575 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    3D Design Review Systems in Immersive Environments

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    Design reviews play a crucial role in the development process, ensuring the quality and effectiveness of designs in various industries. However, traditional design review methods face challenges in effectively understanding and communicating complex 3D models. Immersive technologies, particularly Head-Mounted Displays (HMDs), offer new opportunities to enhance the design review process. In this thesis, we investigate using immersive environments, specifically HMDs, for 3D design reviews. We begin with a systematic literature review to understand the current state of employing HMDs in industry for design reviews. As part of this review, we utilize a detailed taxonomy from the literature to categorize and analyze existing approaches. Additionally, we present four iterations of an immersive design review system developed during my industry experience. Two of these iterations are evaluated through case studies involving domain experts, including engineers, designers, and clients. A formal semi-structured focus group is conducted to gain further insights into traditional design review practices. The outcomes of these evaluations and the focus group discussions are thoroughly discussed. Based on the literature review and the focus group findings, we uncover a new challenge associated with using HMDs in immersive design reviews—asynchronous and remote collaboration. Unlike traditional design reviews, where participants view the same section on a shared screen, HMDs allow independent exploration of areas of interest, leading to a shift from synchronous to asynchronous communication. Consequently, important feedback may be missed as the lead designer disconnects from the users' perspectives. To address this challenge, we collaborate with a domain expert to develop a prototype that utilizes heatmap visualization to display 3D gaze data distribution. This prototype enables lead designers to quickly identify areas of review and missed regions. The study incorporates the Design Critique approach and provides valuable insights into different heatmap visualization variants (top view projection, object-based, and volume-based). Furthermore, a list of well-defined requirements is outlined for future spatio-temporal visualization applications aimed at integrating into existing workflows. Overall, this thesis contributes to the understanding and improvement of immersive design review systems, particularly in the context of utilizing HMDs. It offers insights into the current state of employing HMDs for design reviews, utilizes a taxonomy from the literature to analyze existing approaches, highlights challenges associated with asynchronous collaboration, and proposes a prototype solution with heatmap visualization to address the identified challenge

    Efficient Security Algorithm for Provisioning Constrained Internet of Things (IoT) Devices

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    Addressing the security concerns of constrained Internet of Things (IoT) devices, such as client- side encryption and secure provisioning remains a work in progress. IoT devices characterized by low power and processing capabilities do not exactly fit into the provisions of existing security schemes, as classical security algorithms are built on complex cryptographic functions that are too complex for constrained IoT devices. Consequently, the option for constrained IoT devices lies in either developing new security schemes or modifying existing ones as lightweight. This work presents an improved version of the Advanced Encryption Standard (AES) known as the Efficient Security Algorithm for Power-constrained IoT devices, which addressed some of the security concerns of constrained Internet of Things (IoT) devices, such as client-side encryption and secure provisioning. With cloud computing being the key enabler for the massive provisioning of IoT devices, encryption of data generated by IoT devices before onward transmission to cloud platforms of choice is being advocated via client-side encryption. However, coping with trade-offs remain a notable challenge with Lightweight algorithms, making the innovation of cheaper secu- rity schemes without compromise to security a high desirable in the secure provisioning of IoT devices. A cryptanalytic overview of the consequence of complexity reduction with mathematical justification, while using a Secure Element (ATECC608A) as a trade-off is given. The extent of constraint of a typical IoT device is investigated by comparing the Laptop/SAMG55 implemen- tations of the Efficient algorithm for constrained IoT devices. An analysis of the implementation and comparison of the Algorithm to lightweight algorithms is given. Based on experimentation results, resource constrain impacts a 657% increase in the encryption completion time on the IoT device in comparison to the laptop implementation; of the Efficient algorithm for Constrained IoT devices, which is 0.9 times cheaper than CLEFIA and 35% cheaper than the AES in terms of the encryption completion times, compared to current results in literature at 26%, and with a 93% of avalanche effect rate, well above a recommended 50% in literature. The algorithm is utilised for client-side encryption to provision the device onto AWS IoT core

    Ciguatoxins

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    Ciguatoxins (CTXs), which are responsible for Ciguatera fish poisoning (CFP), are liposoluble toxins produced by microalgae of the genera Gambierdiscus and Fukuyoa. This book presents 18 scientific papers that offer new information and scientific evidence on: (i) CTX occurrence in aquatic environments, with an emphasis on edible aquatic organisms; (ii) analysis methods for the determination of CTXs; (iii) advances in research on CTX-producing organisms; (iv) environmental factors involved in the presence of CTXs; and (v) the assessment of public health risks related to the presence of CTXs, as well as risk management and mitigation strategies

    Dynamic life of a microtubule: From birth, growth and stabilization to damage and destruction

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    Microtubules are one of the major types of cytoskeletal filaments in cells. They are very dynamic polymers composed of αβ-tubulin dimers arranged longitudinally in head-to-tail fashion as well as laterally to assemble 13-protofilament hollow cylindrical tubes. The incorporation of GTP-bound αβ-tubulin dimers generates a fast growing plus end exposing β-tubulin and a slow growing minus end exposing α-tubulin. In cells, microtubules are assembled de novo from a template, called γ-TuRC, which interacts with α-tubulin. Microtubules can either remain capped by γ-TuRC and anchored to the microtubule-organizing centers (MTOCs) or be released if they are cut by microtubule severing enzymes like katanin. The release of microtubules from MTOC generates free minus ends, which are then stabilized by minus-end binding proteins called CAMSAPs. However, the plus ends remain very dynamic and undergo transitions from growth to shrinkage, termed “catastrophes”, and the opposite transitions termed “rescues”. Numerous microtubule regulatory proteins act at the plus ends, minus ends and the microtubule shafts connecting the two ends to control the organization and density of cellular microtubule networks. In this thesis, we focused on each of these aspects and explored the dynamic life of microtubules by reconstituting these processes in vitro using purified proteins. We first focused on the birth and growth of microtubules. We reconstituted microtubule nucleation using purified γ-TuRC and microtubule regulatory proteins and showed that CDK5RAP2, CLASP2 and chTOG promoted microtubule nucleation from γ-TuRC. We discovered that CAMSAPs can bind to γ-TuRC-capped microtubule minus ends and displace γ-TuRC from these ends, generating free and stable microtubule minus ends. Furthermore, we found out that CDK5RAP2, but not CLASP2 or chTOG, can inhibit CAMSAP binding and microtubule release. We propose that the destiny of a microtubule depends on the type of protein complex that activates its nucleation. We then described a mechanism for stabilization of microtubule lattice by TRIM46, a neuronal protein, which can bundle parallel microtubules and promote microtubule rescues within these bundles. We also revealed that Ankyrin-G, a scaffold protein, can recruit TRIM46-stabilized microtubule bundles to the axonal membrane to drive the assembly of the axon initial segment in neurons. We also uncovered a new role of CLASP2 as a microtubule repair factor participating in microtubule maintenance. We demonstrated that CLASP2, an anti-catastrophe factor, can promote complete repair of damaged microtubule lattices by inhibiting microtubule depolymerization and promoting tube closure at the damage sites, causing lattice renewal. Finally, we described a three-protein module involving katanin, CAMSAPs, and WDR47 that can regulate microtubule polymer mass and minus-end stability. We showed that katanin can cut and amplify CAMSAP2/3-stabilized microtubule minus ends. WDR47 can inhibit the binding of katanin to CAMSAP2/3-stabilized minus ends and protect them from severing. The presence of WDR47 shifts the balance from microtubule amplification to minus-end growth regulation. To conclude, we obtained mechanistic insights into the regulation of microtubule nucleation, minus-end dynamics, lattice stabilization and maintenance, microtubule number and the interplay between microtubule regulatory proteins. These insights will help to understand how microtubule arrays are organized in cells

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

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    Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
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