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Efficiently Improving the Wi-Fi-Based Human Activity Recognition, Using Auditory Features, Autoencoders, and Fine-Tuning
Full text linkHuman activity recognition (HAR) based on Wi-Fi signals has attracted significant attention due to its convenience and the availability of infrastructures and sensors. Channel State Information (CSI) measures how Wi-Fi signals propagate through the environment. However, many scenarios and applications have insufficient training data due to constraints such as cost, time, or resources. This poses a challenge for achieving high accuracy levels with machine learning techniques. In this study, multiple deep learning models for HAR were employed to achieve acceptable accuracy levels with much less training data than other methods. A pre-trained encoder trained from a Multi-Input Multi-Output Autoencoder (MIMO AE) on Mel Frequency Cepstral Coefficients (MFCC) from a small subset of data samples was used for feature extraction. Then, fine-tuning was applied by adding the encoder as a fixed layer in the classifier, which was trained on a small fraction of the remaining data. The evaluation results (K-fold cross-validation and K=5) showed that using only 30% of the training and validation data (equivalent to 24% of the total data), the accuracy was improved by 17.7% compared to the case where the encoder was not used (with an accuracy of 79.3% for the designed classifier, and an accuracy of 90.3% for the classifier with the fixed encoder). While by considering more calculational cost, achieving higher accuracy using the pre-trained encoder as a trainable layer is possible (up to 2.4% improvement), this small gap demonstrated the effectiveness and efficiency of the proposed method for HAR using Wi-Fi signals
The perceived impact of counselling training on students' personal relationships
Full text linkBackgroundDespite the urban myth of “the divorce course,” there is little research to support this perception of counselling training programmes. Studies exploring the lived experiences of counsellors in training have referred to relationship changes, but none have made these their primary focus. Research is needed to enhance our understanding of this formative stage for counsellors.AimsThe purpose of this ideographic study is to gain new perspectives on the perceived impact of counselling training on trainees' personal relationships by exploring the lived experiences of five counsellors who completed their training in the last 18 months.MethodThrough a process of volunteer and snowball sampling, qualified counsellors were invited to participate in individual semistructured interviews, which were recorded and transcribed. Due to the small sample size and the emphasis on how each participant made meaning of their own experience, interpretative phenomenological analysis was chosen to interpret the findings.FindingsDetailed analysis and interpretation of the data filtered into two superordinate themes:1. permission to change; and 2. the challenge of integration.ConclusionThe data revealed that training had a significant multifaceted impact on the personal relationships of all participants. Participants' experience of their training group and of personal therapy was found to be an important factor in this change, as were themes of agency and identity. The majority of ruptures in extant relationships took place early in training. Further research is needed, perhaps into the experiences of loved ones in relationships with student counsellors
‘Lots of Black people are on meds because they're seen as aggressive’: STOMP, COVID-19 and anti-racism in community learning disability services
Full text linkBackgroundThe STOMP agenda (Stopping Over-Medication of People with learning disabilities, autism, or both) drew focus to individuals with a diagnosis of a learning disability being prescribed psychotropic medication to manage ‘behaviours that challenge’. The following study is an audit of two community learning disability services in the London boroughs of Westminster and Kensington and Chelsea for compliance with national guidance on the use of medication in this population, the impact of the COVID-19 pandemic, and equality, diversity and anti-racism.MethodRoutinely collected data were audited relating to clients identified in each service, totalling 54 participants. Data were audited against five standards: minimum effective dose, medication reviews, alternative multidisciplinary input, the impact of the COVID-19 pandemic and equality, diversity and anti–racism. Comparisons were made to the overall caseload (N = 365) where appropriate.ResultsEvidence demonstrated a greater risk of receiving psychotropic medication to manage behaviours that challenge for service users from racialised backgrounds, further evidencing institutional and/or individualised racism within practice for this population. Prescriptions also increased in dosage during the COVID-19 pandemic exacerbated by insufficient provision of alternative input and regular multi-disciplinary review as required by national guidance.ConclusionsCommunity learning disability teams require dedicated, co-produced STOMP pathways to review those at risk of over-medication. Additional research is required to explore individual and systemic factors contributing to ethnic disparities in medication prescription for behaviours that challenge among people with learning disabilities. Further recommendations are considered around developing data collection, service user involvement, and future directions
A reinforcement learning recommender system using bi-clustering and Markov Decision Process
Full text linkCollaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning
Influence of aesthetic design elements on residential satisfaction in apartment Based on Seoul apartment complex
Full text linkThis study aimed to examine the influence of aesthetic design elements on residential satisfaction in urban apartment complexes, focusing on elements that are generally considered less important. A total of 65 apartment complexes in Seoul, a city predominantly characterized by middle-class apartment living, were surveyed to assess residential satisfaction. Using multiple regression analysis, the relationships between the dependent variable (post-occupancy evaluation) and 28 independent variables were analyzed. The results revealed significant correlations between residential satisfaction and various independent variables. Specifically, three out of eight aesthetic design factors, namely the main complex entrance design, exterior mass design, and landscape design, were found to have a significant impact on residential satisfaction, collectively accounting for 17.16% of the total satisfaction variance. This finding suggests that aesthetic design elements play an increasingly important role in metro cities. The practical implications of this study are twofold. Firstly, it provides housing providers with strategic guidelines, emphasizing the significance of incorporating aesthetically pleasing design elements to enhance residential satisfaction. Secondly, the study offers potential customers valuable information regarding the importance of aesthetic design in their decision-making process when choosing residential properties. Overall, this research contributes to a better understanding of the relationship between aesthetic design elements and residential satisfaction in urban apartment complexes, shedding light on the growing importance of aesthetics in the housing market
Unlocking potential: the transformative power of coaching for doctoral students
Full text linkCoaching doctoral students to enhance their well-being involves personalised guidance to improve emotional, social and academic skills, fostering self-awareness, and promoting positive coping mechanisms. The goal is to navigate challenges collaboratively, set meaningful goals, and develop strategies for sustained personal and academic success. This paper proposes a model that, by introducing additional support from a coach, can mitigate issues that often lead to students failing to complete their studies. The model is designed to support supervisors and students in managing their work environment, building confidence, and aiding supervisors in supporting students. The potential benefits of this model are significant, given the limited literature on postgraduate studies, coaching and well-being, despite the growing interest in this area
Outdoor learning in urban schools: Effects on 4–5 year old children's noise and physiological stress
Full text linkNatural outdoor environments reduce physiological stress. But in an urban school context, does outdoor learning still have beneficial effects even where nature exposure is more limited? The current, pre-registered study used wearable devices including heart rate monitors and actigraphs to examine physiological stress in 4–5 year old children across 8 matched indoor and outdoor sessions (N = 76 children, N = 601 sessions in total). Results revealed that children's resting heart rates while seated and listening to a teacher were significantly lower when outside compared to indoors (p < 0.001, d = 0.512). Children also moved more while seated during indoor sessions (p < 0.001, d = 0.546). Despite activities and resources being matched across conditions, outdoor learning sessions were significantly quieter than indoor ones, both when children were seated, listening to a teacher (p = 0.004, d = −0.455) and when actively engaged in play and learning activities (p < 0.001, d = 1.064). There was a significant positive correlation between noise levels and resting heart rate in the indoor condition (r(97) = 0.364, p < 0.001) but not in the outdoor condition. These findings suggest that learning outdoors, even in urban settings, associates with lower physiological stress in children and that this effect may partly be due to reduced noise. The fact that noise associates with resting heart rate indoors but not outdoors may indicate that being outside buffers children against the stressful effects of excess noise
Adopting Security Practices in Software Development Process: Security Testing Framework for Sustainable Smart Cities
Full text linkThe dependence on smart city applications has expanded in recent years. Consequently, the number of cyberattack attempts to exploit smart application vulnerabilities significantly increases. Therefore, improving smart application security during the software development process is mandatory to ensure sustainable smart cities. But the challenge is how to adopt security practices in the software development process. There are Several established and mature security testing frameworks exist that consider security requirements and testing during Several already established and mature security testing frameworks exist that consider security requirements and testing during Software Development Life Cycle (SDLC), but there is a unique challenges posed by smart city applications and the need for a comprehensive approach to address the evolving threat landscape in this context. This paper proposed a framework that adopts security testing practices in all phases of the software development process. The proposed framework identifies several security activities and steps that can be applied in each phase of the software development process