User recommendation algorithm in social tagging system based on hybrid user trust

With the rapid growth of web 2.0 technologies, tagging become much more important today to facilitate personal organization and also provide a possibility for users to search information or discover new things with Collaborative Tagging Systems. However, the simplistic and user-centered design of this kind of systems cause the task of finding personally interesting users is becoming quite out of reach for the common user. Collaborative Filtering (CF) seems to be the most popular technique in recommender systems to deal with information overload issue but CF suffers from accuracy limitation. This is because CF always been attack by malicious users that will make it suffers in finding the truly interesting users. With this problem in mind, this study proposes a hybrid User Trust method to enhance CF in order to increase accuracy of user recommendation in social tagging system. This method is a combination of developing trust network based on user interest similarity and trust network from social network analysis. The user interest similarity is derived from personalized user tagging information. The hybrid User Trust method is able to find the most trusted users and selected as neighbours to generate recommendations. Experimental results show that the hybrid method outperforms the traditional CF algorithm. In addition, it indicated that the hybrid method give more accurate recommendation than the existing CF based on user trust

Crowd behavior classification based on generic descriptors

Crowd behavior analysis plays an important role in high security interests in public areas such as railway stations, shopping centres, and airports, where large populations gather. The crowded scenes vary in various densities, structures and occlusion. It brings enormous challenges in identifying generic descriptors to describe motion dynamics caused by pedestrians walk in different directions with extremely diverse behaviors. Therefore, this research is proposal an approach for crowd behavior analysis to recognize the common properties across different crowded scenes. The recognized common properties are then used to identify generic descriptors from group-level for crowd behavior classification

A crowd video retrieval framework using generic descriptors

In the era of data mining and analytics, retrieval of crowd video with desired motion pattern segmentation plays a significant role in surveillance video management. The retrieval of crowd video with desired motion pattern segmentation poses challenges in finding generic descriptors to describe crowd patterns and similarity matching. This paper presents a novel crowd video retrieval framework using generic descriptors to overcome the above challenges. The anticipated structure comprises of four core components, namely motion feature extraction, group detection, learning generic descriptors, and crowd video retrieval. Results obtained indicate that the proposed framework can improve performance of crowd video retrieval compared with the existing crowd motions on CUHK Crowd Dataset

Collective interaction filtering approach for detection of group in diverse crowded scenes

Crowd behavior analysis research has revealed a central role in helping people to find safety hazards or crime optimistic forecast. Thus, it is significant in the future video surveillance systems. Recently, the growing demand for safety monitoring has changed the awareness of video surveillance studies from analysis of individuals behavior to group behavior. Group detection is the process before crowd behavior analysis, which separates scene of individuals in a crowd into respective groups by understanding their complex relations. Most existing studies on group detection are scene-specific. Crowds with various densities, structures, and occlusion of each other are the challenges for group detection in diverse crowded scenes. Therefore, we propose a group detection approach called Collective Interaction Filtering to discover people motion interaction from trajectories. This approach is able to deduce people interaction with the Expectation-Maximization algorithm. The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other. It also tackles grouping consistency between frames. Experiments on the CUHK Crowd Dataset demonstrate that approach used in this study achieves better than previous methods which leads to latest results

User recommendation algorithm in social tagging system based on user trust method

Collaborative Tagging Systems such as Flickr, del.icio.us, and BibSonomy are examples of Web 2.0 applications that have recently gained widespread popularity, where users label digital resources by means of personalized tags. The simplistic and user-centered design of those systems have encouraged many Web users to annotate their data using tags to provide easy search and retrieval of non-textual Web sources such as photos or videos, hence resulting in huge amount of data and metadata becoming available over the Web. This causes the task of searching to be out of reach especially among the common Internet users. This is where recommendation systems or tools come in handy. A lot of methods can be used for the purpose of recommendation. Collaborative filtering is the most popular technique among recommendation system that makes use only past Muser activities such as transaction history or user satisfaction expressed in ratings. Collaborative filtering has been a substantial success; however they do not rely on the actual content of the items. To improve recommendation quality, metadata such as content information in items and tags have been typically used as additional knowledge. Nonetheless, this type of recommendation is not entirely reliable since the knowledge are sourced from people whom we do not know or trust. The accuracy of recommendation system will generally be improved through incorporation of user trust information into the systems due to the fact that acquaintances might share professional interest while social friends might share hobbies. Unfortunately, the level of existing recommendation accuracy to date is still at unsatisfactory level among the users. In effort to improve recommendation in terms of accuracy and coverage, we propose a hybrid method for user recommendation approach based on User Trust method to allow users to easily find other users with similar interest in social tagging system. This method is a combination of developing trust network based on user interest similarity and trust network from social network analysis. The user interest similarity is derived from personalized user tagging information. The User Trust method is able to find the similar users and selected them as neighbors to make automated recommendations. The proposed method is tested using the Del.icio.us dataset. The experiment results showed that the proposed User Trust method outperforms the user-based collaborative filtering in making recommendations with the Pearson Correlation Coefficient (PCC) (Resnick et al., 1994) around 49%, Tidal Trust (TT) (Golbeck, 2006) around 32%, UserRec (Zhou et al., 2010) around 39%, tag-based Similarity Trust (ST) (Bhuiyan et al., 2010) around 45%, as well as incorporation of social network information in collaborative filtering (PCC-SN) (Liu and Lee, 2010) around 29%

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

Mechanical properties of rCB-pigment masterbatch in rLDPE: The effect of processing aids and water absorption test

Homogenization of pigment is the key to coloring a plastic product evenly. In this article, the tensile properties of recovered carbon black merge with low molecular weight lubricants and other compounding ingredients in the form of pigment masterbatch (PM) added in a recycled low-density polyethylene (rLDPE) resin were evaluated. The prepared masterbatch with the varying amount and types of processing aids (A and B) was first compounded using the heated two-roll mill. Subsequently, the manually mixed masterbatch in rLDPE was put through an injection molding machine for the shaping process to produce an rLDPE pigment masterbatch composite (PMC). The tensile test was performed on the samples to evaluate the mechanical properties of the PMC. Meanwhile, the melt flow index test was executed to justify the composite flow characteristics. Fourier-transform infrared spectroscopy analysis and scanning electron microscopy were also carried out to analyze the PM and PMC chemical properties and their constructed surface morphology. Besides, X-ray diffraction analysis was performed to determine the changes in degree of crystallinity before and after the water absorption test. The addition of PM in rLDPE has slightly increased the rLDPE matrix tensile properties. While, the usage of more processing aid B in the PMC has turned out to secure better tensile properties compared to the addition of higher amount of processing aid A in the PMC. Interestingly, the tensile properties of all composites after the water absorption test were enhanced, suggesting that a stronger bond was formed during the immersion period