Reviewerly/exHarmony huggingface dataset:
Know Your International User behind the Screen: A Conversation among Chinese Students and Librarians Regarding Virtual Reference Services (VRS)

Librarians need feedback from users to improve library services. On the other hand, users need assistance from librarians in order to gain a better understanding of library services. This paper focuses on how Chinese students use U.S. academic libraries’ virtual reference services (VRS), and how academic librarians use VRS. By collecting data from Chinese students and librarians, this paper hopes to create a better understanding of these two factors in order to improve VRS.
Dynamic Image Enhancement Algorithm in Heterogeneous Environments

Image acquisition in heterogeneous environments tends to lead to inadequate information and poor image quality.In order to improve the image quality of dynamic data in heterogeneous environments, an enhanced technology of dynamic data information based on Radon scale transformation is proposed.The gray histogram feature information parameters of images in heterogeneous environments are extracted.The feature quantities are fused and optimized in the central region of image clustering, and the multi-scale Retinex color feature components of dynamic data are extracted.Radon scale transformation is used to extract image centers in heterogeneous environments, enhance dynamic data of image centers, and improve image quality.The simulation results show that this method can enhance the dynamic data information of image centers in heterogeneous environments, and the output images have better imaging performance.The normalized correlation coefficient and peak signal-to-noise ratio (psnr) of the output images are higher than those of the traditional methods, which improves the peak signal-to-noise ratio (psnr) of the output image and improves the recognition performance of the image.
FAS-Net: Construct Effective Features Adaptively for Multi-Scale Object Detection

Feature pyramid is the mainstream method for multi-scale object detection. In most detectors with feature pyramid, each proposal is predicted based on feature grids pooled from only one feature level, which is assigned heuristically. Recent studies report that the feature representation extracted using this method is sub-optimal, since they ignore the valid information exists on other unselected layers of the feature pyramid. To address this issue, researchers present to fuse valid information across all feature levels. However, these methods can be further improved: the feature fusion strategies, which use common operation (element-wise max or sum) in most detectors, should be replaced by a more flexible way. In this work, a novel method called feature adaptive selection subnetwork (FAS-Net) is proposed to construct effective features for detecting objects of different scales. Particularly, its adaption consists of two level: global attention and local adaptive selection. First, we model the global context of each feature map with global attention based feature selection module (GAFSM), which can strengthen the effective features across each layer adaptively. Then we extract the features of each region of interest (RoI) on the entire feature pyramid to construct a RoI feature pyramid. Finally, the RoI feature pyramid is sent to the feature adaptive selection module (FASM) to integrate the strengthened features according to the input adaptively. Our FAS-Net can be easily extended to other two-stage object detectors with feature pyramid, and supports to analyze the importance of different feature levels for multi-scale objects quantitatively. Besides, FAS-Net can also be further applied to instance segmentation task and get consistent improvements. Experiments on PASCAL07/12 and MSCOCO17 demonstrate the effectiveness and generalization of the proposed method.
Selection of null models in nestedness pattern detection of highly asymmetric mycorrhizal networks

Aims Null model is an important basis for nesting judgment.Highly asymmetric structures often appear in plant symbolic fungal networks.This study aims to explore the influence of matrix asymmetric changes on network nesting judgment. MethodsThe study was conducted based on various null model construction methods.Important findings Constraints vary with changing null models, with reducing null space when additional qualifications were added during null model establishment.Highly constrained nulls are prone to causing type II errors.Highly asymmetric networks increase matrix temperature (NT) deviation based on random (Equiprobableequiprobable, r00) null model while reducing overlap and decreasing fill (NODF) deviation.Values of z-score show that highly asymmetric networks contribute to the significant determination level of NT and NODF.The impacts on the judgment of nestedness of asymmetric networks differ between row and column fixed null models.The effects of network asymmetry change on nesting detection based on column constrained (c0) nulls are similar to that of random null model, but with smaller nesting deviation and standard deviations.No significant differences in both NT and NT deviations were observed among different asymmetry networks based on the row fixed (r0) nulls, with a lower NODF deviation in highly asymmetric network based on c0 nulls.To more accurately determine whether the asymmetric networks would have nested structures, we recommend using a combination of random and constrained null models.Our results also demonstrate that the r0 null model performs better than either the r00 null model or the c0 null model when comparing nesting level of different asymmetric networks.
Calculation Of Sedimentation Rate at Sempor Reservoirs in 2018 Using Modified Universal Soil Loss Equation Method (MUSLE)

Sempor Reservoir located in the Telomoyo River Basin in the Serayu River region is categorized as one of the priority watersheds based on the 2015-2019 Strategic Plan of the Ministry of Environment and Forestry. Priority watershed is a watershed that gets more attention in controlling water pollution, one of which is sedimentation. Watershed observation and management activities are needed for the reservoir to operate optimally. One of the observations and management activities of the Sempor Watershed is the calculation of sedimentation rate. In this research the sedimentation rate was calculated using the MUSLE method that included runoff parameter, erodibility, slope, land cover and conservation factor. The Sempor watershed boundary is obtained through delineation of DEM. The calculation is done by utilizing a Geographic Information System (GIS) through analysis of soil type maps, length and slope maps, land cover and conservation maps with the boundaries of the Sempor Reservoir. Based on this result, the Sempor Reservoir has a sedimentation rate of 166,173,500 tons/year and a sedimentation thickness rate of 3,7259 mm/year. According to the provisions of the Ministry of Forestry in 2009, the rate of sedimentation thickness in the Sempor Reservoir is included in the medium class. The river as one of the potential means of sediment transport contributes to the rate of sediment thickness of 3.7256 mm /year, while for areas outside the river coverage it only 0.003 mm/year. The river has a major influence on sedimentation in the Sempor Reservoir.
PENGARUH MINAT DAN MOTIVASI BELAJAR TERHADAP PRESTASI AKADEMIK MAHASISWA SEKOLAH TINGGI BAHASA HARAPAN BERSAMA

Mastering foreign language are used as a step to improve competence when someone get into the workforce.That awareness lead many Indonesians interested in taking Chinese major for their further study.Interest and motivation are one of the factors which could affect learning achievements.A person with low interest while starting something new will have a sense of laziness and without high learning motivation can make a person lost their interest to continue their activities.Therefore, researchers would like to know if there is an impact of interest and motivation on student achievements at Sekolah Tinggi Bahasa Harapan Bersama.The research method used in this research is descriptive method with data collection techniques through questionnaires and documentary studies where students of Sekolah Tinggi Bahasa Harapan Bersama become the members of population.There are three variables studied in this study, namely: learning interest (X1), learning motivation (X2) and learning achievement (Y).The data analysis technique used is qualitative and quantitative analysis techniques using the SPSS 17.0 program with multiple linear regression to determine whether there is a relationship between
Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations

Assemblies of modular subsystems are being pressed into service to perform sensing, reasoning, and decision making in high-stakes, time-critical tasks in areas such as transportation, healthcare, and industrial automation. We address the opportunity to maximize the utility of an overall computing system by employing reinforcement learning to guide the configuration of the set of interacting modules that comprise the system. The challenge of doing system-wide optimization is a combinatorial problem. Local attempts to boost the performance of a specific module by modifying its configuration often leads to losses in overall utility of the system's performance as the distribution of inputs to downstream modules changes drastically. We present metareasoning techniques which consider a rich representation of the input, monitor the state of the entire pipeline, and adjust the configuration of modules on-the-fly so as to maximize the utility of a system's operation. We show significant improvement in both real-world and synthetic pipelines across a variety of reinforcement learning techniques.
The Implementation of Teaching Materials Reading Graphic Organizers in Elementary Schools

Reading is a very important skill that must be mastered by elementary school students, in the implementation of learning the teacher also provides a large contribution so that it is required to be able to develop teaching materials as a professional requirement. This research is a classroom action research using graphic organizer teaching materials in reading in the fourth grade of SDN 38 Pekanbaru with a total of 24 students. Before the implementation of learning in advance the implementation plan of learning was validated by several experts and practitioners with the results of the identity aspect validation obtained an average of 87 with a very decent category, and the assessment aspect obtained an average of 98 with a very decent feasible category. The results of the implementation of teacher activities in cycle I have obtained an average of 79% at meeting 1, meeting 2 obtained an average of 83%, meeting 3 obtained an average of 88% and meeting 4 obtained an average of 92%. The results of the research activities of meeting 1 student have obtained an average of 75%, meeting 2 was obtained an average of 79%, meeting 3 was obtained an average of 83%, and meeting 4 obtained an average of 88%. The results of the assessment of reading comprehension with the use of graphic organizer teaching materials from a base score of 42.50, the first cycle obtained an average of 71.88, the second cycle obtained an average of 76.88, with an increase from the base score to the first cycle obtained by 29, 38% while from the basic score to the second cycle obtained an average of 34.38%. The results of this study prove that the implementation of teaching materials to read the graphic organizer is able to improve reading comprehension skills.
Addition of Divisors on Hyperelliptic Curves via Interpolation Polynomials

Two problems are addressed: reduction of an arbitrary degree non-special divisor to the equivalent divisor of the degree equal to genus of a curve, and addition of divisors of arbitrary degrees. The hyperelliptic case is considered as the simplest model. Explicit formulas defining reduced divisors for some particular cases are found. The reduced divisors are obtained in the form of solution of the Jacobi inversion problem which provides the way of computing Abelian functions on arbitrary non-special divisors. An effective reduction algorithm is proposed, which has the advantage that it involves only arithmetic operations on polynomials. The proposed addition algorithm contains more details comparing with the known in cryptography, and is extended to divisors of arbitrary degrees comparing with the known in the theory of hyperelliptic functions.
Hierarchical Modes Exploring in Generative Adversarial Networks

In conditional Generative Adversarial Networks (cGANs), when two different initial noises are concatenated with the same conditional information, the distance between their outputs is relatively smaller, which makes minor modes likely to collapse into large modes. To prevent this happen, we proposed a hierarchical mode exploring method to alleviate mode collapse in cGANs by introducing a diversity measurement into the objective function as the regularization term. We also introduced the Expected Ratios of Expansion (ERE) into the regularization term, by minimizing the sum of differences between the real change of distance and ERE, we can control the diversity of generated images w.r.t specific-level features. We validated the proposed algorithm on four conditional image synthesis tasks including categorical generation, paired and un-paired image translation and text-to-image generation. Both qualitative and quantitative results show that the proposed method is effective in alleviating the mode collapse problem in cGANs, and can control the diversity of output images w.r.t specific-level features.
Subspace Clustering by Relaxed Block Diagonal Representation

The deluge of high dimensional data brings great challenges to data analysis, processing and storage. Subspace clustering aims to solve this dilemma by uncovering the latent low-dimensional structure inherent in high dimensional data. The most popular methods are self-representation (SR) based methods, which learn an affinity matrix by using the SR of the dataset and then apply the spectral clustering to obtain the final clustering results. The SR basically determines the clustering performance; therefore, existing methods use various regularity to impose clustering advantageous structure on the SR. In this work, we incorporate an orthogonal matrix in the block diagonal regularity (BDR) and adapt the BDR model as the relaxed BDR(RBDR) model. Our model enforces the block diagonal structure of SR, but allows an orthogonal matrix difference. We also present an alternative minimization algorithm to solve our model. Extended experimental results show that our model can greatly improve the performance of the BDR, especially when the dataset is not arranged cluster by cluster, which is true in practice. Therefore, our method fits better for practical data.
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks

We propose Absum, which is a regularization method for improving adversarial robustness of convolutional neural networks (CNNs). Although CNNs can accurately recognize images, recent studies have shown that the convolution operations in CNNs commonly have structural sensitivity to specific noise composed of Fourier basis functions. By exploiting this sensitivity, they proposed a simple black-box adversarial attack: Single Fourier attack. To reduce structural sensitivity, we can use regularization of convolution filter weights since the sensitivity of linear transform can be assessed by the norm of the weights. However, standard regularization methods can prevent minimization of the loss function because they impose a tight constraint for obtaining high robustness. To solve this problem, Absum imposes a loose constraint; it penalizes the absolute values of the summation of the parameters in the convolution layers. Absum can improve robustness against single Fourier attack while being as simple and efficient as standard regularization methods (e.g., weight decay and L1 regularization). Our experiments demonstrate that Absum improves robustness against single Fourier attack more than standard regularization methods. Furthermore, we reveal that robust CNNs with Absum are more robust against transferred attacks due to decreasing the common sensitivity and against high-frequency noise than standard regularization methods. We also reveal that Absum can improve robustness against gradient-based attacks (projected gradient descent) when used with adversarial training.
Towards Accurate Low Bit-Width Quantization with Multiple Phase Adaptations

Low bit-width model quantization is highly desirable when deploying a deep neural network on mobile and edge devices. Quantization is an effective way to reduce the model size with low bit-width weight representation. However, the unacceptable accuracy drop hinders the development of this approach. One possible reason for this is that the weights in quantization intervals are directly assigned to the center. At the same time, some quantization applications are limited by the various of different network models. Accordingly, in this paper, we propose Multiple Phase Adaptations (MPA), a framework designed to address these two problems. Firstly, weights in the target interval are assigned to center by gradually spreading the quantization range. During the MPA process, the accuracy drop can be compensated for the unquantized parts. Moreover, as MPA does not introduce hyperparameters that depend on different models or bit-width, the framework can be conveniently applied to various models. Extensive experiments demonstrate that MPA achieves higher accuracy than most existing methods on classification tasks for AlexNet, VGG-16 and ResNet.
Development Of Students' Valeological Culture

Until now the issues of valeologization of education have been widely studied. The purpose of the study is to reveal the dynamics of values-based orientation and motivation of valeological activity of students and to provide further assessment of valeological culture. The authors performed an experiment to assess the state of health of students, revealed the levels of valeological culture and held the Health Track School. We introduced forms and methods of valeological education into the educational process. The purpose was achieved through specification of tasks solved under the conditions of subject-subject relations. This included the purpose of valeological education of future teachers; approbation of program and introduction of changes into theoretical and practical sections, home assignments, independent activities (according to topics of program modules); collection of data on valeological culture. The study revealed changes of all valeological culture indicators. During the experiment we observed students, change of their attitude to valeological activity, values-based orientations, motivation for health, development of healthy lifestyle. The reflexive environment within the study was ensured through the creation of reflexive situations in activity, thinking, communication. It was efficient due to high level of problematicity within the personal space of each participant and a group activity. It was necessary to ensure the procedure of group reflection and self-reflection of students, which made it possible to register individual and group discoveries, achievements in the field of self-knowledge, and group interaction.
Formation of the World Picture and Tasks of Modern Education

The purpose of the article is analysis of a stable holistic world picture formation ways in university students. The relevance of the issue is due to the fact that the formation of the most significant concepts that form the basis of the modern scientific world picture, often suffers from fragmentation, the knowledge gained by students in different sections of the same course, are little connected with each other, do not form a unified system. To ensure the unity of scientific knowledge, it is necessary to provide for the implementation of «comprehensive» generalizations throughout the whole course. The following methods were applied during the study: theoretical, analysis, comparison. This study proposes the criteria for selecting the main generalizing ideas and the conditions necessary for their successful work. The results of the study are concretized on the example of the space-time representations formation in the course of general physics in the university. The choice is due to the fact that the categories of space and time belong to the most fundamental concepts of science and culture, are a reflection of the universal attributes of matter. To solve the tasks, the analysis of the state of the problem in the methodology of teaching physics was carried out. It is concluded that the main ideas can serve as a basis for the space-time representations formation. The proposed provisions summarize the personal experience of teaching the course of general physics at the Far Eastern Federal University.
Dual Protection on Message Transmission based on Chinese Remainder Theorem and Rivest Cipher 4

This research proposes a combination of dual protection on text messages transmission using Chinese Remainder Theorem (CRT) steganography and Rivest Cipher 4 (RC4) encrypting method. This combination aims to optimize the performance of encryption and message insertion into an image. Security This message is done by encrypting text messages using RC4 first, then the results are embedded in the grayscale type container image with the CRT method. The evaluation standards that will be used in this research are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM), and Character Error Rate (CER). MSE, PSNR and SSIM are used as a measure of the quality of stego images. To determine the performance of the proposed method, message insertion is carried out in three types of sizes, namely maximum payload, half payload and one quarter payload. While the CER is used to find out the results of decryption of text messages. The resulting CER value is 0, this indicates the message was extracted and decrypted perfectly.
INSTRUCTORS’ CONTRIBUTION TO PROSPECTIVE TEACHERS’ EXPERIENTIAL LEARNING IN PRESERVICE SECONDARY SCHOOL TEACHER EDUCATION PROGRAMME

Ethiopian public universities are expected to employ experiential approaches to learning in pre-service secondary school teacher education.Accordingly, this study was carried out in three public universities to investigate participants' perception toward instructors' role in facilitating trainees' experiential learning.Parallel mixed methods design was employed in the research.Quantitative data were collected from 311 trainees using questionnaire.On the other hand, qualitative data were gathered from 9 instructors and 30 trainees employing interview and focus group discussion respectively.The t-test results uncovered that the instructor's support to the trainees' experiential learning was significantly less than the expected one.The t-test results also disclosed lack of significant difference in the instructors' support to the trainees' experiential learning across gender and teaching experience.Likewise, the one way ANOVA results uncovered the absence of significant difference in the instructors' support to the trainees' experiential learning across CGPA.The instructors' limitation in flexibly reorganizing the trainees' seating arrangement, utilizing learning activities that facilitate experiential learning, initiating and facilitating the trainees' classroom interaction and discussion, and providing the trainees with timely feedback and support in the instruction hampered the trainees' experiential learning.Thus, the instructors are required to take measures that improve these obstructive practices.