Representation learning for minority and subtle activities in a smart home environment

Daily human activity recognition using sensor data can be a fundamental task for many real-world applications, such as home monitoring and assisted living. One of the challenges in human activity recognition is to distinguish activities that have infrequent occurrence and less distinctive patterns. We propose a dissimilarity representation-based hierarchical classifier to perform two-phase learning. In the first phase, the classifier learns general features to recognise majority classes, and the second phase is to collect minority and subtle classes to identify fine difference between them. We compare our approach with a collection of state-of-the-art classification techniques on a real-world third-party dataset that is collected in a two-user home setting. Our results demonstrate that our hierarchical classifier approach outperforms the existing techniques in distinguishing users in performing the same type of activities. The key novelty of our approach is the exploration of dissimilarity representations and hierarchical classifiers, which allows us to highlight the difference between activities with subtle difference, and thus allows the identification of well-discriminating features.Postprin

Visualization as Intermediate Representations (VLAIR) for human activity recognition

Ambient, binary, event-driven sensor data is useful for many human activity recognition applications such as smart homes and ambient-assisted living. These sensors are privacy-preserving, unobtrusive, inexpensive and easy to deploy in scenarios that require detection of simple activities such as going to sleep, and leaving the house. However, classification performance is still a challenge, especially when multiple people share the same space or when different activities take place in the same areas. To improve classification performance we develop what we call a Visualization as Intermediate Representations (VLAIR) approach. The main idea is to re-represent the data as visualizations (generated pixel images) in a similar way as how visualizations are created for humans to analyze and communicate data. Then we can feed these images to a convolutional neural network whose strength resides in extracting effective visual features. We have tested five variants (mappings) of the VLAIR approach and compared them to a collection of classifiers commonly used in classic human activity recognition. The best of the VLAIR approaches outperforms the best baseline, with strong advantage in recognising less frequent activities and distinguishing users and activities in common areas. We conclude the paper with a discussion on why and how VLAIR can be useful in human activity recognition scenarios and beyond.Postprin

Being Black Is Not a Risk Factor: A Strengths-Based Look at the State of the Black Child

Including nine essays from experts and five "points of proof" organization case studies, this publication challenges the prevailing discourse about black children and intends to facilitate a conversation around strengths, assets, and resilience. It addresses the needs of policymakers, advocates, principals, teachers, parents, and others

Understanding STEM Identity Construction: An ethnography of an all-girls STEM club

This qualitative ethnography follows 11 high-school girls through their experiences in an all-girls after-school STEM club in a privileged school setting. This study uses Gee’s concept of identity and a feminist poststrutural framework to understand their experiences and how they use the club to re/construct their gendered STEM identities. Through interviews, focus groups, observations, and document analysis, this study found that the after-school club offers girls a space to not only learn about STEM, but also provides a space for girls to understand the gendered nature of their interactions with peers and adults in STEM classrooms. Data shows that girls encounter gender bias and stereotyping in STEM classrooms and that GEMS helps girls identify these experiences. Regular and sustained participation in the club allows girls to develop peer-mentorship relationships, helps them to identify barriers they may face, and to create meaning from their experiences in the club. The results of this study show how Gee’s discourse-identity and affinity-identity can work together to offer an alternative pathway for girls to develop a STEM identity. Additionally, feminist poststucturalism highlights the ways that patriarchal discourse of STEM is infused into classroom spaces and how this club, and those like it, provides a space for girls to develop agency, resistance and freedom and an opportunity to re-create a more inclusive STEM discourse that informs their gendered STEM identity. The STEM identity that girls develop in GEMS supports their active and informed resistance of barriers and creation of more gender equitable STEM spaces. Other studies that examine after school STEM clubs are mostly situated in middle schools or colleges and rarely examine sites of privilege. This study starts to fill a gap in the literature by examining the experiences of high school girls in an affluent school

Black and minority ethnic trainees’ experiences of physical education initial teacher training

The official published version can be accessed at the link below.This report draws together the findings of research that aimed to explore black and minority ethnic (BME) trainees’ experiences of Physical Education (PE) initial teacher training (ITT). Although the numbers of BME trainees opting to enter teaching have improved considerably over the last few years, PE remains one of three specific subject areas where they remain significantly under-represented. Current figures suggest that PE attracts approximately 3% of trainees from BME backgrounds, compared with 11% for new entrants into teaching overall. The relative lack of success in attracting BME trainees into PE teaching compared to other subject areas suggests that the subculture of the subject may be a compounding factor. Over the last decade or so, a number of studies have explored the impact of ethnicity on teachers’ professional socialisation and their experiences as teachers in school, but none have focused on experiences within specific subject cultures. The centrality of the body in PE, and the link between this and the perceived low status of the subject, are influencing factors highlighted in the broader literature, including sports studies. For example, research exploring racism and the under-representation of BME participants in sport has highlighted the prevalence of stereotypical attitudes about their physicality and abilities held by coaches, administrators and spectators. Other research has suggested that some minority ethnic groups favour higher status, better paid, careers in areas such as law or medicine rather than teaching. As yet, there has been little attention to ‘race’ and ethnicity within PEITT, although studies have shown the impact of gender on trainees’ developing professional identities, and how teachers’ gendered bodies are important ‘tools’ of their work. In addition, there has been little research that has acknowledged trainees’ multiple identities, or the complex ways in which ‘race’, ethnicity, class and gender and other identity markers intersect to impact on the professional socialisation process. The research on which this report is based sought to fill some of these gaps in our understandings of BME trainees’ experiences of PEITT, and to identify strategies that might help in their recruitment and retention in the longer term. The research was funded through a small Recruitment and Retention Challenge Grant from the Teacher Development Agency (TDA). These grants form part of the TDA’s wider policy agenda to widen the diversity of new intakes opting into teaching. Higher education institutions have been encouraged, through targets and financial support and incentives, to develop specific strategies aimed at widening the diversity of their cohorts. Examples of such strategies include the provision of specialist admission help for BME prospective trainees; opportunities to gain experience in schools; open days and ‘taster’ events; advertising in the ethnic minority media, and the development of good practice guides and staff training to help ITT providers address issues of ‘race’ and ethnicity. 5 The impetus for this research resulted, in part, from presentations and discussions at a one day PEITT Network1 staff seminar on diversity held in October, 2007. The quantitative research conducted by the Association for Physical Education (AfPE) and the Ethnic Minority Foundation (EMF) presented here, showed the extent of the national under-representation of BME students in PEITT. Although the day focused on addressing reasons for BME under representation and strategies that might be used for improving recruitment, we felt it was also important to learn about the qualitative experiences of trainees that have been attracted into PEITT. Understanding the experiences of our current BME trainees might offer useful insights into how we might recruit and retain future such trainees. Our choice of qualitative research was supported by a national study published shortly after the network day, investigating the links between gender, ethnicity and degree attainment (Higher Education Academy, HEA, 2008), which specifically calls for further qualitative studies of students’ experiences of different subject areas.Funding from the Training and Development Agency (TDA

Cultural Competency in a Post-Model Rule 8.4(g) World

Självmord är en av de vanligaste dödsorsakerna i Sverige. Personer som begått självmord lämnar efter sig närstående med en stor sorg. Syftet med studien var att undersöka huruvida närstående till personer som begått självmord får det stöd som de önskar. Studien är baserad på en kvantitativ metod genom ett webb-baserat frågeformulär med 66 deltagare. Urvalet utgår ifrån en kontakt med en ansvarig för anhörigorganisationen SPES (Riksförbundet för SuicidPrevention och Efterlevandes Stöd). Resultatet analyserades sedan med en kvantitativ deskriptiv statistik och kvalitativ innehållsanalys. Studiens resultat visade att 30% av deltagarna var det mer än sex år sedan självmordet skett. Nästan alla av deltagarna söktes upp eller blev erbjudna, på ett eller annat sätt, minst ett slags stöd. Majoriteten av deltagarna sökte i första hand stöd hos sin familj, släkt, vänner eller arbetskamrater. Av de närstående svarade 33% att de var nöjda med det stödet. Hälften av de närstående som fått något slags professionellt stöd upplevde att stödet var bra. Behov av att få stöd i vardagen påtalades av 29%. Studien visade sammanfattningsvis att många av deltagarna är missnöjda med det professionella stöd de erhållit. Deltagarna har ett stort behov av att uttrycka sina tankar och känslor trots att det gått lång tid sedan självmordet skett

I Know It (Racism) Still Exists Here: African American Males at a Predominantly White Institution (PWI)

This qualitative study examined the experiences of African American males at a PWI (Predominantly White Institution). The focus on African American males is deliberate because, as a group, they have the highest attrition rate of any college demographic. Utilizing in-depth interview data from 12 African American males at a PWI, this project delineated the nefarious station of black males who experienced racism and racial microaggressions in a purportedly post-racial, colorblind society. Qualitative analysis of the data revealed the following two themes: (1) Racism and Racial Microaggressions and (2) The African American experience is not important to faculty and the university. Recommendations for how PWIs can foster the academic success of black males will be provided

Detecting abnormal events on binary sensors in smart home environments

With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies.PostprintPeer reviewe