Drivers’ behaviour modelling for virtual worlds

In this paper we present a study that looks at modelling drivers’ behaviour with a view to contribute to the problem of road rage. The approach we adopt is based on agent technology, particularly multi-agent systems. Each driver is represented by a software agent. A virtual environment is used to simulate drivers’ behaviour, thus enabling us to observe the conditions leading to road rage. The simulated model is then used to suggest possible ways of alleviating this societal problem. Our agents are equipped with an emotional module which will make their behaviours more human-like. For this, we propose a computational emotion model based on the OCC model and probabilistic cognitive maps. The key influencing factors that are included in the model are personality, emotions and some social/personal attributes

Hybrid image representation methods for automatic image annotation: a survey

In most automatic image annotation systems, images are represented with low level features using either global methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is beneficial in annotating images. In this paper, we provide a survey on automatic image annotation techniques according to one aspect: feature extraction, and, in order to complement existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation

Group Formation Techniques in Computer-Supported Collaborative Learning: A Systematic Literature Review

Group formation is an essential process for group development lifecycle. It has been a growing concern to many researchers to be applied automatically in collaborative learning contexts. Forming a group is an atomic process that is affected by various factors. These factors differ depending on the group members characteristics, the context of the grouping process and the techniques used to form the group(s). This paper surveys the recently published work in group formation process providing a systematic literature review in which 30 relevant studies were analyzed. The findings of this review propose two taxonomies. The first one is for the attributes of group formation while the second is for the grouping techniques. Furthermore, we present the main findings and highlight the limitations of existing approaches in computer supported collaborative learning environment. We suggest some potential directions for future research with group formation process in both theoretical and practical aspects. In addition, We emphasize other improvements that may be inter-related with other computing areas such as cloud computing and mobility

Blockchain technology and related security risks: towards a seven-layer perspective and taxonomy

Blockchain technology can be a useful tool to address issues related to sustainability. From its initial foundation based on cryptocurrency to the development of smart contracts, blockchain technology promises significant business benefits for various industry sectors, including the potential to offer more trustworthy modes of governance, reducing the risks for environmental and economic crises. Notwithstanding its known benefits, and despite having some protective measures and security features, this emerging technology still faces significant security challenges within its different abstract layers. This paper classifies the critical cybersecurity threats and vulnerabilities inherent in smart contracts based on an in-depth literature review and analysis. From the perspective of architectural layering, each layer of the blockchain has its own corresponding security issues. In order to have a detailed look at the source of security vulnerabilities within the blockchain, a seven-layer architecture is used, whereby the various components of each layer are set out, highlighting the related security risks and corresponding countermeasures. This is followed by a taxonomy that establishes the inter-relationships between the vulnerabilities and attacks in a smart contract. A specific emphasis is placed on the issues caused by centralisation within smart contracts, whereby a “one-owner” controls access, thus threatening the very decentralised nature that blockchain is based upon. This work offers two main contributions: firstly, a general taxonomy that compiles the different vulnerabilities, types of attacks, and related countermeasures within each of the seven layers of the blockchain; secondly, a specific focus on one layer of the blockchain namely, the contract layer. A model application is developed that depicts, in more detail, the security risks within the contract layer, while enlisting the best practices and tools to use to mitigate against these risks. The findings point to future research on developing countermeasures to alleviate the security risks and vulnerabilities inherent to one-owner control in smart contracts

Process Analysis and e-Business Adoption in Nigerian SBEs: A Report on Case Study Research

It is generally acknowledged that e-business technologies can provide internal value as well as opportunities to reach different local and international markets, for both large and small organisations. Although the use of web-based systems and technologies to improve business processes has increased steadily over the past decade, there remains a dearth of research in this field in developing countries such as Nigeria. This paper examines how e-business is being used in two Nigerian small businesses, using a process mapping technique, system profiling and two main models from the existing literature. The results indicate that these models provide a valid framework for the initial analysis of e-business in this environment, and that these companies are indeed benefitting from the deployment of e-business technologies, particularly in their customer facing processes and functions

Ontology-based semantic classification of satellite images: Case of major disasters

The International Charter 'Space and Major Disasters' is regularly activated during a catastrophic event and offers rescue teams comprehensive damage maps. Most of these maps are built by means of satellite image manual processing, which is often complex and demanding in terms of time and energy. Automatic processing supplies prompt treatment. Nevertheless, it usually presents a semantic gap handicap. The exploitation of ontologies to bridge the semantic gap has been widely recommended due to their quality of knowledge representation, expression, and discovery. In this work, we present an ontology-based semantic hierarchical classification method to undertake this problem. Ontology components are translated to image-based parameters and exploited to assist the classification process at two levels, and using 12 classes. The region of interest is selected from the first level and exhaustively analyzed and classified at the second level. The 2010 Haiti earthquake was selected as study area for this work. Experiments were performed using very high resolution multi-temporal QuickBird imagery and eCognition software

Geographic ontology for major disasters: methodology and implementation

During a catastrophic event, the International Charter1 "Space and Major Disasters" is regularly activated and provides the rescue teams damage maps prepared by a photo-interpreter team basing on pre and post-disaster satellite images. A satellite image manual processing must be accomplished in most cases to build these maps, a complex and demanding process. Given the importance of time in such critical situations, automatic or semiautomatic tools are highly recommended. Despite the quick treatment presented by automatic processing, it usually presents a semantic gap issue. Our aim is to express expert knowledge using a well-defined knowledge representation method: ontologies and make semantics explicit in geographic and remote sensing applications by taking the ontology advantages in knowledge representation, expression, and knowledge discovery. This research focuses on the design and implementation of a comprehensive geographic ontology in the case of major disasters, that we named GEO-MD, and illustrates its application in the case of Haiti 2010 earthquake. Results show how the ontology integration reduces the semantic gap and improves the automatic classification accuracy

Model-based multi-critical optimisation of combustion engine fuel consumption and emissions

The combustion engine is a typical nonlinear multi-input multi-output (MIMO) system with strong couplings, actuator constraints, and fast dynamics This paper addresses a model-based multi-critical optimisation approach in diesel engines, which allows to improve emission performance and to provide a reference for the design and optimisation of the diesel engine system. The first part of this paper introduces a data based modelling method that appears particularly suitable for emission modelling. The Design of Experiments (DoE) method helps to generate and collect the required measurement for data-based modelling in a short time, despite the increasing number of manipulated variables. The second part establishes a new model-based multi-critical optimisation approach that supports the optimisation of fuel consumption and emissions based on engine models. This proposed model-based framework consists of system identification and multi-critical optimisation. This framework has the ability to achieve the fast and precise solving of multi-critical optimisation problem and is suitable for implementation in the engine control unit. The experiment results illustrate that the model-based multi-critical optimisation significantly improves the engine exhaust emissions and fuel consumption against the original ECU

Data-Based Gain-Scheduled Modeling and Nonlinear Control of Engine Intake and Exhaust System

In this paper, the parameterized dynamical model of the diesel engine intake and exhaust system using a data-based method, namely a Gain-Scheduled model is proposed and designed based on the data from a virtual engine test bench under normal load conditions. In the first step, the Multiple Input Multiple Output model structure is defined with five inputs and two outputs. Using the constructed model, it is possible to establish the relations between intake Manifold Pressure, Air Mass Flow, the control signals, and changes of the load. Then, the model is further used to design a Nonlinear Model Predictive Control controller, aimed at optimizing the efficiency of the combustion system in terms of the control reference value tracking with respect to emission reduction. This paper follows a model-based design approach to construct the Nonlinear Model Predictive Control objective function for the engine intake Manifold Pressure and Air Mass Flow nonlinear control problem. The proposed data-based dynamical modeling method is shown to increase the flexibility for the modeling of nonlinear plant at a low cost in computational requirements. The experimental results illustrate that the optimized nonlinear control approach significantly improve the control reference tracking performance and the exhaust emissions against the standard decentralized Single Input Single Output control in the standard production Engine Control Unit