Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology learning: revisted

The term "ontology" comes from the field of philosophy that is concerned with the study of being or existence. In general computer science defines ontology as an "explicit specification of a conceptualization," which is, "the objects, concepts, and other entities that are presumed to exist in some area of interest and the relationships that hold among them". Ontologies hold a great importance to modern knowledge based systems. They enable shared knowledge and reuse where information resources can be communicated between human or software agentsand should be machine readable. Manual construction of ontologies is an expensive and time consuming task. An answer to this problem is to provide an automatic or semi- automatic tool for ontology construction. Over the past years, this field of research has not yet reached the goal of fully automating the ontology development process. In this paper we will review the ontology creation process with the help of ontology learning (OL) and extend our previous OL framework. We will examine OL applications with respect to the extensions of our framework. And last we will define a roadmap for future work.
Real Time Closeness and Betweenness Centrality Calculations on Streaming Network Data

Closeness and betweenness are among the most important metrics in social network analysis. They are essential to the evaluation of various research problems such as viral marketing, network stability and network traffic predictions, which play an important role in social media research. However, both of these metrics are expensive to compute. We propose an efficient online algorithm framework to handle both closeness and betweenness in the situation where network structure changes frequently. Whenever a link change is received as the input, the algorithm utilizes existing facts about the calculation to update centrality values with minimal effort. Experimental results on data sets collected from online social media websites show that our approach is 4-7 orders of magnitude faster for closeness and 2-4 orders of magnitude faster for betweenness calculations over baseline methods. We also show how those two metrics share some common calculations so that the running time can be dramatically reduced when calculated together. To the best of our knowledge, this is the first work to improve the running time when those two algorithms are calculated at the same time on streaming network data.
Outer membrane protein shifts in biocide‐resistant Pseudomonas aeruginosa PAO1

Benzisothiazolone (BIT), N‐methylisothiazolone (MIT) and 5‐chloro‐N‐methylisothiazolone (CMIT) are highly effective biocidal agents and are used as preservatives in a variety of cosmetic preparations. The isothiazolones have proven efficacy against many fungal and bacterial species including Pseudomonas aeruginosa. However, some species are beginning to exhibit resistance towards this group of compounds after extended exposure. This experiment induced resistance in cultures of Ps. aeruginosa exposed to incrementally increasing sub‐minimum inhibitory concentrations (MICs) of the isothiazolones in their pure chemical forms. The induced resistance was observed as a gradual increase in MIC with each new passage. The MICs for all three test isothiazolones and a thiol‐interactive control compound (thiomersal) increased by approximately twofold during the course of the experiment. The onset of resistance was also observed by reference to the altered presence of an outer membrane protein, designated the T‐OMP, in SDS‐PAGE preparations. T‐OMP was observed to disappear from the biocide‐exposed preparations and reappear when the resistance‐induced cultures were passaged in the absence of biocide. This reappearance of T‐OMP was not accompanied by a complete reversal of induced resistance, but by a small decrease in MIC. The induction of resistance towards one biocide resulted in the development of cross‐resistance towards other members of the group and the control, thiomersal. It has been suggested that the disappearance of T‐OMP from these preparations is associated with the onset of resistance to the isothiazolones in their Kathon™ form (CMIT and MIT).
An Efficient Shortest Triangle Paths Algorithm for Uncertainty-based Multi Camera Calibration

Extrinsically calibrating a multi camera system based on scene images only is in general a very difficult problem. One promising approach computes the extrinsic parameters from sufficiently many pairwise relative poses, which can be reasonably estimated even in case of very many incorrectly matched point correspondences. Obviously, the quality of the calibration highly depends on the correctness of the relative pose estimates. As a limited number of relative poses suffices, we propose automatically selecting only the most reliable ones based on an uncertainty measure. Our selection criterion is equivalent to computing a shortest subgraph consisting of shortest triangle paths. The contribution of this paper consists of a mathematically rigorous proof of the correctness of an efficient algorithm for that problem. In experiments on synthetic and real data, we show that our selection algorithm produces greatly improved calibration results, both, in case of varying portions of outliers as well as varying noise.
Estimation of Chlorophyll-a Concentration and the Trophic State of the Barra Bonita Hydroelectric Reservoir Using OLI/Landsat-8 Images

Reservoirs are artificial environments built by humans, and the impacts of these environments are not completely known. Retention time and high nutrient availability in the water increases the eutrophic level. Eutrophication is directly correlated to primary productivity by phytoplankton. These organisms have an important role in the environment. However, high concentrations of determined species can lead to public health problems. Species of cyanobacteria produce toxins that in determined concentrations can cause serious diseases in the liver and nervous system, which could lead to death. Phytoplankton has photoactive pigments that can be used to identify these toxins. Thus, remote sensing data is a viable alternative for mapping these pigments, and consequently, the trophic. Chlorophyll-a (Chl-a) is present in all phytoplankton species. Therefore, the aim of this work was to evaluate the performance of images of the sensor Operational Land Imager (OLI) onboard the Landsat-8 satellite in determining Chl-a concentrations and estimating the trophic level in a tropical reservoir. Empirical models were fitted using data from two field surveys conducted in May and October 2014 (Austral Autumn and Austral Spring, respectively). Models were applied in a temporal series of OLI images from May 2013 to October 2014. The estimated Chl-a concentration was used to classify the trophic level from a trophic state index that adopted the concentration of this pigment-like parameter. The models of Chl-a concentration showed reasonable results, but their performance was likely impaired by the atmospheric correction. Consequently, the trophic level classification also did not obtain better results.
IKMC: An Improved K-Medoids Clustering Method for Near-Duplicated Records Detection

An improved K-medoids clustering algorithm (IKMC) to resolve the problem of detecting the near-duplicated records is proposed in this paper. It considers every record in database as one separate data object, uses edit-distance method and the weights of attributes to get similarity value among records, then detect duplicated records by clustering these similarity value. This algorithm can automatically adjust the number of clusters through comparing the similarity value with the preset similarity threshold, and avoid a large numbers of I/O operations used by traditional "sort/merge" algorithm for sequencing. Through the experiment, this algorithm is proved to have good detection accuracy and high availability. Keywords-Near-duplicated; record; K-medoids clustering; Edit-distance
INTEGRATION OF LASER AND PHOTOGRAMMETRIC DATA FOR CALIBRATION PURPOSES

Laser scanners are becoming popular since they provide fast and dense geometric surface information. However, sudden elevation changes along the surface are not clearly visible in the laser data due to the sparse distribution of captured points. In general, laser data provides high density surface information in homogenous areas and low density surface information elsewhere (i.e., object space break-lines). Photogrammetry, on the other hand, provides less dense surface information but with high quality, especially along object space discontinuities. Hence, a natural synergy of both systems can be inferred and consequently integration of the respective data would lead to higher quality surface information than that obtained through the use of a single sensor. However, prior to such integration, both systems should be precisely calibrated and aligned. The calibration is usually carried-out for each system independently using additional control information. In this paper, the calibration of the laser and photogrammetric systems is evaluated by checking the quality of fit between co-registered photogrammetric and laser surfaces. The paper starts by introducing a registration procedure where a set of linear features is extracted from both sets. First, planar surfaces from laser data are extracted and adjacent planes are intersected to determine three-dimensional straight line segments. Secondly, linear features from the photogrammetric dataset are obtained through aerial triangulation. A mathematical model for expressing the necessary constraints for the alignment of conjugate photogrammetric and laser straight lines is established. The model ensures that corresponding straight lines will be collinear after registering the two datasets relative to a common reference frame. The quality of fit between the registered surfaces is then used to evaluate and/or improve the calibration parameters of the photogrammetric and laser systems. In this paper, an experiment with real data is used to illustrate this concept. The registered surfaces in this example revealed the presence of systematic inconsistencies between the photogrammetric and laser systems. The pattern of these inconsistencies is found to resemble the effect of un-calibrated lens distortion. In this case, the laser data is used as control information for the determination of lens distortion, which when considered leads to a better fit between the registered surfaces. The estimated lens distortion using the laser was found to be very close to that determined through a rigorous camera calibration procedure.
Development of Transgenic Mice Harboring Ovine Beta Lactoglobulin-Calcitonin Transgene

Expression of foreign proteins in mammalian milk is becoming a widespread strategy for high-level production of recombinant pharmaceuticals, especially those with the most complex post-translational modifications. A gene construct was generated, consisting of 10.7 kbp of the ovine beta-lactoglobulin (oBLG) gene including its promoter and 3´ flanking region with the calcitonin coding sequences inserted in-frame into the oBLG fifth exon. The gene construct was purified using CsCl gradient, released from vector, and gel-purified. It was microinjected into fertilized mouse oocytes. These oocytes were then transferred to pseudo-pregnant foster mice. The pups born from foster mice were genotyped using PCR, slot blot, and Southern blot techniques. Among 9 mice which showed positive PCR results, only 6 mice transmitted the transgene to the next generation. Therefore, 6 transgenic lines were established which stably transmitted their transgene to their progeny.
Software Architectural Design Meets Security Engineering

Security requirements strongly influence the architectural design of complex IT systems in a similar way as other non-functional requirements. Both security engineering as well as software engineering provide methods to deal with such requirements. However, there is still a critical gap concerning the integration of the methods of these separate fields. In this paper we close this gap with respect to security requirements by proposing a method that combines software engineering approaches with state-of-the-art security engineering principles. This method establishes an explicit alignment between the non-functional goal, the principles in the field of security engineering, and the implementation of a security architecture. The method aims at designing a system's security architecture based on a small, precisely defined, and application-specific trusted computing base. We illustrate this method by means of a case study which describes distributed enterprise resource planning systems using web services to implement business processes across company boundaries.
Image enhancement and segmentation using dark stretching technique for Plasmodium Falciparum for thick blood smear

This paper present the results of applying dark stretching technique to enhance and segment the Plasmodium Falciparum based on thick blood smear images. Image enhancement is the process to improve the quality (clarity) of images for human viewing. Removing blurring and noise, increasing contrast, and revealing details are examples of enhancement operations. Reducing the noise and blurring and increasing the contrast range could enhance the image. The original image might have areas of very high and very low intensity, which mask details. Segmentation is to cluster pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural parts of objects.
Construction of strongly secure wiretap channel code based on hash property

A strongly secure wiretap channel code is proposed. The construction is based on the strong hash property introduced in Proc. ISIT2010, pp. 575–579. Since an ensemble of sparse matrices satisfies the conditions for the strong hash property, the rate of the proposed code using sparse matrices can achieve the secrecy capacity.
The Kinesin Motor KIF3A Is a Component of the Presynaptic Ribbon in Vertebrate Photoreceptors

Kinesin motors are presumed to transport various membrane compartments within neurons, but their specific in vivofunctions, cargoes, and expression patterns in the brain are unclear. We have investigated the distribution of KIF3A, a member of the heteromeric family of kinesins, in the vertebrate retina. We find KIF3A at two distinct sites within photoreceptors: at the basal body of the connecting cilium axoneme and at the synaptic ribbon. Immunoelectron microscopy of the photoreceptor ribbon synapse shows KIF3A to be concentrated both at the ribbon matrix and on vesicles docked at the ribbon, a result that is consistent with the presence of both detergent-extractable and resistant KIF3A fractions at these synapses. KIF3A is also present in the inner plexiform layer, again at presynaptic ribbons. These findings suggest that within a single cell, the photoreceptor, one kinesin polypeptide, KIF3A, can serve two distinct functions, one specific for ribbon synapses.
On the Search of Organization Measures for a Kohonen Map Case Study: Speech Signal Recognition

Unsupervised learning scheme like the self-organizing map (SOM) has been used to classify speech sounds in an ordered manner. SOM is able to extract the most salient features of the input signal and provides a simple way of visualizing them. The distance between two units on the map was used as an objective measure of their perceptual similarity. This paper presents a study of the evaluation of a SOM trained by a sequential learning algorithm integrating information enrichment principal. Two complementary analyses are proposed: quantitative analysis and qualitative one. SOM has been used to visualize speech database as a phenotypic map. The latter was used to generate quantitative measures of the input space. In this paper, we propose different organization measures of a Kohonen map. Some of them are restricted to evaluate a map without referring to data manifold. Others are restricted to quantify map organization with respect to data manifold. We propose also how these organization measures act as fitness measures. These organizations measures are evaluated on the case study of phoneme classification of TIMIT acoustic-phonetic continuous speech corpus. The experimental results show that the proposed combined organization measures provide more significant values according to map sizes.
Single-shot diffuser-encoded light field imaging

We capture 4D light field data in a single 2D sensor image by encoding spatio-angular information into a speckle field (causticpattern) through a phase diffuser. Using wave-optics theory and a coherent phase retrieval method, we calibrate the system by measuring the diffuser surface height from through-focus images. Wave-optics theory further informs the design of system geometry such that a purely additive ray-optics model is valid. Light field reconstruction is done using nonlinear matrix inversion methods, including ℓ1 minimization. We demonstrate a prototype system and present empirical results of 4D light field reconstruction and computational refocusing from a single diffuser-encoded 2D image.
On the evaluation and improvement of feature-based configuration techniques in software product lines

Our work builds upon previous research on software product lines and formal analysis of feature diagrams carried out since several years at the University of Namur. This PhD thesis aims at evaluating and improving existing feature-based configuration techniques to ease their uptake by practitioners and their integration into the software product line engineering process. The affordability and scalability of the delivered languages, methods and tools are major concerns. Evaluation will take place in the context of an open source development project.
Safe limits on voltage reduction efficiency in GPUs: A direct measurement approach

Energy efficiency of GPU architectures has emerged as an important aspect of computer system design. In this paper, we explore the energy benefits of reducing the GPU chip's voltage to the safe limit, i.e. Vmin point. We perform such a study on several commercial off-the-shelf GPU cards. We find that there exists about 20% voltage guardband on those GPUs spanning two architectural generations, which, if "eliminated" completely, can result in up to 25% energy savings on one of the studied GPU cards. The exact improvement magnitude depends on the program's available guardband, because our measurement results unveil a program dependent Vmin behavior across the studied programs. We make fundamental observations about the program-dependent Vmin behavior. We experimentally determine that the voltage noise has a larger impact on Vmin compared to the process and temperature variation, and the activities during the kernel execution cause large voltage droops. From these findings, we show how to use a kernel's microarchitectural performance counters to predict its Vmin value accurately. The average and maximum prediction errors are 0.5% and 3%, respectively. The accurate Vmin prediction opens up new possibilities of a cross-layer dynamic guardbanding scheme for GPUs, in which software predicts and manages the voltage guardband, while the functional correctness is ensured by a hardware safety net mechanism.
A natural language technology-enhanced mobile sales assistant for in-store shopping situations

Sales talks between customers and sales personnel are efficient and preferred means for exchanging information that is relevant for purchase decisions on non-commodity products. Dialogs used in sales talks are governed by complex and in many respects conflicting intentions on both sides. While previous Decision Support Systems (DSS) are designed by the principle of congruent intentions of communication partners, we present an approach that extends this by congruent and opposing intentions of communication partners. We use a design methodology for dialog-based purchase DSS that use Natural Language Technologies (NLT) for dynamically creating question-answer-based sales dialogs. It is first shown how dialog schemata are obtained by a field study and evaluated by subjects. In the second part, these schemata are integrated in a Natural Language Technology-enhanced Mobile Sales Assistant (NLT-MSA). The role of NLT-MSA is to take the position of a sales person with the task to balance congruent and conflicting intentions during sales dialogs. Results of a lab experiment (n=54) are discussed by which the use of a NLT-MSA prototype in sales situations were tested. As part of this study, test persons rated application domains for NLT-MSA that will guide future field experiments in the large
The New Minimal Supersymmetric GUT

The Supersymmetric SO(10) GUT based on the 210⊕ 10⊕ 120⊕ 126⊕ 126 Higgs system and only spontaneous CP violation has 24 real superpotential parameters. It is compatible with all current low energy data and yet has enough freedom to accommodate the still unknown Leptonic CP violation and neutrino mass scale parameters. It is thus the New Minimal Supersymmetric Grand Unified Theory(NMSGUT). We compute the superheavy mass spectrum, couplings and associated gauge evolution threshold effects and determine the regions of the parameter space compatible with perturbative unification, baryon stability and Weinberg angle while permitting spontaneous CKM CP violation. We show that the structural requirements of spontaneous CP violation always tend to raise the unification scale MX and with it the mass of all superheavy multiplets and thus to suppress Baryon decay. These scales can be raised even close to the Planck scale so that the NMSGUT gauge Landau pole becomes innocuous. This scenario lends support to our earlier speculations regarding GUT dynamical symmetry breaking and induced gravity via UV condensation at this Landau pole. Fermion mass formulae and the effective superpotential for Baryon violating d = 5 processes which contains novel channels due to the 120 Higgs are derived to prepare for the next round of detailed analysis of the phenomenology of this model.
The effect of unlabeled data on generative classifiers, with application to model selection

semisupervised learning, labeled and unlabeled data problem, classification, machine learning In this paper we investigate the effect of unlabeled data on generative classifiers in semi-supervised learning. We first characterize situations where unlabeled data cannot change estimates obtained with labeled data, and argue that such situations are unusual in practice. We then report on a large set of experiments involving labeled and unlabeled data, and demonstrate that unlabeled data can degrade classification performance when modeling assumptions are incorrect. To improve classification performance, we propose a method to switch assumed model structure based on the effect of unlabeled data.
Spin transitions in a small Si quantum dot

We have studied the magnetic field dependence of the ground state energies in a small Si quantum dot. At low fields the first five electrons are added in a spin-up -- spin-down sequence minimizing the total spin. This sequence does not hold for larger number of electrons in the dot. At high fields the dot undergoes transitions between states with different spins driven entirely by Zeeman energy. We identify some features that can be attributed to transitions between different spin configurations preserving the total spin of the dot. For a few peaks we observed large linear shifts that correspond to the change of the spin of the dot by 3/2. Such a change requires that an electron in the dot flips its spin during every tunneling event.