Abstract. In this paper, we review different context classification systems that have been used to define elements of context. Although existing classification systems cover various types of context, in the development of context aware applications, only a few types of context have been used. In this work, we aim to build a context classification model based on Activity Theory that provides a basis both for dialogue amongst context awareness researchers and for the implementation of a context awareness architecture.

We have developed a gesture input system that provides a common interaction technique across mobile, wearable and ubiquitous computing devices of diverse form factors. In this paper, we combine our gestural input technique with speech output and test whether or not the absence of a visual display impairs usability in this kind of multimodal interaction. This is of particular relevance to mobile, wearable and ubiquitous systems where visual displays may be restricted or unavailable. We conducted the evaluation using a prototype for a system combining gesture input and speech output to provide information to patients in a hospital Accident and Emergency department. A group of participants was instructed to access various services using gestural inputs. The services were delivered by automated speech output. Throughout their tasks, these participants could see a visual display on which a GUI presented the available services and their corresponding gestures. Another group of participants performed the same tasks but without this visual display. It was predicted that the participants without the visual display would make more incorrect gestures and take longer to perform correct gestures than the participants with the visual display. We found no significant difference in the number of incorrect gestures made. We also found that participants with the visual display took longer than participants without it. It was suggested that for a small set of semantically distinct services with memorable and distinct gestures, the absence of a GUI visual display does not impair the usability of a system with gesture input and speech output.

Behavioral and psychological symptoms of dementia (BPSD) are common in patients with moderate-severe dementia and have negative impacts on both patients and caregivers. There is a lack of a tool for caregivers to monitor patients' BPSD by themselves. This study aimed to develop and validate a mobile application for caregivers to use in monitoring BPSD.Methods: A total of 104 pairs of patients with moderate-severe dementia and their caregivers completed the study. The Neuropsychiatric Inventory (NPI) was modified and digitally transformed to a caregiver-rating mobile application to quantify nine domains of BPSD for their frequency and impact on the emotion of the caregivers. Data collected from the application were compared with the paper-and-pencil NPI for prevalence, concurrent validity (Spearman's rho) and internal consistency (Cronbach's alpha).Results: The application was able to detect 93% of BPSD compared with the NPI. Concurrent validity was good-very good when compared with the Frequency × Severity score (ρ = 0.77, P < 0.001) and the burden score (ρ = 0.85, P < 0.001) from the NPI. Levels of internal consistency were acceptable for both frequency (α = 0.73) and impact (α = 0.79) scores. 80% of the caregivers reported that the application was "very likely to be helpful in caregiving". Conclusions:The mobile application for monitoring BPSD in patients with moderatesevere dementia had an excellent sensitivity, and good-very good validity and consistency. The caregivers had a positive perception of the application as an aid in caregiving. Geriatr Gerontol Int 2021; 21: 472-477.

In Thailand, it is very common for people to own at least one mobile phone. This is also the case for elderly people.They tend to feel that mobile phone is necessary for their safety and convenient in everyday living. This paper discusses our mobile application that is aimed to support independent living for the Thai elderly. Falls in the elderly people often cause serious physical injury such as fracture, cerebral hemorrhaging or even death. To be able to detect falls as early as possible is a very important method in rescuing the subjects and to facilitate the rehabilitation in the future. In this paper, we use a tri -axial accelerometer that is embedded in the mobile phone to monitor the movements of human body. The application also supports children and caters so that they are being notified when the fall is detected. As a result, they are also able to show their care and love to the elderly at the right time. We proposed a thresholdbased concept to be applied to the changes of the parameters from the tri -axial accelerometer. The results show that this application can detect the falls and notify children and care takers of the elderly effectively.

Most studies relating to bug reports aims to automatically identify necessary information from bug reports for software bug fixing. Unfortunately, the study of bug reports focuses only on one issue, but more complete and comprehensive software bug fixing would be facilitated by assessing multiple issues concurrently. This becomes a challenge in this study, where it aims to present a method of identifying bug reports at severe level from a bug report repository, together with assembling their related bug reports to visualize the overall picture of a software problem domain. The proposed method is called “mining bug report repositories”. Two techniques of text mining are applied as the main mechanisms in this method. First, classification is applied for identifying severe bug reports, called “bug severity classification”, while “threshold-based similarity analysis” is then applied to assemble bug reports that are related to a bug report at severe level. Our datasets are from three opensource namely SeaMonkey, Firefox, and Core:Layout downloaded from the Bugzilla. Finally, the best models from the proposed method are selected and compared with two baseline methods. For identifying severe bug reports using classification technique, the results show that our method improved accuracy, F1, and AUC scores over the baseline by 11.39, 11.63, and 19% respectively. Meanwhile, for assembling related bug reports using threshold-based similarity technique, the results show that our method improved precision, and likelihood scores over the other baseline by 15.76, and 9.14% respectively. This demonstrate that our proposed method may help increasing chance to fix bugs completely.

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The travel and tourism industry is one of the main sources of income in Thailand. People choose to travel around the world, as a way to relax and enjoy their time. Users search information from many resources before and during their travel. We grouped the mobile tourism applications into two main groups in order to analyse the nature of information in the tourism applications. Social network provides rich collaborative user-generated information. We found that most tourism applications require personal information and pre-existing association in order to get information. We argue that, in tourism, a user requires instant and easy access to information. So the social network might not be an appropriate option. Therefore we propose a simple collaborative user-generated content application, which has a location awareness chat system. We aim to provide an application with self-sufficient information that allows the user to instantly share, search, and comment on information at anytime and anywhere. Moreover, the location awareness chat system is introduced to provide instant firsthand information to the users as well.

Recommendation system relies on information of user preference and user behavior in order to recommend the useful information. The existing recommendation systems still have problems for new users and new items. This research proposes a new hybrid method to develop the conceptual framework of recommendation system that deals with new user and new movie data. The data used consists of a data from MovieLens and the internet movie database (IMDB). This work introduces a hybrid recommendation system which based on a combination of content-based filtering (CBF) and collaborative filtering (CF). Pre-filtering data is performed by finding an optimal number of clusters by calculating the total within cluster sum of square. In order to reduce the complexity of data and increase the relevance of the user-item ratings, the fuzzy c-mean (FCM) is employed. Then the similarity is calculated by using item-based method, the K-nearest neighbors and weight sum of the rating are applied. Finally, to recommend the movies, the research found that for new user data the precision is at 85% and mean absolute eror (MAE) value 2.1011. For new item data, the result of research obtains the precision at 87% and MAE value 2.0031. In conclusion, the new hybrid method developed can recommend movie efficiently.