Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are su...
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Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.
Automated code completion, aiming at generating subsequent tokens from unfinished code, has significantly benefited from recent progress in pre-trained Large Language Models (LLMs). However, these models often suffer ...
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Automated code completion, aiming at generating subsequent tokens from unfinished code, has significantly benefited from recent progress in pre-trained Large Language Models (LLMs). However, these models often suffer from coherence issues and hallucinations when dealing with complex code logic or extrapolating beyond their training data. Existing Retrieval Augmented Generation (RAG) techniques partially address these issues by retrieving relevant code with a separate encoding model where the retrieved snippet serves as contextual reference for code completion. However, their retrieval scope is subject to a singular perspective defined by the encoding model, which largely overlooks the complexity and diversity inherent in code semantics. To address this limitation, we propose ProCC, a code completion framework leveraging prompt engineering and the contextual multi-armed bandits algorithm to flexibly incorporate and adapt to multiple perspectives of code. ProCC first employs a prompt-based multi-retriever system which crafts prompt templates to elicit LLM knowledge to understand code semantics with multiple retrieval perspectives. Then, it adopts the adaptive retrieval selection algorithm to incorporate code similarity into the decision-making process to determine the most suitable retrieval perspective for the LLM to complete the code. Experimental results demonstrate that ProCC outperforms a widely-studied code completion technique RepoCoder by 7.92% on the public benchmark CCEval, 3.19% in HumanEval-Infilling, 2.80% on our collected open-source benchmark suite, and 4.48% on the private-domain benchmark suite collected from Kuaishou Technology in terms of Exact Match. ProCC also allows augmenting fine-tuned techniques in a plug-and-play manner, yielding an averaged 6.5% improvement over the fine-tuned model.
Since its first edition in 2003, the XML Database Symposium series (XSym) has been a forum for academics, practitioners, users and vendors, allowing all to discuss the use of and synergy between database management sy...
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ISBN:
(数字)9783642156847
ISBN:
(纸本)9783642156830
Since its first edition in 2003, the XML Database Symposium series (XSym) has been a forum for academics, practitioners, users and vendors, allowing all to discuss the use of and synergy between database management systems and XML. The symposia have provided many opportunities for timely discussions on a broad range of topics pertaining to the theory and practice of XML data management and its applications. XSym 2010 continued this XSym tradition with a program consisting of 11 papers and a keynote shared with the 36th International Conference on Very Large Data Bases (VLDB 2010). We received 20 paper submissions, out of which 8 papers were accepted as full papers, and 3 as short papers. Each submitted paper underwent a rigorous and careful review by four referees. The contributions in these proceedings are a fine sample of the current research in XML query processing, including XPath satisfiability, approximate joins, pattern matching, linear index construction for trees, dynamic labeling, and XQuery update translation based on schema. The papers focus on recent advances in detecting fu- tional dependencies, modeling complex XML twig pattern output, promoting sem- tics capability of XML keys, and searchable compression of Microsoft office do- ments. In addition, we include a paper that shares lessons learned from real XML database development.
These two volumes consIstmg of Foundations and Applications provide the current status of theoretical and empirical developments in "computing with words". In philosophy, the twentieth century is said to be ...
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ISBN:
(数字)9783790818734
ISBN:
(纸本)9783790812176;9783662113622
These two volumes consIstmg of Foundations and Applications provide the current status of theoretical and empirical developments in "computing with words". In philosophy, the twentieth century is said to be the century of language. This is mainly due to Wittgenstein who said: "The meaning of a word is its use in the language game". "The concept game is a concept with blurred edges". In the first phrase, "the language game" implies the everyday human activity with language, and in the latter, "game" simply implies an ordinary word. Thus, Wittgenstein precisely stated that a word is fuzzy in real life. Unfortunately this idea about a word was not accepted in the conventional science. We had to wait for Zadeh's fuzzy sets theory. Remembering Wittgenstein's statement, we should consider, on the one hand, the concept of "computing with words" from a philosophical point of view. It deeply relates to the everyday use of a word in which the meaning of a word is fuzzy in its nature.
This book features a collection of high-quality research papers presented at the International Conference on Intelligent and Cloud computing (ICICC 2019), held at Siksha 'O' Anusandhan (Deemed to be University...
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ISBN:
(数字)9789811562020
ISBN:
(纸本)9789811562013;9789811562044
This book features a collection of high-quality research papers presented at the International Conference on Intelligent and Cloud computing (ICICC 2019), held at Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India, on December 20, 2019. Including contributions on system and network design that can support existing and future applications and services, it covers topics such as cloud computing system and network design, optimization for cloud computing, networking, and applications, green cloud system design, cloud storage design and networking, storage security, cloud system models, big data storage, intra-cloud computing, mobile cloud system design, real-time resource reporting and monitoring for cloud management, machine learning, data mining for cloud computing, data-driven methodology and architecture, and networking for machine learning systems.
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of appl...
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ISBN:
(数字)9783030054144
ISBN:
(纸本)9783030054137
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.
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