This paper deals with the concept of informal learning in virtual communities on the Internet. Initially we discuss the need for continuing education and its relation with informal learning. Virtual communities are next defined and then compared to real communities. Case studies are employed, focused on some specific kinds of virtual communities. We examine how they operate, how their members interact, what values they share and what kind of knowledge they gather. The learning process within virtual communities is then examined. We look at the kind of information and knowledge available in some particular virtual communities, and comment on its organisation. Next, the learning process of virtual communities is compared to that of Open Universities. Finally, we claim that the participation in virtual communities is not only a form of continuing education but also a contribution towards the multiliteracies needed for working as well as living in the 21st century.
This paper presents a secure image cryptography telecom system based on a Chua's circuit chaotic noise generator. A chaotic system based on synchronised Master-Slave Chua's circuits has been used as a chaotic true random number generator (CTRNG). Chaotic systems present unpredictable and complex behaviour. This characteristic, together with the dependence on the initial conditions as well as the tolerance of the circuit components, make CTRNGs ideal for cryptography. In the proposed system, the transmitter mixes an input image with chaotic noise produced by a CTRNG. Using thresholding techniques, the chaotic signal is converted to a true random bit sequence. The receiver must be able to reproduce exactly the same chaotic noise in order to subtract it from the received signal. This becomes possible with synchronisation between the two Chua's circuits: through the use of specific techniques, the trajectory of the Slave chaotic system can be bound to that of the Master circuit producing (almost) identical behaviour. Additional blocks have been used in order to make the system highly parameterisable and robust against common attacks. The whole system is simulated in Matlab. Simulation results demonstrate satisfactory performance, as well as, robustness against cryptanalysis. The system works with both greyscale and colour jpg images.
In today's world, due to the evolution in technology and science, old knowledge is fast becoming obsolete while new knowledge is produced in exponential rates. Therefore, lifelong learning is essential for knowledge-intensive persons such as engineers and scientists, as well as, organisations which want to stay competitive in today's globalised environment. It is the aim of this chapter to examine the use of MOOCs in the continuing education of employees in knowledge-intensive workplaces, as well as, organisations based on the knowledge economy. Initially, the main characteristics of the modern learning landscape will be presented; the need for continuing education and lifelong learning will be discussed. An extensive presentation of MOOCs will take place. A survey of the open research problems in the study of MOOCs will be presented. Finally, educational policies, enterprise policies and suggestions for self-directed continuing and lifelong learning via MOOCs will be proposed.
Malware creators generate new malicious software samples by making minor changes in previously generated code, in order to reuse malicious code, as well as to go unnoticed from signature-based antivirus software. As a result, various families of variations of the same initial code exist today. Visualization of compiled executables for malware analysis has been proposed several years ago. Visualization can greatly assist malware classification and requires neither disassembly nor code execution. Moreover, new variations of known malware families are instantly detected, in contrast to traditional signature-based antivirus software. This paper addresses the problem of identifying variations of existing malware visualized as images. A new malware detection system based on a two-level Artificial Neural Network (ANN) is proposed. The classification is based on file and image features. The proposed system is tested on the ‘Malimg’ dataset consisting of the visual representation of well-known malware families. From this set some important image features are extracted. Based on these features, the ANN is trained. Then, this ANN is used to detect and classify other samples of the dataset. Malware families creating a confusion are classified by a second level of ANNs. The proposed two-level ANN method excels in simplicity, accuracy, and speed; it is easy to implement and fast to run, thus it can be applied to antivirus software, smart firewalls, web applications, etc.
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