Developing a categorisation system for rapists' speech

Case linkage, the linking of crimes into series, is used in policing in the UK and other countries. Previous researchers have proposed using rapists' speech in this practice; however, researching this application requires the development of a reliable coding system for rapists' speech. A system was developed based on linguistic theories of pragmatics which allowed for the categorization of an utterance into a speech act type (e.g. directive). Following this classification, the qualitative properties of the utterances (e.g. the degree of threat it carried) could be captured through the use of rating scales. This system was tested against a previously developed system using 188 rapists' utterances taken from victims' descriptions of rape. The pragmatics-based system demonstrated higher inter-rater reliability whilst enabling the classification of a greater number of rapists' utterances. Inter-rater reliability for the subscales was also tested using a sub-sample of 50 rapists' utterances and inter-item correlations were calculated. Seventy-six per cent of the subscales had satisfactory to high inter-rater reliability. Based on these findings and the inter-item correlations, the classification system was revised. The potential use of this system for the practices of case linkage and offender profiling is discussed.

Quality performance rating

This Thesis reports on the principles and usefulness of Performance Rating as developed by the writer over a number of years. In Part one a brief analysis is made of the Quality scene and its development up to the present. The need is exposed for Performance Rating as a tool for all areas of management*. At the same time a system of Quality Control is described which the writer has further developed under the title of 'Operator Control'. This system is based on the integration of all Quality control functions with the creative functions required for Quality achievement. The discussions are mainly focussed on the general philosophy of Quality, its creation and control and that part of Operator Control which affects Performance Rating. Whereas it is shown that the combination of Operator Control and Performance Rating is both economically and technically advantageous, Performance Rating can also usefully be applied under inspection control conditions. Part two describes the principles of Area Performance Rating. *The need for, and the advantages of, Performance Rating are particularly demonstrated in Case study No.1. From this a summation expression is derived which gives the key for grouping of areas with similar Performance Rating (P). A model is devised on which the theory is demonstrated. Relevant case studies, carried out in practice in factories are quoted in Part two, Chapter 4, one written by the Quality manager of that particular factory. Particular stress is laid in the final conclusions on management's function in the Quality field and how greatly this function is eased and improved through the introduction of Area Performance Rating.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.