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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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TC4SE: A high-performance Trusted Channel Mechanism for Secure Enclave-Based Trusted Execution Environments 26th

TC4SE: A High-Performance Trusted Channel Mechanism for Secu...

引用

26th International conference on Information Security (ISC)

作者： Hamidy, Gilang Mentari Yulianti, Sri Philippaerts, Pieter Joosen, Wouter Katholieke Univ Leuven DistriNet IMEC Leuven Belgium

ISBN: (纸本)9783031491863;9783031491870

We present TC4SE, a trusted channel mechanism suitable for secure enclave-based trusted execution environments, such as Intel SGX, that leverages the existing security properties provided by the TEE remote attestation scheme and Transport Layer Security (TLS) protocol. Unlike previous works that integrate attestation into the TLS handshake, TC4SE separates these two processes and binds the trust to the authentication primitives used by the TLS protocol. TC4SE avoids modifying the TLS protocol itself, thereby avoiding extra overhead, dependencies, and inadvertent introduction of security vulnerabilities. We argue that TC4SE provides the same level of security assurance as related works, while offering superior performance and implementation advantages, comparable to the regular TLS protocol.

关键词： Intel SGX attestation trusted channel Transport Layer Security Trusted Execution Environment

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Decoupled Multimodal Distilling for Emotion Recognition

Decoupled Multimodal Distilling for Emotion Recognition

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Li, Yong Wang, Yuanzhi Cui, Zhen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Key Lab Intelligent Percept & Syst High Dimens In Minist EducPCA Lab Nanjing Peoples R China

ISBN: (纸本)9798350301298

Human multimodal emotion recognition (MER) aims to perceive human emotions via language, visual and acoustic modalities. Despite the impressive performance of previous MER approaches, the inherent multimodal heterogeneities still haunt and the contribution of different modalities varies significantly. In this work, we mitigate this issue by proposing a decoupled multimodal distillation (DMD) approach that facilitates flexible and adaptive crossmodal knowledge distillation, aiming to enhance the discriminative features of each modality. Specially, the representation of each modality is decoupled into two parts, i.e., modality-irrelevant/-exclusive spaces, in a self-regression manner. DMD utilizes a graph distillation unit (GD-Unit) for each decoupled part so that each GD can be performed in a more specialized and effective manner. A GD-Unit consists of a dynamic graph where each vertice represents a modality and each edge indicates a dynamic knowledge distillation. Such GD paradigm provides a flexible knowledge transfer manner where the distillation weights can be automatically learned, thus enabling diverse crossmodal knowledge transfer patterns. Experimental results show DMD consistently obtains superior performance than state-of-the-art MER methods. Visualization results show the graph edges in DMD exhibit meaningful distributional patterns w.r.t. the modality-irrelevant/-exclusive feature spaces. Codes are released at https://***/mdswyz/DMD.

关键词： body gesture Humans: Face movement pose

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high-fidelity 3D Human Digitization from Single 2K Resolution Images

High-fidelity 3D Human Digitization from Single 2K Resolutio...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Han, Sang-Hun Park, Min-Gyu Yoon, Ju Hong Kang, Ju-Mi Park, Young-Jae Jeon, Hae-Gon Gwangju Inst Sci & Technol GIST Gwangju South Korea Korea Elect Technol Inst KETI Seongnam South Korea

ISBN: (纸本)9798350301298

high-quality 3D human body reconstruction requires high-fidelity and large-scale training data and appropriate network design that effectively exploits the high-resolution input images. To tackle these problems, we propose a simple yet effective 3D human digitization method called 2K2K, which constructs a large-scale 2K human dataset and infers 3D human models from 2K resolution images. The proposed method separately recovers the global shape of a human and its details. The low-resolution depth network predicts the global structure from a low-resolution image, and the part-wise image-to-normal network predicts the details of the 3D human body structure. The high-resolution depth network merges the global 3D shape and the detailed structures to infer the high-resolution front and back side depth maps. Finally, an off-the-shelf mesh generator reconstructs the full 3D human model, which are available at https://***/SangHunHan92/2K2K. In addition, we also provide 2,050 3D human models, including texture maps, 3D joints, and SMPL parameters for research purposes. In experiments, we demonstrate competitive performance over the recent works on various datasets.

关键词： 3D from single images

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Optimizing the performance of the ECLAT Algorithm using K-means Clustering for Frequently Itemset Mining for Large Datasets 5

Optimizing the Performance of the ECLAT Algorithm using K-me...

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5th International conference on Advanced Information Technologies, ICAIT 2024

作者： Lin, Nandar Win, Thanda Yangon Technological University Computer Engineering and Information Technology Yangon Myanmar

ISBN: (纸本)9798331510046

Data mining is an approach to identify the key pattern in Frequent Itemset Mining (FIM) and to find from historical data into very useful data. ECLAT is an aspect of association rule that is applied to determine the relationship connection in item supports. By intersecting the transaction id sets according to the corresponding kth item sets based on depth-first search, ECLAT algorithm computes the support items. During searching best-selling products, ECLAT requires a lot of memory and processing time for large dataset because of the amount of transaction id sets. By merging the K-means clustering algorithm and ECLAT algorithm, the proposed method aims to determine how the K-means clustering algorithm enhances the performance of ECLAT algorithm to generate support items for large dataset. The retail data items from 100000 to 400000 UK consumers were used to retrieve the support items of the most popular selling goods. Firstly, K-means algorithm grouped the same purchased products to cluster the top selling items and then ECLAT algorithm searches the support items from clustering the famous selling products. According to the experimental results, it can be seen that the silhouette score value in this research is 0.586 to indicate a high-quality cluster, the ideal value of k is 8 clusters between 100,000 and 400,000 data items. ECLAT can search the support items based on the clustering of the top eight selling supplies by setting a minimum support value of 0.00001. The proposed method reduces memory usage and execution time to retrieve the support elements for most popular selling items than the ECLAT algorithm for massive dataset. © 2024 IEEE.

关键词： K-means clustering

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Global Contrastive Learning with high-Quality Data in Large Vision-Language Models for Pathological Question Answering

Global Contrastive Learning with High-Quality Data in Large ...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kuang, Hulin Liu, Suoni Zhang, Haojie Qu, Zhe Kan, Shichao Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha410083 China

ISBN: (纸本)9798350386226

Pathological question answering (PQA) is vital in computational pathology, as it involves interpreting pathological images and answering questions posed by humans. This interaction offers an effective means of engaging with users and enhancing the understanding of pathology-related information. Recent methods developed using large vision-language models (LVLMs), such as QUILT-LLAVA, have made significant progress in advancing PQA. However, existing models, such as those using the QUILT-1M dataset, neglect the quality of the training set during the fine-tuning stage, leading to sub-optimal performance. We recognize that high-quality training data can significantly enhance model performance. Therefore, we design a model-based data filtering strategy to remove images with obvious impurities from the instruction fine-tuning dataset. The filtered high-quality images are then used to fine-tune the model. Additionally, most existing vision-language alignment strategies focus primarily on aligning local features through next-word prediction, leading to a relatively homogeneous granularity in inter-modal alignment. To address this issue, we propose a global-wise alignment module, which introduces global-level contrastive learning during the pretraining stage to establish multi-granularity alignment between pathological images and language descriptions. Based on the above two processes, we design our method, named Global Contrastive Learning with high-Quality Data (GCL-HQD) for pathological question answering in LVLMs. Extensive experiments on two types of experimental settings demonstrate the effectiveness of the GCL-HQD method. © 2024 IEEE.

关键词： Contrastive Learning

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An Improved Template-Based JIT Compiler for Java 34

An Improved Template-Based JIT Compiler for Java

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34th Annual International conference on Collaborative Advances in Software and Computing, CASCON 2024

作者： Kaur, Harpreet Pirvu, Marius Kent, Kenneth B. University of New Brunswick Faculty of Computer Science FrederictonNB Canada IBM Canada Java JIT Compiler Development TorontoON Canada

ISBN: (纸本)9798331504830

programs that run on a Java virtual machine (JVM)-like Eclipse OpenJ9-are initially interpreted. To improve performance, a Just-in- Time (JIT) compiler may be employed at run time to translate the whole or parts of the program to native code, which can be executed directly instead of being interpreted. In certain environments, where the overhead of an optimizing compiler-the default Testarossa JIT-may be too high to be useful, adding a non-optimizing compiler may prove beneficial to reduce startup times. MicroJIT is such a template-based, lightweight JIT compiler that generates native code with low overhead. This native code runs faster than interpreting the source code, but slower than the optimized code generated by the full JIT compiler. This work proposes improvements to MicroJIT, which include handling polymorphism in a template-based compiler, incorporating exception handling, supporting floating-point types and implementing additional bytecodes. Polymorphism will allow for invocation of virtual methods in Java;exception handling will help maintain the normal desired flow in case of unexpected events;supporting floating-point types will enable compiling methods involving float and double data types;and additional bytecode implementation will increase the bytecode support of MicroJIT. The implementation was tested using the regression framework and evaluated using the DaCapo workloads, most of which depicted increased performance. © 2024 IEEE.

关键词： program compilers

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Exploring Motion Ambiguity and Alignment for high-Quality Video Frame Interpolation

Exploring Motion Ambiguity and Alignment for High-Quality Vi...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhou, Kun Li, Wenbo Han, Xiaoguang Lu, Jiangbo CUHK Shenzhen SSE Shenzhen Peoples R China SmartMore Corp Hong Kong Peoples R China CUHK Hong Kong Peoples R China

ISBN: (纸本)9798350301298

For video frame interpolation (VFI), existing deep-learning-based approaches strongly rely on the ground-truth (GT) intermediate frames, which sometimes ignore the non-unique nature of motion judging from the given adjacent frames. As a result, these methods tend to produce averaged solutions that are not clear enough. To alleviate this issue, we propose to relax the requirement of reconstructing an intermediate frame as close to the GT as possible. Towards this end, we develop a texture consistency loss (TCL) upon the assumption that the interpolated content should maintain similar structures with their counterparts in the given frames. Predictions satisfying this constraint are encouraged, though they may differ from the predefined GT. Without the bells and whistles, our plug-and-play TCL is capable of improving the performance of existing VFI frameworks consistently. On the other hand, previous methods usually adopt the cost volume or correlation map to achieve more accurate image or feature warping. However, the O(N-2) (N refers to the pixel count) computational complexity makes it infeasible for high-resolution cases. In this work, we design a simple, efficient O(N) yet powerful guided cross-scale pyramid alignment (GCSPA) module, where multi-scale information is highly exploited. Extensive experiments justify the efficiency and effectiveness of the proposed strategy.

关键词： Image and video synthesis and generation

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Bootstrap Diffusion Model Curve Estimation for high Resolution Low-Light Image Enhancement 20th

Bootstrap Diffusion Model Curve Estimation for High Resolut...

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20th Pacific Rim International conference on Artificial Intelligence, PRICAI 2023

作者： Huang, Jiancheng Liu, Yifan Chen, Shifeng ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819970247

Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement. However, most of them still suffer from two main problems: expensive computational cost in high resolution images and unsatisfactory performance in simultaneous enhancement and denoising. To address these problems, we propose BDCE, a bootstrap diffusion model that exploits the learning of the distribution of the curve parameters instead of the normal-light image itself. Specifically, we adopt the curve estimation method to handle the high-resolution images, where the curve parameters are estimated by our bootstrap diffusion model. In addition, a denoise module is applied in each iteration of curve adjustment to denoise the intermediate enhanced result of each iteration. We evaluate BDCE on commonly used benchmark datasets, and extensive experiments show that it achieves state-of-the-art qualitative and quantitative performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image enhancement

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high-fidelity Facial Avatar Reconstruction from Monocular Video with Generative Priors

High-fidelity Facial Avatar Reconstruction from Monocular Vi...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Bai, Yunpeng Fan, Yanbo Wang, Xuan Zhang, Yong Sun, Jingxiang Yuan, Chun Shan, Ying Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Tencent AI Lab Shenzhen Peoples R China Ant Grp Hangzhou Peoples R China Tsinghua Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

high-fidelity facial avatar reconstruction from a monocular video is a significant research problem in computer graphics and computer vision. Recently, Neural Radiance Field (NeRF) has shown impressive novel view rendering results and has been considered for facial avatar reconstruction. However, the complex facial dynamics and missing 3D information in monocular videos raise significant challenges for faithful facial reconstruction. In this work, we propose a new method for NeRF-based facial avatar reconstruction that utilizes 3D-aware generative prior. Different from existing works that depend on a conditional deformation field for dynamic modeling, we propose to learn a personalized generative prior, which is formulated as a local and low dimensional subspace in the latent space of 3D-GAN. We propose an efficient method to construct the personalized generative prior based on a small set of facial images of a given individual. After learning, it allows for photo-realistic rendering with novel views, and the face reenactment can be realized by performing navigation in the latent space. Our proposed method is applicable for different driven signals, including RGB images, 3DMM coefficients, and audio. Compared with existing works, we obtain superior novel view synthesis results and faithfully face reenactment performance. The code is available here https://***/bbaaii/HFA-GP.

关键词： Image and video synthesis and generation

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Improve and Stabilize the Classification Results of DataRaceBench

Improve and Stabilize the Classification Results of DataRace...

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2023 International conference on high performance Computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Jenke, Joachim Schwitanski, Simon High-Performance Computing It Center Rwth Aachen University Aachen Germany

ISBN: (纸本)9798400707858

DataRaceBench is a benchmark using small kernel applications to classify the detection capabilities of data race detection tools. During our experiments of applying Archer to the benchmark suite we observed different short-comings. With recently added kernels, the turn-around time of a basic benchmark run increased from several minutes to more than an hour. Furthermore, we observed non-deterministic and unexpected results. In this paper, we propose several changes to existing kernels to address these short-comings. In addition, we propose to use variants of the kernels with non-deterministic runtime schedules that explicitly enforce these different schedules. Finally, we provide an evaluation of the updated benchmark with Archer running in thread-centric and task-centric mode. © 2023 ACM.

关键词： Classification (of information)

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