Reviewerly/exHarmony huggingface dataset:
Reviewer Acknowledgements

Applied Economics and Finance (AEF) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether AEF publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 7, Number 1 Ayoub Taha Sidahmed, SIU, SudanHe Nie, Jinan University, ChinaIan McFarlane, University of Reading, UKIbrahim Baghdadi, Lebanese University, LebanonRajeev Rana, APB Govt. P.G. College, IndiaRichard Nguyen, Alliant International University, USARomeo Victor Ionescu, Dunarea de Jos University, RomaniaSteven V. Cates, Purdue University-Global, USAVictoria Cociug, Academy of Sciences of Moldova, Moldova
An Extractive Summarization Technique for Text Documents

In order to read as well as search information quickly, there was a need to reduce the size of the documents without any changes to its content. Therefore, in order to solve this problem, there was a solution to it by introducing a technique called as automatic text summarization which is used to generate summaries from the input document by condensing large sized input documents into smaller documents without losing its meaning as well as relevancy with respect to the original document. Text summarization stands for shortening of text into accurate, meaningful sentences. The paper shows an implementation of summarization of the original document by scoring the sentence based on term frequency and inverse document frequency matrix. The entire record was compressed so that only the relevant sentences in the document were retained. This technique can be applicable in various applications like automating text documents, quicker understanding of documents because of summarization.
Distributed recommender system using multi-agent for social networks

Social networks have become significant tools due to the vast and useful information existing in them. The social platforms also act as the storage of entered choices of millions of users for various applications such as political surveys, research studies, marketing product preferences and many more. Social network recommender systems exploit this information and direct users in selecting their choices. It is clear that recommender systems should be efficient enough to be able to process the huge magnitude of data that has been generated in recent years by social network users. This research proposes a foundation of an efficient and scalable recommender system to be able to process large amount of data (i.e. Big data) in a short amount of time. The main goal is providing scalability and efficiency of the recommender system. The simulation of the prototype of such a distributed recommender system by using multi-agent based technologies shows promising results. These prototypes provide recommendations to users about other users with the similar interests in online and distributed manner as real recommender systems. The agents can simulate users or can be used as the containers of algorithms for comparing the similarity between users by different approaches, such as cosine similarity and clustering methods for testing and examining real scenarios. To be able to test these prototypes in agent-based simulation environment an agent-based framework is developed. This framework has three modules named social network crawler, social network simulator and employed prototype of the distributed recommender system that use different text and data mining algorithms. Finally, newly developed performance metric (called Scalability Factor) is introduced that shows the minimum number of servers needed to be able to run the agent systems in parallel. This thesis shows using a distributed and parallel model for recommender systems is the key to increase the speed of recommendation convergence and as a result to provide scalability. Multi-agent based simulation results, coupled with numerical analysis affirm that the proposed solution provides scalability and efficiency for recommender systems.
Preface

On account of the serious situation of COVID-19, the 2020 2nd International Conference on Resources and Environmental Research (ICRER 2020) was held as virtual conference during November 19-21, 2020. Travel restrictions were made to minimize the risk of people spreading the COVID-19 through physical contact. Therefore, conference committees decided to hold ICRER 2020 by Zoom application and it aims to provide a platform for Resources and Environmental Research fields to share views and experiences. ICRER 2020 is co-organized by Xiamen University of Technology, supported by South-South Collaborative and Sustainable Development Center and International Society for Environmental Information Sciences (ISEIS). This three-day conference focused on the research fields in Resources and Environmental Research. Environmental Resources refer to the environment as a whole as the sum of resources. All kinds of natural resources, including water, air, land, animals and plants, minerals and their combination of various states, are the material basis for human survival and development. Irrational development and utilization of environmental resources have led to increasingly serious damage, such as water resource shortage and pollution, ozone layer destruction, soil desertification, forest area reduction and the imminent exhaustion of some rare species and minerals, which will have a corresponding impact on human social progress and economic development. The conference is an international conference for the presentation of technological advances and research results in the fields of Resources and Environmental Research. The conference has brought together more than 70 leading researchers, engineers and scientists in the domain of interest from Indonesia, Portugal, Australia, Canada, Japan, Romania, China, Thailand, Italy, Argentina, UK and so on. Six distinguished experts in total have given their 35 minutes’ speech as keynote speakers for the conference. They are Prof. Yongping Li from Beijing Normal University, China; Assoc. Prof. Farhad Shahnia from Murdoch University, Australia; Prof. Guilin Zheng from Wuhan University, China; Prof. Wei-Jen Lee from University of Texas at Arlington, USA; Assoc. Prof. S. M. Muyeen from Curtin University, Australia; Prof. Zhijun Peng from University of Bedfordshire, UK. Their insightful speeches had triggered heated discussion during keynote speech session of the conference. In addition, each presenter was allocated 12 minutes to deliver speech and 3 minutes for Q&amp;A one by one. Only one presentation has been selected as the best one for each session. List of Committees are available in the pdf.
Ecological world outlook from the early childhood!

The ten-year experience of Zelenogorsk Children’s Library in ecological and environmental education is discussed; on the library’s premises, The Ant Museum is established; various events are held. Special activities are offered to the children in summer.
Content Based Retrieval of Liver Images for Computer Aided Diagnosis

Liver cancer is a serious disease caused by a variety of factors that damage the liver region. Early detection of this disease is necessary to diagnose and to cure it completely. Enormous increase in medical database images has lead to development of Content Based Image Retrieval (CBIR) system to retrieve relevant liver images from medical database consisting of abdominal Computed Tomography (CT) images. In the proposed method Content Based Medical Image Retrieval (CBMIR) system is designed to search and retrieve relevant liver images from medical image database. Adaptive Region Growing Algorithm (ARGA) and Simple Linear Iterative Clustering (SLIC) are used for liver and tumor segmentation. Features are extracted using Gray Level Co-occurrence Matrix (GLCM), Average Correction High order Local Autocorrelation Coefficients (ACHLAC) and Legendre Moments (LM). Based on the distance metric, distances between extracted features of query image and images in the database are measured. Euclidean distance metric is used to retrieve relevant medical images.
Constrained Local Models (CLM) For Facial Feature Extraction using CLNF and SVR as Patch Experts

Methods for detection of facial characteristics have again developed greatly in recent times. However, they also argue in the presence of poor lighting conditions for amazing pose or occlusions. A well-established group of strategies for facial feature extraction is the Constrained Local Model (CLM). Recently, they are bringing cascaded regression-built methodologies out of favor. This is because the failure of presenting nearby CLM detectors to model the highly complex special signature look affected to a small degree by voice, illumination, facial hair and make-up. This paper keeps tabs on execution to collect facial features for the Constrained Local Model (CLM). CLM model relies on patch model to collect facial image demand features. In this paper patch model built using Support Vector Regression (SVR) and Constrained Local Neural Field (CLNF). We show that the CLNF model exceeds SVR by a large margin on the LFPW database to identify facial landmarks.
The Image Enhancement and Recognition Technology Based on YOLOv3 and Dark Channel Prior

The recognition technology based on image convolution is the main artificial intelligence recognition method at present. The YOLO Algorithm frame, RCNN algorithm frame and so on can be effectively applied to airport, elevator, station, etc. it has a good application prospect in the field of danger alarm. Based on the background of street vehicle recognition, this paper presents a method of image enhancement by dark channel prior, which is based on the Yolov3 Algorithm, aiming at the problem that the image is not clear under the day smog weather and the strong street light at night, and improve the accuracy of the recognition. The experimental results show that this method is helpful to improve the accuracy of recognition.
R4 Model for Case-Based Reasoning and Its Application for Software Fault Prediction

Making R4 model effective and efficient I have introduced some new features, i.e., renovation of knowledgebase (KBS) and reducing the maintenance cost by removing the duplicate record from the KBS. Renovation of knowledgebase is the process of removing duplicate record stored in knowledgebase and adding world new problems along with world new solutions. This paper explores case-based reasoning and its applications for software quality improvement through early prediction of error patterns. It summarizes a variety of techniques for software quality prediction in the domain of software engineering. The system predicts the error level with respect to LOC and with respect to development time, and both affects the quality level. This paper also reviews four existing models of case-based reasoning (CBR). The paper presents a work in which I have expanded our previous work (Rashid et al., 2012). I have used different similarity measures to find the best method that increases reliability. The present work is also credited through introduction of some new terms like coefficient of efficiency, i.e., developer's ability.
Self-Service Checkout System for Groceries

Self-service checkout systems allow shoppers as active participants into a retailer’s business processes. This paper focused on the development of electronic payment in groceries using Near Field Technology. This was done with the use of Radio Frequency Identification (RFID) cards which will replace cash outs and other online-based transactions that are prone to system failures. Several tests were conducted to determine how the self-service checkout system in this study will perform in real- world scenario. The transactions using the self-service checkout system can be finished in less than 41.13 seconds for five items and less. The test in the deduction of current balance by the system yielded 100% accuracy. In terms of security, the system does not rely only on video surveillance of a particular establishment because it has its own security mechanism that is enabled by a microcontroller. The security mechanism that matches the weight of the groceries calculated by the software program and the weight of the groceries weighed by the shopper yielded 100% accuracy during the testing. The self-service checkout solution in this study can provide a more time-efficient shopping experience to time-constrained users.
Conclusion and Outlook

In this concluding Chapter we provide a brief overview of the results discussed in the book and an outlook on possible research directions.
Proving the Associated Factor That Contributing the Caries Status among Pre-School Children in Bachok Kelantan

This paper focuses on the proving of the associated factor for the caries status among preschool children in Bachok, Kelantan. This research paper is mainly focused on the potential factor that most contributing to Early Childhood Caries (ECC). There are two methodologies approaches in this research paper which is Decision Tree Analysis (DTA) and Multi-Layer Perceptron (MLP). The results from both analyses can be used to assist the public and also the stakeholder to control the prevalence of ECC in the future. Results from both analyses are also very useful to redesign the health treatment among pre-school children, to educate the parents, teachers, and to improve the service which offered by the ministry of health from time to time by focusing the most influential factors which lead to ECC in the local community. According to the result of Decision Tree Analysis, the most factor that leads to caries status among preschool children are father’s occupation, household income, children’s weight and the type of water used in their house. While using Multi-Layer Perceptron (MLP) neural networks modeling, the factor can be summarized as household income factor, children’s weight, father occupation and also the type of water used in their house. From the results of the Decision Tree and Multi-Layer Perceptron (MLP) reveals that the top three factors lead to ECC were household income factor, children’s weight, father’s occupation. This information will provide a very useful information to forecast ECC status among preschool children.
Decoupling Kernels from Symmetric Encryption in Rasterization

Various system administrators would agree that, had it not been for Byzantine adjustment to inner disappointment, the improvement of IPv4 may never have occurred. Given the present status of wearable correspondence, structure officials amazingly need the appraisal of RPCs, which encapsulates the private gauges of low-essentialness cryptography. Audit, our new estimation for mixed techniques, is the response for these issues.
Examining the prospects of library use data integration in university information systems: the.

Li n ki ng sociaL capitaL to knowLe dge productivityWhy are some networks more successful in achieving innovation than others?Why do some groups within organizations manage to operate without hierarchical boundaries, and at the same time achieve extraordinary, innovative results?How do they learn?And what kinds of network characteristics do they cherish?This book presents the process and findings of a four-year PhD study on these questions.With empirical data of more than seventeen networks a theoretical framework is presented in which characteristics of social capital are linked to knowledge-productive learning processes.This book invites you to connect to the world of social capital, participative research and innovative learning and at the same time offers a practical guide to get started within your own social network.
Assured Way to Manage Various Controls in Cloud

Secure cloud garage, which is a rising cloud carrier, is designed to guard the confidentiality of outsourced statistics however also to offer bendy statistics get entry to for cloud customers whose information is out of bodily control. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is seemed as one of the maximum promising strategies that may be leveraged to cozy the guarantee of the provider. However, the use of CP-ABE may additionally yield an inevitable security breach that's referred to as the misuse of access credential (i.e. Decryption rights), because of the intrinsic “all-or-nothing” decryption feature of CP-ABE. In this paper, we check out the 2 most important instances of get right of entry to credential misuse: one is on the semi-trusted authority facet, and the opposite is at the aspect of cloud consumer. To mitigate the misuse, we recommend the first responsible authority and revocable CP-ABE based cloud garage system with white-field traceability and auditing, referred to as CryptCloud+. We also gift the safety evaluation and further exhibit the utility of our system thru experiments.
Formalizations of AAS

Four (monadic first order, sentential, dyadic first order, and natural deduction) formalizations of AAS are presented, their syntax and syntactical interrelationships are discussed.
Novel Intraoperative Radiotherapy (IORT) Utilizing Prefabricated Custom Three-Dimensionally Printed High Dose Rate (HDR) Applicators

Intraoperative radiotherapy (IORT) is an effective strategy for the delivery of high doses of radiotherapy to a residual tumor or resection cavity with relative sparing of nearby healthy tissues. This strategy is an important component of the multimodality management of pediatric soft tissue sarcomas, particularly in cases where patients have received prior courses of external beam radiotherapy (EBRT). Tumor beds with significant topographic irregularity remain a therapeutic challenge since existing IORT technologies are typically most reliable with flat surfaces. To address this limitation, we have developed a novel strategy to create custom, prefabricated HDR IORT applicators designed to match the morphology of an anticipated surgical cavity. We describe the successful fabrication process for the 3D customized applicators. We also detail our experience utilizing this strategy in two pediatric patients who underwent HDR-IORT with Iridium-192 as part of complex head and neck sarcoma resections.
Robot Path Planning based on an Improved Q-learning Method

Path planning is one of the most basic and pivotal aspects in research of robots, which is to solve the walking problem of robots.As a widely used reinforcement learning method, Q-learning is employed when the robot has no prior knowledge of how its actions affect its environment.For Q-learning method, there is a problem of explorationutilization in robot path planning.Therefore, robot path planning based on an improved Q-learning method is proposed.According to the environment in which the robot is located, a Markov decision model is established to design the rewardpunishment mechanism and action strategy of the robot in the path planning.During the robot training process, a heuristic search function is defined and added to the value iteration algorithm in order to reduce the invalid path exploration in the environment.The experimental results show that the proposed method not only reduces the length of path and improves the efficiency of path planning, but also accelerates the speed of robot learning.This indicates the effectiveness of the proposed method.
A Robust Image Cryptography Scheme using Optimal Band Selection and Dynamic Key Embedding

Secure Image transmission over media like internet has become very important these days. Several techniques for enhancing security during image transmission like cryptography, steganography, etc. evolved over a period of time. In all these techniques, the images are converted to a form that is not detectable by the intermediate user except the sender and the authenticated user. Still these techniques have limitations in ensuring security in the transmission of secret images due to several attacks in the channel through which they are transmitted. In this paper, an approach of embedding secret information in a host image is carried out using a method of selecting the room for embedding in an optimal way to make the model robust against attacks. Further, an optimal way of key embedding is carried out on the selected band. The encrypted image obtained during the above mentioned steps is subjected to several attacks and the effected image is decrypted and the results are compared with the original host image with respect to the metrics like Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE) and Normalized Cross Correlation (NCC). This proposed method is applied on several images and the above-mentioned metrics are obtained. The proposed approach proves to outperform the conventional LL band selection and LSB Key embedding methods.
A Novel Method on Developing Superblocks and the Transistor using Apodryal

Read-Write correspondence and gigantic multiplayer online pretending recreations have collected constrained enthusiasm from both cyber informatician and analysts over the most recent quite a while. Here, we dis confirm the blend of Byzantine adaptation to non-critical failure, which typifies the essential standards of steganography. In this position paper, we demonstrate that despite the fact that the Turing machine and semaphores can synchronize to settle this test, hash tables and reserve rationality are to a great extent inconsistent.Dynamic systems and irregular calculations, whereas common in thought, have not till currently been viewed as nice measured. Following quite whereas of individual examination into reproduced toughening, we tend to contend the simulation of checksums that encapsulates the engineered up thoughts of factory-made intelligence.MottySkag, our new programming for the enclosure broad web, is that the declare these inconveniences
Algorithmic and advanced Programming in Python - Syllabus in Computer Science, Decision Making &amp;amp; Data - Masterclass 3

This paper is the third of a series of 10 lecture notes in Algorithmic and Advanced Programming in Python that are part of the Syllabus of Dauphine PSL's Master in Computer Science, Decision Making & Data. It taught advanced data structure in python with a focus on core and practical details to learn the ins and outs of advanced data structures. In this lecture, you will learn how to build dynamic website using Flask, SQLite and SQLAlchemy
SUDAN: Protesters Demand Reforms

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 57, Issue 6 p. 22777B-22780C National Security SUDAN: Protesters Demand Reforms First published: 20 July 2020 https://doi.org/10.1111/j.1467-825X.2020.09538.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume57, Issue6July 2020Pages 22777B-22780C RelatedInformation