In aquaculture, feeding cost usually takes a lot from the total budget. However, a significant portion of the feed often gets uneaten by the fish. To overcome this problem, many ways have been developed using artifici...
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Information Technology (IT) project management is important for the growth of organizations. It involves planning and executing projects to achieve strategic objectives. This paper reviews IT project management method...
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For large companies with branch offices spread across various regions, it is not enough to rely on traditional wide area network (WAN) technology to connect networks between data centers, head offices, and branch offi...
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Large Language Models (LLMs), emerging from advancements in Natural Language Processing (NLP) tasks, allow chatbots to provide more sophisticated and human-like text generation by leveraging their vast model sizes, of...
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Large Language Models (LLMs), emerging from advancements in Natural Language Processing (NLP) tasks, allow chatbots to provide more sophisticated and human-like text generation by leveraging their vast model sizes, often exceeding billions of parameters, to provide deep knowledge across various domains. Hence, they are becoming more integrated into our daily lives, serving as personal assistants or even as experts across various domains. Initial language models relied on rule-based systems and early neural networks like Recurrent Neural Networks (RNNs). However, it remained an issue to handle long-term dependency and to understand context over extended conversation. The advent of the Attention mechanism and Transformer architecture enables contextually natural text generation and compresses the burden of processing entire source information into singular vectors. Based on these two main ideas, model sizes gradually increases to accommodate more precise and comprehensive information, leading to the current state-of-the-art LLMs being very large, with parameters around 70 billion. As the model sizes are growing, the demand for substantial storage and computational capacity increases. This leads to the development of high-bandwidth memory and accelerators, as well as a variety of model architectures designed to meet these requirements. We note that LLM architectures have increasingly converged. This paper analyzes how these converged architectures perform in terms of layer configurations, operational mechanisms, and model sizes, considering various hyperparameter settings. In this paper, we conduct a concise survey of the history of LLMs by tracing the evolution of their operational improvements. Furthermore, we summarize the performance trends of LLMs under various hyperparameter settings using the RTX 6000, which features the state-of-the-art Ada Lovelace architecture. We conclude that even the same model can exhibit different behaviors depending on the hyperparamete
This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offeringaninnovativealternativetotraditionalmetamodel-basedsimulations. We unde...
This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offeringaninnovativealternativetotraditionalmetamodel-basedsimulations. We undertake an in-depth analysis of DoppelGANger, a prominent GAN variant for time series data and metadata generation, evaluating its efficiency and efficacy. The sensor data for this investigation was sourced from the National Health and Nutrition Examination Survey, which served as the foundational training set. We scrutinized the synthesized sensor data corresponding to various physical attributes, focusing on the temporal and multi-dimensional statistical properties. Our empirical findings underscore the potential of GANs to adeptly capture the time-dependent correlations and the intricate statistical characteristics inherent in multi-dimensional data. This insight into GANs’ capabilities is a crucial step towards more sophisticated synthetic data generation, with significant implications for future applications in wearable technology and personalized health monitoring systems.
Coffee is one of the plantation crops that has long been a cultivated plant in Indonesia. The classification of coffee fruit maturity manually still has several weaknesses and requires a long process, has low accuracy...
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This paper aims to analyze passenger needs for network connectivity on the ship during traveling at the shipping line of Indonesian XYZ Company. The current connectivity infrastructure on board is supported by VSAT te...
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Rainfall frequency analysis, an essential work for water resources management, is often conducted by using the annual maximum rainfall series. For rainfall stations with short record lengths and outliers presence, the...
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Based on the achievements regarding the 2020 targets, the Renewable Energy Directive established a new, higher target for 2030, reflecting a significant increase of interest for renewable energy sources (RES). As a re...
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This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, sta...
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ISBN:
(数字)9781665464543
ISBN:
(纸本)9781665464550
This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software—both commercial and open-source—and concluding with a qualitative evaluation of the results.
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