Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical study based on machine learning approach to assess the QoS/QoE correlation

The appearance of new emerging multimedia services have created new challenges for cloud service providers, which have to react quickly to end-users experience and offer a better Quality of Service (QoS). Cloud service providers should use such an intelligent system that can classify, analyze, and adapt to the collected information in an efficient way to satisfy end-users' experience. This paper investigates how different factors contributing the Quality of Experience (QoE), in the context of video streaming delivery over cloud networks. Important parameters which influence the QoE are: network parameters, characteristics of videos, terminal characteristics and types of users' profiles. We describe different methods that are often used to collect QoE datasets in the form of a Mean Opinion Score (MOS). Machine Learning (ML) methods are then used to classify a preliminary QoE dataset collected using these methods. We evaluate six classifiers and determine the most suitable one for the task of QoS/QoE correlation.
On the use of Various Power Levels to Improve Wireless LAN-based Positioning - Using Multiple Power Levels With Fingerprinting Algorithms

When talking about location-based services, one of the key factors influencing the overall service quality is the positioning accuracy that the system can rely upon. Nowadays, wireless LAN hardware supports the variation of the transmission power in a wide range. In this paper, two novel algorithms suitable for extension of common wireless LAN-based positioning systems are presented. These novel algorithms exploit the additional information that can be gained by using one or more non-standard transmission power levels. Our findings indicate that the overall positioning accuracy and reliability can be increased with such an approach.
A computer-aided visualization system for orthodontic treatment

This system present a feasibility of computer-aided diagnosis, planning and simulation for orthodontic treatment. In the Visualization Toolkit (VTK) and Microsoft Foundation Classes (MFC) integrated environment, a flexible, friendly and functional interface for orthodontic treatment was designed. The methods of how to integrate VTK and MFC in different system are described, and the three-dimensional model of the dentition is reconstructed by using marching-cubes (MC) algorithms. Finally, the three-dimensional model for rendering and interaction is also introduced.
Exploring tradeoffs in pleiotropy and redundancy using evolutionary computing

Evolutionary computation algorithms are increasingly being used to solve optimization problems as they have many advantages over traditional optimization algorithms. In this paper we use evolutionary computation to study the trade-off between pleiotropy and redundancy in a client-server based network. Pleiotropy is a term used to describe components that perform multiple tasks, while redundancy refers to multiple components performing one same task. Pleiotropy reduces cost but lacks robustness, while redundancy increases network reliability but is more costly, as together, pleiotropy and redundancy build flexibility and robustness into systems. Therefore it is desirable to have a network that contains a balance between pleiotropy and redundancy. We explore how factors such as link failure probability, repair rates, and the size of the network influence the design choices that we explore using genetic algorithms.
A Bayesian Framework for Image Segmentation With Spatially Varying Mixtures

A new Bayesian model is proposed for image segmentation based upon Gaussian mixture models (GMM) with spatial smoothness constraints. This model exploits the Dirichlet compound multinomial (DCM) probability density to model the mixing proportions (i.e., the probabilities of class labels) and a Gauss-Markov random field (MRF) on the Dirichlet parameters to impose smoothness. The main advantages of this model are two. First, it explicitly models the mixing proportions as probability vectors and simultaneously imposes spatial smoothness. Second, it results in closed form parameter updates using a maximum a posteriori (MAP) expectation-maximization (EM) algorithm. Previous efforts on this problem used models that did not model the mixing proportions explicitly as probability vectors or could not be solved exactly requiring either time consuming Markov Chain Monte Carlo (MCMC) or inexact variational approximation methods. Numerical experiments are presented that demonstrate the superiority of the proposed model for image segmentation compared to other GMM-based approaches. The model is also successfully compared to state of the art image segmentation methods in clustering both natural images and images degraded by noise.
Infection of Keratinocytes with Trichophytum rubrum Induces Epidermal Growth Factor-Dependent RNase 7 and Human Beta-Defensin-3 Expression

Human keratinocytes are able to express various antimicrobial peptides (AMP) to protect the skin from exaggerated microbial colonization and infection. Recently, in vitro growth-inhibiting activity of the skin-derived AMP psoriasin, RNase 7 and human beta-defensin (hBD)-2 against dermatophytes such as Trichophyton (T.) rubrum have been reported. To evaluate whether keratinocytes are able to respond to T. rubrum infection by an induced expression of AMP we exposed primary keratinocytes to living conidia of T. rubrum. This led to conidia germination and mycelial growth which was paralleled by a strong gene induction of the skin-derived AMP RNase 7 and hBD-3. Gene expression of the AMP psoriasin (S100A7) and hBD-2 were only slightly induced. The T. rubrum-mediated RNase 7 gene induction was accompanied by increased secretion of RNase 7. Parallel treatment of the keratinocytes with T. rubrum and the cytokine combination IL-17A/IFN-γ resulted in synergistic induction of RNase 7 and hBD-3 expression. Since patients receiving therapy by inhibition of the epidermal growth factor receptor (EGFR) more often suffer from dermatophytoses we investigated whether EGFR may be involved in the T. rubrum-mediated RNase 7 and hBD-3 induction. Primary keratinocytes incubated with an EGFR blocking antibody as well as with the EGFR antagonist AG1478 showed a significantly diminished RNase 7 and hBD-3 induction upon exposure of the keratinocytes to T. rubrum indicating that EGFR is involved in the T. rubrum-mediated induction of RNase 7 and hBD-3. The growth of T. rubrum in vitro was inhibited by hBD-3 in a dose-dependent manner suggesting that hBD-3 may contribute to cutaneous innate defense against T. rubrum. Taken together our data indicate that keratinocytes are able to initiate a fast defense response towards T. rubrum by the increased expression of AMP active against T. rubrum. A dysregulation of AMP may contribute to chronic and recurring dermatophytoses.
An Automatic Crude Registration of Two Partially Overlapping Range Images

We present a crude registration algorithm for two partially overlapping range images acquired from a rigid object. Our proposal is characterized by two novel features: drastic data reduction and expressive shape descriptor. The data reduction is achieved on the basis of the observation that for crude alignments only the overall shape of image is of interest and the enhancement of the expression power of the shape descriptor is accomplished by integrating in it both local and global shape information. Experimental results show that our algorithm delivers good initial estimate to a fine registration program.
Platform-based design of an embedded control system for three-dimensional robotic fish

This paper addresses a platform-based design method of a real-time embedded system for controlling three-dimensional (3-D) swimming of link-based robotic fish. It involves mechanical design, hardware implementation, software design, and central pattern generators (CPG) inspired 3-D swimming control. With the ARM-based microprocessor AT91RM9200, the established hardware is well suited for complex computation environments. Moreover, concerning software design within the framework of the real-time operating system muC/OS-II, each function of the controller represented by a task can be easily added to the embedded system. Both the core hardware and software are realized by a standardized method, which makes the platform easily expandable and manageable. Without significant modification, the developed 3D locomotion controller for robotic fish based on the platform concept can further extend to other control systems.
Deep Learning Approach for Software Maintainability Metrics Prediction

Software maintainability predicts changes or failures that may occur in software after it has been deployed. Since it deals with the degree to which an application may be understood, repaired, or enhanced, it also takes into account the overall cost of the project. In the past, several measures have been taken into account for predicting metrics that influence software maintainability. However, deep learning is yet to be explored for the same. In this paper, we perform deep learning for software maintainability metrics’ prediction on a large number of datasets. Unlike the previous research works, we have relied on large datasets from 299 software and subsequently applied various metrics and functions to the same; 29 object-oriented metrics have been considered along with their impact on software maintainability of open source software. Several metrics have been analyzed and descriptive statistics of these metrics have been pointed out. The proposed long short term memory has been evaluated using measures, such as mean absolute error, root mean square error and accuracy. Five machine learning algorithms, namely, ridge regression with variable selection, decision tree, quantile regression forest, support vector machine, and principal component analysis have been applied to the original datasets, as well as, to the refined datasets. It was found that this paper provides results in the form of metrics that may be used in the prediction of software maintenance and the proposed deep learning model outperforms all of the other methods that were considered. Furthermore, the results of experiment affirm the efficiency of the proposed deep learning model for software maintainability prediction.
EDDE Monte Carlo event generator

EDDE is a Monte Carlo event generator, under construction, for different Exclusive Double Diffractive Events. The program is based on the extended Regge-eikonal approach for "soft" processes. Standard Model and its extensions are used for "hard" fusion processes. An interface to PYTHIA, CMSJET and CMKIN is provided.
Robust Textural Approach for False Positive Reduction in Mammographic Mass

In the last few years and over the last decade, different algorithms have been introduced for mass detection in digital mammograms. However, approximately all the proposed methods present a high number corresponding to false positif. In this paper, we propose a novel approach dealing with the problem of false positif reduction. This proposal is based on the use of a textural approach for the description of the masses texture properties. The least square estimation is applied to minimize the local difference in order to obtain more stable directional features, this scheme is called adaptative LBP (ALBP) to improve the features extracted using LBP efficiently. Artificial Neural Network (ANN) are used for the classification of the breast tissue. The evaluation of this approach is based on 1792 Regions Of Interest (ROIs) obtained from the Digital Database Of Screening Mammography (DDSM). The obtained results show that the proposed textural approach is effective in feature extraction and allows mass false positive reduction. Moreover, this proposal demonstrates better performance where compared to the methods of the state of the art. General Terms Mass detection, classification.
Enhancing Semantic and Social Navigation in Information-Rich Virtual Worlds

Virtual Worlds are a popular medium for communication and collaboration in 3D and they are being utilized as shared information spaces in various application areas, such as education, culture and entertainment. Being presented as 3D visualization spaces they adopt a spatial model of navigation, in which information artifacts have a certain position in space and users can access them through their avatar. However, compared to traditional hypermedia and webbased information systems, Virtual Worlds offer little support for discovering semantically related artifacts and for following the preferences and actions of other users. Furthermore, there is a lack of interoperability with other information systems that would enhance the world’s objects with semantically related content. In this paper we present a framework for extending virtual worlds with interactions and visualizations that support improved semantic and social browsing in the 3D environment, and propose an integrated architecture for their interface with external information sources through the employment of Linked Data. Following the proposed framework, we have implemented a prototype environment that presents a gallery of famous paintings and we have performed a user evaluation in order to assess its usability, focusing especially on its novel features for semantic and social browsing.
Control-theoretic properties of structural acoustic models with thermal effects, II. Trace regularity results

We consider a structural acoustic problem with the flexible wall modeled by a thermoelastic plate, subject to Dirichlet boundary control in the thermal component. We establish sharp regularity results for the traces of the thermal variable on the boundary, in the case the system is supplemented with clamped mechanical boundary conditions. These regularity estimates are most crucial for validity of the optimal control theory developed in [Acquistapace et al., Adv. Differential Equations, 2005], which ensures well-posedness of the corresponding differential Riccati equations. The proof takes full advantage of the exceptional boundary regularity of the mechanical component of the clamped thermoelastic system as well as of the sharp trace theory pertaining to wave equations with Neumann boundary data.
RECENT RESULTS ON SIMULATIONS OF HIGH INTENSITY BEAMS IN CYCLOTRONS

A summary of activities that envisage a source to target simulation of the PSI cyclotron complex is presented. Our aim is to gain a quantitative understanding of complex phenomena in our machines and beam lines, including three-dimensional space charge effects, beam cavity interaction, collimation, beam neutralisation and extraction mechanism. An introduction to the mathematical and computational methods used is presented together with a comparison of simulations with measurements.
Disruption of Fractalkine Signaling Leads to Microglial Activation and Neuronal Damage in the Diabetic Retina

Fractalkine (CX3CL1 or FKN) is a membrane-bound chemokine expressed on neuronal membranes and is proteolytically cleaved to shed a soluble chemoattractant domain. FKN signals via its unique receptor CX3CR1 expressed on microglia and other peripheral leukocytes. The aim of this study is to determine the role of CX3CR1 in inflammatory-mediated damage to retinal neurons using a model of diabetic retinopathy. For this, we compared neuronal, microglial, and astroglial densities and inflammatory response in nondiabetic and diabetic (Ins2Akita) CX3CR1-wild-type and CX3CR1-deficient mice at 10 and 20 weeks of age. Our results show that Ins2Akita CX3CR1-knockout mice exhibited (a) decreased neuronal cell counts in the retinal ganglion cell layer, (b) increased microglial cell numbers, and (c) decreased astrocyte responses comparable with Ins2Akita CX3CR1-Wild-type mice at 20 weeks of age. Analyses of the inflammatory response using PCR arrays showed several inflammatory genes differentially regulated in diabetic tissues. From those, the response in Ins2Akita CX3CR1-deficient mice at 10 weeks of age revealed a significant upregulation of IL-1β at the transcript level that was confirmed by enzyme-linked immunosorbent assay in soluble retinal extracts. Overall, IL-1β, VEGF, and nitrite levels as a read out of nitric oxide production were abundant in Ins2Akita CX3CR1-deficient retina. Notably, double immunofluorescence staining shows that astrocytes act as a source of IL-1β in the Ins2Akita retina, and CX3CR1-deficient microglia potentiate the inflammatory response via IL-1β release. Collectively, these data demonstrate that dysregulated microglial responses in absence of CX3CR1 contribute to inflammatory-mediated damage of neurons in the diabetic retina.
AUDIO-RATE CONTROL OF FFT-BASED PROCESSING USING FEW PARAMETERS

Though the use of the Fast Fourier Transform (FFT) for signal processing in music applications has been widespread, applications in real-time systems for dynamic spectral transformation has been quite limited. The limitations have been largely due to amount of computation required for the operations. With faster machines, and with suitable implementations for frequency-domain processing, real-time dynamic control of high-quality spectral processing can be accomplished with great efficiency and simple approach. This paper will focus on dynamic real-time control of frequencydomain-based signal processing, and will describe the author's latest work (hi-resolution filtering and spatialization implementations) in this area. General background on the implementation and the development environment (Max Signal Processing, MSP) will also be provided.
Canopus: A Domain-Specific Language for Modeling Performance Testing

Despite all the efforts to reduce the cost of the testing phase in software development, it is still one of the most expensive phases. In order to continue to minimize those costs, in this paper, we propose a Domain-Specific Language (DSL), built on top of MetaEdit+ language workbench, to model performance testing for web applications. Our DSL, called Canopus, was developed in the context of a collaboration1 between our university and a Technology Development Laboratory (TDL) from an Information Technology (IT) company. We present, in this paper, the Canopus metamodels, its domain analysis, a process that integrates Canopus to Model-Based Performance Testing, and applied it to an industrial case study.
The Effects of Transmediation on Students' Creativity Through Collaborative Composing

The purpose of this action research was to investigate the process and the effect of the transmediation (translation of content from one media to another) on students’ creativity. The goal was achieved by focusing on: (a) students’ collaborative strategies of translating nonmusical sources to musical compositions; (b) the effects of the level of abstraction of the sources on students’ creative processes and products; and (c) the effects of transmediation on students’ general and domain-specific creativity compared to the general music classes. This study was conducted in a Grade 8 beginner strings’ class of thirteen students. The study was divided into two phases: the teacher taught six classes, then the students were divided up into groups of four or five to compose musical pieces through transmediating the given source. Six classes were spent translating verbal, visual, and video sources, with each mode containing one direct and one abstract source. Students were tested three times throughout the study (before and after phase 1, and after phase 2) with two tests on musical and general creativity. The results revealed that students were able to use multiple modes of communication effectively to face the challenge of transmediation: verbal expression to describe and give precise directions; musical sound to demonstrate musical ideas; and gestures to point, demonstrate, and express rejection or acceptance of ideas. Second, when students were working with more abstract
Utilizing temporal associations for view-based 3-D object recognition

The authors propose an architecture for the recognition of three-dimensional objects on the basis of viewer-centered representations and temporal associations. Motivated by biological findings and by successful computational implementations they have chosen a viewer-centered representation scheme. In contrast to other implementations, special attention is paid to the temporal order of the views, which proves useful for learning and recognition purposes. Their recognition system combines different kinds of artificial neural networks into a four stage architecture: preprocessing by a Gaborjet transform is followed by an extended dynamic link matching algorithm which implements recognition and learning of the view classes. A STORE network records the temporal order of the views by transforming a sequence of view classes into an item-and-order coding. Subsequently, a Gaussian-ARTMAP architecture is used for the classification of the sequences and for their mapping onto object classes by means of supervised learning. The presented results demonstrate that the system is capable to autonomously learn and to discriminate similar objects. Additionally, the examples show how the utilization of the temporal context improves object recognition by making ambiguous views manageable and facilitating an increased insensitiveness against misclassifications.
Surface Flow from Visual Cues

In this paper we study the estimation of dense, instantaneous 3D motion fields over non-rigidly moving surface observed by multi-camera systems. The motivation arises from multi-camera applications that require motion information for arbitrary subjects, in order to perform tasks such as surface tracking or segmentation. To this aim, we present a novel framework that allows to efficiently compute dense 3D displacement fields using low level visual cues and geometric constraints. The main contribution is a unified framework that combines flow constraints for small displacements with temporal feature constraints for large displacements and fuses them over the surface using local rigidity constraints. The resulting linear optimization problem allows for variational solutions and fast implementations. Experiments conducted on synthetic and real data demonstrate the respective interests of flow and feature constraints as well as their efficiency to provide robust surface motion cues when combined.
Ray Tracing Arbitrary Objects on the GPU

Adapting ray tracing algorithms to programmable graphics hardware has gained momentum recently by using the parallelism of the GPU to reduce the work done on the CPU. GPU methods for ray tracing scenes consisting of only triangles have since been proposed. In this paper, we present a general method for ray tracing objects other than triangles on the GPU. Using more advanced primitives allows for faster and better results by reducing the number of intersection tests, as well as providing a more accurate representation of the surface. Realistic images can then be produced containing complex shapes without being limited to a model completely made up of triangles.
Densest Subgraph in Streaming and MapReduce

The problem of finding locally dense components of a graph is an important primitive in data analysis, with wide-ranging applications from community mining to spam detection and the discovery of biological network modules. In this paper we present new algorithms for finding the densest subgraph in the streaming model. For any e > 0, our algorithms make O(log1+e n) passes over the input and find a subgraph whose density is guaranteed to be within a factor 2(1 + e) of the optimum. Our algorithms are also easily parallelizable and we illustrate this by realizing them in the MapReduce model. In addition we perform extensive experimental evaluation on massive real-world graphs showing the performance and scalability of our algorithms in practice.