Personalised meta-learning for human activity recognition with few-data.

State-of-the-art methods of Human Activity Recognition(HAR) rely on a considerable amount of labelled data to train deep architectures. This becomes prohibitive when tasked with creating models that are sensitive to personal nuances in human movement, explicitly present when performing exercises and when it is infeasible to collect training data to cover the whole target population. Accordingly, learning personalised models with few data remains an open challenge in HAR research. We present a meta-learning methodology for learning-to-learn personalised models for HAR; with the expectation that the end-user only need to provide a few labelled data. These personalised HAR models benefit from the rapid adaptation of a generic meta-model using provided few end-user data. We implement the personalised meta-learning methodology with two algorithms, Personalised MAML and Personalised Relation Networks. A comparative study shows significant performance improvements against state-of-the-art deep learning algorithms and other personalisation algorithms in multiple HAR domains. Also, we show how personalisation improved meta-model training, to learn a generic meta-model suited for a wider population while using a shallow parametric model

The Influence of Chemical Risk Communication on Consumer Behavior in Purchasing Foods: A Psychological Study

Consumers prefer a nutritious and delicious diet which is natural. Hence, they pay attention to research which focuses on chemical added food and risks involved in such consumption. Communication media plays a major role in deciding consumer willingness. Therefore, it is necessary to identify the influence of chemical risk communication on consumer behavior in purchasing foods. A qualitative and quantitative research method has been followed by the author to collect data, and questionnaires and in-depth interviews have been used to collect data. Data obtained are analyzed using SPSS for quantitative data whereas; the qualitative data is analyzed thematically. The data were collected from 100 Householders in the Badulla district. This study analyses the problem of how the messages on health risk influence consumer psychological behavior in purchasing food. The main objective of this research is to identify how the messages on health risk due to chemical additives in food influence the psychological behavior of consumers in purchasing food. The findings of the study reveal that consumers are not clear about the term, organic. The major factor that inhibited people from buying organic food was the high price. A majority of respondents had expressed interest in healthy and nutritionally rich food as well as environmental concerns and sustainability. Ultimately this study indicated that consumer awareness effectively advances the demand for organic products. It could be concluded that adoption of proper awareness programs would help in promoting the organic product consumption.DOI: http://doi.org/10.31357/fhss/vjhss.v05i01.0

Evaluating the transferability of personalised exercise recognition models.

Exercise Recognition (ExR) is relevant in many high impact domains, from health care to recreational activities to sports sciences. Like Human Activity Recognition (HAR), ExR faces many challenges when deployed in the real-world. For instance, typical lab performances of Machine Learning models, are hard to replicate, due to differences in personal nuances, traits and ambulatory rhythms. Thus effective transferability of a trained ExR model, depends on its ability to adapt and personalise to new users or user groups. This calls for new experimental design strategies that are also person-aware, and able to organise train and test data differently from standard ML practice. Speciffically, we look at person-agnostic and person-aware methods of train-test data creation, and compare them to identify best practices on a comparative study of personalised ExR model transfer. Our findings show that ExR when compared to results with other HAR tasks, to be a far more challenging personalisation problem and also confirms the utility of metric learning algorithms for personalised model transfer

Towards facilities information management through BIM

Information plays a significant role in managing built environment facilities. These information are generated at different lifecycle stages, by different parties, which also provide different values to a variety of stakeholders. The acquisition of appropriate information efficiently and effectively is two of highly important considerations in facilities management because of the nature of information flows, number of information providers and users. Building Information Modelling (BIM) is one of the popular mechanisms, which has adopted in construction sector to manage its information. This preliminary paper investigates how construction information is valued in facilities management. This is an initial step of understanding the possibilities and hindrance of using BIM as an effective vehicle to manage information during the facilities management stage. To achieve this aim, data were collected through literature review and 13 semi-structured interviews among construction professionals. Data were analysed thematically. The literature reveals BIM is an efficient mechanism to manage construction information. However, there is a difficulty of transferring appropriate information from construction stage to facility management. The study further identified the types of construction information that are highly usable for completing FM tasks, their uses and value attached to them

EVALUATING THE ABILITY OF BIM TO ENHANCE VALUE IN FACILITIES INFORMATION MANAGEMENT

The concern towards information management in construction industry has been changed over the past decade with the introduction of Building Information Modelling (BIM). With this influence the Government Soft Landing Policy focuses on early end user engagement to enhance the in-use performance of buildings. Literature reveals number of advantages that BIM promises on enhancing the efficient management of buildings. However, many of these findings explain what BIM can do and only limited effort has been taken to reveal the mechanism to exploit those good practices. This knowledge gap has slowed down the adoption of BIM beyond government projects. The success of BIM is based on information it holds. Hence this paper attempts to investigate the value of construction information to the facilities management to understand optimum level of information to be handed over through BIM. Also, it further attempts to explain how BIM can be used as a vehicle to improve such value. 14 interviews were conducted among construction professionals to gather the value perception of information. The qualitative data were analysed through thematic analysis based on grounded theory. The information value matrix was developed to assist facilities managers on understanding information requirement and value of information

Corrigendum to "Identifying mismatch and match between clinical needs and mental healthcare use trajectories in people with anxiety and depression:Results of a longitudinal study (vol 297, pg 657, 2022)

The authors regret that the assignment of the affiliations to the co-author “Frederike Jörg” has not been correctly implemented. The correct affiliations of Dr. Frederike Jörg are: b) Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, University of Groningen, University Medical Center Groningen, the Netherlands and c) Department of Education and Research, Friesland Mental Health Care Services, Leeuwarden, the Netherlands. The authors would like to apologise for any inconvenience caused

Identifying mismatch and match between clinical needs and mental healthcare use trajectories in people with anxiety and depression:Results of a longitudinal study

BACKGROUND: Mismatch between need and mental healthcare (MHC) use (under-and overuse) has mainly been studied with cross-sectional designs, not accurately capturing patterns of persistence or change in clinical burden and MHC-use among persons with depressive and/or anxiety disorders. AIMS: Determining and describing [mis]match of longitudinal trajectories of clinical burden and MHC-use. METHODS: Six-year longitudinal burden and MHC-use data came from the Netherlands Study of Depression and Anxiety (n=2981). The sample was split into four subgroups: I) no clinical burden but constant MHC use, II) constant clinical burden but no MHC-use, III) changing clinical burden and MHC-use, and IV) healthy non-users. Within subgroups I)-III), specific clinical burden and MHC trajectories were identified (growth mixture modeling). The resulting classes’ associations with predisposing, enabling, and need factors were investigated (regression analysis). RESULTS: Subgroups I-III revealed different trajectories. I) increasing MHC without burden (4.1%). II) slightly increasing (1.9%), strongly increasing (2.4%), and decreasing (9.5%) burden without MHC. III) increasing (41.4%) or decreasing (19.4%) burden and concurrently increasing MHC use (first underuse, then matched care), thus revealing delayed MHC-use. Only having suicidal ideation (p<.001, Cohen's d= .6-1.5) was a significant determinant of being in latter classes compared to underusers (strongly increasing burden without MHC-use). LIMITATIONS: More explanatory factors are needed to explain [mis]match. CONCLUSION: Mismatch occurred as constant underuse or as delayed MHC-use in a high-income country (Netherlands). Additionally, no meaningful class revealed constantly matched care on average. Presence of suicidal ideation could influence the probability of symptomatic individuals receiving matched MHC or not

Potensi Bakteri Endofit Dalam Meningkatkan Pertumbuhan Tanaman Tembakau Yang Terinfeksi Nematoda Puru Akar (Meloidogyne Spp.)

Potensi Bakteri Endofit dalam Meningkatkan Pertumbuhan Tanaman Tembakau yang Terinfeksi Nematoda Puru Akar (Meloidogyne spp.). Nematoda puru akar (Meloidogyne spp.) pada tanaman tembakau merupakan penyakit penting yang dihadapi oleh perkebunan tembakau di Indonesia. Beberapa teknik pengendalian telah dilakukan, seperti penggunaan nematisida, kultur teknis dan penambahan bahan organik namun belum efektif mengendalikan patogen ini. Pengendalian biologi dengan bakteri endofit merupakan alternatif pengendalian Meloidogyne spp. Penelitian ini bertujuan untuk mendapatkan bakteri endofit asal akar nilam meningkatkan pertumbuhan tanaman tembakau yang terinfeksi nematoda puru akar (Meloidogyne spp.). Penelitian ini dilaksanakan di Laboratorium Penyakit Tumbuhan Fakultas Pertanian Universitas Sumatera Utara pada bulan Juli – Desember 2014. Penelitian ini menggunakan Rancangan Acak Lengkap nonfaktorial dengan perlakuan pemberian beberapa jenis bakteri endofit yaitu: Kontrol (diaplikasikan nematoda 500ekor/pot), Bacillus spp.1, Pseudomonas spp. dan Bacillus spp.2. Hasil penelitian menunjukan laju pertumbuhan dan pertambahan jumlah daun terbaik terdapat pada perlakuan Pseudomonas spp. sedangkan berat basah dan kering akar tertinggi terdapat pada tanaman yang diaplikasikan Bacillus spp 1

The current and future role of visual question answering in eXplainable artificial intelligence.

Over the last few years, we have seen how the interest of the computer science research community on eXplainable Artificial Intelligence has grown in leaps and bounds. The reason behind this rise is the use of Artificial Intelligence in many daily life tasks, and the consequent necessity of people to understand the intelligent systems' behaviour. Computer vision-related tasks are not an exception, for example, Visual Question Answering tasks. The Artificial Intelligence models that carry out this specific task make an effort to answer questions about what we can watch in a particular image. In this work, we review the existing work about eXplainable Artificial Intelligence on Visual Question Answering which is a problem on which there is still much work to be done. Moreover, we open the discussion about the challenges to overcome regarding this topic, like the future role of Visual Question Answering to address eXplainable Artificial Intelligence issues or difficulties

Conceptual modelling of explanation experiences through the iSeeonto ontology.

Explainable Artificial Intelligence is a big research field required in many situations where we need to understand Artificial Intelligence behaviour. However, each explanation need is unique which makes it difficult to apply explanation techniques and solutions that are already implemented when faced with a new problem. Therefore, the task to implement an explanation system can be very challenging because we need to take the AI model into account, user's needs and goals, available data, suitable explainers, etc. In this work, we propose a formal model to define and orchestrate all the elements involved in an explanation system, and make a novel contribution regarding the formalisation of this model as the iSeeOnto ontology. This ontology not only enables the conceptualisation of a wide range of explanation systems, but also supports the application of Case-Based Reasoning as a knowledge transfer approach that reuses previous explanation experiences from unrelated domains. To demonstrate the suitability of the proposed model, we present an exhaustive validation by classifying reference explanation systems found in the literature into the iSeeOnto ontology