检索结果-内蒙古大学图书馆

Dual Latent State Learning: Exploiting Regional Network Similarities for QoS Prediction

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wang, Ziliang Zhang, Xiaohong Zhang, Kechi Li, Ze Shi Yan, Meng Ministry of Education School of Computer Science Peking University Beijing China Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing401331 China The Department of Computer Science the University of Victoria Canada

Individual objects (users or services) within a region usually have similar network states to each other because they usually come from the same region. Despite the similarity within regional networks, many existing techniques overlook this potential, thereby limiting the accuracy of Quality of Service (QoS) predictions. In this paper, we introduce the regional-based dual latent state learning network(R2SL), a novel deep learning framework designed to overcome the pitfalls of traditional individual object-based prediction techniques for QoS prediction. R2SL first captures the regional network states by deriving two distinct regional network latent states: the physical-regional latent state and the virtual-regional latent state. These states are built using aggregated data from a physical area (such as the same city) or a virtual network area (such as the same service provider) rather than individual object data. Then R2SL provides a specific Mixture of Experts(MOE) network based on sparse activation to realize the classification learning task of latent features of different regions. Thirdly, R2SL adopts an enhanced smooth Huber loss function that adjusts Huber loss’s linear loss component to alleviate the impact caused by label imbalance. Finally, the perceptual network was used to interpret the integrated features, thereby realizing the QoS prediction. Experimental results show that compared with the typical methods and the latest methods, the proposed method can obtain higher prediction accuracy, thus improving the research level of QoS prediction. © 2023, CC0.

关键词： Contrastive Learning

Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Yan, Jiexuan Huang, Sheng Mu, NanKun Huangfu, Luwen Liu, Bo School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China Fowler College of Business San Diego State University San DiegoCA United States School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Real-world data consistently exhibits a long-tailed distribution, often spanning multiple categories. This complexity underscores the challenge of content comprehension, particularly in scenarios requiring Long-Tailed Multi-Label image Classification (LTMLC). In such contexts, imbalanced data distribution and multi-object recognition pose significant hurdles. To address this issue, we propose a novel and effective approach for LTMLC, termed Category-Prompt Refined Feature Learning (CPRFL), utilizing semantic correlations between different categories and decoupling category-specific visual representations for each category. Specifically, CPRFL initializes category-prompts from the pretrained CLIP’s embeddings and decouples category-specific visual representations through interaction with visual features, thereby facilitating the establishment of semantic correlations between the head and tail classes. To mitigate the visual-semantic domain bias, we design a progressive Dual-Path Back-Propagation mechanism to refine the prompts by progressively incorporating context-related visual information into prompts. Simultaneously, the refinement process facilitates the progressive purification of the category-specific visual representations under the guidance of the refined prompts. Furthermore, taking into account the negative-positive sample imbalance, we adopt the Asymmetric Loss as our optimization objective to suppress negative samples across all classes and potentially enhance the head-to-tail recognition performance. We validate the effectiveness of our method on two LTMLC benchmarks and extensive experiments demonstrate the superiority of our work over baselines. The code is available at https://***/jiexuanyan/CPRFL. © 2024, CC BY-NC-SA.

关键词： Semantics

Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

学校读者我要写书评

暂无评论

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

Deformable Kernel Expansion Model for Efficient Arbitrary-Shaped Scene Text Detection

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： He, Tao Huang, Sheng Tang, Wenhao Liu, Bo School of Big Data and Software Engineering Chongqing University No.55 Daxuecheng South Rd. Chongqing Shapingba401331 China Walmart Global Tech San Francisco Bay Area CA94087 United States

Scene text detection is a challenging computer vision task due to the high variation in text shapes and ratios. In this work, we propose a real-time arbitrary-shaped scene text detector named Deformable Kernel Expansion (DKE), which incorporates the merits of both segmentation and contour-based detectors. DKE employs a segmentation module to segment the shrunken text region as the text kernel, then expands the kernel to obtain text boundary by regressing the vertex-wise offsets. Generating the text kernel by segmentation enables DKE to inherit the arbitrary-shaped text region modeling capability of segmentation-based detectors. Regressing the kernel with control points enables DKE to avoid the complicated pixel-level post-processing and better learn contour deformation as the contour-based detectors. Moreover, we propose an Optimal Bipartite Graph Matching Loss that measures the matching error between the predicted points and the ground truth, which efficiently minimizes the global matching distance. Extensive experiments on five standard benchmarks demonstrate that DKE achieves a good tradeoff between accuracy and efficiency in scene text detection. © 2023, The Authors. All rights reserved.

关键词： Expansion

iSeg: An Iterative Refinement-based Framework for Training-free Segmentation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Sun, Lin Cao, Jiale Xie, Jin Khan, Fahad Shahbaz Pang, Yanwei The School of Electrical and Information Engineering Tianjin University Tianjin300072 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Linkoping University Sweden

Stable diffusion has demonstrated strong image synthesis ability to given text descriptions, suggesting it to contain strong semantic clue for grouping objects. The researchers have explored employing stable diffusion for training-free segmentation. Most existing approaches refine cross-attention map by self-attention map once, demonstrating that self-attention map contains useful semantic information to improve segmentation. To fully utilize self-attention map, we present a deep experimental analysis on iteratively refining cross-attention map with self-attention map, and propose an effective iterative refinement framework for training-free segmentation, named iSeg. The proposed iSeg introduces an entropy-reduced self-attention module that utilizes a gradient descent scheme to reduce the entropy of self-attention map, thereby suppressing the weak responses corresponding to irrelevant global information. Leveraging the entropy-reduced self-attention module, our iSeg stably improves refined cross-attention map with iterative refinement. Further, we design a category-enhanced cross-attention module to generate accurate cross-attention map, providing a better initial input for iterative refinement. Extensive experiments across different datasets and diverse segmentation tasks (weakly-supervised semantic segmentation, open-vocabulary semantic segmentation, unsupervised segmentation, and mask generation on synthetic dataset) reveal the merits of proposed contributions, leading to promising performance on diverse segmentation tasks. For unsupervised semantic segmentation on Cityscapes, our iSeg achieves an absolute gain of 3.8% in terms of mIoU compared to the best existing training-free approach in literature. Moreover, our proposed iSeg can support segmentation with different kinds of images and interactions. The project is available at https://***/iSeg. © 2024, CC BY.

关键词： Semantic Segmentation

Weak Network Oriented Mobile Distributed Storage: A Hybrid Fault-Tolerance Scheme Based on Potential Replicas

学校读者我要写书评

暂无评论

Weak Network Oriented Mobile Distributed Storage: A Hybrid F...

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xinlei Wei Yujuan Tan Duo Liu Daitao Wu Yu Wu Xianzhang Chen Jian Li College of Computer Science Chongqing University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

Node failure is one of the most typical issues in distributed storage systems. The classic fault-tolerance methods can meet the fault tolerance needs of systems deployed in edge storage, 5G IoT, and other high-performance data centers. However, mobile distributed systems contend with inconsistent network signals and relatively low bandwidth in weak network environments. The traditional fault-tolerance schemes have difficulty ensuring distributed storage and data repair simultaneously, posing a considerable challenge to data storage reliability. To address this problem, we proposed a hybrid fault-tolerance scheme based on potential replicas, called HFPR, which trades off storage overhead and performance for weak network mobile distributed storage systems. HFPR first stores data with the erasure code redundancy and then gradually increases data redundancy by reserving potential replicas, enabling the system to disperse network transmission pressure and reduce network transmission. To maximize replica utilization and control the storage overhead of potential replicas, we design a node state prediction mechanism and a file lifecycle management mechanism. Evaluation results show that, compared with the traditional hybrid fault-tolerant scheme with the same storage overhead, HFPR achieves better replica-level repair efficiency in data repair and a reduced probability of degraded reads by approximately 50%. During data distribution, HFPR can minimize data write amplification by up to 1.1× and additional network transfers by up to 77.5%.

关键词： data centers Fault tolerant systems Redundancy Distributed databases Memory Bandwidth Maintenance engineering

Feature Noise Resilient for QoS Prediction with Probabilistic Deep Supervision

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wang, Ziliang Zhang, Xiaohong Li, Ze Shi Huang, Sheng Yan, Meng Ministry of Education School of Computer Science Peking University Beijing China The Department of Computer Science The University of Victoria Canada Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing401331 China

Accurate Quality of Service (QoS) prediction is essential for enhancing user satisfaction in web recommendation systems, yet existing prediction models often overlook feature noise, focusing predominantly on label noise. In this paper, we present the Probabilistic Deep Supervision Network (PDS-Net), a robust framework designed to effectively identify and mitigate feature noise, thereby improving QoS prediction accuracy. PDS-Net operates with a dual-branch architecture: the main branch utilizes a decoder network to learn a Gaussian-based prior distribution from known features, while the second branch derives a posterior distribution based on true labels. A key innovation of PDS-Net is its condition-based noise recognition loss function, which enables precise identification of noisy features in objects (users or services). Once noisy features are identified, PDS-Net refines the feature's prior distribution, aligning it with the posterior distribution, and propagates this adjusted distribution to intermediate layers, effectively reducing noise interference. Extensive experiments conducted on two real-world QoS datasets demonstrate that PDS-Net consistently outperforms existing models, achieving an average improvement of 8.91% in MAE on dataset D1 and 8.32% on dataset D2 compared to the ate-of-the-art. These results highlight PDS-Net's ability to accurately capture complex user-service relationships and handle feature noise, underscoring its robustness and versatility across diverse QoS prediction environments. © 2023, CC0.

关键词： Prediction models

Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

学校读者我要写书评

暂无评论

Detecting Backdoors During the Inference Stage Based on Corr...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiaogeng Liu Minghui Li Haoyu Wang Shengshan Hu Dengpan Ye Hai Jin Libing Wu Chaowei Xiao School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Software Engineering Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab Arizona State University

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo) 1 1 https://***/CGCL-codes/TeCo, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.

关键词：

Experimentally Study on the Effect of RIE Etching Power on Etching Rate of $\beta-\text{Ga}_{2}\mathrm{O}_{3}$ Thin Film

学校读者我要写书评

暂无评论

Experimentally Study on the Effect of RIE Etching Power on E...

Joint e-Manufacturing and Design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Wang Xu Ran Jing Yang Yang Lai Yang Fa shun Ma Kui College of Big Data and Information Engineering Guizhou University Guizhou Provincial Key Laboratory of Micro and nano Electronics and Software Technology Guiyang City Guizhou Province

ISBN: (纸本)9781665471343

As a member of ultra-wide band gap semiconductor materials, $\beta-\text{Ga}_{2}\mathrm{O}_{3}$ materials have attracted wide attention from researchers in the semiconductor field in recent years. Etching process is crucial to realize semiconductor devices and integrated circuits based on $\beta-\text{Ga}_{2}\mathrm{O}_{3}$ materials. Based on the reaction ion etching process commonly used in silicon-based semiconductor technology, the etching experiment research of $\beta-\text{Ga}_{2}\mathrm{O}_{3}$ thin film is carried out. The $\beta-\text{Ga}_{2}\mathrm{O}_{3}\text{film}$ is etched with SF6, based on the induction coupled reaction ion etching. The effect of RIE etching power, excitation power and bias power, on etching rate of $\beta-\text{Ga}_{2}\mathrm{O}_{3}$ thin film has been studied. SEM characterization results show that the etching rate is the highest at 600W excitation power. The etching rate increases with the increase of bias power. The etching rate at 200W bias power is slightly higher than that at 150W bias power. However, the photoresist used as the etch mask will be damaged at 200W bias power.

关键词： Integrated circuits Photonic band gap Semiconductor materials Semiconductor devices Sulfur hexafluoride Resists Semiconductor device manufacture