Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting and Learning Semantics from Social Web Data

Making machines understand natural language is a dream of mankind that existed since a very long time. Early attempts at programming machines to converse with humans in a supposedly intelligent way with humans relied on phrase lists and simple keyword matching. However, such approaches cannot provide semantically adequate answers, as they do not consider the specific meaning of the conversation. Thus, if we want to enable machines to actually understand language, we need to be able to access semantically relevant background knowledge. For this, it is possible to query so-called ontologies, which are large networks containing knowledge about real-world entities and their semantic relations. However, creating such ontologies is a tedious task, as often extensive expert knowledge is required. Thus, we need to find ways to automatically construct and update ontologies that fit human intuition of semantics and semantic relations. More specifically, we need to determine semantic entities and find relations between them. While this is usually done on large corpora of unstructured text, previous work has shown that we can at least facilitate the first issue of extracting entities by considering special data such as tagging data or human navigational paths. Here, we do not need to detect the actual semantic entities, as they are already provided because of the way those data are collected. Thus we can mainly focus on the problem of assessing the degree of semantic relatedness between tags or web pages. However, there exist several issues which need to be overcome, if we want to approximate human intuition of semantic relatedness. For this, it is necessary to represent words and concepts in a way that allows easy and highly precise semantic characterization. This also largely depends on the quality of data from which these representations are constructed. In this thesis, we extract semantic information from both tagging data created by users of social tagging systems and human navigation data in different semantic-driven social web systems. Our main goal is to construct high quality and robust vector representations of words which can the be used to measure the relatedness of semantic concepts. First, we show that navigation in the social media systems Wikipedia and BibSonomy is driven by a semantic component. After this, we discuss and extend methods to model the semantic information in tagging data as low-dimensional vectors. Furthermore, we show that tagging pragmatics influences different facets of tagging semantics. We then investigate the usefulness of human navigational paths in several different settings on Wikipedia and BibSonomy for measuring semantic relatedness. Finally, we propose a metric-learning based algorithm in adapt pre-trained word embeddings to datasets
Leveraging sparse and dense features for reliable state estimation in urban environments

An ultimate goal of mobile robotics research is the development of intelligent platforms that are capable of undertaking a variety of tasks in everyday life for their users. Over the previous decade, robots have become more integrated into our daily lives, performing tasks in numerous environments including industrial settings such as assembly and manufacturing, indoor scenes such as home assistance and educational tasks, and outdoor areas such as lawn mowing and parcel delivery. Despite the significant strides achieved in the various application areas, reliably deploying robots in urban environments remains an open challenge. In order to realize the goal of ubiquitous robotics, the ability of mobile robots to reliably estimate their state as well as the state of the agents in their vicinity is crucial for their successful deployment. However, in order to achieve this goal, robots need to overcome several challenges. The choice of sensor modality employed for extracting information about the environment plays a major role in the representational capabilities of the localization module. While LiDAR sensors are able to provide geometric information of the environment, cameras provide a low cost alternative with rich color and texture information which is crucial for reasoning about the scene. However, accurately estimating the pose of the robot using only cameras is an arduous task especially in urban scenarios. The complex nature of urban environments due to the presence of multiple repetitive structures and glass facades of buildings render the task of reliable localization extremely challenging. Furthermore, the varying weather and illumination conditions in addition to the frequently changing nature of the scene due to constructions necessitates the constant maintenance of the localization module to enable accurate state estimation. Urban environments commonly are of a highly dynamic and stochastic nature caused by moving pedestrians, cars and cyclists. Each of these agents often traverses a different trajectory and obeys different traffic rules. This in turn makes the task of estimating the state of surrounding agents extremely challenging. The aforementioned challenges render highly accurate generalizable hand-crafted solutions to the state estimation problem unattainable, as it is infeasible for an expert to anticipate and pre-program solutions for all potential scenarios. A promising solution for this problem is robots that are able to leverage the abundant rich information in the environment such as semantic, structural and geometric information in order to learn models for reliable and accurate state estimation. In this thesis, we address the problem of reliable state estimation in urban environments by introducing novel techniques that address these challenges through exploiting sparse
The importance of unexpectedness: Discovering buzzing stories in anomalous temporal graphs

The real-time nature and massive volume of social-media data has converted news portals and micro-blogging platforms into social sensors, causing a ﬂourishing of research on story or event detection in online user-generated content and social-media text streams. Existing approaches to story identiﬁcation broadly fall into two categories. Approaches in the ﬁrst category extract stories as cohesive substructures in a graph representing the strength of association between terms. The latter category includes approaches that analyze the temporal evolution of individual terms and identify stories by grouping terms with similar anomalous temporal behavior. Both categories have their own limitations. Approaches in the ﬁrst category are unable to distinguish ever-popular concepts from stories that buzz in a time interval of interest, i.e., attract an amount of attention that deviates signiﬁcantly from the typical level observed. The second category ignores term co-associations and the wealth of information captured by them. In this work we advance the literature on story identiﬁcation by proﬁtably combining the peculiarities of the two main state-of-the-art approaches. We propose a novel method that characterizes abnormal association between terms in a certain time window and leverages the graph structure induced by such anomalous associations so as to identify stories as subsets of terms that are cohesively associated in this graph. Experiments performed on two datasets extracted from a real-world web-search query log and a news corpus, respectively, attest the superiority of the proposed method over the two main existing story-identiﬁcation approaches.
Reconstruction of building objects from point clouds of built environment and construction sites

Point clouds obtained by laser scanning or stereo matching of images are frequently used in the description of the built environment in the inventory as well as for buildings in the construction phase. The surfaces of measured object are typically represented by 3D points, which, however, do not carry topological and semantic information. The aim of this research is the development of methods for the reconstruction of building objects in urban scenes and construction sites, whereby new algorithms in the areas of segmentation, classification, and model representation are presented. The paper provides contributions on three aspects, namely (i) developing and testing optimized data structures, (ii) developing features for robust and discriminative feature descriptions, and (iii) graph-based optimization to refine results. To optimize the data structure, voxel-based processing is performed, which includes voxel-based filtering, octree-based indexing, supervoxel based over-segmentation, and the extraction of local features based on context of supervoxels. When developing features, both unary and binary features are investigated for elements such as points, voxels, and supervoxels. For unary features of points, a novel linear shape descriptor LSSHOT is presented that extracts linear structures from points. For unary features of voxels or supervoxels, eigenvalues are derived which allow a detailed description of groups of points within the voxel and the supervoxel. For unary features of the local context of voxels or supervoxels, the detrended geometric feature is developed using the multi-scale strategy, removing local low-level geometry trends for the purposes of distinctness. For describing binary features between elements, grouping laws of perceptual psychology are used, for example, to analyze relationships based on proximity, similarity, and continuity. In graph-based optimization, both local and global graphical models are presented and used for segmentation and classification. By optimizing a weighted graphical model, robust point segmentation can be achieved and the classification results refined. The experiments for various applications have shown that the proposed voxel-based data structures, the designed features, and the optimization of graphical models enable efficient and effective segmentation of points and significantly improve the classification.
Quorum Quenching Lactonase Strengthens Bacteriophage and Antibiotic Arsenal Against Pseudomonas aeruginosa Clinical Isolates

Many bacteria use quorum sensing (QS), a bacterial communication system based on the diffusion and perception of small signaling molecules, to synchronize their behavior in a cell-density dependent manner. QS regulates the expression of many genes associated with virulence factor production and biofilm formation. This latter is known to be involved in antibiotic and phage resistance mechanisms. Therefore, disrupting QS, a strategy known as quorum quenching (QQ), appears to be an interesting way to reduce bacterial virulence and increase antibiotic and phage treatment efficiency. In this study, the ability of the QQ enzyme SsoPox-W263I, a lactonase able to degrade acyl-homoserine lactones, was investigated for quenching both virulence and biofilm formation in clinical isolates of Pseudomonas aeruginosa from diabetic foot ulcers, as well as in the PA14 model strain. These strains were further evolved to resist to bacteriophage cocktails. Overall, 10 antibiotics or bacteriophage resistant strains were evaluated and SsoPox-W263I was shown to decrease pyocyanin, protease and elastase production in all strains. Furthermore, a reduction of more than 70% of biofilm formation was achieved in six out of ten strains. This anti-virulence potential was confirmed in vivo using an amoeba infection model, showing enhanced susceptibility toward amoeba of nine out of ten P. aeruginosa isolates upon QQ. This amoeba model was further used to demonstrate the ability of SsoPox-W263I to enhance the susceptibility of sensitive and phage resistant bacteria to bacteriophage and antibiotic.
Influence of base course gradation on response of granular bases under cyclic loading : a micromechanical study Influence au cours de la gradation de base , en reaction avec les bases granulaires sous chargement cyclique : une etude en micromechanique

Performance of unpaved granular bases relies on the shear strength and stiffness of granular particles. These two mechanical properties depend on shape, size, and gradation of the particles. This paper presents a micromechanical study on the influence of particle gradation on the response of unreinforced and geogrid-reinforced granular bases under cyclic loading. Commercial software, Particle Flow Code (PFC) 2D based on the discrete element method (DEM), was used for this purpose. Different biaxial assemblies of uniform particles and well-graded particles were sheared at low confining stresses for their stress-strain relationships and friction angles. The geogrid was simulated using one layer of bonded particles with different sizes to mimic its grid structures. Two base courses consisting of uniform particles and well-graded particles, which were subjected to a 5kN cyclic ramp load at a frequency of 0.76 Hz using a wheel, were modeled. The DEM study showed that the assembly of well-graded particles had a higher angle of internal friction than the assembly of uniform particles. Further, the addition of the geogrid reduced the deformation after each load cycle. RÉSUMÉ La performance des fondations granulaires non pavées est liée à la résistance au cisaillement et à la rigidité de ces composants granulaires. Ces deux propriétés mécaniques dépendent de la forme, de la taille, et de la granulométrie des particules. Cet article présente une étude « micromécanique » sur l'influence de la granulométrie sur la réponse des bases granulaires non renforcées et renforcées par un géotextile sous un chargement cyclique. Pour cette fin, le logiciel “Particle Flow Code (PFC) 2D”, basé sur la méthode aux éléments discrets (MED), a été utilisé. Différents combinaisons bi-axiales de particules à granulométrie étalée et uniforme ont été cisaillées à basse contrainte de confinement afin de déterminer la relation contrainte-déformation ainsi que les angles de frottement. Le géotextile a été simulé en utilisant une couche de particules liée ayant différentes tailles de grain afin de reproduire le plus possible son squelette. La modélisation s’est faite sur deux types de fondation granulaire (granulométrie étalée puis uniforme), qui ont été soumises à une charge de circulation cyclique de 5 kN à une fréquence de 0,76 hertz en simulant une roue. Les résultats issus de la MED ont montré que la matrice à granulométrie étalée possédait un angle de frottement interne plus élevé que celle à granulométrie uniforme. De plus, l'addition d’un géotextile réduisait la déformation après chaque cycle de chargement.
Soft Constraints in MiniBrass: Foundations and Applications

Over-constrained problems are ubiquitous in real-world decision and optimization problems, in particular, those emerging from self-organizing, autonomous systems where the full problem specification is only available at runtime. To address over-constrainedness, several theoretical formalisms to describe soft constraints have been proposed, including weighted, fuzzy, or probabilistic constraints. All of them were shown to be instances of algebraic structures such as valuation structures or c-semirings. In terms of implemented modeling languages and solvers, however, the field of soft constraints lags far behind the state of the art in classical constraint optimization. Therefore, this dissertation describes MiniBrass, a versatile soft constraint modeling language building on the unifying algebraic framework of partially-ordered valuation structures (PVS). It is implemented as an extension of MiniZinc and MiniSearch. The most important characteristics of MiniBrass, and the ones that distinguish it from previous work, are that it is extensible and modular, supports a variety of concrete soft constraint formalisms, works with many solvers (inherited from MiniZinc), admits a graphical modeling language, and has been applied to several real-life case studies. Contributions of this dissertation include the following: (1) The design, implementation, and performance evaluation of MiniBrass using 28 benchmark problems and six solvers, (2) A formal foundation that includes the systematic derivation of partially-ordered valuation structures and c-semirings from partial orders using basic category theory, (3) The qualitative soft constraint formalism constraint preferences, and (4) concepts for multiagent optimization with (possibly) antagonistic preferences, including lexicographic and Cartesian products as well as voting operators.
Effects of Low Frequency Residual Hearing on Music Perception and Psychoacoustic Abilities in Pediatric Cochlear Implant Recipients

Studies have demonstrated the benefits of low frequency residual hearing in music perception and for psychoacoustic abilities of adult cochlear implant (CI) users, but less is known about these effects in the pediatric group. Understanding the contribution of combined electric and acoustic stimulation in this group can help to gain a better perspective on decisions regarding bilateral implantation. We evaluated the performance of six unilaterally implanted children between 9 and 13 years of age with contralateral residual hearing using the Clinical Assessment of Music Perception (CAMP), spectral ripple discrimination (SRD), and temporal modulation transfer function (TMTF) tests and compared findings with previous research. Our study sample performed similarly to normal hearing subjects in pitch direction discrimination (0.81 semitones) and performed well above typical CI users in melody recognition (43.37%). The performance difference was less in timbre recognition (48.61%), SRD (1.47 ripple/octave), and TMTF for four modulation frequencies. These findings suggest that the combination of low frequency acoustic hearing with the broader frequency range of electric hearing can help to increase clinical CI benefit in pediatric users and decisions regarding second-side implantation should consider these factors.
Synchronized pushdown systems for pointer and data-flow analysis

Static data-flow analysis reasons about behaviour of software without executing it. A precise data-flow analysis transforms the program into context-sensitive, flow-sensitive, and field-sensitive approximation of the software. It is challenging to design an analysis of this precision efficiently. Contextand field-sensitive data-flow analysis, if fully precise, is undecidable, and any model of such precision cannot avoid an approximation. This thesis presents a new data-flow approximation for context-, fieldand flowsensitive data-flow analysis. The solution, called synchronized pushdown systems (SPDS), solves precise distributive data-flow analysis problems by relying on two pushdown systems, one system models field-sensitivity, the other one contextsensitivity. The SPDS then only accepts results that are contextand fieldsensitive. SPDS approximates only in corner cases that are rare in practice: at statements where both problems are satisfied but not along the same data-flow path. Experiments comparing SPDS to the standard model for field-sensitivity, k-limited access path, show that SPDS are almost as efficient as k-limiting with k = 1 although their precision equals to k =∞. Static data-flow analysis needs to resolve pointer relations when data escapes to the heap. Flows of pointers are difficult and costly to resolve because pointer relations are non-distributive. Nevertheless, this thesis shows that pointer analysis can be solved by subdividing pointer relations into multiple distributive computations, for each computation a SPDS can be consulted. Based on this design, the thesis presents the demand-driven pointer analysis Boomerang. Apart from relying on efficiently coordinating multiple SPDS, Boomerang minimizes it computational effort by only resolving the minimal part of pointer relations necessary to answer a points-to query. Another contribution of this thesis is IDE, a generic and efficient framework for data-flow analyses, e.g., typestate analysis or mining of application programming interfaces (APIs). IDE resolves pointer relations automatically and efficiently by the help of Boomerang. This reduces the burden of implementing pointer relations into an analysis. Further on, IDE performs strong updates which makes the analysis sound and precise. Apart from the fundamental problem of finding the right balance between precision and efficiency of a general static data-flow analysis, this thesis elaborates on a concrete application of Boomerang and IDE within a data-flow analysis that detects complex security vulnerabilities. Applying this data-flow analysis on large scale shows once more that synchronized pushdown systems enable a promising compromise between efficiency and precision.
Real-time Tracking of Objects in Image Sequences: Beyond Axis-Aligned Boxes

Single-object tracking is one of the fundamental problems in Computer Vision and has been actively researched for many years. Nonetheless, the object tracking capabilities of humans are still far superior to the current state-of-the-art tracking algorithms. Humans are not only able to track almost any object, even previously unseen ones, in difficult sequences more robustly than current algorithms, they are also able to do this effortlessly within a few milliseconds. Since tracking is usually only a single component in a Computer Vision application, the real-time capabilities of object trackers are an essential property in order for the complete system to work in real-time. The overall goal of this thesis is to bridge the gap between human and machine tracking by developing tracking algorithms that are accurate, robust, and real-timecapable. This leads to the following four main contributions: First, we extend the standard single-object tracking evaluation protocol to support pixel-precise ground truths and trackers. The new evaluation protocol is able to better measure the accuracy of trackers and additionally estimates the tracker’s capability of detecting scale change. Furthermore, we present upper bounds for the accuracy of trackers that are restricted to bounding boxes given pixel-precise ground truth data. Second, we present a new tracking dataset with pixel-precise ground truth. The precise ground truth labels are created automatically from a photo-realistic synthetic dataset. The dataset is focused on sequences in the domain of self driving cars. The sequences are very challenging and include long sequences with full occlusions and different lighting conditions. Together with the previously mentioned evaluation protocol, the pixel-precise labels allow to evaluate trackers with a new level of precision. Third, we develop a new single-object tracker that is able to track homogeneous regions that undergo arbitrary deformations. It is based on component-trees and is computationally efficient. The tracker is successfully used for 2D temporal tracking and for 3D object segmentation. Fourth, we develop an edge-based object tracker. The tracker has a failure mode detection, is robust to nonlinear illumination changes, and can cope with occlusions. It is very accurate and virtually drift-free even for long sequences. Furthermore, it is inherently capable of object re-detection when tracking fails. The tracker is thoroughly evaluated through a number of qualitative and quantitative experiments. It is able to perform on par with the current state-of-the-art deep-learning trackers but is at least 45 times faster.