Reviewerly/exHarmony huggingface dataset:
Overview of Deep Learning

AbstractAs a machine learning model based on neural networks, deep learning is particularly advantageous in fields like computer vision, speech recognition and natural language processing. This chapter mainly introduces the basic knowledge related to deep learning, including the history, the components of neural networks, the types of deep learning neural networks, and the common problems researchers may encounter in the deep learning projects.
Database Design Fundamentals

AbstractDatabase design refers to constructing a suitable database schema for specific application objects according to the characteristics of database system, establishing database and corresponding applications, so that the whole system can effectively collect, store, process and manage data to meet the usage requirements of various users.
Huawei Atlas AI Computing Solution

AbstractThis chapter mainly introduces Huawei Ascend AI processor and Huawei Atlas AI computing solution, focusing on the hardware and software architecture of Ascend AI processor and Huawei’s full-stack and all-scenario AI solution.
Degree Design Thinking: integrated design frameworks for emerging online degrees in higher education

&#x0D; &#x0D; &#x0D; This paper proposes a new conceptual framework for curriculum design that incorporates the principles of both educational and service design. Traditionally efforts in designing high quality online learning have relied on learning design and not on broader principles drawn from other fields of studies such as service design. This paper presents a case study of creating a quality online course on digital learning leadership to argue for the importance of an integrated approach to educational design. This new postgraduate degree in Digital Learning Leadership was aimed at the community of professionals working in the field of digital learning. The case study presents an integrated approach that combines design thinking and a Community of Inquiry framework as a way of cultivating a sense of belonging online for a network of digital learning professionals.&#x0D; &#x0D; &#x0D;
Objectives of modern management psychology

Management psychology is an interdisciplinary science that is successfully applied in modern realities in the field of personnel work. Here we will discuss the main features of this science, the objects and subject of management psychology, its goals and objectives, other methods, the structure of management psychology. Familiarity with this science makes all work and management processes in such a way as to increase overall work efficiency. Management psychology - a branch of psychology studies the psychology of management activity. The main task of management psychology is to analyze the psychological conditions and characteristics of management activities in order to have efficiency and workability in the management system. Is applied to the activities of the manager, in which management psychology emphasizes the points of system management: Diagnosis and status of management subsystem changes; The formation of the subordinates' activity program, aimed at changing the state of the management objects in a specific purpose; Organization of the execution of the decision. In the manager's personality, management psychology, the difference between his managerial needs and abilities, as well as his individual managerial concept, managerial plans and management principles and rules accepted internally by the person. The role of a leader is multifaceted.
RETHINKING INITIAL TEACHER TRAINING MODEL OF SECONDARY SCHOOL CHINESE LANGUAGE TEACHERS IN CAMEROON

Chinese language teaching and learning in Cameroon has developed rapidly. Teaching Chinese as a foreign language in secondary schools in Cameroon requires teachers' professional training. Due to the complexity and challenges of the Chinese language teaching, teachers need to be equipped with the necessary linguistic, didactic, communicative, and cultural competencies. Based on a critical and in-depth analysis of documents, statistical analysis and interviews, this study has analysed the current situation of the initial teacher training (ITT) model for secondary school Chinese language teachers in Cameroon in various aspects, including the training objective, duration, curriculum methods and lecturers. The results of this study show that the initial teacher training of secondary school Chinese language teachers in Cameroon has undergone many steps in its development. At the same time, it faces some challenges such as, the limited number of Chinese language lecturers, outdated training curriculum and insufficient practical courses. To address these issues, the authors made some practical recommendations such as the urgent need to recruit and train more cameroonian lecturers, strengthen and diversify cooperation with other international higher education institutions, follow up on cameroonian students who are studying in China, set up a system which will encourages graduates to come back home after their studies to participate in the country’s development, enrich the training curriculum by revising the current programme, focus more on practical courses and cultural animations and finally set up a micro-teaching programme.
Governing social media with users: tracing the evolution in Weibo community policies

Launched in 2009 and later becoming the most popular microblogging service in China, Weibo once had a bold experiment to address the unprecedented challenge of large-scale inappropriate content on its platform. It imitated the common law and jury institution and established a community committee system to involve ordinary users in the content moderation process. However, this innovation with democratic components later got reversed and transformed into a more platform-centric design where users only played an assistive role. This paper traces all available policy documents and web archives to map out how Weibo’s community governance evolved from a half-autonomous system to a rigidly controlled model since 2012. This paper suggests that a democratic content moderation system cannot last under external pressures on the social media company and without the solid empowerment of users.
STUDENT PERCEPTIONS ON PARTS OF SPEECH AFTER TAKING INTEGRATED ENGLISH

This article discussed about perception students on parts of speech after taking Integrated English. The purpose of this research is to know the student’s perception about Parts of Speech such as noun, pronoun, verbs, adjective, adverb, preposition, conjunction and interjection. Problems is students still confuse to put words of parts of speech in a sentence, so the researcher wanted to know what is students’ perceptions on Parts of Speech after taking Integrated English. The method of this study is qualitative research which data was obtained with taking a note from WhatsApp. Michael Patton and Michael Cohran (2002,3) in Sihatul, M (2020) Qualitative research is characteristic by it is aim, which relate to understanding some aspects of social life, and it is methods which (in general) generate words, rather that numbers, as data for analysis. Based on the result of the research, the researchers found the students perception on the parts of speech; First, from ten students as taken data only five students got felt difficult to learn Parts of speech; Second, the students’ perception about Parts of Speech are Positive.
A systematic Literature Review of Internet of Things for Higher Education: Architecture and Implementation

The objectives of this paper are to analyze the implementation and architecture of the Internet of Things (IoT) from previous studies, specifically in the world of higher education; offer recommendations for future research, as well as strengthen the theory of IoT architecture that already exists in higher education. This study employed a systematic literature review (SLR) with data collection utilizing Funnel Diagrams to select articles based on their relevance to the research question. The three publisher databases (Scopus, Emerald, and EBSCO) and index journals were utilized in the search for articles. A total of 1,200 articles were gathered from these three sources, with distributions of up to 800 in Scopus, 150 in Emerald, and 250 in EBSCO. The findings demonstrate that the existing IoT architecture has a more sophisticated model than the fundamental idea, which has three layers, implying that using IoT in education may have a significant influence on user convenience. This is due to the increasingly complicated requirements of higher education's many business procedures. This study serves as an inspiration and reference for future research for higher education institutions that include the Internet of Things in their implementation to build an efficient teaching and learning environment.
Factors Affecting in Achievement of Universal Courses Objective by Using Distance Education during COVID-19 Pandemic

The Coronavirus pandemic (COVID-19) has caused countries to resort to using distance education, as the study tried to uncover the factors that affect in the achievement of universal courses objective by using distance education during (COVID-19) pandemic. The researchers used the relational descriptive Methodology through the questionnaire Instrument. The questionnaire consisted of (24) items distributed on (6) factors (Effectiveness of Students, Effectiveness of faculty members, Methods of Distance Teaching, Motivation of Students, Availability of facilities and Achieving of course objectives). The study sample consisted of (1320) students from university colleges in Jordan, which were chosen by the Available sample method. The appropriate statistical analysis represented by the path analysis was found through the IBM SPSS Statistics. The results of the study showed the validity of the study hypotheses and that the specific factors (Effectiveness of Students, Effectiveness of faculty members, Methods of Distance Teaching, Motivation of Students and Availability of facilities) affect the Achieving of course objectives. According to the results of the research, the researchers recommended concerned universities in taking the factors found in the study to improve the effectiveness of distance education. Where the research contributes to supporting e-learning by developing it as a method of distance education and identifying the most important factors that affect its achievement of the objectives of the courses, especially in countries where the use of e-learning is considered a preliminary experience due to the Corona pandemic, which has not happened previously, and the state did not resort Jordanian to rely on him previously.
URUK 4D DISCRETE CHAOTIC MAP FOR SECURE COMMUNICATION APPLICATIONS

In this paper, URUK, a discrete four-dimensional chaotic map, is proposed for secure communication. The 0-1 tests, the Lyapunov Exponent (LE) tests, and the National Institute of Standard and Technology (NIST) tests, which are typically used to verify the randomness of bits, are employed to evaluate the dynamic behavior of the system based on a variety of criteria. Based on the results of tests demonstrating the system's characteristic chaotic and random behavior, it is reliable for use in covert communications and image encryption.
X-Gamma Lomax Distribution with Different Applications

The X-Gamma Lomax (XGLo) distribution, a new three-parameter modification of the Lomax distribution, was introduced and examined in this study. This distribution's features for reliability and hazard rate are addressed. The methods for estimating the XGLo distribution parameters using maximum likelihood estimation (MLE) and maximum product spacing (MPS) are explained. To compare the MLE and MPS estimate approaches, a numerical investigation is conducted Monte-Carlo simulation. Three real data sets as the cancer data includes failure rates in weeks, 109 days of continuous coal mining occurrences in Great Britain, and remission periods (in months) of a random sample of 128 bladder cancer patients. are used to examine the adaptability and potential of the XGLo distribution. The likelihood ratio test and Kolmogorov- Smirnov test have been used to check the XGLo model is better fits than Lomax model.
Message Security Enhanced By Bit Cycling Encryption and Bi-LSB Technique

Steganography is a powerful and effective data-hiding technique that has received great interest in recent years due to its effectiveness in maintaining the security of data transferred over the internet.To provide safe data transfer, various steganography technologies have lately been presented and developed.Steganography and encryption are basic methods of securing data exchange.A new algorithm is encrypted based on text messages and exchanges the bit positions around a key point using the Bi-LSB (Bi-Least Significant Bit) method.After adding the hidden message, the changed audio file appears to be the original carrier file.In comparison to previous experiments, the suggested approach increases hiding capacity by 30% while maintaining an acceptable stego.The Signal to noise (SNR) is 69.2 DB.
Teacher's Effort In Improving Early Children's Language Development

This study aims to determine the efforts that can be made to improve early childhood language development in schools. Derived from the results of previous studies, it is known that there is still a lack of language development in children at an early age. This can be influenced by many factors, including boring learning in school, stuck learning methods, and the lack of creativity of teachers in creating an exciting learning environment. The research method used in this research is literature study research, where the system data retrieval comes from books or journals analyzed based on existing problems. The results of the research show that in improving children's language development, stimulation is needed. In early childhood learning, the role of the teacher is vital because the teacher will become a motivator, facilitator and mediator. There are many ways that teachers can improve children's language development so that learning becomes fun and enjoyable for children, such as using the play method, namely using serial image media, picture media in a sandbox, and storytelling methods. From the results of this study, it is concluded that the development of children's language skills can improve.
An Approach to Enhance Quality of Services Aware Resource Allocation in Cloud Computing

A new technology called cloud computing has revolutionized the way services are delivered to businesses and consumers. As an online service, it offers a variety of options to registered users. Quality of service (QoS) requirements must be reached in order for the customer to be completely satisfied. As a result of its impact on other issues faced by cloud users and providers alike, QoS-aware resource allocation is the most essential issue in resource allocation. There is no effective solution that meets both the needs of the service provider and the consumer, yet it is still regarded a difficulty by many. This research aims to reduce the amount of time needed to assign cloud resources, improving overall performance. The social spider algorithm (SSA) is presented to map resources with the suitable job in order to fulfill the specified objectives and handle the complexity of the resource allocation issue. In order to simulate spider foraging behavior, SSA created an algorithm. It focuses on the spider, its prey, and the strength of its vibrations. This is how a victim gets out of the spider web: by attempting to release itself from the web, which creates vibrations in the web. At that point, every spider in that web was able to pick up on the vibration. The more fit the sufferer is, the greater the strength of the vibrations. Vibration intensity created on the web determines the victim’s potential. In the cloud, the job is the spider, and the resource is the prey. In terms of resource fitness, task fitness is seen as the ability to make effective use of available resources. Using DEV-C++ to construct the suggested technique, tests have shown that it saves execution time by up to 10% while simultaneously improving service quality. In terms of execution time, the SSA algorithm with first fit exceeds the SSA algorithm with best fit, while the best fit excels in terms of utilization. Furthermore, when the SSA algorithm is compared to the SSCWA method, the SSA algorithm performs better in terms of execution time, usage, and throughput. The SSA results in improved resource allocation, which results in higher QoS parameters and performance. Additional QoS considerations, such as resource dependability, are a conceivable possibility in the future. Additional research may be done to speed up the execution time even more.
Double Generalized Majorization

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; In this paper, we give a complete, explicit and constructive solution to the double generalized majorization problem. Apart from purely combinatorial interest, double generalized majorization problem has strong impact in Matrix and Matrix Pencils Completion Problems, Bounded Rank Perturbation Problems, and it has additional nice interpretation in Representation Theory of Kronecker Quivers.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Emotional Sentiment Analysis of Social Media content for Mental Health Safety

Abstract Text sentiment analysis is mostly used for the assessment of the author’s mood depending on the context. The purpose of sentiment analysis (SA) is to discover the exactness of the underlying emotion in a given situation. It has been applied to various fields, including stock market predictions , social media data on product evaluations, psychology, the judiciary, forecasting, illness prediction, agriculture, and more. Many researchers have worked on these topics and generated important insights. These outcomes are useful in the field because they (outcomes) help people comprehend the general summary quickly, Additionally, SA aids in limiting the harmful effects of some posts on various social media sites such as Facebook and Twitter. For these reasons and more, we are proposing an approach to filter the social media content that could be emotionally harmful to the user, through getting the SM content(we will refer to social media as SM), for that we have used Twitter API to get the user posts (Twitter as an example of SM), then we have used API natural understanding language API tool to extract and classify the emotions of the Twitter content into five basic emotional categories: Joy, sadness, anger, fear, disgust. into an array of emotions, after that, we have defined a perfect emotion array from over 450 words from the English language. The main purpose of this comprehensive research article is to examine the proposed solution that we have conducted to improve the quality of content displayed to users emotionally.
Detect and Mitigate Blockchain-Based DDoS Attacks Using Machine Learning and Smart Contracts

The key target of Distributed Denial-of-Service (DDoS) attacks is to interrupt and suspend any available online services either executed for professional or personal gains. These attacks originate from the fast advancement in the number of insecure technologies. The attacks are caused due to the easy access to internet and advent of technology resulting to exponential growth of traffic volumes. DDoS attack remains most leading security risks to provisioning services. Also, the current embraced security mechanism for defense lacks flexibility and adequate resources to combat these attacks. Hence, there is need to embrace various other critical resources, where they can share the problem of mitigation. In addition, emerging technologies for instance smart contracts and blockchain offers for the sharing of these potential attacks information in an entirely automated and distributed manner. This paper recommends for a blockchain design which combines smart contracts and Machine Learning (ML) technologies, by presenting new ideal opportunities towards efficient DDoS mitigation solutions in variety of cooperative domains. Furthermore, the key advantage and benefits of this structure is deployment of still existing distributed and public infrastructure to blacklisted IP address or even advertise white, and the application of such an infrastructure with further defense mechanisms to current attacks of DDoS, deprived of considering distribution mechanisms or specialized registries, which facilitates the implementation of procedures across diverse domains. This paper further presents the demonstration and implementation features of this blockchain structure, discussion and study findings over these smart contracts and ML technologies. The study further concludes by recommending use of smart contract in collaborative block-chain design with ML for mitigating future attack of DDoS.
Towards Real-Time Human Behavior Understanding: A Suboptimal Shape Descriptor

In this study, two novel shape descriptors are proposed to be used in human behavior understanding problem. First is optimal shape descriptor, which has high performance but works very slow due to high algorithmic complexity. Second is suboptimal shape descriptor, performance of which is very close to optimal one, but works much more faster. Optimal means using minimum data to represent maximum knowledge. Algorithms are run on Weizmann dataset and results are shown both as figure and video link. Classification was performed using 12 statistical features extracted from the data sets' human silhouettes. An accuracy rating of 92 percent was obtained by using Euclidean distance in classification.
Managing health supply chain using blockchain technology: state of art challenges and solution

The COVID-19 flare-up in late 2019 caused a worldwide medical crisis. In a littlemore than a quarter of a year, the quantity of COVID new cases has risen to in excessof 1,000,000 around the world.The fast transmission of the infection prompts newcases to be accounted for all around the world constantly. At the same time, mortalityrate and contaminations keep on rising rapidly. Therefore, the COVID-19 pandemichas led to the implementation of lockdowns and social distancing rules that negativelyimpact global economies. It has upturned many challenges which are mentioned inthis paper,we proposed a solution to these major problems, blockchain technology canplay a vital role in closing maximum escape clauses in the present system to improvevisibility and traceability and to provide a clear far vision for the stakeholders presentin pharmaceutical industry.
Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem

Math word problem solver requires both precise relation reasoning about quantities in the text and reliable generation for the diverse equation. Current sequence-to-tree or relation extraction methods regard this only from a fixed view, struggling to simultaneously handle complex semantics and diverse equations. However, human solving naturally involves two consistent reasoning views: top-down and bottom-up, just as math equations also can be expressed in multiple equivalent forms: pre-order and post-order. We propose a multi-view consistent contrastive learning for a more complete semantics-to-equation mapping. The entire process is decoupled into two independent but consistent views: top-down decomposition and bottom-up construction, and the two reasoning views are aligned in multi-granularity for consistency, enhancing global generation and precise reasoning. Experiments on multiple datasets across two languages show our approach significantly outperforms the existing baselines, especially on complex problems. We also show after consistent alignment, multi-view can absorb the merits of both views and generate more diverse results consistent with the mathematical laws.