Reviewerly/exHarmony huggingface dataset:
Enhanced Text Mining Methodology in Social Media Platform

For past few years, the World Wide Web has turned into an immense wellspring of client produced content and opinionative information. Utilizing web-based life, for example, Instagram, Facebook, twitter, etc., users share their views and feelings in an advantageous manner. Web-based social networking, for example, Instagram, Facebook, twitter, etc., where a huge number of individuals prompt their individual perspectives in their everyday communication, which may be their assumptions &amp; sentiments about specific thing. These consistently developing emotional information are, without a doubt, an incredibly rich wellspring of data for any sort of basic leadership method. To computerize examination of such information, region of Sentiment Analysis has been developed. It goes for recognizing opinionative information in the Web &amp; characterizing it as indicated by their polarities., regardless of whether they convey a positive/negative implication. Assessment Study is an issue of content-based examination, yet there are a few difficulties that make it troublesome when contrasted with conventional content-based investigation This unmistakably expresses there is need of an endeavor to move in the direction of these issues and it has opened up a few open doors for future research for taking care of refutations, concealed assumptions ID, slangs, polysemy. Be that as it may, the developing size of information requests programmed information examination strategies. In this paper, a literature review on various strategies utilized in the Sentiment Analysis is done to comprehend dimension of the work.
BURKINA FASO: Kabore Wins

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 57, Issue 11 p. 22939C-22941A Internal Developments BURKINA FASO: Kabore Wins First published: 20 December 2020 https://doi.org/10.1111/j.1467-825X.2020.09745.x Read 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 No abstract is available for this article. Volume57, Issue11December 2020Pages 22939C-22941A RelatedInformation
NEURAL NETWORK CLUSTER ANALYSIS OF AREAL GEOPHYSICAL DATA OF THE KHIBINY-LOVOZERO VOLCANO-PLUTONIC ORE-BEARING COMPLEX (KOLA PENINSULA) BY SELF-ORGANIZING MAPS OF KOHONEN METHOD

The method of automatic express-interpretation of areal geophysical data is described using the example of the Khibiny-Lovozero volcano-plutonic complex.Within the framework of this method a complex geo-
Scientific Ontology and Speculative Ontology

A contrast is drawn between two approaches to ontology: scientific ontology, which is constrained by scientific knowledge, and speculative ontology, which is not. Three primary arguments are provided against speculative ontology: its factual falsity, its appeal to intuitions as a source of knowledge, and its lack of attention to scale variance. The need to formulate criteria judging the appropriateness of philosophical idealizations is suggested and reasons are given not to take scientists’ ontological positions as a guide to the correct ontology either for a given theory or more generally.
Smart Classrooms and E-learning

This paper deals with the growing popularity of smart classrooms and E-learning in the field of education. Technology these days has spread its reach to every sector and has become one of those major things to rely on. It is definitely handier for students and teachers. E-learning refers to the use of electronic media and information and communication technologies (ICT) in education. A renewed and focused attention is being given to universalization of computer literacy and integration of ICT in schools and other institutions in the tertiary sector. The extent to which e-learning assists other learning and teaching approaches is variable, ranging on a continuum from none to fully online distance learning. A variety of descriptive terms have been employed (somewhat inconsistently) to categorize the extent to which the technology is used.&lt;br/&gt; The term "hybrid learning" may refer to classroom aids and laptops, or may refer to approaches in which traditional classroom time is reduced but not eliminated, and is replaced with some online learning.
Collective entity linking via greedy search and Monte Carlo calculation

Facing the large amount of entities appearing on the web, entity linking becomes popular recently. It assigns an entrance of a resource to one entity to help users grasp the meaning of this entity. Apparently, the entities that usually co-occur are related and can be considered together to find their best assignments. This approach is called collective entity linking and is often conducted based on entity graph. However, traditional collective entity linking methods either consume much time due to the large-scale of entity graph or obtain low accuracy due to simplifying graph to boost speed. To improve both accuracy and efficiency, this paper proposes a novel collective entity linking method based on greedy search and Monte Carlo calculation. Experimental results show that our linking algorithm can obtain both accurate results and low running time meanwhile.
Floating Funds in a Shrinking Lake

SUMMARY. The Longwood University Library has implemented an allocation formula for its acquisitions budget based on credit hour production. The formula provides maximum flexibility for the library and campus departments to respond to pressures on the library’s budget. This presentation includes specific details of the allocation formula and an initial assessment of its success in improving relationships between the library and campus departments. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;https://www.HaworthPress.com&gt;]
CoPS - Cooperative Provenance System with ZKP using Ethereum Blockchain Smart Contracts

The redesign of cloud storage with the amalgamation of cooperative cloud and an immutable and unhackable distributed database blockchain thrives towards a strong CIA triad and secured data provenance. The conspiracy ideology associated with the traditional cloud has economized with cooperative cloud storage like Storj and Sia, decentralized storage, which allows renting the unused hard drive space and getting monetary compensation in an exchange with cryptocurrency. In this article, the authors explain how confidentiality, integrity and availability can be progressed with cooperative cloud storage along with tamper-proof data provenance management with ethereum smart contracts using zero-knowledge proof (ZKP). A contemporary architecture is proposed with regards to storing data on the cooperative cloud and collecting and verifying the provenance data from the cloud and publishing the provenance data into blockchain network as transactions.
Analyses and Modeling of Neural Machine Translation for English-to-Khasi

Language barrier is a common issue faced by humans who move from one community or group to another. Statistical machine translation has enabled us to solve this issue to a certain extent, by formulating models to translate text from one language to another. Statistical machine translation has come a long way but they have their limitations in terms of translating words that belongs to an entirely different context that is not available in the training dataset. This has paved way for neural Machine Translation (NMT), a deep learning approach in solving sequence to sequence translation. Khasi is a language popularly spoken in Meghalaya, a north-east state in India. Its wide and unexplored. In this paper we will discuss about the modeling and analyzing of a NMT base model and a NMT model using Attention mechanism for English to Khasi.
A framework for automatically constructing a dataset for training a vehicle detector

Object detection based on a trained detector has been widely applied to diverse tasks such as pedestrian, face, and vehicle detection. In such approach, detectors are learned offline with an enormous number of training samples. However, the approach has a significant drawback that heavy intervention and effort, as well as domain knowledge, of a human are essentially required to construct a reliable training dataset. To remedy this drawback, we propose a framework to collect and label training samples automatically. By analysing information of foreground blobs obtained from background subtraction results, a training dataset can be constructed without any human's effort. Also, condition investigation of scenes is performed periodically to check the suitability of sample candidates. As a result, it generates an accurate vehicle detector. With the proposed method, training samples can be automatically collected only when vehicle blobs in the given scene provide suitable appearance information. The effectiveness of the proposed framework is demonstrated from vehicle detection tasks under real traffic environments.
Smart Storage with the Internet of Things and Voice Recognition

In the era of automation ruling the world by coming into each and every field, now it has entered into the field of Storage. Automation has reduced the time complexity and the manual power in the entire field it has intruded. And likewise it will reduce the time complexity and tracking of the stored items and retrieving the same from the storage. This model of storage can be done with the help of Internet of Things, Cloud computing and machine learning. Cloud computing plays a major role due to its robustness and its portability which does give an extra edge in the business. To survive in business today you need to make smart choices. Storage can be a small business savior. This model can be used in many fields like medicine, business etc. Tracking and retrieving in these large amounts of storage can be made easier with the help of database.
Preface

2019 2nd International Conference on Computer Information Science and Artificial Intelligence (CISAI 2019) was successfully held on October 25-27, 2019 in Xi’an, China. This International conference is an annually held symposium with aims to bring together innovative academics and industrial experts in the field of computer science and artificial intelligence to a common forum. CISAI 2019 included keynote and oral presentations from scholars working in the areas of Computer Information Science and Artificial Intelligence. We were honored to have three distinguished professors to present keynote speeches, who are Prof. Zhihong Qian from Jilin University / College of Communication Engineering, China, Assoc. Prof. Mohd Asyraf Zulkifley from Universiti Kebangsaan Malaysia, and Dr. Mahmoud Ahmad Al-Khasawneh from Al-Madinah International University, Malaysia. They gave advanced talks referred to Convolutional Neural Networks and IOT Opportunities and Challenges, which gained big applause on site. The oral speeches were given by Yuqi Zhang from Shanghai Jiao Tong University, China, and Raushan Myrzashova from Anhui University of Science and Technology, China, who discussed Paragraph-Parallel based Neural Machine Translation Model and AI based systems for diabetes treatment. CISAI 2019 aims to present the latest research and results of scientists related to computer science and artificial intelligence topics. Therefore, we compiled a lot of excellent papers featuring the latest advanced studies on the following topics: Computer Science, Artificial Intelligence, Robotics Science and Engineering, Vision Science and Engineering, Software Process and Data Mining, and other related topics. All the papers have been through rigorous review and process to meet the requirements of International publication standard. Thanks to the supports of all distinguished speakers, Chairman, and participants, this conference was a big success. We hope this conference can greatly promote research and developmental activities in computer science and artificial intelligence and make it an ideal platform for people to share views and experiences in this field. We are expecting more and more experts and scholars form all over the world to join this international event next year. The Organizing Committee of CISAI 2019 List of Committee members are available in the pdf.
Chinese–Vietnamese Bilingual News Event Summarization Based on Distributed Graph Ranking

Multi-language news event summarization aims to quickly obtain important information from lots of related news texts written in different languages automatically. Considering that the main expressed information for the same event is similar no matter what language it is presented, the paper proposes a novel unified approach to summarize important information from the monolingual and Chinese–Vietnamese bilingual news sets simultaneously. Firstly, analyzing the sentence dependence relationship, making rules to segment sentences into different grammatical parts, a bilingual dictionary is used to set up a bilingual feature space. Secondly, Chinese–Vietnamese sentence graph model is calculated distributively. Finally, using the features that graph nodes can boost each other and fusing context information, the sentences are ranked based on whether they can represent the important information. The experimental result shows that our method is effective.
A Proficient Pattern of using Multi Keyword Search In Private Cloud For Multiuser Environment

A cloud system with computational services allowing users to access data, process and store features providing on-demand, a group of virtual remote servers, scalability, security, redundancy and elasticity. A model with many server for enabling convenient keys for a on-demand network access with a service provider interaction released with minimal management effort. The computing resources like networks, servers can be rapidly provisioned with a cloud based applications, and services to compute the shared pool of configurable devices, servers, storage. The encrypted data limits all the usage of networks due to the difficulty of searching over the usability of outsourced data to cloud. The privacy information are uploaded to the cloud and outsourced which are typically encrypted before likely to contain sensitive keywords for its consistent. In our paper, we present a scheme which overcomes the above complexity. Here we use two different techniques FMS-I AND FMS-II. First comes the simple keyword search using relevance score and preference factors. On other hand it supports the logic gate operations like “AND”,”OR”,”NOT” to find the relationship between the keywords and the document. By using these techniques the user can get a improvised search experience in encrypted cloud area. Finally, we tend to improve the single user search to multi user search by using many to many cardinality.
Getting acquainted

Abstract Before working through a real example of getting insights from data, we need to become acquainted with some tools we will use. Learning a bit about the tools first can help us feel more confident and comfortable when we then come to use them for real. Hence this chapter takes some time to introduce you to R and RStudio, including writing some simple commands, writing scripts, what to do when things seem to go wrong (as they often do!), what are functions, what are add-on packages, how to get help, and some common pitfalls to avoid.
Analitik Hiyerarşi Süreci ve Veri Madenciliği Teknikleriyle Hibrit bir Karar Destek Sistemi Uygulaması: Tam Kan Sayımı Değerleri ile Kovid19 Tanısı

Objective: Data mining techniques have a significant impact on enhancing the precision of diagnostics based on artificial intelligence. In this research, it was aimed to develop a web-based decision support that predicts the status of a person who comes to the hospital with Covid-19 suspicion by using complete blood count results until the imaging and PCR test results are obtained.&#x0D; Method: In this study, firstly data pre-processing techniques on the data set were applied, then feature selection was made using data mining approaches. After reducing the number of variables, the analytical hierarchy process method (AHP), a prominent multi-criteria decision-making approach, was utilized. Through the AHP method combined with expert opinions, the priorities of the variables determined by machine learning were ascertained, leading to the development of a decision model using publicly accessible data. A web-based application of this decision model was subsequently crafted to provide the decision support system to the end-users. Furthermore, an evaluation was conducted to gauge the usability of the decision support system and the satisfaction of its users.&#x0D; Results: RFE-SVM feature selection algorithm identified seven pivotal variables: Basophil, Eosinophil, Lymphocyte, Leukocyte, Neutrophil, Platelet, and Monocyte. Consultations were held with six expert physicians spanning diverse specialties relevant to COVID-19 diagnosis decision-making with the AHP method. Out of the 42 expert users (57.1% were male, with an average age of 37.30±10.56) were evaluated the system. The System Usability Scale (SUS) score averaged 81.43±15.64, indicating high usability.&#x0D; Conclusion: Consequently, this system might enable faster isolation of the patient and the commencement of preliminary treatment.
Semantic Sampling: Enhancing Recommendation Diversity and User Engagement in the Headspace Meditation App

In this paper, we present a clever approach to enhance the performance of sequential recommendation systems, specifically in the context of meditation recommendations within the Headspace app. Our method, termed “Semantic Sampling”, leverages the power of language embeddings and clustering techniques to introduce diversity and novelty in the recommen-dations. We augment the Time Interval Aware Self-Attention for Sequential Recommendation (TiSASRec) model with semantic sampling, where the next recommended item is randomly sampled from a cluster of semantically similar items. Our empirical evaluation, conducted on a sample set of 276,700 users, reveals a statistically significant increase of 2.26 % in content start rate for the treatment group (TiSASRec with semantic sampling) compared to the control group (TiSASRec alone). Furthermore, our approach demonstrates improved coverage and rarity, indi-cating a broader range of recommendations and higher novelty. The results underscore the potential of Semantic Sampling in enhancing user engagement and satisfaction in recommendation systems.
SmishGuard: Leveraging Machine Learning and Natural Language Processing for Smishing Detection

SMS facilitates the transmission of concise text messages between mobile phone users, serving a range of functions in personal and business domains such as appointment confirmation, authentication, alerts, notifications, and banking updates. It plays a vital role in daily communication due to its accessibility, reliability, and compatibility. However, SMS unintentionally generates an environment where smishing can occur. This is because SMS is extensively available and reliable. Smishing attackers exploit this trust to trick victims into divulging sensitive information or performing malicious actions. Early detection saves users from being victimized. Researchers introduced different methods for accurately detecting smishing attacks. Machine Learning models, coupled with Language Processing methods, are promising approaches for combating the escalating menace of SMS phishing attacks by analyzing large datasets of SMS messages to differentiate between legitimate and fraudulent messages. This paper presents two methods (SmishGaurd) to detect smishing attacks that leverage machine learning models and language processing techniques. The results indicate that TF-IDF with the LDA method outperforms Weight Average Word2Vec in precision and F1-Score, and Random Forest and Extreme Gradient Boosting demonstrate higher accuracy.
Arabic Regional Dialect Identification (ARDI) using Pair of Continuous Bag-of-Words and Data Augmentation

Author profiling is the process of finding characteristics that make up an author’s profile. This paper presents a machine learning-based author profiling model for Arabic users, considering the author’s regional dialect as a crucial characteristic. Various classification algorithms have been implemented: decision tree, KNN, multilayer perceptron, random forest, and support vector machines. A pair of Continuous Bag-of-Word (CBOW) models has been used for word representation. A well-known data set has been used to evaluate the proposed model and a data augmentation process has been implemented to improve the quality of training data. Support vector machines achieved a 50.52% f1-score, outperforming other models.
Performance evaluation systems for research and teaching staff: The attitude of university teachers

This article summarizes the results of a survey and semi-structured interviews of Russian university teachers regarding the systems of performance evaluation of research and teaching staff and their effectiveness. The results of the study revealed that university teachers have a positive attitude to the methods of stimulating research and teaching staff performance applied in higher education. At the same time, the degree of teachers’ satisfaction and evaluation of systems’ effectiveness directly depend on the specifics of their application in a particular higher education institution. The results of the study confirm the overall trend towards increasing importance of scientific components in the performance evaluation of research and teaching staff at universities. The results can become a prerequisite for the development of new criteria for ensuring the effectiveness of social and labor relations with university teachers.