Miracle-dz/UniFAR-FTUs huggingface dataset:
Face Recognition under Variation of Pose and Illumination using Independent Component Analysis

Abstract This paper addresses the problem of face recognition under variation of illumination and poses with large rotation angles using Independent Component Analysis (ICA). Face recognition using ICA, based on information theory concepts, seek a computational model that best describes face, by extracting most relevant information contained in that face. ICA approach used here to extract global features seems to be an adequate method due to its simplicity, speed and learning capability. The preprocessing is done by Principle Component Analysis (PCA) before applying the ICA algorithm for training of images. The independent components obtained by ICA algorithm are used as feature vectors for classification. The Euclidian distance classifier is used for testing of the images. The variation in illumination and facial poses up to 180 0 rotation angle is used by the proposed method and result shows that the recognition improved significantly. Keywords: Face recognition, Independent component analysis (ICA), Principle component analysis (PCA), Pose variance, Illumination variance.
Semantic Descriptors: The Case of Reflexive Verbs

This paper presents a semantic classification of reflexive verbs in Bulgarian, augmenting the morphosyntactic classes of verbs in the large Bulgarian Lexical Data Base - a language resource utilized in a number of Language Engineering (LE) applications. Thesemantic descriptors conform to the Unified Eventity Representation (UER), developed by Andrea Schalley. The UER is a graphical formalism, introducing the object-oriented system design to linguistic semantics. Reflexive/non-reflexive verb pairs are analyzed where the non-reflexive member of the opposition, a two-place predicate, is considered the initial linguistic entity from which the reflexive correlate is derived. The reflexive verbs are distributed into initial syntactic-semantic classes which serve as the basis for defining the relevant semantic descriptors in the form of EVENTITY FRAME diagrams. The factors that influence the categorization of the reflexives are the lexical paradigmaticapproach to the data, the choice of only one reading for each verb, top level generalization of the semantic descriptors. The language models described in this paper provide the possibility for building linguistic components utilizable in knowledge-driven systems.
An overview of object detection and tracking

Over the last couple of years, object detection and tracking reserachers have been developing many new techniques, which has been used widely by others. In this article, we present an extensive overview of object detection and tracking methods. At the same time, we also introduces some related theoretical knowledge (e.g., feature and classification). The reason why the object detection and tracking in summarized together, is because the object detection can be said to be the foundation of the object tracking, and they all need to choose the right features and training effective classification. Due to the application fields and emphasis may be different, the number of features which we can select is large. This paper mainly introduces some common features, such as color, histogram of gradients edges and optical flow. Then classifications are introduced, which are all classical classifications. There are many methods of detection and tracking, but now researchers will mainly consider some of the key factors, which include context, silhouette and background. Finally, we respectively introduced some common methods for object detection and object tracking. And discuss the advantages and disadvantages of principles.
On the Robustness of Most Probable Explanations

In Bayesian networks, a Most Probable Explanation (MPE) is a complete variable instantiation with the highest probability given the current evidence. In this paper, we discuss the problem of finding robustness conditions of the MPE under single parameter changes. Specifically, we ask the question: How much change in a single network parameter can we afford to apply while keeping the MPE unchanged? We will describe a procedure, which is the first of its kind, that computes this answer for all parameters in the Bayesian network in time O(n exp(w)), where n is the number of network variables and w is its treewidth.
Super-Resolution Techniques Evaluated in the Context of HD Endoscopic Imaging

In this work we evaluate different reconstruction-based super-resolution algorithms in order to enhance the spatial resolution of endoscopic images acquired with an HD endoscope. The main question we try to answer in this work is whether it is feasible to employ super-resolution algorithms in order to increase the resolution of our HD images to reveal new details which may have got lost due to the limited endoscope magnification inherent to the HD endoscope used (e.g. mucosal structures). For this purpose we first briefly explain the principles of super-resolution algorithms. We then describe the evaluated algorithms in more detail, which is followed by experiments using different super-resolution algorithms. We show that for artificially generated sequences and non-endoscopic real-world sequences the SR algorithms are indeed able to deliver a higher quality as compared to a simple bicubic interpolation. In case of endoscopic sequences, however, there is no real improvement observable. But we also show that the quality measures used very frequently show a rather high disagreement upon an improvement.
Using Invariants for Anomaly Detection: The Case Study of a SaaS Application

Invariants represent properties of a system that are expected to hold when everything goes well. Thus, the violation of an invariant most likely corresponds to the occurrence of an anomaly in the system. In this paper, we discuss the accuracy and the completeness of an anomaly detection system based on invariants. The case study we have taken is a back-end operation of a SaaS platform. Results show the rationality of the approach and discuss the impact of the invariant mining strategy on the detection capabilities, both in terms of accuracy and of time to reveal violations.
A Multiple Patterns Matching Method Based on Added Information

The multiple patterns matching algorithm based on added information is advanced in this paper. It can overcome language problem in some patterns matching algorithm. To reduce collision, the information of multiple continuous bytes is used in matched text. It adopts AVL tree managing index to reduce average searching times. The algorithm also makes use of specifically character of pattern collection to eliminate more collision. It adapts to matching on bigger pattern collection. This algorithm is easy to be translated into parallel pattern matching algorithm.
Are Traditional Retirements a Thing of the Past? New Evidence on Retirement Patterns and Bridge Jobs

Purpose: This paper investigates whether permanent, one-time retirements are coming to an end just as the trend towards earlier and earlier retirements did nearly 20 years ago. We explore how common bridge jobs are among today’s retirees, and how uncommon traditional retirements have become. Methods: Using data from the Health and Retirement Study (HRS), we explore the work histories and retirement patterns of a cohort of retirees aged 51 to 61 in 1992 over a ten-year time period in both a cross-sectional and longitudinal context. Bridge job determinants are examined using bivariate comparisons and a multinomial logistic regression model of the bridge job decision. Results: We find that one-half to two-thirds of the HRS respondents with full-time career jobs take on bridge jobs before exiting the labor force completely. We also find that bridge job behavior is most common among younger respondents, respondents without defined-benefit pension plans, and respondents at the lower- and upper-end of the wage distribution. Implications: The evidence suggests that changes in the retirement income landscape since the 1980s appear to be taking root. Going forward, traditional retirements will be the exception rather than the rule.
Predictors of Functional Health Status of End Stage Renal Disease Patients

Potential predictors of the functional health status of 125 end stage renal disease (ESRD) patients were studied cross-sectionally. When health status was assessed by the physician with the Karnofsky Index, younger patient age, lower ESRD severity of illness, lower comorbidity severity, and higher albumin levels were predictors of better health [R-square=0.48]. When patients self-reported their health status with the Duke Health Profile, African-American race, higher family support, lower family stress, and lower ESRD severity were positive predictors [R-square=0.23]. The importance of measuring functional status, severity of illness, and social support and stress of ESRD patients is supported by these findings.
Community Diversity of Nitrosobacteria in SBR Denitrified System for Treating the Livestock Wastewater

Short-cut nitrification and denitrification had achieved in this Sequencing Batch Reactor (SBR). At the aerating stage, nitrosobacteria had survived much better than nitrobacteria and the concentration ratio of [NO2--N]/[NO3--N]was exceed 50%. After one cycle running, the total ammonia was removed completely. We wished to discover if we could gain greater insights into the relationship between the community construction of nitrosobacteria and the accumulation of nitrite nitrogen in this reactor. This examination used the molecular biology technology by amplifying the target fragment of amoA gene from total DNA of activated sludge, by coupling the target gene with pGM-Tvector and transforming them into E.coli DH5a competent cells. Randomly, picked some white clones out from the LBsolid nutritive medium, and used RFLP markers to obtain the restriction patterns. RFLP analysis showed that there were 8kinds of restriction patterns among these clones. After comparing homologies with standard strains, the results confirmed that there were 3 genera of nitrosobacteria in this reactor: Nitrosospira sp. , Nitrosomomas sp. and Nitrosococcussp. , the distributing frequencies were 0.285, 0.392 and 0.035, respectively. Preliminary analysis, Nitrosolobus multiform sand Nitrosomomas europaea had played the predominant role in accumulating nitrite nitrogen.
An Index Structure for Efficient k-NN Query Processing in Location Based Services

Many studies on index structures for k-NN query processing have been done in Location Based Services(LBS). However, they are not suitable for the moving objects because their update costs are so expensive due to the frequent location updates of the moving objects. In this paper, we propose Incremental Search R-tree(ISR-tree) that processes k-NN queries efficiently in the environment with static objects and moving clients. In ISR-tree, the leaf-nodes maintain the entries pointing to their relevant neighbor nodes and the entries help to find the neighbor leaf-nodes without visiting internal nodes when k-NN queries are processed. We prove the superiority of ISR-tree through the comparison with previous works. As a result, since ISR-tree saves the costs to visit internal nodes, it significantly improves the performance over the existing index structure.
An efficient cast shadow removal for motion segmentation

Accompanied by the rapid development of Computer Vision, Visual surveillance has achieved great evolution with more and more complicated processing. However there are still many problems to be resolved for robust and reliable visual surveillance and the cast shadow occurring in motion detection process is one of them. Shadow pixels are often misclassified as object pixels so that they cause errors in localization, segmentation, tracking and classification of objects. This paper proposes a novel cast shadow removal method. As opposed to previous conventional methods, which considers pixel properties like intensity properties, color distortion, HSV color system, and etc., the proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the background scene. Then, the product of the outcomes of application determines whether the blob pixels in the foreground mask comes from object blob regions or shadow regions. The proposed method is simple but turns out practically very effective for Gaussian Mixture Model, which is verified through experiments.
Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way

It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets and, therefore, a hierarchical visualization system is desirable. In this paper, we extend an existing locally linear hierarchical visualization system PhiVis in several directions: 1) We allow for nonlinear projection manifolds. The basic building block is the Generative Topographic Mapping (GTM). 2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. 3) Using tools from differential geometry, we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the ancestor visualization plots which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 18-dimensional data sets.
Design of Strong Fuzzy Partitions from Cuts

The adoption of triangular fuzzy sets to define Strong Fuzzy Partitions (SFPs) is a common practice in the research community: due to their inherent simplicity, triangular fuzzy sets can be easily derived from data by applying suitable clustering algorithms. However, the choice of triangular fuzzy sets may be limiting for the modeling process. In this paper we focus on SFPs built up starting from cuts (points of separation between cluster projections on data dimensions), showing that a SFP based on cuts can always be defined by trapezoidal fuzzy sets. Different mechanisms to derive SFPs from cuts are presented and compared by employing DC*, an algorithm for extracting fuzzy information granules from classified data.
Biomarkers of Subclinical Atherosclerosis in Patients with Autoimmune Disorders

Atherosclerosis is accelerated in rheumatoid arthritis (RA) and psoriatic arthritis (PsA). We investigated a possible association of oxidized low-density lipoproteins (ox-LDLs), nitric oxide (NO), 3-nitrotyrosine, vitamin A, vitamin E, and β-carotene serum levels with subclinical atherosclerosis in RA and PsA. By the use of ELISA, we observed higher ox-LDL levels in patients with intima-media thickness (IMT) > 1 than in patients with IMT ≤ 1 and a negative correlation between NO levels and IMT values. By the use of high-performance liquid chromatography, we determined higher levels of vitamin A in patients with PsA and IMT ≤ 1 than in controls and lower levels of β-carotene in patients with RA and PsA than in controls. β-carotene concentrations were negatively correlated to the duration of disease in RA. Our study confirms that ox-LDLs and NO may be markers of accelerated atherosclerosis in RA and PsA whereas vitamins seem to be associated only to the presence of the autoimmune disorders.
Realizing the physics of motile cilia synchronization with driven colloids

Cilia and flagella in biological systems often show large scale cooperative behaviors such as the synchronization of their beats in "metachronal waves". These are beautiful examples of emergent dynamics in biology, and are essential for life, allowing diverse processes from the motility of eukaryotic microorganisms, to nutrient transport and clearance of pathogens from mammalian airways. How these collective states arise is not fully understood, but it is clear that individual cilia interact mechanically,and that a strong and long ranged component of the coupling is mediated by the viscous fluid. We review here the work by ourselves and others aimed at understanding the behavior of hydrodynamically coupled systems, and particularly a set of results that have been obtained both experimentally and theoretically by studying actively driven colloidal systems. In these controlled scenarios, it is possible to selectively test aspects of the living motile cilia, such as the geometrical arrangement, the effects of the driving profile and the distance to no-slip boundaries. We outline and give examples of how it is possible to link model systems to observations on living systems, which can be made on microorganisms, on cell cultures or on tissue sections. This area of research has clear clinical application in the long term, as severe pathologies are associated with compromised cilia function in humans.
Evolution and Coevolution in Mutualistic Networks

A major current challenge in evolutionary biology is to understand how networks of interacting species shape the coevolutionary process. We combined a model for trait evolution with data for twenty plant-animal assemblages to explore coevolution in mutualistic networks. The results revealed three fundamental aspects of coevolution in species-rich mutualisms. First, coevolution shapes species traits throughout mutualistic networks by speeding up the overall rate of evolution. Second, coevolution results in higher trait complementarity in interacting partners and trait convergence in species in the same trophic level. Third, convergence is higher in the presence of super-generalists, which are species that interact with multiple groups of species. We predict that worldwide shifts in the occurrence of super-generalists will alter how coevolution shapes webs of interacting species. Introduced species such as honeybees will favour trait convergence in invaded communities, whereas the loss of large frugivores will lead to increased trait dissimilarity in tropical ecosystems.
Chitosan and β-Cyclodextrin-epichlorohydrin Polymer Composite Film as a Plant Healthcare Material for Carbendazim-Controlled Release to Protect Rape against Sclerotinia sclerotiorum (Lib.) de Bary

The influence of β-cyclodextrin-epichlorohydrin (β-CD-EP) polymers on the improvement of the solubility and antifungal activity of carbendazim has been investigated. Meanwhile, the potential of the chitosan and β-CD-EP composite film used as a plant healthcare material for carbendazim-controlled release to protect rape against Sclerotinia sclerotiorum (Lib.) de Bary has been evaluated. β-CD-EP-1 and 2 (β-CD content, 750 mg/g and 440 mg/g, respectively) were found to significantly improve the solubility of the guest molecule carbendazim (17.9 and 18.5 times, respectively) and the 1:1 stoichiometry of the host-guest was confirmed by the Job’s plot. A slight synergism was observed for the β-CD-EP/carbendazim complex against S. sclerotiorum (Lib.) de Bary, indicating an enhancement to the bioavailability of carbendazim. The in vitro release studies revealed that β-CD-EP polymers could efficiently modulate carbendazim release behaviors, such as the release retard and rate. The in vivo efficacy experiments demonstrated that the β-CD-EP/carbendazim and chitosan composite film could significantly prolong the effective duration of carbendazim at a concentration of 100 μg/mL compared with spraying carbendazim at 500 μg/mL. Thereby, a highly useful and strategic concept in plant disease control by a plant healthcare material—the chitosan and polymeric β-CD-EP composite film—is provided, which could also serve as a concept for related plant diseases.
Modeling Si-Ge interdiffusion by a vacancy exchange mechanism

The vacancy exchange mechanism was used to model the inter diffusion of Si and Ge in Si/Si1-xGex/Si single quantum well structures. Diffusion was modeled for samples grown by molecular beam epitaxy and annealed at 900degC, 1000degC and 1100degC for different times in inert and oxidizing ambients. Intrinsic self diffusivities of Si and Ge as a function of Ge fraction in the structure used as the fitting parameters match values reported in the literature. Once these parameters were fixed for the inert ambient, the diffusion behavior under oxidizing conditions was completely accounted for by the model with no additional fitting parameters. Excellent fits were obtained in all the cases
Diversity in logarithmic opinion pools

Conditional random fields are state-of-the-art models for sequencing tasks such as named entity recognition. However, being globally conditioned, they have a tendency to overfit to a greater extent than other sequencing models. We introduce an approach to combat this overfitting called a logarithmic opinion pool (LOP). A LOP consists of a weighted combination of constituent models. We present the theory behind LOPs, and show that effective LOPs require constituent models that are diverse from one another. We examine different ways to introduce such diversity, including an approach that involves training the constituent models together, interactively. Our results show that, as expected from the underlying theory, explicitly optimising for constituent model diversity can improve performance over standard approaches to regularisation.
A Change Impact Analysis Approach for the Software Development Phase

Software undergoes changes at all stages of the software development process. Accepting too many changes will cause expense and delay and rejecting the changes may cause customer dissatisfaction. One of the inputs that help the software project management to decide whether to accept or reject the changes is by having reliable predictions of the impact of the changes. Change impact analysis is one of the methods that can be used to provide the predictive information. Many current impact analysis approaches have been developed for the software maintenance phase. These approaches assume that all classes in the class artifact are completely developed and the class artifact is used as a source of analysis since it represents the final user requirements. However, these assumptions are not practical for impact analysis in the software development phase as some classes in the class artifact are still under development or partially developed. This leads to inaccuracy. This paper presents a novel impact analysis approach to be used in the software development phase. The significant achievements of the approach are demonstrated through an extensive experimental validation using several case studies. The experimental analysis shows improvement in the accuracy over current impact analysis results.