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KEAMANAN DI KAWASAN ASIA-PASIFIK DAN PERTAHANAN JEPANG
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-10, OKTOBER (No. 10) |
| Halaman | : | 891-902 |
| Abstrak | : | - |
PERANAN AMERIKA SERIKAT DI KAWASAN PASIFIK
| Pengarang | : | Jusuf Wanandi |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-10, OKTOBER (No. 10) |
| Halaman | : | 878-890 |
| Abstrak | : | - |
KERJA SAMA DI PASIFIK BARAT-DAYA: SEBUAH PANDANGAN ASEAN
| Pengarang | : | Hadi Soesastro |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-10, OKTOBER (No. 10) |
| Halaman | : | 865-877 |
| Abstrak | : | - |
A Survey and Comparative Analysis of Number Systems for Deep Neural Networks
| Pengarang | : | Ghada Alsuhli; Vasilis Sakellariou; Hani Saleh; Mahmoud Al-Qutayri; Baker Mohammad; Thanos Stouraitis |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 2) |
| Halaman | : | 172-207 |
| Abstrak | : | Deep neural networks (DNNs) are indispensable in various artificial intelligence (AI) applications. However, their inherent complexity presents significant challenges, particularly when deploying them on resource-constrained devices. To overcome these hurdles, academia and industry are actively seeking ways to accelerate and optimize DNN implementations. A significant area of research revolves around discovering more effective methods to represent the enormous data volumes processed by DNNs. Traditional number systems (NSs) have proven nonoptimal for this task, prompting extensive exploration into alternative and bespoke systems for DNNs. This survey aims to comprehensively discuss various NSs utilized to efficiently represent DNN data. These systems are categorized mainly based on their impact on DNN performance and hardware implementation. This survey offers an overview of these categorized NSs and delves into different subsystems within each, outlining their effect on DNN performance and hardware design. Furthermore, these systems are compared quantitatively and qualitatively concerning their expected quantization error, memory utilization, and computational requirements. This survey also emphasizes the challenges linked with each system and the diverse proposed solutions to address them. Insights into the utilization of these NSs for sophisticated DNNs are also presented in this survey. Readers will acquire a deeper understanding of the importance of efficient NSs for DNNs, explore commonly used systems, comprehend the tradeoffs between these systems, delve into design considerations influencing their impact on DNN performance, and discover recent trends and potential research avenues in this field. |
PERUBAHAN STRUKTURAL BEBERAPA NEGARA INDUSTRI DI KAWASAN ASIA-PASIFIK DAN KESEMPATAN BAGI INDONESIA
| Pengarang | : | J. Panglaykim. |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-10, OKTOBER (No. 10) |
| Halaman | : | 835-864 |
| Abstrak | : | - |
How the Brain Formulates Memory: A Spatio-Temporal Model
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 2) |
| Halaman | : | 56-68 |
| Abstrak | : | Memory is a complex process across different brain regions and a fundamental function for many cognitive behaviors. Emerging experimental results suggest that memories are represented by populations of neurons and organized in a categorical and hierarchical manner. However, it is still not clear how the neural mechanisms are emulated in computational models. In this paper, we present a spatio-temporal memory (STM) model using spiking neurons to explore the memory formulation and organization in the brain. Unlike previous approaches, this model employs temporal population codes as the neural representation of information and spiketiming-based learning methods to formulate the memory structure. It explicitly demonstrates that the complex spatio-temporal patterns are the internal neural representations of memory items. Two types of memory processes are analyzed and emulated: associative memory, i.e., spatio-temporal patterns driven by intra-assembly connections, and episodic memory, i.e., temporally separated spatio-temporal patterns linked by inter-assembly connections. Our model will provide a computational substrate based on lowlevel neural circuits for developing neuromorphic cognitive systems with wide applications. |
Probabilistic Graphical Models on Multi-Core CPUs Using Java 8
| Pengarang | : | Borchani, Hanen,Martinez, Ana M.,Masegosa, Andrés R. |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 2) |
| Halaman | : | 41-54 |
| Abstrak | : | In this paper, we discuss software design issues related to the development of parallel computational intelligence algorithms on multi-core CPUs, using the new Java 8 functional programming features. In particular, we focus on probabilistic graphical models (PGMs) and present the parallelization of a collection of algorithms that deal with inference and learning of PGMs from data. Namely, maximum likelihood estimation, importance sampling, and greedy search for solving combinatorial optimization problems. Through these concrete examples, we tackle the problem of defining efficient data structures for PGMs and parallel processing of same-size batches of data sets using Java 8 features. We also provide straightforward techniques to code parallel algorithms that seamlessly exploit multicore processors. The experimental analysis, carried out using our open source AMIDST (Analysis of MassIve Data STreams) Java toolbox, shows the merits of the proposed solutions. |
Facilitating Creativity in Collaborative Work with Computational Intelligence Software
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 2) |
| Halaman | : | 29-40 |
| Abstrak | : | The use of computational intelligence for leveraging social creativity is a relatively new approach that allows organizations to find creative solutions to complex problems in which the interaction between stakeholders is crucial. The creative solutions that come from joint thinking-from the combined knowledge and abilities of people with diverse perspectivescontrast with traditional views of creativity that focus primarily on the individual as the main contributor of creativity. In an effort to support social creativity in organizations, in this paper we present com- putational intelligence software tools for that aim and an architecture for creating software mashups based on the concept ?o of affinity space.The affinity space defines a digital setting to facilitate specific scenarios in collaborative business environments. The solution presented includes a set of free and open source software tools rang- ing from newly developed brainstorming applications to an expertise recommender for enhancing social creativity in the enterprise. The current paper addresses software design issues and presents reflections on the research work undertaken in the COLLAGE project between 2012 and 2015. |
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
| Pengarang | : | Chuan Qin; Le Zhang; Yihang Cheng; Rui Zha; Dazhong Shen; Qi Zhang; Xi Chen; Ying Sun; Chen Zhu; Hengshu Zhu; Hui Xiong |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 2) |
| Halaman | : | 125-171 |
| Abstrak | : | In today’s competitive and fast-evolving business environment, it is critical for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of big data and artificial intelligence (AI) techniques has revolutionized human resource management (HRM). The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which, in turn, delivers intelligence for real-time decision-making and effective talent management for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for HRM, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of HRM. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios at different levels: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics. |
JFCS: A Color Modeling Java Software Based on Fuzzy Color Spaces
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 2) |
| Halaman | : | 16-28 |
| Abstrak | : | This paper introduces JFCS (Java Fuzzy Color Space), an open source software for modeling colors on the basis of fuzzy color spaces, which is able to fill the semantic gap between the color representation in computers and the subjective human perception. Fuzzy colors allow introducing semantics in the description of color by using linguistic labels, taking into account the fuzzy boundaries between the representation of color terms. The methodology for building fuzzy coloror spaces implemented in JFCS was proposed by the authors in previous works, it is based on the paradigm of conceptual spaces including fuzzy boundaries and considering a collection of color names and corresponding crisp color representatives. In this sense, the necessary information for modeling fuzzy color spaces can be easily obtained from non-expert users, since the only information required is a representative crisp color for each linguistic color term to be modeled.The software is implemented in Java and it includes several graphical tools for building different types of spaces with different characteristics in an easy way. These are needed to be consistent with the different nature of the colors a user wants to model. Membership degrees of pixels in images to each fuzzy color in a certain fuzzy color space can be obtained. Tools for 3D visualization of fuzzy color spaces, as well as for describing colors and mapping pixels in images in term of linguistic labels, are also included in JFCS. |