Reviewerly/exHarmony huggingface dataset:
Machine Learning Approach Based on the Finite Element Method to Predict Reaction Forces

Machine learning is an important tool used in many science branches. In this paper, neural network is created to predict reaction forces in the plate under tension. The automatized Python script is used to generate model, solve model, obtain results and process data in SIMULIA Abaqus environment. The script was then used with over 15,000 combinations of 6 independent input parameters. Reaction forces in three directions were obtained in this process. Created in this way, dataset is normalized and used as the training dataset for a neural network. Errors obtained for simple structure in this approach are small and comparable to the error of the finite element method itself. This approach demands preparing a large dataset, but it can be worth it if the application lets analysis in real time, i.e., construction monitoring or medicine.
The Research on Fishery Metadata in Bohai Sea Based on Semantic Web

Abstract In this paper, a data sharing and management mechanism suitable for the characteristics of fishery industry was established to clarify the phenomenon of heterogeneous Web data and Information Island based on Semantic Web technology, and unified interface specification information platform was established. Form the specification of metadata from the physical and chemical database, developing and publishing the corresponding metadata management tool, assisting, assisting and guiding a specialized database centre, completing the construction of metadata from the professional database.
Further Developments

We have come to the end of this book. We will reflect on what we have learned. In this chapter we will review the main themes and essential lessons, and we will look to the future.
Using Inertial Measurement Units (IMU) and Comparative Trajectory Analysis for Modeling Micro-level Human Motion Dysfunction

Ubiquitous sensing from smartphones and wearable devices has proven to be useful for applications ranging from sports to modern medicine. The aim of this paper is to propose a visualization framework to illustrate the points in time when a query trajectory is deviating the most from a reference trajectory. Validation is performed through the use of a novel post ACL reconstruction dataset. Validation is performed through wearable sensing data collected from 11 patients recovering from ACL reconstruction and 10 healthy participants. Results provide promising insights about how this method can be used to visualize anomalies in motion trajectories and to detect abnormal motion patterns.
Wireless Ultraviolet Light Mimo Assisted Uav Direction Perception and Collision Avoidance Method

In order to solve the problem of UAV's direction perception and collision prevention in the limited radio communication environment, we use wireless ultraviolet (UV) communication as an auxiliary communication method in UAVs. We improved the traditional artificial potential field method (IAPF), and designed a UV MIMO model installed on the UAV. On this basis, the determination of the threat source direction in the artificial potential field method is further simplified, and the IAPF-UVMIMO algorithm is proposed. Experimental results show that IAPF effectively solves the problems of unreachable targets, path oscillations and local minimums that exist in the traditional artificial potential field method. In addition, IAPF-UVMIMO also improves the tolerance of the threat source direction. Compared with IAPF, the number of collisions with actual collision threats is reduced by 22.6%.
Yi Optimizer for Single Objective Optimization

Yin-Yang provides a philosophical framework to describe the dynamics of opposing but complementary forces. It has inspired the design of the Yin-Yang pair optimization (YYPO) algorithm, which has low time complexity. While YYPO implemented opposing forces, the complementary forces were not considered. To incorporate complementary forces, we develop the Yi optimization (YI) algorithm by modifying the Yin-yang pair in YYPO by using Yi-point, which balances exploration and exploitation. Comparative analysis between the proposed Yi with YYPO is performed using IEEE CEC 2017 benchmark. The experimental results demonstrate that Yi is superior to YYPO.
Blockchain technology: a special survey for new era

People have been experiencing their dependency on the centralized agencies. However, there are still many transforms that need to be witnessed. The objective of this paper is focused upon creating awareness about the technology among the people on which people can have faith and trust. Provided an overview of the technology and its existing architecture and futuristic integrations of blockchain technology. A special case of India has been taken by researcher to pointing out the application of blockchain in a country. The technology has specific solutions for different sectors as it has been discussed in this paper.
A Novel Evaluation Model of Data Security Protection Capability in Edge Computing Environment

With the rapid development and wide application of 5G networks, artificial intelligence, and big data, traditional cloud computing cannot handle the massive data generated by edge terminals. Edge computing has gradually been widely used as a computing method close to objects. However, due to the open features of edge computing, such as content perception, real-time computing, and parallel processing, the data security issues that already exist in the cloud computing environment have become more prominent. Data security protection capability evaluation is an important part of the improvement of data security capabilities. However, current protection capability evaluations are mostly qualitative evaluations and lack quantitative evaluation models. Aiming at the edge computing network architecture, this paper proposes a data security protection capability evaluation model based on weight presets. By studying the edge architecture data security protection capability score and cost curve, the data security protection model is adaptively selected. Experiments show that the method can quantitatively calculate the data security protection capability in the edge computing environment, and can guide the construction of the protection model through the score-cost curve.
Conceptual Analysis and Applications of Bigdata in Smart Society

Big data is a study that refers to dealing with datasets that are too substantial and complex for standard database software tools to handle. Big Data assists in representing data, managing data, analyzing data, searching, sharing, transferring data, encapsulating data, querying data, updating data, information privacy, and the data source. Smart cities are applications of upcoming technologies in creating or enhancing the City’s infrastructure. Big data plays a pivotal role in the creation of Smart Cities. Its techniques are necessary for almost every field. In this chapter, Big data applications related to epidemiology, mental health, and astrophysics are discussed.
Learning From Failure: Data Capture in an Australian Aboriginal Community

Most low resource language technology development is premised on the need to collect data for training statistical models.When we follow the typical process of recording and transcribing text for small Indigenous languages, we hit up against the so-called "transcription bottleneck."Therefore it is worth exploring new ways of engaging with speakers which generate data while avoiding the transcription bottleneck.We have deployed a prototype app for speakers to use for confirming system guesses in an approach to transcription based on word spotting.However, in the process of testing the app we encountered many new problems for engagement with speakers.This paper presents a close-up study of the process of deploying data capture technology on the ground in an Australian Aboriginal community.We reflect on our interactions with participants and draw lessons that apply to anyone seeking to develop methods for language data collection in an Indigenous community.
Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences

The need to analyze information from streams arises in a variety of applications. One of its fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the presence of the pattern in transactions but pay no attention to the series of itemsets and their multiple occurrences. The pattern over a window of itemsets stream and their multiple occurrences, however, provides additional capability to recognize the essential characteristics of the patterns and the inter-relationships among them that are unidentifiable by the existing presence-based studies. In this paper, we study such a new sequential pattern mining problem and propose a corresponding sequential miner with novel strategies to prune the search space efficiently. Experiments on both real and synthetic data show the utility of our approach.
Comparative analysis of Lotka-Volterra type models with numerical methods using residuals in Mathematica

This paper details the approximate solution to the Lotka-Volterra type models with no closed-form by employing the use of two numerical techniques; Runge-Kutta Fehlberg and the 4-stage Runge-Kutta method. From the numerical techniques used, a comparative analysis is carried out, and an approximate solution is obtained. Two cases were considered, case 1 details on indistinguishable graphs but different numerical values (not enough to conclude on the efficient technique) are obtained using the two techniques and while case 2 shows that both numerical techniques give same graphical representation and numerical values, hence this necessitate the reason for this investigation; by so doing the log-plots and residuals was introduced to obtain the most effective and efficient technique under such condition.
chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://pedagogy-vestnik.ksu.kz/apart/2021-104-4/12.pdf

During the first wave of the COVID-19 pandemic, Kazakhstan followed strict global procedures. An important government decision was made to close schools and move to online learning. The sudden transition from traditional education to a distance learning format has caused some problems. In this regard, a comprehensive review of the problems of organizing online education during the first wave of the pandemic is significant. Therefore, there is now an immediate need for more detailed research in order to understand the factual effectiveness of various program measures during crisis conditions (pandemic, natural disaster, etc.). However, in the context of Kazakhstan, there is a limited amount of scientifically based research on the problems of organizing online learning during the pandemic, compared with studies by scientists from different countries. In the article, the authors pay special attention to the process of organizing online learning in the context of Kazakhstan during the beginning of the pandemic, in order to give a realistic vision of a new way of learning in a crisis situation. This study complements the existing literature, demonstrating the role of online learning in supporting educational services in crisis situations.
Social Network Analysis Algorithms, Techniques and Methods

Understanding how social structures with the use of a network have been an active field of study for academics in the past five years alone. The need to properly comprehends how Social Network Analysis (SNA) is being studied grows more and more in recent years. In this article, we propose a Systematic Literature Review (SLR) to the SNA to see how the algorithms, techniques, and methods are used also discuss their findings. We select thirty-one research studies on SNA. We found different algorithms and techniques that are being used. It is found that the selected research could be categorized into five different main topics which is academic, health, social media, communication, and technology. From all of the research paper discussed, it is also found that many algorithms and techniques are being used to enhanced the SNA, most of them are being machine learning algorithm such as Decision Tree, Random Forest, Support Vector Machine, Na&#x00EF;ve Bayes, Logistic Regression, K Nearest Neighbor, and Whale Optimization Algorithm. While the common features of the datasets used in the research comes as different arrays of user information from social media platforms, Tweets and posts from multiple platforms, also a photographic input such as self-images, portraits, and context related pictures. This article will serve as a single reference for future researchers to the discovery of the latest SNA findings.
The Need for Certification-Based Security Curriculum: A Matter of National Security

Recognizing the importance of Information Security in industry today, the relevant strategy literature offers several anecdotal and prescriptive narratives to strengthen security curriculum. The National Security Agency reports employees are lacking sufficient knowledge in Information Security. This educational crisis is compromising companies and countries at a cost of billions of dollars every year. Current literature provides neither clear information nor an organizing framework to improve Information Security education. This paper presents methods for design and implementation of an effective Information Security curriculum at institutions of higher education.
Large-Scale Measurement of Encrypted TLS Server Name Indication (ESNI): How Far Have We Come?

With the development of Internet techniques, the Transport Layer Security (TLS) protocol has become the most popular protocol for traffic encryption. ESNI, which is an extension of the TLS 1.3 protocol and enables the encryption of SNI, greatly protects the privacy of users. Currently, the measurement and exploration of ESNI applications and deployment are quite lacking. In this paper, we conduct the first study of ESNI deployment and use since its standardization by the IETF. We propose a novel ESNI measurement method combining active DNS TXT and TLS scanning. We collect a large amount of domain data based on passive monitoring and open source sites, and acquire 5.5 billion data during 3 months. We perform a global measurement of ESNI by deploying high-performance active detection services. According to comprehensive analysis, we find that 0.73&#x0025; of global sites and 3.4&#x0025; second-level domains support ESNI. The higher proportion correspond to China (.cn) and Russia (.ru), being 21.13&#x0025; and 15.63&#x0025; respectively. We also establish and investigate the critical contribution of hosting services and CDNs to rapid deployment and initial uptake of the ESNI. They will accelerate the deployment of ESNI applications. Especially, Cloudflare is the only vendor that supports ESNI on the server side. Finally, we validate the measurement framework and the results demonstrate the feasibility of the measurement method.
Classification of Lumbar Disc Disorder from MRI and CT images using Iterative Differential Approach

The prime objective of this study is to recognize and segment Lower Lumber Spine from the collected sample and then perform classification to separate affected and non-affected regions by lower lumbar spine disease. The proposed model first of all performs identification and separation of regions from the sample. This was performed by converting RGB cell image into gray colour scale. Background subtraction algorithm was applied to extract only cell structures from the image by eliminating the background completely and region-props. In second phase, features from the segmented regions were extracted. These features include homogeneity, contrast, energy, correlation and some hybrid features. In the third phase, digital differential analyzer optimization(DDAO) algorithm was applied to select the significant features. In the final phase, different classifiers were used to validate the performance of proposed optimization approach. The proposedmodel was applied on well-known benchmarked dataset. The obtained results corresponding to identification and separation were 92, 88 and 80&#x0025; of segmentation accuracy, sensitivity and specificity, respectively. This result was best among other published papers worked on same dataset. Classification accuracy was notably higher as compared to other models not following DDA optimization algorithm. Validation of results was further extended through feature reduction ratio and still remarkable results in terms classification accuracy of 90&#x0025; was achieved.
xRSA: Construct Larger Bits RSA on Low-Cost Devices

As the most widely applied public-key cryptographic algorithm, RSA is now integrated into many low-cost devices such as IoT devices. Due to the limited resource, most low-cost devices only ship a 2048-bit multiplier, making the longest supported private key length as 2048 bits. Unfortunately, 2048-bit RSA keys are gradually considered insecure. Utilizing the existing 2048-bit multiplier is challenging because a 4096-bit message cannot be stored in the multiplier. In this paper, we perform a thorough study of RSA and propose a new method that achieves the 4096-bit RSA cryptography with the existing hardware. We use the Montgomery modular multiplication and the Chinese Remainder Theorem to reduce the computational cost and construct the necessary components to compute the RSA private key operation. To further validate the correctness of the method and evaluate its performance, we implement this method on a micro-controller and build a testbed named CanoKey with three commonly used cryptography protocols. The result shows that our method is over 200x faster than the naive method, a.k.a., software-based big number multiplications.
Self Check-In Access Control Kiosk with Automated Card Dispensing and Storage System

A high number of office buildings especially in Indonesia still using a system where a receptionist exchange ID cards for temporary building pass. This project aims to automate such access control procedure. By combining a self-service user interface, automated card storage and retrieval systems, and optical character recognition (OCR), the processing time can be reduced and the system will help security staff to focus on the more important human observation tasks. This work will involve producing a prototype to execute this process, including integrating the system with a cloud database for access tracking. This will allow for better monitoring and documentation of building access.
Designing Broadcast Services in Hyperledger Fabric Framework for Scalability and Performance System

Blockchain is a distributed ledger technology that has received much attention in the world. Blockchain networks allow more than one party to make transactions through a transparent and verifiable peer-to-peer protocol. Blockchain networks are divided into two types, namely public and permissioned, and one example is through the Hyperledger Fabric network, where each participant knows each other in this authenticated network and can identify each other but does not fully trust. In previous research, a blockchain architecture was applied to a purchasing information system by adopting the transactional concept of Hyperledger Fabric to solve budget transparency and transaction validation in the buying &#x0026; selling process. However, in that study, there are still shortcomings where it is not clear how the channel creation and chain calling code is done, including performance and scalability issues that decrease as the number of data increases. Based on these findings, the contribution of the research is developing a blockchain architecture based on previous research by adding broadcast service and pipeline &#x0026; chunking methods to handle the channel creation process, chain code calling, performance, and scalability with a focus on improving system performance along with the increasing amount of data using the broadcast service method, the number of outputs that can be done through the pipelining method, and architectural scalability by moving some critical processes to separate services.
Subset Feature Selection with Structural Variables

In this work we propose a novel method for subset feature selection based on mutual information. We introduce structural parameters that define the probability of selecting each feature. These parameters are adjusted by maximizing the information that sampled groups of features have about a class and at the same time minimizing the size of the group. After training, the parameters are used to select a subset of features. Results on four synthetic datasets are reported, where each dataset poses a different challenge, ranging from finding synergy to avoiding redundancy. We compare these results with those of eight other mutual information based feature selection methods. Our method outperforms the other eight feature selection methods on the four synthetic datasets.
POLLUTION CLASS AND ECO-TOXICOLOGICAL RISK ASSESSMENT OF HEAVY METALS IN SEDIMENT ALONG EL-KHADRAWIA DRAIN

El-Khadrawia drain has number of anthropogenic activities including fishing, irrigation and discharge of Qusena industrial zone and industrial Mubark city that pose a risk of heavy metals in the drain and reduce economic environment.Field and laboratory analyses were carried out to assess contamination level and potential environmental risk on sediment along El-Khadrawia drain.Steel grab sampler was used to collect six sediment samples in triplicate sediment layers with 0-20 cm for quality control and quality assurance of sampling technique.Procedures of sediment chemistry characterization including extraction (Suspension extraction (decantation method) & microwave digestion technique) and geochemical analysis for eco-toxicology using Sediment Quality Guideline (SQGs), Sediment Contamination Index (SCI) and Potential Ecological Risk Index (PERI) were carried out for trace elements Aluminum: (Al), Nickel (Ni), Iron (Fe), Manganese (Mn), Cobalt (Co), Cadmium (Cd), Chromium (Cr), Zinc (Zn), Cupper (Cu) and Lead (Pb) in sediment samples along El-Khadrawia drain and El-Rayah El-Menoufy (self-investment development) to assess and predict risk for the environment and public.The research showed all metal concentrations in sediment samples, except Cr in all samples were greater than the permission limits of (SQGs) and had effect low range for adverse biology for organisms.Sediment Contamination Index (SCI) and Potential ecological Risk Index (PERI) reported all sites samples were dangers and had high rank of toxicology.The other metals concentration had variables incidence of adverse biological effect (6-70%).
Fusion of Machine Learning for Teaching Case Research on Algorithm Course

“Algorithm Design and Analysis” is a professional compulsory course for computer undergraduates. A solid grasp of the content of the course is of great importance for students to engage in relevant positions after graduation such as algorithm engineers or to further study. Nowadays, a large number of application problems need to be solved by machine learning algorithms. In fact, the underlying implementation details of many machine learning algorithms are also derived from these basic algorithms. However, there are few descriptions related to machine learning algorithms in current algorithm courses. In the process of teaching basic algorithms, if automatic association with machine learning algorithms can be realized, knowledge will root in the core knowledge base of students, thereby realizing continuous extension of knowledge. Based on this, this paper effectively associates divide and conquer and greedy algorithms with the machine learning representative --- decision tree algorithm, so as to improve the students' analogy of relevant contents and knowledge and draw inferences from one instance.