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7th international conference on Business Intelligence, CBI 2022

7th International Conference on Business Intelligence, CBI 2...

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7th international conference on Business Intelligence, CBI 2022

ISBN: (纸本)9783031064579

the proceedings contain 23 papers. the special focus in this conference is on Business Intelligence. the topics include: Intelligent System Based on GAN Model for Decision Support in Brain Tumor Segmentation;a Convolutional Neural Networks-Based Approach for Potato Disease Classification;a Novel Hybrid Approach for Improving the Accuracy of the Supervised Link Prediction Based on Graph Structure Features in Social Networks;Analysis of Several algorithms for DOA Estimation in Two Different Communication Models by a Comparative Study;how far can Deep Learning Improve Arabic Part of Speech Tagging?;analysis of Decision Tree algorithms for Diabetes Prediction;automatic Change Detection Based on the Independent Component Analysis and Fuzzy C-Means Methods;automatic Text Summarization for Moroccan Arabic Dialect Using an Artificial Intelligence Approach;mining Frequents Itemset and Association Rules in Diabetic Dataset;Increasing Student Engagement in Lessons and Assessing MOOC Participants through Artificial Intelligence;comparative Study of Deep Learning Models for Detection and Classification of Intracranial Hemorrhage;optimization Focused on parallel Fuzzy Deep Belief Neural Network for Opinion Mining;deep Learning Model for Educational Recommender Systems;sentiment Analysis Decision System for Tracking Climate Change Opinion in Twitter;an Exploration of Student Grade Retention Prediction Using Machine Learning algorithms;deep Reinforcement Learning for Bitcoin Trading;machine Learning Approach to Automate Decision Support on Information System Attacks;Facial Expression Recognition Using a Hybrid ViT-CNN Aggregator;decision Boundary to Improve the Sensitivity of Deep Neural Networks Models;Performance Investigation of a Proposed CBIR Search Engine Using Deep Convolutional Neural Networks;preface.

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Winograd-CNN Accelerator Compatible with Sparse and Non-sparse Models 23

Winograd-CNN Accelerator Compatible with Sparse and Non-spar...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Huazhen Chen, Weiting Wang, Jiangtao East China Normal University Hangzhou Hikvision Digital Technology Company Ltd Hikvision Research Institute Moe Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China East China Normal University Moe Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9781665494571

the inference speed of convolutional neural networks is a key factor for their application in the industry. the two research directions of non-sparse model acceleration and sparse model acceleration are now studied independently. In this paper, we propose a hybrid granular pruning method and a fixed-length binary pruning index scheme, based on which a compatible acceleration architecture is further designed. the hybrid granular pruning method is realized by executing fine-grained pruning after coarse-grained pruning. the binary pruning index scheme ensures a simple fixed-length binary mask to index valid weight positions no matter the model is pruned or not, thus makes it possible for an accelerator compatible with both non-sparse models without pruning and sparse models with hybrid granular pruning. An acceleration architecture is further designed, using a new data layout, a buffer-pipeline mechanism for the data buffering, and converting the Winograd algorithm into matrix operations with parallelism carried out from three aspects. the experimental results suggest that, our proposed acceleration solution shows not only better acceleration performance but also excellent compatibility, and it can be directly applied for the acceleration of models at different pruning levels without recompilation. © 2021 IEEE.

关键词： Industries Smart cities Layout Optimization methods parallel processing Data models Sparse matrices

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AP3: Adaptive Power Prediction Framework based on Spatial Partition Multi-Phase Model 23

AP3: Adaptive Power Prediction Framework based on Spatial Pa...

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作者： Chen, Juan Ou, Zhixin Guo, Yifei Qi, Xinxin Sun, Yuyang Deng, Lin Chen, Hongyu Lin, Zihan College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665494571

the accuracy of processor power modeling is an important foundation for power management and optimization on parallel computing system. It is difficult to build a high-accuracy instantaneous CPU/DRAM power prediction model. One of the main reasons for the low accuracy is that the processor architecture sometimes influence greatly on the accuracy of power prediction. For example, the accuracy of processor power model is affected by inaccurate modeling of the uncore part of processor. Another reason comes from the limitation of static models for instantaneous power prediction. Despite the existence of various optional linear/nonlinear models, the fixed training set and model coefficients are insufficient to make high-accuracy instantaneous predictions for various target programs. Aiming at the above two issues, this paper proposes an Adaptive Power Prediction framework based on spatial Partition multi-phase model (AP^th). Spatial partition mainly solves the impact of uncore power on the prediction accuracy, and adaptability solves the limitation of the static model on the power prediction accuracy. According to the experimental results on both ARM-based and x86-based processor platforms, AP^th greatly increases the CPU and DRAM power instantaneous prediction accuracy. Spatial partition reduce the prediction error (MRE) by 0.3%-8.2% compared to previous single model, while adaptive update further reduces the error (MRE) by 1.7%-7.1% compared to previous static model. © 2021 IEEE.

关键词： Training Adaptation models Smart cities Multicore processing Computational modeling Power system management Random access memory

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the parallelized Cuckoo Filter for Cold Data Representation 23

The Parallelized Cuckoo Filter for Cold Data Representation

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作者： Sun, Bowen Luo, Lailong Li, Shangsen Chen, Yingwen Guo, Deke College of Computer National University of Defense Technology China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China College of Computer National University of Defense Technology National Lab for Parallel and Distributed Processing China

ISBN: (纸本)9781665494571

Cold data contributes a large portion of the big data today and is usually stored in secondary storage. Various sketch data structures are implemented to represent the stored elements and provide constant-time membership queries. Considering the large volume and infrequent access of cold data, we envision three rationales of cold data representation: 1) parallelized insertion, 2) accurate representation, and 3) constant-time query/deletion. the existing sketches, however, fail to achieve these rationales simultaneously. To this end, we propose a novel sketch named parallelized cuckoo Filter (PCF) in this paper. PCF divides the standard cuckoo filter into multiple homogenous segments. An additional hash function is introduced to assign an element to a segment. the element will be inserted into the selected segment. With such a design, element insertions in PCF can be naturally parallelized. Besides, as a variant of the cuckoo filter, PCF naturally realizes the accurate representation and constant-time query/deletion requirements. Comprehensive experiments further indicate that PCF achieves the three rationales simultaneously. © 2021 IEEE.

关键词： Hash functions Smart cities Big Data Data structures Standards

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Efficient deep Neural Network architectures for Subsurface Scattering Approximation 7

Efficient deep Neural Network Architectures for Subsurface S...

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7th international conference on Image and Signal processing and their Applications, ISPA 2022

作者： Abbas, Faycal Malah, Mehdi Babahenini, Mohamed Chaouki University of Abbes Laghrour Khenchela Department of Computer Science Khenchela Algeria University of Mohamed Khider Department of Computer Science Biskra Algeria

ISBN: (纸本)9781665480420

Subsurface rendering has become essential for photo-realistic rendering. therefore, an accurate simulation is too costly due to the interactions between the particles composing the material and the amount of light passing through it. In this paper, we propose a method to estimate the subsurface light scattering effects. Our method comprises two steps: the first step consists of generating synthetic images using a reference technique to train the neural network. the second step consists in generating an approximation of the reference image after a learning step. Our model is based on convolutional architecture. the neural network is fed in input by images retrieved from the G-buffer(depth map, normal map,albedo map), while in output, it generates an approximation of the subsurface diffusion. We show that our method produces effective approximations of subsurface scattering. Furthermore, our method generates images comparable to the reference method and does not require processing of the output image. therefore, it does not do any calculation in the three-dimensional space of 3D scene. © 2022 IEEE.

关键词： Deep neural networks

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Multimodal Summarization with Modality-Aware Fusion and Summarization Ranking 24th

Multimodal Summarization with Modality-Aware Fusion and Su...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Ye, Xuming Chaomurilige Liu, Zheng Luo, Haoyu Dong, Jun Luo, Yingzhe Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China Faculty of Data Science City University of Macau Macau China

ISBN: (纸本)9789819615278

Existing multimodal summarization methods primarily focus on multimodal fusion to efficiently utilize the visual information for summarization. However, they fail to exploit the deep interaction between textual and visual modality. Moreover, optimizing the model by maximum likelihood estimation (MLE) leads to exposure bias, causing the model to generate the next word that is based on the previously generated erroneous words during inference. To address these challenges, we propose a novel modality-aware fusion module (MAF) with a summarization ranking (SumR) training objective. Specifically, the MAF module exploits the interaction in the multimodal input through multiple fusion layers, and SumR aims to align the probability order predicted by the model with actual quality metrics, therefore it is able to reduce the exposure bias problem during inference. Extensive experiments on a large-scale dataset demonstrate that our method outperforms existing models, achieving superior results in both automatic and human evaluation metrics. the generated multimodal summaries provide richer context and enhance user’s comprehension by combining the key textual information with the relevant visual content. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Maximum likelihood estimation

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DOMAIN DECOMPOSITION algorithms FOR REAL-TIME HOMOGENEOUS DIFFUSION INPAINTING IN 4K 47

DOMAIN DECOMPOSITION ALGORITHMS FOR REAL-TIME HOMOGENEOUS DI...

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47th IEEE international conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Kaemper, Niklas Weickert, Joachim Saarland Univ Fac Math & Comp Sci Math Image Anal Grp Campus E1-7 D-66041 Saarbrucken Germany

ISBN: (纸本)9781665405409

Inpainting-based compression methods are qualitatively promising alternatives to transform-based codecs, but they suffer from the high computational cost of the inpainting step. this prevents them from being applicable to time-critical scenarios such as real-time inpainting of 4K images. As a remedy, we adapt state-of-the-art numerical algorithms of domain decomposition type to this problem. they decompose the image domain into multiple overlapping blocks that can be inpainted in parallel by means of modern GPUs. In contrast to classical block decompositions such as the ones in JPEG, the global inpainting problem is solved without creating block artefacts. We consider the popular homogeneous diffusion inpainting and supplement it with a multilevel version of an optimised restricted additive Schwarz (ORAS) method that solves the local problems with a conjugate gradient algorithm. this enables us to perform real-time inpainting of 4K colour images on contemporary GPUs, which is substantially more efficient than previous algorithms for diffusion-based inpainting.

关键词： Inpainting Homogeneous Diffusion Domain Decomposition Restricted Additive Schwarz Method

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sKokkos: Enabling Kokkos with Transparent Device Selection on Heterogeneous Systems using OpenACC 24

sKokkos: Enabling Kokkos with Transparent Device Selection o...

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7th international conference on High Performance Computing in Asia-Pacific Region (HPC Asia)

作者： Valero-Lara, Pedro Lee, Seyong Denny, Joel Teranishi, Keita Vetter, Jeffrey S. Gonzalez-Tallada, Marc Oak Ridge Natl Lab Oak Ridge TN 37830 USA Univ Politecn Cataluna Barcelona Spain

ISBN: (纸本)9798400708893

this paper presents a new feature to enable Kokkos with transparent device selection. For application developers, it is not easy to identify which device is the most appropriate to use in a heterogeneous system, since this depends on the characteristics of both the application and the hardware. In Kokkos, a backend is associated with one specific programming model/hardware. Programmers decide which backend to use at compilation time. this new feature implemented on the OpenACC backend eliminates the burden of deciding which device to use, providing a highly productive programming solution for Kokkos applications. this work includes implementation details and a performance study conducted with a set of mini-benchmarks (i.e., AXPY and dot product), kernels (Lattice-Bolzmann method), and two mini-apps (LULESH and miniFE) on two heterogeneous systems with different hardware capabilities. this new Kokkos feature provides high accelerations of up to 35x thanks to automatic and transparent device selection.

关键词： Kokkos OpenACC C plus plus Metaprogramming Heterogeneous Systems CPU GPU parallel Programming Models Auto-tuning

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A Multi-holder Role and Strange Attractor-Based Data Possession Proof in Medical Clouds 24th

A Multi-holder Role and Strange Attractor-Based Data Possess...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Guo, Jinyuan Sun, Lijuan Wu, Jingchen Chu, Chiawei Gao, Yutong Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Cybersecurity Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University Beijing China Intellectual Property Information Service Center BHU Beihang University Beijing China City University of Macau Taipa China Li’an International Education Innovation Pilot Zone Hainan International College of Minzu University of China Hainan Beijing China

ISBN: (纸本)9789819615278

Proof of Data Possession is a technique for ensuring the integrity of data stored in cloud storage. However, most audit schemes assume only one role for data owners, which is not suitable for complex Smart Healthcare Network with multiple roles. In such schemes addressing the risk of third-party auditors (TPA) colluding with other entities, blockchain is typically employed. However, this necessitates extensive computation, resulting in unacceptably high energy consumption and prolonged verification times. In this paper, we propose a Proof of Data Possession scheme tailored for medical network environments, which incorporates protocols designed for both healthcare professionals and patients, considering the diverse roles that generate data within medical network applications and the varying performance of the devices they use. During the generation of metadata for verification, the protocol incorporates the varying security risk levels associated with medical data. For subsequent metadata selection, an audit block extraction strategy based on strange attractors is employed, effectively mitigating the risk of collusion by third-party TPA, thereby enhancing the security and efficiency of the scheme. Additionally, we conducted experimental analysis and comparisons, demonstrating higher security and practical applicability. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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Enhancing Text-Image Person Re-identification via Intra-Class Relevance Learning 24th

Enhancing Text-Image Person Re-identification via Intra-Cla...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： He, Wenbin Gao, Yutong Liu, Wenjian Chaomurilige Liu, Zheng Guo, Ao Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan Lingshui Li572499 China City University of Macau China

ISBN: (纸本)9789819615278

Text-Image Person Re-Identification (TIReID) is a computer vision task that involves identifying person in images or videos based on textual descriptions. Current works mainly employ Vision Language Pretrained (VLP) models for cross-modal alignment and to reestablish text-image fine-grained associations for re-identification. However, these methods neglect the variations in intra-class sample features, hindering accurate cross-modal alignment and discrimination of fine-grained features for the same person. In this paper, we propose a Cross-modal Intra-Class Learning (CICL) framework to enhance the model’s capability of learning fine-grained cross-modal features and intra-class sample variations. We propose an Image-Text Intra-class Relevance Learning (ITRL) method that considers sample relevance during text-image matching, boosting re-identification capability while maintaining fine-grained text-image alignment. Meanwhile, we propose a Bidirectional Masked Matching (BiMM) method that introduces masks to images and texts, prompting the model to attend to different content regions and establish finer-grained cross-modal associations. We test our approach using the CLIP model on three TIReID benchmarks, achieving results that surpass state-of-the-art performance on multiple metrics. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image matching

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