Globalisation and MA TESOL programs in the UK

This article reports the results of a mixed-methods approach to investigating the association between globalisation and MATESOL in UK universities. Qualitative and quantitative data collected from academic staff through eight emails, four interviews and 41 questionnaires indicate that the globalised context of higher education have affected these programmes in a number of ways including an increasing interest in recruiting more international students and a growing awareness about the need for curriculum and content modifications. The analysis of the data suggests that although change has been an inherent characteristic of these MAs over the past decade, it has been implemented gradually and conservatively, often relying on a dialectic relationship between academic staff and universities’ policies. The results imply that factors other than globalisation have also been at work. Many of the participants contend that globalisation has not lowered the quality of these MAs or standards of good practice.

Discourse in Action: Introducing Mediated Discourse Analysis

From emails relating to adoption over the Internet to discussions in the airline cockpit, the spoken or written texts we produce can have significant social consequences. The area of Mediated Discourse Analysis considers texts in their social and cultural contexts to explore the actions individuals take with texts - and the consequences of those actions. Discourse in Action: brings together leading scholars from around the world in the area of Mediated Discourse Analysis reveals ways in which its theory and methodology can be used in research into contemporary social situations explores real situations and draws on real data in each chapter shows how analysis of texts in their social contexts broadens our understanding of the real world. Taken together, the chapters provide a comprehensive overview to the field and present a range of current studies that address some of the most important questions facing students and researchers in linguistics, education, communication studies and other fields.

L’image comme outil didactique dans l’enseignement du français dans des collèges et des lycées en Suède

Le travail actuel a été rédigé dans le but d’éclaircir en quoi consiste le rôle de l’image comme outil didactique aux cours de français aux collèges et aux lycées suédois. Nous avons abordé notre thème sous des perspectives différentes : d’un côté, nous avons analysé les images dans deux livres, puisque les manuels font souvent partie de chaque leçon de français ; de l’autre côté, nous avons fait un questionnaire que nous avons distribué aux professeurs à travers les médias sociaux et par email. Nous avons également étudié les données récoltées aux cours des entretiens avec trois professeurs de français au lycée suédois, pour savoir comment les professeurs intègrent des images dans la pratique. Selon les résultats de notre étude, il nous a été possible d’affirmer que l’image est un outil qui n’est pas omniprésent, mais qui est parfois utilisé afin de rendre les leçons plus variées. La photographie est le type d’image le plus répandu dans la pratique des professeurs et dans les manuels ; le format numérique est le plus fréquent en tenant en compte de la révolution technologique et de l’habitude des jeunes à travailler avec les ordinateurs. Cependant, l’image n’est pas un outil indépendant. Elle est très souvent complétée par d’autres outils didactiques et sert principalement à accompagner la lecture, à développer l’expression orale et le vocabulaire et aussi à illustrer des phénomènes culturels. En conclusion, il ressort de notre mémoire, qu’il est important que le professeur accompagne le travail avec des images avec des instructions et des commentaires, afin de rendre l’apprentissage plus efficace.

Developing and testing a model of knowledge utilisation through marketing research

Little research has examined the return on marketing research, be that financial or knowledge acquisition. Furthermore, there has been insufficient research into the factors affecting the conduct of marketing research. This paper investigates and reports on a conceptual model proposed by Yaman (2000), which explores knowledge acquisition, dissemination, and utilisation through marketing research. The study specifically explores and attempts to replicate the model’s conceptual structure. The data were collected electronically via emails and an HTML web-form questionnaire, with a sample of 182 being obtained. Using structural equation modelling, the results obtained indicated an adequate fit for a modified Yaman model to the data from this particular sample.

An email classification model based on rough set theory

The communication via email is one of the most popular services of the Internet. Emails have brought us great convenience in our daily work and life. However, unsolicited messages or spam, flood our email boxes, which results in bandwidth, time and money wasting. To this end, this paper presents a rough set based model to classify emails into three categories - spam, no-spam and suspicious, rather than two classes (spam and non-spam) in most currently used approaches. By comparing with popular classification methods like Naive Bayes classification, the error ratio that a non-spam is discriminated to spam can be reduced using our proposed model.

Spam filtering based on preference ranking

When the average number of spam messages received is continually increasing exponentially, both the Internet service provider and the end user suffer. The lack of an efficient solution may threaten the usability of the email as a communication means. In this paper we present a filtering mechanism applying the idea of preference ranking. This filtering mechanism will distinguish spam emails from other email on the Internet. The preference ranking gives the similarity values for nominated emails and spam emails specified by users, so that the ISP/end users can deal with spam emails at filtering points. We designed three filtering points to classify nominated emails into spam email, unsure email and legitimate email. This filtering mechanism can be applied on both middleware and at the client-side. The experiments show that high precision, recall and TCR (total cost ratio) of spam emails can be predicted for the preference based filtering mechanisms.

An innovative spam filtering model based on support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, an innovative and intelligent spam filtering model has been proposed based on support vector machine (SVM). This model combines both linear and nonlinear SVM techniques where linear SVM performs better for text based spam classification that share similar characteristics. The proposed model considers both text and image based email messages for classification by selecting an appropriate kernel function for information transformation.

Managing email overload with an automatic nonparametric clustering approach

Email overload is a recent problem that there is increasingly difficulty people have faced to process the large number of emails received daily. Currently this problem becomes more and more serious and it has already affected the normal usage of email as a knowledge management tool. It has been recognized that categorizing emails into meaningful groups can greatly save cognitive load to process emails and thus this is an effective way to manage email overload problem. However, most current approaches still require significant human input when categorizing emails. In this paper we develop an automatic email clustering system, underpinned by a new nonparametric text clustering algorithm. This system does not require any predefined input parameters and can automatically generate meaningful email clusters. Experiments show our new algorithm outperforms existing text clustering algorithms with higher efficiency in terms of computational time and clustering quality measured by different gauges.

Architecture of adaptive spam filtering based on machine learning algorithms

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to distinguish between spam and legitimate email messages. Much work has been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection FP problem is unacceptable sometimes. In this paper, an adaptive spam filtering model has been proposed based on Machine learning (ML) algorithms which will get better accuracy by reducing FP problems. This model consists of individual and combined filtering approach from existing well known ML algorithms. The proposed model considers both individual and collective output and analyzes them by an analyzer. A dynamic feature selection (DFS) technique also proposed in this paper for getting better accuracy.

Dynamic feature selection for spam filtering using support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to differentiate spam from legitimate email. Much work have been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In this paper, architecture of spam filtering has been proposed based on support vector machine (SVM,) which will get better accuracy by reducing FP problems. In this architecture an innovative technique for feature selection called dynamic feature selection (DFS) has been proposed which is enhanced the overall performance of the architecture with reduction of FP problems. The experimental result shows that the proposed technique gives better performance compare to similar existing techniques.

Email categorization using multi-stage classification technique

This paper presents an innovative email categorization using a serialized multi-stage classification ensembles technique. Many approaches are used in practice for email categorization to control the menace of spam emails in different ways. Content-based email categorization employs filtering techniques using classification algorithms to learn to predict spam e-mails given a corpus of training e-mails. This process achieves a substantial performance with some amount of FP tradeoffs. It has been studied and investigated with different classification algorithms and found that the outputs of the classifiers vary from one classifier to another with same email corpora. In this paper we have proposed a multi-stage classification technique using different popular learning algorithms with an analyser which reduces the FP (false positive) problems substantially and increases classification accuracy compared to similar existing techniques.

An innovative analyser for email classification based on grey list analysis

In this paper we propose a new technique of email classification based on grey list (GL) analysis of user emails. This technique is based on the analysis of output emails of an integrated model which uses multiple classifiers of statistical learning algorithms. The GL is a list of classifier/(s) output which is/are not considered as true positive (TP) and true negative (TN) but in the middle of them. Many works have been done to filter spam from legitimate emails using classification algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection the FP problem is unacceptable, sometimes. The proposed technique will provide a list of output emails, called "grey list (GL)", to the analyser for making decisions about the status of these emails. It has been shown that the performance of our proposed technique for email classification is much better compare to existing systems, in order to reducing FP problems and accuracy.

Gatekeepers : going, going, gone -the challenge of citizenship journalism to traditional practice

Commentators believe that the reporting of the London bombings of July 2005 ushered in a new era of the citizen joumalist. News outlets in Britain were  flooded with emails and mobile phone pictures. But with the sheer quantity of material heading into the editor's inbox, how can we be sure of its veracity? This paper looks at The Herald Sun. The Age, The Sydney Morning Herald, The Daily Telegraph and The Adelaide Advertiser to investigate the current systems in place for checking incoming leads and material. The paper raises questions regarding the reliability of current systems and puts forward the possibility that new approaches and systems may be needed to meet the new challenges. The paper further explores if newspapers are still acting as gatekeepers of the traditional system or if they are letting the gate swing ajar in response to  changed circumstances.

Cyber bullying and cyber safety

Cyberbullying is a concern for governments, educational authorities, teachers, parents and children alike. Using technology (i.e. mobile phones, emails, instant messaging, blogs, websites) to annoy, harass, intimidate, abuse or threaten others has become known as cyberbullying.

Email categorization using (2+1)-tier classification algorithms

In this paper we have proposed a spam filtering technique using (2+1)-tier classification approach. The main focus of this paper is to reduce the false positive (FP) rate which is considered as an important research issue in spam filtering. In our approach, firstly the email message will classify using first two tier classifiers and the outputs will appear to the analyzer. The analyzer will check the labeling of the output emails and send to the corresponding mailboxes based on labeling, for the case of identical prediction. If there are any misclassifications occurred by first two tier classifiers then tier-3 classifier will invoked by the analyzer and the tier-3 will take final decision. This technique reduced the analyzing complexity of our previous work. It has also been shown that the proposed technique gives better performance in terms of reducing false positive as well as better accuracy.