RoleSim* : scaling axiomatic role-based similarity ranking on large graphs

RoleSim and SimRank are among the popular graph-theoretic similarity measures with many applications in, e.g., web search, collaborative filtering, and sociometry. While RoleSim addresses the automorphic (role) equivalence of pairwise similarity which SimRank lacks, it ignores the neighboring similarity information out of the automorphically equivalent set. Consequently, two pairs of nodes, which are not automorphically equivalent by nature, cannot be well distinguished by RoleSim if the averages of their neighboring similarities over the automorphically equivalent set are the same. To alleviate this problem: 1) We propose a novel similarity model, namely RoleSim*, which accurately evaluates pairwise role similarities in a more comprehensive manner. RoleSim* not only guarantees the automorphic equivalence that SimRank lacks, but also takes into account the neighboring similarity information outside the automorphically equivalent sets that are overlooked by RoleSim. 2) We prove the existence and uniqueness of the RoleSim* solution, and show its three axiomatic properties (i.e., symmetry, boundedness, and non-increasing monotonicity). 3) We provide a concise bound for iteratively computing RoleSim* formula, and estimate the number of iterations required to attain a desired accuracy. 4) We induce a distance metric based on RoleSim* similarity, and show that the RoleSim* metric fulfills the triangular inequality, which implies the sum-transitivity of its similarity scores. 5) We present a threshold-based RoleSim* model that reduces the computational time further with provable accuracy guarantee. 6) We propose a single-source RoleSim* model, which scales well for sizable graphs. 7) We also devise methods to scale RoleSim* based search by incorporating its triangular inequality property with partitioning techniques. Our experimental results on real datasets demonstrate that RoleSim* achieves higher accuracy than its competitors while scaling well on sizable graphs with billions of edges

Automatic target recognition with deep metric learning.

An Automatic Target Recognizer (ATR) is a real or near-real time understanding system where its input (images, signals) are obtained from sensors and its output is the detected and recognized target. ATR is an important task in many civilian and military computer vision applications. The used sensors, such as infrared (IR) imagery, enlarge our knowledge of the surrounding environment, especially at night as they provide continuous surveillance. However, ATR based on IR faces major challenges such as meteorological conditions, scale and viewpoint invariance. In this thesis, we propose solutions that are based on Deep Metric Learning (DML). DML is a technique that has been recently proposed to learn a transformation to a representation space (embedding space) in end-to-end manner based on convolutional neural networks. We explore three distinct approaches. The first one, is based on optimizing a loss function based on a set of triplets [47]. The second one is based on a method that aims to capture the explicit distributions of the different classes in the transformation space [45]. The third method aims to learn a compact hyper-spherical embedding based on Von Mises-Fisher distribution [64]. For these methods, we propose strategies to select and update the constraints to reduce the intra-class variations and increase the inter-class variations. To validate, analyze and compare the three different DML approaches, we use a large real benchmark data that contain multiple target classes of military and civilian vehicles. These targets are captured at different viewing angles, different ranges, and different times of the day. We validate the effectiveness of these methods by evaluating their classification performance as well as analyzing the compactness of their learned features. We show that the three considered methods can learn models that achieve their objectives

Using entropy and AHP-TOPSIS for comprehensive evaluation of internet shopping malls and solution optimality

Consumers are switching from offline to online to buy everything due to this reason nowadays Internet shopping malls (ISMs) are setting up a very crucial role in the economy. For assessment and ranking are basically a critical work which could be exploitation of Internet shopping malls information resources when consider in a scientific way, there are many methods for the evaluation and ranking of e-commerce sites. Taking into consideration Traffic Rank, Inbound Links, Competition, Speed, and Keyword Statistics, in literature Multi Criteria Decision Making (MCDM) methods are rarely used by the researchers to find the rank of Internet Shopping Malls (ISMs) on the basis of primary/secondary data of these influencing factors. This study, therefore, is unique to narrow down the gap in literature by employing MCDM methods i.e. Entropy and Analytic Hierarchy Process (AHP) to collect the weight of influencing factors and Technique for Order Preference by Similarity to Ideal (TOPSIS) to find the rank of Internet Shopping Malls (ISMs). After finding out the rank of selected criteria, solution optimality needs to be done to find the average ideal solution matrix. Conclusion and managerial implications of the study are also discussed.N/

Context-dependent fusion with application to landmine detection.

Traditional machine learning and pattern recognition systems use a feature descriptor to describe the sensor data and a particular classifier (also called expert or learner ) to determine the true class of a given pattern. However, for complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be viable alternative to using a single classifier. In this thesis we introduce a new Context-Dependent Fusion (CDF) approach, We use this method to fuse multiple algorithms which use different types of features and different classification methods on multiple sensor data. The proposed approach is motivated by the observation that there is no single algorithm that can consistently outperform all other algorithms. In fact, the relative performance of different algorithms can vary significantly depending on several factions such as extracted features, and characteristics of the target class. The CDF method is a local approach that adapts the fusion method to different regions of the feature space. The goal is to take advantages of the strengths of few algorithms in different regions of the feature space without being affected by the weaknesses of the other algorithms and also avoiding the loss of potentially valuable information provided by few weak classifiers by considering their output as well. The proposed fusion has three main interacting components. The first component, called Context Extraction, partitions the composite feature space into groups of similar signatures, or contexts. Then, the second component assigns an aggregation weight to each detector\u27s decision in each context based on its relative performance within the context. The third component combines the multiple decisions, using the learned weights, to make a final decision. For Context Extraction component, a novel algorithm that performs clustering and feature discrimination is used to cluster the composite feature space and identify the relevant features for each cluster. For the fusion component, six different methods were proposed and investigated. The proposed approached were applied to the problem of landmine detection. Detection and removal of landmines is a serious problem affecting civilians and soldiers worldwide. Several detection algorithms on landmine have been proposed. Extensive testing of these methods has shown that the relative performance of different detectors can vary significantly depending on the mine type, geographical site, soil and weather conditions, and burial depth, etc. Therefore, multi-algorithm, and multi-sensor fusion is a critical component in land mine detection. Results on large and diverse real data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our experiments have also indicated that the context-dependent fusion outperforms all individual detectors and several global fusion methods

Análisis, caracterización y modelación 3D de fugas de agua en sistemas de abastecimiento de agua mediante imágenes de GPR

Tesis por compendio[ES] Los esfuerzos que hacen los países, en conjunto con organizaciones mundiales, tales como IWA (por International Water Association), ONU-Agua y OMS (Organización Mundial de la Salud), para mitigar el impacto ambiental en el campo de la hidráulica urbana son considerados de vital importancia. Sin embargo, la escasez de los recursos hídricos en el mundo aumenta diariamente. Esto viene dado por el aumento constante de la demanda en los sectores industrial, agrícola y urbano, provocado por el aumento poblacional y el cambio climático. Los administradores de los sistemas de abastecimiento de agua (WSSs, por sus siglas en inglés, water supply systems) se han visto desafiados a suplir la creciente demanda de los diferentes sectores con la cantidad, calidad y eficiencia necesarios y, a su vez, reducir el desperdicio y el mal uso del recurso. Desde esta perspectiva, las fugas de agua son el mayor problema que enfrentan los administradores de estas empresas de servicios públicos. Las fugas en una red provocan problemas de salud, de escasez, económicos y medioambientales. El uso de técnicas de inspección no destructivas debe favorecer una rápida identificación de problemas, para realizar acciones posteriores de reparación en la red. Este trabajo hace uso del GPR (siglas en inglés de ground penetrating radar) como técnica de inspección no destructiva porque: favorece la exploración del subsuelo sin causar alteraciones al medio, es de fácil aplicación y, además, permite obtener pseudo imágenes del subsuelo. Uno de los objetivos de este documento es identificar y extraer características de una fuga en un WSS mediante imágenes de GPR, con el fin último de recrear las fugas a través de modelos 3D. Se realizaron ensayos de laboratorio bajo condiciones controladas donde se emuló una parcela en la cual se había enterrado una tubería con una pequeño orifico que simula una fuga de agua; tras introducir agua al sistema, se realizaron prospecciones con el GPR. Una vez finalizada la exploración del subsuelo, dado que las imágenes de GPR en bruto obtenidas no son fácilmente interpretables por personal no experto, tales imágenes fueron sometidas a procesamiento de datos que favorezcan su fácil interpretación. Este documento presenta dos metodologías de procesamiento de datos que permiten obtener imágenes a partir de las cuales es posible identificar tanto los componentes del sistema como la fuga y su alcance. Las metodologías de tratamiento de datos aplicadas en este documento son una metodología basada en sistemas multi-agente y el filtro de varianza, metodología basada en parámetros estadísticos de segundo orden. Posteriormente, tras aplicar estas metodologías de procesamiento a las imágenes, se sometieron los resultados a un análisis que facilitase la mejor elección evitando la subjetividad del experto. Bajo este concepto, este documento propone el uso conjunto de técnicas multicriterio. Se utilizó el Proceso de Jerarquía Analítica Difusa (FAHP, por sus siglas en inglés, Fuzzy Analytical Hierarchy Process), que permite ponderar varios criterios de evaluación, con el propósito de mitigar la incertidumbre que caracterizan los juicios de los expertos, en conjunto con el método ELECTRE III para obtener la clasificación final de alternativas, todo esto de la manera más objetiva posible. Los resultados de este documento son satisfactorios, permitiendo obtener amplio conocimiento de las fugas y su interacción con el subsuelo, proporcionando pautas para desarrollar posteriormente metodologías de automatización que permitan localizar, seguir y predecir problemas en los WSSs.[CA] Els esforços que fan els països en conjunt amb organitzacions mundials, como ara IWA (per International Water Association), ONU-Agua i OMS (per Organització Mundial de la Salut), per a mitigar l'impacte ambiental en el camp de la hidràulica urbana són considerats de vital importància. No obstant això, l'escassetat dels recursos hídrics en el món augmenta diàriament, donat per l'augment constant de la demanda en els sectors industrial, agrícola i urbà, provocat per l'augment poblacional i el canvi climàtic. Els administradors dels sistemes d'abastiment d'aigua (WSSs, per les seus sigles en anglès, water supply systems) s'han vist desafiats a suplir la creixent demanda dels diferents sectors amb la quantitat, qualitat i eficiència necessaris i, al seu torn, reduir el desaprofitament i el mal ús del recurs. Enfocant aquesta perspectiva, les pèrdues d'aigua són el problema més gran fet front pels directors d'aquestes utilitats. Les pèrdues d'aigua en una xarxa provoquen problemes de salut, d'escassetat, econòmics i mediambientals. L'ús de tècniques d'inspecció no destructives que afavoreixen una ràpida identificació per a realitzar accions de reparació posteriors en la xarxa. Aquest treball fa ús del GPR (sigles en anglès per ground penetrating radar) com a tècnica d'inspecció no destructiva perquè afavoreix l'exploració del subsol sense causar alteracions al entorn, és de fàcil aplicació i a més permet obtenir pseudo imatges del subsol. Un dels objectius d'aquest document és identificar i extraure característiques d'una pèrdua en un WSS mitjançant imatges de GPR, amb la fi última de recrear les pèrdues a través de models 3D. Es van realitzar assajos de laboratori sota condicions controlades on es va emular una parcel¿la en la qual s'ha enterrat una canonada amb una xicotet forat que simula una pèrdua d'aigua; després d'introduir aigua al sistema, s'obtenen prospeccions amb el GPR. Una vegada finalitzada l'exploració del subsol, atès que les imatges de GPR en brut obtingudes no són fàcilment interpretables per personal no expert, són sotmeses a processament de dades que afavorisquen la seua fàcil interpretació. Aquest document presenta dues metodologies de processament de dades que permeten obtenir imatges de les quals és possible identificar tant els components del sistema com la pèrdua i el seu abast. Les metodologies de tractament de dades aplicades en aquest document són una metodologia basada en multi-agents (MABS, per les seves sigles en anglès, multi-agent-based systems) i el filtre de variància, metodologia basada en paràmetres estadístics de segon ordre. Posteriorment, després d'aplicar aquestes metodologies de processament a les imatges se sotmeten els resultats a una anàlisi que faciliti la millor elecció evitant la subjectivitat de l'expert. Sota aquest concepte, aquest document proposa l'ús conjunt de tècniques de decisió multi-criteri (MCDM, per les seves sigles en anglès, multi-criteria decision-making). Es va utilitzar el Procés de Jerarquia Analítica Difusa (FAHP, per les seves sigles en anglès, Fuzzy Analytical Hierarchy Process) el qual s'utilitza per a ponderar diversos criteris d'avaluació, amb el propòsit de mitigar la incertesa que caracteritzen els judicis dels experts, en conjunt amb el mètode ELECTRE III, per a obtenir la classificació final d'alternatives, tot això de la manera més objectiva possible. Els resultats d'aquest document són satisfactoris, permetent obtenir ampli coneixement de les pèrdues d'aigua i la seua interacció amb el subsol, donant-nos la pauta per a desenvolupar posteriorment metodologies d'automatització que permeten localitzar, seguir i predir problemes en els WSSs.[EN] The efforts made by the countries in collaboration with world organizations, such as IWA (for International Water Association), UN-Water and WHO (for World Health Organization), to mitigate the environmental impact in the field of urban hydraulics are considered of vital importance. However, the scarcity of water resources in the world increases daily, given by the constant increase in demand in the industrial, agricultural and urban sectors, caused by the population increase and the climate change. Managers of water supply systems (WSSs) are challenged to supply the growing demand of different sectors with sufficient quantity, quality and efficiency and, in turn, reduce waste and misuse of the resource. Focusing this perspective, water leaks are the biggest problem faced by the managers of these utilities. Leaks in a network cause health, shortage, economic and environmental problems. The use of non-destructive inspection techniques favors rapid identification to carry out subsequent repair actions on the network. This work makes use of the GPR (ground penetrating radar) as a non-destructive inspection technique because: it favors the exploration of the ground without causing alterations to the environment, it is easy to apply, and also allows to obtain pseudo images of the subsoil. This document presents two data processing methodologies that allow obtaining images from which it is possible to identify both the system components and the leak and its scope. The data treatment methodologies applied in this document are a multi-agent-based system (MABS) methodology and the variance filter, a methodology based on second-order statistical parameters. Subsequently, after applying these processing methodologies to the images, the results are subjected to an analysis that eases the best choice, avoiding expert's subjectivity. Under this concept, this document proposes the joint use of two multi-criteria decision-making (MCDM) methods. The Fuzzy Analytical Hierarchy Process (FAHP) is used first to weight various evaluation criteria, in order to mitigate the uncertainty that characterize the experts' judgments, in conjunction with the ELECTRE III method, to obtain the final classification of alternatives in the most objective way. The results of this document are satisfactory, allowing to obtain extensive knowledge of leaks and their interaction with the subsoil, giving a guideline to subsequently develop automation methodologies that allow locating, monitoring and predicting problems in WSSs.Part of this work has been developed under the support of Fundación Carolina PhD and short-term scholarship programOcaña Levario, SJ. (2021). Análisis, caracterización y modelación 3D de fugas de agua en sistemas de abastecimiento de agua mediante imágenes de GPR [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/163677TESISCompendi

Dynamic scene understanding: Pedestrian tracking from aerial devices.

Multiple Object Tracking (MOT) is the problem that involves following the trajectory of multiple objects in a sequence, generally a video. Pedestrians are among the most interesting subjects to track and recognize for many purposes such as surveillance, and safety. In the recent years, Unmanned Aerial Vehicles (UAV’s) have been viewed as a viable option for monitoring public areas, as they provide a low-cost method of data collection while covering large and difficult-to-reach areas. In this thesis, we present an online pedestrian tracking and re-identification from aerial devices framework. This framework is based on learning a compact directional statistic distribution (von-Mises-Fisher distribution) for each person ID using a deep convolutional neural network. The distribution characteristics are trained to be invariant to clothes appearances and to transformations. In real world scenarios, during deployment, new pedestrian and objects can appear in the scene and the model should detect them as Out Of Distribution (OOD). Thus, our frameworks also includes an OOD detection adopted from [16] called Virtual Outlier Synthetic (VOS), that detects OOD based on synthesising virtual outlier in the embedding space in an online manner. To validate, analyze and compare our approach, we use a large real benchmark data that contain detection tracking and identity annotations. These targets are captured at different viewing angles, different places, and different times by a ”DJI Phantom 4” drone. We validate the effectiveness of the proposed framework by evaluating their detection, tracking and long term identification performance as well as classification performance between In Distribution (ID) and OOD. We show that the the proposed methods in the framework can learn models that achieve their objectives

Characterizing structural relationships in scenes using graph kernels

Modeling virtual environments is a time consuming and expensive task that is becoming increasingly popular for both professional and casual artists. The model density and complexity of the scenes rep-resenting these virtual environments is rising rapidly. This trend suggests that data-mining a 3D scene corpus to facilitate collabora-tive content creation could be a very powerful tool enabling more efficient scene design. In this paper, we show how to represent scenes as graphs that encode models and their semantic relation-ships. We then define a kernel between these relationship graphs that compares common virtual substructures in two graphs and cap-tures the similarity between their corresponding scenes. We apply this framework to several scene modeling problems, such as find-ing similar scenes, relevance feedback, and context-based model search. We show that incorporating structural relationships allows our method to provide a more relevant set of results when compared against previous approaches to model context search

Characterizing structural relationships in scenes using graph kernels

Quelles relations entretiennent les mouvements sociaux de 1968 et les mouvements féministes en Europe et en Amérique du Nord ? Telle est la question posée dans cet essai par Brigitte Studer, professeure d’histoire suisse et d’histoire moderne à l’Université de Berne. Cependant, ce ne sont pas tant les aspects théoriques – bien qu’il soit difficile d’en faire l’économie – qui sont au cœur de son propos mais bien la constitution d’une puissance agissante qui est au centre du questionnement de l..

Musical timbre: bridging perception with semantics

Musical timbre is a complex and multidimensional entity which provides information regarding the properties of a sound source (size, material, etc.). When it comes to music, however, timbre does not merely carry environmental information, but it also conveys aesthetic meaning. In this sense, semantic description of musical tones is used to express perceptual concepts related to artistic intention. Recent advances in sound processing and synthesis technology have enabled the production of unique timbral qualities which cannot be easily associated with a familiar musical instrument. Therefore, verbal description of these qualities facilitates communication between musicians, composers, producers, audio engineers etc. The development of a common semantic framework for musical timbre description could be exploited by intuitive sound synthesis and processing systems and could even influence the way in which music is being consumed. This work investigates the relationship between musical timbre perception and its semantics. A set of listening experiments in which participants from two different language groups (Greek and English) rated isolated musical tones on semantic scales has tested semantic universality of musical timbre. The results suggested that the salient semantic dimensions of timbre, namely: luminance, texture and mass, are indeed largely common between these two languages. The relationship between semantics and perception was further examined by comparing the previously identified semantic space with a perceptual timbre space (resulting from pairwise dissimilarity rating of the same stimuli). The two spaces featured a substantial amount of common variance suggesting that semantic description can largely capture timbre perception. Additionally, the acoustic correlates of the semantic and perceptual dimensions were investigated. This work concludes by introducing the concept of partial timbre through a listening experiment that demonstrates the influence of background white noise on the perception of musical tones. The results show that timbre is a relative percept which is influenced by the auditory environment