A new distributed MIKEY mode to secure e-health applications.

Securing e-health applications in the context of Internet of Things (IoT) is challenging. Indeed, resources scarcity in such environment hinders the implementation of existing standard based protocols. Among these protocols, MIKEY (Multimedia Internet KEYing) aims at establishing security credentials between two communicating entities. However, the existing MIKEY modes fail to meet IoT specificities. In particular, the pre-shared key mode is energy efficient, but suffers from severe scalability issues. On the other hand, asymmetric modes such as the public key mode are scalable, but are highly resource consuming. To address this issue, we combine two previously proposed approaches to introduce a new hybrid MIKEY mode. Indeed, relying on a cooperative approach, a set of third parties is used to discharge the constrained nodes from heavy computational operations. Doing so, the pre-shared mode is used in the constrained part of the network, while the public key mode is used in the unconstrained part of the network. Preliminary results show that our proposed mode is energy preserving whereas its security properties are kept safe.

An evaluation of a sketching interface for interaction with concatenative sound synthesis.

This paper presents the evaluation of morpheme a sketching interface for the control of sound synthesis. We explain the task that was designed in order to assess the effectiveness of the interface, detect usability issues and gather participants’ responses regarding cognitive, experiential and expressive aspects of the interaction. The evaluation comprises a design task, where partici-pants were asked to design two soundscapes using the morpheme interface for two video footages. Responses were gathered using a series of likert type and open-ended questions. The analysis of the data gathered revealed a number of usability issues, however the performance of morpheme was satisfactory and participants recognised the creative potential of the interface and the synthesis methods for sound design applications.

Sonifying the solar system.

Sound is potentially an effective way of analysing data and it is possible to simultaneously interpret layers of sounds and identify changes. Multiple attempts to use sound with scientific data have been made, with varying levels of success. On many occasions this was done without including the end user during the development. In this study a sonified model of the 8 planets of our solar system was built and tested using an end user approach. The sonification was created for the Esplora Planetarium, which is currently being constructed in Malta. The data requirements were gathered from a member of the planetarium staff, and 12 end users, as well as the planetarium representative tested the sonification. The results suggest that listeners were able to discern various planetary characteristics without requiring any additional information. Three out of eight sound design parameters did not represent characteristics successfully. These issues have been identified and further development will be conducted in order to improve the model.

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

Distributed and compressed MIKEY mode to secure end-to-end communications in the Internet of things.

Multimedia Internet KEYing protocol (MIKEY) aims at establishing secure credentials between two communicating entities. However, existing MIKEY modes fail to meet the requirements of low-power and low-processing devices. To address this issue, we combine two previously proposed approaches to introduce a new distributed and compressed MIKEY mode for the Internet of Things. Indeed, relying on a cooperative approach, a set of third parties is used to discharge the constrained nodes from heavy computational operations. Doing so, the preshared mode is used in the constrained part of network, while the public key mode is used in the unconstrained part of the network. Furthermore, to mitigate the communication cost we introduce a new header compression scheme that reduces the size of MIKEY’s header from 12 Bytes to 3 Bytes in the best compression case. Preliminary results show that our proposed mode is energy preserving whereas its security properties are preserved untouched.

Bitcoin mining is about to become a lot less profitable.

Imagine being told that your wage was going to be cut in half. Well, that’s what’s soon going to happen to those who make money from Bitcoin mining, the process of earning the online currency Bitcoin. The current expected date for this change is 11 July 2016. Many see this as the day when Bitcoin prices will rocket and when Bitcoin owners could make a great deal of money. Others see it as the start of a Bitcoin crash. At present no one quite knows which way it will go. Bitcoin was created in 2009 by someone known as Satoshi Nakamoto, borrowing from a whole lot of research methods. It is a cryptocurrency, meaning it uses digital encryption techniques to create bitcoins and secure financial transactions. It doesn’t need a central government or organisation to regulate it, nor a broker to manage payments. Conventional currencies usually have a central bank that creates money and controls its supply. Bitcoin is instead created when individuals “mine” for it by using their computers to perform complex calculations through special software. The algorithm behind Bitcoin is designed to limit the number of bitcoins that can ever be created. All Bitcoin transactions are recorded on a public database known as a blockchain. Every time someone mines for Bitcoin, it is recorded with a new block that is transmitted to every Bitcoin app across the network, like a bank updating its online records.

Digital engagement workshop for community councils: Glasgow 2016 report.

This report covers a workshop on digital engagement for Community Councils supported by the School of Computing's public engagement fund. It was held in Glasgow on 22 March 2016. The workshop combined presentations by subject experts with attendee-led round-table discussions. It was well received and felt by delegates to be of immediate benefit. There is clear demand for follow-up events, potentially more focussed on training.

Evolution feature oriented model driven product line engineering approach for synergistic and dynamic service evolution in clouds: A04BPEL3.0 proposal.

Model Driven based approach for Service Evolution in Clouds will mainly focus on the reusable evolution patterns' advantage to solve evolution problems. During the process, evolution pattern will be driven by MDA models to pattern aspects. Weaving the aspects into service based process by using Aspect-Oriented extended BPEL engine at runtime will be the dynamic feature of the evolution.

PCA-FNN based performance prediction for water injection in oilfields.

In order to solve the problem of uncertain cycle of water injection in the oilfield, this paper proposed a numerical method based on PCA-FNN, so that it can forecast the effective cycle of water injection. PCA is used to reduce the dimension of original data, while FNN is applied to train and test the new data. The correctness of PCA-FNN model is verified by the real injection statistics data from 116 wells of an oilfield, the result shows that the average absolute error and relative error of the test are 1.97 months and 10.75% respectively. The testing accuracy has been greatly improved by PCA-FNN model compare with the FNN which has not been processed by PCA and multiple liner regression method. Therefore, PCA-FNN method is reliable to forecast the effectiveness cycle of water injection and it can be used as an decision-making reference method for the engineers.

When is bigger better? The effects of group size on the evolution of helping behaviours.

Understanding the evolution of sociality in humans and other species requires understanding how selection on social behaviour varies with group size. However, the effects of group size are frequently obscured in the theoretical literature, which often makes assumptions that are at odds with empirical findings. In particular, mechanisms are suggested as supporting large-scale cooperation when they would in fact rapidly become ineffective with increasing group size. Here we review the literature on the evolution of helping behaviours (cooperation and altruism), and frame it using a simple synthetic model that allows us to delineate how the three main components of the selection pressure on helping must vary with increasing group size. The first component is the marginal benefit of helping to group members, which determines both direct fitness benefits to the actor and indirect fitness benefits to recipients. While this is often assumed to be independent of group size, marginal benefits are in practice likely to be maximal at intermediate group sizes for many types of collective action problems, and will eventually become very small in large groups due to the law of decreasing returns. The second component is the response of social partners on the past play of an actor, which underlies conditional behaviour under repeated social interactions. We argue that under realistic conditions on the transmission of information in a population, this response on past play decreases rapidly with increasing group size so that reciprocity alone (whether direct, indirect, or generalised) cannot sustain cooperation in very large groups. The final component is the relatedness between actor and recipient, which, according to the rules of inheritance, again decreases rapidly with increasing group size. These results explain why helping behaviours in very large social groups are limited to cases where the number of reproducing individuals is small, as in social insects, or where there are social institutions that can promote (possibly through sanctioning) large-scale cooperation, as in human societies. Finally, we discuss how individually devised institutions can foster the transition from small-scale to large-scale cooperative groups in human evolution.