Academic literature on the topic 'Reconstruction intelligente'
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Journal articles on the topic "Reconstruction intelligente"
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Epskamp-Dudink, Chantal, and Jan Martin Winter. "Benefits of scenario reconstruction in cold case investigations." Journal of Criminal Psychology 10, no. 2 (April 1, 2020): 65–78. http://dx.doi.org/10.1108/jcp-09-2019-0035.
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Purpose The purpose of this paper is to propose a methodological approach to increase the clearance rate of cold cases in The Netherlands and to contribute to the development of intelligence-driven criminal investigations in general police practice and within cold case investigations in particular. Design/methodology/approach This proposal is based on practical investigative experience and academic knowledge. Findings Reconstructing scenarios helps convert cold case information into intelligence, which is beneficial to law enforcement agencies in terms of time, resources and prioritising cold cases. Intelligence contributes to the formulation of more effective queries and to a more efficient adaptation of new and existing investigation methods, leading to a higher cold case clearance rate. Moreover, scenario reconstruction creates a link between intelligence and investigation, i.e. between science and practice, which adds to the further development of intelligence-driven investigations. Practical implications When carrying out scenario reconstructions, practical implications are expected, as intelligence products are currently not or barely used in practice and science is not yet embedded in the investigational practice. Originality/value To move from scenario reconstruction to intelligence-driven criminal investigation, the gap between science and the investigational practice needs to be bridged by persons familiar with both. This study hopes to provide the necessary and relevant impetus to this dialogue.
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Condorelli, Francesca, and Maurizio Perticarini. "Comparative Evaluation of NeRF Algorithms on Single Image Dataset for 3D Reconstruction." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 73–79. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-73-2024.
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Abstract. The reconstruction of three-dimensional scenes from a single image represents a significant challenge in computer vision, particularly in the context of cultural heritage digitisation, where datasets may be limited or of poor quality. This paper addresses this challenge by conducting a study of the latest and most advanced algorithms for single-image 3D reconstruction, with a focus on applications in cultural heritage conservation. Exploiting different single-image datasets, the research evaluates the strengths and limitations of various artificial intelligence-based algorithms, in particular Neural Radiance Fields (NeRF), in reconstructing detailed 3D models from limited visual data. The study includes experiments on scenarios such as inaccessible or non-existent heritage sites, where traditional photogrammetric methods fail. The results demonstrate the effectiveness of NeRF-based approaches in producing accurate, high-resolution reconstructions suitable for visualisation and metric analysis. The results contribute to advancing the understanding of NeRF-based approaches in handling single-image inputs and offer insights for real-world applications such as object location and immersive content generation.
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Wu, Yi-Chang, Pei-Shan Chiang, Yao-Cheng Liu, and Ru-Yi Huang. "Use of artificial intelligence in banknote reconstruction." IAES International Journal of Robotics and Automation (IJRA) 13, no. 4 (December 1, 2024): 410. http://dx.doi.org/10.11591/ijra.v13i4.pp410-422.
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Banknotes may be damaged during various events, such as floods, fires, insect infestations, and mechanical or manual shredding. Disaster victims might need to perform banknote reconstruction when applying for currency exchange, or investigative agencies might need to conduct such reconstruction during evidence collection. When the number of banknote fragments is small, they can be manually assembled; however, when this number is large, manual assembly becomes increasingly difficult and time-consuming. Therefore, an automated and effective method is required for banknote reconstruction. The process of banknote reconstruction can be considered similar to solving a large-scale jigsaw puzzle. This study employed an artificial intelligence (AI) system to reconstruct damaged banknotes. A robotic arm was used to replace manual separation and automated digital image processing techniques, and AI image registration technology, deep learning, and logical operations were utilized. A deep convolutional neural network was used to estimate the relative homography between images, and fragmented banknotes were mapped to a reference banknote for image transformation, thereby reconstructing the damaged banknotes. Additionally, a repetitive matching method was established to optimize the matching results to achieve the best possible mapping and enhance validation efficiency.
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Báskay, János, Dorottya Pénzes, Endre Kontsek, Adrián Pesti, András Kiss, Bruna Katherine Guimarães Carvalho, Miklós Szócska, et al. "Are Artificial Intelligence-Assisted Three-Dimensional Histological Reconstructions Reliable for the Assessment of Trabecular Microarchitecture?" Journal of Clinical Medicine 13, no. 4 (February 15, 2024): 1106. http://dx.doi.org/10.3390/jcm13041106.
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This study aimed to create a three-dimensional histological reconstruction through the AI-assisted classification of tissues and the alignment of serial sections. The secondary aim was to evaluate if the novel technique for histological reconstruction accurately replicated the trabecular microarchitecture of bone. This was performed by conducting micromorphometric measurements on the reconstruction and comparing the results obtained with those of microCT reconstructions. A bone biopsy sample was harvested upon re-entry following sinus floor augmentation. Following microCT scanning and histological processing, a modified version of the U-Net architecture was trained to categorize tissues on the sections. Detector-free local feature matching with transformers was used to create the histological reconstruction. The micromorphometric parameters were calculated using Bruker’s CTAn software (version 1.18.8.0, Bruker, Kontich, Belgium) for both histological and microCT datasets. Correlation coefficients calculated between the micromorphometric parameters measured on the microCT and histological reconstruction suggest a strong linear relationship between the two with ⍴-values of 0.777, 0.717, 0.705, 0.666, and 0.687 for BV/TV, BS/TV, Tb.Pf Tb.Th, and Tb.Sp, respectively. Bland–Altman and mountain plots suggest good agreement between BV/TV measurements on the two reconstruction methods. This novel method for three-dimensional histological reconstruction provides researchers with a tool that enables the assessment of accurate trabecular microarchitecture and histological information simultaneously.
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Okano, Teruo. "Thermo-Intelligent Surfaces for Cell Culture." Advances in Science and Technology 53 (October 2006): 70–73. http://dx.doi.org/10.4028/www.scientific.net/ast.53.70.
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In order to avoid several complications resulted from biodegradable scaffolds or single cell injection, we have developed “cell sheet engineering”. Our concept is novel tissue reconstruction not from single cells but from cell sheets. In order to prepare cell sheets, temperature-responsive culture dishes are utilized. Since temperature-responsive polymers are covalently grafted on the dishes, various types of cells adhere and proliferate on them at 37°C, but are spontaneously detached only by reducing temperature below 32°C without any need for proteolytic enzyme. All the confluent cells are noninvasively harvested as a single contiguous cell sheets with intact cell-cell junctions and deposited extracellular matrix. We have utilized these harvested cell sheets for various tissue reconstructions including ocular surfaces, periodontal ligament, cardiac patches as well as bladder.
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LI, WEI, NINGCHUAN SHEN, and JU WANG. "R-CALCULUS: A LOGICAL APPROACH FOR KNOWLEDGE BASE MAINTENANCE." International Journal on Artificial Intelligence Tools 04, no. 01n02 (June 1995): 177–200. http://dx.doi.org/10.1142/s0218213095000103.
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The concepts of maintenance sequences of a knowledge base and their limits are introduced. Some concepts used in maintenance of knowledge bases, such as new laws, user’s rejections, and reconstructions of a base are defined; the related theorems are proved. A maintenance procedure scheme is defined. The maintenance sequences generated by the procedure are convergent, and their limits are the set of true sentences of the model. Some computational aspects of reconstructions are studied; an R-calculus is given to deduce a reconstruction when a knowledge base meets a rejection. Especially, an R-calculus for logic programming is provided and implemented in Prolog. Finally, our research is compared with AGM’s theory of belief revision and other relevant work.
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De Francesco, Francesco, Nicola Zingaretti, Pier Camillo Parodi, and Michele Riccio. "The Evolution of Current Concept of the Reconstructive Ladder in Plastic Surgery: The Emerging Role of Translational Medicine." Cells 12, no. 21 (November 3, 2023): 2567. http://dx.doi.org/10.3390/cells12212567.
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Plastic surgeons have used the reconstructive ladder for many decades as a standard directory for complex trauma reconstruction with the goal of repairing body structures and restoring functionality. This consists of different surgical maneuvers, such as secondary intention and direct tissue closure, as well as more complex methods such as local tissue transfer and free flap. The reconstructive ladder represents widely known options achievable for tissue reconstruction and wound closure that puts at the bottom rung the simplest methods of reconstruction and strengthens the complexity by moving upward. Regenerative medicine and surgery constitute a quickly spreading area of translational research that can be employed by minimally invasive surgical strategies, with the aim of regenerating cells and tissues in vivo in order to reestablish normal function through the intrinsic potential of cells, in combination with biomaterials and appropriate biochemical stimuli. These translational procedures have the aim of creating an appropriate microenvironment capable of supporting the physiological cellular function to generate the desired cells or tissues and to generate parenchymal, stromal, and vascular components on demand, and above all to produce intelligent materials capable of determining the fate of cells. Smart technologies have been grown that give extra “rungs” on the classic reconstructive ladder to integrate a more holistic, patient-based approach with improved outcomes. This commentary presents the evolution of the traditional concept of the reconstructive ladder in the field of plastic surgery into a new course with the aim of achieving excellent results for soft tissue reconstruction by applying innovative technologies and biologically active molecules for a wide range of surgical diseases.
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Salomon, Gavriel. "AI in Reverse: Computer Tools That Turn Cognitive." Journal of Educational Computing Research 4, no. 2 (May 1988): 123–39. http://dx.doi.org/10.2190/4lu7-vw23-egb1-aw5g.
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This article posits artificial intelligence (AI), as applied in instruction, is a more efficient means to old ends and that newer ends can be thought of. In particular, it is argued on the basis of some past research and on the basis of a Vygotskian view, that intelligent computer tools can not only simulate human cognition but, given specific conditions, humans can simulate computer's intelligence. That is, learners can internalize computers' intelligent tools and use them as cognitive ones. Internalization is discussed in terms of cognitive reconstruction of cultural artifacts of particular characteristics — tool-like nature, relative novelty of function, compatibility with learners' schemata, and most importantly — explicitness of operation. The article also discusses learning conditions that might facilitate the process of tool internalization, particularly “high road” learning whereby learners are intentionally mindful of a tool's mode of operation and logic. A preliminary study is briefly described showing that children are capable of internalizing the metacognitive guidance provided by a semi-intelligent Reading Aid and that they show evidence of internalizing the tool's “intelligence” thereby manifesting improved reading comprehension as well as improved essay writing. Four fundamental questions are raised for future research as well as questions of ethics.
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Pu, Jane J., Samer G. Hakim, James C. Melville, and Yu-Xiong Su. "Current Trends in the Reconstruction and Rehabilitation of Jaw following Ablative Surgery." Cancers 14, no. 14 (July 7, 2022): 3308. http://dx.doi.org/10.3390/cancers14143308.
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The reconstruction and rehabilitation of jaws following ablative surgery have been transformed in recent years by the development of computer-assisted surgery and virtual surgical planning. In this narrative literature review, we aim to discuss the current state-of-the-art jaw reconstruction, and to preview the potential future developments. The application of patient-specific implants and the “jaw-in-a-day technique” have made the fast restoration of jaws’ function and aesthetics possible. The improved efficiency of primary reconstructive surgery allows for the rehabilitation of neurosensory function following ablative surgery. Currently, a great deal of research has been conducted on augmented/mixed reality, artificial intelligence, virtual surgical planning for soft tissue reconstruction, and the rehabilitation of the stomatognathic system. This will lead to an even more exciting future for the functional reconstruction and rehabilitation of the jaw following ablative surgery.
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Sun, Bing, Xiao Jin Zhu, Xiao Ping Qiao, Lina Jiang, and Jin Cong Yi. "Analysis and Design of a FBG Intelligent Flexible Structure Based on Orthogonal Curvatures." Applied Mechanics and Materials 39 (November 2010): 67–72. http://dx.doi.org/10.4028/www.scientific.net/amm.39.67.
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With the research model of intelligent flexible structure by Fiber Bragg Grating (FBG) sensor, the dissertation deals with a design and manufacture methodology for FBG sensor array based on orthogonal curvatures, and offers the experiential analysis and validation. On basis of brief introduction to the principle of FBG sensor to detect curvatures and methodology of structural morphological reconstruction, detailed description of design solutions and manufacture processes have yielded based on orthogonal distributed FBG sensor array which includes some technological parts as the configuration of orthogonal distributed FBG sensor array, the technology of perception range-proportion and orthogonally contrapuntal-pasting, the method of casing and encapsulation, calibration for model structure and so on. Fitting and reconstructing of the model structure and validation of visualization is split up based on intelligent flexible model structure we make and experimental platform we develop. Thus, the result of the experiment adequately shows the FBG intelligent flexible structure designed and produced in the dissertation gives a splendid on fitting and reconstruction, in which the feasibility and validity of the design methodology and key technology has been proved deeply.
Dissertations / Theses on the topic "Reconstruction intelligente"
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Bonvard, Aurélien. "Algorithmes de détection et de reconstruction en aveugle de code correcteurs d'erreurs basés sur des informations souples." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0178.
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Les dernières décennies ont connu l’essor des communications numériques. Ceci a donné lieu à la prolifération des standards de communication, ce qui demande une plus grande adaptabilité des systèmes de communication. Une manière de rendre ces systèmes plus flexibles consiste à concevoir un récepteur intelligent qui serait capable de retrouver l’ensemble des paramètres de l’émetteur. Dans ce manuscrit, nous nous intéressons à l’identification en aveugle des codes correcteurs d’erreurs. Nous proposons des méthodes originales, basées sur le calcul de distances euclidiennes entre des séquences de symboles bruités. Tout d’abord, un premier algorithme de classification permet la détection d’un code puis l’identification de la longueur de ses mots de code. Un second algorithme basé sur le nombre de collisions permet quand à lui d’identifier la longueur des mots d’informations. Ensuite, nous proposons une autre méthode utilisant cette fois les distances euclidiennes minimales pour l’identification de la longueur d’un code en bloc. Enfin, une méthode de reconstruction du code dual d’un code correcteur d’erreurs est présentéeRecent decades have seen the rise of digital communications. This has led to a proliferation of communication standards, requiring greater adaptability of communication systems. One way to make these systems more flexible is to design an intelligent receiver that would be able to retreive all the parameters of the transmitter from the received signal. In this manuscript, we are interested in the blind identification of error-correcting codes. We propose original methods based on the calculation of Euclidean distances between noisy symbol sequences. First, a classification algorithm allows the detection of a code and then the identification of its code words lenght. A second algorithm based on the number of collisions allows to identify the length of the information words. Then, we propose another method using the minimum Euclidean distances to identify block codes length. Finally, a method for reconstructing the dual code of an error-correcting code is presented
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El, Hage Josiana. "Smart Reconstruction after a natural or man-made disaster : Feedback, methodology, and application to the Beirut Harbor Disaster." Electronic Thesis or Diss., Université de Lille (2022-....), 2024. http://www.theses.fr/2024ULILN015.
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L'objectif de cette étude est de développer un cadre intelligent pour la reconstruction post-catastrophe des bâtiments, en se concentrant sur l'explosion de Beyrouth comme étude de cas, en raison de son contexte géopolitique complexe, de l'étendue de ses dommages et des crises socio-économiques qui ont frappé le pays pendant ces dernières années. L'étude explore diverses dimensions comprenant l'état structurel, l'économique et le social pour prioriser les groupes communautaires marginalisés dans les projets de reconstruction et plaider en faveur de l'approche "Build-Back-Better", selon les recommandations de « Sendai Framework For Disaster Risk Reduction ».Pour atteindre ces objectifs, la thèse commence par l'état de l'art du sujet (Chapitre 1) pour identifier les lacunes de recherche et les cadres de reconstruction post-catastrophe existants. S'appuyant sur cette revue, une méthodologie de recherche est formulée pour combler ces lacunes en mettant l'accent sur la ville de Beyrouth au Liban (Chapitre 2). Elle comprend l'étude du contexte local, les méthodes d'analyse des données et une compréhension des défis de la reconstruction post-catastrophe, plus particulièrement à Beyrouth. Un cadre complet pour évaluer les bâtiments post-catastrophe à Beyrouth suite à l'explosion est développé (Chapitre 3), comprenant 12 indicateurs couvrant les attributs physiques du bâtiment et le profil socio-économique de ses habitants. Ce cadre facilite le calcul d'un indice de priorité pour un grand ensemble de bâtiments endommagés à Beyrouth (Chapitre 4). L'évaluation aide les décideurs et les parties prenantes impliquées dans le processus de reconstruction à gérer et à surveiller les projets de rénovation des bâtiments tout en encourageant l'engagement de la communauté affectée. Elle donne la priorité aux individus les plus vulnérables, favorisant ainsi une approche centrée sur les personnes pour le rétablissement, soutenue par les principes de "Build-Back-Better" et d'inclusivité.Le cadre basé sur les données et les résultats présentés dans cette thèse constituent une avancée dans le domaine de la reconstruction post-catastrophe. Cependant, cette recherche montre certaines limitations, notamment la collecte de données via la méthode de crowdsourcing et le manque de participation des personnes, la dynamique et la complexité du contexte post-catastrophe, ainsi que la concentration uniquement sur le secteur du bâtiment. Les futures recherches pourraient se concentrer sur (i) la prise en compte de tous les secteurs affectés par la catastrophe, (ii) l'étude de l'acceptation sociale pour participer au processus de collecte de données, (iii) et la diversification des sources de collecte de donnéesThe objective of this study is to develop a smart framework for post-disaster reconstruction of buildings, with a focus on the Beirut explosion as a case study, due to its complex geopolitical context, extensive damage, and socio-economic crises. The study delves into various dimensions encompassing physical, economic, and social to prioritize marginalized community groups in the recovery efforts and advocate for the “Build-Back-Better approach”, according to the recommendations of « Sendai Framework For Disaster Risk Reduction ».To attain these objectives, the thesis starts with a literature review (Chapter 1) to identify research gaps and existing post-disaster reconstruction frameworks. Drawing from this review, a research methodology is formulated to address these gaps with emphasis on Beirut city in Lebanon (Chapter 2). It includes the local context study, the data analysis methods, and an understanding of the challenges facing the post-disaster reconstruction with a focus on Beirut. A comprehensive framework for assessing post-disaster buildings in Beirut following the explosion is developed (Chapter 3), comprising 12 indicators spanning physical attributes of the building and socio-economic profile of its residents. This framework facilitates the calculation of a Priority Index for a large set of damaged buildings in Beirut (Chapter 4). The assessment assists decision-makers and stakeholders involved in the reconstruction process manage and monitor building renovation projects while encouraging the affected community engagement. It prioritizes the most vulnerable individuals, thereby fostering a people-centric approach to recovery, underpinned by the principles of building-back-better and inclusivity.The data-based framework and results presented in this thesis form a step forward in the post-disaster reconstruction field. However, this research shows some limitations including the data collection via crowdsourcing and the lack of people participation, the dynamics and the complexity of the post-disaster context, and the focus on the building sector only. Future research could focus on (i) considering all the sectors affected by the disaster, (ii) investigating the social acceptance for participating in the data collection process, (iii) and diversifying the data collection sources
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Mallik, Mohammed Tariqul Hassan. "Electromagnetic Field Exposure Reconstruction by Artificial Intelligence." Electronic Thesis or Diss., Université de Lille (2022-....), 2023. https://pepite-depot.univ-lille.fr/ToutIDP/EDENGSYS/2023/2023ULILN052.pdf.
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Le sujet de l'exposition aux champs électromagnétiques a fait l'objetd'une grande attention à la lumière du déploiement actuel du réseaucellulaire de cinquième génération (5G). Malgré cela, il reste difficilede reconstituer avec précision le champ électromagnétique dans unerégion donnée, faute de données suffisantes. Les mesures in situ sontd'un grand intérêt, mais leur viabilité est limitée, ce qui renddifficile la compréhension complète de la dynamique du champ. Malgré legrand intérêt des mesures localisées, il existe encore des régions nontestées qui les empêchent de fournir une carte d'exposition complète. Larecherche a exploré des stratégies de reconstruction à partird'observations provenant de certains sites localisés ou de capteursdistribués dans l'espace, en utilisant des techniques basées sur lagéostatistique et les processus gaussiens. En particulier, desinitiatives récentes se sont concentrées sur l'utilisation del'apprentissage automatique et de l'intelligence artificielle à cettefin. Pour surmonter ces problèmes, ce travail propose de nouvellesméthodologies pour reconstruire les cartes d'exposition aux CEM dans unezone urbaine spécifique en France. L'objectif principal est dereconstruire des cartes d'exposition aux ondes électromagnétiques àpartir de données provenant de capteurs répartis dans l'espace. Nousavons proposé deux méthodologies basées sur l'apprentissage automatiquepour estimer l'exposition aux ondes électromagnétiques. Pour la premièreméthode, le problème de reconstruction de l'exposition est défini commeune tâche de traduction d'image à image. Tout d'abord, les données ducapteur sont converties en une image et l'image de référencecorrespondante est générée à l'aide d'un simulateur basé sur le tracédes rayons. Nous avons proposé un réseau adversarial cGAN conditionnépar la topologie de l'environnement pour estimer les cartes d'expositionà l'aide de ces images. Le modèle est entraîné sur des images de cartesde capteurs tandis qu'un environnement est donné comme entréeconditionnelle au modèle cGAN. En outre, la cartographie du champélectromagnétique basée sur le Generative Adversarial Network estcomparée au simple Krigeage. Les résultats montrent que la méthodeproposée produit des estimations précises et constitue une solutionprometteuse pour la reconstruction des cartes d'exposition. Cependant,la production de données de référence est une tâche complexe car elleimplique la prise en compte du nombre de stations de base actives dedifférentes technologies et opérateurs, dont la configuration du réseauest inconnue, par exemple les puissances et les faisceaux utilisés parles stations de base. En outre, l'évaluation de ces cartes nécessite dutemps et de l'expertise. Pour répondre à ces questions, nous avonsdéfini le problème comme une tâche d'imputation de données manquantes.La méthode que nous proposons prend en compte l'entraînement d'un réseauneuronal infini pour estimer l'exposition aux champs électromagnétiques.Il s'agit d'une solution prometteuse pour la reconstruction des cartesd'exposition, qui ne nécessite pas de grands ensembles d'apprentissage.La méthode proposée est comparée à d'autres approches d'apprentissageautomatique basées sur les réseaux UNet et les réseaux adversairesgénératifs conditionnels, avec des résultats compétitifsThe topic of exposure to electromagnetic fields has received muchattention in light of the current deployment of the fifth generation(5G) cellular network. Despite this, accurately reconstructing theelectromagnetic field across a region remains difficult due to a lack ofsufficient data. In situ measurements are of great interest, but theirviability is limited, making it difficult to fully understand the fielddynamics. Despite the great interest in localized measurements, thereare still untested regions that prevent them from providing a completeexposure map. The research explored reconstruction strategies fromobservations from certain localized sites or sensors distributed inspace, using techniques based on geostatistics and Gaussian processes.In particular, recent initiatives have focused on the use of machinelearning and artificial intelligence for this purpose. To overcome theseproblems, this work proposes new methodologies to reconstruct EMFexposure maps in a specific urban area in France. The main objective isto reconstruct exposure maps to electromagnetic waves from some datafrom sensors distributed in space. We proposed two methodologies basedon machine learning to estimate exposure to electromagnetic waves. Forthe first method, the exposure reconstruction problem is defined as animage-to-image translation task. First, the sensor data is convertedinto an image and the corresponding reference image is generated using aray tracing-based simulator. We proposed an adversarial network cGANconditioned by the environment topology to estimate exposure maps usingthese images. The model is trained on sensor map images while anenvironment is given as conditional input to the cGAN model.Furthermore, electromagnetic field mapping based on the GenerativeAdversarial Network is compared to simple Kriging. The results show thatthe proposed method produces accurate estimates and is a promisingsolution for exposure map reconstruction. However, producing referencedata is a complex task as it involves taking into account the number ofactive base stations of different technologies and operators, whosenetwork configuration is unknown, e.g. powers and beams used by basestations. Additionally, evaluating these maps requires time andexpertise. To answer these questions, we defined the problem as amissing data imputation task. The method we propose takes into accountthe training of an infinite neural network to estimate exposure toelectromagnetic fields. This is a promising solution for exposure mapreconstruction, which does not require large training sets. The proposedmethod is compared with other machine learning approaches based on UNetnetworks and conditional generative adversarial networks withcompetitive results
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Kentzoglanakis, Kyriakos. "Reconstructing gene regulatory networks : a swarm intelligence framework." Thesis, University of Portsmouth, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523619.
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Zhao, Yu. "Channel Reconstruction for High-Rank User Equipment." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-256064.
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In a 5 Generation massive Multiple Input Multiple Output radio network, the Channel State Information is playing a central role in the algorithm design and system evaluation. However, Acquisition of Channel State Information consumes system resources (e.g. time, frequency) which in turn decrease the link utilization, i.e. fewer resources left for actual data transmission. This problem is more apparent in a scenario when User Equipment terminals have multi-antennas and it would be beneficial to obtain Channel State Information between Base Station and different User Equipment antennas e.g. for purpose of high rank (number of streams) transmission towards this User Equipment. Typically, in current industrial implementations, in order to not waste system resources, Channel State Information is obtained for only one of the User Equipment antennas which then limits the downlink transmission rank to 1. Hence, we purpose a method based on Deep learning technique. In this paper, multi-layer perception and convolutional neural network are implemented. Data are generated by MATLAB simulator using the parameters provided by Huawei Technologies Co., Ltd. Finally, the model proposed by this project provides the best performance compared to the baseline algorithms.I ett 5-generationsmassivt massivt multipel-inmatningsradio-nätverk spelar kanalstatens information en central roll i algoritmdesignen och systemutvärderingen. Förvärv av Channel State Information konsumerar emellertid systemresurser (t.ex. tid, frekvens) som i sin tur minskar länkanvändningen, dvs färre resurser kvar för faktisk dataöverföring. Detta problem är mer uppenbart i ett scenario när användarutrustningsterminaler har flera antenner och det skulle vara fördelaktigt att erhålla kanalstatusinformation mellan basstationen och olika användarutrustningsantenner, t.ex. för överföring av hög rang (antal strömmar) till denna användarutrustning. I nuvarande industriella implementeringar erhålls kanalstatusinformation för endast en av användarutrustningens antenner för att inte slösa bort systemresurser, vilket sedan begränsar överföringsrankningen för nedlänkning till 1. Därför syftar vi på en metod baserad på Deep learning-teknik. I detta dokument implementeras flerskiktsuppfattning och inblandat neuralt nätverk. Data genereras av MATLAB-simulator med hjälp av parametrarna som tillhandahålls av Huawei Technologies Co., Ltd. Slutligen ger modellen som föreslås av detta projekt bästa prestanda jämfört med baslinjealgoritmerna.
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Elias, Rimon. "Towards obstacle reconstruction through wide baseline set of images." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/29104.
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In this thesis, we handle the problem of extracting 3D information from multiple images of a robotic work site in the context of teleoperation. A human operator determines the virtual path of a robotic vehicle and our mission is to provide him with the sequence of images that should be seen by the teleoperated robot moving along this path.
The environment, in which the robotic vehicle moves, has a planar ground surface. In addition, a set of wide baseline images are available for the work site. This implies that a small number of points may be visible in more than two views. Moreover, camera parameters are known approximately. According to the sensor error margins, the parameters read lie within some range. Obstacles of different shapes are present in such an environment. In order to generate the sequence, this ground plane as well as the obstacles must be represented. The perspective image of the ground plane can be obtained through a homography matrix. This is done through the virtual camera parameters and the overhead view of the work site. In order to represent obstacles, we suggest different methods; these are volumetric and planar.
Our algorithm to represent obstacles starts with detecting junctions. This is done through a new fast junction detection operator we propose. This operator provides the location of the junction as well as the orientations of the edges surrounding it. Junctions belonging to the obstacles are identified against those belonging to the ground plane through calculating the inter-image homography matrices.
Fundamental matrices relating images can be estimated roughly through the available camera parameters. Strips surrounding epipolar lines are used as a search range for detecting possible matches. We introduce a novel homographic correlation method to be applied among candidates by reconstructing the planes of junctions in space. Two versions of homographic correlation are proposed; these are SAD and VNC. Both versions achieve matching results that outperform non-homographic correlation. The match set is then turned into a set of 3D points through triangulation. At this point, we propose a hierarchical structure to cluster points in space. This results in bounding boxes containing obstacles. A more accurate volumetric representation for the obstacle can be achieved through a voxelization approach.
Another representation is suggested. That is to represent obstacles as planar patches. This is done through mapping among original and synthesized images. Finally, steps of the different algorithms presented throughout the thesis are supported by examples to show the usefulness we claim of our approaches.
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關福延 and Folk-year Kwan. "An intelligent approach to automatic medical model reconstruction fromserial planar CT images." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31243216.
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Gayed, Said Simone. "Skull reconstruction through shape completion." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24057/.
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In this study, we present a shape completion approach to skull reconstruction. Our final goal is to reconstruct the complete mesh of a skull starting from its defective point cloud. Our approach is based on an existing deep neural network, opportunely modified, trained to reconstruct a complete 3D point cloud from an incomplete one. The complete point clouds are then processed through a multi-step pipeline in order to reconstruct the original skull surface. Moreover, we analyze and refine the Sant'Orsola skull dataset, designing functional pipelines for its processing. On the test set, the proposed approach is able to complete missing areas effectively, reaching high accuracy in terms of the predicted point locations and a good qualitative approximation of the complete skull.
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Papadopoulos, Georgios. "Towards a 3D building reconstruction using spatial multisource data and computational intelligence techniques." Thesis, Limoges, 2019. http://www.theses.fr/2019LIMO0084/document.
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La reconstruction de bâtiments à partir de photographies aériennes et d’autres données spatiales urbaines multi-sources est une tâche qui utilise une multitude de méthodes automatisées et semi-automatisées allant des processus ponctuels au traitement classique des images et au balayage laser. Dans cette thèse, un système de relaxation itératif est développé sur la base de l'examen du contexte local de chaque bord en fonction de multiples sources d'entrée spatiales (masques optiques, d'élévation, d'ombre et de feuillage ainsi que d'autres données prétraitées, décrites au chapitre 6). Toutes ces données multisource et multirésolution sont fusionnées de manière à extraire les segments de ligne probables ou les arêtes correspondant aux limites des bâtiments. Deux nouveaux sous-systèmes ont également été développés dans cette thèse. Ils ont été conçus dans le but de fournir des informations supplémentaires, plus fiables, sur les contours des bâtiments dans une future version du système de relaxation proposé. La première est une méthode de réseau de neurones à convolution profonde (CNN) pour la détection de frontières de construction. Le réseau est notamment basé sur le modèle SRCNN (Dong C. L., 2015) de super-résolution à la pointe de la technologie. Il accepte des photographies aériennes illustrant des données de zones urbaines densément peuplées ainsi que leurs cartes d'altitude numériques (DEM) correspondantes. La formation utilise trois variantes de cet ensemble de données urbaines et vise à détecter les contours des bâtiments grâce à une nouvelle cartographie hétéroassociative super-résolue. Une autre innovation de cette approche est la conception d'une couche de perte personnalisée modifiée appelée Top-N. Dans cette variante, l'erreur quadratique moyenne (MSE) entre l'image de sortie reconstruite et l'image de vérité de sol (GT) fournie des contours de bâtiment est calculée sur les 2N pixels de l'image avec les valeurs les plus élevées. En supposant que la plupart des N pixels de contour de l’image GT figurent également dans les 2N pixels supérieurs de la reconstruction, cette modification équilibre les deux catégories de pixels et améliore le comportement de généralisation du modèle CNN. Les expériences ont montré que la fonction de coût Top-N offre des gains de performance par rapport à une MSE standard. Une amélioration supplémentaire de la capacité de généralisation du réseau est obtenue en utilisant le décrochage. Le deuxième sous-système est un réseau de convolution profonde à super-résolution, qui effectue un mappage associatif à entrée améliorée entre les images d'entrée à basse résolution et à haute résolution. Ce réseau a été formé aux données d’altitude à basse résolution et aux photographies urbaines optiques à haute résolution correspondantes. Une telle différence de résolution entre les images optiques / satellites optiques et les données d'élévation est souvent le cas dans les applications du monde réelBuilding reconstruction from aerial photographs and other multi-source urban spatial data is a task endeavored using a plethora of automated and semi-automated methods ranging from point processes, classic image processing and laser scanning. In this thesis, an iterative relaxation system is developed based on the examination of the local context of each edge according to multiple spatial input sources (optical, elevation, shadow & foliage masks as well as other pre-processed data as elaborated in Chapter 6). All these multisource and multiresolution data are fused so that probable line segments or edges are extracted that correspond to prominent building boundaries.Two novel sub-systems have also been developed in this thesis. They were designed with the purpose to provide additional, more reliable, information regarding building contours in a future version of the proposed relaxation system. The first is a deep convolutional neural network (CNN) method for the detection of building borders. In particular, the network is based on the state of the art super-resolution model SRCNN (Dong C. L., 2015). It accepts aerial photographs depicting densely populated urban area data as well as their corresponding digital elevation maps (DEM). Training is performed using three variations of this urban data set and aims at detecting building contours through a novel super-resolved heteroassociative mapping. Another innovation of this approach is the design of a modified custom loss layer named Top-N. In this variation, the mean square error (MSE) between the reconstructed output image and the provided ground truth (GT) image of building contours is computed on the 2N image pixels with highest values . Assuming that most of the N contour pixels of the GT image are also in the top 2N pixels of the re-construction, this modification balances the two pixel categories and improves the generalization behavior of the CNN model. It is shown in the experiments, that the Top-N cost function offers performance gains in comparison to standard MSE. Further improvement in generalization ability of the network is achieved by using dropout.The second sub-system is a super-resolution deep convolutional network, which performs an enhanced-input associative mapping between input low-resolution and high-resolution images. This network has been trained with low-resolution elevation data and the corresponding high-resolution optical urban photographs. Such a resolution discrepancy between optical aerial/satellite images and elevation data is often the case in real world applications. More specifically, low-resolution elevation data augmented by high-resolution optical aerial photographs are used with the aim of augmenting the resolution of the elevation data. This is a unique super-resolution problem where it was found that many of -the proposed general-image SR propositions do not perform as well. The network aptly named building super resolution CNN (BSRCNN) is trained using patches extracted from the aforementioned data. Results show that in comparison with a classic bicubic upscale of the elevation data the proposed implementation offers important improvement as attested by a modified PSNR and SSIM metric. In comparison, other proposed general-image SR methods performed poorer than a standard bicubic up-scaler.Finally, the relaxation system fuses together all these multisource data sources comprising of pre-processed optical data, elevation data, foliage masks, shadow masks and other pre-processed data in an attempt to assign confidence values to each pixel belonging to a building contour. Confidence is augmented or decremented iteratively until the MSE error fails below a specified threshold or a maximum number of iterations have been executed. The confidence matrix can then be used to extract the true building contours via thresholding
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Hajjdiab, Hassan. "Vision-based localization, map building and obstacle reconstruction in ground plane environments." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/29109.
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The work described in this thesis develops the theory of 3D obstacle reconstruction and map building problems in the context of a robot, or a team of robots, equipped with one camera mounted on board. The study is composed of many problems representing the different phases of actions taken by the robot.
This thesis first studies the problem of image matching for wide baseline images taken by moving robots. The ground plane is detected and the inter-image homography induced by the ground plane is calculated. A novel technique for ground plane matching is introduced using the overhead view transformation.
The thesis then studies the simultaneous localization and map building (SLAM) problem for a team of robots collaborating in the same work site. A vision-based technique is introduced in this thesis to solve the SLAM problem.
The third problem studied in this thesis is the 3D obstacle reconstruction of the obstacles lying on the ground surface. In this thesis a Geometric/Variational level set method is proposed to reconstruct the obstacles detected by the robots.
Books on the topic "Reconstruction intelligente"
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Peng, Chen, Chuanliang Cheng, and Ling Wang. Reconstruction and Intelligent Control for Power Plant. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5574-7.
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Abdelguerfi, Mahdi. 3D Synthetic Environment Reconstruction. Boston, MA: Springer US, 2001.
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Cipolla, Roberto, Sebastiano Battiato, and Giovanni Maria Farinella. Computer vision: Detection, recognition and reconstruction. Berlin: Springer, 2010.
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Blake, Andrew. Visual reconstruction. Cambridge, Mass: MIT, 1987.
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United States. Congress. Senate. Select Committee on Intelligence. Report of the Select Committee on Intelligence on prewar intelligence assessments about postwar Iraq together with additional and minority views. Washington: U.S. G.P.O., 2007.
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Chuvikov, Dmitriy. Models and algorithms for reconstruction and examination of emergency events of road accidents based on logical artificial intelligence. 2nd ed. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1220729.
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The purpose of the monograph is to create a methodology, combined expert and simulation models, as well as algorithms and software-modeling tools for reconstruction and examination of accident events for automating decision-making by an expert center employee.
The methodology of combining and algorithms of joint work of an expert system based on logical artificial intelligence (mivar approach) and a simulation system for solving problems of reconstruction and examination of road accidents are developed; model reconstruction and examination of the accident in the formalism of the knowledge base bipartite oriented mivar nets, including analysis formulas braking qualities of the vehicle, determining the speed of a car's performance in terms of specific DTS, the formula for calculating different occasions: - slip car when braking, driving on curved sections of the road, hitting a car on the pedestrian in uniform motion and unlimited visibility; a method of generation of interfaces for designer expert systems based on the concept of mivar approach; special software in the form of expert systems "Analysis of road accident" in order to reduce the complexity of the process of calculating the disputed accidents, errors in the calculation and improve the accuracy and objectivity of the results obtained and the speed and quality of the calculations.
It can be useful to specialists of expert institutions, insurance companies, educational institutions in the field of expertise, as well as unmanned vehicles in terms of objective analysis and examination of road accidents.
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Dubovský, Peter. Hezekiah and the Assyrian spies: Reconstruction of the neo-Assyrian intelligence services and its significance for 2 Kings 18-19. Roma: Pontificio Istituto biblico, 2006.
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Dubovsky, Peter. Hezekiah and the Assyrian spies: Reconstruction of the neo-Assyrian intelligence services and its significance for 2 Kings 18-19. Roma: Pontificio Istituto biblico, 2006.
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United States. Congress. Senate. Select Committee on Intelligence. The Dayton Accords: Hearing before the Select Committee on Intelligence of the United States Senate, One Hundred Fourth Congress, second session ... Wednesday, July 24, 1996. Washington: U.S. G.P.O., 1996.
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Pisani, Sallie. The CIA and the Marshall Plan. Lawrence, Kan: University Press of Kansas, 1991.
Find full textBook chapters on the topic "Reconstruction intelligente"
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Hynek, Nik, and Anzhelika Solovyeva. "Reconstruction." In Militarizing Artificial Intelligence, 30–48. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003045489-4.
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Wang, Jingyue, and Xuan Jue. "Artificial Intelligence and Employment." In Reconstructing Our Orders, 99–127. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2209-9_4.
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Wang, Bin, Huanhuan Liu, Ping An, Qing Li, Kai Li, Ling Chen, Qi Zhang, Jingwu Zhang, Xinpeng Zhang, and Shenshen Gu. "Artificial Intelligence and Education." In Reconstructing Our Orders, 129–61. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2209-9_5.
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Zhang, Shanshan, Cheng Yang, Nina Qian, Qingye Tang, Xiangfeng Luo, Tuo Leng, Xiaoqiang Li, and Yuexing Han. "Artificial Intelligence and People’s Consensus." In Reconstructing Our Orders, 1–27. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2209-9_1.
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Sun, Weiping. "Artificial Intelligence and Ethical Principles." In Reconstructing Our Orders, 29–59. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2209-9_2.
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Lou, Jun, Shaorong Xie, Weidong Zhang, Yang Yang, Na Liu, Yan Peng, Huayan Pu, et al. "Artificial Intelligence and Safety Control." In Reconstructing Our Orders, 163–93. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2209-9_6.
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Wang, Jiabao, Xiaoyu Yu, Jie Li, and Xiaoling Jin. "Artificial Intelligence and International Norms." In Reconstructing Our Orders, 195–229. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2209-9_7.
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Malik, Rabia, and Asif Masood. "Fingerprint Enhancement and Reconstruction." In Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence, 660–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04020-7_71.
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Li, Junfeng, Ying Liu, Lin Yue, Fengliang Jin, Qi Guo, and Cong Xu. "Artificial Intelligence Governed by Laws and Regulations." In Reconstructing Our Orders, 61–97. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2209-9_3.
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Feng, Qiao, Yebin Liu, Yu-Kun Lai, Jingyu Yang, and Kun Li. "Monocular Real-Time Human Geometry Reconstruction." In Artificial Intelligence, 594–98. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20503-3_54.
Full textConference papers on the topic "Reconstruction intelligente"
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Chen, Jicai, Shengli Liu, Peiyi Zhu, Jiantao Yuan, Celimuge Wu, Xianfu Chen, Weidang Lu, and Rui Yin. "Adaptive Reconstruction Offloading for Digital Twin-assisted Intelligent Transportation Systems." In 2024 IEEE/CIC International Conference on Communications in China (ICCC), 2119–24. IEEE, 2024. http://dx.doi.org/10.1109/iccc62479.2024.10682032.
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Yang, Yang, and Songjie Wu. "Industrial CT Truncated Projection Data Reconstruction System Based on Artificial Intelligence." In 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC), 1151–57. IEEE, 2024. http://dx.doi.org/10.1109/spic62469.2024.10691399.
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Ramazanov, S. "Intelligent decision-making technologies in the conditions of war threats, dangers and post-war reconstruction of Ukraine." In international scientific-practical conference. MYKOLAYIV NATIONAL AGRARIAN UNIVERSITY, 2024. http://dx.doi.org/10.31521/978-617-7149-78-0-107.
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The report examines the role of intelligent technologies, in particular artificial intelligence, machine learning and big data analysis, in making effective decisions in the face of military threats, hazards and during the post-war recovery of Ukraine. The author highlights the areas of application of these technologies: emergency management, infrastructure reconstruction, evacuation planning, forecasting the consequences of war, and countering disinformation. The author emphasises the need for interdisciplinary cooperation, development of specialised solutions, access to quality data and training. Ethical aspects and cybersecurity issues are outlined. The importance of investing in the development of intelligent technologies to strengthen Ukraine's resilience and security is emphasised
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Varlaki, Peter, Istvan Palyi, Lajos Toth, and Ildiko Gombaszogi. "Reconstruction Decision Model for Trasportation Infrastructure Systems." In 2007 International Symposium on Computational Intelligence and Intelligent Informatics. IEEE, 2007. http://dx.doi.org/10.1109/isciii.2007.367382.
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Koczy, Laszlo T., and Tamas D. Gedeon. "Context Dependent Reconstructive Communication." In 2007 International Symposium on Computational Intelligence and Intelligent Informatics. IEEE, 2007. http://dx.doi.org/10.1109/isciii.2007.367354.
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Liu, Shaofan, Junbo Chen, and Jianke Zhu. "HVOFusion: Incremental Mesh Reconstruction Using Hybrid Voxel Octree." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/757.
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Incremental scene reconstruction is essential to the navigation in robotics. Most of the conventional methods typically make use of either TSDF (truncated signed distance functions) volume or neural networks to implicitly represent the surface. Due to the voxel representation or involving with time-consuming sampling, they have difficulty in balancing speed, memory storage, and surface quality. In this paper, we propose a novel hybrid voxel-octree approach to effectively fuse octree with voxel structures so that we can take advantage of both implicit surface and explicit triangular mesh representation. Such sparse structure preserves triangular faces in the leaf nodes and produces partial meshes sequentially for incremental reconstruction. This storage scheme allows us to naturally optimize the mesh in explicit 3D space to achieve higher surface quality. We iteratively deform the mesh towards the target and recovers vertex colors by optimizing a shading model. Experimental results on several datasets show that our proposed approach is capable of quickly and accurately reconstructing a scene with realistic colors. Code is available at https://github.com/Frankuzi/HVOFusion
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Xu, Ruiling, Song Gao, Zhiheng Wang, and Guang Xi. "Reconstruction of Two-Dimensional to Three-Dimensional Flow Transition Fields Using Neural Network-Based Generative Adversarial Networks." In ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/gt2024-128663.
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Abstract Flow transition is a common phenomenon in fluid dynamics, widely observed, particularly in scientific and technological fields such as turbomachine. However, accurately measuring and predicting flow transition poses a significant challenge due to its inherent complexity, and acquiring comprehensive data for the entire fluid domain is often a daunting task. Artificial intelligence (AI) emerges as a promising avenue for conveniently and efficiently obtaining typical information about flow transition, thereby assisting conventional experimental and computational methods. In this study, the Generative Adversarial Network (GAN) was extensively used in the fundamental neural network training model for reconstructing the three-dimensional flow fields in a compressor cascade. Through the joint training of the generator (G) and discriminator (D) based on GAN, the velocity distribution was constructed. The training dataset comprised 200 consecutive snapshots, with an additional 200 consecutive snapshots reserved for testing. To ensure a high degree of accuracy, the data for the compressor cascade flow was obtained through Direct Numerical Simulation (DNS). The trained generator model in this study successfully reconstructed three-dimensional flow field data, capturing the flow transition in smooth blade passages as well as the bypass transition induced by roughness elements. In-depth analysis of the data resulted in accurate predictions and reconstructions of the transition location, range, and structure. This study has achieved the reconstruction of information from two-dimensional to three-dimensional flow fields for time instances not included in the training data using the 2D3DGAN model. The implementation of this aspect implies that the 2D3DGAN model can be utilized to infer the flow field status at unknown time instances by leveraging partial information. This capability opens up greater potential applications in the engineering domain, thereby reducing experimental costs and enhancing engineering efficiency.
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Ju, Yakun, Kin-Man Lam, Yang Chen, Lin Qi, and Junyu Dong. "Pay Attention to Devils: A Photometric Stereo Network for Better Details." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/97.
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We present an attention-weighted loss in a photometric stereo neural network to improve 3D surface recovery accuracy in complex-structured areas, such as edges and crinkles, where existing learning-based methods often failed. Instead of using a uniform penalty for all pixels, our method employs the attention-weighted loss learned in a self-supervise manner for each pixel, avoiding blurry reconstruction result in such difficult regions. The network first estimates a surface normal map and an adaptive attention map, and then the latter is used to calculate a pixel-wise attention-weighted loss that focuses on complex regions. In these regions, the attention-weighted loss applies higher weights of the detail-preserving gradient loss to produce clear surface reconstructions. Experiments on real datasets show that our approach significantly outperforms traditional photometric stereo algorithms and state-of-the-art learning-based methods.
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Wang, Tianming, Xiaojun Wan, and Shaowei Yao. "Better AMR-To-Text Generation with Graph Structure Reconstruction." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/542.
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AMR-to-text generation is a challenging task of generating texts from graph-based semantic representations. Recent studies formalize this task a graph-to-sequence learning problem and use various graph neural networks to model graph structure. In this paper, we propose a novel approach that generates texts from AMR graphs while reconstructing the input graph structures. Our model employs graph attention mechanism to aggregate information for encoding the inputs. Moreover, better node representations are learned by optimizing two simple but effective auxiliary reconstruction objectives: link prediction objective which requires predicting the semantic relationship between nodes, and distance prediction objective which requires predicting the distance between nodes. Experimental results on two benchmark datasets show that our proposed model improves considerably over strong baselines and achieves new state-of-the-art.
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Dell, Robert F., Pablo E. Román, and Juan D. Velásquez. "Web User Session Reconstruction Using Integer Programming." In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2008. http://dx.doi.org/10.1109/wiiat.2008.181.
Full textReports on the topic "Reconstruction intelligente"
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Hendricks, Kasey. Data for Alabama Taxation and Changing Discourse from Reconstruction to Redemption. University of Tennessee, Knoxville Libraries, 2021. http://dx.doi.org/10.7290/wdyvftwo4u.
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At their most basic level taxes carry, in the words of Schumpeter ([1918] 1991), “the thunder of history” (p. 101). They say something about the ever-changing structures of social, economic, and political life. Taxes offer a blueprint, in both symbolic and concrete terms, for uncovering the most fundamental arrangements in society – stratification included. The historical retellings captured within these data highlight the politics of taxation in Alabama from 1856 to 1901, including conflicts over whom money is expended upon as well as struggles over who carries their fair share of the tax burden. The selected timeline overlaps with the formation of five of six constitutions adopted in the State of Alabama, including 1861, 1865, 1868, 1875, and 1901. Having these years as the focal point makes for an especially meaningful case study, given how much these constitutional formations made the state a site for much political debate. These data contain 5,121 pages of periodicals from newspapers throughout the state, including: Alabama Sentinel, Alabama State Intelligencer, Alabama State Journal, Athens Herald, Daily Alabama Journal, Daily Confederation, Elyton Herald, Mobile Daily Tribune, Mobile Tribune, Mobile Weekly Tribune, Morning Herald, Nationalist, New Era, Observer, Tuscaloosa Observer, Tuskegee News, Universalist Herald, and Wilcox News and Pacificator. The contemporary relevance of these historical debates manifests in Alabama’s current constitution which was adopted in 1901. This constitution departs from well-established conventions of treating the document as a legal framework that specifies a general role of governance but is firm enough to protect the civil rights and liberties of the population. Instead, it stands more as a legislative document, or procedural straightjacket, that preempts through statutory material what regulatory action is possible by the state. These barriers included a refusal to establish a state board of education and enact a tax structure for local education in addition to debt and tax limitations that constrained government capacity more broadly. Prohibitive features like these are among the reasons that, by 2020, the 1901 Constitution has been amended nearly 1,000 times since its adoption. However, similar procedural barriers have been duplicated across the U.S. since (e.g., California’s Proposition 13 of 1978). Reference: Schumpeter, Joseph. [1918] 1991. “The Crisis of the Tax State.” Pp. 99-140 in The Economics and Sociology of Capitalism, edited by Richard Swedberg. Princeton University Press.
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BARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, December 2021. http://dx.doi.org/10.12731/er0519.07122021.
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The auroral activity indices AU, AL, AE, introduced into geophysics at the beginning of the space era, although they have certain drawbacks, are still widely used to monitor geomagnetic activity at high latitudes. The AU index reflects the intensity of the eastern electric jet, while the AL index is determined by the intensity of the western electric jet. There are many regression relationships linking the indices of magnetic activity with a wide range of phenomena observed in the Earth's magnetosphere and atmosphere. These relationships determine the importance of monitoring and predicting geomagnetic activity for research in various areas of solar-terrestrial physics. The most dramatic phenomena in the magnetosphere and high-latitude ionosphere occur during periods of magnetospheric substorms, a sensitive indicator of which is the time variation and value of the AL index. Currently, AL index forecasting is carried out by various methods using both dynamic systems and artificial intelligence. Forecasting is based on the close relationship between the state of the magnetosphere and the parameters of the solar wind and the interplanetary magnetic field (IMF). This application proposes an algorithm for describing the process of substorm formation using an instrument in the form of an Elman-type ANN by reconstructing the AL index using the dynamics of the new integral parameter we introduced. The use of an integral parameter at the input of the ANN makes it possible to simulate the structure and intellectual properties of the biological nervous system, since in this way an additional realization of the memory of the prehistory of the modeled process is provided.
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Chornodon, Myroslava. FEAUTURES OF GENDER IN MODERN MASS MEDIA. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11064.
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The article clarifies of gender identity stereotypes in modern media. The main gender stereotypes covered in modern mass media are analyzed and refuted. The model of gender relations in the media is reflected mainly in the stereotypical images of men and woman. The features of the use of gender concepts in modern periodicals for women and men were determined. The most frequently used derivatives of these macroconcepts were identified and analyzed in detail. It has been found that publications for women and men are full of various gender concepts that are used in different contexts. Ingeneral, theanalysisofthe concept-maximums and concept-minimum gender and their characteristics is carried out in the context of gender stereotypes that have been forme dand function in the society, system atizing the a ctual presentations. The study of the gender concept is relevant because it reveals new trends and features of modern gender images. Taking into account the special features of gender-labeled periodicals in general and the practical absence of comprehensive scientific studies of the gender concept in particular, there is a need to supplement Ukrainian science with this topic. Gender psychology, which is served by methods of various sciences, primarily sociological, pedagogical, linguistic, psychological, socio-psychological. Let us pay attention to linguistic and psycholinguistic methods in gender studies. Linguistic methods complement intelligence research tasks, associated with speech, word and text. Psycholinguistic methods used in gender psychology (semantic differential, semantic integral, semantic analysis of words and texts), aimed at studying speech messages, specific mechanisms of origin and perception, functions of speech activity in society, studying the relationship between speech messages and gender properties participants in the communication, to analyze the linguistic development in connection with the general development of the individual. Nowhere in gender practice there is the whole arsenal of psychological methods that allow you to explore psychological peculiarities of a person like observation, experiments, questionnaires, interviews, testing, modeling, etc. The methods of psychological self-diagnostics include: the gender aspect of the own socio-psychological portrait, a gender biography as a variant of the biographical method, aimed at the reconstruction of individual social experience. In the process of writing a gender autobiography, a person can understand the characteristics of his gender identity, as well as ways and means of their formation. Socio-psychological methods of studying gender include the study of socially constructed women’s and men’s roles, relationships and identities, sexual characteristics, psychological characteristics, etc. The use of gender indicators and gender approaches as a means of socio-psychological and sociological analysis broadens the subject boundaries of these disciplines and makes them the subject of study within these disciplines. And also, in the article a combination of concrete-historical, structural-typological, system-functional methods is implemented. Descriptive and comparative methods, method of typology, modeling are used. Also used is a method of content analysis for the study of gender content of modern gender-stamped journals. It was he who allowed quantitatively to identify and explore the features of the gender concept in the pages of periodicals for women and men. A combination of historical, structural-typological, system-functional methods is also implemented in the article. Descriptive and comparative methods, method of typology, modeling are used. A method of content analysis for the study of gender content of modern gender-labeled journals is also used. It allowed to identify and explore the features of the gender concept quantitatively in the periodicals for women and men. The conceptual perception and interpretation of the gender concept «woman», which is highlighted in the modern gender-labeled press in Ukraine, requires the elaboration of the polyfunctionality of gender interpretations, the comprehension of the metaphorical perception of this image and its role and purpose in society. A gendered approach to researching the gender content of contemporary periodicals for women and men. Conceptual analysis of contemporary gender-stamped publications within the gender conceptual sphere allows to identify and correlate the meta-gender and gender concepts that appear in society.