The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City

When providing directions to a place, web and mobile mapping services are all able to suggest the shortest route. The goal of this work is to automatically suggest routes that are not only short but also emotionally pleasant. To quantify the extent to which urban locations are pleasant, we use data from a crowd-sourcing platform that shows two street scenes in London (out of hundreds), and a user votes on which one looks more beautiful, quiet, and happy. We consider votes from more than 3.3K individuals and translate them into quantitative measures of location perceptions. We arrange those locations into a graph upon which we learn pleasant routes. Based on a quantitative validation, we find that, compared to the shortest routes, the recommended ones add just a few extra walking minutes and are indeed perceived to be more beautiful, quiet, and happy. To test the generality of our approach, we consider Flickr metadata of more than 3.7M pictures in London and 1.3M in Boston, compute proxies for the crowdsourced beauty dimension (the one for which we have collected the most votes), and evaluate those proxies with 30 participants in London and 54 in Boston. These participants have not only rated our recommendations but have also carefully motivated their choices, providing insights for future work.Comment: 11 pages, 7 figures, Proceedings of ACM Hypertext 201

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Contributions of Graph Theory and Algorithms to Animal Behaviour and Neuroscience

Η θεωρία γραφημάτων και οι αλγόριθμοι προσφέρουν πολύτιμες εργαλειοθήκες για τη μοντε- λοποίηση καθώς και την ανάλυση πολυάριθμων φαινομένων στις φυσικές επιστήμες. Εδώ παρουσιάζεται μια ανασκόπηση της σύγχρονης βιβλιογραφίας, χωρισμένη σε τέσσερα κύρια κεφάλαια, δίνοντας κάποιες ενδείξεις για το πώς οι έννοιες αυτών των δύο κλάδων μπορούν να χρησιμοποιηθούν για τη μελέτη της συμπεριφοράς των ζώων και της νευροεπιστήμης. Κατ ’εξαίρεση, το πρώτο μέρος του πρώτου κεφαλαίου παρέχει μια σύντομη συζήτηση σχετικά με τις εφαρμογές της θεωρίας γραφημάτων στη μοριακή βιολογία. Η επιλογή αυτή έγινε προκειμένου να καταστεί η εργασία αυτή πληρέστερη και να δοθεί στους αναγνώστες με διαφορετικό υπόβαθρο, όσο το δυνατόν περισσότερο, συνολική άποψη για τη δυνητική χρησι- μότητα τέτοιων διεπιστημονικών προσεγγίσεων. Τα υπόλοιπα δύο τμήματα του πρώτου κεφα- λαίου εστιάζουν σε δίκτυα του εγκεφάλου και σε κεντρικές έννοιες της θεωρίας γραφημάτων, όπως η κεντρικότητα, στη μελέτη τους. Το δεύτερο κεφάλαιο εισάγει μερικές έννοιες της κοινωνικότητας των ζώων και αναφέρεται σε μελέτες της συνεργασίας στο ζωικό βασίλειο, εστιάζοντας στην εξελικτική θεωρία γραφημάτων και παιγνίων. Επιπλέον, στη τελευταία ενότητα αυτού του κεφαλαίου συζητείται η συλλογική κίνηση ομάδων ζώων, παρέχοντας εκτός των άλλων, εισαγωγή βασικών όρων για το επόμενο τρίτο κεφάλαιο. Η διεπιστημονική έρευνα, με στόχο την ενοποίηση μεθόδων από διαφορετικούς τομείς, λαμβάνει χώρα ευρέως για να απαντήσει βιολογικά ερωτήματα. Εντούτοις, όπως παρουσιάζεται παρακάτω, η έρευνα στους αλγορίθμους και στη βιολογία μπορούν να συμβάλλουν στην ανάπτυξη η μια της άλλης. Ως εκ τούτου, το τρίτο κεφάλαιο παρέχει πληροφορίες σχετικά με αλγόριθμους των οποίων ο σχεδιασμός έχει εμπνευστεί από τη (συλλογική) συμπεριφορά των ζώων στο φυσικό περιβάλλον. Τέλος, το τέταρτο κεφάλαιο αποκλίνει εκ νέου από το επίκεντρο των προηγούμε- νων κεφαλαίων και κάνει μια σύντομη εισαγωγή στο σημαντικό, αλλά και αμφιλεγόμενο, υπολογιστικό χαρακτήρα της νόησης και κατ’ επέκταση της συμπεριφοράς. Συνολικά, μπορεί κανείς να παρατηρήσει ότι η συνεργασία των προαναφερθέντων πεδίων είναι εκτεταμένη ενώ η πραγματοποιημένη έρευνα ανοίγει νέα ερωτήματα που μπορούν να μελετηθούν μόνο υπό το φως τέτοιων διεπιστημονικών συνεργασιών.Graph theory and algorithms offer precious toolboxes for the modelling as well as the analysis of numerous phenomena in natural sciences. Here a review of the modern bibliography is pre- sented, divided in four main chapters, giving some indications on how the concepts of these two disciplines can be used for the study of animal behaviour and neuroscience. As an exception the premier part of the first chapter provides a short discussion on the applications of graph theory on molecular biology. This choice made in order to make this work more complete and give to the readers from various backgrounds an, as much as possible, overall view of the future potential of such interdisciplinary approaches. The rest two sections of the first chapter deals with brain networks and central terms of graph theory, such as centrality, in their study. The second chapter introduces some concepts of animal sociality and refers to studies of animal cooperation, focusing on evolutionary graph and game theory. Moreover, in the last section of this chapter the collective motion of animal groups is discussed providing, into the bargain, an introduction of basic terms for the subsequent third chapter. Interdisciplinary research, aiming to unite methods from different fields, is vastly used in order to answer biological questions. Although, as it is presented below, both the fields of algorithms and biology can contribute to the elaboration of each other. Hence, the third chapter provides information about algorithms whose design has been inspired by the (collective) behaviour of animals in the nature. Finally, the fourth chapter deviates anew from the central focus of the previous chapters and makes a short introduction in the substantial controversial computational nature of cognition and by extension behaviour. Overall, one can observe that the cooperation of the above mentioned fields is extensive while the accomplished research opens new questions which can be studied only in the light of such collaborations

Performance Evaluation of Network Anomaly Detection Systems

Nowadays, there is a huge and growing concern about security in information and communication technology (ICT) among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. Attacks, problems, and internal failures when not detected early may badly harm an entire Network system. Thus, this thesis presents an autonomous profile-based anomaly detection system based on the statistical method Principal Component Analysis (PCADS-AD). This approach creates a network profile called Digital Signature of Network Segment using Flow Analysis (DSNSF) that denotes the predicted normal behavior of a network traffic activity through historical data analysis. That digital signature is used as a threshold for volume anomaly detection to detect disparities in the normal traffic trend. The proposed system uses seven traffic flow attributes: Bits, Packets and Number of Flows to detect problems, and Source and Destination IP addresses and Ports, to provides the network administrator necessary information to solve them. Via evaluation techniques, addition of a different anomaly detection approach, and comparisons to other methods performed in this thesis using real network traffic data, results showed good traffic prediction by the DSNSF and encouraging false alarm generation and detection accuracy on the detection schema. The observed results seek to contribute to the advance of the state of the art in methods and strategies for anomaly detection that aim to surpass some challenges that emerge from the constant growth in complexity, speed and size of today’s large scale networks, also providing high-value results for a better detection in real time.Atualmente, existe uma enorme e crescente preocupação com segurança em tecnologia da informação e comunicação (TIC) entre a comunidade científica. Isto porque qualquer ataque ou anomalia na rede pode afetar a qualidade, interoperabilidade, disponibilidade, e integridade em muitos domínios, como segurança nacional, armazenamento de dados privados, bem-estar social, questões econômicas, e assim por diante. Portanto, a deteção de anomalias é uma ampla área de pesquisa, e muitas técnicas e abordagens diferentes para esse propósito surgiram ao longo dos anos. Ataques, problemas e falhas internas quando não detetados precocemente podem prejudicar gravemente todo um sistema de rede. Assim, esta Tese apresenta um sistema autônomo de deteção de anomalias baseado em perfil utilizando o método estatístico Análise de Componentes Principais (PCADS-AD). Essa abordagem cria um perfil de rede chamado Assinatura Digital do Segmento de Rede usando Análise de Fluxos (DSNSF) que denota o comportamento normal previsto de uma atividade de tráfego de rede por meio da análise de dados históricos. Essa assinatura digital é utilizada como um limiar para deteção de anomalia de volume e identificar disparidades na tendência de tráfego normal. O sistema proposto utiliza sete atributos de fluxo de tráfego: bits, pacotes e número de fluxos para detetar problemas, além de endereços IP e portas de origem e destino para fornecer ao administrador de rede as informações necessárias para resolvê-los. Por meio da utilização de métricas de avaliação, do acrescimento de uma abordagem de deteção distinta da proposta principal e comparações com outros métodos realizados nesta tese usando dados reais de tráfego de rede, os resultados mostraram boas previsões de tráfego pelo DSNSF e resultados encorajadores quanto a geração de alarmes falsos e precisão de deteção. Com os resultados observados nesta tese, este trabalho de doutoramento busca contribuir para o avanço do estado da arte em métodos e estratégias de deteção de anomalias, visando superar alguns desafios que emergem do constante crescimento em complexidade, velocidade e tamanho das redes de grande porte da atualidade, proporcionando também alta performance. Ainda, a baixa complexidade e agilidade do sistema proposto contribuem para que possa ser aplicado a deteção em tempo real

Swarm-Organized Topographic Mapping

Topographieerhaltende Abbildungen versuchen, hochdimensionale oder komplexe Datenbestände auf einen niederdimensionalen Ausgaberaum abzubilden, wobei die Topographie der Daten hinreichend gut wiedergegeben werden soll. Die Qualität solcher Abbildung hängt gewöhnlich vom eingesetzten Nachbarschaftskonzept des konstruierenden Algorithmus ab. Die Schwarm-Organisierte Projektion ermöglicht eine Lösung dieses Parametrisierungsproblems durch die Verwendung von Techniken der Schwarmintelligenz. Die praktische Verwendbarkeit dieser Methodik wurde durch zwei Anwendungen auf dem Feld der Molekularbiologie sowie der Finanzanalytik demonstriert

Mining Aircraft Telemetry Data With Evolutionary Algorithms

The Ganged Phased Array Radar - Risk Mitigation System (GPAR-RMS) was a mobile ground-based sense-and-avoid system for Unmanned Aircraft System (UAS) operations developed by the University of North Dakota. GPAR-RMS detected proximate aircraft with various sensor systems, including a 2D radar and an Automatic Dependent Surveillance - Broadcast (ADS-B) receiver. Information about those aircraft was then displayed to UAS operators via visualization software developed by the University of North Dakota. The Risk Mitigation (RM) subsystem for GPAR-RMS was designed to estimate the current risk of midair collision, between the Unmanned Aircraft (UA) and a General Aviation (GA) aircraft flying under Visual Flight Rules (VFR) in the surrounding airspace, for UAS operations in Class E airspace (i.e. below 18,000 feet MSL). However, accurate probabilistic models for the behavior of pilots of GA aircraft flying under VFR in Class E airspace were needed before the RM subsystem could be implemented. In this dissertation the author presents the results of data mining an aircraft telemetry data set from a consecutive nine month period in 2011. This aircraft telemetry data set consisted of Flight Data Monitoring (FDM) data obtained from Garmin G1000 devices onboard every Cessna 172 in the University of North Dakota\u27s training fleet. Data from aircraft which were potentially within the controlled airspace surrounding controlled airports were excluded. Also, GA aircraft in the FDM data flying in Class E airspace were assumed to be flying under VFR, which is usually a valid assumption. Complex subpaths were discovered from the aircraft telemetry data set using a novel application of an ant colony algorithm. Then, probabilistic models were data mined from those subpaths using extensions of the Genetic K-Means (GKA) and Expectation- Maximization (EM) algorithms. The results obtained from the subpath discovery and data mining suggest a pilot flying a GA aircraft near to an uncontrolled airport will perform different maneuvers than a pilot flying a GA aircraft far from an uncontrolled airport, irrespective of the altitude of the GA aircraft. However, since only aircraft telemetry data from the University of North Dakota\u27s training fleet were data mined, these results are not likely to be applicable to GA aircraft operating in a non-training environment

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so