Lecture Notes in Computer Science (Lect Notes Comput Sci)

Publisher: Springer Verlag

Journal description

The series Lecture Notes in Computer Science (LNCS), including its subseries Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI), has established itself as a medium for the publication of new developments in computer science and information technology research and teaching - quickly, informally, and at a high level. The cornerstone of LNCS's editorial policy is its unwavering commitment to report the latest results from all areas of computer science and information technology research, development, and education. LNCS has always enjoyed close cooperation with the computer science R & D community, with numerous renowned academics, and with prestigious institutes and learned societies. Our mission is to serve this community by providing a most valuable publication service.

Current impact factor: 0.51

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2005 Impact Factor 0.302
2004 Impact Factor 0.251
2002 Impact Factor 0.515
2001 Impact Factor 0.415
2000 Impact Factor 0.253
1999 Impact Factor 0.53

Impact factor over time

Impact factor

Additional details

5-year impact 0.00
Cited half-life 0.00
Immediacy index 0.00
Eigenfactor 0.00
Article influence 0.00
Website Lecture Notes in Computer Science website
Other titles Lecture notes in computer science, Lecture notes in artificial intelligence, Lecture notes in computer science
ISSN 0302-9743
OCLC 3719235
Material type Series, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publisher details

Springer Verlag

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on pre-print servers such as arXiv.org
    • Author's post-print on author's personal website immediately
    • Author's post-print on any open access repository after 12 months after publication
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany link to published version (see policy)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification
    ​ green

Publications in this journal

  • Lecture Notes in Computer Science 07/2015;
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    ABSTRACT: Motion capture data have been widely used in applications ranging from video games and animations to simulations and virtual environments. Moreover, all data-driven approaches for analysis and synthesis of motions are depending on motion capture data. Although multiple large motion capture data sets are freely available for research, there is no system which can provide a centralized access to all of them in an organized manner. In this paper we show that using a relational database management system (RDBMS) to store data does not only provide such a centralized access to the data, but also allows to include other sensor modalities (e.g. accelerometer data) and various semantic annotations. We present two applications for our system: A motion capture player where motions sequences can be retrieved from large datasets using SQL queries and the automatic construction of statistical models which can further be used for complex motion analysis and motions synthesis tasks.
    Lecture Notes in Computer Science 06/2015; ICCSA 2015.
  • Lecture Notes in Computer Science 06/2015;
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    ABSTRACT: The articles of this volume will be reviewed individually. For the preceding workshop see Zbl 1222.68014.
    Lecture Notes in Computer Science 04/2015; DOI:10.1007/978-3-642-32897-8
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    ABSTRACT: Climate change is one of this century’s greatest unbalancing forces that affect our planet. Mining the public awareness is an essential step towards the assessment of current climate policies, dedication of sufficient resources, and construction of new policies for business planning. In this paper, we present an exploratory data mining method that compares two types of networks. The first type is constructed from a set of words collected from a Climate Change corpus, which we consider as ground-truth (i.e., base of comparison). The other type of network is constructed from a reasonably large data set of 72 million tweets; it is used to analyze the public awareness of climate change on Twitter. The results show that the social-language used on Twitter is more complex than just single word expressions. While the term climate and the hashtag (#climate) scored a lower rank, complex terms such as (“Cli- mate Change”) and (“Climate Engineering”) were more dominant using hashtags. More interestingly, we found the (#ClimateChange) hashtag is the top ranked term, among all other features, used on Twitter to signal climate familiarity expressions. This is indeed striking evidence that demonstrates a great deal of awareness and provides much hope for a better future dealing with Climate Change issues.
    Lecture Notes in Computer Science 04/2015;
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    ABSTRACT: Novel models for cell differentiation and proliferation in the urothelium are presented. The models are simulated with the Glazier-Graner-Hogeweg technique using CompuCell3D. From a variety of tested models, the contact model is the best candidate to explain cell proliferation in the healthy urothelium. Based on this model, four variations were compared to highlight the key variations that best fit real urothelium. All simulations were quantified by a fitness function designed for the requirements of the urothelium. The findings suggest that adhesion and a nutrient dependent growth may play a crucial role in the maintenance of the urothelium. Aberrations in either adhesion or nutrient dependent growth led to the development of polyp-like formations. This work mimics the regeneration process and the steady state of the urothelium with a spatial and adhesion dependent approach for the first time.
    Lecture Notes in Computer Science 04/2015; 9044:375-385. DOI:10.1007/978-3-319-16480-9_37
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    ABSTRACT: A strong effort has been made during the last years in the autonomous and automatic recognition of human activities by using wearable sensor systems. However, the vast majority of proposed solutions are designed for ideal scenarios, where the sensors are pre-defined, wellknown and steady. Such systems are of little application in real-world settings, in which the sensors are subject to changes that may lead to a partial or total malfunctioning of the recognition system. This work presents an innovative use of ontologies in activity recognition to support the intelligent and dynamic selection of the best replacement for a given shifted or anomalous wearable sensor. Concretely, an upper ontology describing wearable sensors and their main properties, such as measured magnitude, location and internal characteristics is presented. Moreover, a domain ontology particularly defined to neatly and unequivocally represent the exact placement of the sensor on the human body is presented. These ontological models are particularly aimed at making possible the use of standard wearable activity recognition in data-driven approaches.
    Lecture Notes in Computer Science 04/2015; 9044:431-443.
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    ABSTRACT: Given the prominence of IoT applications integrating mobile Internet-connected objects (ICOs), e.g., wearable sensors and mobile devices with built-in sensors, novel solutions are required to discover and collect data from mobile sensors producing data streams from varying locations, while taking into account sensor accuracy, energy-efficiency, and potential data redundancy. The OpenIoT platform offers support for mobile sensors by means of its publish/subscribe middleware solution entitled CloUd-based Publish/Subscribe middleware for the IoT (CUPUS). The CUPUS publish/subscribe component is used to collect data from mobile ICOs in a flexible and energy-efficient manner and to provide preprocessed data into the OpenIoT cloud. Moreover, CUPUS in collaboration with a Quality of Service (QoS) Manager component enables mobility management of ICOs and quality-driven data acquisition from mobile sensors to satisfy the global sensing coverage requirements while taking into account data redundancy and ICO battery lifetime.
    Lecture Notes in Computer Science 03/2015; 9001:46-61. DOI:10.1007/978-3-319-16546-2_5