This article addresses the annular finite-time $H_{2}/H_{\infty }$ control for mean-field jump-diffusion systems (MFJDSs), where the state equation is influenced by both Wiener and Poisson noises. Initially, a new concept termed annular finite-time $H_{2}/H_{\infty }$ control is introduced, which simultaneously ensures the system's annular finite-time bounded-ness (AFTB) in the mean-square sense and the minimization of $H_{2}$ and $H_{\infty }$ performance indices. Moreover, its superiority over finite-time $H_{2}/H_{\infty }$ control is analyzed. Next, several innovative and less conservative sufficient conditions for both state feedback and observer-based annular finite-time (SFAFT and OBAFT) $H_{2}/H_{\infty }$ control are proposed. Further, a new algorithm is devised. When $ \gamma $ is a fixed value, this algorithm can be used to obtain the range of stability parameters $ \mu $ and $ \pi $ . When $\gamma $ is a varying value, this algorithm can be employed to determine the relationship between the $H_{2}$ and $H_{\infty }$ performance indices under different values of $\mu $ and $\pi $ . Finally, a comprehensive design example is presented to showcase the practical advantages of the proposed methodologies.

This article investigates the finite-time annular domain guaranteed cost control (FTADGCC) problem for uncertain mean-field stochastic systems with both Wiener and Poisson noises (UMFSSwWP). First, the definition of FTADGCC is introduced, which ensures that the state trajectories remain within an upper and lower bound during a fixed time interval and minimizes the performance cost of systems. Moreover, its superiority to finite-time guaranteed cost control is analyzed. Second, utilizing the Itô-Levy formula and the reverse differential Gronwall inequality (RDGI), several novel and less conservative sufficient conditions are presented for the state feedback and observer-based FTADGC (SFaOBFTADGC) controllers. Moreover, a new numerical algorithm is provided to minimize the cost function and give a broader range of parameters. Finally, a practical electrical circuit is offered to demonstrate the effectiveness and feasibility of our proposed findings.

With the rise of the Internet and social media, social mobilization - large-scale mobilization manpower for scientific, social, and political activities through crowdsourcing - has become a widespread practice. Despite the success, social mobilization is not without its limitations. Local trapping of diffusion and the dependence on highly connected individuals to mobilize people in distance locations affect the effectiveness of social mobilization. Furthermore, as empirical studies on people's responses to various social mobilization approaches are lacking, it is a significant challenge for artificial intelligence (AI) researchers to design effective and efficient decision support mechanisms to help manage this emerging phenomenon. In my thesis, I conduct large-scale empirical studies to help the AI research community establish baseline personal variabilities in different people's response patterns to social mobilization approaches. Based on the collected dataset, I will further propose computational algorithmic crowdsourcing mechanisms which leverage the empirical evidence to improve the effectiveness and efficiency of social mobilization, towards achieving superlinear productivity. Throughout this process, I will also incorporate human factors into the computational models to benefit social mobilization efforts.

We consider the problem of making efficient quality-time-cost trade-offs in collaborative crowdsourcing systems in which different skills from multiple workers need to be combined to complete a task. We propose CrowdAsm - an approach which helps collaborative crowdsourcing systems determine how to combine the expertise of available workers to maximize the expected quality of results while minimizing the expected delays. Analysis proves that CrowdAsm can achieve close to optimal profit for workers in a given crowdsourcing system if they follow the recommendations.

The current generation of senior citizens are enjoying unparalleled levels of good health than previous generations. The need for personal fulfilment after retirement has driven many of them to participate in productive aging activities such as volunteering. This paper outlines the Silver Productive (SP) mobile app, a system powered by the RTS-P intelligent personalized task sub-delegation approach with dynamic worker effort pricing functions. It provides an algorithmic crowdsourcing platform to enable seniors to contribute their effort through productive aging activities and help organizations efficiently utilize seniors' collective productivity.

A true semantic Web repository must scale both in terms of number of ontologies and quantity of data. It should also support reasoning using different points of view about the meanings and relationships of concepts and roles. Our DLDB2 system has these features. Our system is sound and complete on a sizable subset of description horn logic when answering extensional conjunctive queries, but more importantly also computes many entailments from OWL DL. By delegating TBox reasoning to a DL reasoner, we focus on the design of the table schema, database views, and algorithms that achieve essential ABox reasoning over an RDBMS. We evaluate the system using synthetic benchmarks as well as real-world data and queries.

A key challenge for the semantic Web is to acquire the capability to effectively query large knowledge bases. As there will be several competing systems, we need benchmarks that will objectively evaluate these systems. Development of effective benchmarks in an emerging domain is a challenging endeavor. In this paper, we propose a requirements driven framework for developing benchmarks for semantic Web knowledge base systems (SW KBSs). In this paper, we make two major contributions. First, we provide a list of requirements for SW KBS benchmarks. This can serve as an unbiased guide to both the benchmark developers and personnel responsible for systems acquisition and benchmarking. Second, we provide an organized collection of techniques and tools needed to develop such benchmarks. In particular, the collection contains a detailed guide for generating benchmark workload, defining performance metrics, and interpreting experimental results