The reception of Cameroon Films and home videos in student residential areas: the case of Bambili – Cameroon

Film as a medium of communication uses visual and auditory signs between senders and receivers. It is considered as one of the most influential areas of media. The art form in Cameroon is valorised more through the home video system caused principally by the closure of theatre halls and the advent of cable TV channels dedicated to the broadcasting of movies for home consumption and the emergence of new media. This study looks at the reception of these home videos particularly in the student residential area of Bambili – Cameroon. A total number of 500 students served as sample for the study whose results show that a majority of viewers choose films according to different criteria but mostly influenced by their horizons of expectations.

Crowd Annotations Can Approximate Clinical Autism Impressions from Short Home Videos with Privacy Protections

Artificial Intelligence (A.I.) solutions are increasingly considered for telemedicine. For these methods to adapt to the field of behavioral pediatrics, serving children and their families in home settings, it will be crucial to ensure the privacy of the child and parent subjects in the videos. To address this challenge in A.I. for healthcare, we explore the potential for global image transformations to provide privacy while preserving behavioral annotation quality. Crowd workers have previously been shown to reliably annotate behavioral features in unstructured home videos, allowing machine learning classifiers to detect autism using the annotations as input. We evaluate this method with videos altered via pixelation, dense optical flow, and Gaussian blurring. On a balanced test set of 30 videos of children with autism and 30 neurotypical controls, we find that the visual privacy alterations do not drastically alter any individual behavioral annotation at the item level. The AUROC on the evaluation set was 90.0% +/- 7.5% for the unaltered condition, 85.0% +/- 9.0% for pixelation, 85.0% +/- 9.0% for optical flow, and 83.3% +/- 9.3% for blurring, demonstrating that an aggregation of small changes across multiple behavioral questions can collectively result in increased misdiagnosis rates. We also compare crowd answers against clinicians who provided the same annotations on the same videos and find that clinicians are more sensitive to autism-related symptoms. We also find that there is a linear correlation (r=0.75, p<0.0001) between the mean Clinical Global Impression (CGI) score provided by professional clinicians and the corresponding classifier score emitted by the logistic regression classifier with crowd inputs, indicating that the classifier's output probability is a reliable estimate of clinical impression of autism from home videos. A significant correlation is maintained with privacy alterations, indicating that crowd annotations can approximate clinician-provided autism impression from home videos in a privacy-preserved manner.

The Museum as a collector of vernacular born-digital photographs: a case study of the family camera network collection at the Royal Ontario Museum.

Active from April 2016 to March 2019, The Family Camera Network was a collaborative project that explored the relationship between family and photography. The project established a public archive at the Royal Ontario Museum (ROM) and The ArQuives. The collection is composed of photographs, albums, home videos and miscellaneous objects. Among the objects collected by the ROM are 126 born-digital photographs. This thesis focuses on the development of cataloguing methods for born-digital vernacular photographs using existing fields in the museum’s collection catalogue, TMS. Through the use of digital metadata, this thesis describes and analysis how information embedded in the born-digital archives can assist in the production of valuable collection records.

The Museum as a collector of vernacular born-digital photographs: a case study of the family camera network collection at the Royal Ontario Museum.

Active from April 2016 to March 2019, The Family Camera Network was a collaborative project that explored the relationship between family and photography. The project established a public archive at the Royal Ontario Museum (ROM) and The ArQuives. The collection is composed of photographs, albums, home videos and miscellaneous objects. Among the objects collected by the ROM are 126 born-digital photographs. This thesis focuses on the development of cataloguing methods for born-digital vernacular photographs using existing fields in the museum’s collection catalogue, TMS. Through the use of digital metadata, this thesis describes and analysis how information embedded in the born-digital archives can assist in the production of valuable collection records.

Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD

Time is a key factor to consider in Autism Spectrum Disorder. Detecting the condition as early as possible is crucial in terms of treatment success. Despite advances in the literature, it is still difficult to identify early markers able to effectively forecast the manifestation of symptoms. Artificial intelligence (AI) provides effective alternatives for behavior screening. To this end, we investigated facial expressions in 18 autistic and 15 typical infants during their first ecological interactions, between 6 and 12 months of age. We employed Openface, an AI-based software designed to systematically analyze facial micro-movements in images in order to extract the subtle dynamics of Social Smiles in unconstrained Home Videos. Reduced frequency and activation intensity of Social Smiles was computed for children with autism. Machine Learning models enabled us to map facial behavior consistently, exposing early differences hardly detectable by non-expert naked eye. This outcome contributes to enhancing the potential of AI as a supportive tool for the clinical framework.

Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection

Standard medical diagnosis of mental health conditions requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd of non-experts can efficiently annotate behavioral features needed for accurate machine learning detection of the common childhood developmental disorder Autism Spectrum Disorder (ASD) for children under 8 years old. We implement a novel process for identifying and certifying a trustworthy distributed workforce for video feature extraction, selecting a workforce of 102 workers from a pool of 1,107. Two previously validated ASD logistic regression classifiers, evaluated against parent-reported diagnoses, were used to assess the accuracy of the trusted crowd’s ratings of unstructured home videos. A representative balanced sample (N = 50 videos) of videos were evaluated with and without face box and pitch shift privacy alterations, with AUROC and AUPRC scores > 0.98. With both privacy-preserving modifications, sensitivity is preserved (96.0%) while maintaining specificity (80.0%) and accuracy (88.0%) at levels comparable to prior classification methods without alterations. We find that machine learning classification from features extracted by a certified nonexpert crowd achieves high performance for ASD detection from natural home videos of the child at risk and maintains high sensitivity when privacy-preserving mechanisms are applied. These results suggest that privacy-safeguarded crowdsourced analysis of short home videos can help enable rapid and mobile machine-learning detection of developmental delays in children.

Intelligent Video Highlights Generation with Front-Camera Emotion Sensing

In this paper, we present HOMER, a cloud-based system for video highlight generation which enables the automated, relevant, and flexible segmentation of videos. Our system outperforms state-of-the-art solutions by fusing internal video content-based features with the user’s emotion data. While current research mainly focuses on creating video summaries without the use of affective data, our solution achieves the subjective task of detecting highlights by leveraging human emotions. In two separate experiments, including videos filmed with a dual camera setup, and home videos randomly picked from Microsoft’s Video Titles in the Wild (VTW) dataset, HOMER demonstrates an improvement of up to 38% in F1-score from baseline, while not requiring any external hardware. We demonstrated both the portability and scalability of HOMER through the implementation of two smartphone applications.

Queering the Nigerian Cinema and Politics of Gay Culture

The advocacy for gayism and lesbianism in Nigeria is informed by transnational cultural processes, transculturalism, interculturalism, multiculturalism and globalisation. Although critical dimensions on Lesbian, Gay, Bisexual and Transgender (LGBT) are becoming recurrent subjects in Nigerian scholarship, scholarly works on LGBT, sexual identity and Nigerian cinema remain scarce. Perhaps, this is because of indigenous Nigerian cultural processes. While Chimamanda Adichie, a Nigerian novelist cum socio-political activist, campaigns against marginalisation and subjugation of gays and lesbians and for their integration into the Nigerian cultural system, numerous African socio-cultural and political activists hold a view that is dialectical to Adichie’s. The position of the members of the anti-gay group was further strengthened with the institution of stringent laws against gay practice in Nigeria by the President Goodluck Jonathan led government in 2014. In recent times, the gay, bisexual, transgender and lesbian cultures have been a source of raw material for filmmakers. Some of the thematic preoccupations of films have bordered on questions such as: what does it mean to be gay? Why are gays marginalised? Are gays socially constructed? What is the future of the advocacy for gay and lesbian liberation in Nigeria? Although most Nigerian film narratives are destructive critiques of the gay culture, the purpose of this research is not to cast aspersion on the moral dimension of LGBT. Rather, I argue that films on LGBT create spaces and maps for a critical exploration of the gay question. While the paper investigates the politics of gay culture in Nigerian cinema, I also posit that gays and lesbians are socio-culturally rather than biologically constructed. This research adopts literary and content analysis methods to engage Moses Ebere’s Men in Love with reference to other home videos on the gay and lesbian motifs.