129,994 research outputs found
Managing evolution and change in web-based teaching and learning environments
The state of the art in information technology and educational technologies is evolving constantly.
Courses taught are subject to constant change from organisational and subject-specific reasons. Evolution
and change affect educators and developers of computer-based teaching and learning environments alike â
both often being unprepared to respond effectively. A large number of educational systems are designed
and developed without change and evolution in mind. We will present our approach to the design and
maintenance of these systems in rapidly evolving environments and illustrate the consequences of evolution
and change for these systems and for the educators and developers responsible for their implementation and
deployment. We discuss various factors of change, illustrated by a Web-based virtual course, with the
objective of raising an awareness of this issue of evolution and change in computer-supported teaching and
learning environments. This discussion leads towards the establishment of a development and management
framework for teaching and learning systems
SwiftCache: Model-Based Learning for Dynamic Content Caching in CDNs
We introduce SwiftCache, a "fresh" learning-based caching framework designed
for content distribution networks (CDNs) featuring distributed front-end local
caches and a dynamic back-end database. Users prefer the most recent version of
the dynamically updated content, while the local caches lack knowledge of item
popularity and refresh rates. We first explore scenarios with requests arriving
at a local cache following a Poisson process, whereby we prove that the optimal
policy features a threshold-based structure with updates occurring solely at
request arrivals. Leveraging these findings, SwiftCache is proposed as a
model-based learning framework for dynamic content caching. The simulation
demonstrates near-optimal cost for Poisson process arrivals and strong
performance with limited cache sizes. For more general environments, we present
a model-free Reinforcement Learning (RL) based caching policy without prior
statistical assumptions. The model-based policy performs well compared to the
model-free policy when the variance of interarrival times remains moderate.
However, as the variance increases, RL slightly outperforms model-based
learning at the cost of longer training times, and higher computational
resource consumption. Model-based learning's adaptability to environmental
changes without retraining positions it as a practical choice for dynamic
network environments. Distributed edge caches can utilize this approach in a
decentralized manner to effectively meet the evolving behaviors of users.Comment: arXiv admin note: text overlap with arXiv:2401.0361
DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments
With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
Evolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ensemble learning for mining non-stationary data streams
can be found in the literature, most of them are crafted under a static base
classifier and revisits preceding samples in the sliding window for a
retraining step. This feature causes computationally prohibitive complexity and
is not flexible enough to cope with rapidly changing environments. Their
complexities are often demanding because it involves a large collection of
offline classifiers due to the absence of structural complexities reduction
mechanisms and lack of an online feature selection mechanism. A novel evolving
ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in
this paper. pENsemble differs from existing architectures in the fact that it
is built upon an evolving classifier from data streams, termed Parsimonious
Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism,
which estimates a localized generalization error of a base classifier. A
dynamic online feature selection scenario is integrated into the pENsemble.
This method allows for dynamic selection and deselection of input features on
the fly. pENsemble adopts a dynamic ensemble structure to output a final
classification decision where it features a novel drift detection scenario to
grow the ensemble structure. The efficacy of the pENsemble has been numerically
demonstrated through rigorous numerical studies with dynamic and evolving data
streams where it delivers the most encouraging performance in attaining a
tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
Proceedings of the ECSCW'95 Workshop on the Role of Version Control in CSCW Applications
The workshop entitled "The Role of Version Control in Computer Supported Cooperative Work Applications" was held on September 10, 1995 in Stockholm, Sweden in conjunction with the ECSCW'95 conference. Version control, the ability to manage relationships between successive instances of artifacts, organize those instances into meaningful structures, and support navigation and other operations on those structures, is an important problem in CSCW applications. It has long been recognized as a critical issue for inherently cooperative tasks such as software engineering, technical documentation, and authoring. The primary challenge for versioning in these areas is to support opportunistic, open-ended design processes requiring the preservation of historical perspectives in the design process, the reuse of previous designs, and the exploitation of alternative designs.
The primary goal of this workshop was to bring together a diverse group of individuals interested in examining the role of versioning in Computer Supported Cooperative Work. Participation was encouraged from members of the research community currently investigating the versioning process in CSCW as well as application designers and developers who are familiar with the real-world requirements for versioning in CSCW. Both groups were represented at the workshop resulting in an exchange of ideas and information that helped to familiarize developers with the most recent research results in the area, and to provide researchers with an updated view of the needs and challenges faced by application developers. In preparing for this workshop, the organizers were able to build upon the results of their previous one entitled "The Workshop on Versioning in Hypertext" held in conjunction with the ECHT'94 conference. The following section of this report contains a summary in which the workshop organizers report the major results of the workshop. The summary is followed by a section that contains the position papers that were accepted to the workshop. The position papers provide more detailed information describing recent research efforts of the workshop participants as well as current challenges that are being encountered in the development of CSCW applications. A list of workshop participants is provided at the end of the report.
The organizers would like to thank all of the participants for their contributions which were, of course, vital to the success of the workshop. We would also like to thank the ECSCW'95 conference organizers for providing a forum in which this workshop was possible
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