Titlebook: Innovations for Community Services; 24th International C Frank Phillipson,Gerald Eichler,Günter Fahrnberger Conference proceedings 2024 The

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書目名稱Innovations for Community Services影響因子(影響力)




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書目名稱Innovations for Community Services網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Innovations for Community Services被引頻次學(xué)科排名




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書目名稱Innovations for Community Services年度引用學(xué)科排名




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書目名稱Innovations for Community Services讀者反饋學(xué)科排名

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SATQUBOLIB: A Python Framework for?Creating and?Benchmarking (Max-)3SAT QUBOs domain to immediately explore and use quantum techniques as a potential solver for their domain-specific problems, as long as they can be encoded as satisfiability problems. As a second contribution, we created a dataset of 6000 practically hard and satisfiable SAT instances that are also small eno

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Examining Smart Neighborhood Platforms: A Qualitative Exploration of Features and Applicationsd applications of smart neighborhood platforms, providing valuable insights for further research as well as urban planners, policymakers, and technology developers seeking to create smarter and more livable communities.

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Development and?Validation of?AI-Driven NLP Algorithms for?Chatbots in?Requirement Engineeringl, as well as classifying them based on ISO 25010 (quality criteria for software). Both versions utilized the DistilBERT models as their foundation. A GPT-2 model was trained to generate the inquiry. This approach utilized ambiguous user inputs and generated inquiries to get further information. To

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BURSA

Oblivious Graph Algorithms for?Solving TSP and?VRP Using FHE and?MPCly) computing a short tour along various destinations (known as the traveling salesman problem, or TSP), without seeing the privacy-sensitive input data of the user, namely which destinations the user wishes to visit. We also study a generalization of this problem with multiple tours being planned,

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Spanning Thread: A Multidimensional Classification Method for?Efficient Data Center Managementdomly selected data. Additionally, we present OST (Ordered Spanning Thread), which commences with the minimum virtual data and concludes with the maximum virtual data. Both methods are evaluated using an open dataset, wherein we measure the frequency of class changes to assess their effectiveness.

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