Usability heuristics for fast crime data anonymization in resource-constrained contexts

This thesis considers the case of mobile crime-reporting systems that have emerged as an effective and efficient data collection method in low and middle-income countries. Analyzing the data, can be helpful in addressing crime. Since law enforcement agencies in resource-constrained context typically do not have the expertise to handle these tasks, a cost-effective strategy is to outsource the data analytics tasks to third-party service providers. However, because of the sensitivity of the data, it is expedient to consider the issue of privacy. More specifically, this thesis considers the issue of finding low-intensive computational solutions to protecting the data even from an "honest-but-curious" service provider, while at the same time generating datasets that can be queried efficiently and reliably. This thesis offers a three-pronged solution approach. Firstly, the creation of a mobile application to facilitate crime reporting in a usable, secure and privacy-preserving manner. The second step proposes a streaming data anonymization algorithm, which analyses reported data based on occurrence rate rather than at a preset time on a static repository. Finally, in the third step the concept of using privacy preferences in creating anonymized datasets was considered. By taking into account user preferences the efficiency of the anonymization process is improved upon, which is beneficial in enabling fast data anonymization. Results from the prototype implementation and usability tests indicate that having a usable and covet crime-reporting application encourages users to declare crime occurrences. Anonymizing streaming data contributes to faster crime resolution times, and user privacy preferences are helpful in relaxing privacy constraints, which makes for more usable data from the querying perspective. This research presents considerable evidence that the concept of a three-pronged solution to addressing the issue of anonymity during crime reporting in a resource-constrained environment is promising. This solution can further assist the law enforcement agencies to partner with third party in deriving useful crime pattern knowledge without infringing on users' privacy. In the future, this research can be extended to more than one low-income or middle-income countries

Are Social Media Design Practices Marginalising Other Cultures?

We argue that the current practice of social media design might have inadvertently ‘othered’ African cultures in place of stereotypical traditions and values that inform the practice of innovation globally. It is our understanding that sustainable and sensitive approaches that integrate with the aspirations and lived conditions of diverse marginalised communities ought to form the basis for the approaches used in understanding and designing social media platforms. We also emphasise the need to critically and sensibility analyse the sociocultural implication of using mobile technologies and social platforms to the process of harnessing the social practices of the communities that they get adopted and used. It is our position that examining the multitude of Nigerian cultures and values might bring about a better understanding of the societal implications of ‘Instagramming in Nigeria’ and the use of mobile technologies in intimate spaces among married couples and adults in relationships

Inclusive Positionality:How HCI Brought us Together

This position paper documents the reasoning behind working together as interdisciplinary researchers thinking and investigating technological issues with and for the Nigerian population. It presents ideas that point to how our differences in positionalities, specifically cultural identity, gender, social values, religious beliefs, power relations, language and intellectual locale might influence and impact the practice of examining the social implications of mobile technologies and social platforms non-use, use, misuse and over-use to the psychological and physiological wellbeing and digital safety of different actors. We hope that our research and co-design practice would point to how differences in researchers and research participants values are negotiated and absorbed while providing inclusive and sustainable design insights

A Deep Learning Model for Identical National Flag Recognition in Selected African Countries

The national flags are among the symbolic representations of a country. They make us understand the country of interest in a particular issue. Therefore, they are commonly used in both private and government organizations. It has been discovered in recent times that the younger generation mostly and idly and spend its time online; hence, knowing little about national flags. Additionally, some national flags (particularly in West Africa) are identical in nature. The likeness is in terms of layout, colours, shapes and objects on the national flags. Hence, there is a need to have a model for flag recognition. In this paper, national flag images of some West African countries were gathered to form a dataset. After this, the images were preprocessed by cropping out the irrelevant parts of the images. VGG-16 was used to extract necessary features and to develop the deep learning model. This contrasted with the existing handcrafted feature extraction and traditional machine learning techniques used on this subject matter. It was observed from this study that the proposed approach performed excellently well in predicting national flags; with an Accuracy of 98.20%, and an F1 score of 98.16%. In the future, it would be interesting to incorporate the national flag recognition into Human-Computer Interaction System. For instance, it could be used as flag recognition in some mobile and web applications for individuals with colour blindness. This research work presents a robust model because of nature of the dataset used in this work compared to previous works

Impact analysis of COVID-19 on Nigerian workers’ productivity using multiple correspondence analysis

As the COVID-19 pandemic became a global health concern, many business activities have had to adjust to the protocols required to keep people safe, thereby altering the work structures of many professionals. With data gathered from 466 respondents in Nigeria, of which approximately 70% are from the South-West, this study shows how the factors associated with the health crisis have affected work productivity during this period. The snowball survey research design techniques with the two-way interaction model were employed. Multiple Correspondence Analysis was used to analyse and understand multiple and pairwise qualitative factors that influence productivity. The first part of the analysis identified boredom, remuneration, internet availability, fear of COVID-19 and depressing news of COVID-19 as the factors that had significant impacts on workers’ productivity. The second part of the analysis shows how the categories of the five significant factors were either associated or not with productivity. An analysis of each of these factors showed that fear of the disease was associated with slight productivity but access to internet facilities and remuneration were strongly associated with improved work productivity, while boredom and depressing news about COVID-19 were associated with non-productivity during this period. Further evidence also showed that training and new skills acquisition might improve workers’ productivity much more. We, therefore, recommend dynamic skills acquisition, training, and investment in tools and services that will enhance flexibility with the changing work structure that comes because of global crises

What if?.. : Fabulating African HCI Futures within the Veil of HCI

This workshop aims to bring together researchers and practitioners (African and otherwise) engaging with diverse communities and industries to discuss the geopolitics of knowledge production in computing research and design. Our proposal builds on existing works that have explored the modalities of the knowledge economy - particularly as knowledge is created, legitimized, and circulated as objective truth via the exercise of power. For example, engaging indigenous communities in conferences and knowledge fairs have sought to challenge disciplinary silos and boundaries imposed on non-Western academics and practitioners. This workshop, as a platform for interaction and discussion with no “sage on the stage”, is the first of its kind within AfriCHI and it seeks to explore and promote more subtle discussions about HCI knowledge production and dissemination practices

Poultry fecal imagery dataset for health status prediction: A case of South-West Nigeria

Feces is one quick way to determine the health status of the birds and farmers rely on years of experience as well as professionals to identify and diagnose poultry diseases. Most often, farmers lose their flocks as a result of delayed diagnosis or a lack of trustworthy experts. Prevalent diseases affecting poultry birds may be quickly noticed from image of poultry bird's droppings using artificial intelligence based on computer vision and image analysis. This paper provides description of a dataset of both healthy and unhealthy poultry fecal imagery captured from selected poultry farms in south-west of Nigeria using smartphone camera. The dataset was collected at different times of the day to account for variability in light intensity and can be applied in machine learning models development for abnormality detection in poultry farms. The dataset collected is 19,155 images; however, after preprocessing which encompasses cleaning, segmentation and removal of duplicates, the data strength is 14,618 labeled images. Each image is 100 by 100 pixels size in jpeg format. Additionally, computer vision applications like picture segmentation, object detection, and classification can be supported by the dataset. This dataset's creation is intended to aid in the creation of comprehensive tools that will aid farmers and agricultural extension agents in managing poultry farms in an effort to minimize loss and, as a result, optimize profit as well as the sustainability of protein sources

Enhancing poultry health management through machine learning-based analysis of vocalization signals dataset

Population expansion and rising consumer demand for nutrient-dense meals have both contributed to an increase in the consumption of animal protein worldwide. A significant portion of the meat and eggs used for human consumption come from the poultry industry. Early diagnosis and warning of infectious illnesses in poultry are crucial for enhancing animal welfare and minimizing losses in the breeding and production systems for poultry. On the other hand, insufficient techniques for early diagnosis as well as infectious disease control in poultry farms occasionally fail to stop declining productivity and even widespread death.Individual physiological, physical, and behavioral symptoms in poultry, such as fever-induced increases in body temperature, abnormal vocalization due to respiratory conditions, and abnormal behavior due to pathogenic infections, frequently represent the health status of the animal. When birds have respiratory problems, they make strange noises like coughing and snoring. The work is geared towards compiling a dataset of chickens that were both healthy and unhealthy.100 day-old poultry birds were purchased and split into two groups at the experimental site, the poultry research farm at Bowen University. For respiratory illnesses, the first group received treatment, whereas the second group did not. After that, the birds were separated and caged in a monitored environment. To eliminate extraneous sounds and background noise that might affect the analysis, microphones were set a reasonable distance away from the birds. The data was gathered using 24-bit samples at 96 kHz. For 65 days, three times per day (morning, afternoon, and night) of audio data were continually collected. Food and water are constantly provided to the birds during this time. During this time, the birds have constant access to food and water. After 30 days, the untreated group started to sound sick with respiratory issues. This information was also noted as being unhealthy. Chickens' audio signals were recorded, saved in MA4, and afterwards converted to WAV format.This dataset's creation is intended to aid in the design of smart technologies capable of early detection and monitoring of the status of birds in poultry farms in a continuous, noninvasive, and automated way