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An Efficient Memetic Algorithm for Influence Maximization in Social Networks
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| Pengarang | : | Maoguo Gong,Chao Song,Chao Duan,Lijia Ma,Bo Shen |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 3) |
| Halaman | : | 22-33 |
| Abstrak | : | Influence maximization is to extract a small set of nodes from a social network which influences the propagation maximally under a cascade model. In this paper, we propose a memetic algorithm for community-based influence maximization in social networks. The proposed memetic algorithm optimizes the 2-hop influence spread to find the most influential nodes. Problem-specific population initialization and similarity-based local search are designed to accelerate the convergence of the algorithm. Experiments on three realworld datasets demonstrate that our algorithm has competitive performances to the comparing algorithms in terms of effectiveness and efficiency. For example, on a real-world network of 15233 nodes and 58891 edges, the influence spread of the proposed algorithm is 12.5%, 13.2% and 173.5% higher than the three comparing algorithms Degree, PageRank and Random, respectively. |
Leveraging Cross-Domain Social Media Analytics to Understand TV Topics Popularity
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 3) |
| Halaman | : | 10-21 |
| Abstrak | : | The way we watch television is changing with the introduction of attractive Web activities that move users away from TV to other media. The social multimedia and user-generated contents are dramatically changing all phases of the value chain of contents (production, distribution and consumption). We propose a concept-level integration framework in which users' activities on different social media are collectively represented, and possibly enriched with external knowledge, such as information extracted from the Electronic Program Guides, or available ontological domain knowledge. The integration framework has a knowledge graph as its core data model. It keeps track of active users, the television events they talk about, the concepts they mention in their activities, as well as different relationships existing among them. Temporal relationships are also captured to enable temporal analysis of the observed activity. The data model allows different types of analysis and the definition of global metrics in which the activity on different media concurs with the measure of success. |
Computational Intelligence for Big Social Data Analysis
| Pengarang | : | Cambria, Erik,Howard, Newton,Yunqing Xia,Tat-Seng Chua |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 3) |
| Halaman | : | 8-9 |
| Abstrak | : | - |
Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection
| Pengarang | : | Junke Wang; Zhenxin Li; Chao Zhang; Jingjing Chen; Zuxuan Wu; Larry S. Davis; Yu-Gang Jiang |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 3) |
| Halaman | : | 287-311 |
| Abstrak | : | Online media data, in the form of images and videos, are becoming mainstream communication channels. However, recent advances in deep learning (DL), particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost, which not only poses a serious threat to the trustworthiness of digital information but also has severe societal implications. This motivates a growing interest in research in media tampering detection (TD), i.e., using DL techniques to examine whether media data have been maliciously manipulated. Depending on the content of the targeted images, media forgery could be divided into image tampering and Deepfake techniques. The former typically moves or erases the visual elements in ordinary images, while the latter manipulates the expressions and even the identity of human faces. Accordingly, the means of defense include image TD and Deepfake detection (DFD), which share a wide variety of properties. In this article, we provide a comprehensive review of the current media TD approaches and discuss the challenges and trends in this field for future research. |
MASALAH-MASALAH KEAMANAN ASIA TENGGARA MENURUT SUATU PANDANGAN JEPANG
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-10, OKTOBER (No. 10) |
| Halaman | : | 918-928 |
| Abstrak | : | - |
CIS Publication Spotlight
| Pengarang | : | Haibo He,Chin-Teng Lin,Kay Chen Tan,Kendall, Graham,Yaochu Jin |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 3) |
| Halaman | : | 6-7 |
| Abstrak | : | - |
EMG Acquisition and Processing for Hand Movement Decoding on Embedded Systems: State of the Art and Challenges
| Pengarang | : | Simone Benatti; Elisa Donati; Ali Moin; Marcello Zanghieri; Mattia Orlandi; Alessio Burrello; Fiorenzo Artoni; Silvestro Micera; Luca Benini; Jan M. Rabaey |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 3) |
| Halaman | : | 256-286 |
| Abstrak | : | The electromyography (EMG) signal is particularly useful in monitoring muscle activity, and it can be acquired noninvasively on the skin surface. Thanks to these key characteristics, EMG-based human–machine interfaces (HMIs) for prosthetic myocontrol, as well as gesture recognition, are becoming widespread. A key challenge in this context is to design embedded systems to process EMG signals and generate motor commands with miniaturized, unobtrusive, and low-power devices, reliably and in real time, at a relatively low cost to provide continuous monitoring without causing stigma or discomfort. This article presents an in-depth review of the current status and future research challenges in systems and circuits for EMG acquisition and processing. We start by illustrating the sensor interfaces and acquisition systems required for signal analysis to provide efficient and effective ways of understanding the signal and its nature. We, then, focus on conventional state-of-the-art (SoA) EMG gesture recognition algorithms as well as novel architectures that tackle EMG processing challenges, i.e., hyperdimensional computing (HDC), blind source separation (BSS), and spiking neural networks (SNNs). Finally, we discuss open challenges, such as EMG variability, natural control, and efficient computation, to bring the myocontrol completely out of the laboratory, filling the gap between research prototypes and real-world applications. |
GAGASAN MASYARAKAT PASIFIK: SUATU TINJAUAN SINGKAT
| Pengarang | : | Endi Rukmo |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-10, OKTOBER (No. 10) |
| Halaman | : | 911-917 |
| Abstrak | : | - |
MENUJU KERJA SAMA DAN INTERDEPENDENSI
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-10, OKTOBER (No. 10) |
| Halaman | : | 903-910 |
| Abstrak | : | - |
A Review of Safe Reinforcement Learning Methods for Modern Power Systems
| Pengarang | : | Tong Su; Tong Wu; Junbo Zhao; Anna Scaglione; Le Xie |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 3) |
| Halaman | : | 213-255 |
| Abstrak | : | Given the availability of more comprehensive measurement data in modern power systems, reinforcement learning (RL) has gained significant interest in operation and control. Conventional RL relies on trial-and-error interactions with the environment and reward feedback, which often leads to exploring unsafe operating regions and executing unsafe actions, especially when deployed in real-world power systems. To address these challenges, safe RL has been proposed to optimize operational objectives while ensuring safety constraints are met, keeping actions and states within safe regions throughout both training and deployment. Rather than relying solely on manually designed penalty terms for unsafe actions, as is common in conventional RL, safe RL methods reviewed here primarily leverage advanced and proactive mechanisms. These include techniques such as Lagrangian relaxation, safety layers, and theoretical guarantees like Lyapunov functions to rigorously enforce safety boundaries. This article provides a comprehensive review of safe RL methods and their applications across various power system operations and control domains, including security control, real-time operation, operational planning, and emerging areas. It summarizes existing safe RL techniques, evaluates their performance, analyzes suitable deployment scenarios, and examines algorithm benchmarks and application environments. This article also highlights real-world implementation cases and identifies critical challenges such as scalability in large-scale systems and robustness under uncertainty, providing potential solutions and outlining future directions to advance the reliable integration and deployment of safe RL in modern power systems. |