In the realm of cloud native environments, Ku-bernetes has emerged as the de facto orchestration system for containers, and the service mesh architecture, with its interconnected microservices, has become increasingly prominent. Efficient scheduling and resource allocation for these microservices play a pivotal role in achieving high performance and maintaining system reliability. In this paper, we introduce a novel approach for container scheduling within Kubernetes clusters, leveraging Graph Attention Networks (GATs) for representation learning. Our proposed method captures the intricate dependencies among containers and services by constructing a representation graph. The deep Q-learning algorithm is then employed to optimize scheduling decisions, focusing on container-to-node placements, CPU request-response allocation, and adherence to node affinity and anti-affinity rules. Our experiments demonstrate that our GATs-based method outperforms traditional scheduling strategies, leading to enhanced resource utilization, reduced service latency, and improved overall system throughput. The insights gleaned from this study pave the way for a new frontier in cloud native performance optimization and offer tangible benefits to industries adopting microservice-based architectures.

The presence of proper wrinkles is important while modeling realistic virtual garments. Unlike previously used full 3D information methods, our approach achieves detailed garment generation from a single image. First, we retrieve a garment image similar to the initial virtual garment based on content-based image retrieval (CBIR) method. Then, we preprocess the image with a combination of human body reshaping, image segmentation and shape recovery, to obtain the 3D wrinkle details. Finally, the garment height are synthesized into the virtual garment. For better suit the posture of the human body, excess garment energy are released to remove the unmatched wrinkles. We apply our method to various styles of virtual garments, and it enable virtual characters in general pose to be dressed in these garments and complete wrinkle generation. Compared with existing garment modeling methods, the experimental results show that the proposed method could quickly capture the realistic wrinkles of virtual garments with less manual operation and achieve more realistic wrinkles for virtual garments.

Participatory design is a technique which is being used by system designers to involve the end users and product owners throughout the design process. Even though utilizing this approach brings customers to the design process, implementing it requires a budget, a place, time, and other resources. This chapter demonstrates a model-based approach to facilitate the selection of interviews for each design phase such as listing elements for the interface, choosing location for components, making decision for the general look of the component, finally making the component interactable. Interface designers can use the model to choose different type of interview method for different design phases such as interface components, sketching, lo-fi prototyping and hi-fi prototyping, according to their resources. The research focus is on four different participatory design interview method, which are GUI-ii face-to-face, GUI-ii screen-sharing, GUI-ii Ozlab, and traditional face-to-face interview.

Graphical user interface interaction interview (GUI-ii), is a recently purposed method in which designers can remotely co-design and review GUI prototypes by eliminating the need for physical presence of co-designers. However, there are some concerns regarding the accuracy of such remote interview methods, as users do not have any physical interaction with the designers during their interview. In this work, for the first time, we compare GUI-ii methods with the traditional face-to-face interview processes to study their effectiveness in various design phases. The result shows that GUI-ii method is most effective when used in Ozlab.    

Online Medical Applications (OMA) has evolved dramatically in the last few years, and, consequently, the number of patients using it has also grown exponentially. Patients who are seeking non-emergency but immediate medical services or consultant may save time and money by approaching online doctors through OMAs instead of visiting them physically in healthcare centers. Additionally, medical doctors quantity contributing to OMAs growth, which can affect patients average waiting time and the queue size in healthcare centers. In this paper, We have developed a Discrete Event Simulation (DES) model to study the effects of using OMA on the patients healing time and doctors utilization by comparing it with the same process in healthcare centers. Additionaly, we compared patients average queue size, maximum number of patients in the queue, and total number of healed patients in our study. The results of this simulation showed that the healing process in OMA could serve the same number of patients in ~46% shorter time compared to healthcare centers with ~5.7% less doctors' utilization.