Techniques for text classification: Literature review and current trends

Automated classification of text into predefined categories has always been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. This kind of web information, popularly known as the digital/electronic information is in the form of documents, conference material, publications, journals, editorials, web pages, e-mail etc. People largely access information from these online sources rather than being limited to archaic paper sources like books, magazines, newspapers etc. But the main problem is that this enormous information lacks organization which makes it difficult to manage. Text classification is recognized as one of the key techniques used for organizing such kind of digital data. In this paper we have studied the existing work in the area of text classification which will allow us to have a fair evaluation of the progress made in this field till date. We have investigated the papers to the best of our knowledge and have tried to summarize all existing information in a comprehensive and succinct manner. The studies have been summarized in a tabular form according to the publication year considering numerous key perspectives. The main emphasis is laid on various steps involved in text classification process viz. document representation methods, feature selection methods, data mining methods and the evaluation technique used by each study to carry out the results on a particular dataset

Improving intrusion detection model prediction by threshold adaptation

This research was supported and funded by the Government of the Sultanate of Oman represented by the Ministry of Higher Education and the Sultan Qaboos University.Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the accuracy of anomaly-based network intrusion detection systems (IDS) that are built using predictive models in a batch learning setup. This work investigates how adapting the discriminating threshold of model predictions, specifically to the evaluated traffic, improves the detection rates of these intrusion detection models. Specifically, this research studied the adaptability features of three well known machine learning algorithms: C5.0, Random Forest and Support Vector Machine. Each algorithm’s ability to adapt their prediction thresholds was assessed and analysed under different scenarios that simulated real world settings using the prospective sampling approach. Multiple IDS datasets were used for the analysis, including a newly generated dataset (STA2018). This research demonstrated empirically the importance of threshold adaptation in improving the accuracy of detection models when training and evaluation traffic have different statistical properties. Tests were undertaken to analyse the effects of feature selection and data balancing on model accuracy when different significant features in traffic were used. The effects of threshold adaptation on improving accuracy were statistically analysed. Of the three compared algorithms, Random Forest was the most adaptable and had the highest detection rates.Publisher PDFPeer reviewe

Code offloading on real-time multimedia systems: a framework for handling code mobility and code offloading in a QoS aware environment

Actualmente, os smartphones e outros dispositivos móveis têm vindo a ser dotados com cada vez maior poder computacional, sendo capazes de executar um vasto conjunto de aplicações desde simples programas de para tirar notas até sofisticados programas de navegação. Porém, mesmo com a evolução do seu hardware, os actuais dispositivos móveis ainda não possuem as mesmas capacidades que os computadores de mesa ou portáteis. Uma possível solução para este problema é distribuir a aplicação, executando partes dela no dispositivo local e o resto em outros dispositivos ligados à rede. Adicionalmente, alguns tipos de aplicações como aplicações multimédia, jogos electrónicos ou aplicações de ambiente imersivos possuem requisitos em termos de Qualidade de Serviço, particularmente de tempo real. Ao longo desta tese é proposto um sistema de execução de código remota para sistemas distribuídos com restrições de tempo-real. A arquitectura proposta adapta-se a sistemas que necessitem de executar periodicamente e em paralelo mesmo conjunto de funções com garantias de tempo real, mesmo desconhecendo os tempos de execução das referidas funções. A plataforma proposta foi desenvolvida para sistemas móveis capazes de executar o Sistema Operativo Android.Smartphones and other mobile devices are becoming more powerful and are capable of executing several applications in a concurrent manner. Although the hardware capabilities of mobile devices are increasing in an unprecedented way, they still do not possess the same features and resources of a common desktop or laptop PC. A potential solution for this limitation might be to distribute an application by running some of its parts locally while running the remaining parts on other devices. Additionally, there are several types of applications in domains such as multimedia, gaming or immersive environments that require soft real-time constraints which have to be guaranteed. In this work we are targeting highly dynamic distributed systems with Quality of Service (QoS) constraints, where the traditional models of computation are not sufficient to handle the users’ or applications’ requests. Therefore, new models of computation are needed to overcome the above limitations in order to satisfy the applications’ or users’ requirements. Code offloading techniques allied with resource management seem very promising as each node may use neighbour nodes to request for help in order to perform demanding computations that cannot be done locally. In this demanding context, a full-fledged framework was developed with the objective of integrating code offloading techniques on top of a middleware framework that provides QoS and real-time guarantees to the applications. This paper describes the implementation of the above-mentioned framework in the Android platform as well as a proof-of-concept application to demonstrate the most important concepts of code offloading, QoS and real-time scheduling

Modeling software artifact count attribute with s-curves

The estimation of software project attributes, such as size, is important for software project resource planning and process control. However, research regarding software attribute modeling, such as size, effort, and cost, are high-level and static in nature. This research defines a new operation-level software project attribute that describes the operational characteristic of a software project. The result is a measurement based on the s-curve parameter that can be used as a control variable for software project management. This result is derived from modeling the count of artifact instances created by the software engineering process, which are stored by software tools. Because of the orthogonal origin of this attribute in regard to traditional static estimators, this s-curve based software attribute can function as an additional indicator of software project activities and also as a quantitative metric for assessing development team capability

Mathematics in Software Reliability and Quality Assurance

This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment

Web information systems: A study of maintenance, change and flexibility

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Information Systems (IS’s) have provided organisations with huge efficiency gains and benefits over the years; however an outstanding problem that is yet to be successfully tackled is that of the troublesome maintenance phase. Consuming vast resources and thwarting business progression in a competitive global market place, system maintenance has been recognised as one of the key areas where IS is failing organisations. Organisations are too often faced with the dilemma of either replacement or the continual upkeep of an unwieldy system. The ability for IS’s to be able to adapt to exogenous influences is even more acute today than at any time in the past. This is due to IS’s namely, Web Information Systems (WIS’s) increasingly and continually having to accommodate the needs of organisations to interconnect with a plethora of additional systems as well as supporting evolving business models. The richness of the interconnectivity, functionalities and services WIS’s now offer are shaping social, cultural and economic behaviour on a truly global scale, making the maintenance of such systems and evermore pertinent issue. The growth and proliferation of WIS’s shows no sign of abating which leads to the conclusion that what some have termed as the ‘maintenance iceberg’ should not be ignored. The quandary that commercial organisations face is typically driven by two key aspects; firstly, systems are built on the cultural premise of using fixed requirements, with not enough thought or attention being paid to systems abilities to deviate from these requirements. Secondly, systems do not generally cope well with adapting to unpredictable change arising from outside of the organisations environment. Over the recent past, different paradigms, approaches and methods have attempted to make software development more predictable, controllable and adaptable, however, the benefits of such measures in relation to the maintenance dilemma have been limited. The concept of flexible systems that are able to cope with such change in an efficient manner is currently an objective that few can claim to have realised successfully. The primary focus of the thesis was to examine WIS post-development change in order to empirically substantiate and understand the nature of the maintenance phase. This was done with the intention to determine exactly ‘where’ and ‘how’ flexibility could be targeted to address these changes. This study uses an emergent analytical approach to identify and catalogue the nature of change occurring within WIS maintenance. However, the research framework design underwent a significant revision as the initial results indicated that a greater emphasis and refocus was required to achieve the research objective. To study WIS’s in an appropriate and detailed context, a single case study was conducted in a web development software house. In total the case study approach was used to collect empirical evidence from four projects that investigated post-development change requests in order to identify areas of the system susceptible to change. The maintenance phases of three WIS projects were considered in-depth, resulting in the collection of over four hundred change requests. The fourth project served as a validation case. The results are presented and the findings are used to identify key trends and characteristics that depict WIS maintenance change. The analytical information derived from the change requests is consolidated and shown diagrammatically for the key areas of change using profile models developed in this thesis. Based on the results, the thesis concludes and contributes to the ongoing debate that there is a discernable difference when considering WIS maintenance change compared to that of traditional IS maintenance. The detailed characteristics displayed in the profile models are then used to map specific flexibility criteria that ultimately are required to facilitate change. This is achieved using the Flexibility Matrix of Change (FMoC) tool which was developed within the remit of this research. This tool is a qualitative measurement scheme that aligns WIS maintenance changes to a reciprocal flexibility attribute. Thus, the wider aim of this thesis is to also expand the awareness of flexibility and its importance as a key component of the WIS lifecycle

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems