Media pressures and corporate disclosure of social responsibility performance : a case study of two global clothing and sports retail companies

This paper investigates the social and environmental disclosure practices of two large multi-national companies, specifically Nike and Hennes and Mauritz. Utilising a joint consideration of legitimacy theory and media agenda setting theory, we investigate the linkage between negative media attention, and positive corporate social and environmental disclosures. Our results generally support a view that for those issues attracting the greatest amount of negative media attention, corporations react by providing positive social and environmental disclosures. The results were particularly significant in relation to labour practices in developing countries – the issue attracting the greatest amount of negative media attention for the companies in question.

Multi-classifier classification of spam email on an ubiquitous multi-core architecture

This paper presents an innovative fusion based multi-classifier email classification on a ubiquitous multi-core architecture. Many approaches use text-based single classifiers or multiple weakly trained classifiers to identify spam messages from a large email corpus. We build upon our previous work on multi-core by apply our ubiquitous multi-core framework to run our fusion based multi-classifier architecture. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our proposed multi-classifier based filtering system. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at the average cost of 1.4 ms. We also reduced the instance of false positive, which is one of the key challenges in spam filtering system, and increases email classification accuracy substantially compared with single classification techniques.

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.

Spam filtering using classification algorithms

This thesis proposes an innovative adaptive multi-classifier spam filtering model, with a grey-list analyser and a dynamic feature selection method, to overcome false-positive problems in email classification. It also presents additional techniques to minimize the added complexity. Empirical evidence indicates the success of this model over existing approaches.

Progressive traditionalists : the emergence of a new discourse in Indonesia's Nahdlatul Ulama during the Addurrahman Wahid era

Studies the emergence of progressive thought amongst younger ulama, or Islamic scholars in Indonesia. Unlike most earlier traditionalist scholars, who were essentially limited to engaging with the intellectual heritage of mediaeval ulama, these young ulama are able to bridge the past and the present by synthesizing knowledge traditions.

An automated classification system based on the strings of trojan and virus families

Classifying malware correctly is an important research issue for anti-malware software producers. This paper presents an effective and efficient malware classification technique based on string information using several wellknown classification algorithms. In our testing we extracted the printable strings from 1367 samples, including unpacked trojans and viruses and clean files. Information describing the printable strings contained in each sample was input to various classification algorithms, including treebased classifiers, a nearest neighbour algorithm, statistical algorithms and AdaBoost. Using k-fold cross validation on the unpacked malware and clean files, we achieved a classification accuracy of 97%. Our results reveal that strings from library code (rather than malicious code itself) can be utilised to distinguish different malware families.

Peak to average power ratio analysis for LTE systems

The 3rd generation partnership project (3GPP) long term evolution (LTE) standard uses single carrier frequency division multiple access (SCFDMA) scheme for the uplink transmissions and orthogonal frequency division multiplexing access (OFDMA) in downlink. SCFDMA uses DFT spreading prior to OFDMA modulation to map the signal from each user to a subset of the available subcarriers i.e., single carrier modulation. The efficiency of a power amplifier is determined by the peak to average power ratio (PAPR) of the modulated signal. In this paper, we analyze the PAPR in 3GPP LTE systems using root raised cosine based filter. Simulation results show that the SCFDMA subcarrier mapping has a significantly lower PAPR compared to OFDMA. Also comparing the three forms of SCFDMA subcarrier mapping, results show that interleave FDMA (IFDMA) subcarrier mapping with proposed root raised cosine filter reduced PAPR significantly than localized FDMA (LFDMA) and distributed (DFDMA) mapping. This improves its radio frequency (RF) power amplifier efficiency and also the mean power output from a battery driven mobile terminal.

Spam filtering for network traffic security on a multi-core environment

This paper presents an innovative fusion-based multi-classifier e-mail classification on a ubiquitous multicore architecture. Many previous approaches used text-based single classifiers to identify spam messages from a large e-mail corpus with some amount of false positive tradeoffs. Researchers are trying to prevent false positive in their filtering methods, but so far none of the current research has claimed zero false positive results. In e-mail classification false positive can potentially cause serious problems for the user. In this paper, we use fusion-based multi-classifier classification technique in a multi-core framework. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our multi-classifier-based filtering system in terms of running time, false positive rate, and filtering accuracy. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at an average cost of 1.4 ms. We also reduced the instances of false positives, which are one of the key challenges in a spam filtering system, and increases e-mail classification accuracy substantially compared with single classification techniques.

Minimizing the limitations of GL analyser of fusion based email classification

In the last decade, the Internet email has become one of the primary method of communication used by everyone for the exchange of ideas and information. However, in recent years, along with the rapid growth of the Internet and email, there has been a dramatic growth in spam. Classifications algorithms have been successfully used to filter spam, but with a certain amount of false positive trade-offs. This problem is mainly caused by the dynamic nature of spam content, spam delivery strategies, as well as the diversification of the classification algorithms. This paper presents an approach of email classification to overcome the burden of analyzing technique of GL (grey list) analyser as further refinements of our previous multi-classifier based email classification [10]. In this approach, we introduce a “majority voting grey list (MVGL)” analyzing technique with two different variations which will analyze only the product of GL emails. Our empirical evidence proofs the improvements of this approach, in terms of complexity and cost, compared to existing GL analyser. This approach also overcomes the limitation of human interaction of existing analyzing technique.

Multi-tier phishing email classification with an impact of classifier rescheduling

Protecting user's mailbox from infiltration of phishing email is a significant research issue now a day. Many researches are going on filtering phishing using classification based algorithms and achieve substantial performance. It has been studied and investigated with different classification algorithms and observed that the outputs of the classifiers vary from one another with same corpora. This paper presents the impact of classifier rescheduling of multi-tier classification of phishing email to observe the best scheduling in the classification process. In our method, the features of phishing email will be extracted and classified in a sequential fashion by using the multi-tier classification and the outputs will be sent to the decision fusion process. Empirical evidence proofs that the impact of rescheduling of classifiers among the tiers gives diverse outcomes in terms of accuracy as well as number of false positive instances.

Minimizing the drawbacks of grey-list analyser in synthesis based spam filtering

In the last decade, the rapid growth of the Internet and email, there has been a dramatic growth in spam. Spam is commonly defined as unsolicited email messages and protecting email from the infiltration of spam is an important research issue. Classifications algorithms have been successfully used to filter spam, but with a certain amount of false positive trade-offs, which is unacceptable to users sometimes. This paper presents an approach of email classification to overcome the burden of analyzing technique of GL (grey list) analyzer as further refinements of synthesis based email classification technique. In this approach, we introduce a “majority voting grey list (MVGL)” analyzing technique which will analyze the GL emails by using the majority voting (MV) algorithm. We have presented two different variations of the MV system, one is simple MV (SMV) and other is the Ranked MV (RMV). Our empirical evidence proofs the improvements of this approach compared to existing GL analyzer [7].

MVGL analyser for multi-classifier based spam filtering system

In the last decade, the rapid growth of the Internet and email, there has been a dramatic growth in spam. Spam is commonly defined as unsolicited email messages and protecting email from the infiltration of spam is an important research issue. Classifications algorithms have been successfully used to filter spam, but with a certain amount of false positive trade-offs, which is unacceptable to users sometimes. This paper presents an approach to overcome the burden of GL (grey list) analyzer as further refinements to our multi-classifier based classification model (Islam, M. and W. Zhou 2007). In this approach, we introduce a ldquomajority voting grey list (MVGL)rdquo analyzing technique which will analyze the generated GL emails by using the majority voting (MV) algorithm. We have presented two different variations of the MV system, one is simple MV (SMV) and other is the ranked MV (RMV). Our empirical evidence proofs the improvements of this approach compared to the existing GL analyzer of multi-classifier based spam filtering process.