This volume contains papers selected for presentation at the 31st Annual C- ference on Current Trends in Theory and Practice of Informatics – SOFSEM 2005, held on January 22–28, 2005 in LiptovskyJ ´ an, ´ ...
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ISBN:
(数字)9783540305774
ISBN:
(纸本)9783540243021
This volume contains papers selected for presentation at the 31st Annual C- ference on Current Trends in Theory and Practice of Informatics – SOFSEM 2005, held on January 22–28, 2005 in LiptovskyJ ´ an, ´ Slovakia. The series of SOFSEM conferences, organized alternately in the Czech - public and Slovakia since 1974, has a well-established tradition. The SOFSEM conferences were originally intended to break the Iron Curtain in scienti?c - change. After the velvet revolution SOFSEM changed to a regular broad-scope international conference. Nowadays, SOFSEM is focused each year on selected aspects of informatics. This year the conference was organized into four tracks, each of them complemented by two invited talks: – Foundations of computer Science (Track Chair: Bernadette Charron-Bost) – Modeling and Searching Data in the Web-Era (Track Chair: Peter Vojt´ a? s) – softwareengineering (Track Chair: M´ aria Bielikova) ´ – Graph Drawing (Track Chair: Ondrej Syk ´ ora) The aim of SOFSEM 2005 was, as always, to promote cooperation among professionalsfromacademiaandindustryworkinginvariousareasofinformatics. Each track was complemented by two invited talks. The SOFSEM 2005 Program Committee members coming from 13 countries evaluated 144 submissions (128 contributed papers and 16 student research - rum papers). After a careful review process (counting at least 3 reviews per paper), followed by detailed discussions in the PC, and a co-chairs meeting held on October 8, 2005 in Bratislava, Slovakia, 44 papers (overall acceptance rate 34.
Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. ...
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Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.
Recent years have witnessed the increasing prevalence of smart home applications, where digital twin (DT) is popularly employed for creating virtual models that interact with physical devices in real time. Empowered b...
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Recent years have witnessed the increasing prevalence of smart home applications, where digital twin (DT) is popularly employed for creating virtual models that interact with physical devices in real time. Empowered by artificial intelligence (AI), these DT-created virtual models have more intelligent decision-making capabilities to ensure reliable performance of a smart home system. In this paper, a DT based smart home framework is investigated. It is capable of achieving intelligent control, healthcare prediction and graphical monitoring. First, the human body and device are individually modeled, and then assembled into a DT system, and the corresponding model interfaces are provided for visual monitoring. Then, an intelligent algorithm fusing VGG, LSTM and attention mechanism is developed for healthcare monitoring, i.e., the screening out of the irregular ECG rhythms. The system results are provided, including various high-fidelity interactive DT interfaces as well as the effectiveness and advantages of the intelligent algorithms for arrhythmia detection.
This LNCS volume contains the papers presented at SEAL 2008, the 7th Int- nationalConference on Simulated Evolutionand Learning,held December 7–10, 2008, in Melbourne, Australia. SEAL is a prestigious international c...
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ISBN:
(数字)9783540896944
ISBN:
(纸本)9783540896937
This LNCS volume contains the papers presented at SEAL 2008, the 7th Int- nationalConference on Simulated Evolutionand Learning,held December 7–10, 2008, in Melbourne, Australia. SEAL is a prestigious international conference series in evolutionary computation and learning. This biennial event was ?rst held in Seoul, Korea, in 1996, and then in Canberra, Australia (1998), Nagoya, Japan (2000), Singapore (2002), Busan, Korea (2004), and Hefei, China (2006). SEAL 2008 received 140 paper submissions from more than 30 countries. After a rigorous peer-review process involving at least 3 reviews for each paper (i.e., over 420 reviews in total), the best 65 papers were selected to be presented at the conference and included in this volume, resulting in an acceptance rate of about 46%. The papers included in this volume cover a wide range of topics in simulated evolution and learning: from evolutionarylearning to evolutionary optimization, from hybrid systems to adaptive systems, from theoretical issues to real-world applications. They represent some of the latest and best research in simulated evolution and learning in the world.
The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT service...
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The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge Computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.
The two-volume set LNAI 8397 and LNAI 8398 constitutes the refereed proceedings of the 6th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2014, held in Bangkok, Thailand, in April 2014. The 1...
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ISBN:
(数字)9783319054766
ISBN:
(纸本)9783319054759
The two-volume set LNAI 8397 and LNAI 8398 constitutes the refereed proceedings of the 6th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2014, held in Bangkok, Thailand, in April 2014. The 125 revised papers presented were carefully reviewed and selected from 300 submissions. The papers address the following topics: natural language and text processing, intelligent information retrieval, semantic Web, social networks and recommendation systems, intelligent database systems, decision support systems, computer vision techniques, and machine learning and data mining. The papers are organized in topical sections on multiple model approach to machine learning, MMAML 2014, computational intelligence, CI 2014, engineering knowledge and semantic systems, IWEKSS 2014, innovations in intelligent computation and applications, IICA 2014, modeling and optimization techniques in information systems, database systems and industrial systems, MOT 2014, innovation via collective intelligences and globalization in business management, ICIGBM 2014, intelligent supply chains, ISC 2014, and human motion: acquisition, processing, analysis, synthesis and visualization for massive datasets, HMMD 2014.
This two volume set LNCS 8055 and LNCS 8056 constitutes the refereed proceedings of the 24th International Conference on Database and Expert Systems Applications, DEXA 2013, held in Prague, Czech Republic, August 23-2...
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ISBN:
(数字)9783642401732
ISBN:
(纸本)9783642401725
This two volume set LNCS 8055 and LNCS 8056 constitutes the refereed proceedings of the 24th International Conference on Database and Expert Systems Applications, DEXA 2013, held in Prague, Czech Republic, August 23-29, 2013. The 43 revised full papers presented together with 33 short papers, and 3 keynote talks, were carefully reviewed and selected from 174 submissions. These papers discuss a range of topics including: search queries; indexing; discovery of semantics; parallel processing; XML and RDF; enterprise models; query evaluation and optimization; semantic Web; sampling; industrial applications; communities; AI and databases; matching and searching; information extraction; queries, streams, and uncertainty, storage and compression; query processing; security; distributed data processing; metadata modeling and maintenance; pricing and recommending; and security and semantics.
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