Image making: representations of women in the art and career of Safeya Binzagr from 1968 to 2000

This thesis examines a selection of work by the Saudi female artist Safeya Binzagr (b.1940) from the years 1968 to 2000. It is argued that in order to claim agency for Saudi women and fight negative stereotypes Binzagr focused in her work on highlighting their authoritative traditional roles in the pre-oil society. Binzagr changed their status in the cultural discourse by producing images that compensate for the lack of visual representations of Saudi women, and also she perpetuated the influence of these images by placing them in a museum that functions as an education centre. The thesis examines how space segregation and the conservative nature of Saudi society neither limited the artist’s sense of control, nor forced her to overtly conflict with its norms. The first and second chapters highlight the cultural significance of Saudi society during the period in question and how it shaped Binzagr’s work and career plans. The third chapter analyses Binzagr’s representations of domestic life in old Jeddah, and how in her work she gave women an authoritative position over men. The fourth and fifth chapters examine the socio-religious boundaries of image making and explore how Binzagr’s style and subject matter helped her breach this prohibition. Moreover, they demonstrate how Binzagr’s sense of authority over her cultural heritage drove her to intervene and amend images of Saudi women in Orientalist photography. The sixth chapter highlights the artist’s relationship to the ‘home’. It examines how family, ethnicity and class were used strategically to expand her audience group leading her to establish the first and only art museum in Saudi Arabia. Finally, the thesis concludes with a re-ordering of the crucial stages that shaped her career and style, and suggests that as an important part of , Saudi heritage religious based debates for Binzagr were an influential tool for negotiation. Volume 2 - Appendices is EMBARGOED PERMANENTL

An Adaptive Epidemiology-Based Approach to Swarm Foraging with Dynamic Deadlines

Swarm robotics is an emerging field that can offer efficient solutions to real-world problems with minimal cost. Despite recent developments in the field, however, it is still not sufficiently mature, and challenges clearly remain. The dynamic deadline problem is neglected in the literature, and thus, time-sensitive foraging tasks are still an open research problem. This paper proposes a novel approach—ED_Foraging—that allows simple robots with limited sensing and communication abilities to perform complex foraging tasks that are dynamic and time constrained. A new mathematical model is developed in this paper to utilize epidemiological modeling and predict the dynamics of resource deadlines. Moreover, an improved dynamic task allocation (DTA) method is proposed to assign robots to the most critical region, where a deadline is represented by a state and time. The main goal is to reduce the number of expired resources and collect them as quickly as possible by giving priority to those that are more likely to expire if not collected. The deadlines are unknown and change dynamically. Thus, the robots continuously collect local information throughout their journeys and allocate themselves dynamically to the predicted hotspots. In the experiments, the proposed approach is adapted to four DTA methods and tested with different setups using simulated foot-bot robots. The flexibility, scalability, and robustness of this approach are measured in terms of the foraging and expiration rates. The empirical results support the hypothesis that epidemiological modeling can be utilized to handle foraging tasks that are constrained by dynamic deadlines. It is also confirmed that the proposed DTA method improves the results, which were found to be flexible, scalable, and robust to changes in the number of robots and the map size

A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions

A number of AI-generated tools are used today to clone human voices, leading to a new technology known as Audio Deepfakes (ADs). Despite being introduced to enhance human lives as audiobooks, ADs have been used to disrupt public safety. ADs have thus recently come to the attention of researchers, with Machine Learning (ML) and Deep Learning (DL) methods being developed to detect them. In this article, a review of existing AD detection methods was conducted, along with a comparative description of the available faked audio datasets. The article introduces types of AD attacks and then outlines and analyzes the detection methods and datasets for imitation- and synthetic-based Deepfakes. To the best of the authors’ knowledge, this is the first review targeting imitated and synthetically generated audio detection methods. The similarities and differences of AD detection methods are summarized by providing a quantitative comparison that finds that the method type affects the performance more than the audio features themselves, in which a substantial tradeoff between the accuracy and scalability exists. Moreover, at the end of this article, the potential research directions and challenges of Deepfake detection methods are discussed to discover that, even though AD detection is an active area of research, further research is still needed to address the existing gaps. This article can be a starting point for researchers to understand the current state of the AD literature and investigate more robust detection models that can detect fakeness even if the target audio contains accented voices or real-world noises

Mining Pharmacy Database Using Evolutionary Genetic Algorithm

Medication management is an important process in pharmacy field. Prescribing errors occur upstream in the process, and their effects can be perpetuated in subsequent steps. Prescription errors are an important issue for which conflicts with another prescribed medicine could cause severe harm for a patient. In addition, due to the shortage of pharmacists and to contain the cost of healthcare delivery, time is also an important issue. Former knowledge of prescriptions can reduce the errors, and discovery of such knowledge requires data mining techniques, such as Sequential Pattern. Moreover, Evolutionary Algorithms, such as Genetic Algorithm (GA), can find good rules in short time, thus it can be used to discover the Sequential Patterns in Pharmacy Database. In this paper GA is used to assess patient prescriptions based on former knowledge of series of prescriptions in order to extract sequenced patterns and predict unusual activities to reduce errors in timely manner

Dynamic task allocation in an uncertain environment with heterogeneous multi-agents

Abstract Dynamic task allocation (DTA) is a key feature in collaborative robotics. It affects organizations’ profits and allows agents to perform more tasks when efficiently designed. Although some work has been done on DTA, allocating tasks dynamically in an uncertain environment between heterogeneous multi-agents has rarely been investigated. The solutions proposed so far have inefficiently managed uncertainty, and none of them has utilized the semantics of heterogeneous agents’ capabilities. Studies measuring the performance of these techniques on real robots are also scarce. Therefore, this paper proposes an online DTA method, which introduces new functionalities that can be applied in a real environment. In particular, an uncertain incremental cost function is developed with a distributed semantic negotiation strategy that reflects heterogeneous capabilities without needing to communicate them. The proposed method is tested in a dynamic environment and experiments on heterogeneous real/virtual robots are conducted with different numbers of agents. Different statistical and visualization tools are used to analyze the results, including bar graphs for the waiting time metrics, histograms for the waiting time frequency, scatter plots for the result distribution and variance, and critical difference diagrams for ANOVA–Tukey results. The results indicate that the proposed DTA balances allocation quality and reliability, allowing the agents to serve targets equally without neglecting certain targets at the expense of the total performance. Evidently, updating the cost incrementally allows agents to update their allocation and choose better routes to finish the task earlier. Understanding the capability also gives priority to the capable agents that complete the task faster

Group Profiling in E-Service Portals

Abstract- Nowadays Service activities are social and involve different types of individuals that should be considered. Thus, group profiling can be used to connect similar customers together and recommend providers based on group evaluation. Accordingly, this paper proposes a new approach that recommends providers, based on group profiling and ranking, to be used in e-service portals. This approach focuses on group profiling and cluster users based on Ant Colony Clustering (ACC) method. In addition, the approach has been tested and three measures have been recorded including speed, aggregation precision, and result accuracy. The result of such test has shown that this approach is promising and has a high quality result