Academic journals serve as pivotal bridges for knowledge dissemination and technology innovation, playing a crucial role in promoting scientific research, industrial progress, and societal development. However, traditional models of journal management and operation, hindered by prolonged peer review processes, overarching publication inefficiencies, and surging page expenditures, are increasingly powerless to address challenges posed by ever-faster knowledge updates, broad information dissemination, and interdisciplinary scholarly work. This has led to an urgent need for smart organizations and intelligent operations of journals. In view of this, the paper identifies the primary issues in current journal management and proposes the concept of smart journals based on TRUE Autonomous Organizations and Operations (TRUE DAO or TAO). This paper introduces the foundational architecture of smart journals and proposes incentive mechanisms for a more dynamic publishing ecosystem. Moreover, a simulation experiment is designed to evaluate an adaptive incentive mechanism, demonstrating significant improvements in review quality and accuracy through personalized incentive allocation strategies. Smart journals not only enable digital transformation but also foster profound innovation and long-term sustainability in publishing.

Aiming to boost production efficiency and reduce human workload, human-centricity has emerged as the core concept of Industry 5.0 (I5.0). However, current works have not established a unified automation and autonomous framework for human-centric smart manufacturing across various real world applications. Addressing this gap, this research introduces an innovative automated framework, ParallelWorkforce, which integrates blockchain intelligence and decentralized autonomous organizations and operations (DAOs) to drive the evolution from digital twins to parallel intelligence. First, this research conducts a comprehensive investigation into smart manufacturing in I5.0, summarizing the ongoing evolution. Next, a detailed exploration of ParallelWorkforce is provided to offer customized strategies for managing different levels of out-of-distribution events, significantly alleviating the workload on biological workers and maximizing the potential of both digital and robotic workers. Finally, the development of ParallelWorkforce across various key applications of smart manufacturing is demonstrated, including autonomous transportation, task assignment, and worker management. This research provides a viable solution for the further development of human-centered smart manufacturing and paves the way for the realization of “6S” goals in I5.0.

As digital transformation accelerates, data has become a core driver of technological innovation and economic growth. However, a key challenge in data utilization is the semantic misalignment between data supply and the demands of business scenarios. This misalignment significantly hinders efficient data flow and collaborative utilization. To address this issue, this article proposes a federated service solution integrating blockchain and decentralized autonomous organizations and operations (DAOs), large language models (LLMs) and scenarios engineering, federated learning and edge computing, as well as encryption technologies and privacy-computing. A five-layer federated service framework is introduced, consisting of the foundation layer, the data-scenario layer, the semantic coordination layer, the incentive-security layer, and the application layer, which is designed to ensure efficient and context-aware data supply–demand matching while preserving privacy and scalability. Moreover, the core mechanisms for semantic coordination are proposed, and a detailed solution process for resolving semantic misalignment with these mechanisms, as well as an illustrative example, is also presented. The proposed federated service framework offers an effective solution to semantic misalignment in supply–demand matching, fostering seamless data collaboration across diverse business scenarios. This work provides an intelligent service paradigm that leverages distributed data co-governance to address semantic challenges in the digital economy.

In recent years, with the advancement of artificial intelligence technology, autonomous driving technologies have gradually emerged. 3D object detection using point clouds has become a key in this field. Multi-frame fusion of point clouds is a promising technique to enhance 3D object detection for autonomous driving systems. However, most existing multi-frame detection methods focus primarily on utilizing vehicle-side lidar data. Infrastructure-side detection remains relatively unexplored, yet can enhance vital vehicle-road coordination capabilities. To help with this coordination, we propose an efficient multi-frame aggregation multi-scale fusion network specifically for infrastructure-side 3D object detection. First, our key innovation is a novel multi-frame feature aggregation module that effectively integrates information from multiple past point cloud frames to improve detection accuracy. This module comprises a feature pyramid network to fuse multi-scale features, as well as a cross-attention mechanism to learn semantic correlations between different frames over time. Next, we incorporate deformable attention, which reduces the computational overhead of aggregation by sampling locations. We designed Multi-frame and Multi-scale modules, thereby we named the model MAMF-Net. Finally, through extensive experiments on two infrastructure-side datasets including the V2X-Seq-SPD dataset which was released by Baidu corporation, we demonstrate that MAMF-Net delivers consistent accuracy improvements over single frame detectors such as PointPillars, PV-RCNN and TED-S, especially boosting pedestrian detection by 5%. Our approach also surpasses other multi-frame methods designed for vehicle-side point clouds such as MPPNet.

The movement of mobile robots in a known environment can be viewed as a Multi-Agent Path Finding (MAPF) problem. MAPF is influenced by various factors such as the application scenarios, the scale and attributes of the agents. However, MAPF assumes agents to be abstract homogeneous nodes, without considering differences in agent attributes. Therefore, this paper proposes a general method to improve the abstract MAPF algorithms into the specific Multi-Attribute Heterogeneous MAPF (MAH-MAPF) algorithms. The method consists of three steps: 1) Define the MAH-MAPF problem based on the attributes of the robots in the application scenario. 2) Based on the agent attributes and definition, transform the MAPF algorithm into MAH-MAPF algorithm by modifying the homogeneous abstract agent assumption. 3) Reduce path conflicts caused by agent attributes to improve the performance of the MAH-MAPF algorithm. The experimental results indicate that the method proposed in this paper is effective and general.

去中心化自治组织(DAO)的兴起, 为传统的社会协作关系带来全新的可能性, 而DAO组织的核心驱动力和权利基础就在于其创新的运作机制. 然而, DAO作为一个同时具有社会复杂性和工程复杂性的复杂系统, 其机制需要能够动态适应不断变化的外部环境, 这同时也是其面临的巨大挑战. 传统的自上而下的设计方法并不能有效解决这些问题. 循环因果理论将复杂系统的运作视作是一个持续演化的动态过程, 从而为解决以上问题提供了新的视角. 因此, 基于平行智能理论和循环因果理论, 提出一种用于DAO机制设计和验证的工程方法. 在这种方法中, 采用了计算机辅助复杂自适应系统的动态设计工具(cad2CAS)简化DAO机制的设计, 并使用复杂自适应系统的计算机辅助动态设计系统(casCAD2)验证和引导这些机制, 从而建立一个因果循环. 通过提出这种方法, 希望能提高DAO治理系统的效率、安全性和适应性, 为更加鲁棒和弹性的去中心化组织发展奠定基础.

With the increasing application of technology in the healthcare industry, it has become imperative to establish a robust medical information ecosystem for effective management of medical information secure storage and sharing. This article proposes a healthier ecosystem in collaboration with the main consortium chain and data side chain, using multiblockchain architecture. In the implementation methods for this ecosystem, we store medical information in JavaScript Object Notation (JSON) format within different side chain structures. Additionally, we introduce an improved Practical Byzantine Fault Tolerant (PBFT) consensus mechanism based on a point nomination system and a dynamic RBAC access mechanism. By simulating a blockchain environment with multiple nodes, we analyze the efficiency of this method in terms of record retrieval, consensus mechanism, and storage execution time. The results demonstrate that compared to a single chain structure, the proposed method in this article achieves a substantial 30% improvement in query time efficiency. Moreover, the improved PBFT outperforms the traditional PBFT algorithm without dishonest nodes. Medical records stored in JSON format require shorter storage and execution time compared to text format records. This research contributes toward enhancing the security and efficiency of medical information storage and sharing among different medical subject information systems, thereby fostering a healthier healthcare alliance ecosystem.

Welcome to the second issue of IEEE Transactions on Computational Social Systems (TCSS) of 2024. This issue showcases an impressive array of 104 regular papers alongside our Special Issue on Big Data and Computational Social Intelligence for Guaranteed Financial Security, highlighting cutting-edge research aimed at harnessing big data and computational techniques to fortify financial security amidst the digital finance evolution. With a focus on addressing the intricate challenges of financial big data, enhancing the efficacy of artificial intelligence, and covering critical topics from data mining to digital currencies, this issue underscores the vital role of cross-disciplinary efforts in mitigating financial security risks.

This article proposes a novel parallel management mode based on decentralized autonomous organizations (DAOs) for enterprises by utilizing the artificial systems, computational experiments, parallel execution (ACP) approach, parallel intelligence theory, and blockchain technologies, to realize the distributed management of an enterprise. The artificial enterprise DAO (EnDAO) corresponding to the actual enterprise is constructed, and they constitute a parallel system via virtual–real interaction and parallel execution. Through the non-fungible token (NFT)-based incentive mechanism, metaverse-based virtual learning and training, as well as DAO-based distributed management and decision-making, the management and control of the actual enterprise as well as its employees can be carried out. By virtue of the virtual–real interactions of three types of employees, as well as the virtual–real feedback of three closed loops in the parallel systems, DAO-based parallel management for enterprises can realize descriptive intelligence, predictive intelligence, and prescriptive intelligence. On this basis, this article takes the recruitment-oriented key performance indicator (KPI) management of a startup technology enterprise as the case to introduce the operation processes and illustrate the superiorities of the proposed DAO-based enterprise parallel management mode.

As the next-generation Internet characterized by readability, writability, and ownability, Web 3.0 necessitates the fusion of blockchain and artificial intelligence (AI) technologies to realize its vision of decentralization, user autonomy, and intelligent openness. To this end, this article proposes the integration of “AI for blockchain” and “blockchain for AI” to form a bidirectional enhancement loop, for establishing genuinely intelligent blockchains and ushering in a new paradigm referred to as blockchain intelligence. On this basis, the technical architecture of intelligent blockchains is proposed, which infuses intelligence into every layer of traditional blockchain architectures while enables the parallel execution between virtual and artificial intelligent blockchain systems. This architecture facilitates blockchain systems to cultivate an ecosystem of intelligence, extending from foundation intelligence to application intelligence. Moreover, the core attributes of blockchain intelligence are examined, from the perspectives of smart contracts, data, identity, and governance. Furthermore, the main challenges and research issues faced by blockchain intelligence are outlined. This article is committed to the advancement of blockchain intelligence, laying the groundwork for Web 3.0 and the impending era of smart societies.

With the advent of Web 3.0, the evolution of cities and societies is increasingly oriented toward virtual spaces. This shift signifies an inevitable trend where the integration of virtual and real elements becomes vital to their development. Such a transition will bring huge changes to the organizational structure and methods, development modes, as well as operating mechanisms of cities and societies. In the virtual-real integrated cities and societies, traditional economic and management principles and models are no longer applicable. Consequently, it is crucial to explore new economic and management models tailored to Web 3.0. This article integrates virtual cities/societies with actual cities/societies, and proposes the innovative paradigm of MetaCities/MetaSocieties. Based on parallel intelligence theory and metaverse technologies, the research framework of MetaCities/MetaSocieties is established, and its main participants and operating mode are discussed. In addition, in view of the new economic and management issues faced in MetaCities/MetaSocieties, the innovative paradigms of MetaEconomics and MetaManagement are proposed, and the operational logic and models of MetaEconomics, as well as the MetaManagement big models and management-oriented operating systems, are proposed. This work aims to offer valuable insights for the evolution of cities and societies in the upcoming intelligent era, and inspire the development of new MetaEconomics and MetaManagement models in MetaCities/MetaSocieties.

This paper presents Management-Oriented Operating Systems (M2OS) that leverages the power of parallel intelligence theory, Decentralized Autonomous Organizations (DAOs) and foundation models to revolutionize the manner of management in Cyber-Physical-Social Systems (CPSS). The parallel architecture on M2OS is proposed, including the parallel interactive actual M2OS and artificial M2OS. Among them, the artificial M2OSs provide digital infrastructures for organizations to operate, collaborate, and make decisions in the virtual space, and conduct computational experiments to evaluate management decisions and predict future states of the actual M2OS. Through parallel execution and closed-loop feedback between the artificial and actual M2OSs, the management and control, experimentation and evaluation, as well as learning and training of the actual M2OS can be realized. Moreover, the functional layers of M2OS, including the infrastructure layer, data layer, scenario layer, modeling layer, decision layer, and application layer, are discussed. These layers work together to support the intelligent, autonomous, collaborative, and adaptive nature of the M2OS, and facilitate data-driven decision-making, optimize business operations, and empower managers with real-time actionable insights. The proposed M2OS paradigm has great potential to transform the management paradigm and opens up new possibilities for intelligent and collaborative decision-making.

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 been gaining popularity in recent years due to their promise of realizing the decentralized Web 3.0. However, most DAOs rely heavily on token-centric value systems as well as allocate decision-making authority and yield-sharing rights according to the held tokens, which often lead to monopolization of power and rights. To address this issue, this paper contributes to propose a truly democratic organization model, named True Autonomous Organizations and Operations (TAOs), that does not count upon tokens and is guided by principles of contribution-based and on-demand allocation. We first discuss the design of TAOs, including their infrastructures, power structures, and value systems, and then provide a technical roadmap for implementing TAOs in the DeSci context. This research can provide a valuable guidance for the construction and application of TAOs.

Data trading has been hindered by privacy concerns associated with user-owned data and the infinite reproducibility of data, making it challenging for data owners to retain exclusive rights over their data once it has been disclosed. Traditional data pricing models relied on uniform pricing or subscription-based models. However, with the development of Privacy-Preserving Computing techniques, the market can now protect the privacy and complete transactions using progressively disclosed information, which creates a technical foundation for generating greater social welfare through data usage. In this study, we propose a novel approach to modeling multi-round data trading with progressively disclosed information using a matchmaking-based Markov Decision Process (MDP) and introduce a Social Welfare-optimized Data Pricing Mechanism (SWDPM) to find optimal pricing strategies. To the best of our knowledge, this is the first study to model multi-round data trading with progressively disclosed information. Numerical experiments demonstrate that the SWDPM can increase social welfare 3 times by up to 54 % in trading feasibility, 43 % in trading efficiency, and 25 % in trading fairness by encouraging better matching of demand and price negotiation among traders.

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.

Crypto management is proposed to tackle the management decision-making challenges under data asymmetry and trust asymmetry that cannot be solved merely by technical means. It emphasizes the novel management model for the real-time generation of reliable, trustworthy, and usable management decisions based on blockchain and blockchain-driven technologies. First, the framework model of crypto management with detailed descriptions of each technique is introduced, where blockchain is the underlying technology, decentralized autonomous organization (DAO) is the management structure, federated data is the decision basis, smart contract is the decision method, and non-fungible token (NFT) is the main decision incentive. Then, its collaboration mechanisms of on-blockchain DAO and off-blockchain organization as well as intra-organization and extra-organization nodes are discussed. Moreover, the potential applications of crypto management are addressed, and a case of task-oriented performance management is given to state how crypto management works to generate the real-time management decisions. Toward the end, the future research directions are pointed out in this emerging new area.

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.

In the future, management in smart societies will revolve around knowledge workers and the works they produce. This article is committed to explore new management framework, model, paradigm, and solution for organizing, managing, and measuring knowledge works. First, the parallel management framework is presented that would allow for the virtual-real interactions of humans in social space, robots in physical space, and digital humans in cyberspace to realize descriptive, predictive, and prescriptive intelligence for management. Then, the management foundation models are proposed by fusing scenarios engineering with artificial intelligence foundation models and cyber–physical-social systems. Moreover, the new management paradigm driven by decentralized autonomous organizations and operations is formulated for the advancement of smart organizations and intelligent operations. On these basis, the management operating systems that highlight features of simple intelligence, provable security, flexible scalability, and ecological harmony are finally put forward as new management solution.

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.

Bitcoin is gaining ground in recent years. In the Bitcoin system, miners provide computing power to confirm transactions in pursuit of transaction fees, while users compete by bidding transaction fees for faster confirmation. This process is in essence analogous to online ad auctions, where advertisers bid for more prominent ad slots. Therefore, inspired by ad auction research, we propose to apply the Generalized Second Price (GSP) auction mechanism in the dynamic confirmation game on Bitcoin transactions. Our model is targeted to deal with the problems caused by instability and low efficiency in the currently-adopted Generalized First Price (GFP) auction model in Bitcoin confirmation games. Besides, we use the “rank-by-cost” rule to replace the “rank-by-fee” rule, where each transaction’s cost is calculated by the user-submitted fee and the waiting time. Aiming to probe users’ equilibrium strategy, we first discuss the GSP game with complete information under synchronous submissions, and show that it has the Locally Envy-Free equilibrium. Then, we study the GSP game with incomplete information under asynchronous submissions, and define two types of strategies, i.e., the Farsighted Balanced (FB) strategy and the Instant Balanced (IB) strategy. The FB strategy is in line with users’ practical needs of determining fees so as to maximize the long-term payoffs; however it cannot generate a stable equilibrium. Alternatively, the IB strategy focuses on the instant payoff maximization, and if all users follow the IB strategy, their equilibrium fees can finally converge to a stable profile. Finally, we design computational experiments to validate our theoretical models and analysis. Our research findings indicate that this novel GSP mechanism is superior to the currently adopted GFP mechanism. Besides, the convergence of the GSP game under the IB strategy has also been illustrated by the computational experiments.

To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed. Our two-stage system leverages on an ad-hoc score combination based on two CNN classifiers: (i) the first CNN classifies acoustic inputs into one of three broad classes, and (ii) the second CNN classifies the same inputs into one of ten finergrained classes. Three different CNN architectures are explored to implement the two-stage classifiers, and a frequency sub-sampling scheme is investigated. Moreover, novel data augmentation schemes for ASC are also investigated. Evaluated on DCASE 2020 Task 1a, our results show that the proposed ASC system attains a state-of-the-art accuracy on the development set, where our best system, a two-stage fusion of CNN ensembles, delivers a 81.9% average accuracy among multi-device test data, and it obtains a significant improvement on unseen devices. Finally, neural saliency analysis with class activation mapping (CAM) gives new insights on the patterns learnt by our models.

Urban transportation systems are shaped by factors that include people, vehicles, roads, and the environment, forming a complex and giant system with dynamics, diversity, and uncertainty. Physical signal-driven intelligent transportation systems (ITSs) typically lack the ability to capture social behaviors or crowd willingness, and they achieve only information automation for transportation decision support. The crowdsourcing social signals consist of timely, extensive, comprehensive, and rich intelligence that concern urban dynamics, social behaviors, and traffic environments. Such social signals provide a new paradigm for operating ITS with unstructured semantic data, making knowledge automation for decision intelligence a possibility. This article reviews the knowledge automation paradigms for cyber-physical-social systems (CPSSs) compared with traditional information automation paradigms for cyber-physical systems (CPSs) in ITS, from the perspective of data-driven, modeling space, analytical methodologies, and decision support services. To investigate the key methodology in social spaces that enhance information automation into knowledge automation, we summarize the current research into a multisource heterogeneous social signal-based traffic decision knowledge automation framework and further exploit the computational paradigm and applications scenarios of this framework. Finally, we discuss future challenges for designing and realizing knowledge automation on CPSS in transportation.

Welcome to the last issue of IEEE Transactions on Computational Social Systems (IEEE TCSS) in 2021. For IEEE TCSS, 2021 is an exciting year. TCSS has been added to the ISI Web of Science Sources Citation Index Expanded (SCIE) database in 2021, and all articles published since 2018 have been indexed by SCIE. This is an important milestone in the development of TCSS. We would like to take this opportunity to thank and congratulate everyone for their great efforts and supports. We are looking forward to working together to further improve the publication quality and speed up the review process of TCSS in the upcoming 2022.

Since early 2020, the COVID-19 global pandemic has significantly impacted almost every aspect of the human society throughout the world. Until now, middle of 2021, although with all the efforts on pandemic intervention and vaccination, COVID-19 is still hovering around the world, resulting in more than 177 million confirmed cases and 3.8 million deaths.

In this paper, an automatic extraction method of multi-type built-up areas in SAR images is proposed. In order to adapt to the architectural differences in different regions, we improve the residual U -Net, one is to introduce multi -scale pyramid structure into the structure, the other is to use the diss loss function. The proposed method was verified using GF-3 SAR data in four regions of China and compared with FCN and PSPNET. Finally, the accuracy evaluation results show that the overall accuracy of the extraction results is greater than 86%, indicating that the method has a certain application potential.

Welcome to the first issue of the IEEE Transactions on Computational Social Systems (TCSS) of 2020, and Happy New Year to You! We would like to take this opportunity to express our sincere thanks to our editors, reviewers, authors, and readers for your great support and effort devoted to the TCSS, along with our best wish and hope that everyone has a happy, healthy, and fruitful 2020.

The dramatic spread of online social network services, such as Facebook, Twitter, Instagram, and Google+, has led to increasing awareness of the power of incorporating social elements into a variety of data-centric applications. These applications, in recent years, apply various sensors with social media platforms to continuously collect massive data that can be directly associated with human interactions. This phenomenon has led to the creation of numerous social sensing systems, such as Biketastic, BikeNet, CarTel, and Pier, which use social sensors (i.e., users) for a variety of social sensing systems and applications. Social sensing has become an emerging and promising sensing paradigm that relies on the voluntary cooperation of users equipped with embedded or integrated sensors.

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.

Welcome to the fourth issue of the IEEE Transactions on Computational Social Systems (TCSS), which includes 16 regular papers and a brief discussion on social computing. We would also like to inform you that IEEE will conduct its regular 5-year review for TCSS at its TAB meeting in November at Boston. Any suggestions for our review report are welcome!

Welcome to the first issue of the IEEE Transactions on Computational Social Systems (TCSS) this year, and Happy New Year to everyone. We would like to take this opportunity to express sincere gratitude to our editors, reviewers, authors, and readers for your support and great efforts devoted to TCSS. Also, we want to express the best wishes to you all, and hope you have a happy, healthy, and fruitful 2019.

Welcome to the last issue of the IEEE Transactions on Computational Social Systems (TCSS) of this year, with a special focus on “blockchainbased secure and trusted computing for IoT.” Here, we have 18 regular articles and a brief discussion on social intelligence. I would like to take this opportunity to thank and congratulate everyone, especially our editorial board for a great job well done. Looking forward to working with you all in 2020!

Bitcoin is a novel protocol with the potential of enabling a decentralized and trustless cryptographic currency, and its underlying technology named blockchain operates on a worldwide basis via a complex set of rules originally proposed by Nakomoto in 2008. In Bitcoin blockchain, miners provide computational services (i.e. mining) to get profits from the fixed rewards of newly found block and also transaction fees from recording the users’ transactions to the blocks. With the decreasing of the fixed new block reward, transaction fees will play the role as the main profit source of miners, thus provide important supports for the sustainability and vitality of the Bitcoin system. Therefore, it is of great necessity to research transaction fees. In this paper, we investigate transaction fees in a queuing game with non-preemptive priority, in which both the miners’ mining rewards and the users’ time cost are highlighted. Then, we conduct theoretical analysis of the game, getting five types of Nash equilibria of the game. We also find that the over-long waiting time will bring negative marginal profits on transaction fees to some users with low time cost, therefore, they will not be willing to offer transaction fees.

With the rapid development of cryptocurrency and its underlying blockchain technologies, platforms such as Ethereum and Hyperledger began to support various types of smart contracts. Smart contracts are computer protocols intended to digitally facilitate, verify, or enforce the negotiation or performance of a contract. Smart contracts have broad range of applications, such as financial services, prediction markets and Internet of Things (IoT), etc. However, there are still many challenges such as security issues and privacy disclosure that await future research. In this paper, we present a comprehensive overview on blockchain powered smart contracts. First, we give a systematic introduction for smart contracts, including the basic framework, operating mechanisms, platforms and programming languages. Second, application scenarios and existing challenges are discussed. Finally, we describe the recent advances of smart contract and present its future development trends, e.g., parallel blockchain. This paper is aimed at providing helpful guidance and reference for future research efforts.

In recent years, social media platforms and instant messaging applications have been witnessed to play an increasingly important role as a communication channel among employees to build effective organizations, which results in a considerably large part of individual activities moving onto the cyber-based workspace. The online behaviors of employees rely heavily on the policies or mechanisms formulated for the cyber-space, and are expected to impose a significant influence on their performance appraisal in the physical world. In this paper, we focus on the typical online reporting behavior, and empirically study how the work reporting policies formulated by supervisors influence the employees' strategies and in turn their performance, using a unique real-world dataset collected from the Chinese social media platform called WeChat. We consider two types of work reporting policies: the one is concerned with the reporting time and the other with the duplication degree of reporting contents. First, we establish an optimization model, taking both the reputation-based and performance-related utilities into consideration, so as to examine the employees' reporting strategies and their actual work performance. On this basis, we make further discussions about the reporting policy optimization from the perspective of organization supervisors. Then, an empirical study on a median-size organization in China is conducted to validate our proposed model and analysis. The results prove our conclusion that the reporting policy will influence not only employees' reporting strategies, but also their actual work performance.

In recent years, social media platforms and, especially, instant messaging applications have been witnessed to play an increasingly important role as a communication channel among employees to build effective organizations, which results in a considerably large part of individual activities moving onto the cyber-based workspace. In this paper, we collect the unique real-world data set from a WeChat-based work performance evaluation system built and used by a medium-sized organization in China, in order to study the optimization of the employees’ work strategies, especially the work time determination, with the purpose of maximizing the total utility comprised of the work and nonwork utilities. In the system, the performance evaluation is completely based on the employees’ work reports and includes three major measures including score, level, and rank. First, we formulate a dynamic optimization model, incorporating the work utility into the reputational and the substantive utility, so as to examine the employees’ work strategy. Then, we conduct both empirical studies and computational experiments to make an in-depth analysis to probe the employees’ optimal work strategies. The main results include: 1) the optimal work time is greatly affected by the weight of the reputational utility; 2) the optimal work time is always the threshold time to achieve a certain level or a certain rank; 3) the total utility is with the trend of decreasing after increasing with the growing work time; and 4) the optimal work time does not always make the employee achieve a good work performance.

With the application of big data analytics in online marketing, real-time bidding (RTB) has developed to be the primary business model and also the major online advertising channel. Due to the precise analysis of Web Cookies, RTB platforms can target the visiting audiences and then forward their generated ad impressions to demanding advertisers who bid on the best-matched audience in a real-time fashion. In RTB markets, the reserve price plays the vital role as a tuner to exclude over-low bids, and hence guarantee the desirable sales prices and revenues for publishers from ad impression sales. In this paper, we strive to study the publisher's strategy on the reserve price and probe its impact on his/her revenues. We first discuss the reserve price of ad impression in a single-channel sales model, including the online RTB channel or the off-line direct channel, aimed to study its impact on the publisher' revenue. Then, we further analyze the impact of the reserve price in the multi-channel settings. Finally, we conduct experiments using empirical log data collected from real-world RTB markets to validate our models and analyses, and the experimental results indicate that: 1) in the single-channel sales model, publishers should set the reserve price for only the online-channel ad impressions while not for the off-line-channel ones and 2) in the multi-channel ad impression sales, publishers should set both off-line and online reserve prices for revenue maximization.

Welcome to the third issue of the IEEE Transactions on Computational Social Systems (TCSS) for 2018.

With the rapid development of big data analytics in online marketing, real-time bidding (RTB) has emerged as a promising business model in recent years, and now becomes one of the major online advertising channels. Based on analysis of Web Cookies, RTB platforms are able to precisely identify the features and preferences of target audiences visiting publishers' websites, and forward the generated ad impressions to competing advertisers who submit bids for their best-matched audience in real-time ad auctions. In RTB markets, reserve price serves as an important tuner to exclude advertisers with low estimated values, and hence can guarantee a desirable result for the publisher from ad impression auctions. In this paper, we strive to study publishers' strategy on the reserve price, and probe the impact of reserve price on their revenues. We first analyze the ad impression auction under a direct auction mechanism. We then introduce the reserve price and study its impact on publishers' revenues under an indirect auction mechanism, and our research findings indicate that a rational positive reserve price will always improve publishers' revenues even if it is not optimal. Also, the optimal reserve price is figured out based on the advertisers' bid distributions for publishers' revenue maximization. Finally, experiments using empirical log data from real-world RTB markets are designed to validate our model and analysis, and the results provide strong support to our theoretical analysis. The experimental results also indicate that although the number of bids does not impose any influence on the optimal reserve price, it has significant impacts on publishers' revenues.

Real time bidding (RTB) has become an emerging online advertising with the development of Internet big data in recent years. In the whole RTB ecosystem, the Demand Side Platform (DSP) plays a central role, and it realizes the programmatic and accurate buying of the advertisements for the advertisers via a two-stage auction. In RTB business logics, DSP plays as an intermediary between the advertisers and the center platform. Due to the principle-agent relationship between the advertisers and the DSP, the DSP aims not only to maximize the revenue for the advertisers, but also gain its revenue in this process. So far there are two revenue modes for DSP, namely the two-stage resale model and the commission model, respectively. In this paper, we mainly consider the revenue model for DSP in RTB advertising market. We aim to study the properties of the two revenue models, and compare the revenues for the DSP and the advertisers under these two models. We also provide an example to illustrate our proposed models and their properties. The results show that under small ratio of the commission, the advertisers are more likely to choose the commission model, but the DSP is more likely to choose the two-stage resale model and set a larger weight, while under large ratio of the commission, the advertisers are more likely to choose the two-stage resale model, but the DSP is more likely to choose the commission model. Our research work highlights the importance of the revenue model on the revenues of the advertisers and the DSP, and is intended to provide a useful reference for DSPs in RTB advertising markets.

In social marketing practice, it is usually important to anticipate the long-term impact of the target application to maintain a long-lasting marketing effect, whereas a new product or technology should spread as quickly as possible to establish a competitive advantage. To find a balance between them, we tackle this challenge by modelling the problem as an issue of time-discounted influential sustainability. Given a threshold μ, the goal of the problem is finding a small subset of nodes as seeds and deciding the optimal timing to activate each seed that could maximize the time-discounted number of iterations, each of which actives more than μ nodes. We prove that solving the problem is NP-hard and the objective function is non-negative, non-monotonic, and non-submodular. Therefore we propose a greedy approach to approximately solve this problem. Our experimental results demonstrate that our solution outperforms two baseline algorithms. In order to provide meaningful advices for advertisers on selecting proper initial seed users, we further analyze and compare the performance of four seeding strategies on three typical types of social networks.

With the rapid development of big data analytics in online marketing, real-time bidding (RTB) has emerged as a promising business model in recent years and now becomes one of the major online advertising channels. Based on analysis of Web Cookies, RTB platforms are able to precisely identify the features and preferences of target audiences visiting publishers' websites, and forward the information to competing advertisers submitting bids for their best-matched audience in real-time ad auctions. As the supplier of ad impressions, publishers typically have multiple channels to sell their ad impressions (i.e., ad inventory), making their strategies for allocating ad inventory one of the most critical research problems. In this paper, we strive to study publishers' optimal strategy of allocating ad inventory across online channel of RTB-based auctions and offline channel prevailingly realized in the form of guaranteed contracts. Considering the ad reserve price as the control variable, we establish the optimization model. We also explicitly take the default penalty in offline channels into consideration, so as to balance the short-term online revenue and long-term offline revenue. In our work, we analyze altogether three kinds of strategies for publishers to allocate their ad inventory in pursuit of the optimal strategy, and validate our model and analysis via computational experiments. We find that there is no dominant strategy that can outperform others in all cases, and interestingly, publishers using the hybrid-channel strategy do not always gain more revenues than those using the single-channel strategy.

Real Time Bidding (RTB) is an emerging business model and a popular research topic of online advertising markets. Using cookie-based big-data analysis, RTB advertising platforms have the ability to precisely identify the features and preferences of online users, segment them into various kinds of niche markets, and thus achieve the precision marketing via delivering advertisements to the best-matched users. The segmentation granularity used by such platforms, typically referred to as the Demand Side Platforms (DSPs), plays a central role in the effectiveness and efficiency of the RTB ecosystem. In practice, fine-grained user segmentations may lead to increased value-per-clicks and bid prices from advertisers, but at the same time reduced competition and possibly decreased bid prices in each niche market. This motivates our research on the optimal segmentation granularity to solve this dilemma faced by DSPs. Using a RTB market model with two-stage resales, we analyzed DSPs' segmentation strategies taking the revenues of both advertisers and DSPs into consideration. We also validated our proposed model and analysis using the computational experiment approach, and the experimental results indicate that with the increasing of segmentation granularity, the weighted sum of the DSP and advertisers' revenues tends to first rise and then decline in all weight-value cases, and the optimal granularity is greatly influenced by the value of weights. Our work highlights the need for DSPs of moderately using, instead of overusing, the online big data for maximized revenues.

As the size of high performance applications increases, four major challenges including heterogeneity, programmability, failure resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approaches in resource utilization, task scheduling and programming paradigm should be reconsidered. As Hadoop has handled data-intensive applications well in Clouds, GPU has demonstrated its acceleration effectiveness for computation-intensive ones. This paper intends to integrate Hadoop with CUDA to exploit both CPU and GPU resources. Hadoop will schedule MapReduce's Map and Reduce functions across multiple nodes, whereas CUDA code helps accelerate them further on local GPUs. All available heterogeneous computational power will be utilized. MapReduce in Hadoop will ease the programming task by hiding communication details. Hadoop Distributed File System will help achieve data-level fault resilience. GPU's energy efficiency characteristics help reduce the power consumption of the whole system. To achieve Hadoop and GPU integration, four approaches including Jcuda, JNI, Hadoop Streaming, and Hadoop Pipes, have been accomplished. Experimental results have demonstrated their effectiveness.

Lattice based cryptography is attractive for its quantum computing resistance and efficient encryption/decryption process. However, the big data problem has perplexed lattice based cryptographic systems with the slow processing speed. This paper intends to analyze one of the major lattice-based cryptographic systems, Nth-degree truncated polynomial ring (NTRU), and accelerate its execution with Graphic Processing Unit (GPU) for acceptable processing performance. Three strategies, including single GPU with zero copy, single GPU with data transfer, and multi-GPU versions are proposed. GPU computing techniques such as stream and zero copy are applied to overlap the computation and communication for possible speedup. Experimental results have demonstrated the effectiveness of GPU acceleration of NTRU. As the number of involved devices increases, better NTRU performance will be achieved.

How to rationally allocate the limited advertising budget is a critical issue in sponsored search auctions. There are plenty of uncertainties in the mapping from the budget into the advertising performance. This paper presented some preliminary efforts to deal with uncertainties in search marketing environments, following principles of a hierarchical budget optimization framework (BOF). We proposed a stochastic, risk-constrained budget strategy, by considering a random factor of clicks per unit cost to capture a kind of uncertainty at the campaign level. Uncertainties of random factors at the campaign level lead to risk at the market/system level. We also proved its theoretical soundness through analyzing some desirable properties. Some computational experiments were made to evaluate our proposed budget strategy with real-word data collected from reports and logs of search advertising campaigns. Experimental results illustrated that our strategy outperforms two baseline strategies. We also noticed that 1) the risk tolerance has great influences on the determination of optimal budget solutions; 2) the higher risk tolerance leads to more expected revenues.

Budget optimization is one of the primary decision-making issues faced by advertisers in search auctions. A quality budget optimization strategy can significantly improve the effectiveness of search advertising campaigns, thus helping advertisers to succeed in the fierce competition of online marketing. This paper investigates budget optimization problems in search advertisements and proposes a novel hierarchical budget optimization framework (BOF), with consideration of the entire life cycle of advertising campaigns. Then, we formulated our BOF framework, made some mathematical analysis on some desirable properties, and presented an effective solution algorithm. Moreover, we established a simple but illustrative instantiation of our BOF framework which can help advertisers to allocate and adjust the budget of search advertising campaigns. Our BOF framework provides an open testbed environment for various strategies of budget allocation and adjustment across search advertising markets. With field reports and logs from real-world search advertising campaigns, we designed some experiments to evaluate the effectiveness of our BOF framework and instantiated strategies. Experimental results are quite promising, where our BOF framework and instantiated strategies perform better than two baseline budget strategies commonly used in practical advertising campaigns.

Recently, popularity of multimedia content sharing among the Internet users and development of wireless mobile devices have promoted a trend of deploying Peer-to-Peer (P2P) networks over mobile ad hoc networks (MANETs) for mobile content distribution. However, due to nodes' frequent movement, limited radio transmission range, sparse distribution and power limitations, MANETs may become Delay Tolerant Network (DTN). Therefore, this wireless mobile P2P networks over DTN have to be studied. Sharing multimedia files among users is an important application in wireless mobile P2P networks. Even though various multimedia content sharing mechanism in P2P networks over MANET have been proposed in the literature. However, DTN experiences frequent and long-duration partitions, therefore, the original method does not apply to DTN scenario. In this work, we propose a Distributed Multisource Parallel Coadjutant Transmission (DMPCT) mechanism of multimedia based on P2P lookup protocol. The proposed transmission scheme enables multimedia files to be sent to the receiver fast and reliably in wireless mobile P2P networks over DTN. Simulation results demonstrate that the proposed scheme significantly improves the performance of the file delivery rate and file delivery delay compared with the existing scheme.