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作者： Lee, Seungyong Lee, Hyokeun Lee, Hyuk-Jae Kim, Hyun Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Electrical and Information Engineering Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul Korea Republic of

Phase-Change Memory (PCM) is known as the next generation of memory thanks to its outstanding properties, such as fast speed, non-volatility, scalability, and low power consumption. Based on these characteristics, PCM can be used in larger expanded memory or faster storage compared to HDD or NAND flash memory. Therefore, various companies are trying to deploy PCM-based memory products. However, studies on deciding the target system of PCM are still insufficient, which is an obstacle to the commercialization of PCM. In this paper, a file system benchmark, Filebench, is evaluated with various operating options to find the most appropriate workload for PCM as storage. An experiment was conducted in a virtual system by mounting PCM with a PCM-aware file system. The results demonstrate that the PCM-based system performs up to 500 times better than traditional storage if executing workloads with a significant amount of write operations and synchronization operations. A number of applications were tested on various configurations of systems, and workload characteristics suitable for the PCM-based system are presented. © 2021 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Phase change memory

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1000 FPS HDR Video with a Spike-RGB Hybrid Camera

1000 FPS HDR Video with a Spike-RGB Hybrid Camera

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yakun Chang Chu Zhou Yuchen Hong Liwen Hu Chao Xu Tiejun Huang Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University National Key Laboratory of General AI School of Intelligence Science and Technology Peking University

Capturing high frame rate and high dynamic range (HFR&HDR) color videos in high-speed scenes with conventional frame-based cameras is very challenging. The increasing frame rate is usually guaranteed by using shorter exposure time so that the captured video is severely interfered by noise. Alternating exposures can alleviate the noise issue but sacrifice frame rate due to involving long-exposure frames. The neuromorphic spiking camera records high-speed scenes of high dynamic range without colors using a completely different sensing mechanism and visual representation. We introduce a hybrid camera system composed of a spiking and an alternating-exposure RGB camera to capture HFR&HDR scenes with high fidelity. Our insight is to bring each camera's superiority into full play. The spike frames, with accurate fast motion information encoded, are firstly reconstructed for motion representation, from which the spike-based optical flows guide the recovery of missing temporal information for long-exposure RGB images while retaining their reliable color appearances. With the strong temporal constraint estimated from spike trains, both missing and distorted colors cross RGB frames are recovered to generate time-consistent and HFR color frames. We collect a new Spike-RGB dataset that contains 300 sequences of synthetic data and 20 groups of real-world data to demonstrate 1000 FPS HDR videos outperforming HDR video reconstruction methods and commercial high-speed cameras.

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The Parables of the Mustard Seed and the Yeast: Extremely Low-Budget, High-Performance Nighttime Semantic Segmentation 39

The Parables of the Mustard Seed and the Yeast: Extremely Lo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Shiqin Xu, Xin Chen, Haoyang Jiang, Kui Wang, Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)157735897X

Nighttime Semantic Segmentation (NSS) is essential to many cutting-edge vision applications. However, existing technologies overly rely on massive labeled data, whose annotation is time-consuming and laborious. In this paper, we pioneer a new task focusing on exploring the potential of training strategy and framework design with limited annotation to achieve high-performance NSS. Insufficient information at very low labeling budgets can easily lead to under-optimization or overfitting of the model. Our solution comprises two main components: i) a novel region-based active sampling strategy called Contextual-Aware Region Query (CARQ), which identifies highly informative target nighttime regions for labeling;and ii) an innovative Fragmentation Synergy Active Domain Adaptation framework (FS-ADA), which progressively broadcasts the limited annotation to the unlabeled regions, achieving high performance with a minimal annotation budget. Extensive experiments demonstrate that our method outperforms state-of-the-art UDA-NSS & ADA-SS methods across four day-to-nighttime benchmarks, and generalizes well to foggy, rainy, & snowy scenes. In particular only with 1% target nighttime data annotation, our method is on par with the mainstream fully-supervised methods on the BDD100K-Night val dataset. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Budget control

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ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy

ACGraph: Accelerating Streaming Graph Processing via Depende...

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Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids

Integrating Noise Classification and Speech Enhancement Mode...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Shin-Chi Lai Wen-Ho Juang Ming-Hwa Sheu Jeng-Dao Lee Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm.

关键词： Accuracy Noise Noise reduction Speech recognition Speech enhancement Hearing aids Classification algorithms Convolutional neural networks Human voice Older adults

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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Abnormal Respiratory Sound Identification Using Audio-Spectrogram Vision Transformer

Abnormal Respiratory Sound Identification Using Audio-Spectr...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Whenty Ariyanti Kai-Chun Liu Kuan-Yu Chen Yu-Tsao Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Research Center for Information Technology Innovation (CITI) at Academia Sinica Taipei Taiwan

Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spectrogram vision transformer (AS-ViT), a new approach for identifying abnormal respiration sounds, was developed. The sounds of the lungs are converted into visual representations called spectrograms using a technique called short-time Fourier transform (STFT). These images are then analyzed using a model called vision transformer to identify different types of respiratory sounds. The classification was carried out using the ICBHI 2017 database, which includes various types of lung sounds with different frequencies, noise levels, and backgrounds. The proposed AS-ViT method was evaluated using three metrics and achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20 split ratio in terms of unweighted average recall and overall scores respectively for respiratory sound detection, surpassing previous state-of-the-art results.

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Hierarchy-Aware Quaternion Embedding for Knowledge Graph Completion

Hierarchy-Aware Quaternion Embedding for Knowledge Graph Com...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qiuyu Liang Weihua Wang Jie Yu Feilong Bao College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Knowledge graph completion is an essential task in the fields of graph mining and graph machine learning. Most contemporary approaches rely on geometric transformation to achieve knowledge graph completion, as geometry offers a well-defined mathematical foundation. For example, rotation transformations in rigid body transformation are frequently employed within quaternion spaces to model complex relation types in knowledge graphs. However, these models cannot effectively handle the hierarchical structure in the knowledge graph. As a result, the performance of knowledge graph completion suffers. To address this shortcoming of quaternion space, we propose a novel model that integrates hyperbolic space. Specifically, we perform a translation transformation in a hyperbolic space to obtain support vector embeddings that imply relation embedding. We then perform a rotation transformation with the Hamilton product in tangent space, treating the relation embedding as a rotation from the head entity embedding to the tail entity embedding. We verify the validity and generalization ability of our model on standard benchmark datasets including WN18RR, FB15k-237 and YAGO3-10. The experimental results show that our model achieves competitive results on MRR and H@K metrics. Our code is publicly available at https://***/llqy123/HAQE-master.

关键词： Analytical models Quaternions Neural networks Knowledge graphs Tail Benchmark testing Mathematical models

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Multi-criteria Radio Frequency Identification Approach for Manufacturing Company Selection Based on Partitioned Maclaurin Symmetric Mean Operators Under Complex Intuitionistic Fuzzy Set Environment

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International Journal of Fuzzy Systems 2024年第5期26卷 1730-1748页

作者： Azam, Muhammad Jana, Chiranjibe Khan, Muhammad Sajjad Ali Pal, Madhumangal Xin, Qin Yang, Shilin Sarkar, Biswajit Faculty of Science Beijing University of Technology Beijing100022 China Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore721102 India Department of Mathematics Khushal Khan Khattak University Karak Khyber Pakhtunkhwa Karak27200 Pakistan Faculty of Science and Technology University of the Faroe Islands Tórshavn Faroe Islands Department of Industrial Engineering Yonsei University 50 Yonsei-ro Sinchon-dong Seodaemun-gu Seoul03722 Korea Republic of Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu 600077 Chennai India Department of Industrial Engineering and Management National Taipei University of Technology Taipei City 106344 Taiwan Chennai602105 India

The theory of intuitionistic fuzzy sets (IFSs) plays an essential role to deal with uncertainty and ambiguity. However, the IFSs deal only with anticipation, not periodicity. But, complex IFSs (CIFS) can handle both uncertainties and periodicity at a time. Therefore, this paper focuses on a new multi-criteria decision making (MCDM) approach using Maclaurin symmetric mean (MSM) operator in connection with the CIFS setting. Then, we develop some CIF partitioned based MSM (CIFPMSM) operators and their weighted form by considering that all the criteria can be arranged into some groups. The proposed operators not only deal with the interrelationship among criteria but also deal with the partitioned relationship among criteria. We discuss the properties of these proposed operators and investigate their cases. Finally, a decision-making approach for radio frequency identification (RFID) for manufacturing companies is developed based on the proposed operators and compared the results with the existing method to show the application and feasibility of the proposed method. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2024.

关键词： Decision making

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SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models

arXiv

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arXiv 2025年

作者： Duc, Chuc Man Fukui, Hiromichi Department of Computer Science Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam International Digital Earth Applied Science Research Center Chubu University Aichi Kasugai487-8501 Japan

Foundation models refer to deep learning models pretrained on large unlabeled datasets through self-supervised algorithms. In the Earth science and remote sensing communities, there is growing interest in transforming the use of Earth observation data, including satellite and aerial imagery, through foundation models. Various foundation models have been developed for remote sensing, such as those for multispectral, high-resolution, and hyperspectral images, and have demonstrated superior performance on various downstream tasks compared to traditional supervised models. These models are evolving rapidly, with capabilities to handle multispectral, multitemporal, and multisensor data. Most studies use masked autoencoders in combination with Vision Transformers (ViTs) as the backbone for pretraining. While the models showed promising performance, ViTs face challenges, such as quadratic computational scaling with input length, which may limit performance on multiband and multitemporal data with long sequences. This research aims to address these challenges by proposing SatMamba, a new pretraining framework that combines masked autoencoders with State Space Model, offering linear computational scaling. Experiments on high-resolution imagery across various downstream tasks show promising results, paving the way for more efficient foundation models and unlocking the full potential of Earth observation data. The source code is available in https://***/mdchuc/HRSFM. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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