Miracle-dz/UniFAR-FTUs huggingface dataset:
LogA-DM: An Approach of Dynamic Log Analysis

In ubiquitous computing high levels of connectivity are needed. Considering that, preoccupations related with security aspects are indispensable. One strategy that can be applied for improve security is the log analysis. Such strategies can be used to promote systems' understanding, in particular, the detection of intrusion attempts. The operation of modern computing systems, as the ones used in ubiquitous computing, tend to generate a large number of log records, which require the use of automatic tools to an easier analysis. Tools that employ data mining techniques to log analysis have been used in order to detect attempted attacks on computer systems, assisting security management. Thus, this paper proposes an approach to perform log analysis with intuit to prevent attack situations. The proposed solution explores two fronts: (i) log records of applications, and (ii) log records from the network and transport layers. To evaluate the proposed approach was designed a prototype that employs modules for collection and normalization of data. The normalization module also adds contextual information in order to assist the analysis of critical security situations. To conserve the system's autonomic operation, the records of the network and transport layers are collected and evaluated from connections in progress. Tests were developed in the proposed solution, showing good result for typical categories of attack.
Non-monotonic recruitment of ventromedial prefrontal cortex during remote memory recall

Systems-level consolidation refers to the time-dependent reorganisation of memory traces in the neocortex, a process in which the ventromedial prefrontal cortex (vmPFC) has been implicated. Capturing the precise temporal evolution of this crucial process in humans has long proved elusive. Here, we used multivariate methods and a longitudinal functional MRI design to detect, with high granularity, the extent to which autobiographical memories of different ages were represented in vmPFC and how this changed over time. We observed an unexpected time-course of vmPFC recruitment during retrieval, rising and falling around an initial peak of 8-12 months, before re-engaging for older two and five year old memories. This pattern was replicated in two independent sets of memories. Moreover, it was further replicated in a follow-up study eight months later with the same participants and memories, where the individual memory representations had undergone their hypothesised strengthening or weakening over time. We conclude that the temporal engagement of vmPFC in memory retrieval seems to be non-monotonic, revealing a complex relationship between systems-level consolidation and prefrontal cortex recruitment that is unaccounted for by current theories.
PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES

Otizm spektrum bozuklugu (OSB) sosyal etkilesim ve iletisim zayifliklari seklinde ortaya cikan kalitsal ve norolojik bir gelisimsel bozukluktur. OSB hastaliginin teshisi icin klinik yontemlerin yaninda teshis suresini kisaltmak ve basarimi artirmak icin makine ogrenmesi yontemleri de basariyla uygulanmaktadir. Makine ogrenmesi yontemleri yuksek boyutlu ve cesitli biyomedikal verilerin analizi icin sunduklari objektif algoritmalar ile hastaliklarin teshisi konusunda yuksek performans gostermektedir. Makine ogrenmesi yontemleri, verilerdeki cok degiskenli iliskileri yakaladigi ve bu nedenle verilerdeki ince farklari tespit edebildigi icin OSB gibi heterojen durumlar iceren davranissal bozukluklarin tespit edilmesinde basarili olmaktadir. Bu calismada OSB ergen tarama verileri kullanilarak destek vektor makineleri (DVM), k-en yakin komsu (kNN) ve rastgele orman (RO) makine ogrenmesi yontemleriyle OSB durumunun hizli ve dogru olarak teshis edilmesine yonelik analizler yapilmis ve bu yontemlerin performanslari karsilastirilmistir. DVM, kNN ve RO yontemleri kullanilarak 10-kat capraz dogrulama ile yapilan ikili siniflandirma islemi sonucunda sirasiyla %95, %89 ve %100 dogruluk oranlarina erisilmistir. Ayrica, RO yontemi ile yapilan siniflamadan % 100 duyarlilik ve belirlilik degerleri elde edilmistir. Bu calisma ile OSB ergen tarama verilerini kullanarak RO yontemi ile yapilan siniflama sonucunda OSB olgularinin tam bir basari ile tespit edilebildigi gosterilmistir.
Accurate object retrieval for high-resolution remote-sensing imagery using high-order topic consistency potentials

We propose incorporating semantic topic information into a hierarchical conditional random fields (CRFs) framework to promote object recognition and retrieval accuracy. Specially, we devise convenient yet effective methods based on multiple segmentations to perform accurate image retrieval tasks for rigid and amorphous man-made objects. Through a robust topic consistency potential (RTCP) modelling approach, we perform accurate multi-class segmentation on high-resolution remote-sensing images. The generated segments can be readily used for object recognition and discovery. We report satisfactory the performance on two sets of high-resolution remote-sensing images that cover a highly populated urban area and a rural area, respectively. Experimental results demonstrate that our approach outperforms the state-of-the-art CRF models, due to its ability to capture inherent semantic information for efficient object recognition and boundary discovery.
Characterization of Drosophila Nidogen/entactin reveals roles in basement membrane stability, barrier function and nervous system plasticity

Basement membranes (BMs) are specialized layers of extracellular matrix (ECM) mainly composed of Laminin, type IV Collagen, Perlecan and Nidogen/entactin (NDG). While the essential and evolutionary conserved functions of Laminin, Collagen and Perlecan are well documented in Drosophila and other species, the proposed role of NDG as the major ECM linker molecule has been challenged by several in vivo studies revealing that NDG is dispensable for viability and BM formation. Here, we report the characterization of the single Ndg gene in Drosophila. Embryonic Ndg expression differed from that of other BM components and was primarily observed in mesodermal tissues and the chordotonal organs, whereas NDG protein localized to all BMs. While loss of Laminin strongly affected BM-localization of NDG, Ndg null mutants exhibited no overt changes in the distribution of BM core components. However, loss of NDG led to ultrastructural BM defects compromising barrier function and stability in vivo. Although Ndg mutants were viable, loss of NDG led to decreased fecundity in flies as well as impaired crawling behavior and reduced response to vibrational stimuli in larvae. Further morphological analysis revealed accompanying defects in the larval peripheral nervous system especially in the chordotonal organs and the neuromuscular junction (NMJ), where Ndg genetically interacted with the Leukocyte-antigen-related-like (Lar) receptor gene to regulate NMJ extension and synaptic differentiation. Taken together, our analysis suggests that NDG is not essential for BM assembly but mediates BM stability and ECM-dependent neural plasticity during Drosophila development. Summary Statement In this study we characterize Drosophila Nidogen/Entactin (Ndg) mutants revealing that loss of Ndg impairs basement membrane (BM) stability and permeability as well as proper function of the nervous system.
An Overview of Biotransformation and Toxicity of Diterpenes

Diterpenes have been identified as active compounds in several medicinal plants showing remarkable biological activities, and some isolated diterpenes are produced at commercial scale to be used as medicines, food additives, in the synthesis of fragrances, or in agriculture. There is great interest in developing methods to obtain derivatives of these compounds, and biotransformation processes are interesting tools for the structural modification of natural products with complex chemical structures. Biotransformation processes also have a crucial role in drug development and/or optimization. The understanding of the metabolic pathways for both phase I and II biotransformation of new drug candidates is mandatory for toxicity and efficacy evaluation and part of preclinical studies. This review presents an overview of biotransformation processes of diterpenes carried out by microorganisms, plant cell cultures, animal and human liver microsomes, and rats, chickens, and swine in vivo and highlights the main enzymatic reactions involved in these processes and the role of diterpenes that may be effectively exploited by other fields.
Dehydrojuncusol, a Natural Phenanthrene Compound Extracted from Juncus maritimus Is a New Inhibitor of Hepatitis C Virus Replication

Recent emergence of direct acting antivirals (DAAs) targeting hepatitis C virus (HCV) proteins has considerably enhanced the success of antiviral therapy. However, the appearance of DAA resistant-associated variants is a cause of treatment failure, and the high cost of DAAs renders the therapy not accessible in countries with inadequate medical infrastructures. Therefore, search for new inhibitors and with lower cost of production should be pursued. In this context, crude extract of Juncus maritimus Lam. was shown to exhibit high antiviral activity against HCV in cell culture. Bio-guided fractionation allowed isolating and identifying the active compound, dehydrojuncusol. A time-of-addition assay showed that dehydrojuncusol significantly inhibited HCV infection when added after virus inoculation of HCV genotype 2a (EC50 = 1.35 µM). This antiviral activity was confirmed with a HCV subgenomic replicon and no effect on HCV pseudoparticle entry was observed. Antiviral activity of dehydrojuncusol was also demonstrated in primary human hepatocytes. No in vitro toxicity was observed at active concentrations. Dehydrojuncusol is also efficient on HCV genotype 3a and can be used in combination with sofosbuvir. Interestingly, dehydrojuncusol was able to inhibit replication of two frequent daclatasvir resistant mutants (L31M or Y93H in NS5A). Finally, resistant mutants to dehydrojuncusol were obtained and showed that HCV NS5A protein is the target of the molecule. In conclusion, dehydrojuncusol, a natural compound extracted from J. maritimus, inhibits infection of different HCV genotypes by targeting NS5A protein and is active against HCV resistant variants frequently found in patients with treatment failure. Importance Tens of millions of people are infected with hepatitis C virus (HCV) worldwide. Recently marketed direct acting antivirals (DAAs) targeting HCV proteins have enhanced the efficacy of the treatment. However, due to its high cost, this new therapy is not accessible to the vast majority of infected patients. Furthermore, treatment failures have also been reported due to appearance of viral resistance. Here we report on the identification of a new HCV inhibitor, dehydrojuncusol that targets HCV NS5A and is able to inhibit replication of replicons harboring resistance mutations to anti-NS5A DAAs used in current therapy. Dehydrojuncusol is a natural compound isolated from Juncus maritimus, a halophilic plant species very common in the coastlines worldwide. This molecule might serve as a lead for the development of new therapy more accessible to hepatitis C patients in the future.
Control Structure Design in Power Systems

Most available control theories assume that a control structure is given at the outset, so they do not use the provisions of control structure design. Control structure design tries to close the gap between theory and practice. Control structure design is clearly more important in large scale systems. Power systems are large scale systems that are subject to low frequency oscillations. These oscillations degrade the system performance. Power system stabilizers (PSSs) used to improve the performance of power systems, determination of the suitable signal for applying to power system stabilizer and determination of their locations is an important issue in power systems. Some of the control structure design measures are introduced. These measures are used to determine the inputs, outputs and control configuration based on a systematic procedure that reduces the reliance on experiment and the art of the designer. Selection of a suitable signal for applying to a power system stabilizer and determination of the PSS locations are the major contributions of this paper.
Prediction of Protein Function Using Gaussian Mixture Model in Protein-Protein Interaction Networks

Summary Predicting protein function is one of the most important problems in the post-genomic era. Recent high-throughput experiments have determined proteome-scale protein physical interaction maps for several organisms. In this paper, a new method, which is based on Gaussian Mixture Model, is introduced to predict protein function from protein-protein interaction data. In the proposed method, A global information are taken into account by representing a protein using all the functional annotations of all proteins assigned with that term and have a shortest path with target protein in the all protein interaction network. We apply our method to a constructed data set for yeast and fly based upon protein function classifications of GO scheme and upon the interaction networks collected from IntAct protein-protein interaction. The results obtained by leave-one-cross-validation test show that the proposed method can obtain desirable results for protein function prediction and outperforms some existing approaches based on protein-protein interaction data.
Decision level fusion of multi-frequency polarimetric SAR and optical data with Dempster-Shafer evidence theory

This paper investigated and tested the multi-source data fusion of multi-frequency SAR data with optical sensor, utilizing the mathematical theory of evidence. In this type of data fusion, the correct definition of uncertainty and mass function is very important. Firstly each mono-source data is independently classified using the Bayesian maximum likelihood classification method and the fusion is processed during the decision making stage. For the representation of support and plausibility, mass of evidence is assigned to the candidate labels associated with the frequencies of neighborhood pixels' decision. Then the mass is weighted again by both the distance effect and the global source-specific reliability. In the combination and fusion stage, unions of classes get same mass values to emboss its uncertainty of decision making. Finally Dempster's orthogonal sum method was used to combine the evidence information and the associated level of ignorance, and then the maximum support rule decides the final land-cover types. Classification accuracies before and after each step of fusion processing are compared with reference training data set. We applied this method to the NASA/JPL polarimetric AIRSAR data and KOMPSAT-1 (KOrea Multi-Purpose SATellite-1) EOC (Electro Optical Camera) panchromatic data and obtained noticeably better land-cover classification results.
Neural networks for array processing: from DOA estimation to blind separation of sources

In many signal processing applications, signals are received on an array of sensors, and the problem consists in estimating the directions of arrival (DOA) of the signals, and/or in estimating the sources. Basically, the techniques proposed for its solution use either information about the geometry of the array, or information about the statistics of the sources. Efficient neural-based approaches for both kinds of situations are proposed in this paper. When geometrical knowledge is available, the weights and structure of the neural networks are constrained according to the geometry of the array. When statistical information is available, neural networks which optimize a statistical criterion (namely the measure of dependence) are developed. Furthermore, neural networks provide the opportunity to fuse both approaches in a unified framework, and to take profit simultaneously of both kind of information. <<ETX>>
Face Detection and Recognition Using Recorded Video's

The goal of paper is to described the recorded video face detection by manipulating the machine and image processing. This Platform is differentiating in three main parts. First is that representing the image by integrating the image or face. The second is learning, algorithm based on Paul Viola and Michael Jones, which is method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions (1). The third way is extract the small number of critical recorded video features from the numbers of set and submit extremely well ordered classifiers. The serial (cascade) can be viewed as an object or face particular focus mechanism which is different from previous parts address the surety that removable regions are unlikely to contain the object of interest. In that the sub part of face detection the system provides the detection rates or values comparable to the best previous systems. It use in Real Time application and also Non Real Time applications, simultaneously the detector runs at 15 frames per second without differencing image or its skin color detection.
Nectar ecology of the endemic epiphytic hummingbird-pollinated bromeliad Vriesea altodaserrae: secretion dynamics and pollinator visitation pattern

Hummingbirds are the main pollinators of most bromeliad species, whose nectar traits usually respond to the selective pressures imposed by pollinators. Considering the specialization of hummingbird-pollinated bromeliads, we expect a close relationship between nectar ecophysiology and the needs of the main pollinators. In this sense, we studied the nectar ecology of the endemic epiphytic bromeliad Vriesea altodaserrae by assessing its nectar traits to address the following questions: i) do flowers respond to successive experimental removals of nectar? ii) is hummingbird visitation frequency related to nectar secretion pattern? We found that V. altodaserrae depended completely on hummingbirds for sexual reproduction, and nectar composition was consistent with that of most hummingbirdpollinated species. Most of the nectar was secreted at bud stage and, if not removed, flowers reabsorb it at the end of their lifespan. Total nectar production did not change after successive removals, and nectar secretion rhythm did not affect the frequency of hummingbird visits. Vriesea altodaserrae was visited by two-thirds of the hummingbird species recorded at the study site, but especially by those of Trochilinae subfamily, suggesting specialization for this group of hummingbirds and highlighting the importance this endemic bromeliad as a keystone species in areas of highland Atlantic forest.
Block adaptive binarisation of ill-conditioned business card images acquired in a PDA using a modified quadratic filter

A block adaptive binarisation method using a modified quadratic filter (MQF) to binarise business card images having ill conditions of very poor resolution, weak and irregular illumination, shadow and noise acquired in hand-held personal digital assistant cameras, is proposed. In the proposedmethod, a business card image is first partitioned into blocks of 8times8, among which character blocks (CBs) are extracted based on their block activity in DCT domain. Each CB is next enhanced by the MQF that is a result of modifying the quadratic filter (QF) to be proper for locally adaptive enhancement. The enhanced CB is then binarised with the same threshold used in the enhancement so that the enhancement and binarisation are associated consistently with each other. All the remaining blocks are next classified as background blocks and binarised as background. A binarised image is finally obtained by tiling each binarised block at its original position. Experimental results show that the quality of binarised images obtained by the proposed method is much better than that by the conventional global binarisation (GB) using QF and that by Rodtook and Rangsanseri's (2001) pixel adaptive binarisation. It is also better than that by Sauvola and Pietikaumlinen's (2000) pixel adaptive binarisation and that by Yang and Yan's (2000) adaptive logical level binarisation. In addition, the proposed method yields about 32, 28.2, 5.6, and 6.4% improvement of character recognition rate over GB using QF (GB-QF), Rodtook and Rangsanseri's method, Sauvola and Pietikaumlinen's method, and Yang and Yan's method, respectively
MP-16 Characterization of Avibacterium paragallinarum Caused Infectious coryza/Snot: Satellite Colony Phenomenon

Infectious coryza (IC) is an acute upper respiratory disease of poultry that can appear in all ages. Some of clinical signs that are commonly seen in IC are rhinitis, facial swelling or edema, lacrimation, anorexia, and retarded growth in young poultry [1.2. 3]. The disease can be found worldwide, especially in tropical countries [4]. Infectious coryza is very important in the chicken farm industry in developed and developing countries, including Indonesia [5]. The large economic losses due to IC such as increased number of culling, decreased egg production (10-40%), decreased body weight, stunting growth, and some mortality (2-10%) [4]. Avibacterium paragallinarum which was previously classified as Haemophilus paragallinarum is a causative agent of infectious coryza in laying and broiler chickens, quail, pearl chicken, turkey, and peacocks [4,6,7,8]. The bacteria is commensal in the mucous membrane of the upper respiratory system, is sensitive to preservation and does not last long outside the host body [8]. Factors X and V are needed for the growth of several types of A. paragallinarum . According to in vitro growth requirements, A. paragallinarum can be either nicotinamide adenine dinucleotide (NAD) independent or NAD-dependent. The reduced form of NAD (NADH; 1.56-25 µg/ml) and the oxidized form (20-100 µg/ml) is required for in vitro growth in most isolates A. paragallinarum that show satellitic colony on a medium [9]. The description of the need for V factor of field isolates A. paragalinarum has been few reported. The aim of this research is to find out the phenomenon of satellite colonies from a variety of poultry isolates.
A Neural-networks Associative ClassificationMethod For Association Rule Mining

Association rule theory has been used to analyze relationships between data in a large database. It has been applied in various fields including market analysis, customer habit study, detection, classification, clustering and so on. However, it consumes a great amount of time. In the Apriori approach, all frequent patterns are found and then generate association rules from above frequent patterns. However, this approach generates a huge time-intensive query called “iceberg query”. Various researches have been done under the Apriori-like approach to improve performance of the frequent pattern mining task but the results were not as much as expected due to many scans on the dataset. This research aims to propose a flexible way of mining frequent patterns by extending the idea of the Associative Classification method. For better performance, the Neural-Network Associative Classification (NAC) method is proposed here to be one of the approaches for building accurate and efficient classifiers. In this paper, the Neural Networks Associative Classification method is used in order to improve its accuracy. The structure of the network reflects the knowledge uncovered in the previous discovery phase. The trained network is then used to classify unseen data. The accuracy rates obtained from the datasets show promising results.