Applied Computing Review最新文献_第6页

Exploiting Machine-learning Prediction for Enabling Real-time Pixel-scaling Techniques in Mobile Camera Applications 利用机器学习预测在移动相机应用中实现实时像素缩放技术

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577770

S. Wei, Sheng-Da Tsai, Chun-Han Lin

引用次数: 0

RMC-PVC: A Multi-Client Reusable Verifiable Computation Protocol rmmc - pvc:一个多客户端可复用的可验证计算协议

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577680

Gael Marcadet, P. Lafourcade, Léo Robert

引用次数: 1

Adaptive Context Caching for Efficient Distributed Context Management Systems 高效分布式上下文管理系统的自适应上下文缓存

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577602

Shakthi Weerasinghe, A. Zaslavsky, S. Loke, A. Abken, A. Hassani, A. Medvedev

{"title":"Adaptive Context Caching for Efficient Distributed Context Management Systems","authors":"Shakthi Weerasinghe, A. Zaslavsky, S. Loke, A. Abken, A. Hassani, A. Medvedev","doi":"10.1145/3555776.3577602","DOIUrl":"https://doi.org/10.1145/3555776.3577602","url":null,"abstract":"We contend that performance metrics-driven adaptive context caching has a profound impact on performance efficiency in distributed context management systems (CMS). This paper proposes an adaptive context caching approach based on (i) a model of economics-inspired expected returns of caching particular items, and (ii) learning from historical context caching performance, i.e., our approach adaptively (with respect to statistics on historical performance) caches \"context\" with the objective of minimizing the cost incurred by a CMS in responding to context queries. Our novel algorithm enables context queries and sub-queries to reuse and repurpose cached context in an efficient manner, different from traditional data caching. The paper also proposes heuristics and adaptive policies such as eviction and context cache memory scaling. The method is evaluated using a synthetically generated load of sub-queries inspired by a real-world scenario. We further investigate optimal adaptive caching configurations under different settings. This paper presents and discusses our findings that the proposed statistical selective caching method reaches short-term cost optimality fast under massively volatile queries. The proposed method outperforms related algorithms by up to 47.9% in cost efficiency.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90098562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Detecting and Measuring the Polarization Effects of Adversarial Botnets on Twitter Twitter上对抗性僵尸网络极化效应的检测和测量

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577730

Yeonjung Lee, M. Ozer, S. Corman, H. Davulcu

引用次数: 0

Towards Deployment of Mobile Robot driven Preference Learning for User-State-Specific Thermal Control in A Real-World Smart Space 在现实世界的智能空间中，移动机器人驱动的偏好学习用于用户状态特定的热控制

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577760

Geon Kim, Hyunju Kim, Dongman Lee

引用次数: 1

On the Effect of Low-Ranked Documents: A New Sampling Function for Selective Gradient Boosting 关于低排序文档的效果:一种新的用于选择性梯度增强的抽样函数

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577597

C. Lucchese, Federico Marcuzzi, S. Orlando

{"title":"On the Effect of Low-Ranked Documents: A New Sampling Function for Selective Gradient Boosting","authors":"C. Lucchese, Federico Marcuzzi, S. Orlando","doi":"10.1145/3555776.3577597","DOIUrl":"https://doi.org/10.1145/3555776.3577597","url":null,"abstract":"Learning to Rank is the task of learning a ranking function from a set of query-documents pairs. Generally, documents within a query are thousands but not all documents are informative for the learning phase. Different strategies were designed to select the most informative documents from the training set. However, most of them focused on reducing the size of the training set to speed up the learning phase, sacrificing effectiveness. A first attempt in this direction was achieved by Selective Gradient Boosting a learning algorithm that makes use of customisable sampling strategy to train effective ranking models. In this work, we propose a new sampling strategy called High_Low_Sampl for selecting negative examples applicable to Selective Gradient Boosting, without compromising model effectiveness. The proposed sampling strategy allows Selective Gradient Boosting to compose a new training set by selecting from the original one three document classes: the positive examples, high-ranked negative examples and low-ranked negative examples. The resulting dataset aims at minimizing the mis-ranking risk, i.e., enhancing the discriminative power of the learned model and maintaining generalisation to unseen instances. We demonstrated through an extensive experimental analysis on publicly available datasets, that the proposed selection algorithm is able to make the most of the negative examples within the training set and leads to models capable of obtaining statistically significant improvements in terms of NDCG, compared to the state of the art.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81259953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

S-ViT: Sparse Vision Transformer for Accurate Face Recognition S-ViT:用于精确人脸识别的稀疏视觉变换

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577640

Geunsu Kim, Gyudo Park, Soohyeok Kang, Simon S. Woo

{"title":"S-ViT: Sparse Vision Transformer for Accurate Face Recognition","authors":"Geunsu Kim, Gyudo Park, Soohyeok Kang, Simon S. Woo","doi":"10.1145/3555776.3577640","DOIUrl":"https://doi.org/10.1145/3555776.3577640","url":null,"abstract":"Most of the existing face recognition applications using deep learning models have leveraged CNN-based architectures as the feature extractor. However, recent studies have shown that in computer vision tasks, vision transformer-based models often outperform CNN-based models. Therefore, in this work, we propose a Sparse Vision Transformer (S-ViT) based on the Vision Transformer (ViT) architecture to improve the face recognition tasks. After the model is trained, S-ViT tends to have a sparse distribution of weights compared to ViT, so we named it according to these characteristics. Unlike the conventional ViT, our proposed S-ViT adopts image Relative Positional Encoding (iRPE) method for positional encoding. Also, S-ViT has been modified so that all token embeddings, not just class token, participate in the decoding process. Through extensive experiment, we showed that S-ViT achieves better performance in closed-set than the other baseline models, and showed better performance than the baseline ViT-based models. For example, when using ArcFace as the loss function in the identification protocol, S-ViT achieved up to 3.27% higher accuracy than ResNet50. We also show that the use of ArcFace loss functions yields greater performance gains in S-ViT than in baseline models. In addition, S-ViT has an advantage in cost-performance trade-off because it tends to be more robust to the pruning technique than the underlying model, ViT. Therefore, S-ViT offers the additional advantage, which can be applied more flexibly in the target devices with limited resources.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81609493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating Phenotypic Characteristics of Tuberculosis Bacteria 估计结核杆菌的表型特征

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3578609

D. Sloan, E. Dombay, W. Sabiiti, B. Mtafya, Ognjen Arandelovic, Marios Zachariou

{"title":"Estimating Phenotypic Characteristics of Tuberculosis Bacteria","authors":"D. Sloan, E. Dombay, W. Sabiiti, B. Mtafya, Ognjen Arandelovic, Marios Zachariou","doi":"10.1145/3555776.3578609","DOIUrl":"https://doi.org/10.1145/3555776.3578609","url":null,"abstract":"Microscopy analysis of sputum images for bacilli screening is a common method used for both diagnosis and therapy monitoring of tuberculosis (TB). Nonetheless, it is a challenging procedure, since sputum examination is time-consuming and needs highly competent personnel to provide accurate results which are important for clinical decision-making. In addition, manual fluorescence microscopy examination of sputum samples for tuberculosis diagnosis and treatment monitoring is a subjective operation. In this work, we automate the process of examining fields of view (FOVs) of TB bacteria in order to determine the lipid content, and bacterial length and width. We propose a modified version of the UNet model to rapidly localise potential bacteria inside a FOV. We introduce a novel method that uses Fourier descriptors to exclude contours that do not belong to the class of bacteria, hence minimising the amount of false positives. Finally, we propose a new feature as a means of extracting a representation fed into a support vector multi-regressor in order to estimate the length and width of each bacterium. Using a real-world data corpus, the proposed method i) outperformed previous methods, and ii) estimated the cell length and width with a root mean square error of less than 0.01%.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85130465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Sanitization Design for LSM-based Key-Value Store over 3D MLC NAND Flash 基于lsm的3D MLC NAND闪存键值存储的高效消毒设计

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577780

Liang-Chi Chen, Shu-Qi Yu, Chien-Chung Ho, Wei-Chen Wang, Yung-Chun Li

引用次数: 0

Rotated-DETR: an End-to-End Transformer-based Oriented Object Detector for Aerial Images 旋转- detr:一种基于端到端变换的航空图像定向目标检测器

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577745

Gil-beom Lee, Jinbeom Kim, Taejune Kim, Simon S. Woo

{"title":"Rotated-DETR: an End-to-End Transformer-based Oriented Object Detector for Aerial Images","authors":"Gil-beom Lee, Jinbeom Kim, Taejune Kim, Simon S. Woo","doi":"10.1145/3555776.3577745","DOIUrl":"https://doi.org/10.1145/3555776.3577745","url":null,"abstract":"Oriented object detection in aerial images is a challenging task due to the highly complex backgrounds and objects with arbitrary oriented and usually densely arranged. Existing oriented object detection methods adopt CNN-based methods, and they can be divided into three types: two-stage, one-stage, and anchor-free methods. All of them require non-maximum suppression (NMS) to eliminate the duplicated predictions. Recently, object detectors based on the transformer remove hand-designed components by directly solving set prediction problems via performing bipartite matching, and achieve state-of-the-art performances in general object detection. Motivated by this research, we propose a transformer-based oriented object detector named Rotated DETR with oriented bounding boxes (OBBs) labeling. We embed the scoring network to reduce the tokens corresponding to the background. In addition, we apply a proposal generator and iterative proposal refinement module in order to provide proposals with angle information to the transformer decoder. Rotated DETR achieves state-of-the-art performance on the single-stage and anchor-free oriented object detectors on DOTA, UCAS-AOD, and DIOR-R datasets with only 10% feature tokens. In the experiment, we show the effectiveness of the scoring network and iterative proposal refinement module.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89429724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0