Reviewerly/exHarmony huggingface dataset:
Watershed analysis for geochemical mapping in Japan based on a hydrologic model: The concentrations of 53 elements and the dominant lithology in a drainage basin

Recent interest in geochemical maps produced for environmental assessment has led to multi-purpose applications of such maps in various other fields including provenance studies for agriculture and archaeology. Research subjects of these provenance studies closely relate to surficial rocks. Fine stream sediment samples used for Japanese geochemical mapping originate from parent rocks present in the corresponding drainage basins. Consequently, as well as the chemical composition of stream sediment samples, information regarding the parent lithology is imperative for the applications mentioned above. In this work, the exposed areas of different lithologies in river drainage basins were calculated using a digitalized geological map and watershed polygons obtained using a hydrologic model. Stream sediment samples were classified into 18 different types in a larger-scale classification and into 92 more specific types in a smaller-scale classification based on the following assumption: when a specific rock type crops out over more than 50% of the watershed area, it is the dominant rock type controlling the chemical composition of stream sediments. This assumption was confirmed to be reasonable using a one-way analysis of variance. Chemical Index Alteration values suggest that the chemical weathering process is not dominant in the formation of stream sediments from their source rocks. Accordingly, the chemical compositions of stream sediments are approximately comparable to that of their source rocks. Finally, the percentages of the different lithologies in the watershed areas were tabulated for all stream sediment samples (n = 3024). The median values of 53 elemental concentrations were calculated for samples classified according to the dominant lithology. This new database lists the chemical compositions and the dominant lithology in the watershed area for each sample and constitutes an extensive source of information relevant to understanding of the chemical, mineralogical, and petrological variety of surface materials in this island arc setting, which helps to respond to existing academic or industrial requests.
READING COMPREHENSION OF EDUCATIONAL TEXTS AMONG 4–8 GRADES STUDENTS AS INDICATOR OF THEIR ABILITY TO LEARN

Introduction. Within the framework of the actual problem of the ability to learn for a modern school the peculiarities of children’s reading comprehension of edu-cational texts were studied. The analysis showed that the problem of reading com-prehension is not sufficiently developed from the standpoint of Cultural Activity Approach, since in existing studies (I.I. Ilyasov, I.V. Usacheva, O.E., Malskaya, etc.), the activity aspect is expressed only in a detailed description of the actions that provide reading comprehension and their structure, but these works do not “grasp” the general meaning of these actions related to the students’ understanding of the indicative purpose of educational texts — that it is from their content that the indicative basis of the subject actions performed should be ex-tracted. The most clearly indicative purpose of a special type of educational text — the definition of a concept — is justified in studies of the formation of the recogni-tion action (RA) (P. Ya. Galperin, N.F. Talyzina, L.F. Obukhova, etc.), where the “understanding” of the definition is the ability to recognize the objects that are described by it. The goal: to study the ability of students to perceive educational texts (ordinary educational texts and definitions of concepts) as tools for completing of specific tasks. Methods and sample. A sample of students in grades 4–8 (N = 280) was conducted: 1) assessment of the formation of the ability to work with definitions of concepts (concept’ recognition tasks, developed on the basis of the principles of diagnostics by N.F. Talyzina), 2) assessment of the ability to highlight the main thing in the text (the method of O.E. Malskaya and A.A. Sidelnikova “Selection of main sentences” for grades 4–8), 3) assessment of the ability to structure the text (the task of selecting paragraphs and their heading), 4) two GIT subtests (“Execution of rules” and “Numerical series”). Results and conclusions. It was shown that the understanding of definitions significantly and positively correlates with the understanding of educational texts. It was also revealed that the level of formation of both the ability to work with definitions and the ability to work with texts does not significantly increase after the 4th grade and up to the 8th grade there is a “failure” in these parameters, despite the smooth positive dynamics in some intellectual abilities.
Statistical Filters for Processing and Reconstruction of 3D Brain MRI

Construction of 3D brain MRI images from 2D brain MRI images is an important step towards an accurate diagnosis and for a correct treatment plan for any disease. The volume segmentation is a challenging task due to the poor quality of DTI brain images. This paper aims to evaluate the efficacy of the spatial statistical Maximum (Max) and Minimum (Min) filters in the construction of 3D brain MRI images. These well-known simple filters were used to analyze the 3D image construction together with noise removal techniques operated by them. Using these semi-processed images, a binarization operation has been applied and a similarity investigation was performed using the Dice score. A dataset of 2D processing images is used for the construction of the 3D images by means of the volume imaging technique. The effectiveness of the 3D construction imaging algorithm was investigated through the mean of the volume computation. 3D volume measurements were performed using ImageJ and a real-time database of brain MRI images that contains image stacks from both healthy and ischemic stroke patients. The Minimum filter provides the best results by keeping the image characteristics and local geometries.
SafeOSL: Ensuring memory safety of C via ownership‐based intermediate language

Abstract The unsafe features of C make it a big challenge to ensure memory safety of C programs, and often lead to memory errors that can result in vulnerabilities. Various formal verification techniques for ensuring memory safety of C have been proposed. However, most of them either have a high overhead, such as state explosion problem in model checking, or have false positives, such as abstract interpretation. In this article, by innovatively borrowing ownership system from Rust, we propose a novel and sound static memory safety analysis approach, named SafeOSL. Its basic idea is an ownership‐based intermediate language, called ownership system language (OSL), which captures the features of the ownership system in Rust. Ownership system specifies the relations among variables and memory locations, and maintains invariants that can ensure memory safety. The semantics of OSL is formalized in K‐framework, which is a rewriting‐logic based tool. C programs to be checked are first transformed into OSL programs and then detected by OSL semantics. Experimental results have demonstrated that SafeOSL is effective in detecting memory errors of C. Moreover, the translations and experiments indicate that the intermediate language OSL could be reused by other programming languages to detect memory errors.
SSA-HIAST: A Novel Framework for Code Clone Detection

In the recent era of software development, reusing software is one of the major activities that is widely used to save time. To reuse software, the copy and paste method is used and this whole process is known as code cloning. This activity leads to problems like difficulty in debugging, increase in time to debug and manage software code. In the literature, various algorithms have been developed to find out the clones but it takes too much time as well as more space to figure out the clones. Unfortunately, most of them are not scalable. This problem has been targeted upon in this paper. In the proposed framework, authors have proposed a new method of identifying clones that takes lesser time to find out clones as compared with many popular code clone detection algorithms. The proposed framework has also addressed one of the key issues in code clone detection i.e., detection of near-miss (Type-3) and semantic clones (Type-4) with significant accuracy of 95.52% and 92.80% respectively. The present study is divided into two phases, the first method converts any code into an intermediate representation form i.e., Hash-inspired abstract syntax trees. In the second phase, these abstract syntax trees are passed to a novel approach “Similarity-based self-adjusting hash inspired abstract syntax tree” algorithm that helps in knowing the similarity level of codes. The proposed method has shown a lot of improvement over the existing code clones identification methods.
Generative Image Inpainting for Retinal Images using Generative Adversarial Networks

The diagnosis and treatment of eye diseases is heavily reliant on the availability of retinal imagining equipment. To increase accessibility, lower-cost ophthalmoscopes, such as the Arclight, have been developed. However, a common drawback of these devices is a limited field of view. The narrow-field-of-view images of the eye can be concatenated to replicate a wide field of view. However, it is likely that not all angles of the eye are captured, which creates gaps. This limits the usefulness of the images in teaching, wherefore, artist’s impressions of retinal pathologies are used. Recent research in the field of computer vision explores the automatic completion of holes in images by leveraging the structural understanding of similar images gained by neural networks. Specifically, generative adversarial networks are explored, which consist of two neural networks playing a game against each other to facilitate learning. We demonstrate a proof of concept for the generative image inpainting of retinal images using generative adversarial networks. Our work is motivated by the aim of devising more realistic images for medical teaching purposes. We propose the use of a Wasserstein generative adversarial network with a semantic image inpainting algorithm, as it produces the most realistic images.Clinical relevance— The research shows the use of generative adversarial networks in generating realistic training images.
PyRoboCar: A Low-cost Deep Neural Network-based Autonomous Car

In this innovative practice WIP paper, we present PyRoboCar, a low-cost deep neural network-based autonomous car project. PyRoboCar is a small-scale replication of a real self-driving car using a deep convolutional neural network (CNN), which takes images from a front fisheye camera as input and produces car steering angles as output. PyRoboCar uses a similar network architecture as industry-level autonomous cars and can drive itself in real-time using a camera, an additional Tensor Processing Unit, and a Raspberry Pi 4 platform. We have also made this project open online, including the code and instructions.
Mutation Analysis of STR Locus on 23 Autosomes in Hainan Population

Objective] To analyze the mutation signature and regularity of STR locus on 23 autosomes in paternity testing cases in Hainan.[Methods] A total of 2715 paternity testing cases accepted by the Forensic Medical Identification Centre of our hospital from 2017 to 2020 derived from counties and cities in Hainan Province were collected, the cases containing gene mutations were selected, the mutation rate and details of each locus were counted, and the mutation regularity of 23 STR loci was analyzed.[Results] Of the 2715 cases identified as "support", 1487 were triplet cases and 1640 were dyad cases, totaling 4614 meioses; There were 50 gene mutation events (including 17 triplet mutations and 33 dyad mutations), with an average mutation rate of 0.0047% and a cumulative mutation rate of 1.0837%.A total of 19 of the 23 STR loci were mutated, with a mutation rate of 0.1301% at the D12S391 locus and 0.0217% at five loci, TPOX, D1S1656, D2S441, D22S1045, and PentaD, while no mutation events were found at four loci, D19S433, TH01, D13S317, and D7S820.Of the 50 mutation events, 47 were one-step mutations, 1 was two-step, and 2 were three-step.There were 35 paternal mutations (13 triplets and 22 dyads), 6 maternal mutations (4 triplets and 2 dyads), and 9 indeterminate paternal/maternal mutations, with a paternal to maternal mutation ratio of 5.83:1.[Conclusion] The mutation rate of D12S391 locus is the highest, and the mutation rate of TPOX, D1S1656, D2S441, D22S1045 and PentaD loci is the lowest in Hainan population, and paternal mutations are more than maternal mutations.In the paternity test, if 1 -3 STR loci do not conform to the genetic law, especially when the mutant locus is homozygous or the next of kin is identified, it is necessary to use other kits to review and increase the number of loci or use the second-generation sequencing technology to confirm, carefully determine the mutation and ensure the accuracy of the identification conclusion.
Can Blockchain be a Solution to IoT Technical and Security Issues

The Internet of Things (IoT) is a growing trend in technology that interconnects millions of physical devices from any location anytime. Currently, IoT devices have become an integral part of human lives, as such organizations are deeply concerned with its security and technical issues. Blockchain system comprises a distributed digital ledger which is shared among community of users on the Internet; validated and recorded transactions in the ledger which cannot be altered or removed. We presented the challenges of IoT devices and how blockchain can be used to alleviate these problems. An outline of how to integrate blockchain with IoT was tackled, highlighting the challenges of IoT and how blockchain can remedy the issues. It was concluded that blockchain has the capability to curb the challenges posed by IoT devices.
Optimization of the stereo system calibration parameters for photogrammetric measurements in the conditions of field experiments

Abstract Optical methods for deformation diagnostics and surface shape measurement are often used in scientific research and industry. Most of these methods are based on the triangulation of a set of two-dimensional points from different images corresponding to the three-dimensional points of an object in space. Triangulation is based on the stereo system calibration parameters, which are determined before the experiment. Measurements during conditions with increased vibration loads can lead to a change in the relative position of the cameras of the stereo system (decalibration). This leads to a change in the actual calibration parameters and an increase in the measurement error. This work aims to solve the problem of increasing the measurement accuracy of the photogrammetric method in the case of high vibration loads. For this, it is proposed to use an optimization algorithm for calibration parameters to minimize the reprojection error of three-dimensional points calculated using triangulation. The paper presents the results of a computer simulation of decalibration of a video camera stereo system, an algorithm for optimizing the external parameters of a stereo system, and an assessment of its performance.
A Systematic approach towards enhancing of Security and usability of graphical password through cognitive computing and data mining

This In this modern era of online world, one requires to have an online account.It may be a social, transactional, online shopping or an e-mail account, with a heap of so many accounts and the burden of memorizing the difficult passwords, user tends to compromise on the password, thereby weakening the security aspect.Most of the passwords are prone to shoulder surfing attack.In this paper we propose a novel Graphical authentication system named Confusys.Cofusys system takes care of the shoulder surfing assault, as it deals with information from different images.The befog module does not provide any information to the attacker; above all it confuses the attacker more .The befog module differs for every user as it is purely based on the click point from the user.The slightest change in the click point from the user results in a massive change of data.A prototype of this system is implemented to check for various parameters of this system and the results proved the chances of breaking into the system is very less within the range of 7.63183 e -15 to 1.38936 e-17 , and also provides password entropy between 2 58 to 2 67 .
A Digital Forensics Approach for Lost Secondary Partition Analysis using Master Boot Record Structured Hard Disk Drives

The development and widespread use of computer systems has increased the need for secure storage of data. At the same time, the analysis of digital data storage devices is very important for forensic IT professionals who aim to access information to clarify the crime. File systems of disk drives use partition structures to securely store data and prevent problems such as corruption. In this study, deletion or corruption of partitions on commonly used DOS / Master Boot Record (MBR) configured hard disk drives are investigated by using forensic tools. In order to analyze hard disk drives, Forensic Tool Kit (FTK), Magnet AXIOM, Encase, Autopsy and The Sleuth Kit (TSK), which are widely used as commercial and open source, are analyzed by using a presented scenario. In the scenario, the primary partition and the extended partition are created using the DOS / MBR partitioning structure on the test disk. Test files are added to the sections and the sections are deleted. The digital forensics tools were tested on the presented scenario. According to the obtained results, TSK and Encase are successful tools for DOS / MBR structured HDD analysis. However, FTK, Magnet AXIOM and Autopsy could not achieve information detection on DOS/MBR structured disks. These results clearly demonstrated that crime data can be hidden in MBR structured HDD. To carve these data, the correct methodology should be selected.
Enhanced Image Compression and Processing Scheme

Image compression refers to the process of encoding image using fewer number of bits. The major aim of lossless image compression is to reduce the redundancy and irreverence of image data for better storage and transmission of data in the better form. The lossy compression scheme leads to high compression ratio while the image experiences lost in quality. However, there are many cases where the loss of image quality or information due to compression needs to be avoided, such as medical, artistic and scientific images. Efficient lossless compression become paramount, although the lossy compressed images are usually satisfactory in divers’ cases. This paper titled Enhanced Lossless Image Compression Scheme is aimed at providing an enhanced lossless image compression scheme based on Bose, Chaudhuri Hocquenghem- Lempel Ziv Welch (BCH-LZW) lossless image compression scheme using Gaussian filter for image enhancement and noise reduction. In this paper, an efficient and effective lossless image compression technique based on LZW- BCH lossless image compression to reduce redundancies in the image was presented and image enhancement using Gaussian filter algorithm was demonstrated. Secondary method of data collection was used to collect the data. Standard research images were used to validate the new scheme. To achieve these, an object approach using Java net beans was used to develop the compression scheme. From the findings, it was revealed that the average compression ratio of the enhanced lossless image compression scheme was 1.6489 and the average bit per pixel was 5.416667. Gaussian filter image enhancement was used for noise reduction and the image was enhanced eight times the original.
An Experimental Performance Evaluation of Cloud-API-Based Applications

Cloud Application Programming Interfaces (APIs) have been developed to link several cloud computing applications together. API-based applications are widely used to provide flexible and reliable services over cloud platforms. Recently, a huge number of services have been attached to cloud platforms and widely utilized during a very short period of time. This is due to the COVID-19 lockdowns, which forced several businesses to switch to online services instantly. Several cloud platforms have failed to support adequate services, especially for extended and real-time-based applications. Early testing of the available platforms guarantees a level of suitability and reliability for the uploaded services. In this work, we first selected two different API-based applications from education and professional taxonomies, the two most recently used applications that have switched to the cloud environment. Then, we aimed to evaluate the performance of different API-based applications under different cloud platforms, in order to measure and validate the ability of these platforms to support these services. The advantages and drawbacks of each platform were experimentally investigated for each application.
Spatiotemporal RDF Data Query Based on Subgraph Matching

Resource Description Framework (RDF), as a standard metadata description framework proposed by the World Wide Web Consortium (W3C), is suitable for modeling and querying Web data. With the growing importance of RDF data in Web data management, there is an increasing need for modeling and querying RDF data. Previous approaches mainly focus on querying RDF. However, a large amount of RDF data have spatial and temporal features. Therefore, it is important to study spatiotemporal RDF data query approaches. In this paper, firstly, we formally define spatiotemporal RDF data, and construct a spatiotemporal RDF model st-RDF that is used to represent and manipulate spatiotemporal RDF data. Secondly, we present a spatiotemporal RDF query algorithm stQuery based on subgraph matching. This algorithm can quickly determine whether the query result is empty for queries whose temporal or spatial range exceeds a specific range by adopting a preliminary query filtering mechanism in the query process. Thirdly, we propose a sorting strategy that calculates the matching order of query nodes to speed up the subgraph matching. Finally, we conduct experiments in terms of effect and query efficiency. The experimental results show the performance advantages of our approach.
Inductive and Coinductive Predicate Liftings for Effectful Programs

We formulate a framework for describing behaviour of effectful higher-order recursive programs.Examples of effects are implemented using effect operations, and include: execution cost, nondeterminism, global store and interaction with a user.The denotational semantics of a program is given by a coinductive tree in a monad, which combines potential return values of the program in terms of effect operations.Using a simple test logic, we construct two sorts of predicate liftings, which lift predicates on a result type to predicates on computations that produce results of that type, each capturing a facet of program behaviour.Firstly, we study inductive predicate liftings which can be used to describe effectful notions of total correctness.Secondly, we study coinductive predicate liftings, which describe effectful notions of partial correctness.The two constructions are dual in the sense that one can be used to disprove the other.The predicate liftings are used as a basis for an endogenous logic of behavioural properties for higher-order programs.The program logic has a derivable notion of negation, arising from the duality of the two sorts of predicate liftings, and it generates a program equivalence which subsumes a notion of bisimilarity.Appropriate definitions of inductive and coinductive predicate liftings are given for a multitude of effect examples.The whole development has been fully formalized in the Agda proof assistant.