Miracle-dz/UniFAR-FTUs huggingface dataset:
Swarm intelligence and neural network for data classification

Classification process is one of the most important operations implemented on the huge data warehouses in order to classify the data. Availability of huge amounts of data increased the need for effective techniques to analyze and classify data accurately. Many algorithms in the field of swarm intelligence are able to contribute to improve the classification accuracy using the optimal algorithm methods. The optimal algorithms are used to select optimal features set. From this perspective, these algorithms are used to select the optimal feature of weights and biases for artificial neural network. The proposed method in this article is based on the two algorithms in the field of swarm intelligence, which are used as the new training method for artificial neural network in order to overcome the deficiency in the traditional training algorithms and get a high classification accuracy. The hybrid ABC and PSO is used as new training method for feed-forward neural network, the proposed method is tested in terms of classification accuracy on several datasets. The result show that the performance of classification accuracy of the method is better than the other classification algorithms.
Novel Meta-Analysis-Derived Type 2 Diabetes Risk Loci Do Not Determine Prediabetic Phenotypes

Background Genome-wide association (GWA) studies identified a series of novel type 2 diabetes risk loci. Most of them were subsequently demonstrated to affect insulin secretion of pancreatic β-cells. Very recently, a meta-analysis of GWA data revealed nine additional risk loci with still undefined roles in the pathogenesis of type 2 diabetes. Using our thoroughly phenotyped cohort of subjects at an increased risk for type 2 diabetes, we assessed the association of the nine latest genetic variants with the predominant prediabetes traits, i.e., obesity, impaired insulin secretion, and insulin resistance. Methodology/Principal Findings One thousand five hundred and seventy-eight metabolically characterized non-diabetic German subjects were genotyped for the reported candidate single nucleotide polymorphisms (SNPs) JAZF1 rs864745, CDC123/CAMK1D rs12779790, TSPAN8/LGR5 rs7961581, THADA rs7578597, ADAMTS9 rs4607103, NOTCH2 rs10923931, DCD rs1153188, VEGFA rs9472138, and BCL11A rs10490072. Insulin sensitivity was derived from fasting glucose and insulin concentrations, oral glucose tolerance test (OGTT), and hyperinsulinemic-euglycemic clamp. Insulin secretion was estimated from OGTT data. After appropriate adjustment for confounding variables and Bonferroni correction for multiple comparisons (corrected α-level: p = 0.0014), none of the SNPs was reliably associated with adiposity, insulin sensitivity, or insulin secretion (all p≥0.0117, dominant inheritance model). The risk alleles of ADAMTS9 SNP rs4607103 and VEGFA SNP rs9472138 tended to associate with more than one measure of insulin sensitivity and insulin secretion, respectively, but did not reach formal statistical significance. The study was sufficiently powered (1-β = 0.8) to detect effect sizes of 0.19≤d≤0.25 (α = 0.0014) and 0.13≤d≤0.16 (α = 0.05). Conclusions/Significance In contrast to the first series of GWA-derived type 2 diabetes candidate SNPs, we could not detect reliable associations of the novel risk loci with prediabetic phenotypes. Possible weak effects of ADAMTS9 SNP rs4607103 and VEGFA SNP rs9472138 on insulin sensitivity and insulin secretion, respectively, await further confirmation by larger studies.
Predicting the location of users on Twitter from low density graphs

In this paper we present an algorithm for the estimation of geographic locations of Twitter users. The algorithm is based on the graph representation of communication patterns on Twitter and employs an effective clustering algorithm for estimating locations of users from their connections in the communication graph. While using a graph based approach to estimate geo-location for Twitter users is not new, most of the existing methods require very dense networks which generally require collecting data over a considerable period of time. In this paper, we present a new method that is able to achieve good accuracy and coverage for geolocation estimation of Twitter users using a much less dense graph and only requiring a little over one month's worth of data. The approach is based on label propagation and a new inference location method that uses a clustering of geographical points. We test it on two versions of the Twitter graph built from data in June-July of 2014 and December 2014-January 2015. Analysis of the results demonstrates high accuracy - 65% of geo-labeled users have distance error between true and inferred locations less than 50 km. We were able to geolocate up 87% of users from our datasets. When comparing results between the two graphs, we show that around 40% of users move more than 25 km over the 6 month period.
Use of Sense Marking for Improving WordNet Coverage

WordNet is a crucial resource that aids in several Natural Language Processing (NLP) tasks. The WordNet development activity for 18 Indian languages has been initiated in INDIA by the IndoWordNet1 consortium using the expansion approach with the Hindi WordNet developed by IIT Bombay, as the source. After linking 20K synsets, it was decided that each of these languages should find the coverage of their respective language WordNets by using sense marker tool released by IIT Bombay. The sense marking activity mainly helped in validation of WordNet and improving the WordNet coverage. In this paper, the various effects that sense marking activity had on the Konkani2 language WordNet development are presented.
Carbon Monoxide and Dust Column Densities: The Dust-to-Gas Ratio and Structure of Three Giant Molecular Cloud Cores

We have observed emission in the three lowest rotational transitions of the optically thin species C18O and the dust continuum emission at three millimeter/submillimeter wavelengths. By employing the proper combination of the intensities of the three lowest rotational transitions of C18O, we can obtain the total molecular column density, with relatively little sensitivity to density and temperature variations along the line of sight. We use the line and continuum data to determine column densities of the dust and gas across three giant molecular cloud cores. We find that two of the three sources, M17 and Cepheus A, have the same gas column density-to-dust optical depth ratio, given by log [N(C18O)/τ(790 μm)] = 18.8. In the third source, the Orion molecular cloud, the gas-to-dust ratio is typically a factor of 3 lower than in the other two sources. The gas-to-dust ratio shows only a small (factor ≤ 3) variation across the region of M17 that we have mapped and a comparable reduction at the center of Cepheus A relative to the cloud edge. We have good evidence for the correlation of the continuum emission in different bands for the Orion molecular cloud and find the frequency dependence of the optical depth in the densest regions near the embedded sources to be given by τ ∝ ν1. 9. For positions away from the embedded sources, there is a larger scatter in the data points, with a suggestion that the frequency dependence is steeper, such that τ ∝ ν2. 4. This may be an indication of a change in the grain properties between less dense and very dense regions and is consistent with the results of grain growth. Using standard values for the fractional abundance of C18O relative to H2, the mean densities of the cloud cores are 3-5 × 104 cm-3 . These regions appear to be close to virial equilibrium. The dense gas [revealed by multiple transition studies of tracers such as CS and HC3N to have n(H2) ≃ 106 cm-3] has a volume filling factor of a few percent. Assuming a fractional abundance of C18O equal to 1.7 × 10-7, we find that the 790 μm dust optical depth to mass column density ratio for M17 and Cepheus A is 0.0062 cm2 g-1, while the average value for the Orion molecular cloud is a factor of 3 larger.
Markless tracking based on natural feature for Augmented Reality

This paper presents a solution for AR based product information displaying with virtual 3D model inserted into real world scene. In particular we developed an AR system based on markless tracking which can deal with different illumination conditions during the tracking stage, partial occlusions and wide view angle. Through FAST algorithm the AR system can real-time detect corner on image. In order to confirm the relative position of the camera and the image L-K optical flow algorithm is used to track the feature points in this AR system. The algorithm of K-D tree and RANSAC are also used to help the system to determine the camera parameter. Experimental results show the efficiency, accuracy and robustness of this proposed approach.
A Diagnosis and Repair Framework for DL-Lite A Knowledge Bases

Several logical formalisms have been proposed in the literature for expressing structural and semantic integrity constraints of Linked Open Data (LOD). Still, the data quality of the datasets published in the LOD Cloud needs to be improved, as published linked data often violate such constraints. This lack of consistency may jeopardise the value of applications consuming linked data in an automatic way. A major challenge in this respect, is to provide to the curators of linked data knowledge bases (KBs), the tools that will help them in detecting the violations of integrity constraints and in resolving them, in order to render the knowledge base valid and improve its data quality. In this work, we propose a novel, fully automatic framework for detecting violations of integrity constraints (diagnosis) in KBs, by executing the appropriate queries over the data, as well as for resolving those violations (repair), by removing invalid data from the KB. Our approach takes into consideration both explicit and inferred ontology knowledge, by relying on the ontology language DL-LiteA for the expression of several useful types of logical constraints and for the detection of data that are inconsistent with those constraints, while maintaining good computational properties. The framework that is proposed in this work is modular, allowing each component to be implemented in a manner independent to the other components. This way, we are able to implement our framework with using off-the-shelf, state-of-theart tools for several features, such as reasoning, query execution, etc. We have implemented and evaluated our framework, showing that it is scalable for large datasets and numbers of invalidities, which are exhibited in reality by reference linked datasets, such as DBpedia. The evaluation also shows that our framework can be used over already deployed knowledge bases, without any further reconfiguration.
Formalizing fuzzy objects from uncertain classification results

Concepts of fuzzy objects have been put forward by various authors (Burrough and Frank 1996) to represent objects with indeterminate boundaries. In most of these proposals the uncertainties in thematic aspects and the geometric aspects are treated separately. Furthermore little attention is paid to methods for object identification, whereas it is generally in this stage that the uncertainty aspects of objects become manifest. When objects are to be extracted from image data then the uncertainty of image classes will directly effect the uncertainty of the determination of the spatial extent of objects. Therefore a complete and formalized description of fuzzy objects is needed to integrate these two aspects and analyse their mutual effects. The syntax for fuzzy objects (Molenaar 1998), was developed as a generalization of the formal syntax model for conventional crisp objects by incorporating uncertainties. This provides the basic framework for the approach presented in this paper. However, the model still needs further development in order to represent objects for different application contexts. Moreover, the model needs to be tested in practice. This paper proposes three fuzzy object models to represent objects with fuzzy spatial extents for different situations. The Fuzzy-Fuzzy object (FF-object) model represents objects that have an uncertain thematic description and an uncertain spatial extent, these objects may spatially overlap each other. The Fuzzy-Crisp object (FC-object) model represents objects with an uncertain spatial extent but a determined thematic content and the Crisp-Fuzzy object (CF-object) model represents objects with a crisp boundary but uncertain content. The latter two models are suitable for representing fuzzy objects that are spatially disjoint. The procedure and criteria for identifying the conditional spatial extent and boundaries based upon fuzzy classification result are discussed and are formalized based upon the syntactic representation. The identification of objects by these models is illustrated by two cases: one from coastal geomorphology of Ameland, The Netherlands and one from land cover classification of Hong Kong.
The AN2 Protein Is a Novel Marker for the Schwann Cell Lineage Expressed by Immature and Nonmyelinating Schwann Cells

The expression of the 330 kDa AN2 glycoprotein was studied in the rodent peripheral nervous system. AN2 is expressed by immature Schwann cells in vitro and in vivo and downregulated as the cells upregulate myelin genes. A subpopulation of nonmyelinating Schwann cells in the adult sciatic nerve retains expression of AN2. In rat sciatic nerve crushes, where Schwann cell numbers increase after initial axonal loss and markers of immature Schwann cells show an upregulation, no increased expression of AN2 was observed. In contrast, AN2 expression was upregulated in nerves from peripheral myelin protein-22-transgenic rats, where immature Schwann cells expand without axonal loss. Furthermore, coculture with neurons upregulated AN2 expression on Schwann cells in vitro. Polyclonal antibodies against AN2 inhibited the migration of an immortalized Schwann cell clone in an in vitro migration assay, and the purified AN2 protein was shown to be neither inhibitory nor permissive for outgrowing dorsal root ganglion neurites. AN2 is thus a novel marker for the Schwann cell lineage. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis of purified AN2 from early postnatal mouse brain demonstrated that AN2 is the murine homolog of the rat NG2 proteoglycan.
Toward a Model of Intelligence in Pedagogical Agents

Pedagogical agents have made significant strides over the past few years as a new species of learning technologies. This paper is a review on the current state of the art in pedagogical agents, believability and human-likeness, and instructional affordance of the autonomous agents in Intelligent Tutoring Systems. It reviews the challenges that concern pedagogical agents, and proposes research and technology development activities that can allow for learner-oriented intelligent efforts.
Differential Methylation as a Biomarker of Response to Etanercept in Patients With Rheumatoid Arthritis

Biologic drug therapies represent a huge advance in the treatment of rheumatoid arthritis (RA). However, very good disease control is achieved in only 30% of patients, making identification of biomarkers of response a research priority. We undertook this study to test our hypothesis that differential DNA methylation patterns may provide biomarkers predictive of response to tumor necrosis factor inhibitor (TNFi) therapy in patients with RA.
Validation of Simulated Robots with Realistically Modeled Dimensions and Mass in USARSim

In the case of simulators of mobile robots, it is necessary to clarify the similarity between actual and simulated robots in order to improve the efficiency and transparency of simulators as research tools. Because of its real-time performance, USAR-Sim [1] has been popularly used in studies involving human-robot interactions. However, since USARSim uses approximations and abbreviations in its physical computations, there are some doubts over its computational accuracy. The objective of this study is to compare the actual and simulated robots in order to investigate whether the simulated environments can be used as a training tool. The authors modeled two types of response robots -IRS Soryu [2] and Kenaf [3]- and compared the abilities of the simulated robots and the actual robots in navigating obstacles such as steps and trenches. The dimensions such as heights, widths and lengths and total mass of the real robots were accurately modeled in the simulation and the other physical parameters were assigned default values. It was observed that the maximum error in the sizes of the traversable objects was 18 % of the body length of the robot. It was also found out that the angular velocities of the robots were significantly different for the real and simulated robots. The conclusion is that a training system can be built based on USARSim when the simulated environments involve simple obstacles because there are not significant discrepancies between the basic traversing abilities of the actual robots and those of the simulated robots.
A Novel Endometriosis Inducing Factor In Women with Endometriosis

Aim: To confirm the hypothesis of the presence of a possible endometriosis inducing factor(s) (EIF) in the blood of women with endometriosis. Patients and Methods: Forty infertile women were studied. The study group compromised of fifteen women of each three different degrees of endometriosis and fifteen women without endometriosis as a control group. Stem cells are characterized by being spindle shaped and proliferate in appropriate culture indefinitely. The women sera were co-cultured with mesenchymal stem cells (MSCs) which were followed up weekly to look for morphological changes and to detect Annexin 1 marker and ß-actin gene by reverse transcriptase polymerase chain reaction. Results: MSCs cultured with sera of cases with, mild, moderate and severe endometriosis, showed morphological changes to be columnar and cuboidal shaped cells -resembling endometrial cells and glands- by the 4th week in 60%, 60% & 100% respectively. These cells were detected from as early as the first week in women with moderate and severe types (20% for each group). The percentage of the change into endometrial like cells increased among the three groups where it was 30±25.8%, 45±29.9% and 75±37.9% respectively. Moreover, increasing number of endometrial like cells are detected weekly, the more severe the disease is. None of the cultures of serum of the control group had made such changes all over the study. Furthermore, with more differentiation there was a considerable decrease in number of stem cells. These differentiated cells expressed the Annexin-1 marker. Conclusion: It was evident that serum of women with endometriosis posses a factor(s) that enables the MSCs to be transformed into endometrial like cells and glands in vitro. This finding supports a new theory for the etiology of endometriosis. This observation may have a tremendous effect on the therapeutic implications of this debilitating condition.
HIF‐1α regulates EMT via the Snail and β‐catenin pathways in paraquat poisoning‐induced early pulmonary fibrosis

Paraquat (PQ) poisoning‐induced pulmonary fibrosis is one of the primary causes of death in patients with PQ poisoning. Hypoxia‐inducible factor‐1α (HIF‐1α) and epithelial‐mesenchymal transition (EMT) are involved in the progression of pulmonary fibrosis. Snail and β‐catenin are two other factors involved in promoting EMT. However, the relationship among HIF‐1α, Snail and β‐catenin in PQ poisoning‐induced pulmonary fibrosis is not clear. Our research aimed to determine whether the regulation of HIF‐1α in EMT occurs via the Snail and β‐catenin pathways in PQ poisoning‐induced pulmonary fibrosis. Sixty‐six Sprague–Dawley rats were randomly and evenly divided into a control group and a PQ group. The PQ group was treated with an intragastric infusion of a 20% PQ solution (50 mg/kg) for 2, 6, 12, 24, 48 and 72 hrs. A549 and RLE‐6TN cell lines were transfected with HIF‐1α siRNA for 48 hrs before being exposed to PQ. Western blotting, real‐time quantitative PCR, immunofluorescence, immunohistochemistry and other assays were used in our research. In vivo, the protein levels of HIF‐1α and α‐SMA were increased at 2 hrs and the level of ZO‐1 (Zonula Occluden‐1) was reduced at 12 hrs. In vitro, the transient transfection of HIF‐1α siRNA resulted in a decrease in the degree of EMT. The expression levels of Snail and β‐catenin were significantly reduced when HIF‐α was silenced. These data demonstrate that EMT may be involved in PQ poisoning‐induced pulmonary fibrosis and regulated by HIF‐1α via the Snail and β‐catenin pathways. Hypoxia‐inducible factor‐1α may be a therapeutic target for the treatment of PQ poisoning‐induced pulmonary fibrosis.
Dimension reduction using evolutionary Support Vector Machines

This paper presents a novel approach of hybridizing two conventional machine learning algorithms for dimension reduction. Genetic algorithm (GA) and support vector machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set using principles of evolutionary process, after which the reduced dataset is presented to the SVMs. Simulation results show that GA-SVM hybrid is able to produce good classification accuracy and a high level of consistency. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes. This correlation measure injects greater diversity and increases the overall fitness of the population.
Energy optimization for latency- and quality-constrained video applications

The trade-off between energy consumption and media quality is a fundamental design issue in mobile multimedia. This energy optimization technique based on frame skipping and buffering exploits the characteristics of video applications, particularly their tolerance to variation in latency and video quality to increase slack time and its use in dynamic voltage scaling. The technique adapts the frame rate to the degree of motion activity by skipping redundant frames. Thus, it intentionally creates slack time as long as video quality is not unacceptably degraded. We implemented this technique with only a slight modification of the original encoder algorithm and no modification to the decoder.
Analyzing the Effects of Instance Features and Algorithm Parameters for Max–Min Ant System and the Traveling Salesperson Problem

Ant colony optimization (ACO) performs very well on many hard optimization problems, even though no good worst case guarantee can be given. Understanding the effects of different ACO parameters and the structural features of the considered problem on algorithm performance has become an interesting problem. In this paper, we study structural features of easy and hard instances of the Traveling Salesperson problem for a well-known ACO variant called Max Min Ant System MMAS) for several parameter settings. The four considered parameters are the importance of pheromone values, the heuristic information, the pheromone update strength and the number of ants. We further use this knowledge to predict the best parameter setting for a wide range of instances taken from TSPLIB.