Reviewerly/exHarmony huggingface dataset:
Iranian regional l ament zones and the location of Cu, Pb, Zn and Fe deposits

Background . Large-scale geological structures, such as lineaments, are of great research interest due to their potential to mark the presence of ore fields. Aim . To determine the relationship between ore deposits, lineaments and large-scale faults in Iran using satellite imagery, digital relief modelling and structural mapping. Materials and methods . In this study, we compiled a map of lineaments in the Iran, which were determined both automatically and manually. A database of the distribution of ore deposits and lineaments was compiled from available maps, publications and reports on ore deposits in Iran. These maps were overlaid to study the relationship between the locations of the lineaments, faults and ore deposits. Results . An analysis of the lineaments revealed on geological maps indicates the presence of four dominant strike directions of large and small lineaments. The developed lineament map shows the main northwest trending longitudinal faults, which are parallel to the main strike of the Zagros orogenic belt; sublatitudinal and submeridional trending oblique faults; northeast trending transverse faults. A “Combined map of lineaments and deposits of copper, lead, zinc and iron in Iran” was compiled by the ArcGis software using maps of lineaments and mineral deposits. The ore deposits identified were analysed to determine the distribution of the distances between each deposit using the counting and cumulative methods the ArcGis software. Conclusions . About 90% and 50% of ore deposits are located at a distance of less than 15 km and 5 km, respectively, from the centre line of the associated lineament. A direct relationship between the density of lineaments and the presence of deposits was observed. The obtained results demonstrate the potential of this method for assessing the prospects of ore fields in hard-to-reach and poorly studied regions.
Study of information literacy skills behind the prison wall in Sri Lanka

The study explores and recognizes the information literacy skills and the information resources used by the Sri Lankan prisoners. This study was carried out in the Welikada Prison, which is also known as the Magazine Prison: a maximum-security prison, as well as the largest prison in Sri Lanka. The survey research design was employed in the study, equipped with a simple questionnaire as the instrument. The subjects of the study were 80 male prisoners who were selected on the basis of random sampling method. The study gave birth to a number of findings with utmost importance, in relation to the information literacy skills of the inmates. Some of the major findings recommend that the prison administrators should have and maintain an effective collaboration with the prison library officers, while stressing the need of introducing a library hour for the inmates, facilitating them with adequate information sources and creating a conducive environment with in the prison walls, in order to promote the access to information sources and encourage the inmates to use the information literacy skills and available resources for their betterment.
Representations of Meaningful Objects in Visual Long-Term Memory Have Greater Invariance and Resist Proactive Interference

Visual long-term memory is better when the encoded items are meaningful. Three experiments were conducted, each investigating why a representation that includes both visual and semantic content may increase VLTM performance. A behavioral calibration procedure was used to sort images of tools (broadly defined) into a familiar set (known function; both visual and semantic information available for encoding) and unfamiliar set (unknown function, mostly just visual information available for encoding). In all three experiments, participants viewed 50 images of objects (either familiar or unfamiliar) in sequence, and then completed a 2AFC memory test in which they were required to indicate which of two simultaneously-presented objects was previously shown. In the first two experiments, the hypothesis that meaningful, familiar objects are encoded in a format more invariant to incidental viewing conditions was tested. Experiment one showed that familiar objects are less affected by removal of color information between study and test. Experiment two showed that familiar objects are less affected by partial occlusion at test. The third experiment tested the hypothesis that visual memory representations of meaningful, familiar objects are more robust to proactive interference. Using a pre-exposure procedure, the results showed that familiar objects benefit from lower levels of interference, provided the interfering objects viewed during pre-exposure are not semantically similar to the test set. When the pre-exposure objects shared both visual and semantic information with the test set, performance for familiar objects decreased to the same level as the unfamiliar objects, suggesting that the advantage of additional semantic content was entirely lost. The results of all three experiments demonstrate how incorporating semantic information into memory representations enhances visually-guided recognition. Specifically, visual-semantic representations are more invariant to viewing conditions and less susceptible to proactive interference.
Background complexity decreases evidence accumulation rates during parallel processing in efficient search

Parallel processing in efficient search tasks involves a rejection of non-target items via a process of evidence accumulation. This evidence accumulation process results in a logarithmic increase in response times as a function of set size. The slope of this logarithmic function indexes the rate of accumulation; the greater the target-item similarity, the slower the rate, the steeper the slope. Although almost all of visual search in the real world involves items against backgrounds, evidence accumulation thus far has only been examined without backgrounds. Here, we examined the effect of background information in efficient search tasks. In Experiment 1, search stimuli were displayed against a background that was either a scene, phase-scrambled, or solid-colored. When target-distractor similarity was low, there was no effect of background type on both the slope and intercept of the logarithmic function. When target- distractor similarity was high, the slope for the scene background was steeper than that for the scrambled background, which was in turn steeper than that for the single-colored background. Thus, the greater the complexity of the background, the slower the rate of evidence accumulation of individual items. In Experiment 2, we examined the effect of meaningful but unstructured backgrounds by replacing the solid-colored background with an upside-down scene. Regardless of target-distractor similarity, the upside-down background produced the shallowest slope (fastest accumulation rate) and the highest intercept. Consistent with previous findings, our results suggest that processing of scene gist is automatic. When the scene is meaningful but unstructured, a constant processing cost (increased intercept) is incurred. This could arise either from the discounting of the inconsistent background, or a longer time to obtain scene gist. However, object segmentation is easier since objects do not blend in with the upside-down scene structure, resulting in a faster accumulation rate.
Development Of Discovery Learning Based IPS Learning Module For Class IV Students Primary School

DEVELOPMENT OF DISCOVERY LEARNING BASED IPS LEARNINGMODULE FOR CLASS IV STUDENTSPRIMARY SCHOOLPutri Arrahmah¹, Yulfia Nora¹, Rieke Alyusfitri ¹¹Program Studi Pendidikan Guru Sekolah DasarFakultas Keguruan dan Ilmu PendidikanUniversitas Bung Hattae-mail:putriiarrahmah@gmail.comABSTRACTThis study aims to produce a Discovery Learning-based module in social studieslearning for grade IV SD that meets valid and practical criteria. This type of researchis development research. The development research model used is a 4-D developmentmodel which is modified into 3-D, namely define, design and develop. This researchwas conducted in the even semester of the 2019/2020 school year with the theme of 6my aspirations. The research instrument included a validity sheet and a practicalitysheet (teacher and student response questionnaires). In the validation process, themodule is validated by 1 material expert, 1 design expert and 1 linguist. In thepractical process the module was tested on 1 teacher and 8 students. Based on theresults of research based on Discovery Learning modules in social studies class IVtheme 6, my ideals that meet the criteria are very valid with an assessment percentageof 93.3% and meet very practical criteria with a percentage of 96.8%. So it can beconcluded that the Discovery Learning-based module in social studies class IV theme6 is valid and practically used as social studies learning media in grade IV SD(Elementary School).Keywords: Modules, Social Sciences, Discovery Learning
중학교 1∼3학년 읽기 능력 검사 도구 개발 및 IRT 분석을 통한 타당화 연구

The middle school students’ reading inventory is a standardized test that can objectively and systematically diagnose and evaluate reading ability. It aims to be a valid and reliable test tool that can comprehensively diagnose an individual’s reading ability in a reading environment that has changed through a broadened interpretation of reading. By reflecting the latest domestic and international theories from the literature review stage onward, the researchers sought to develop a tool that could comprehensively diagnose an individual’s reading ability by sufficiently including various factors of reading ability.This research aimed to closely study the development process of middle school students’ reading inventory. It also intended to review the results of the quality analysis of questions through preliminary examination under Item Response Theory (IRT). Furthermore, after discussing unfit questions from the preliminary examination and ways to improve the questions, the primary selection of final questions could be verified. The aim is to examine the process of developing a valid and reliable reading inventory.Based on this systematic verification, it is hoped that this reading inventory can be fully utilized in schools to provide practical help in improving students’ reading ability. On the other hand, the development of this reading inventory is not yet complete, and more improvement in the reading inventory is expected by gathering the various opinions from educators who have actually employed it.
Image Processing Techniques Based Crowd Size Estimation

Image processing algorithms are the basis for image computer analysis and machine Vision. Employing a theoretical foundation, image algebra, and powerful development tools, Visual C++, Visual Fortran, Visual Basic, and Visual Java, high-level and efficient computer vision techniques have been developed. This paper analyzes different image processing algorithms by classifying them in logical groups. In addition, specific methods are presented illustrating the application of such techniques to the real world images. In most cases more than one method is used. This allows a basis for comparison of different methods as advantageous features as well as negative characteristics of each technique is delineated. The main objective of this paper is to use image processing techniques to estimate the size of a crowd from a still photograph. The simulation results show that the different images have different efficiencies.
WELL DETECTION USING IMAGE PROCESSING

Counting and locating active wells is deemed important while implementing rural development projects involving irrigation.Irrigation needs to be planned more thoroughly as it is a quite expensive thing to do.Government institutions like NBSS & LUP manually detect the wells on Google Earth Pro, which is a quite cumbersome process.This paper aims to present an overview of a project developed to introduce an element of automation to this hectic procedure and explores the open source library Scikitimage which is an image processing and image recognition library that implements the basic image manipulation and processing using the core scientific modules NumPy and SciPy.
A Generative Model for Tumor Stimuli Synthesis

Recent studies have shown that previous visual stimuli can affect current visual perception. It is believed that such serial dependency can help to increase perceptual stability since our visual world tends to be stable over space and time. However, when radiologists review mammographies in a sequence, their visual world does not necessarily have the assumed stability due to variations in the patients, scanners, and tumor types. Serial dependency may thus strongly influence radiologists' decisions and diagnoses. Understanding the mechanism could potentially lead to new strategies that prevent radiologists from making biased decisions. In order to study the role of serial dependency in radiography recognition, we need to be able to generate visually related stimuli in a sequence. Synthetic tumor stimuli are typically generated by applying simple spatial deformation and intensity filtering operations such as blurring in masked areas. However, synthetic scans from such image manipulations often appear implausible to a radiologist, because they are not metamers for real tumors and are often anatomically inconsistent with the surrounding tissues. Our goal is to synthesize realistic new tumor images from a small set of real scans. We leverage recent advances in deep learning to generate synthetic mammographies that conform to the statistical pattern distributions exhibited in the real scans. We build such a generative model based on Digital Database for Screening Mammography (DDSM) dataset, which has 2,620 cases of normal and tumor scans. Our model can synthesize new scans that have tumors similar to source images, seamlessly embedded into the target background image. We are exploring additional Generative Adversarial Network (GAN) models that produce high-resolution synthetic scans with realistic variations in both foreground tumor regions and surrounding tissues.
Influence of Information and Communication (ICT) Maturity on Nigerian Correctional Education Services

With the global acceptance of ICT in correctional education, there is the need to investigate how its adoption and maturity influenced rehabilitation, reformation and increasing recidivism common in Nigerian correctional services. Therefore, this study is on the influence of information and communication (ICT) maturity on Nigerian correctional education services. Descriptive survey of ex post facto type was adopted with four objectives, four research questions and a null hypothesis. A Modified Questionnaire ‘Information and Communication Technology Maturity Scale’ with 0.73 reliability was administered to 120 participants selected using multi-stage sampling techniques. Frequency distribution, percentage and multiple correlation matrix was used to test for null hypothesis at 0.05 alpha level. Results of the relationship between independent and dependent variables show that skill dependence (r= 0.28), task performance (r= -0.29), restricted ability(r= -0.37), high ability (r= -0.26) and organizational competence (r=0.19). This indicates that the independent variable has positive influence on skill dependence and organisational competence whereas it negatively influenced task performance, restricted ability and high ability. It is concluded that ICT maturity is poor and cannot drive correctional education service delivery efficiently in Nigerian correctional facilities; therefore most inmates do not have access to it. It is recommended that ICT infrastructure should be provided to re-position education service delivery in Nigerian correctional system by the Federal government of Nigeria.Keyword: ICT maturity; education; service delivery.
An Adaptive Neighborhood Retrieval Visualizer

We propose a novel adaptive version of the Neighborhood Retrieval Visualizer (NeRV). The data samples’ neighborhood widths are determined on the basis of the data scattering in the high-dimensional input space. The scattering of input data is measured using the inner-cluster variance quantity, obtained as a result of the preliminary data clustering in the input space. The combination of the pre-clustering and the subsequent NeRV projection can be recognized as a hybrid approach. The experimental study carried out on two different real datasets verified and confirmed the effectiveness of the introduced approach and the correctness of the theoretical claim of the paper.
FUZZY EDGE IMAGE MATCHING ALGORITHM FOR SQUID SPECIES IDENTIFICATION

The image features play important role in matching system. The effectiveness of these Squid species features depends on the global features. The identification of Squid species requires information of their morphology. Body shape is very useful to characterize the one species to another species. In Shape extraction, edge detection is an important aspect. Edge is an important visual feature and it represents visual information with a limited number of pixels. While considering the morphology of Squid, it can have uncertainty due to climatic conditions. Hence, in this study feature extraction is done by fuzzy edge map. In this paper we proposed Fuzzy Image Edge Image Matching Algorithm (FEIMA) for Squid species identification. Similarity metric is used for matching of query and the candidate images in the database and it finally displays the class of species. The proposed algorithm performance is calculated by using Average of precision and recall. &nbsp
Optimized design for the supporting structure of a large aperture mirror

With the continuous development of optical technology in recent years, the pace of human exploration of space has further accelerated. Space remote sensing technology is widely used in surveying and mapping, environmental monitoring and other fields. Therefore, the requirements for space optics technology are gradually increasing. In order to reduce the launch cost and the deformation of the supporting member and the main mirror base under its own gravity, a lightweight design must be carried out. Therefore, under the premise of ensuring the rigid body displacement of the mirror body and the error of the mirror shape, lightweight has become a key requirement for the development of remote sensing technology. By comparing various supporting structures, the spatial freedom of the mirror is calculated. Choose a combination of 9-point post-support and 3-point peripheral support. Compare and select the materials commonly used in the structure of the supporting part and the main mirror base. Although the support structure adopts topology optimization, a very effective support method can be obtained, but the final result cannot be universally applied to the support structure of mirrors with different apertures. Therefore, this paper determines the design structure of the relationship between the mirror support position, the fundamental frequency and the surface shape accuracy and the support structure parameters based on the flexibility matrix. For the rigid parts of the supporting structure and the main mirror base, simulation software was used to optimize the design of the initial design structure to remove excess materials. The final main mirror base lightweight rate was 36.6%, and the triangular plate lightweight rate was 65.9%. The static analysis and modal analysis of the supporting scheme are carried out by analysis software. After optimization, the shape accuracy of the primary mirror under its own weight is better than λ/50. Structural resonance will seriously affect the use and life of the equipment. Therefore, the modal analysis is performed, and the fundamental frequency is within a reasonable range during the optimization process. The simulation results show that the first-order fundamental frequency is 836.55 Hz. The analysis results show that while ensuring the shape accuracy of the primary mirror, the lightweight design of the mirror support assembly is realized.
DATA MINING ALGORITMA C4.5 UNTUK MEMPREDIKSI PENJUALAN BATERAI DI PT VARTA MICROBATTERY INDONESIA

The development information of technology in the current era of globalization is very fast, this requires all companies in the world to be able to compete with each other. The competition in the bussiness world, forcing all of the companies to think of strategies and breakthroughs that can ensure the sustainability of the bussiness that they run of. PT VARTA MICROBATTERY INDONESIA is a company engaged producing battery-based materials. However, looking at the past years many series of battery models have been discontinued. Some series of battery models that have been stopped production. It’s difficult to obtain strategic information such as the level of sales per period, predictions of sales in the coming years, and sales of products produced. Availability large sales data in the database server are often not used optimally, therefore the sales data is only used for daily operational activities. Analysis is needed to see patterns from sales data so as to produce predictions of battery. That big data from customer can be analysis using data mining. The method to use as for predictions is C4.5 Algorithm based on decision tree. This mining activity is expected to provide a decision tree to see patterns of prediction consumer buying behavior the batteries. The results of this mining activity is getting 9 rules with size category knowing as a root and the sales via as a leaf.
LW-Net: A Lightweight Network for Monocular Depth Estimation

Existing self-supervised monocular depth estimation methods usually explore increasingly large networks to achieve accurate estimation results. However, larger networks are more difficult to train and require more storage space. To balance the network size and the computational accuracy, we propose in this article a compact lightweight network for monocular depth estimation, named LW-Net. First, we construct a compact network by designing an iterative decoder with shared weights and a lightweight pyramid encoder. The proposed network includes significantly fewer parameters than most of the existing monocular depth estimation networks. Second, we exploit a self-supervised training strategy by combining the proposed LW-Net model with a pose network, and we then use a hybrid loss function to train the decoder and encoder separately. The proposed training strategy results in the LW-Net model achieving a better performance in terms of estimation accuracy than other methods. Finally, we respectively run the proposed LW-Net model on the KITTI and Make3D datasets to conduct a comprehensive comparison with several state-of-the-art methods. The experimental results demonstrate that our method performs the best in terms of computational accuracy while utilizing the fewest parameters. Specifically, the model parameters of our method are reduced by 46.6%, the time cost is decreased by 7.69%, and the frame rate is increased by 5.19% compared with the existing state-of-the-art method.