Paul Sant

Real-time traversal in grammar-based compressed files

Summary form only given. In text compression applications, it is important to be able to process compressed data without requiring (complete) decompression. In this context it is crucial to study compression methods that allow time/space efficient access to any fragment of a compressed file without being forced to perform complete decompression. We study here the real-time recovery of consecutive symbols from compressed files, in the context of grammar-based compression. In this setting, a compressed text is represented as a small (a few Kb) dictionary D (containing a set of code words), and a very long (a few Mb) string based on symbols drawn from the dictionary D. The space efficiency of this kind of compression is comparable with standard compression methods based on the Lempel-Ziv approach. We show, that one can visit consecutive symbols of the original text, moving from one symbol to another in constant time and extra O(|D|) space. This algorithm is an improvement of the on-line linear (amortised) time algorithm presented in (L. Gasieniec et al, Proc. 13th Int. Symp. on Fund. of Comp. Theo., LNCS, vol.2138, p.138-152, 2001).

Guidelines for Internet of Things Deployment Approaches – The Thing Commandments

Abstract The Internet of Things (IoT) describes the interconnection of objects (or Things) for various purposes including identification, communication, sensing, and data collection. “Things” in this context range from traditional computing devices like Personal Computers (PC) to general household objects embedded with capabilities for sensing and/or communication through the use of technologies such as Radio Frequency Identification (RFID). This conceptual paper, from a philosophical viewpoint, introduces an initial set of guiding principles - also referred to in the paper as commandments - that can be applied by all the stakeholders involved in the IoT during its introduction, deployment and thereafter.

Defining Minimum Requirements of Inter-collaborated Nodes by Measuring the Weight of Node Interactions

In this paper we are focusing on the minimum requirements to be addressed in order to demonstrate a inter-node communication within a Virtual Organisation (VO) using the method of Self-led Critical Friends (SCF). The method is able to decide paths that a node can choose in order to locate neighbouring nodes by aiming at realizing the overhead of each communication. The weight of each path will be measured by the analysis of prerequisites in order to achieve the interaction between nodes. We define requirements as the least fundamentals that a node needs to achieve in order to determine its accessibility factor. The information gathered from an interaction is then stored in a snapshot, a profile that is made available during the discovery stage.

The Forensics Edge Management System: A Concept and Design

This paper describes the design of the Forensics Edge Management System (FEMS), a system that autonomously provides security and forensic services within the home Internet of Things (IoT) or smart home context. Within smart homes, users are increasingly being allowed the flexibility to manage and maintain all the solutions that entail. This is evident from the growing number of commercial smart home IoT solutions which are being designed to be manageable by end users. This IoT requirement for user-manageable solutions (without direct or indirect input from vendors beyond the provision of robust systems and solutions) presents a challenge to the traditional concept of Digital Forensics (DF) which is currently an expert-led domain. The FEMS design aims to meet these requirements for autonomy and independence, it is a system that can be integrated into a home-IoT network to conduct preliminary forensic investigations and to provide basic security services.

Affects of Queuing Mechanisms on RTP Traffic: Comparative Analysis of Jitter, End-to-End Delay and Packet Loss

The idea of converging voice and data into a best-effort service network, such as the Internet, has rapidly developed the need to effectively define the mechanisms for achieving preferential handling of traffic. This sense of QoS assurance has increased due to the enormous growth of users accessing networks, different types of traffic competing for available bandwidth and multiple services running on the core network, defined by different protocols and vendors. VoIP traffic behaviour has become a crucial element of the intrinsic QoS mainly affected by jitter, latency and packet loss rates. This paper focuses on three different mechanisms, DropTail (FIFO), RED and DiffServ, and their effects on real-time voice traffic. Measurements of jitter, end-to-end delay and packet loss, based on simulation scenarios using the NS-2 network simulator are also presented and analyzed.

Towards decentralized grid agent models for continuous resource discovery of interoperable grid Virtual Organisations

Grid technology enables resource sharing among a massive number of dynamic and geographically distributed resources. The significance of such environments is based on the aptitude of grid members to look across multiple grids for resource discovery and allocation. Parallel to grid, agents are autonomous problem solvers capable of self-directed actions in flexible environments. As grid systems require self-sufficiency, agents may be the means by which to achieve a robust autonomy infrastructure. In this direction we propose a resource discovery method of interoperable grid agents which travel within Virtual Organizations (VOs) and by capturing resource information regarding their action domain; they update the internal data of each grid member. Moreover we propose that resource discovery is a systematic and continually updating process that occurs within a VO and allows information exchange to happen. This exchange takes place between various community members at a pre-defined interval, aiming to distribute internal knowledge about the domain.

Efficient Probe Selection in Microarray Design

The DNA microarray technology, originally developed to measure the level of gene expression, had become one of the most widely used tools in genomic study. Microarrays have been proved to benefit areas including gene discovery, disease diagnosis, and multi-virus discovery. The crux of microarray design lies in how to select a unique probe that distinguishes a given genomic sequence from other sequences. However, in cases that the existence of a unique probe is unlikely, e.g., in the context of a large family of closely homologous genes, the use of a limited number of non-unique probes is still desirable. Due to its significance, probe selection attracts a lot of attention. Various probe selection algorithms have been developed in recent years. Good probe selection algorithms should produce as small number of candidate probes as possible. Efficiency is also crucial because the data involved is usually huge. Most existing algorithms usually select probes by filtering, which is usually not selective enough and quite a large number of probes are returned. We propose a new direction to tackle the problem and give an efficient algorithm to select (randomly) a small set of probes and demonstrate that such a small set of probes is sufficient to distinguish each sequence from all the other sequences. Based on the algorithm, we have developed a probe selection software RandPS, which runs efficiently and effectively in practice. A number of experiments have been carried out and the results will be discussed

A Graph Theoretic Framework for Trust - From Local to Global

Traditional approaches to trust, be it in agent-based societies, or within a more theoretical framework often consider trust to be a local phenomenon. Here we propose that trust should be viewed from a global perspective. Our motivation is the area of pervasive computing although we believe that our formal framework applies in many domains. Here we present our framework and formalize it in the form of graph theory. We present some open problems and discuss the wider application of our work

Evolving polynomial neural networks for detecting abnormal patterns

Abnormal patterns, existing e.g. in raw data, affect decision making process and have to be accurately detected and removed in order to reduce the risk of making wrong decisions. Existing Machine Learning (ML) approaches known from the literature require the user to set and experimentally adjust parameters of a decision model to achieve the best result. When artificial neural networks (ANNs) are employed, a typical problem is setting of a proper network structure and learning parameters that are required to minimise possible over-fitting. We propose a new evolutionary strategy of learning an ANN structure of a near optimal connectivity from the given data and show that such structures are less prone to over-fitting. The proposed method starts to learn with one input variable and one neuron and then adds a new input and a new neuron to the network while its validation error decreases. The resultant ANN consists of a reasonably small number of neurons that are concisely described by a set of short-term polynomial functions of variables that make a distinct contribution to the output. The proposed technique has been tested on the ML benchmarks and the results showed that the performance is comparable with that obtained by the conventional ML methods that require ad hoc tuning.

A multi-cloud approach for secure data storage on smart device

Using a mobile device to store sensitive data is not recommended, because of the high possibility of losing or thieving these data; although, neither using single cloud as a storage service is not proper solution for number of reasons; for instance, the data could be captured while uploaded to the cloud, and the data could be stolen from the cloud via using stolen ID. In this paper, we propose a solution that aims to offer a secure data storage for mobile cloud computing users based on multi-clouds scheme. The proposed solution will take the advantages of multi-clouds, data cryptography, and data compression to secure the distributed data; by splitting the data into segments, encrypting the segments, compressing the segments, distributing the segments via multi-clouds while keeping one segment on the mobile device memory; which will prevent extracting the data if the distributed segments have been phished.