A framework for requirements engineering for context-aware services

Context-aware services, especially when made available to mobile devices, constitute an interesting but very challenging domain. It poses fundamental problems for both requirements engineering, software architecture, and their relationship. We propose a novel, reflection-based framework for requirements engineering for this class of applications. The framework addresses the key difficulties in this field, such as changing context and changing requirements. We report preliminary work on this framework and suggest future directions

Single Value Devices

We live in a world of continuous information overflow, but the quality of information and communication is suffering. Single value devices contribute to the information and communication quality by fo- cussing on one explicit, relevant piece of information. The information is decoupled from a computer and represented in an object, integrates into daily life. However, most existing single value devices come from conceptual experiments or art and exist only as prototypes. In order to get to mature products and to design meaningful, effective and work- ing objects, an integral perspective on the design choices is necessary. Our contribution is a critical exploration of the design space of single value devices. In a survey we give an overview of existing examples. The characterizing design criteria for single value devices are elaborated in a taxonomy. Finally, we discuss several design choices that are specifically important for moving from prototypes to commercializable products

CyberGuarder: a virtualization security assurance architecture for green cloud computing

Cloud Computing, Green Computing, Virtualization, Virtual Security Appliance, Security Isolation

CyberLiveApp: a secure sharing and migration approach for live virtual desktop applications in a cloud environment

In recent years we have witnessed the rapid advent of cloud computing, in which the remote software is delivered as a service and accessed by users using a thin client over the Internet. In particular, the traditional desktop application can execute in the remote virtual machines without re-architecture providing a personal desktop experience to users through remote display technologies. However, existing cloud desktop applications mainly achieve isolation environments using virtual machines (VMs), which cannot adequately support application-oriented collaborations between multiple users and VMs. In this paper, we propose a flexible collaboration approach, named CyberLiveApp, to enable live virtual desktop applications sharing based on a cloud and virtualization infrastructure. The CyberLiveApp supports secure application sharing and on-demand migration among multiple users or equipment. To support VM desktop sharing among multiple users, a secure access mechanism is developed to distinguish view privileges allowing window operation events to be tracked to compute hidden window areas in real time. A proxy-based window filtering mechanism is also proposed to deliver desktops to different users. To support application sharing and migration between VMs, we use the presentation streaming redirection mechanism and VM cloning service. These approaches have been preliminary evaluated on an extended MetaVNC. Results of evaluations have verified that these approaches are effective and useful

Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models

In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales. We introduce FEATHER, a computationally efficient algorithm to calculate a specific variant of these characteristic functions where the probability weights of the characteristic function are defined as the transition probabilities of random walks. We argue that features extracted by this procedure are useful for node level machine learning tasks. We discuss the pooling of these node representations, resulting in compact descriptors of graphs that can serve as features for graph classification algorithms. We analytically prove that FEATHER describes isomorphic graphs with the same representation and exhibits robustness to data corruption. Using the node feature characteristic functions we define parametric models where evaluation points of the functions are learned parameters of supervised classifiers. Experiments on real world large datasets show that our proposed algorithm creates high quality representations, performs transfer learning efficiently, exhibits robustness to hyperparameter changes, and scales linearly with the input size.Comment: Source code is available at: https://github.com/benedekrozemberczki/FEATHE

Evaluation of an RTOS on top of a hosted virtual machine system

International audienceIn this paper we evaluate a virtualized RTOS by detailing its internal fine-grained overheads and latencies rather than by providing more global results from an application perspective, as it is usually the case. This approach is fundamental to analyze a mixed criticality real-time system where applications with different levels of criticality must share the same hardware with different operating systems. This evaluation allows to observe how the RTOS behaves when deployed on top of a virtual machine system and to understand what are the key features of the RTOS which impact the performance degradation

Systém na podporu analýzy biomedicínských dat

The analysis of biomedical data is a current task, mainly thanks to the ever-evolving technologies for obtaining and preprocessing biological samples. This diploma thesis presents a software environment supporting experiments with real-world data using machine learning methods based on neural networks. The first part of the work discusses core concepts of machine learning and neural networks. The second part describes requirements, architecture, used technologies and all the capabilities of the application.Analýza biomedicínských dat je aktuální úlohou zejména díky stále se vyvíjejícím technologiím pro získávání a předzpracování biologických vzorků. Tato diplomová práce prezentuje prostředí pro podporu experimentů s metodami z oblasti strojového učení založenými na neuronových sítích. První část práce popisuje základní pojmy strojového učení a neuronových sítí. Druhá část popisuje požadavky, architekturu, použité technologie a všechny možnosti aplikace.460 - Katedra informatikyvýborn

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0