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IEEE International Conference on Cluster computing

作者： Hiroko Midorikawa Motoyoshi Kurokawa Ryutaro Himeno Mitsuhisa Sato Department of Computer and Information Science Seikei University Musashino Tokyo Japan Graduate School of Systems and Information Engineering University of Tsukuba Tennodai Tsukuba Ibaraki Japan Advanced Center of Computing and Communication Wako Saitama Japan Research Program for Computational Science RIKEN Wako Saitama Japan

Emerging 64 bitOSpsilas supply a huge amount of memory address space that is essential for new applications using very large data. It is expected that the memory in connected nodes can be used to store swapped pages efficiently, especially in a dedicated cluster which has a high-speed network such as 10 GbE and Infiniband. In this paper, we propose the distributed large memory system (DLM), which provides very large virtual memory by using remote memory distributed over the nodes in a cluster. The performance of DLM programs using remote memory is compared to ordinary programs using local memory. The results of STREAM, NPB and Himeno benchmarks show that the DLM achieves better performance than other remote paging schemes using a block swap device to access remote memory. In addition to performance, DLM offers the advantages of easy availability and high portability, because it is a user-level software without the need for special hardware. To obtain high performance, the DLM can tune its parameters independently from kernel swap parameters. We also found that DLMpsilas independence of kernel swapping provides more stable behavior.

关键词： Kernel Arrays Memory management Servers Bandwidth Performance evaluation Benchmark testing

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2D Basement Relief Inversion using Sparse Regularization.

arXiv

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arXiv 2024年

作者： Barboza, Francisco Márcio da Cunha Romão Silva, E. Arthur Anthony de Carvalho, Bruno Motta Department of Computing and Technology Federal University of Rio Grande do Norte Rio Grande do Norte Caicó Brazil Graduate Program on Systems and Computing Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil

Basement relief gravimetry is a key application in geophysics, particularly important for oil exploration and mineral prospecting. It involves solving an inverse geophysical problem, where the parameters of a geological model are inferred from observed data. In this context, the geological model consists of the depths of constant-density prisms representing the basement relief, and the data correspond to the gravitational anomalies caused by these prisms. Inverse geophysical problems are typically ill-posed, as defined by Hadamard, meaning that small perturbations in the data can result in large variations in the solutions. To address this instability, regularization techniques, such as those proposed by Tikhonov, are employed to stabilize the solutions. This study presents a comparative analysis of various regularization techniques applied to the gravimetric inversion problem, including Smoothness Constraints, Total Variation, the Discrete Cosine Transform (DCT), and the Discrete Wavelet Transform (DWT) using Daubechies D4 wavelets. Optimization methods are commonly used in inverse geophysical problems because of their ability to find optimal parameters that minimize the objective function—in this case, the depths of the prisms that best explain the observed gravitational anomalies. The Genetic Algorithm (GA) was selected as the optimization technique. GA is based on Darwinian evolutionary theory, specifically the principle of natural selection, where the fittest individuals in a population are selected to pass on their traits. In optimization, this translates to selecting solutions that most effectively minimize the objective function. The results, evaluated using fit metrics and cumulative error analysis, demonstrate the effectiveness of all the regularization techniques and the Genetic Algorithm. Among the methods tested, the Smoothness constraint was briefly the most effective for the first and second synthetic models. For the third model, which was based on re

关键词： Discrete wavelet transforms

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TEAM: Task-Clustering and Enhanced Adaptability for Meta-Reinforcement Learning on Robotics Through Multi-Task Diffusion and Optimization

TEAM: Task-Clustering and Enhanced Adaptability for Meta-Rei...

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IEEE International Conference on Robotic computing (IRC)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Chi-Wei Feng Shih-Yun Sunkuan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

The ability of robots to imitate human learning strategies-rapidly adapting to new tasks without large datasets-has garnered significant attention in meta-learning. Meta-reinforcement learning seeks to enhance robotic agent flexibility across diverse tasks and contexts, offering promise where single-task learning often fails. Despite advancements like multi-task diffusion models and task-weighted optimization mechanisms, effectively training tasks with varying complexities simultaneously remains a major challenge. This paper introduces a novel meta-reinforcement learning method that addresses this issue by clustering the training tasks of robotic arms based on semantic and trajectory similarities, while leveraging adaptive learning rates and task-specific weights proposed by the multitask optimization techniques. Our approach, TEAM, emphasizes performance-driven semantic clustering, optimizing based on robotic task similarity, complexity, and convergence objectives. We also integrate fast adaptive and multi-task optimization of the diffusion model to enhance computational efficiency and adaptability. More specifically, we introduce a cluster-specific optimization technique, using specialized parameters for each group to allow more refined task handling. The experimental validation demonstrates the effectiveness of this scalable method in improving performance, adaptability, and efficiency in real-world, heterogeneous robotic tasks, further advancing robotic computing in meta-reinforcement learning.

关键词： Training Metalearning program processors Semantics Multitasking Diffusion models Complexity theory Trajectory Robots Optimization

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Drum-aware ensemble architecture for improved joint musical beat and downbeat tracking

arXiv

引用

arXiv 2021年

作者： Chiu, Ching-Yu Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

This paper presents a novel system architecture that integrates blind source separation with joint beat and downbeat tracking in musical audio signals. The source separation module segregates the percussive and non-percussive components of the input signal, over which beat and downbeat tracking are performed separately and then the results are aggregated with a learnable fusion mechanism. This way, the system can adaptively determine how much the tracking result for an input signal should depend on the input's percussive or non-percussive components. Evaluation on four testing sets that feature different levels of presence of drum sounds shows that the new architecture consistently outperforms the widely-adopted baseline architecture that does not employ source separation. Copyright © 2021, The Authors. All rights reserved.

关键词： Blind source separation

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FluxProg 2.0 - Teaching Introductory programming Using Flowcharts with Real and Simulated Robots for The Brazilian Robotics Olympiad (OBR)

FluxProg 2.0 - Teaching Introductory Programming Using Flowc...

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IEEE Latin American Robotics Symposium, LARS

作者： João Alberto Fabro Matheus Biscaya Gutierrez Fernando Henrique Ratusznei Caetano DAINF - Academic Department of Informatica PPGCA - Graduate Program on Applied Computing LASER-Laboratory for Advanced Robotics and Embedded Systems UTFPR - Federal University of Technology - Paraná Curitiba PR - Brazil Information Systems academic - UTFPR - Federal University of Technology - Paraná Curitiba PR - Brazil Computer Engineering academic - UTFPR - Federal University of Technology - Paraná Curitiba PR - Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

In this paper, an approach to aid in introductory programming courses is presented, focused on students that had never had any contact with computer programming. The approach, that was introduced in a previous paper [3], presents programming concepts using icons and “flowcharts”, and a 3D robotics simulator (CoppeliaSim), that has free educational use. This time, a real version of the simulated robot, built using Arduino and cheap, easily obtainable electronic components, is also presented. The programs to control the robot are developed by graphically constructing flowcharts on the visual editor, and then following the execution of programs using the simulated (or real) robot, step by step. This graphic program editor is named “FluxProg”, and its 2nd version is now available online, with new features comprising variables, arithmetic and logic expressions, thus providing a complete programming environment.

关键词： Flowcharts Visualization Three-dimensional displays Conferences Education Electronic components Robots

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mietha Anditta Natashya Amartya Laurens Spits Warnars Harco Leslie Hendric Spits Warnars Arief Ramadhan Teddy Siswanto Teddy Mantoro Nurulhuda Noordin Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Departement School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia PJJ Informatika Telkom University Bandung Indonesia Information Systems Department Trisakti University Jakarta Indonesia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.

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Computation of Resistance and Potential of Grounding Grids in Any Geometry

Computation of Resistance and Potential of Grounding Grids i...

引用

IEEE International Conference on Environment and Electrical Engineering

作者： Thyago G. Pires José W. L. Nerys Carlos L. B. Silva Diogo N. Oliveira Antonio M. Silva Filho Wesley P. Calixto Aylton J. Alves School of Electric and Computing Engineering. Federal University of Goias. Goiania Brazil Graduate Program in Electronic and Automation Systems Engineering. University of Brasilia. Brasilia Brazil Experimental & Technologic Research and Study Group. Federal Institute of Education Science and Technology. Goiania Brazil

ISBN: (纸本)9781509023219

This paper deals with the computation of ground resistance, surface voltage, touch voltage and step voltage, to mesh with horizontal wires arranged in different angles. The computer program implemented used in the mathematical modeling is based on the method proposed by Heppe, which allows obtaining the grounding parameters for homogeneous soil and soil stratified in two layers. The results obtained with the proposed method will be compared with other methods in literature. Also will be presented the results of a grounding grid using wires at various angles.

关键词： ground grid leakage current mesh voltage multilayer soil surface potential

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How Good is ChatGPT at Audiovisual Deepfake Detection: A Comparative Study of ChatGPT, AI Models and Human Perception

arXiv

引用

arXiv 2024年

作者： Shahzad, Sahibzada Adil Hashmi, Ammarah Peng, Yan-Tsung Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan The Department of Computer Science National Chengchi University Taipei Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan The Institute of Information Science Academia Sinica Taiwan

Multimodal deepfakes involving audiovisual manipulations are a growing threat because they are difficult to detect with the naked eye or using unimodal deep learning-based forgery detection methods. Audiovisual forensic models, while more capable than unimodal models, require large training datasets and are computationally expensive for training and inference. Furthermore, these models lack interpretability and often do not generalize well to unseen manipulations. In this study, we examine the detection capabilities of a large language model (LLM) (i.e., ChatGPT) to identify and account for any possible visual and auditory artifacts and manipulations in audiovisual deepfake content. Extensive experiments are conducted on videos from a benchmark multimodal deepfake dataset to evaluate the detection performance of ChatGPT and compare it with the detection capabilities of state-of-the-art multimodal forensic models and humans. Experimental results demonstrate the importance of domain knowledge and prompt engineering for video forgery detection tasks using LLMs. Unlike approaches based on end-to-end learning, ChatGPT can account for spatial and spatiotemporal artifacts and inconsistencies that may exist within or across modalities. Additionally, we discuss the limitations of ChatGPT for multimedia forensic tasks. © 2024, CC BY.

关键词： Audiovisual

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Classifying Ore Deposits Based on La-Icp-Ms Geochemistry of Natural Gold

SSRN

引用

SSRN 2024年

作者： Verbel, Angel Augusto Schutesky, Maria Emilia Gregory, Daniel D. Verbel, Arturo University of Brasilia Institute of Geosciences Graduate Program in Geology DF Brasília70910-900 Brazil University of Toronto Department of Earth Sciences TorontoONM5S 3B1 Canada Universidad de los Andes Systems and Computing Engineering Department School of Engineering Bogotá111711 Colombia

Systematic sampling of gold grains and their characterization in abundance and morphology can be used as a vectoring tool for mineral deposits. However, the processes that lead to the formation of an ore deposit are multiscale and nonlinear. As a result, the elemental compositions recorded in gold grains during the mineralization process seem mostly irregular and unpredictable. Here, we took advantage of the capabilities of neural networks to approximate, a natural nonlinear function that better explains the chemical variability observed in gold allowing us to make predictions. this was achieved by using published data of trace elements in gold of four types of gold deposits (i.e., Orogenic, VMS, porphyry and epithermal) from 47 different localities, obtained by laser ablation-inductively coupled plasma-mass spectrometry (LA- ICP-MS). The model was optimized by training five different architectures on the most influential elements, determined by principal component analysis (PCA). As a result, it was found that two hidden layers with 10 neurons each, is the best architecture for using trace elements as predictors of the type of deposit that formed a natural gold grain. In order to encourage the use of the findings made in this paper, we introduce OreGenes, an app developed in Matlab2023b based on the best-obtained model. It allows any user who possesses compatible data, the ability to import and process it to obtain a prediction with an average accuracy level of 88.9% confidence to determine the mineral deposit type that an unknown gold grain came from which grants the user the ability to have a powerful tool for exploration or research. © 2024, The Authors. All rights reserved.

关键词： Deposits

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Speaker-Specific Articulatory Feature Extraction Based on Knowledge Distillation for Speaker Recognition

引用

APSIPA Transactions on Signal and Information Processing 2023年第2期12卷

作者： Qian-Bei Hong Hsin-Min Wang Chung-Hsien Wu Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica and Department of Computer Science and Information Engineering National Cheng Kung University Taiwan

This paper proposes a novel speaker-specific articulatory feature (AF) extraction model based on knowledge distillation (KD) for speaker recognition. First, an AF extractor is trained as a teacher model for extracting the AF profiles of the input speaker dataset. Next, a KD-based speaker embedding extraction method is proposed to distill the speaker-specific information from the AF profiles in the teacher model to a student model based on multi-task learning, in which the lower layers not only capture the speaker characteristics from acoustic features, but also learn the speaker-specific features from the AF profiles for robust speaker representation. Finally, speaker embeddings are extracted from the high-level layer, and the obtained speaker embeddings are further used to train a probabilistic linear discriminant analysis (PLDA) model for speaker recognition. In the experiments, speaker embedding models were trained using the VoxCeleb2 dataset and the AF extractor was trained based on the LibriSpeech dataset, and the performance was evaluated using the VoxCeleb1 dataset. The experiments showed that the proposed KD-based models outperformed the baseline models without KD. Furthermore, feature concatenation of multimodal results can further improve the performance.

关键词： Speaker recognition articulatory feature knowledge distillation

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