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The International Maritime Organization (IMO) enforced stricter sulphur abatement regulations since shipping emission has become one of the most major cause of the atmospheric pollution. Experts from the industry and academicians try to find the balanced solution among low-sulphur fuel, clean energy, and purposely fit scrubber by conventional statistical methods however failed to reach a satisfying conclusion. In addition, maritime datasets are usually massive, multi-source, and heterogeneous, it seems imperative for the maritime industry to adapt to the worldwide trend of intellectualisation and promote sustainable development.This work delineates and compares three main sulphur abatement solutions for ships through a thorough investigation of the current research state, and proposes a new framework based on a fusion model using modern big data and data mining algorithms. This work identifies and summarises major factors (with high impacts) in sulphur abatement solutions in the ocean shipping industry and integrate those high-level impacting factors to the proposed fusion model. The proposed framework can be optimised and utilised in determining suitable solutions for different ships, as well as shipping routes.

Distributed denial of service (DDoS) attacks pose a huge threat to the Internet. Follow the rapid usage of the Internet of things (IoT), the DDoS attack is no longer a mere traffic attack, the original attack on the application layer surpasses the attack on the network layer. Furthermore, DDoS attacks using Bonnets result more destructive effects. This research aims to propose a new collaborated active defense framework between Honeypot and cloud platform to detect and defend future DDoS attacks in the context of the IoT with the instantaneous malicious traffic measured in Terabytes.

This paper considers multiple network attacks in a Mobile Ad hoc Network (MANET). MANET is a type of network that does not require any infrastructure for nodes to communicate with each other. The general assumption in MANET is that each node is a trusted node. However, due to its lack of infrastructure and dynamic topology, the MANET is exposed to network layer attacks. In this paper, some of the network layer attacks such as the black hole attack, worm hole attack and gray hole attack are discussed. Frequent multiple network attacks are highlighted, and the identification of collaborative network attacks is discussed. There are several symptoms and observations in a network that signal the presence of an attack and some of the symptoms of these attacks will be highlighted. Finally, an analysis of existing multiple network attacks will be discussed, and the identification of prospective multiple network attacks is considered.

Facial expressions analysis is a vital part of the research in human-machine interaction. This chapter introduces an automatic recognition system for facial expression from a front view human face image. Obtaining an effective facial representation from initial face images is an essential phase for strong and efficient facial expression system. In this chapter we have developed a facial expression analysis system. Firstly we have tried to evaluate facial analysis base on Active shape model (ASM). In order to detect the face images, Adaboost classifier and Haar-Like feature has been adopted to achieve face detection and tracking. The ASM then has been automatically initiated in the detected face image. Then, discriminates and reliable facial feature points has been extracted applying ASM fitting technique. The geometric displacement among the projected ASM feature points coordinates and the mean shape of ASM were used to evaluate facial expression. Using support vector machine (SVM) classifier, the obtained results has reached a recognition rate of 93 %.Keywords: Facial Expression Recognition, Image

Advertising agencies have a growing interest in finding out if roadside advertisements have reached the target audiences. The drivers’ head position’ would enable us to determine if they have been looking at the billboards. Numerous researches were focusing on the drivers’ attention from inside their cars; however, some have tried to determine the interaction between the drivers and outside objects. In this paper, the proposed Head Pose Estimation (HPE) is able to detect drivers’ head motion/position and try to determine if they are looking at the billboard. This information is vital for the Advertising companies for the evaluation of the effectiveness of their advertisements.

Wireless sensor network technology holds great promise for application in agriculture to improve crop yield, improve quality, and reduce costs. This paper presents wireless sensor network coverage measurements in a mixed crop farmland. As one of its key contributions, this study shows that general vegetation attenuation models do not apply to low power wireless sensor networks. A log-linear model is proposed in this paper and validated for application in mixed crop environment. Unlike in mono-crop environment, this study shows that the network coverage is heterogeneous with asymmetric channel between communicating node pair. Crop specific parameters of the log-linear model are derived and used to simulate network coverage in a 7 acre test-bed farm. An adaptive energy consumption model for each sensor node is proposed and used to compute energy consumption in the network. A cluster head and two antenna heights deployment model is also proposed and simulated to alleviate short network lifetime due to vegetation attenuation of signals. The results show that this network deployment model extends the lifetime of the network by a factor of more than 20 compare to a deployment where cluster heads are not used.

Ontology is the way to provide interoperability of different classes over semantic web. The better the ontology is constructed, the more are the chances for tight integration between different processes, no matter they are within the organization or are indirectly connected to organization, including external stakeholders i.e. Customers, Suppliers and Outsource services. For a properly design Ontology, it is always a mandatory step to apply different methods for Ontology Alignment. This propagates the reusability approach of a single object, thus at different instances, same property or attribute can be declared, rather going for heterogeneous environment. We have different traditional methods to align the ontology, however we are going to propose new method, adopted from the other domain, to better align the ontology. We will advance the concept by introducing algorithm which can help implementing the solution.

During their final year undergraduate project a student may feel under-supported, stressed or isolated. In an internally funded project we set out to investigate the benefits of using a diverse blend of collaboration and communication tools alongside traditional methods of final year project supervision. We established separate formal and informal communication channels between the supervisor and their project students and a community of practice of students and supervisors was set up using twitter or a web forum. Using a wiki as a collaborative workspace and repository, student project pages were created and virtual supervision was blended with face-to-face supervision using electronic logs. at first some students were dubious about our approach and disapproved of at least one of the tools used. The supervisors were also initially sceptical of an increase in workload due to the multiplicity of tools used. In this paper we present how the staff and students benefited precisely because of the diverse range of tools used. The methods used resulted in transparency of students’ and supervisors’ actions, however, lessons were learnt about how to address student concerns about plagiarism in such an open environment.

Modern Critical infrastructures have command and control systems. These command and control systems are commonly called supervisory control and data acquisition (SCADA). In the past, SCADA system has a closed operational environment, so these systems were designed without security functionality. Nowadays, as a demand for connecting the SCADA system to the open network growths, the study of SCADA system security is an issue. A key-management scheme is critical for securing SCADA communications. Numerous key-management structures for SCADA also have been suggested. 11770-2 Mechanism 9 Key establishment Protocol has been used in SCADA communication however a security proof for the 11770-2 Mechanism 9 protocol is needed. The purpose of this paper is to provide a general overview about SCADA system, and its related security issues. Furthermore, we try to investigate the importance of key management protocol and the need of formal security poof.

Business policy reflects way too many things not only restricting what the company should do but also predicting the future direction of the company. It is desirable that the description of business policy can be interchangeable between business descriptions and engineering specifications so that the rules can be integrated into engineering process without further misinterpreting and conflicting. This paper presents our initiatives of a work to investigate and identify suitable ontologies, as well as suitable ontology alignment framework. A set or sets of formal vocabulary will be developed across various ontologies to specify business policy in a way that different levels of business managers, business people and technical people especially engineers can exchange and share business rules without misinterpreting and inconsistency.

This paper presents the first stage of our proposed research work, i.e., integrating a business reporting language into pervasive computing processes and products. In general, our first stage is to identify and review one of the key business reporting languages, namely, Extensible Business Reporting Language (XBRL) and discuss an integrating XBRL into pervasive computing fields. XBRL is an emerging standard based on XML technology, particularly to streamline a diversification of financial information throughout the world for globalised stakeholders. This paper gives an overview and discusses potential of using XBRL for pervasive computing, as well as strengthening XBRL using smart computing technologies, bridging gaps between financial requirements and pervasive computing productions. Furthermore, current development of relevant intelligent computing technologies especially semantic web is investigated, identified and developed to assist XBRL for a more automated manner and to enhance usability of XBRL for both business users and pervasive computing researchers.

To acquire knowledge by learning automatically from the data, through a process of inference, model fitting, or learning from example is one of the rare field of email management. And when an artificial system can perform "intelligent", tasks similar to those performed by the human brain and such is implemented in email classification, such a system will be is extremely intelligent. Using neural network for email content classification with back propagation is where our technique becomes distinct and effective. This paper proposes a new email classification model using a teaching process of multi-layer neural network to implement back propagation algorithm. Our contributions are: the use of empirical analysis to select an optimum, novel collection of features of a user's email message content that enables the rapid detection of most important words, phrases in emails and a demonstration of the effectiveness of two equal sets of emails (training and testing data).

This paper presents the design and implementation of a new system to manage email messages using email evolving clustering method with unsupervised learning approach to group emails base on activities found in the email messages, namely email grouping. Users spend a lot of time reading, replying and organizing their emails. To help users organize their email messages, we propose a new framework to help organise and prioritize email better. The goal is to provide highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time.

Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize and prioritize their email messages, we propose a new framework; email reply prediction with unsupervised learning. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work.

This paper presents the design and implementation of a new system to predict whether email received require a reply, group emails and summarize email messages. The system uses not only subjects and headers fields but also content of email messages to classify emails based on users' activities and generate summaries of each incoming message with unsupervised learning approach. Our framework tackles the problem of email overload, congestion, difficulties in prioritizing and difficulties in finding previously archived messages in the mail box.

This discussion concerns 'metrics'. More specifically, we discuss quantitative metrics for evaluating Internet applications: what should we quantify, monitor and analyse in order to characterise, evaluate and develop Internet applications based on reusing existing Internet applications,which is widely available in the Internet. Due to the distinctive evolution nature of Internet applications, assessing quality of software will provide ease and higher accuracy for Web developers. However, there is a great gap between the rapid development of Internet applications and the slow speed of developing corresponding metric measures. To tackle this issue, we look into measuring the quality of Internet applications and enable Web developers to enhance the quality of their programs and identify reusable components from Internet-based resources.

Nowadays, email has become one of the most critical personal and business applications and email users would experience serious consequences if email messages could not be available or experience high volume of messages which lead to congestions, overloads and limited storage space coupled with unstructured messages in mail boxes. A few years ago, the means of communication are via letters by post, telegraph, fax, couriers to mention a few but now the focus has changed to a faster means of obtaining quick responses and faster ways of communication-emails. We propose a new framework to help organise and prioritize email better; Machine learning email prediction system (MLEPS). The goal is to organise emails better in mail boxes, prioritise emails based on the focus of the email content. The intelligent email prediction system helps to improve email users’ performances, saves time, very effective and efficient tool and is cost effective for businesses and for personal use. The system is evaluated against a corpus of human-judged predictions, reaching satisfactory level of performance.