Collective interaction filtering with graph-based descriptors for crowd behaviour analysis

Crowd behaviour analysis plays an important role in high security interests in public areas such as railway stations, shopping centres, and airports, where large populations gather. Crowd behaviour analysis framework can be divided into low-level, mid-level and high-level. This research is focused on problems of mid-level and high-level. The crowded scenes vary in various densities, structures and occlusion. It brings enormous challenges in effectively dividing detection feature points into cluster to develop dynamic group detector and grouping consistency between frames at mid-level. Besides that, it also poses challenges in identifying generic descriptors to describe motion dynamics caused by pedestrians walk in different directions with extremely diverse behaviours at high-level. Therefore, crowd behaviour analysis framework with enhanced mid and high levels approaches is used in this research to recognise the common properties across different crowded scenes. The recognised common properties are then used to identify generic descriptors from group-level for crowd behaviour classification and crowd video retrieval. At the low-level, motion feature extraction is performed to extract trajectories from each of the video frames. Kanade-Lucas-Tomasi feature point tracker is used to detect and track moving humans, and then tracklets are grouped to form trajectories. At the mid-level, a Collective Interaction Filtering is presented to identify groups by clustering trajectories. It is suitable for group detection in low, medium, and high crowds. At the high-level, the result of Collective Interaction Filtering is used in group motion pattern mining to predict collectiveness, uniformity, stability, and conflict generic descriptors. The generic descriptors identified are represented by graph-based descriptors. Graph-based descriptors are applied to crowd behaviour analysis and crowd video retrieval. All experiments are carried out using CUHK Crowd dataset. The group detection and crowd behaviour analysis ground truth results were provided by related work. The group detection experiment is implemented using the clustering algorithm. Normalized Mutual Information and Rand Index are used to measure the performance of Collective Interaction Filtering. The crowd behaviour analysis experiment is implemented by using non-linear Structural Support Vector Machine with RBF-kernel classifier. Leave-one-out is used to measure the performance of the proposed graph-based descriptors to describe crowd behaviour. The proposed crowd video retrieval approach based on generic descriptors experiment is implemented by using Euclidean distance and Chi-Square distance to measure the similarity matching generic descriptors between the query video and the retrieval set of videos. The crowd video retrieval performance is measured by the average precision in the top k retrieved samples. Experimental results show that the crowd behaviour analysis framework achieves the state-of-the-art performance on the CUHK Crowd dataset. The Collective Interaction Filtering outperforms the related work by achieving 0.55 for Normalized Mutual Information and 0.83 for Rand Index. The average accuracy of the proposed graph-based descriptors for crowd behaviour analysis is 80% compared to the previous works. The proposed crowd video retrieval approach based on graphbased descriptors obtained 49% in average top 10 precision. The performance improvement reveals the effectiveness of the graph-based descriptors for crowd video retrieval in different crowded scenes

A new clustering approach for group detection in scene-independent dense crowds

Despite significant progress in crowd behaviour analysis over the past few years, most of today's state of the art algorithms focus on analysing individual behaviour in a specific-scene. Recently, the widespread availability of cameras and a growing need for public safety have shifted the attention of researchers in video surveillance from individual behavior analysis to group and crowd behavior analysis. However, dangerous and illegal behaviours are mostly occurred from groups of people. Group detection is the main process to separate people in crowded scene into different group based on their interactions. Results of group detection can further to apply in analyze group and crowd behaviour. This paper present a study of the group detection and propose a novel approach for clustering group of people in different crowded scenes based on trajectories. For the clustering of group of people we propose novel formula to compute the weights based on the distance, the occurrence, and the speed correlations of two people in a tracklet cluster to infer the people relationship in a tracklet clusters with Expectation Maximization (EM) in order to overcome occlusion in crowded scenes

Transcriptomic and Genomic Approaches for Unravelling <em>Candida albicans</em> Biofilm Formation and Drug Resistance—An Update

Candida albicans is an opportunistic fungal pathogen, which causes a plethora of superficial, as well as invasive, infections in humans. The ability of this fungus in switching from commensalism to active infection is attributed to its many virulence traits. Biofilm formation is a key process, which allows the fungus to adhere to and proliferate on medically implanted devices as well as host tissue and cause serious life-threatening infections. Biofilms are complex communities of filamentous and yeast cells surrounded by an extracellular matrix that confers an enhanced degree of resistance to antifungal drugs. Moreover, the extensive plasticity of the C. albicans genome has given this versatile fungus the added advantage of microevolution and adaptation to thrive within the unique environmental niches within the host. To combat these challenges in dealing with C. albicans infections, it is imperative that we target specifically the molecular pathways involved in biofilm formation as well as drug resistance. With the advent of the -omics era and whole genome sequencing platforms, novel pathways and genes involved in the pathogenesis of the fungus have been unraveled. Researchers have used a myriad of strategies including transcriptome analysis for C. albicans cells grown in different environments, whole genome sequencing of different strains, functional genomics approaches to identify critical regulatory genes, as well as comparative genomics analysis between C. albicans and its closely related, much less virulent relative, C. dubliniensis, in the quest to increase our understanding of the mechanisms underlying the success of C. albicans as a major fungal pathogen. This review attempts to summarize the most recent advancements in the field of biofilm and antifungal resistance research and offers suggestions for future directions in therapeutics development

Developing computational thinking competencies through constructivist argumentation learning: a problem-solving perspective

Argumentation is a scientific literacy practice focused on developing scientific thinking skills associated with problem-solving. As computing has become an integral part of our world, computational thinking skills are requisite for successful problem-solving. The significant effect of computational thinking applications on the efficacy of scientific literacy practices is increasingly acknowledged. In this article, we propose a framework that conceptualizes the constructivist argumentation as a context for problem-solving by applying five computational thinking dimensions, viz. algorithmic design, decomposition, abstraction, evaluation, and generalization. The framework emphasizes two aspects, students’ problem-solving capability and quality of argumentation. Drawing from the literature on scientific argumentation and problem-solving, we argue that the application of computational thinking dimensions in science learning is currently overlooked in the instructional environment. To nurture higher order thinking skills and to engage effective problem-solvers, our framework incorporates four Computational Thinking-Argumentation design principles to support instructional innovation in the teaching and learning of science at the secondary school level, viz. 1) developing problem-solving competencies and building capability in solving uncertainties throughout scientific inquiry; 2) developing creative thinking and cooperativity through negotiation and evaluation; 3) developing algorithmic thinking in talking and writing; 4) developing critical thinking in the processes of abstraction and generalization

International Collaboration on Palliative Care Development Between ASCO and the Land of Hornbills

PURPOSEPalliative care in Sarawak is mainly provided by health care professionals with limited formal training in palliative care. Therefore, in 2020, collaborative work between Sarawak General Hospital, University Malaysia Sarawak, and ASCO began. This study reports on the outcome of this collaboration.METHODSThe collaboration was initiated with the first ASCO Palliative Care e-course, Train the Trainer program, International Development and Education Award—Palliative Care and translation of ASCO Palliative Care Interdisciplinary Curriculum resources.RESULTSThis collaboration has resulted in the change of practice of palliative care among the oncology team of Sarawak General Hospital.CONCLUSIONIt encourages more timely palliative care referrals to ensure that patients with complex physical, psychosocial, and spiritual needs have the necessary input and support from the palliative care team throughout the course of patients’ illnesses