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A Novel Information Search and Recommendation Services Platform Based on an Indexing Network (Short Paper)

A Novel Information Search and Recommendation Services Platf...

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IEEE international conference on Service-Oriented computing and Applications (SOCA)

作者： Xiaodong Deng Ming Jiang Haichun Sun Yangjie Zhang Junjun Liu Yu Guo Xin Wang Dajie Ge Pengwei Wang Zhijun Ding Hongzhong Chen Department of Computer Science and Technology Tongji University Shanghai China School of Information Sciences University of Pittsburgh Pittsburgh PA USA Tongji University Shanghai Shanghai CN

With the rapid development of Internet technology, information resources on the Internet become more abundant, but also bring some problems like diversity, heterogeneity, disorder, and redundancy. Given a brief expression like search keywords only, users' needs are ambiguous. therefore, current technologies of search applications relying on direct keyword matching cannot meet the requirements of users exactly. Service applications are hoped to be more intelligent and knowledgeable. To solve such challenge, this paper attempts to organize web pages into a semantic association graph based on a novel model - indexing network, which can provide more valuable information and services for users. An information search and recommendation services platform is implemented using cloud distributed systems (Hadoop + Habse + Zookeeper) based on Sugon-Tongji cloud platform that is located at Tongji University. 70 million web pages are crawled on the Internet and their corresponding indexing network model are constructed. Several novel services are implemented and provided on the platform, such as search location, search navigation, category/keyword recommendation, and the interaction interface of the indexing network. System demonstration and experimental analysis show that the proposed platform can provide and support more knowledgeable and valuable information search-related services, thereby better meeting the growing needs of Internet users.

关键词： Semantics Navigation Educational institutions Internet Indexing Data mining

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A Time-Efficient distributed Constant Conditional Functional Dependency Discovery Algorithm for Data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual international Computer Software and Applications conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

the accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. this paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. the Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy Data preprocessing Software algorithms distributed databases Parallel processing Software Partitioning algorithms

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Dissecting the Applicability of HTTP/3 in Content Delivery Networks

Dissecting the Applicability of HTTP/3 in Content Delivery N...

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international conference on distributed computing Systems

作者： Mengying Zhou Yang Chen Shihan Lin Xin Wang Bingyang Liu Aaron Yi Ding Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Huawei Technologies Co. Ltd. China Department of Engineering Systems and Services Delft University of Technology The Netherlands

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

HTTP/3 (H3) has experienced significant growth and extensive adoption in various scenarios, especially in Content Delivery Networks (CDNs). Over the past few years, there have been numerous insightful studies on its deployment in industrial CDNs. However, these studies often separately analyze H3 and CDN, overlooking their synergistic integration. In this work, we explore the applicability of H3 in CDN from a holistic perspective. We analyze 325 websites hosted by seven CDN providers and identify three key characteristics where CDN align perfectly with H3's strengths. Firstly, CDN resources dominate the composition of webpages, where enabling H3 can amplify H3's benefits in connection acceleration. Secondly, CDN providers also exhibit a dominant characteristic, with the majority of CDN resources hosted by a few large providers. this phenomenon makes different webpages share the same provider. When browsing consecutively, H3 helps to skip the connection phase by resuming the connections to the same CDN provider across pages. thirdly, H3 mitigates the congestion problem on webpages serving multiple CDN resources. this work provides a deeper insight into the applicability of H3 in large-scale distributed systems like CDNs, holding promise for informing the development and optimization of industrial H3.

关键词： Content distribution networks distributed computing Optimization

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Comparative Analysis of Methods for Calculating the Interactions Between the Human Brain Regions Based on Resting-State FMRI Data to Build Long-Term Cognitive Architectures 11th

Comparative Analysis of Methods for Calculating the Interact...

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11th Annual international conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, BICA*AI 2020

作者： Poyda, Alexey Sharaev, Maksim Orlov, Vyacheslav Kozlov, Stanislav Enyagina, Irina Ushakov, Vadim NRC Kurchatov Institute Akademika Kurchatova pl. 1 Moscow123182 Russia Skolkovo Institute of Science and Technology Bolshoy Boulevard 30 Moscow Russia NRNU MEPhI Kashirskoe hwy 31 Moscow115409 Russia Institute for Advanced Brain Studies Lomonosov Moscow State University GSP-1 Leninskie Gory Moscow Russia

ISBN: (纸本)9783030655952

In this work, we compared many different methods proposed for calculating the functional interaction of brain regions based on resting-state fMRI data. We compared them according to the criterion of the stability of the results to small changes in the parameters of both the methods themselves and the input data including different levels of noise. By stability, here we mean that small changes in the parameters and level of noise will lead to small changes in the obtained estimates of the interaction. Since fMRI has a temporal resolution of about 2 s, here we focused on long-term architectures (400 s or more). Our study revealed that measures of Correlation and Coherence families show slightly better values for sustainability. this is in a good agreement with the result obtained earlier on synthetic fMRI data when evaluating medium-strength connections, that may be characteristic of resting-state. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Brain

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Comparison of the Impacts of the Financial Crisis and the Covid-19 Pandemic Crisis on Selected Financial Indicator of Construction Companies 7th

Comparison of the Impacts of the Financial Crisis and the Co...

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7th EAI international conference on Management of Manufacturing Systems, MMS 2022

作者： Krajníková, Katarína Smetanková, Jana Ručinský, Rastislav Mésároš, Peter Zemanová, Lucia Technical University of Kosice Faculty of Civil Engineering Institute of Construction Technology Economics and Management Kosice Slovakia Technical University of Kosice Faculty of Civil Engineering Expert’s Institute in Construction Kosice Slovakia

ISBN: (纸本)9783031227189

In the past, there have been several major and minor economic crises in global society. the financial crisis in 2008 was one of the biggest economic crises since the Great Depression in 1928. the crisis was a direct result of the decline in liquidity in global financial markets that arose in the United States as a result of the collapse of the US housing market. the Covid-19 pandemic crisis stunned all aspects of society and saw dramatic effects on society’s socio-economic spectrum. the paper analyzes the effects of selected crises on the profitability of sales. the research analyzed data from companies that belong to the TOP 100 construction companies operating in Slovakia and their activities began before 2008. the data used in the survey were obtained from the annual reports of selected companies and publicly available economic portals. the aim of the paper is to compare the profitability of sales and results of selected construction companies in three periods, namely during the financial crisis in 2008, in 2014, which can be specified as a transitional period, or the market stabilized after the financial crisis and the crisis caused by the Covid-19 pandemic. the survey will result in conclusions and future recommendations that will help eliminate the adverse effects of future crises on the activities of construction companies. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Profitability

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A genetic algorithm based method of early warning rule mining for student performance prediction 4th

A genetic algorithm based method of early warning rule minin...

引用

4th international conference on Cloud computing and Security, ICCCS 2018

作者： Mi, Chunqiao Peng, Xiaoning Cai, Zhiping Deng, Qingyou Zhao, Changhua Huaihua University HuaihuaHunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province HuaihuaHunan418000 China National University of Defense Technology ChangshaHunan410073 China

ISBN: (纸本)9783030000059

Prediction of student failure in course learning has become a very difficult issue due to the large number of factors that can affect student’s low performance, and it is difficult to use classical statistical methods because the results are usually very difficult to being understood by end-user. In this study, a genetic algorithm approach is proposed to deal with these problems using a data set of 576 higher education students’ course learning information. Firstly, a mechanism of chromosome encoding is designed to represent associated individual namely classification rule. Secondly, a flexible fitness function is proposed in order to evaluate the quality of each individual, which can make a trade-off between sensitivity and specificity. thirdly, a set of genetic operators including selection, crossover and mutation are constructed to generate offspring from the fittest individuals so as to select out the best solution to our problem, which can be easily used as an early warning rule to predict student failure in course learning. Finally, by testing the model, consistency was shown between the predicted results and the observed data, indicating that the employed method is promising for identifying at-risk students. the interpretable result is a significant advantage over other classical methods as it can obtain a both accurate and comprehensible classifier for student performance prediction. © Springer Nature Switzerland AG 2018.

关键词： Genetic algorithms

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Research on Consensus Mechanism of Diagnosis and Treatment Conclusion of Consultation 14th

Research on Consensus Mechanism of Diagnosis and Treatment C...

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14th international conference on Management Science and Engineering Management, ICMSEM 2020

作者： Li, Yueyu Li, Xiyang Bu, Qianjun Kuang, Ling Business School Sichuan University ChengduSichuan610064 China Affiliated Hospital of University of Electronic Science and Technology Operational Department Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital ChengduSichuan610072 China

ISBN: (纸本)9783030498283

this paper is to explore how to form the three typical consensus mechanisms of diagnosis and treatment conclusion between consultation specialists in the process of consultation for intractable disease. After reviewing the medical records, each consultation specialist draws his or her own subjective preference of diagnosis and treatment conclusion, which is the subjective probability of diagnosing intractable disease. Meanwhile, each consultation specialist has his or her own authoritative endowment. With the descriptive research paradigm, credible preference and Bayes’ theorem, the scientific nature of the three typical consensus mechanisms to reach on the diagnosis and treatment conclusion for the consultation specialist is discussed theoretically. the first of the typical consensus mechanisms is reasonable;the second is reasonable when the inconsistency is an absolute minority, while the relative minority cannot guarantee the rationality;the third that the diagnosis and treatment conclusions confirmed by the authoritative consultation specialist can only be said to be helpless reasonable, but has the risk of arbitrary when it is difficult to reach a consensus. the three typical consensus mechanisms on diagnosis and treatment conclusion are determined by the preferences and authoritative endowments of consultation specialists. © 2020, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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Migraine Diagnosis Support System Based on Classifier Ensemble

Migraine Diagnosis Support System Based on Classifier Ensemb...

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6th Information and Communication Technologies Innovations 2014 conference, ICT 2014

作者： Jackowski, Konrad Jankowski, Dariusz Simić, Dragan Simić, Svetlana IT4Innovations VSB - Technical University of Ostrava 17. listopadu 15/2172 708 33 Ostrava - Poruba Czech Republic Department of Systems and Computer Networks Wroclaw University of Technology Wybrzeze Wyspiańskiego 27 50-370 Wroclaw Poland University of Novi Sad Trg Dositeja Obradovića 6 21000 Novi Sad Serbia University of Novi Sad Hajduk Veljkova 1-9 21000 Novi Sad Serbia

A valid diagnosis of migraine is a non-trivial decision problem. this is due to the fact that migraine can manifest wide range of varied symptoms. thus, designing a computer aided diagnosis system for that problem remains still a very interesting topic. In this paper we present an ensemble classifier system designed for headache diagnosis. We assumed that the system should make fast initial diagnosis based on an analysis of data collected in the questionnaire only. Such an assumption eliminated possibility of application of most classical classification algorithms as they could not obtain decent level of accuracy. therefore, we decided to apply an ensemble solution. Although it is clear that ensemble should consists of complementary classifiers, there is no guidance on how to choose ensemble size and ensure its diversity. thus, we applied two stages strategy. Firstly, large pool of elementary classifiers were prepared. Its diversity was ensured by selecting algorithms of different types, structures, and learning algorithms. Secondly, we determined optimal size of the ensemble and selected its constituents using exhaustive search approaches. Results of experiments, which were carried on dataset collected in University of Novi Sad, shows that proposed system significantly outperformed all classical methods. Additionally we present analysis of diversity and accuracy correlation for tested systems. © Springer international Publishing Switzerland 2015.

关键词： Computer aided diagnosis

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UAV Smart Navigation: Combining Delaunay Triangulation and the Bat Algorithm for Enhanced Efficiency 1

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9th international conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Jothi, Akshya Sundarrajan, M. Gayana, R. Kadry, Seifedine SRM Institute of Science and Technology Chennai India K. Ramakrishnan College of Engineering Tiruchirappalli India Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

In the realm of unmanned aerial vehicles (UAVs), path planning is crucial. the objective is to create a safe, practical, and optimum flight route for UAVs to fly across unfamiliar terrain while avoiding obstacles. Because of their high computational complexity and inability to handle changing surroundings, traditional route planning algorithms confront hurdles in real-world applications. As a result, academics have created many sophisticated ways to handle UAV route planning challenges. this research provides a unique technique for intelligent UAV route planning. the program first divides the research region using the Delaunay Triangulation method. After obtaining the partitions, the coverage points are located, and the issue is framed as the Traveling Salesperson Problem (TSP). the Bat Optimization Algorithm (BOA) is used to find an optimum route for the TSP. the suggested technique seeks to find the best route for UAVs in dynamic situations. the suggested approach was verified by comparing it to other existing algorithms. the findings demonstrate that the suggested method can construct an optimum route that meets all requirements while avoiding obstacles. Moreover, the method may swiftly identify a new route as the environment changes. the study findings show that the suggested approach beats the other techniques in total computing time, saving roughly 0.03 s. Moreover, the suggested technique reduces the UAV’s route by around 0.02 cm, substantially improving existing algorithms. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Motion planning

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Towards Label-Efficient Deep Learning for Myopic Maculopathy Classification 1

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Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2023

作者： Hou, Junlin Xu, Jilan Xiao, Fan Zhang, Bo Xu, Yiqian Zhang, Yuejie Zou, Haidong Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopic Maculopathy is the leading cause of legal blindness in patients with pathologic myopia. Automated myopic maculopathy diagnosis is of vital importance to early treatment and progression slowdown. However, the scarcity of labeled fundus images with myopic maculopathy makes it challenging to improve diagnostic performance via deep learning models. In this paper, we construct a label-efficient deep learning framework for myopic maculopathy classification. In specific, we exploit two categories of pre-training methods, i.e., vision-language pre-training and self-supervised visual representation learning, to alleviate the overfitting problem caused by the limited number of training images. Moreover, we adopt a semi-supervised learning technique, namely pseudo labeling, to leverage a large number of unlabeled fundus images from external datasets. We also investigate the impact of other key components in model training for better performance, including backbone architecture, input resolution, and loss function. Our method is evaluated in the MICCAI 2023 Myopic Maculopathy Analysis Challenge (MMAC). Among 17 participating teams, our ensembled model ranked 1st on the leaderboard with an average score of 0.8752. the code will be publicly available at https://***/FDU-VTS/MMAC. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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