Human Interaction Recognition with Audio and Visual Cues

The automated recognition of human activities from video is a fundamental problem with applications in several areas, ranging from video surveillance, and robotics, to smart healthcare, and multimedia indexing and retrieval, just to mention a few. However, the pervasive diffusion of cameras capable of recording audio also makes available to those applications a complementary modality. Despite the sizable progress made in the area of modeling and recognizing group activities, and actions performed by people in isolation from video, the availability of audio cues has rarely being leveraged. This is even more so in the area of modeling and recognizing binary interactions between humans, where also the use of video has been limited.;This thesis introduces a modeling framework for binary human interactions based on audio and visual cues. The main idea is to describe an interaction with a spatio-temporal trajectory modeling the visual motion cues, and a temporal trajectory modeling the audio cues. This poses the problem of how to fuse temporal trajectories from multiple modalities for the purpose of recognition. We propose a solution whereby trajectories are modeled as the output of kernel state space models. Then, we developed kernel-based methods for the audio-visual fusion that act at the feature level, as well as at the kernel level, by exploiting multiple kernel learning techniques. The approaches have been extensively tested and evaluated with a dataset made of videos obtained from TV shows and Hollywood movies, containing five different interactions. The results show the promise of this approach by producing a significant improvement of the recognition rate when audio cues are exploited, clearly setting the state-of-the-art in this particular application

Learning Representations for Novelty and Anomaly Detection

The problem of novelty or anomaly detection refers to the ability to automatically identify data samples that differ from a notion of normality. Techniques that address this problem are necessary in many applications, like in medical diagnosis, autonomous driving, fraud detection, or cyber-attack detection, just to mention a few. The problem is inherently challenging because of the openness of the space of distributions that characterize novelty or outlier data points. This is often matched with the inability to adequately represent such distributions due to the lack of representative data. In this dissertation we address the challenge above by making several contributions. (a)We introduce an unsupervised framework for novelty detection, which is based on deep learning techniques, and which does not require labeled data representing the distribution of outliers. (b) The framework is general and based on first principles by detecting anomalies via computing their probabilities according to the distribution representing normality. (c) The framework can handle high-dimensional data such as images, by performing a non-linear dimensionality reduction of the input space into an isometric lower-dimensional space, leading to a computationally efficient method. (d) The framework is guarded from the potential inclusion of distributions of outliers into the distribution of normality by favoring that only inlier data can be well represented by the model. (e) The methods are evaluated extensively on multiple computer vision benchmark datasets, where it is shown that they compare favorably with the state of the art

Recommender Systems in Light of Big Data

The growth in the usage of the web, especially e-commerce website, has led to the development of recommender system (RS) which aims in personalizing the web content for each user and reducing the cognitive load of information on the user. However, as the world enters Big Data era and lives through the contemporary data explosion, the main goal of a RS becomes to provide millions of high quality recommendations in few seconds for the increasing number of users and items. One of the successful techniques of RSs is collaborative filtering (CF) which makes recommendations for users based on what other like-mind users had preferred. Despite its success, CF is facing some challenges posed by Big Data, such as: scalability, sparsity and cold start. As a consequence, new approaches of CF that overcome the existing problems have been studied such as Singular value decomposition (SVD). This paper surveys the literature of RSs and reviews the current state of RSs with the main concerns surrounding them due to Big Data. Furthermore, it investigates thoroughly SVD, one of the promising approaches expected to perform well in tackling Big Data challenges, and provides an implementation to it using some of the successful Big Data tools (i.e. Apache Hadoop and Spark). This implementation is intended to validate the applicability of, existing contributions to the field of, SVD-based RSs as well as validated the effectiveness of Hadoop and spark in developing large-scale systems. The implementation has been evaluated empirically by measuring mean absolute error which gave comparable results with other experiments conducted, previously by other researchers, on a relatively smaller data set and non-distributed environment. This proved the scalability of SVD-based RS and its applicability to Big Data

DEVELOPMENT OF NEW AS-PCR BASED ANALYTICAL APPROACH FOR DETECTING THE SINGLE NUCLEOTIDE POLYMORPHISM OF AGTR.1 GENE

Objective: Angiotensin II is a potent vasoactive peptide that causes blood vessels to constrict, resulting in increased blood pressure. It acts through at least two types of receptors. The angiotensin II receptor type 1 gene (AGTR1) encodes the type 1 receptor (AT1). The AT1 receptor mediates the major cardiovascular effects of angiotensin II. A single nucleotide polymorphism (SNP) in the 3'-untranslated region of the AGTR1 gene (A1166C) has been linked in several studies with essential hypertension. A new analytical approach based on allele-specific polymerase chain reaction (AS-PCR) was developed in this study to detect this SNP.Methods: Allele-specific primers were designed by using appropriate software to allow the PCR amplification only if the nucleotide at the 3'-end of the primer complements the base of the wild-type or variant-type DNA sample. The primers were then tested for uniqueness using the Basic Local Alignment Search Tool search engine. The developed method was tested on 21 samples.Results: The developed method accurately detected the genotype of the SNP. Our results were validated using the reference method for detecting A1166C SNP, PCR-restriction fragment length polymorphism (PCR-RFLP). The use of AS-PCR technique reduced both time and cost of the A1166C AGTR1 genotyping. Moreover, the AS-PCR test is more suitable as it reduces the false results due to incompletely digested PCR products, which can be a problem with PCR-RFLP technique. Conclusion: The use of this method will enable researchers to carry out genetic polymorphism studies for the association of A1166C SNP in AGTR1 gene with essential hypertension and other heart diseases without the use of expensive instrumentation and reagents.Keywords: Essential hypertension, SNP, Angiotensin II type 1 receptor gene (AGTR1), Renin-angiotensin system, AS-PC

Computational analysis of radiative heat transfer due to rotating tube in parabolic trough solar collectors with Darcy Forchheimer porous medium

This attempt numerically investigates the heat transfer in parabolic trough solar collectors due to the rotating tube for the hybrid nanofluid flow over the Riga surface with Darcy Forchheimer’s porous medium under the effect of solar radiation. The influences of viscous dissipation and Joule heating are also considered. Equations governing the fluid flow are non-dimensionalized by implementing appropriate similarity variables. The resulting non-dimensionalized ordinary differential equations are solved using the shooting technique with Adam Bashforth and Adam Moulten’s fourth-order numerical approach. The numerical outcomes for various influential physical parameters regarding the fluid velocity, temperature, Nusselt number, and entropy generation are presented in graphical form. It is observed that the thermal profile escalates with the higher values of Reynold’s number, modified magnetic field parameter, and Prandtl number. Also, the Nusselt number diminishes with augmenting values of the Eckert number, modified magnetic field parameter, Forchheimer number, and Darcy number. The optimization of heat transfer in parabolic trough collectors is essential to improve the performance of solar collectors. The concentrated solar power technology is adequate for storing radiation energy in higher amounts.Author U.F.-G. appreciates the support of the Government of the Basque Country, Grant N. ELKARTEK 22/85 and ELKARTEK 21/10. The research is supported by Researchers Supporting Project number (RSP2023R158), King Saud University, Riyadh, Saudi Arabia

COMPLEX INTUITIONISTIC FUZZY DOMBI PRIORITIZED AGGREGATION OPERATORS AND THEIR APPLICATION FOR RESILIENT GREEN SUPPLIER SELECTION

One of the main problems faced by resilient supply chain management is how to solve the problem of supplier selection, which is a typical multi-attribute decision-making (MADM) problem. Given the complexity of the current decision-making environment, the primary influence of this paper is to propose the theory of Dombi operational laws based on complex intuitionistic fuzzy (CIF) information. Moreover, we examined the theory of CIF Dombi prioritized averaging (CIFDPA) and CIF weighted Dombi prioritized averaging (CIFWDPA), where these operators are the modified version of the prioritized aggregation operators and Dombi aggregation operators for fuzzy, intuitionistic fuzzy, complex fuzzy and complex intuitionistic fuzzy information. Some reliable properties for the above operators are also established. Furthermore, to state the art of the proposed operators, an application example in the presence of the invented operators is evaluated for managing resilient green supplier selection problems. Finally, through comparative analysis with mainstream technologies, we provide some mechanism explanations for the proposed method to show the supremacy and worth of the invented theory

HYBRID GENETIC AND PENGUIN SEARCH OPTIMIZATION ALGORITHM (GA-PSEOA) FOR EFFICIENT FLOW SHOP SCHEDULING SOLUTIONS

This paper presents a novel hybrid approach, fusing genetic algorithms (GA) and penguin search optimization (PSeOA), to address the flow shop scheduling problem (FSSP). GA utilizes selection, crossover, and mutation inspired by natural selection, while PSeOA emulates penguin foraging behavior for efficient exploration. The approach integrates GA's genetic diversity and solution space exploration with PSeOA's rapid convergence, further improved with FSSP-specific modifications. Extensive experiments validate its efficacy, outperforming pure GA, PSeOA, and other metaheuristics

The Effect of Contrast Agents on Dose Calculations of Volumetric Modulated Arc Radiotherapy Plans for Critical Structures

Radiotherapy dose calculation requires accurate Computed Tomography (CT) imaging while tissue delineation may necessitate the use of contrast agents (CA). Acquiring these two sets is a common practice in radiotherapy. This study aims to evaluate the effect of CA on the dose calculations. Two hundred and twenty-six volumetric modulated arc therapy (VMAT) patients that had planning CT with contrast (CCT) and non-contrast CT (NCCT) of different cancer sites (e.g., brain, head, and neck (H&N), chest, abdomen, and pelvis) were evaluated. Treatment plans were recalculated using CCT, then compared to NCCT. The variation in Hounsfield units (HU) and dose distributions for critical structures and target volumes were analyzed using mean HU, mean and maximum relative dose values, D2%, D98%, and 3D gamma analysis. HU variations were statistically significant for most structures. However, this was not clinically significant as the difference in mean HU values was within 30 HU for soft tissue and 50 HU for lungs. Variation in target volumes’ D2% and D98% were insignificant for all sites except brain and nasopharynx. Dose maximum differences were within 2% for the majority of critical structures and target volumes. 3D gamma analysis results revealed that majority of plans satisfied the 2% and 2 mm criteria. CCT may be acquired for VMAT radiotherapy planning purposes instead of NCCT, since there is no clinically significant difference in dose calculations based on either image set

The transition towards a sustainable energy system in Europe: What role can North Africa's solar resources play?

Securing energy supply and speeding up the transition towards a reliable, sustainable, low-carbon energy system are among the major current and future challenges facing Europe. Importing dispatchable solar electricity from North Africa is considered as a potential and attractive option. Nevertheless, as things currently stand, the European Commission focuses mainly on the exploitation of the existing wind power potential in the North Sea, largely ignoring the solar power potential in the Sahara region of North Africa. After discussing the major challenges and issues facing Europe to achieve the assigned ambitious objectives, the paper emphasises the importance of North Africa's solar resources in helping Europe to successfully address the challenge of decarbonising its electricity system, in particular with regards to the security of supply and sustainability. Within these two major challenges, the paper explores the issues of access, barriers and opportunities. The paper highlights why the EU’s energy and climate goals will not be achievable without adequate grid expansion and grid-scale energy storage facilities, as well as other innovative measures to manage demand and ensure a secure energy supply. In this respect, the paper shows how the import of dispatchable electricity from North Africa via specific HVDC links could play a key role in helping the EU achieve its energy targets in a cost effective way without recourse to significant investments in transmission infrastructure and storage facilities. The paper then attempts to identify and analyze the main barriers that continue to inhibit the export of solar electricity from North Africa to Europe. Finally, to make the project more attractive and achievable in the near future, the paper proposes a systematic approach for setting up energy import scenarios. A promising import scenario is presented where energy import via Italy is shown to be a more viable and effective solution than via Spain.Peer reviewe