{"title":"Unsupervised Selective Rank Fusion for Content-based Image Retrieval","authors":"Lucas Pascotti Valem, D. C. G. Pedronette","doi":"10.5753/ctd.2020.11370","DOIUrl":"https://doi.org/10.5753/ctd.2020.11370","url":null,"abstract":"The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image retrieval tasks. These systems are mainly supported by the use of different visual features and machine learning methods. As distinct features produce complementary ranking results with different effectiveness performance, a promising solution consists in combining them. However, how to decide which visual features to combine is a very challenging task, especially when no training data is available. This work proposes three novel methods for selecting and combining ranked lists by estimating their effectiveness in an unsupervised way. The approaches were evaluated in five different image collections and several descriptors, achieving results comparable or superior to the state-of-the-art in most of the scenarios.","PeriodicalId":304800,"journal":{"name":"Anais do Concurso de Teses e Dissertações da SBC (CTD-SBC 2020)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114795664","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}
{"title":"Methods and Algorithms for Knowledge Reuse in Multiagent Reinforcement Learning","authors":"Felipe Leno da Silva, Anna Helena Reali Costa","doi":"10.5753/ctd.2020.11360","DOIUrl":"https://doi.org/10.5753/ctd.2020.11360","url":null,"abstract":"Reinforcement Learning (RL) is a powerful tool that has been used to solve increasingly complex tasks. RL operates through repeated interactions of the learning agent with the environment, via trial and error. However, this learning process is extremely slow, requiring many interactions. In this thesis, we leverage previous knowledge so as to accelerate learning in multiagent RL problems. We propose knowledge reuse both from previous tasks and from other agents. Several flexible methods are introduced so that each of these two types of knowledge reuse is possible. This thesis adds important steps towards more flexible and broadly applicable multiagent transfer learning methods.","PeriodicalId":304800,"journal":{"name":"Anais do Concurso de Teses e Dissertações da SBC (CTD-SBC 2020)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122203336","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}
{"title":"MuTARe: A Multi-Target, Adaptive Reconfigurable Architecture","authors":"M. Brandalero, A. C. S. Beck","doi":"10.5753/wscad_estendido.2019.8706","DOIUrl":"https://doi.org/10.5753/wscad_estendido.2019.8706","url":null,"abstract":"With recent changes in transistor scaling trends, the design of all types of processing systems has become increasingly constrained by power consumption. At the same time, driven by the needs of fast response times, many applications are migrating from the cloud to the edge, pushing for the challenge of increasing the performance of these already power-constrained devices. The key to addressing this problem is to design application-specific processors that perfectly match the application's requirements and avoid unnecessary energy consumption. However, such dedicated platforms require significant design time and are thus unable to match the pace of fast-evolving applications that are deployed in the Internet-of-Things (IoT) every day. Motivated by the need for high energy efficiency and high flexibility in hardware platforms, this thesis paves the way to a new class of low-power adaptive processors that can achieve these goals by automatically modifying their structure at run time to match different applications' resource requirements. The proposed Multi-Target Adaptive Reconfigurable Architecture (MuTARe) is based upon a Coarse-Grained Reconfigurable Architecture (CGRA) that can transparently accelerate already-deployed applications, but incorporates novel compute paradigms such as Approximate Computing (AxC) and Near-Threshold Voltage Computing (NTC) to improve its efficiency. Compared to a traditional system of heterogeneous processing cores (similar to ARM's big.LITTLE), the base MuTARe architecture can (without any change to the existing software) improve the execution time by up to $1.3times$, adapt to the same task deadline with $1.6times$ smaller energy consumption or adapt to the same low energy budget with $2.3times$ better performance. When extended for AxC, MuTARe's power savings can be further improved by up to $50%$ in error-tolerant applications, and when extended for NTC, MuTARe can save further $30%$ energy in memory-intensive workloads.","PeriodicalId":304800,"journal":{"name":"Anais do Concurso de Teses e Dissertações da SBC (CTD-SBC 2020)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128768442","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}
{"title":"Semantic Hyperlapse: a Sparse Coding-based and Multi-Importance Approach for First-Person Videos","authors":"M. Silva, M. Campos, Erickson R. Nascimento","doi":"10.5753/sibgrapi.est.2019.8302","DOIUrl":"https://doi.org/10.5753/sibgrapi.est.2019.8302","url":null,"abstract":"The availability of low-cost and high-quality wearable cameras combined with the unlimited storage capacity of video-sharing websites have evoked a growing interest in First-Person Videos. Such videos are usually composed of long-running unedited streams captured by a device attached to the user body, which makes them tedious and visually unpleasant to watch. Consequently, it raises the need to provide quick access to the information therein. We propose a Sparse Coding based methodology to fast-forward First-Person Videos adaptively. Experimental evaluations show that the shorter version video resulting from the proposed method is more stable and retain more semantic information than the state-of-the-art. Visual results and graphical explanation of the methodology can be visualized through the link: https://youtu.be/rTEZurH64ME","PeriodicalId":304800,"journal":{"name":"Anais do Concurso de Teses e Dissertações da SBC (CTD-SBC 2020)","volume":"12 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132546083","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}