This paper presents a systematic analysis of the existing studies in two research areas: (1) effects that animated objects might have on users, and (2) HCI studies with autistic users. The aim of this work is to broaden current knowledge of the effect of animation on user interface usability. We systematically analysed the state of the art in the related research areas and identified many gaps that should be addressed, as well as proposed the solution to cover these gaps. Our goal is to analyse the performance variance between neurotypical users and autistic individuals in order to gauge task achievement, webpage navigability, and ability to focus.

Task continuity is an important inter-usability attribute, for enhancing user transitioning experience. Mobile devices are commonly used in cross-device interaction, in combination with desktops or laptops devices. However, a lack of studies explored and compared factors affecting task continuity when users transit to and from the different types of mobile user interfaces: native mobile application, mobile website and mobile responsive website. To address this, we conducted a user study, with twenty-four voluntary participants, to investigate factors affecting task continuity when users switch between desktop website and the different types of mobile user interfaces. We used think-aloud protocols, observation and questionnaire to gather data. During our experiments, each participant switched between desktop and mobile interfaces to perform inter-related tasks. Inconsistency was a principle factor affecting task continuity when participants switched from and to mobile native applications and mobile websites. The difficulty in controlling user interface components and the slow loading of contents were two main factors affecting task continuity when users switched to continue working on mobile websites and mobile responsive websites.

It is now common for users to move from one device to another when interacting with a service that is available across platforms. In addition, not all usage contexts involve being seated, potentially leading to usability issues that have not been tested. In this study, we tested the transitioning experience of mobile users while interacting with cross-platform services, across different contextual settings (seated-moving, and seated-seated). Our study findings showed that in each test setting, users were sensitive to the same cross-platform user experience (UX) elements, such as consistency and fluency. However, the seated-moving setting consistently resulted in a larger number of inter-usability issues.

Finding a place of interest (e.g., a restaurant, hotel, or attraction) is often related to a group information need, however, the actual multiparty collaboration in such searches has not been explored, and little is known about its significance and related practices. We surveyed 100 computer science students and found that 94% (of respondents) searched for places online; 87% had done so as part of a group. Search for place by multiple active participants was experienced by 78%, with group sizes typically being 2 or 3. Search occurred in a range of settings with both desktop PCs and mobile devices. Difficulties were reported with coordinating tasks, sharing results, and making decisions. The results show that finding a place of interest is a quite different group‐based search than other multiparty information‐seeking activities. The results suggest that local search systems, their interfaces and the devices that access them can be made more usable for collaborative search if they include support for coordination, sharing of results, and decision making.

Attributing authorship of documents with unknown creators has been studied extensively for natural language text such as essays and literature, but less so for non-natural languages such as computer source code. Previous attempts at attributing authorship of source code can be categorised by two attributes: the software features used for the classification, either strings of n tokens/bytes (n-grams) or software metrics; and the classification technique that exploits those features, either information retrieval ranking or machine learning. The results of existing studies, however, are not directly comparable as all use different test beds and evaluation methodologies, making it difficult to assess which approach is superior. This paper summarises all previous techniques to source code authorship attribution, implements feature sets that are motivated by the literature, and applies information retrieval ranking methods or machine classifiers for each approach. Importantly, all approaches are tested on identical collections from varying programming languages and author types. Our conclusions are as follows: (i) ranking and machine classifier approaches are around 90% and 85% accurate, respectively, for a one-in-10 classification problem; (ii) the byte-level n-gram approach is best used with different parameters to those previously published; (iii) neural networks and support vector machines were found to be the most accurate machine classifiers of the eight evaluated; (iv) use of n-gram features in combination with machine classifiers shows promise, but there are scalability problems that still must be overcome; and (v) approaches based on information retrieval techniques are currently more accurate than approaches based on machine learning. Copyright © 2012 John Wiley & Sons, Ltd.

A novel approach is proposed in this study, which is to evolve visual inspection programs for automatic defect detection on populated printed circuit boards. This GP-based method does not require knowledge of the layout design of a board, nor relevant domain knowledge such as lighting conditions and visual characteristics of the components. Furthermore, conventional image operators are not required to perform the detection. The experiments show that these evolved GP programs can identify all the faults while some suspicious areas are also highlighted. By this GP approach, manual inspection effort can be dramatically reduced. In addition, an evolved GP detection program can readily work on different types of boards without re-training.

Finding a piece of music based on its content is a key problem in music in for music information retrieval . For example, a user may be interested in finding music based on knowledge of only a small fragment of the overall tune. In this paper, we consider the searching of musical audio using symbolic queries. We first propose a relative pitch approach for representing queries and pieces. Experiments show that this technique, while effective, works best when the whole tune is used as a query. We then present an algorithm for matching based on a pitch classes approach, using the longest common subsequence between a query and target. Experimental evaluation shows that our technique is highly effective, with a mean average precision of 0.77 on a collection of 1808 recordings. Significantly, our technique is robust for truncated queries, being able to maintain effectiveness and to retrieve correct answers whether the query fragment is taken from the beginning, middle, or end of a piece. This represents a significant reduction in the burden placed on users when formulating queries.

There have been many proposed music-retrieval systems, based on a variety of principles. How the effectiveness of these systems compares is not clear. The evaluation of some systems has been informal, without the rigor applied in other areas of information retrieval, and comparison of systems is difficult because of the lack of a common data set, queries, or relevance judgments. In this paper we explain how we collected artificial and expert music queries and name-based relevance judgments, and describe software we developed for collection of manual relevance judgments. Together with a collection of downloaded musical instrument digital interface (MIDI) files, these sets of queries and relevance judgments provide valuable tools for measuring music-retrieval systems. As an example of the value of these tools, we use them to compare the effect of using the expert queries and manual judgments to that of the artificial queries and manual judgments used in our earlier experiments.

Abstract We have explored methods for music information retrieval for polyphonic music stored in the MIDI format. These methods use a query, expressed as a series of notes that are intended to represent a melody or theme, to identify similar pieces. Our work has shown that a three‐phase architecture is appropriate for this task in which the first phase is melody extraction, the second is standardization, and the third is query‐to‐melody matching. We have investigated and systematically compared algorithms for each of these phases. To ensure that our results are robust, we have applied methodologies that are derived from text information retrieval: We developed test collections and compared different ways of acquiring test queries and relevance judgments. In this article we review this program of work, compare it to other approaches to music information retrieval, and identify outstanding issues.

With the growth in digital representations of music, and of music stored in these representations, it is increasingly attractive to search collections of music. One mode of search is by similarity, but, for music, similarity search presents several difficulties: in particular, for melodic query support, deciding what part of the music is likely to be perceived as the theme by a listener, and deciding whether two pieces of music with different sequences of notes represent the same theme. In this paper we propose a three-stage framework for matching pieces of music. We use the framework to compare a range of techniques for determining whether two pieces of music are similar, by experimentally testing their ability to retrieve different transcriptions of the same piece of music from a large collection of MIDI files. These experiments show that different comparison techniques differ widely in their effectiveness; and that, by instantiating the framework with appropriate music manipulation and comparison techniques, pieces of music that match a query can be identified in a large collection.

Many types of user would find it valuable to search collections of music via queries representing music fragments, but such searching requires a reliable technique for identifying whether a provided fragment occurs within a piece of music. The problem of matching fragments to music is made difficult by the psychology of music perception, because literal matching may have litile relation to perceived melodic similarity, and by the interactions between the multiple parts of typical pieces of music. In this paper we analyse the propeties of music, music perception, and music database users, and use the analysis to propose alternative techniques for extracting monophonic melodies from polyphonic music; we believe that such melodies can subsequently be used for matching of queries to data. We report on experiments with music listeners, which rank our proposed techniques for extracting melodies.