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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lee, Saehyung Mok, Jisoo Park, Sangha Shin, Yongho Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Qualcomm Korea YH Seoul Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT. © 2024 Neural information processing systems foundation. All rights reserved.

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Generating comprehension checking problems using mutation analysis compensating for shakyo-style learning defects: A case study on binary operators with evaluating its effectiveness

Computer Software

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computer Software 2021年第1期38卷 111-139页

作者： Kamei, Ryota Yoshizuka, Takahiro Sasano, Isao Komiya, Seiichi Department of Electrical Engineering and Computer Science Graduate School of Engineering and Science Shibaura Institute of Technology Emeritus Shibaura Institute of Technology

When learning some programming language it is often the case that novice programmers input some sample code and check the execution results. This is called Shakyo-Style learning, which tends to be a monotonous task without thinking and thus results in insufficient understanding of the syntax and semantics of the language. In this study, we present a new learning method with making minute changes to source code. The changes are made by the techniques in mutation test. In this work we change some binary operators in the original source code and let novice programmers write the standard output of the generated code. Based on the method, we developed a tool and evaluated it by an experiment, which shows that the group with learning by the tool in addition to Shakyo-Style learning improved learning effectiveness, compared with the group with solving exercises similar to problems in the examinations in the experiments. Furthermore, by the results of questionnaires, we found that our method improves the motivation of novice programmers. We also report the implementation of the tool. © 2021 Japan Society for Software science and Technology. All rights reserved.

关键词： Semantics

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White rice stem borer pest detection system using image-based convolution neural network 9th

White rice stem borer pest detection system using image-base...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Saufi, Akhmad Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Preventing agricultural resource loss caused by pests remains a crucial issue. While technological advancements are being achieved, the current agricultural management methods and equipment have yet to meet the required level for precise pest control, a huge portion of the pest population analysis process is still conducted manually. As a solution to this issue, the development of a White Rice Stem Borer pest detection system has been conducted by applying Convolutional Neural Network (CNN) technology to calculate the pest population count at the research location. This system has been specifically designed to detect the White Rice Stem Borer using available traps. The method involves training data from a direct dataset obtained from the field, categorized into two positive and negative classes of the White Stem Borer pests. Six models have been trained from this dataset, utilizing two different architectures. Out of the six trained models, four showed potential overfitting, one exhibited underfitting, and one model demonstrated optimal results. The highest accuracy in image detection achieved by the most optimal CNN model was 97.35%, with a training accuracy of 98.54%. This best-performing model utilized an architecture with three Convolution layers, 50 Epochs, and an automatic data split with an 80:20 training-validation data ratio. From the research findings, it is concluded that this study can assist in automatically analyzing the quantity of White Stem Borer pests in a specific area without directly counting the number of pests from existing traps. However, the study still encounters a limitation-the detection process still requires substantial server resources and cannot be directly processed on the Raspberry PI device installed in the trap. Consequently, the detection relies on transmitting image data from the field device to the server before the detection process can occur. © 2024 The Authors.

关键词： Convolutional neural networks

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Impact of Renewable Energy on the Philippine Electricity Spot Prices: An Ex-Post Analysis Using Multiple Linear Regression

Impact of Renewable Energy on the Philippine Electricity Spo...

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2023 IEEE International Conference on electrical, computer and Energy Technologies, ICECET 2023

作者： Marzo Barcenas, Mark Reden Ang, Gerard Ladignon, Christopher Musni School of Graduate Studies Mapúa University Manila Philippines School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines Nueva Ecija University of Science and Technology Electrical Engineering Department Cabanatuan City Philippines

ISBN: (纸本)9798350327816

The increasing integration of renewable energy (RE) sources impacts the spot price in the electricity market. Decentralized markets observe electricity spot price elasticity in response to the additional supply from the RE sources. This study focused on the empirical investigation of the impact of solar PV, wind, and biomass power generation in the Philippine Wholesale Electricity Spot Market (WESM) prices using multiple linear regression. STATA software was the main driver for statistical modeling and testing. To ensure the result's accuracy and avoid spurious analysis, the hourly market data was initially subjected to data cleaning and evaluation. The heteroskedasticity and serial correlation detected in the initial regression model were addressed using the Newey-West error estimation - the recommended robust technique for the mentioned concerns. The energy dispatch of different power plant types significantly explains the 2019 Philippine electricity spot price variations, hence the model's reliability for the analysis. The proposed methodology may be adopted and found helpful in assessing the impacts of the rising RE grid integration in the electricity spot price. © 2023 IEEE.

关键词： decentralized market electricity spot price multiple linear regression analysis renewable energy sources

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A Case Study on the Effects of Auditory Cues in Influencing Spatial Memory and Representation for a Person who is Blind 1

A Case Study on the Effects of Auditory Cues in Influencing ...

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1st International Workshop on Brain-computer Interfaces (BCI) for Multimedia Understanding, BCIMM 2024, co-located with ACM Multimedia 2024

作者： Nguyen, Minh Zhu, Howe Sun, Huiyuan Nguyen, Vincent Singh, Avinash Jin, Craig Lin, Chin-Teng School of Computer Science University of Technology Sydney Sydney Australia School of Electrical and Information Engineering The University of Sydney Sydney Australia Graduate School of Health University of Technology Sydney Sydney Australia

ISBN: (纸本)9798400711893

Navigation is a coordinated and goal-oriented movement through the environment, in which vision is an integral part of acquiring spatial information. People with blindness and vision impairment rely on alternative senses to navigate in daily life. Auditory cues are critical in building a spatial representation and effective navigation. Our work investigates the neurophysiological response to explore how auditory cues can shape a person’s spatial representation and memory of an environment. In our experiment, a person who is blind was presented with verbal and non-verbal cues, either at close-reaching distance or globally. Electroencephalography, walking trajectories and tactile drawing data were collected to evaluate spatial representation and memory formed by different auditory cues and the role of theta oscillations in such relationships. Our results indicated a consistent increase in theta power at turning and starting points, a higher cognitive load with verbal strategies, and enhanced path recollection with global strategies. These findings emphasise the role of theta as a key indicator of cognitive workload and spatial memory processing. The preliminary results contribute to a broader understanding of the underlying processing mechanism of auditory information processing to support spatial navigation. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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An FM-Index Based High-Throughput Memory-Efficient FPGA Accelerator for Paired-End Short-Read Mapping

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IEEE Transactions on Biomedical Circuits and Systems 2023年第6期17卷 1331-1341页

作者： Yang, Chung-Hsuan Wu, Yi-Chung Chen, Yen-Lung Lee, Chao-Hsi Hung, Jui-Hung Yang, Chia-Hsiang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan GeneASIC Technologies Corporation Hsinchu302 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Electrical Engineering Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan

This article presents an Ferragina-Manzini index (FM-index) based paired-end short-read mapping hardware accelerator. Four techniques are proposed to significantly reduce the number of memory accesses and operations to improve the throughput. First, an interleaved data structure is proposed to reduce the processing time by 51.8% by leveraging the data locality. Second, the boundaries of possible mapping location candidates can be retrieved within only one memory access by constructing a lookup table along with the FM-index. This reduces the number of DRAM accesses by 60% with only a 64 MB memory overhead. Third, an additional step is added to skip the time-consuming repetitive location candidates filtering conditionally, avoiding unnecessary operations. Lastly, an early termination method is proposed to terminate the mapping process if any location candidate with a high enough alignment score is detected, greatly decreasing the execution time. Overall, the computation time is reduced by 92.6% with only a 2% memory overhead in DRAM. The proposed methods are realized on a Xilinx Alveo U250 FPGA. The proposed FPGA accelerator processes 1,085,812,766 short-reads from the U.S. Food and Drug Administration (FDA) dataset within 35.4 minutes at 200 MHz. It achieves a 1.7-to-18.6× higher throughput and the highest 99.3% accuracy by exploiting the paired-end short-read mapping, compared to state-of-the-art FPGA-based designs. © 2023 IEEE.

关键词： Genome

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： El-Sheikh, Mohamed M. Bayoumy, Aya Ahmed, Nada M. Mohamed Eldeeb, Amany Rabea, Fatma I. Nawar, Amira A. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Fully explicit dynamic Gaussian splatting 24

Fully explicit dynamic Gaussian splatting

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Junoh Lee Changyeon Won Hyunjun Jung Inhwan Bae Hae-Gon Jeon School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology AI Graduate School Gwangju Institute of Science and Technology Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science and AI Graduate School Gwangju Institute of Science and Technology

ISBN: (纸本)9798331314385

3D Gaussian Splatting has shown fast and high-quality rendering results in static scenes by leveraging dense 3D prior and explicit representations. Unfortunately, the benefits of the prior and representation do not involve novel view synthesis for dynamic motions. Ironically, this is because the main barrier is the reliance on them, which requires increasing training and rendering times to account for dynamic motions. In this paper, we design Explicit 4D Gaussian Splatting (Ex4DGS). Our key idea is to firstly separate static and dynamic Gaussians during training, and to explicitly sample positions and rotations of the dynamic Gaussians at sparse timestamps. The sampled positions and rotations are then interpolated to represent both spatially and temporally continuous motions of objects in dynamic scenes as well as reducing computational cost. Additionally, we introduce a progressive training scheme and a point-backtracking technique that improves Ex4DGS's convergence. We initially train Ex4DGS using short timestamps and progressively extend timestamps, which makes it work well with a few point clouds. The point-backtracking is used to quantify the cumulative error of each Gaussian over time, enabling the detection and removal of erroneous Gaussians in dynamic scenes. Comprehensive experiments on various scenes demonstrate the state-of-the-art rendering quality from our method, achieving fast rendering of 62 fps on a single 2080Ti GPU.

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Dental Imaging Using a Higher Signal-to-Noise Ratio Optical Coherence Tomography System

Dental Imaging Using a Higher Signal-to-Noise Ratio Optical ...

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2023 IEEE International Conference on electrical, computer and Energy Technologies, ICECET 2023

作者： Azeez, Akeem. A. Mezgebo, Biniyam Franca, Rodrigo Sherif, Sherif S. University of Manitoba Biomedical Engineering Graduate Program Winnipeg Canada University of Manitoba Dept. of Electrical and Computer Engineering Winnipeg Canada University of Manitoba Dept. of Restorative Dentistry Winnipeg Canada

ISBN: (纸本)9798350327816

This paper presents an optical coherence tomography (OCT) system in conjunction with a novel image reconstruction technique employed for in vitro imaging of human teeth. The primary goal is to enhance the signal-to-noise ratio (SNR) in the obtained images. The study entails a comparative analysis between the conventional Fast Fourier Transform (FFT) OCT image reconstruction method and a newly introduced scaled nonuniform discrete Fourier transform (NDFT) approach. The findings reveal that the NDFT method consistently delivers superior results in terms of peak signal-to-noise ratio (PSNR) and overall image quality, even when dealing with redundant and nonuniform frequency domain samples. In light of these results, this paper concludes that integrating NDFT into OCT procedures has the potential to significantly enhance the quality of image reconstructions, thereby fostering its broader application in the field of dental imaging. © 2023 IEEE.

关键词： dental imaging frame theory image reconstruction Optical Coherence Tomography (OCT) signal processing

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Optimizing 3D Clothing Models for VR: A Deep Learning Approach to Triangle Count Reduction

Optimizing 3D Clothing Models for VR: A Deep Learning Approa...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Surasachai Doungtap Varinya Phanichraksaphong Jenq-Haur Wang International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This study presents a deep learning framework optimizing 3D clothing models for VR, using a CNN to significantly reduce the triangle count of models from DeepFashion3D and CAP-UDF datasets. Achieving a balance between efficiency and detail, it cuts triangle count from over 160,000 to below 4,000, maintaining high DPI. The approach automates optimization, promising scalability and efficiency in VR fashion, setting a foundation for future 3D content development, enhancing virtual garment realism and interactivity.

关键词： Deep learning Solid modeling Three-dimensional displays Scalability Clothing Optimization Consumer electronics

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