Under dynamic and heterogenous environment, the need for adaptability and rapid response time to information service systems has become increasingly important. To cope with the continuously changing conditions of service provision and utilization, Faded Information Field (FIF) has been proposed, which is an agent-based distributed information service system architecture. In the case of a mono-service request, the system is designed to improve users' access time and preserve load balancing through the information structure. However, with interdependent requests of multi-service increasing, adaptability, reliability and timeliness have to be assured by the system. In this paper, the relationship between the timeliness and the reliability of correlated services allocation and access is clarified. Based on these factors, the autonomous network-based heterogeneous information services integration technology to provide one-stop service for users' multi-service requests is proposed. We proved the effectiveness of the proposed technology through the simulation and the results show that the integrated service can reduce the total users access time compared with the conventional systems.

In standard fighting videogames, users usually prefer playing against other users rather than against machines because opponents controlled by machines are in a rut and users can memorize their behaviors after repetitive plays. On the other hand, human players adapt to each other's behaviors, which makes fighting videogames interesting. Thus, in this paper, we propose an artificial agent for a fighting videogame that can adapt to its users, allowing users to enjoy the game even when playing alone. In particular, this work focuses on combination attacks, or combos, that give great damage to the opponent. The agent treats combos independently, i.e., it is composed of a subagent for predicting combos the user executes, that for choosing combos the agent executes, and that for controlling the whole agent. Human users evaluated the agent compared to static opponents, and the agent received minimal negative ratings.

This article describes the idea of utilizing Attested Execution Secure Processors (AESPs) that fit into building a secure Self-Sovereign Identity (SSI) system satisfying Sybil-resistance under permissionless blockchains. Today’s circumstances requiring people to be more online have encouraged us to address digital identity preserving privacy. There is a momentum of research addressing SSI, and many researchers approach blockchain technology as a foundation. SSI brings natural persons various benefits such as owning controls; on the other side, digital identity systems in the real world require Sybil-resistance to comply with Anti-Money-Laundering (AML) and other needs. The main idea in our proposal is to utilize AESPs for three reasons: first is the use of attested execution capability along with tamper-resistance, which is a strong assumption; second is powerfulness and flexibility, allowing various open-source programs to be executed within a secure enclave, and the third is that equipping hardware-assisted security in mobile devices has become a norm. Rafael Pass et al.’s formal abstraction of AESPs and the ideal functionality $\color{brown}{\mathcal{G}_\mathtt{att}}$ enable us to formulate how hardware-assisted security works for secure digital identity systems preserving privacy under permissionless blockchains mathematically. Our proposal of the AESP-based SSI architecture and system protocols, $\color{blue}{\Pi^{\mathcal{G}_\mathtt{att}}}$, demonstrates the advantages of building a proper SSI system that satisfies the Sybil-resistant requirement. The protocols may eliminate the online distributed committee assumed in other research, such as CanDID, because of assuming AESPs; thus, $\color{blue}{\Pi^{\mathcal{G}_\mathtt{att}}}$ allows not to rely on multi-party computation (MPC), bringing drastic flexibility and efficiency compared with the existing SSI systems.

Research on emotion recognition using electroencephalogram (EEG) of subjects listening to music has become more active in the past decade. However, previous works did not consider emotional oscillations within a single musical piece. In this research, we propose a continuous music-emotion recognition approach based on brainwave signals. While considering the subject-dependent and changing-over-time characteristics of emotion, our experiment included self-reporting and continuous emotion annotation in the arousal-valence space. Fractal dimension (FD) and power spectral density (PSD) approaches were adopted to extract informative features from raw EEG signals and then we applied emotion classification algorithms to discriminate binary classes of emotion. According to our experimental results, FD slightly outperformed PSD approach both in arousal and valence classification, and FD was found to have the higher correlation with emotion reports than PSD. In addition, continuous emotion recognition during music listening based on EEG was found to be an effective method for tracking emotional reporting oscillations and provides an opportunity to better understand human emotional processes.