Algorithms for Automatic Label Placement

Práce popisuje problém automatického umísťování popisků do mapy. Jednotlivé bodové, čárové a plošné objekty v mapě je třeba označit odpovídajícími textovými či obrázkovými popisky. Tyto popisky je nutné rozmístit tak, aby se vzájemně nepřekrývaly a zároveň byly jasně přiřaditelné k odpovídajícím objektům. O problému je známo, že je NP-těžký a nalezení optimálního rozmístění všech popisků je výpočetně velmi náročné i pro nejjednodušší mapy. Pozornost je věnována umísťování popisků označujících bodové a čárové objekty, včetně prvního kroku obnášejícího přípravu možných pozic pro umístění těchto popisků, při dodržení běžných kartografických pravidel pro rozmísťování popisků. Následně jsou na problém aplikovány tři různé druhy algoritmů -- greedy ("hladové") algoritmy v kombinaci s lokálním prohledáváním, matematická optimalizace (v podobě 0-1 celočíselného programování) a genetické algoritmy. Popsané algoritmy jsou v softwarové části práce implementovány a na závěr porovnány na několika různých datových sadách, vycházejících z reálných geografických podkladů a z náhodně vygenerovaných map. Závěrečné srovnání se zaměřuje na kvalitu výsledného rozmístění (dle metrik definovaných v práci), času potřebnému k nalezení řešení a také na determinističnost daných algoritmů.Thesis describes the problem of automatic map label placement. Various point, line or area features in maps must be marked with matching text or graphic labels. These labels have to be placed so they do not overlap with each other and they are clearly associable with corresponding map features. The problem is known to be NP-hard and finding optimal positions of all map labels is highly computationally expensive, even for the simplest maps. Focus is given to the placement of labels describing point and line map features, including the initial phase of enumerating possible label positions, respecting the basic cartographic rules common for those labels. Afterwards, three different algorithm types are applied to the problem itself -- greedy algorithms (in combination with local search optimization), mathematical optimization (0-1 integer programming) and genetic algorithms. Ultimately, the described algorithms are implemented in the software part of the work and compared on various data sets, based on both real world geographical data and randomly generated maps. The final comparison focuses especially on the quality of the result (scored by the metrics defined in the thesis), time needed to find the solution and determinism of the given algorithms

Um GRASP para o problema da rotulação cartográfica de pontos: novas soluções

O Problema da Rotulação Cartográfica de Pontos (PRCP) é uma importante etapa no processo de geração de mapas em um sistema de informações geográficas e consiste em posicionar os rótulos dos pontos em posições que não ocasionam sobreposições. O PRCP é um problema da classe NP-difícil e por isso, várias abordagens foram propostas usando heurísticas/metaheurísticas para resolvê-lo no sentido de se obter soluções polinomiais e de boa qualidade. Seguindo essa idéia, esse trabalho propõe um GRASP para o PRCP baseado em seu grafo de conflitos. Os resultados encontrados para instâncias da literatura mostram que essa metaheurística é uma boa estratégia, pois a mesma produziu soluções de melhor qualidade que todos os resultados informados na literatura, em um tempo de computacional razoável

Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey

Adversarial attacks and defenses in machine learning and deep neural network have been gaining significant attention due to the rapidly growing applications of deep learning in the Internet and relevant scenarios. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models. Specifically, we conduct a comprehensive classification of recent adversarial attack methods and state-of-the-art adversarial defense techniques based on attack principles, and present them in visually appealing tables and tree diagrams. This is based on a rigorous evaluation of the existing works, including an analysis of their strengths and limitations. We also categorize the methods into counter-attack detection and robustness enhancement, with a specific focus on regularization-based methods for enhancing robustness. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks, and a hierarchical classification of the latest defense methods is provided, highlighting the challenges of balancing training costs with performance, maintaining clean accuracy, overcoming the effect of gradient masking, and ensuring method transferability. At last, the lessons learned and open challenges are summarized with future research opportunities recommended.Comment: 46 pages, 21 figure

Algorithms for Map Generation and Spatial Data Visualization in LIFE

The goal of this master thesis is to construct a software system, named the LIFE Spatial Data Visualization System (LIFE-SDVS), to automatically visualize the data obtained in the LIFE project spatially. LIFE stands for the Leipzig Research Centre for Civilization Diseases. It is part of the Medical Faculty of the University of Leipzig and conducts a large medical research project focusing on civilization diseases in the Leipzig population. Currently, more than 20,000 participants have joined this population-based cohort study. The analyses in LIFE have been mostly limited to non-spatial aspects. To integrate geographical facet into the findings, a spatial visualization tool is necessary. Hence, LIFE-SDVS, an automatic map visualization tool wrapped in an interactive web interface, is constructed. LIFE-SDVS is conceptualized with a three-layered architecture: data source, functionalities and spatial visualization layers. The implementation of LIFE-SDVS was achieved by two software components: an independent, self-contained R package lifemap and the LIFE Shiny Application. The package lifemap enables the automatic spatial visualization of statistics on the map of Leipzig and to the extent of the authors knowledge, is the first R package to achieve boundary labeling for maps. The package lifemap also contains two self-developed algorithms. The Label Positioning Algorithm was constructed to find good positions within each region on a map for placing labels, statistical graphics and as starting points for boundary label leaders. The Label Alignment Algorithm solves the leader intersection problem of boundary labeling. However, to use the plotting functions in lifemap, the users need to have basic knowledge of R and it is a tedious job to manually input the argument values whenever changes on the maps are necessary. An interactive Shiny web application, the LIFE Shiny Application, is therefore built to create a user friendly data exploration and map generation tool. LIFE Shiny Application is capable of obtaining experimental data directly from the LIFE database at runtime. Additionally, a data preprocessing unit can transform the raw data into the format needed for spatial visualization. On the LIFE Shiny Application user interface, users can specify the data to display, including what data to be fetched from database and which part of the data shall be visualized, by using the filter functions provided. Many map features are also available to improve the aesthetic presentation of the maps. The resulting maps can also be downloaded for further usage in scientific publications or reports. Two use cases using LIFE hand grip strength and body mass index data demonstrate the functionalities of LIFESDVS. The current LIFE-SDVS sets a foundation for the spatial visualization of LIFE data. Suggestions on adding further functionalities into the future version are also provided

Genetic algorithms for map labeling

Map labeling is the cartographic problem of placing the names of features (for example cities or rivers) on the map. A good labeling has no intersections between labels. Even basic versions of the problem are NP-hard. In addition, realistic map-labeling problems deal with many cartographic constraints, which pose more demands on how the labels should be placed in relation to their surroundings. For example, a label is preferably placed above and to the right of a city. These two aspects (combinatorially hard and the need of considering cartographic rules) make the problem challenging. Genetic algorithms (GAs) are heuristic solvers for optimization problems. Based on the theory of Darwinian evolution, they are able to "evolve" solutions using a process similar to adaptation in biology. In this thesis we apply GAs to solve map-labeling problems. Problems dealing with point features (like cities) and line features (like rivers) are discussed. It is also shown how additional cartographic rules can be incorporated in the algorithm. Experiments done on randomly-generated maps and real-world data show that the GAs are successful in finding good solutions. The GAs were designed with theoretical insights regarding linkage and mixing in mind. The map-labeling problem is interesting in that its linkage is geometrically determined and therefore reasonably clear. This property was exploited in the design of the GAs. The GAs were also used to verify the predictions of theoretical models from literature (a convergence model and a population-sizing model). The GA was able to match the assumptions of the models thanks to a novel operator, the so-called geometrically local optimizer. Experimental results indeed matched the predictions of the models. As a result, the number of fitness evaluations scales linearly with the input size (the size of the map)

A multi-agent system for on-the-fly web map generation and spatial conflict resolution

Résumé Internet est devenu un moyen de diffusion de l’information géographique par excellence. Il offre de plus en plus de services cartographiques accessibles par des milliers d’internautes à travers le monde. Cependant, la qualité de ces services doit être améliorée, principalement en matière de personnalisation. A cette fin, il est important que la carte générée corresponde autant que possible aux besoins, aux préférences et au contexte de l’utilisateur. Ce but peut être atteint en appliquant les transformations appropriées, en temps réel, aux objets de l’espace à chaque cycle de génération de la carte. L’un des défis majeurs de la génération d’une carte à la volée est la résolution des conflits spatiaux qui apparaissent entre les objets, essentiellement à cause de l’espace réduit des écrans d’affichage. Dans cette thèse, nous proposons une nouvelle approche basée sur la mise en œuvre d’un système multiagent pour la génération à la volée des cartes et la résolution des conflits spatiaux. Cette approche est basée sur l’utilisation de la représentation multiple et la généralisation cartographique. Elle résout les conflits spatiaux et génère les cartes demandées selon une stratégie innovatrice : la génération progressive des cartes par couches d’intérêt. Chaque couche d’intérêt contient tous les objets ayant le même degré d’importance pour l’utilisateur. Ce contenu est déterminé à la volée au début du processus de génération de la carte demandée. Notre approche multiagent génère et transfère cette carte suivant un mode parallèle. En effet, une fois une couche d’intérêt générée, elle est transmise à l’utilisateur. Dans le but de résoudre les conflits spatiaux, et par la même occasion générer la carte demandée, nous affectons un agent logiciel à chaque objet de l’espace. Les agents entrent ensuite en compétition pour l’occupation de l’espace disponible. Cette compétition est basée sur un ensemble de priorités qui correspondent aux différents degrés d’importance des objets pour l’utilisateur. Durant la résolution des conflits, les agents prennent en considération les besoins et les préférences de l’utilisateur afin d’améliorer la personnalisation de la carte. Ils améliorent la lisibilité des objets importants et utilisent des symboles qui pourraient aider l’utilisateur à mieux comprendre l’espace géographique. Le processus de génération de la carte peut être interrompu en tout temps par l’utilisateur lorsque les données déjà transmises répondent à ses besoins. Dans ce cas, son temps d’attente est réduit, étant donné qu’il n’a pas à attendre la génération du reste de la carte. Afin d’illustrer notre approche, nous l’appliquons au contexte de la cartographie sur le web ainsi qu’au contexte de la cartographie mobile. Dans ces deux contextes, nous catégorisons nos données, qui concernent la ville de Québec, en quatre couches d’intérêt contenant les objets explicitement demandés par l’utilisateur, les objets repères, le réseau routier et les objets ordinaires qui n’ont aucune importance particulière pour l’utilisateur. Notre système multiagent vise à résoudre certains problèmes liés à la génération à la volée des cartes web. Ces problèmes sont les suivants : 1. Comment adapter le contenu des cartes, à la volée, aux besoins des utilisateurs ? 2. Comment résoudre les conflits spatiaux de manière à améliorer la lisibilité de la carte tout en prenant en considération les besoins de l’utilisateur ? 3. Comment accélérer la génération et le transfert des données aux utilisateurs ? Les principales contributions de cette thèse sont : 1. La résolution des conflits spatiaux en utilisant les systèmes multiagent, la généralisation cartographique et la représentation multiple. 2. La génération des cartes dans un contexte web et dans un contexte mobile, à la volée, en utilisant les systèmes multiagent, la généralisation cartographique et la représentation multiple. 3. L’adaptation des contenus des cartes, en temps réel, aux besoins de l’utilisateur à la source (durant la première génération de la carte). 4. Une nouvelle modélisation de l’espace géographique basée sur une architecture multi-couches du système multiagent. 5. Une approche de génération progressive des cartes basée sur les couches d’intérêt. 6. La génération et le transfert, en parallèle, des cartes aux utilisateurs, dans les contextes web et mobile.Abstract Internet is a fast growing medium to get and disseminate geospatial information. It provides more and more web mapping services accessible by thousands of users worldwide. However, the quality of these services needs to be improved, especially in term of personalization. In order to increase map flexibility, it is important that the map corresponds as much as possible to the user’s needs, preferences and context. This may be possible by applying the suitable transformations, in real-time, to spatial objects at each map generation cycle. An underlying challenge of such on-the-fly map generation is to solve spatial conflicts that may appear between objects especially due to lack of space on display screens. In this dissertation, we propose a multiagent-based approach to address the problems of on-the-fly web map generation and spatial conflict resolution. The approach is based upon the use of multiple representation and cartographic generalization. It solves conflicts and generates maps according to our innovative progressive map generation by layers of interest approach. A layer of interest contains objects that have the same importance to the user. This content, which depends on the user’s needs and the map’s context of use, is determined on-the-fly. Our multiagent-based approach generates and transfers data of the required map in parallel. As soon as a given layer of interest is generated, it is transmitted to the user. In order to generate a given map and solve spatial conflicts, we assign a software agent to every spatial object. Then, the agents compete for space occupation. This competition is driven by a set of priorities corresponding to the importance of objects for the user. During processing, agents take into account users’ needs and preferences in order to improve the personalization of the final map. They emphasize important objects by improving their legibility and using symbols in order to help the user to better understand the geographic space. Since the user can stop the map generation process whenever he finds the required information from the amount of data already transferred, his waiting delays are reduced. In order to illustrate our approach, we apply it to the context of tourist web and mobile mapping applications. In these contexts, we propose to categorize data into four layers of interest containing: explicitly required objects, landmark objects, road network and ordinary objects which do not have any specific importance for the user. In this dissertation, our multiagent system aims at solving the following problems related to on-the-fly web mapping applications: 1. How can we adapt the contents of maps to users’ needs on-the-fly? 2. How can we solve spatial conflicts in order to improve the legibility of maps while taking into account users’ needs? 3. How can we speed up data generation and transfer to users? The main contributions of this thesis are: 1. The resolution of spatial conflicts using multiagent systems, cartographic generalization and multiple representation. 2. The generation of web and mobile maps, on-the-fly, using multiagent systems, cartographic generalization and multiple representation. 3. The real-time adaptation of maps’ contents to users’ needs at the source (during the first generation of the map). 4. A new modeling of the geographic space based upon a multi-layers multiagent system architecture. 5. A progressive map generation approach by layers of interest. 6. The generation and transfer of web and mobile maps at the same time to users

User hints for optimisation processes

Innovative improvements in the area of Human-Computer Interaction and User Interfaces have en-abled intuitive and effective applications for a variety of problems. On the other hand, there has also been the realization that several real-world optimization problems still cannot be totally auto-mated. Very often, user interaction is necessary for refining the optimization problem, managing the computational resources available, or validating or adjusting a computer-generated solution. This thesis investigates how humans can help optimization methods to solve such difficult prob-lems. It presents an interactive framework where users play a dynamic and important role by pro-viding hints. Hints are actions that help to insert domain knowledge, to escape from local minima, to reduce the space of solutions to be explored, or to avoid ambiguity when there is more than one optimal solution. Examples of user hints are adjustments of constraints and of an objective function, focusing automatic methods on a subproblem of higher importance, and manual changes of an ex-isting solution. User hints are given in an intuitive way through a graphical interface. Visualization tools are also included in order to inform about the state of the optimization process. We apply the User Hints framework to three combinatorial optimization problems: Graph Clus-tering, Graph Drawing and Map Labeling. Prototype systems are presented and evaluated for each problem. The results of the study indicate that optimization processes can benefit from human interaction. The main goal of this thesis is to list cases where human interaction is helpful, and provide an ar-chitecture for supporting interactive optimization. Our contributions include the general User Hints framework and particular implementations of it for each optimization problem. We also present a general process, with guidelines, for applying our framework to other optimization problems

Classification of Explainable Artificial Intelligence Methods through Their Output Formats

Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review aimed to organise these methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats. The reviewed scientific papers were retrieved by conducting an initial search on Google Scholar with the keywords “explainable artificial intelligence”; “explainable machine learning”; and “interpretable machine learning”. A subsequent iterative search was carried out by checking the bibliography of these articles. The addition of the dimension of the explanation format makes the proposed classification system a practical tool for scholars, supporting them to select the most suitable type of explanation format for the problem at hand. Given the wide variety of challenges faced by researchers, the existing XAI methods provide several solutions to meet the requirements that differ considerably between the users, problems and application fields of artificial intelligence (AI). The task of identifying the most appropriate explanation can be daunting, thus the need for a classification system that helps with the selection of methods. This work concludes by critically identifying the limitations of the formats of explanations and by providing recommendations and possible future research directions on how to build a more generally applicable XAI method. Future work should be flexible enough to meet the many requirements posed by the widespread use of AI in several fields, and the new regulation