Design, innovation and case-based reasoning.

The design task is especially appropriate for applying, integrating, exploring and pushing the boundaries of case-based reasoning. In this paper, we briefly review the challenges that design poses for case-based reasoning and survey research on case-based design ranging from early explorations to more recent work on innovative design. We also summarize the theoretical contributions this research has made to case-based reasoning itself

Music recommendation: audio neighbourhoods to discover music in the long tail.

Millions of people use online music services every day and recommender systems are essential to browse these music collections. Users are looking for high quality recommendations, but also want to discover tracks and artists that they do not already know, newly released tracks, and the more niche music found in the 'long tail' of on-line music. Tag-based recommenders are not effective in this 'long tail' because relatively few people are listening to these tracks and so tagging tends to be sparse. However, similarity neighbourhoods in audio space can provide additional tag knowledge that is useful to augment sparse tagging. A new recommender exploits the combined knowledge, from audio and tagging, using a hybrid representation that extends the track's tag-based representation by adding semantic knowledge extracted from the tags of similar music tracks. A user evaluation and a larger experiment using Last.fm user data both show that the new hybrid recommender provides better quality recommendations than using only tags, together with a higher level of discovery of unknown and niche music. This approach of augmenting the representation for items that have missing information, with corresponding information from similar items in a complementary space, offers opportunities beyond content-based music recommendation

Visualisation to explain personal health trends in smart homes.

An ambient sensor network is installed in Smart Homes to identify low-level events taking place by residents, which are then analysed to generate a profile of activities of daily living. These profiles are compared to both the resident's typical profile and to known 'risky' profiles to support recommendation of evidence-based interventions. Maintaining trust presents an XAI challenge because the recommendations are not easily interpretable. Trust in the system can be improved by making the decision-making process more transparent. We propose a visualisation workflow which presents the data in clear, colour-coded graphs

Music recommenders: user evaluation without real users?

Good music recommenders should not only suggest quality recommendations, but should also allow users to discover new/niche music. User studies capture explicit feedback on recommendation quality and novelty, but can be expensive, and may have difficulty replicating realistic scenarios. Lack of effective offline evaluation methods restricts progress in music recommendation research. The challenge is finding suitable measures to score recommendation quality, and in particular avoiding popularity bias, whereby the quality is not recognised when the track is not well known. This paper presents a low cost method that leverages available social media data and shows it to be effective. Not only is it based on explicit feedback from many users, but it also overcomes the popularity bias that disadvantages new/niche music. Experiments show that its findings are consistent with those from an online study with real users. In comparisons with other offline measures, the social media score is shown to be a more reliable proxy for opinions of real users. Its impact on music recommendation is its ability to recognise recommenders that enable discovery, as well as suggest quality recommendations

Refinement complements verification and validation.

Knowledge based systems are being applied in ever increasing numbers. The development of knowledge acquisition tools has eased the Knowledge Acquisition Bottleneck. More recently there has been a demand for mechanisms to assure the quality of knowledge based systems. Checking the contents of the knowledge base and the performance of the knowledge based systems at various stages throughout its life cycle is an important component of quality assurance. Hence, the demand now is for verification and validation tools. However, traditionally, verification and validation have identified possible faults in the knowledge base. In contrast, this paper advocates the use of knowledge refinement to correct identified faults in parallel with the ongoing verification and validation, thus easing the progress towards correct knowledge based systems. An automated refinement tool is described which uses the output from verification and validation tools to assemble evidence from which the refinement process can propose repairs. It is hoped that automated refinement in parallel with validation and verification may ease the Knowledge V &V Bottleneck

Wifi-based human activity recognition using Raspberry Pi.

Ambient, non-intrusive approaches to smart home health monitoring, while limited in capability, are preferred by residents. More intrusive methods of sensing, such as video and wearables, can offer richer data but at the cost of lower resident uptake, in part due to privacy concerns. A radio frequency-based approach to sensing, Channel State Information (CSI),can make use of low cost off-the-shelf WiFi hardware. We have implemented an activity recognition system on the Raspberry Pi 4, one of the world’s most popular embedded boards. We have implemented an classification system using the Pi to demonstrate its capability for activity recognition. This involves performing data collection, interpretation and windowing, before supplying the data to a classification model. In this paper, the capabilities of the Raspberry Pi 4 at performing activity recognition on CSI data are investigated. We have developed and publicly released a data interaction framework, capable of interpreting, processing and visualising data from a range of CSI-capable hardware. Furthermore, CSI data captured for these experiments during various activity performances have also been made publically available. We then train a Deep Convolutional LSTM model to classify the activities. Our experiments, performed in a small apartment, achieve 92% average accuracy on 11 activity classes

Genetic algorithms for feature selection and weighting

Abstract Automated techniques to optimise the retrieval of relevant cases in a CBR system are desirable as a way to reduce the expensive knowledge acquisition phase. This paper concentrates on feature selection methods that assist in indexing the case-base, and feature weighting methods that improve the similarity-based selection of relevant cases. Two main types of method are presented: filter methods use no feedback from the learning algorithm that will be applied; wrapper methods incorporate feedback and hence take account of learning bias. Wrapper methods based on Genetic Algorithms have been found to deliver the best results with a tablet design application, but these generic methods are flexible about the criterion to be optimised, and should be applicable to a wide variety of problems. Introduction The majority of CBR systems rely on a good case-base organisation, an effective index and a (possibly knowledge intensive) similarity matching to select cases, that can then be used to solve a problem, see Many CBR tools provide standard means of constructing indexes. Isoft's ReCall is typical in using a C4.5 [Quinlan 1993] generated decision tree, constructed from the cases in the case-base, as the index. However, induction algorithms like C4.5 apply a greedy selection approach and so the features used by the index are not always the optimal ones. This is a particular problem when the cases contain many features irrelevant to the problem solving The cases identified by the index are next ranked according to their similarity to the new problem. The simplest similarity metric is Euclidean distance between normalised feature vectors. However, a "useful" (from the point of view of solving a problem) similarity should take account of the relative importances of various features. Certainly in a situation where many features are irrelevant to the problem to be solved, a simple similarity measure is insufficient. This problem can be partially solved by identifying and removing irrelevant features as before. However, a more flexible method assigns weights to the features to indicate their relative importance to the problem solving. Although the selection of the relevant features can usually be done quite accurately by an expert, feature weighting can only be done approximately by an expert, often by categorising the relevance as one from a small set of possible degrees of relevance. Therefore, applying an automated algorithm to find feature weights is attractive. Section 2 reviews feature selection and weighting methods. Our tablet formulation problem domain is introduced in Section 3

Improving e-learning recommendation by using background knowledge.

There is currently a large amount of e-Learning resources available to learners on the Web. However, learners often have difficulty finding and retrieving relevant materials to support their learning goals because they lack the domain knowledge to craft effective queries that convey what they wish to learn. In addition, the unfamiliar vocabulary often used by domain experts makes it difficult to map a learner's query to a relevant learning material. We address these challenges by introducing an innovative method that automatically builds background knowledge for a learning domain. In creating our method, we exploit a structured collection of teaching materials as a guide for identifying the important domain concepts. We enrich the identified concepts with discovered text from an encyclopedia, thereby increasing the richness of our acquired knowledge. We employ the developed background knowledge for influencing the representation and retrieval of learning resources to improve e-Learning recommendation. The effectiveness of our method is evaluated using a collection of Machine Learning and Data Mining papers. Our method outperforms the benchmark, demonstrating the advantage of using background knowledge for improving the representation and recommendation of e-Learning materials

Cold-start music recommendation using a hybrid representation.

Digital music systems are a new and exciting way to dis- cover, share, and listen to new music. Their success is so great, that digital downloads are now included alongside tra- ditional record sales in many o cial music charts [10]. In the past listeners would rely on magazine, radio, and friends reviews to decide on the music they listen to and purchase. In the internet age, this style of nding music is being su- perseded by music recommender systems. The shift from listening to hard copies of music, such as CDs, to online copies like MP3s, presents the interesting new challenge of how to recommend music to a listener. In such recommender systems, a user will typically provide a track that they like as a query, often implicitly as they listen to the track. The system must then provide a list of further tracks that the user will want to listen to. Many websites exist that provide such recommender systems, and many of the systems provide very good recommendations. However, there are still scenarios that these systems struggle to han- dle, and where recommendations can be unreliable. Online music systems allow users to tag any track with a free-text description. A recommender system can then determine the similarity between tracks based on these tags, and make recommendations. However, when a track is new to the system it will have no tags. This means that the track is never recommended, and in turn, the track is very unlikely to be tagged. Turnbull et. al [11] show that social tags tend to be very sparse, and that a huge popularity bias exists. This is further con rmed by data released by Last.fm [7] as part of the million song dataset [3]: from a vocabulary of over 500000 tags, each track, on average, has only 17 tags; 46% of tracks have no tags at all. This scenario is often referred to as the cold-start prob- lem; the results of which means large volumes of music are excluded from recommendations, even if they may be an excellent recommendation. The aim of our hybrid repre- sentation is to reduce the e ects of the cold-start problem, therefore increasing the recommendation quality of the over- all system

Music-inspired texture representation.

Techniques for music recommendation are increasingly relying on hybrid representations to retrieve new and exciting music. A key component of these representations is musical content, with texture being the most widely used feature. Current techniques for representing texture however are inspired by speech, not music, therefore music representations are not capturing the correct nature of musical texture. In this paper we investigate two parts of the well-established mel-frequency cepstral coefficients (MFCC) representation: the resolution of mel-frequencies related to the resolution of musical notes; and how best to describe the shape of texture. Through contextualizing these parts, and their relationship to music, a novel music-inspired texture representation is developed. We evaluate this new texture representation by applying it to the task of music recommendation. We use the representation to build three recommendation models, based on current state-of-theart methods. Our results show that by understanding two key parts of texture representation, it is possible to achieve a significant recommendation improvement. This contribution of a music-inspired texture representation will not only improve content-based representation, but will allow hybrid systems to take advantage of a stronger content component