Class Of Pairs Research Articles

Analysis of the inertia tensor of autonomous mobile robot

The object of research is the inertia tensor of an autonomous mobile robot (AMR) with a manipulator with different configurations of their mutual position. As an example of the AMR design of a changing configuration, an all-wheel drive four-wheeled platform with a manipulator is considered, consisting of a docking disk rotating around a vertical axis and rod links of the arm connected by rotational kinematic pairs of the fifth class. The mass of moving structural elements, i. e., a manipulator with a load, is 10–20 % of the mass of the robot platform. Let’s consider that the links of the manipulator and the platform are absolutely rigid and homogeneous bodies with a constant density; let’s neglect the mass of kinematic pairs. The next step in the analysis of the AMR inertia tensor of a changing configuration can be a study taking into account the elastic properties of the manipulator links, the uneven distribution of the masses of the platform, and the characteristics of the kinematic pairs. The dependence of the values of the elements of the AMR inertia tensor of a changing configuration on the values of the generalized coordinates of the moving elements of the structure and the ratio of the mass of the platform and the mass of the moving elements of the structure has been studied. The analysis of the inertia tensor of the AMR with a manipulator at different configurations of their mutual position showed that the values of the centrifugal moments of inertia of the system during the relative motion of the manipulator are commensurate with the value of the axial moments of inertia of the system, even if the mass of the moving structural elements is less than 10% of the mass of the platform. In most existing AMRs, the mass of moving structural elements is up to 20% of the platform mass, therefore, in the general case, the inertia tensor of such a system should be taken as off-diagonal and non-stationary. In the future, this will make it possible to refine the equation of dynamics, take into account the relationship of control channels, simulate the movement of AMR of a changing configuration, and optimize energy costs. Since AMR with the manipulator is an example of the «changing AMR» object class, the results obtained can be applied to all objects of this class.

Multi-view distance metric learning via independent and shared feature subspace with applications to face and forest fire recognition, and remote sensing classification

Distance Metric Learning for Large Margin Nearest Neighbor (LMNN), as a classic distance metric learning (DML) method, has attracted much attention among researchers. However, it, like most of the existing DML methods, cannot be guaranteed to achieve independent and shared feature subspaces from multiple sources or different feature subsets, such that much statistical feature information is ignored in model learning. In this paper, we propose a novel DML model, called Multi-view DML Based on Independent and Shared Feature Subspace (MVML-ISFS), which learns multiple distance metrics to unify the information from multiple views. The proposed method finds a distance metric for each view in an independent feature space to preserve its specific property as well as a sparse representation related to the distance metrics from distinct views in a shared feature space to remain their common properties. The objective problem of MVML-ISFS is formulated based on LMNN, thus encouraging a large margin for each view that makes the distance between each of the same class pairs of samples be smaller than that between each of the different class pairs of samples. The proposed model in MVML-ISFS involves multivariate variables, which are optimized by a gradient descent strategy. The experimental results show the effectiveness of our MVML-ISFS on remote sensing, face, forest fire, and UCI datasets.

Reference-free method for investigating classification uncertainty in large-scale land cover datasets

In the last decade, land cover products are produced at a global scale and updated with an unprecedent speed with the development of earth observation and mapping techniques. However, assessing large-scale land cover datasets is always a challenging task because of lack of ground truth and high dependency on manual inspection. To promote the efficiency of assessment, this study introduces a generic framework that identifies potential land cover classification errors without traditional reference data. Depending on rich features in remote sensing data, the overall procedure of can be regarded as a multi-class anomaly detection problem. To improve the performance in dealing with complex classification scheme, a pairing strategy is firstly proposed for pairwise analysis on each two classes, so that the multi-class problem is decomposed into multiple smaller binary-class problems. Secondly, the proximity matrix of each class pair is generated on the basis of extra trees model, which is applicable to mixed numerical and categorical data and provides robust proximity measurement by including multiple levels of randomness. Finally, an improved proximity-based anomaly detection algorithm is applied, and the detection results of each class pair are ensembled to obtain the final anomaly score of each land instance. Experiments show that the overall area under the receiver operating characteristic curve score reaches approximately 0.9 for synthetic datasets and 0.8 for real-life datasets. The proposed method is expected to provide users valuable data for a more efficient assessment in the absence of ground truth in practice.

О ФОРМИРОВАНИИ БАЗ ПРЕЦЕДЕНТОВ ДЛЯ РЕШЕНИЯ ЗАДАЧ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ

The issues of the formation of bases of precedents related to the one of “big data” in the field of information security are considered. That manifests itself in the lack of opportunities for using classical methods of the data analysis in order to make informed decisions. As a rule, among the basic requirements for the mathematical and software information security include minimizing the cost of computing resources and high rates of generalizing ability based on the results of machine learning. To record those requirements, it is proposed to use several criteria. The application of criteria is associated with the formation of a latent feature space with a smaller dimension than the original one and the study of the structure of object relations in it. The analysis of the structure diversity of objects relations is carried out in the spaces formed by pairs of classes (type of DDOS attacks, normal traffic). A rule is proposed for including an object in the database of precedents. The effectiveness of using the precedent base is demonstrated (based on a test sample) using the k nearest neighbor recognition algorithms, Random Forest, and SVM. One of the types of DDOS attacks of the test sample is not presented in the training. Descriptions of objects are given without selection and with selection of informative features, according to the results of data normalization and without it.

36-Month Randomized Clinical Trial Evaluation of Preheated and Room Temperature Resin Composite.

This study evaluated the effect of preheating resin composites (RCs) on the clinical performance of class I restorations during a 36-month period using a split-mouth, double-blinded randomized design. A total of 35 patients were selected. Every patient received one pair of class I nanofilled resin composite (RC, Filtek Z350 XT) posterior restorations (n=70). One side of the mouth received preheated composites; on the other side, the composite was placed in a nonheated state following the manufacturer's instructions. These restorations were evaluated at 1-week (baseline), 12-months, 24-months, and 36-months using the FDI World Dental Federation criteria. The statistical analyses were also performed using the Wilcoxon and Friedman tests with the level of significance set at 0.05. After 36 months, 33 patients attended the recall visits, and 66 restorations were evaluated. The Friedman and Wilcoxon signed-rank tests revealed insignificant differences between both groups (p>0.05) for all FDI parameters. However, a significant difference was detected for staining as a criterion at 36 months (p=0.01). Moreover, a significant difference in the staining was detected when the baseline and 36 months were compared in the nonheated RC group (p=0.001). For esthetic, functional, and biological properties, the nonheated composite exhibited 93.9%, 100%, and 100% of the clinically accepted scores, respectively, and the preheated group presented 100% for all properties. Four restorations had postoperative sensitivity at baseline for nonheated (11.4%) and five for preheated (14.2%), but the postoperative sensitivity scores were considered highly acceptable at 12-, 24-, and 36-months. After 36 months, preheated nanofilled RCs showed an acceptable clinical performance similar to that of the nonheated ones in class I restorations, but with better resistance to marginal staining.

The Double Difference Property for the Class of Locally Hölder Continuous Functions

In this paper, we show that the pair of classes of locally Holder continuous functions (considered on $\mathbb{R}$ and $\mathbb{R}^{2}$, respectively) has the double difference property.

AI SoC-Based Accelerator for Speech Classification Accélérateur de classification de la parole basé sur un AI SoC

Speech classification acceleration using field-programmable gate arrays (FPGAs) is a well-studied field and enables the potential to gain both speed and better energy efficiency over other processor-intensive classifiers. System-on-chip (SoC) architecture allows for an integrated system between programmable logic and processor and for increased bandwidth communications to on-chip peripherals and memory. This article serves as an investigation of the utility of an edge-based support-vector machine (SVM) implemented onto a Zynq-XC7Z020 multiprocessor system on a chip (MPSoC) for the acceleration of three speech class pairs. The system allows for a parallelized structure, which yielded a faster classifier model. The results were found to be an acceleration factor of 2.08 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> . This appears to have come at the cost of a decrease in prediction accuracy, lowering from 92.5% to 83.5% positive prediction percentage likely due to decreased data resolution. The resolution used in this model was a 16-bit fixed-point format for the hardware interpretation and a floating-point format for the software benchmark. The resource usage of the FPGA was also analyzed for both overlays and can yield a 21% reduction in CPU usage.

Apportioned margin approach for cost sensitive large margin classifiers

We consider the problem of cost sensitive multiclass classification, where we would like to increase the sensitivity of an important class at the expense of a less important one. We adopt an apportioned margin framework to address this problem, which enables an efficient margin shift between classes that share the same boundary. The decision boundary between all pairs of classes divides the margin between them in accordance with a given prioritization vector, which yields a tighter error bound for the important classes while also reducing the overall out-of-sample error. In addition to demonstrating an efficient implementation of our framework, we derive generalization bounds, demonstrate Fisher consistency, adapt the framework to Mercer’s kernel and to neural networks, and report promising empirical results on all accounts.

CONSISTENCY OF THE INSTRUCTIONS FOR THE USE OF PROSPECTIVES FOR DETERMINING THE AREA OF A PARALLELA AND TRIANGLE AREA FOR STUDENTS OF SDN 6 MATARAM

The approach used in this research is a quantitative approach with a quasi-experimental research design. The purpose of this study was to test the consistency and effectiveness of the instruction manual for determining the area of a parallelogram and the area of a triangle for students of SD 6 Mataram. To achieve this goal, tools and instructions for use were used in learning for the 111th-grade students of SDN 6 Mataram and divided into 3 classes. Because the research was conducted during the COVID-19 pandemic, the media was designed to be a learning video that describes the use of props, and their operational guidelines are narrated in the video. The video that contains the use of the tool is considered very in accordance with the instructions for use|| props. The results of the evaluation showed that there was no significant difference in the mean scores of the three pairs of classes (significance level 5% |t_h | &lt; t_table = 2,0002). On the other hand, the completeness obtained by students in each class is &gt;80% (KKM=70). This means that there is complete student learning about the area of a parallelogram and the area of a triangle. Thus, it can be concluded that the teaching aids for determining the area of a parallelogram and the area of a triangle and the instructions for use are consistently effective in helping students achieve complete learning on the material for the area of a parallelogram and the area of a triangle.

Heights of points on elliptic curves over ℚ

In this note we obtain effective lower bounds for the canonical heights of non-torsion points on E ( Q ) E(\mathbb {Q}) by making use of suitable elliptic curve ideal class pairings Ψ E , − D : E ( Q ) × E − D ( Q ) ↦ C L ( − D ) . \begin{equation*} \Psi _{E,-D}: \ E(\mathbb {Q})\times E_{-D}(\mathbb {Q})\mapsto \mathrm {CL}(-D). \end{equation*} In terms of the class number H ( − D ) H(-D) and T E ( − D ) T_E(-D) , a logarithmic function in D D , we prove h ^ ( P ) &gt; | E t o r ( Q ) | 2 ( H ( − D ) + | E t o r ( Q ) | ) 2 ⋅ T E ( − D ) . \begin{equation*} \widehat {h}(P)&gt; \frac {|E_{\mathrm {tor}}(\mathbb {Q})|^2}{\left ( H(-D)+ |E_{\mathrm {tor}}(\mathbb {Q})|\right )^2}\cdot T_E(-D). \end{equation*}

Capturing Discriminative Information Using a Deep Architecture in Acoustic Scene Classification

Acoustic scene classification contains frequently misclassified pairs of classes that share many common acoustic properties. Specific details can provide vital clues for distinguishing such pairs of classes. However, these details are generally not noticeable and are hard to generalize for different data distributions. In this study, we investigate various methods for capturing discriminative information and simultaneously improve the generalization ability. We adopt a max feature map method that replaces conventional non-linear activation functions in deep neural networks; therefore, we apply an element-wise comparison between the different filters of a convolution layer’s output. Two data augmentation methods and two deep architecture modules are further explored to reduce overfitting and sustain the system’s discriminative power. Various experiments are conducted using the “detection and classification of acoustic scenes and events 2020 task1-a” dataset to validate the proposed methods. Our results show that the proposed system consistently outperforms the baseline, where the proposed system demonstrates an accuracy of 70.4% compared to the baseline at 65.1%.

Diversifying Trends

A new method is proposed for disentangling the systematic components from the idiosyncratic components of risk associated with trend-following strategies. A simple statistical approach combined with standard dimension reduction techniques enables to identify the common trending component of futures market prices. This methodology is applied to a large set of futures, covering all asset classes, to extract a common risk factor, called CoTrend. It is shown that common trends are higher for some cross-asset class pairs than for intra-asset class pairs, such as JPY/USD and Gold. This result is used to create sectors in a portfolio diversification context, especially for trend-following strategies. Additionally, the CoTrend factor helps understand arbitrage-based Hedge Fund strategies, which by essence are decorrelated from standard risk factors.

Phytochemistry reflects different evolutionary history in traditional classes versus specialized structural motifs

Foundational hypotheses addressing plant–insect codiversification and plant defense theory typically assume a macroevolutionary pattern whereby closely related plants have similar chemical profiles. However, numerous studies have documented variation in the degree of phytochemical trait lability, raising the possibility that phytochemical evolution is more nuanced than initially assumed. We utilize proton nuclear magnetic resonance (1H NMR) data, chemical classification, and double digest restriction-site associated DNA sequencing (ddRADseq) to resolve evolutionary relationships and characterize the evolution of secondary chemistry in the Neotropical plant clade Radula (Piper; Piperaceae). Sequencing data substantially improved phylogenetic resolution relative to past studies, and spectroscopic characterization revealed the presence of 35 metabolite classes. Metabolite classes displayed phylogenetic signal, whereas the crude 1H NMR spectra featured little evidence of phylogenetic signal in multivariate tests of chemical resonances. Evolutionary correlations were detected in two pairs of compound classes (flavonoids with chalcones; p-alkenyl phenols with kavalactones), where the gain or loss of a class was dependent on the other’s state. Overall, the evolution of secondary chemistry in Radula is characterized by strong phylogenetic signal of traditional compound classes and weak phylogenetic signal of specialized chemical motifs, consistent with both classic evolutionary hypotheses and recent examinations of phytochemical evolution in young lineages.

Konsistensi Petunjuk Penggunaan Alat Peraga untuk Penentukan Luas Daerah Jajaran Genjang dan Luas Daerah Segitiga Bagi Siswa SD 6 Mataram

The purpose of this study is to know the consistency and effectiveness of the instructions for the use of props to determine the area of the parallel ranks and the area of triangles for students of SD 6 Mataram. It is realized that the research was conducted during the COVID 19 pandemic, so that the media is designed to be a learning vedio that describes the use of props and operational guidelines narrated on the vedio. Based on the observations of 9 elementary school teachers as narrative practitioners of operational guidelines, it appears in accordance with the instructions for the use of such props. The subjects of this study were grade VI Mataram elementary school students as young as 3 grades. The evaluation results showed that the average score obtained from the three pairs of classes did not differ significantly at the level of significance of 5 % because it was obtained &lt; ttabel = 2.0002. On the other hand, the completion obtained by students in each class in each region &gt; 80%. This means that students are complete in learning about the area of genakang ranks and the area of triangle area with KKM = 70. On the other hand, the message is the same because the average score that each class gets for each region does not differ significantly. Thus the application of instructions for the use of props determines the formula of the area of the parallel range and the area of the triangle consistently and the use of effective props for students of SD 6 Mataram

IMPROVING THE DESIGN OF KINEMATIC PAIRS OF CRANK-ROCKER DRIVE MECHANISM SSHNU

It is shown that one of the reasons for the failure of the SSHNU is the wear of the connecting rods with the crank, due to uneven loads on the connecting rods, due to the inaccuracy of the manufacture of elements of kinematic pairs, leading to elastic deformation and an increase in friction forces. The replacement of connecting rods with a crank and a traverse with spherical plain bearings corresponding to kinematic pairs of class IV, which exclude elastic deformations of the links, due to the appearance of an additional degree of freedom, is proposed. Calculations show an increase in the service life of spherical plain bearings by 15-17%.

A recurrent curve matching classification method integrating within-object spectral variability and between-object spatial association

Object-based image analysis (OBIA), which has been commonly used for land cover and land use classification, may encounter challenges when satellite images’ spatial resolution achieves at the sub-meter level. An image object may exhibit spectral heterogeneity, causing traditional object-level statistical measures such as mean values of the pixels in an object not suited to represent the feature of the object. Additionally, an image object may have strong spatial association with its surroundings. Traditional OBIA only considers spatial features of individual object, but ignoring spatial arrangement or spatial association between objects. This paper proposes a new OBIA method by integrating within-object spectral variability and between-object spatial association. The within-object spectral variability is captured by the histograms of the pixels in an object across multispectral bands to reflect the heterogeneity of their pixel values. Based on this, the initial classification result is obtained using non-parametric curve matching methods. Then, the between-object spatial association is represented by curves derived from the frequency of pairwise classes in four main directions, also in the form of curves. The curves now composed of both the histograms of spectral feature and the class pair frequency of spatial feature are then fused for another curve matching based classification. This recurrent process is repeated and the spatial association is recaptured from the previous classification result at each iteration until a stopping criterion is satisfied. The curve matching classification method based on histograms of spectral feature is superior to traditional OBIA based on only object-level statistical measures since it fully characterizes spectral variability in the objects. The between-object spatial association works as a spatial filter that considers spatial arrangement of classes in a neighborhood. The developed method is especially suitable for classifying high spatial resolution (HSR) images with land cover/land use classes in typical urban areas.

The Effect of the Discussion Method of Video Analysis of Physics Learning on High Level Thinking Skills of Students

Higher order thinking skills are one of the skills that are used as provisions for students to face competition in the era of globalization. The purpose of this study was to determine the effect of the discussion method of analysis of physics learning videos on students' higher order thinking skills. The subjects of this study were all 5th semester students who took the Physics Curriculum Review lectures in the 2020/2021 academic year at a university in the city of Mataram, totaling 64 people. The research method was quantitative, with a post-test only group design. Higher order thinking skills data were obtained from the essay test given after the treatment. Data analysis to test the hypothesis using one-way ANOVA and calculations using SPSS 25. The results showed that the value of the F coefficient of 16,846 with a significance of 0.000 is much smaller than the alpha value of 0.05. There are differences in higher-order thinking skills between students who take conventional discussion learning, students who take video analysis discussion learning, and students who take problem-solving learning. The follow-up test with the Tukey Test shows that the difference between class 1 and class 2 has a significance of 0.000, the difference between class 1 and class 3 has a significance of 0.003, and the difference between class 2 and class 3 has a significance of 0.048. All comparisons had a significance value less than an alpha value of 0.05, so further tests for all pairs of sample classes were significant. The average score of students who took conventional discussion learning was 78.45, the average score of students who took part in the video analysis discussion learning was 85.20, and the average score of students who took problem solving learning was 82.36. Thus, the class of students who took part in the video analysis discussion learning physics learning had the highest average compared to the other 2 classes. The discussion method of analyzing physics learning videos can be used as an alternative learning method to develop students' higher order thinking skills.

Thousands of protein linear motif classes may still be undiscovered.

Linear motifs are short protein subsequences that mediate protein interactions. Hundreds of motif classes including thousands of motif instances are known. Our theory estimates how many motif classes remain undiscovered. As commonly done, we describe motif classes as regular expressions specifying motif length and the allowed amino acids at each motif position. We measure motif specificity for a pair of motif classes by quantifying how many motif-discriminating positions prevent a protein subsequence from matching the two classes at once. We derive theorems for the maximal number of motif classes that can simultaneously maintain a certain number of motif-discriminating positions between all pairs of classes in the motif universe, for a given amino acid alphabet. We also calculate the fraction of all protein subsequences that would belong to a motif class if all potential motif classes came into existence. Naturally occurring pairs of motif classes present most often a single motif-discriminating position. This mild specificity maximizes the potential number of coexisting motif classes, the expansion of the motif universe due to amino acid modifications and the fraction of amino acid sequences that code for a motif instance. As a result, thousands of linear motif classes may remain undiscovered.

Effect of Different Atmospheric Correction Algorithms on Sentinel-2 Imagery Classification Accuracy in a Semiarid Mediterranean Area

Multi-temporal imagery classification using spectral information and indices with random forest allows improving accuracy in land use and cover classification in semiarid Mediterranean areas, where the high fragmentation of the landscape caused by multiple factors complicates the task. Hence, since data come from different dates, atmospheric correction is needed to retrieve surface reflectivity values. The Sen2Cor, MAJA and ACOLITE algorithms have proven their good performances in these areas in different comparative studies, and DOS is a basic method that is widely used. The aim in this study was to test the feasibility of its application to the data set to improve the values of accuracy in classification and the performance in properly labelling different classes. Additionally, we tried to correct accuracy and separability mixing predictors with different algorithms. The results showed that, using a single algorithm, the general accuracy and kappa index from ACOLITE were the highest, 0.80 ± 0.01 and 0.76 ± 0.01., but the separability between problematic classes was slightly improved by using MAJA. Any combination of the different algorithms tested increased the values of classification, although they may help with separability between some pairs of classes.

Rough-Bayesian approach to select class-pair specific descriptors for HEp-2 cell staining pattern recognition

One of the important problems in computer-aided diagnosis of connective tissue disease is automatic recognition of staining patterns present in HEp-2 cells. In this regard, the paper introduces a novel approach for the recognition of staining patterns by HEp-2 cell indirect immunofluorescence image analysis. The proposed method assumes that a fixed set of local texture descriptors or scales may not be effective for classifying staining patterns into multiple classes. A particular set of descriptors or scales may be significant for classifying a pair of classes, but may not be relevant for other pairs of classes. The proposed approach, therefore, first selects a set of local texture descriptors under appropriate scales for each class-pair, and then forms the final feature set for multiple classes from the relevant descriptors of all possible pairs of classes. A novel framework, termed as Rough-Bayesian model, is introduced to evaluate the relevance of a descriptor and/or a scale. It is based on the merits of rough sets and Bayes decision theory. During the selection of relevant descriptor and/or scale, the proposed method takes care of the presence of both noisy pixels in an HEp-2 cell image and noisy HEp-2 cell images in a staining pattern class. The support vector machine is used to predict the staining patterns present in HEp-2 cell images. The performance of the proposed method, along with a comparison with state-of-the-art methods, is demonstrated on several HEp-2 cell image databases. An important finding is that the accuracy for classifying HEp-2 cell images is significantly increased if class-pair specific descriptors under appropriate scales are considered, instead of selecting a uniform set of descriptors and scales for multiple classes.