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.

A ranking list for information systems journals

There has been an increasing focus internationally on the quality and impact of research outputs in recent years. Several countries, including the United Kingdom and New Zealand have implemented schemes to base the funding of research on research quality. The Australian government is planning to implement a Research Quality Framework (RQF) in the next few years that will impact greatly on funding of research in Australian universities. A key issue for Australian researchers is how the quality and impact of research is defined and measured in their discipline areas. Although peer review is widely used to assess the quality of research outputs, it is expensive and labour intensive. Other surrogate quality measures are often used. This paper focuses on measuring the quality of research outputs in the information systems discipline. We argue that measures such as citation indexes are inappropriate for information systems and that the publication outlet is a more suitable indicator of quality. We present a ranking list of journals for the information systems discipline, and discuss the approach we have taken in developing the list. We discuss how the ranking list may be used in defining and measuring the quality of information systems research outputs, the limitations inherent in the approach and discuss lessons we have learned in developing the list.

Exploring multi-dimensional perceptual ranking of marketing journals by North American academics

This paper develops a weighted multi-dimensional perceptual rankings based on respondents evaluation of a journals prestige, contribution to theory, contribution to practice and contribution to teaching. Comparisons are made between rankings of individual criteria and composite rankings. Comparisons are also made to recent single dimension perceptual-based rankings and citation-based rankings.

At the sharp end : journal ranking and the dreams of academics

Purpose – The purpose of this article is to review and comment on the Australian Government's entry into the journal ranking domain.

Design/methodology/approach – A review and reflection on the approach and potential impact of the direction taken.

Findings – This project is arguably the largest of its type and the effects on academic publishing and the survival of journals could be far reaching.

Originality/value – The article draws together current material on the Australian Government's activities and provides details of the scope of the journal ranking project.

Algorithms and applications of preference based ranking for information retrieval

In this thesis, the author designed three sets of preference based ranking algorithms for information retrieval and provided the corresponsive applications for the algorithms. The main goal is to retrieve recommended, high similar and valuable ranking results to users.

Journal ranking based on social information

Recently, literature analysis has become a hot issue in academic studies. In order to quantify the importance of journals and provide researchers with target vehicles for their work, this poster proposes a novel approach based on the social information through considering the potential relationship between journals quality and authors’ affiliation. Based on the formula proposed in this work, the importance of journals can be estimated and ranked.

Using choquet integrals for evaluating citation indices in journal ranking

The use of citation-based indices to evaluate the quality of journals is becoming increasingly widespread. Recently, ISI Web of Knowledge has begun to include three new indices in their journal statistics, including a 5-year impact factor. Here, we continue our earlier research which modeled the behavior of journal assessors based on some of these indices and the Choquet integral. We interpret the obtained fuzzy measures of many new datasets toward understanding the importance of these newly published indices and how indicative they may be of a journal's quality. The problem is one of ordinal classification, and the values of the best-fitting fuzzy measures can be obtained using the FMtools software package.

The ranking of factors influencing the behaviour of light structures on expansive soils in Victoria, Australia

In this paper a Neural Network Model was used to develop a ranking of the potential damage influences for light structures on expansive soils in Victoria. These influences include geology, Thornthwaite moisture index, vegetation covers, construction foundation type, construction wall type, geographical region and age of building when first inspected. Approximately 400 cases of damage to light structures in Victoria, Australia were considered in this study. Feedforward Backpropagation was adopted to train the data. The ranking of importance was estimated using connection weight approach and then compared to results calculated from sensitivity analysis. From the analysis, the ranking of importance for potential damage factor was noted.

Ranking method for optimizing precision/recall of content-based image retrieval

The ranking method is a key element of Content-based Image Retrieval (CBIR) system, which can affect the final retrieval performance. In the literature, previous ranking methods based on either distance or probability do not explicitly relate to precision and recall, which are normally used to evaluate the performance of CBIR systems. In this paper, a novel ranking method based on relative density is proposed to improve the probability based approach by ranking images in the class. The proposed method can achieve optimal precision and recall. The experiments conducted on a large photographic collection show significant improvements of retrieval performance.

Ranking of pre-treatment technologies for reverse osmosis for water recovery

Agro-industries are a life-line for sustainable future of human kind. However, the wastewater generated by agro-industries poses direct threat to the same sustainable future by polluting the freshwater sources when discharged into those freshwater sources. Thus, we need both advanced treatment technologies to treat those wastewater streams generated and better reuse practices for the treated effluents. Reverse osmosis (RO) is one of the advanced treatments to treat dissolved solids that are present in agricultural wastewater streams. But, RO is very sensitive to suspended solids (SS) present in the wastewater streams. Those SS can foul the RO membrane and make it ineffective in producing treated effluent at desired rates. Therefore, suitable pre-treatment scheme is necessary to treat the agro-wastewater streams before passing through RO. This study focuses on the qualitative and quantitative ranking of the available conventional and modern pre-treatment technologies as pre-treatment for RO. This study considers wastewater that has been treated through a secondary treatment system for example activated sludge process as the target water that needs pre-treatment. Based on qualitative ranking of conventional pre-treatment options, the Lime clarification/Granular Media filtration (GMF) option is ranked as the best; whereas finescreens/ micro-screens option ranked as the least preferred option based on the scores they attained in treating the water quality parameters that are considered essential. Based on the quantitative ranking, the low pressure membrane technology such as ultra-filtration (UF) stood first and microfiltration (MF) stood last.

Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering

Ranking is an important task for handling a large amount of content. Ideally, training data for supervised ranking would include a complete rank of documents (or other objects such as images or videos) for a particular query. However, this is only possible for small sets of documents. In practice, one often resorts to document rating, in that a subset of documents is assigned with a small number indicating the degree of relevance. This poses a general problem of modelling and learning rank data with ties. In this paper, we propose a probabilistic generative model, that models the process as permutations over partitions. This results in super-exponential combinatorial state space with unknown numbers of partitions and unknown ordering among them. We approach the problem from the discrete choice theory, where subsets are chosen in a stagewise manner, reducing the state space per each stage significantly. Further, we show that with suitable parameterisation, we can still learn the models in linear time. We evaluate the proposed models on two application areas: (i) document ranking with the data from the recently held Yahoo! challenge, and (ii) collaborative filtering with movie data. The results demonstrate that the models are competitive against well-known rivals.