Reviewerly/exHarmony huggingface dataset:
Toxicity and Health Effects of Heavy Metals in Soil, Water and Edible Plants in Jos South, Plateau State, Nigeria

The toxicity of heavy metal is obviously a major threat and has various health hazard related to it. This work unveils the accumulation of heavy metals in Jos South using XRF. The results of this study showed that the Geo-Accumulation Index (Igeo) of water for different metals is decreasing in order of Cd (0.15) &gt; Cr and As (0.03) &gt; Pb (-0.13) &gt; Ni (-0.6). Heavy metals in soil decrease in the order of As and Cd (0.4) &gt; Ni, Cr and Pb (0.2). The heavy metals in different plants is in decreasing order of Cd (0.512) &gt; As (0.25) &gt; Pb (0.23) &gt; Ni (0.01) &gt; Ni (-0.06). 26% of these points are uncontaminated, except few points which are found within the uncontaminated to moderately contaminate level. It can be concluded that the area falls within uncontaminated-moderately contamination which calls for regulation. Hence this study can be used as a reference data for regulatory bodies like NNRA and the rest.
A Meta-Analytic Reliability Generalization Study of the Computational Thinking Scale

This study aims to analyze the reliability generalization of the computational thinking scale. There are five dimensions of computational thinking: creativity, algorithmic thinking, coopera-tivity, critical thinking, and problem-solving. A Bonett transformation was used to standardize the reliability coefficient of Cronbach’s alpha. A random-effects meta-analysis was conducted since the heterogeneity among the studies was high. Results supported the RG of the computational thinking scale and its sub-dimensions, which were calculated as 0.843 for general, 0.799 for creativity, 0.848 for algorithmic thinking, 0.863 for cooperativity, 0.799 for critical thinking, and 0.817 for problem-solving. Besides that, the moderator analysis was conducted for the sample type, test length, country, and language of the study. According to the findings, there were no significant moderator effects on the reliability estimation.
Reading Behavior of Chinese Primary and Secondary School Students: A Systematic Review

This paper’s objective is to give a thorough summary of research on the reading behavior of Chinese primary and secondary school students. The majority of existing research in this field focuses on the importance of extensive reading, the status quo of reading practices among Chinese students, factors that influence student reading behavior, and techniques for enhancing student reading accomplishment. It is proposed that this review will generate more interest in the development of basic education students’ reading skills.
Knowledge Management and Marketing Innovation Impact on Manufacturing Firms Performance in Ethiopia

The study aims to examine the joint impact of knowledge management and marketing innovation on manufacturing companies’ performance in Bahir-Dar, Ethiopia. The study employed a quantitative research approach and an explanatory research design. Stratified and simple random sampling techniques were used to pick a sample of 341 respondents from 20 firms based on the Krejcie and Morgan table formula. Structural equation modeling and AMOS software version 23 were used to analyze the data. The founding shows knowledge management has a direct impact on manufacturing firm performance. The study also revealed marketing innovation has a positive direct impact on business performance. Although, marketing innovation has a positive and significant effect on manufacturing firm performance managers should have to focus more on knowledge management strategy since, it boosts firm performance more in relative. DOI : https://doi.org/10.54663/2182-9306.2022.v10.n19.36-65
ininflueDeveloping Aerial Unmanned Effective Decision Makers

Unmanned aerial vehicles (UAVs, drones) and similar unmanned units are becoming more and more involved in various spheres, such as agriculture, emergency situations, battles, etc. however, in decision making there are still a lot they can be improved to avoid human direct involvement in those problems. To advance in the problem we develop tools to make UAV autonomously effective decision makers, particularly, able to analyze properly given situations and then according to assigned goals select appropriate strategies to achieve the goals. In the following work we aim to provide a solution for a single UAV which is able to discover units of interest, and select the target to track, manipulate or hit based on expert specified knowledge, as well as discuss further steps.
A Brief Comparison Between White Box, Targeted Adversarial Attacks in Deep Neural Networks

Today, neural networks are used in various domains, in most of which it is critical to have reliable and correct output. This is why adversarial attacks make deep neural networks less reliable to be used in safety-critical areas. Hence, it is important to study the potential attack methods to be able to develop much more robust networks. In this paper, we review four white box, targeted adversarial attacks, and compare them in terms of their misclassification rate, targeted misclassification rate, attack duration, and imperceptibility. Our goal is to find the attack(s), which would be efficient, generate adversarial samples with small perturbations, and be undetectable to the human eye.
Mobile Application Based Expert System for Cattle Disease Diagnosis and Treatment in Afan Oromo Language

Ethiopia is best known for its cattle population in Africa. Nonetheless, cattle diseases is the major constraint to cattle production and its safe utilization. For the developing countries, cattle disease creates a considerable effect on the livelihood and lives of the farmers. In this research work we have developed a mobile application based expert system in afan oromo language for diagnosing and treating cattle diseases. Knowledge Base regarding the various diseases affecting cattle’s in the study area was acquired by using both secondary and primary sources. The primary knowledge source was collected from West Guji and Bornea zonal area domain experts and the secondary sources of knowledge was collected using document analysis. Purposive sampling technique was used for selecting domain experts for knowledge acquisition process. The proposed expert system was developed using rule based knowledge representation approach and using decision tree knowledge modeling technique. After knowledge is acquired it was represented using rule based knowledge representation method using Java Programming language and a user interface was designed and developed using XML that fits into the present day smart phones and the application will be cross platform. User acceptance testing and overall system performance testing was made after the prototype was developed. Accordingly, the mobile application demonstrated 81% of accuracy and among the users who were made a user experience evaluation, 84% of them got satisfied by the performance of the mobile application.
Valuation of the Relationship of Health Literacy and Healthy Eating Attitude in Obesity

Atıf/Cite as:
Application of Multi-Scale Convolution Neural Network Optimization Image Defogging Algorithm in Image Processing

To improve the ability to detect and identify smog images in complex road traffic scenes, smog images need to be defogged, and an optimized image defogging algorithm on the basis of multi-scale convolutional neural network (MCNN) is proposed.The physical model of road traffic scene smog scattering is constructed, and the image is divided sky area, road surface area and road sky boundary area.The road sky boundary line is the boundary line between road surface and sky area.The dark channel of traffic scene smog image is established by Canny edge detection and MCNN optimization, and the smog image is subjected to detail compensation and gray enhancement processing through prior knowledge.After substituting the atmospheric light value and transmittance map into the atmospheric scattering model (ACM), the MCNN learning model is combined to realize the filtering processing and defogging optimization of smog images in complex road traffic scenes.The color saturation, defogging degree, peak signal-to-noise ratio (PSNR), texture effect as well as other aspects of the image are taken as test indexes for the simulation experiment.The simulation results show that the color saturation, defogging degree and image definition of the defogged haze images in complex road traffic scenes are higher by using this method, which improves the output PSNR of the defogged haze images in complex road traffic scenes, and has a good application value in image defogging.
New Text Steganography Technique based on Multilayer Encoding with Format-Preserving Encryption and Huffman Coding

Steganography is the process of hiding secret data inside other media or cover media.Balancing the requirements for capacity, security, and imperceptibility is the main challenge for any successful steganography system.In text steganography, the data hiding capacity is limited because of the lack of redundant data compared to other digital media, such as images, video, or audio.Other challenges in text steganography are imperceptibility and security.Poor imperceptibility results from the structure of the text file, which is more visually apparent in terms of syntax and grammar than in other media.Low level of security results from the sequential selection of positions for embedding secret data due to insufficient redundant data in a text file.Therefore, an attacker or a third party would notice slight changes in the text file.This paper proposes a new text steganography method that combines cryptography and compression techniques to deal with these issues.This technique is used to conceal secret data to achieve high data hiding capacity in the cover text while maintaining security and imperceptibility.Multilayer encoding and Format-Preserving Encryption (FPE) with Huffman Coding, are applied to secret data before embedding.Invisible Unicode characters are employed to embed secret data into English text files to generate stego files.Results show that the proposed method satisfies capacity and imperceptibility in the cover file by comparing it with previously developed methods.
Web based Mitosis Detection on Breast Cancer Whole Slide Images using Faster R-CNN and YOLOv5

Histological grading quantifies the tumor architecture and the cytology deviation of breast cancer against normal tissue.Nottingham Grading System grades the breast cancer classification and allots tumor scores.Mitotic detection is one of the major components in the Nottingham Grading System.Using a conventional microscope is time-consuming, semiquantitative and has limited histological parameters.Digital scanners scan the tissue slice into high-resolution virtual images called whole slide images.Deep learning models on whole slide images provide a fast and accurate quantitative diagnosis.This paper proposes two deep learning models namely Faster R-CNN and YOLOv5 to detect mitosis on WSI.The proposed Deep Learning models uses 56258 annotated tiles for training/testing and provide F1 score as 84%.The proposed model uses a webbased imaging analysis and diagnosis platform called CADD4MBC for image uploading, Annotation and visualization.This paper proposes an end-to-end web based Deep Learning detection for Breast Cancer Mitosis.
New Algorithm for Exhausting Optimal Permutations for Generalized Feistel Networks

The Feistel construction is one of the most studied ways of building block ciphers. Several generalizations were proposed in the literature, leading to the Generalized Feistel Network (GFN) construction, in which the round function operates on each pair of blocks in parallel until all branches are permuted. At FSE’10, Suzaki and Minematsu studied the diffusion of such construction, raising the question of how many rounds are required so that each block of the ciphertext depends on all blocks of the plaintext. Exhausting all possible permutations up to 16 blocks, they observed that there were always optimal permutations mapping even-number input blocks to odd-number output blocks and vice versa. Recently, both Cauchois et al. and Derbez et al. proposed new algorithms to build optimal even-odd permutations for up to 36 blocks. In this paper, we present a new algorithm based on iterative path building to search for optimal Feistel permutation. This algorithm is much faster in exhausting optimal non-even-odd permutations than all the previous approaches. Our first result is a computational proof that no non-even-odd permutation reaches a better diffusion round than optimal even-odd permutations up to 32 blocks. Furthermore, it is well known that permutations with an optimal diffusion round do not always lead to optimal permutations against differential cryptanalysis. We investigate several new criteria to build permutations leading to more secure GFN.
Improved Truncated Differential Distinguishers of AES with Concrete S-Box

The security of Advanced Encryption Standard (AES) is one of the most important issues in cryptanalysis. In ToSC 2020, Bao et al. proposed an open question about the relation between the input-output indices and the probability of truncated differentials. In this work, we try to answer this question, and accomplish a tighter bound for several types of truncated differential distinguishers based on the differential distribution table (DDT) of the S-box of AES. In order to reduce the computational complexity, we choose the starting point in the middle of the differential instead of the beginning, construct the DDT of 32-bit to 8/16-bit Super-Sboxes adopting an integrated S-box technique, and explore the divide-and-combine algorithm to perform the accurate calculation. For the 4-round truncated differentials with only one active byte in the input difference and one inactive byte in the output difference, we investigate the concrete probability of all 256 combinations of input-output indices. Moreover, our computation algorithms remove the independence assumption of functions in Bao et al.’s work, and can be generalized to compute the probability of truncated differentials ended with two inactive bytes in one column. To take full advantage of the results, we construct statistical model based on conditional probability, and propose 4/5/6-round truncated differential distinguishers, respectively. Our 6-round distinguisher needs $$2^{62.88}$$ chosen-plaintexts and $$2^{63.42}$$ encryptions, which is better than the published 6-round distinguishers in key-independent secret-key setting. For all truncated differentials presented in this work, we perform experimental verifications on Small-AES variants, and the results show our algorithms can provide reliable results. It is noted that the results do not threaten the security of AES.
Discovering Data

This chapter reviews four common sources used to discover data, from both a technical and interaction perspective: search engines (Sect. 4.2), data portals and repositories (Sect. 4.3), academic literature (Sect. 4.4), and social connections (Sect. 4.5). The chapter ends (Sect. 4.6) by drawing four conclusions about how people discover data in these sources.
An algebraic construction technique for codes over Hurwitz integers

Let $\alpha$ be a prime Hurwitz integer. $\mathcal{H}_{\alpha}$, which is the set of residual class with respect to related modulo function in the rings of Hurwitz integers, is a subset of $\mathcal{H},$ which is the set of all Hurwitz integers. In this study, we present an algebraic construction technique, which is a modulo function formed depending on two modulo operations, for codes over Hurwitz integers. We consider left congruent modulo $\alpha,$ and the domain of related modulo function is $\mathbb{Z}_{N(\alpha)},$ which is residual class ring of ordinary integers with $N(\alpha)$ elements. Therefore, we obtain the residue class rings of Hurwitz integers with $N(\alpha)$ size. In addition, we present some results for mathematical notations used in two modulo functions, and for the algebraic construction technique formed depending upon two modulo functions. Moreover, we presented graphs obtained by graph layout methods, such as spring, high-dimensional, and spiral embedding, for the set of the residual class obtained with respect to the related modulo function in the rings of Hurwitz integers.
REVIEW PAPER ON COMPARISON OF DIFFERENT CRYPTOGRAPHY ALGORITHMS IN CLOUD COMPUTING

Cloud computing is the process of maintaining one's own software and hardware environment while outsourcing communications about the IT industry via the internet.It is an on-demand service made possible through CSP (Cloud Service Provider).It is accessible whenever needed, in any quantity, and at a cost based on usage.The main worry in a cloud environment is security.The two primary cryptography algorithms, DES (Data Encryption Standard) and AES (Advanced Encryption Standard) have been elaborated on in this research paper along with a discussion of the fundamentals of cloud computing and the risks present in a cloud environment.Each algorithm's practical implementation has been shown to show how plain text is coconverted to cipher text and vice versa.
ASSESSMENT WEB APPLICATION

To crack any placement drive, technical knowledge is required.In recent years, the IT industry has had a high demand for employees.Various sources are available online for engineering students.Still, there is a need for a dedicated resource for students to prepare for their desired companies.There is a need for hands-on practice in coding as well as technical mocks to crack the selection process.This paper proposes a solution for such students facing the problem of not having proper resource material.The website developed can be used by any institute for engineering students and will provide two sections: a quiz and a code.The administrator will have full control over the system.Lecturers can decide the subjects and types of questions.Students can attempt the test and view their results.The compiler provided will help students enhance their coding skills.Various programming languages can be used, such as C, C++, Java, etc.
Sistem Informasi Administrasi Surat Menyurat Pada Kantor Balai Desa Jatimulyo

The Jatimulyo Village Hall office is a place for administrative services for residents. One example is the creation of correspondence. This correspondence service is carried out by the operator or admin on duty at the service office. However, in carrying out their duties, there are several obstacles, namely residents who do not know the flow in making letters, residents who do not complete the requirements in making letters, and residents who have to queue and wait in making letters. To solve this problem, in this study a correspondence application system was designed so that it could improve the work system of the Jatimulyo Village Hall Office employees. In this research, the method used is the development of the waterfall method and the design system for this research using UML, as well as system testing using Black Box Testing and User Acceptance Test. Testing on this system produces a percentage of 100% for the Black Box Testing and 89.2% for the User Acceptance Test for residents and 94.2% for the Admin/operator User Acceptance Test. The results obtained in this study is a correspondence administration application which is expected to facilitate and assist the staff and residents of Jatimulyo in the process of making letters.
Multi-objective Meta-return Reinforcement Learning for Sequential Recommendation

With the demand for information filtering among big data, reinforcement learning (RL) that considers the long-term effects of sequential interactions is attracting much attention in the sequential recommendation realm. Many RL models have shown promising results on sequential recommendation; however, these methods have two major issues. First, they always apply the conventional exponential decaying summation for return calculation in the recommendation. Second, most of them are designed to optimize a single objective on the current reward or use simple scalar addition to combine heterogeneous rewards (e.g., Click Through Rate [CTR] or Browsing Depth [BD]) in the recommendation. In real-world recommender systems, we often need to simultaneously maximize multiple objectives (e.g., both CTR and BD), for which some objectives are prone to long-term effect (i.e., BD) and others focus on current effect (i.e., CTR), leading to trade-offs during optimization. To address these challenges, we propose a Multi-Objective Meta-return Reinforcement Learning (M $$^2$$ OR-RL) framework for sequential recommendation, which consists of a meta-return network and a multi-objective gating network. Specifically, the meta-return network is designed to adaptively capture the return of each action in an objective, while the multi-objective gating network coordinates trade-offs among multiple objectives. Extensive experiments are conducted on an online e-commence recommendation dataset and two benchmark datasets and have shown the superior performance of our approach.
Blockchain Technology-Based Management of Blood and Its Products - A Case Study in Vietnam

Blood and its products are one of the products that have not found an alternative and play a huge role in the treatment of diseases today. The current supply is obtained from the volunteers’ blood (called donors). However, this supply is also very limited and must go through a rigorous inspection process before reaching the recipient. Depending on the type of product extracted from the blood (e.g., red blood cells, white blood cells, platelets, plasma), we have different preservation procedures and requirements (e.g., duration, temperature, humidity). Therefore, it is extremely necessary to build blood and its products management system. The process is now done manually, where all data entry is done by medical staff without the technique supported. Furthermore, data relating to donors and the centrally stored blood donation process are difficult to assess reliably. In addition to the above difficulties, developing countries (including Vietnam) face infrastructural barriers in managing the supply chain w.r.t blood and its products. In this paper, we introduce a blockchain technology-based blood and its products management model applied to provinces/cities in Vietnam. Specifically, the paper introduces the limitation of the current blood collection and blood-based treatment process, thereby proposing a new approach based on blockchain technology. We also implemented our proofs-of-concept on the Hyperledger Fabric platform. This paper is considered the first attempt to introduce blockchain-based blood and blood product management processes to developing countries (i.e., Vietnam).
Circle Graph Isomorphism in Almost Linear Time

Circle graphs are intersection graphs of chords of a circle. In this paper, we present a new algorithm for the circle graph isomorphism problem running in time $O((n+m)\alpha(n+m))$ where $n$ is the number of vertices, $m$ is the number of edges and $\alpha$ is the inverse Ackermann function. Our algorithm is based on the minimal split decomposition [Cunnigham, 1982] and uses the state-of-art circle graph recognition algorithm [Gioan, Paul, Tedder, Corneil, 2014] in the same running time. It improves the running time $O(nm)$ of the previous algorithm [Hsu, 1995] based on a similar approach.