Reviewerly/exHarmony huggingface dataset:
AI-ENABLED GAMIFICATION FOR LEARNING AND ASSESSMENT

Games and gamification can potentially inform and transform learning by providing meaningful and engaging learning environments.The application of Artificial Intelligence (AI) algorithms and systems in education is envisaged as an opportunity to provide students with adaptive and personalized learning, especially in the context of mobile learning and assessment.However, such endeavors highly contrast with the provision of standardized test-driven curricula, which continue to force assessment practices to endure a meagreness of pedagogical innovation and transformation (Perrotta and Whitelock 2017) and which remain the fixation of formal educational institutions (Hazelkorn 2014).This short paper comprises of the initial theoretical framework for a proposed academic research, which seeks to critically analyze the traditional quantification of learning and the processes for measuring and certifying academic achievement, in view of a more adaptive, personalized and engaging learning and assessment experience, put forth and underpinned by gamification and AI.Consequently, the main research question that will be addressed is: What is the effect of AI-powered gamification on students' learning and assessment experience?A mixed-methods approach involving both qualitative and quantitative data at different stages of the research will be adopted.A number of distinct data sources will be targeted, including students, educators, game designers and AI scientists.The proposed research, has the potential to contribute new and original insights to the body of knowledge and practice in the fields of Gamification and Artificial Intelligence, whilst providing a solid foundation for future academic studies.
ANALYZING MOBILE LEARNING KEY SUCCESS FACTORS IN CATALONIA

This study investigates the factors affecting mobile learning adoption in Catalonia.The main methodology is an expert judgement where 7 Catalan experts participated, including three school leaders, two university experts and two education inspectors.The findings reveal that the factors that affect mobile learning can be grouped into five categories ordered from the highest to lowest impact as follows: leadership, personal character, attitudes and ethics, pedagogical, digital literacy, and technological resources.The findings of these insights are expected to be useful to improve the efficiency of mobile learning adoption.
Intelligent Control and Parameter Calculation of Highway Truck Escape Ramp

To reduce the probability of secondary accidents caused by the direction deviation, body rollover, and excessive deceleration of runaway vehicles during braking on a truck escape ramp (TER), the safety of the occupants and the vehicles must be ensured.Based on the momentum theorem, an intelligent control method for a TER is proposed.In the method, a slope aggregate with a small rolling resistance coefficient is used on the original escape lane, and the information acquisition, braking device control, and braking modules are established.Through these modules, the operation parameters and control parameters of the out-of-control vehicle are obtained in advance, to control the operation state of the runaway vehicle.Achieve the purpose of reducing the braking effect of the slope bed aggregate and ensuring maximum utilization of the braking ramp.Finally, the proposed control method is simulated on the MATLAB/Simulink simulation platform.The results show that the control method can realize safe braking of an out-of-control vehicle at various speeds and different mass conditions, and it can play a highly significant role in the braking of high-speed and heavy-duty vehicles.
Multi-target Capturing Algorithm of Multiple Robots in Dynamic Environment

This paper proposes an algorithm for the multi-target capture problem in a dynamic environment. The algorithm makes full use of the advantages of strong real-time performance and fast processing speed, which is more suitable for dynamic environments. A cost function is established to comprehensively describe the basis for grouping rounds. Grouping of multiple robots is based on the location information of the target points and then set up round-up points dynamically within the group. The robots can reach multiple target points while avoiding static and dynamic obstacles in the environment. Simulation results show that our algorithm is effective and robust.
Secret Communication using Multi-Image Steganography for Military Purposes

Information security plays an important role when considering data transmission. Transmission of sensitive data or communication through the internet turns out to be challenging due to security concerns. Generally, we use cryptography for information hiding and sending secret messages in the form of text. Nowadays, there are several techniques used for hiding information in any medium. One such technique is steganography. Building a secret communication system including Multi-image steganography will result in secure communication between the sender and the receiver without any interference from the hackers. Image steganography is the main aspect of information hiding where the ciphertext is embedded into an image called a cover image which is next to impossible for the intruders to see with their naked eyes. The hiding information can be any form of text, images, audio, and even videos inside a cover image. The conceptual definition of Multi-image Steganography is that the secret code is divided into multiple parts and is etched into multiple cover images. So we proposed two image steganography ideas to make it very challenging for the hackers to conceal the data. Thispaper proposes the Least significant bit(LSB) technique of Steganography and the Advanced Encryption Standard(AES) technique of Cryptography to build a safe and secure system. Here the sender and the receiver use the same key to encrypt and decrypt the data which is popularly termed a symmetric key.
Cooperative 4D Penetration Path Planning of Multi-UAV Using MLS-EDA Algorithm

Aiming at the path planning problem of unmanned aircraft vehicle (UAV) cooperative penetration, a 4D path planning method with the time and space constraints is developed. The simultaneous arrival time requirement of the end state is transformed into the state adaptation in the process, thus, the dimension reduction of the complexity of problem model is realized. Moreover, a reasonable cost function is developed to transform the cooperative penetration path planning problem into a single objective optimization problem, which is solved by MLS-EDA algorithm. The simulation test is carried out by using a UAV cluster with a scale of 20. The results show that the planned path of each UAV can effectively avoid the enemy fire threat and all member can reach the assemble points at the same time, which proves the rationality and feasibility of the proposed model as well as the algorithm.
Detecting Ballot Stuff Collusion Attack in Reputation System for Mobile Agents Security

A Mobile Agent (MA), when dispatched in a decentralized peer-to-peer (P2P) electronic community, is forced to do a transaction with unfamiliar hosts. Such unfamiliar hosts are malicious in nature and can tamper agent’s code, state, and data. To solve integrity, confidentiality, availability, and authenticity threats from hosts, this paper proposes a soft security approach. Under this approach, a trust-based reputation model called MRep is proposed. The model considers first-hand information called Direct Reputation (DR) obtained from trust gathered through Source Host (SH). The model assumes SH to be a pre-trusted host that possesses past transaction experience from the destination host. The destination host (DH) is the target host with which the agent wishes to do a transaction in the future. Indirect Reputation (IDR) is obtained from recommenders having a past transactional history with the DH. A collusion attack takes place when these recommenders collaborate to give false recommendations about DH. Ballot Stuff and Bad Mouth collusion occur when recommenders collude to give a positive and negative rating to dishonest and honest DH, respectively. The methodology is based on Similarity Filtering (SF) that uses Euclidean Distance (ED) and single linkage clustering techniques. ED is calculated between consecutive recommender’s past recommendation value called ‘F-Score’ and recommendation value given by SH for DH. Clustering merges recommenders into two clusters. Scatter plots give two clusters. One cluster contains recommenders that gave an exceptionally high or low rating to DH while the other cluster gave a rating close to the rating given by SH. Bernoulli's trial helps to know the effect of collusion on the Final Reputation (FR) of DH when the number of colluders increases and decreases in the system. The reputation errors are calculated and statistically verified using Binomial Probability Distribution. Validation graphs show that when the chance of collusion (p) is less than 0.5, the probability of reputation error p(x) decreases with an increase in the number of colluders(x). When p is equal to 0.5, p(x) first increases and then decreases with an increase in x and when p is greater than 0.5, p(x) increases with an increase in the number of colluders(x). We compare SF with Bayesian Filtering (BF), Outlier Filtering (OF), and No Filtering (NF) when 20%, 40%, 60%, and 80% collusion arises in the system. The proposed SF approach helps filter ballot stuff colluders. MRep gives less error in FR of DH, even when 80% collusion arises in the system.
The Simple Usefulness of the Secchi Disk

A centuries-old sailor’s hack enters the ecologist’s toolkit.
Test Data Measurement Uncertainty Analysis

The Structural Validation Branch (AFRL/RQVV) provides test services for internal and external customers covering a wide range of research and development programs. Calculating and documenting test data uncertainty is a necessary capability for a test organization. Test data uncertainty analysis provides error limit values based on the combined effects of random and systematic error sources. Error source examination can also be used to determine if corrective actions are necessary and/or possible to eliminate or reduce the measurement errors. This project was initiated to review uncertainty calculation methods and develop the internal processes and procedures that will be used to calculate, document, and report measurement uncertainty values.
Teaching Complex Sentences in ESL Reading: Structural Analysis

Informed by the reading ability of English complex sentences, as well as the syntactic acquisition and cognition, this empirical study, based on a structural analysis approach, investigated 32 Chinese high school students in their ESL (English as Second Language) reading course. The results indicated that: (1) the differences between students&amp;rsquo; pre- and post-test are significant; (2) structural analysis approach was found to have independent positive predictive effects on Chinese high school students&amp;rsquo; syntactic proficiency. The findings can generate implications for TESL (Teaching English as Second Language) and provide insights to theoretical conceptualization of L2 (Second Language) reading.
Bidirectional RNN for Audio Deep Learning in an End-to-End Model

ASR (automated speech recognition) is a feature that enables users of information systems to input data by speaking rather than punching numbers into a terminal. These advancements empower machines to answer human voices precisely and reliably, permitting to convey helpful and significant services. Individuals would pick such a gadget since speaking with a machine through voice is quicker than utilizing a console. Since communicating in language rules human correspondence, it’s just typical for individuals to anticipate voice interfaces for PCs. This can be accomplished by making discourse-to-message programming that empowers a gadget to change over voice orders and correspondence into messages. The acoustic model, language model, and vocabulary model are the three models of a run-of-the-mill ASR framework. Various examples of voice, the environment, which requires foundation commotion, and the speaker’s intonation are altogether hindrances in automatic speech recognition. The key idea is to investigate the spectrogram and MFCC highlights of information sound signals and make state-of-the-art profound learning models. It prepared an acoustic model utilizing RNN-GRU (Recurrent Neural Network-Gated Recurrent Unit) in the Librispeech dataset and then executed a language model utilizing Bert to improve the presentation of the acoustic model carried out. Whenever prepared from start to finish with reasonable regularization, it observes that RNN-GRU accomplishes a higher word blunder rate. When compared to a higher word blunder rate, a lower word blunder rate implies superior discourse acknowledgement execution. In automatic speech recognition, profound learning procedures make it simpler to diminish the pace of word mistake rate.
Deep learning based Novel Vehicle Detection and Counting System

When researchers talk about smart cities, management of traffic on the highway and road tracking of vehicles is very difficult but in the last few decades, technology has played a great role in solving this problem of traffic management on roads and highways. In this paper, we are going to discuss a similar kind of technology. This paper represents an identification technique based on VISION, which means tracking through cameras. It is mainly targeted at detecting vehicles, counting total number of vehicles, and detecting speed of every vehicle inside the frame.
An Advanced Model to Secure the IoT Data using Steganography

The Internet of things describes physical objects with sensors, processing ability, software, and other technologies that connects and exchange data with other devices and systems over the internet or other communication networks. But very few IoT gadgets are being used in some organization, it is very important to know about the security dangers and risks in digital assaults. In cryptography, plain message is converted to encoded message before it is stored in the cloud, and plaining message after correspondence on the opposite side is changed over. On applying the encoded data to this monotonous data in some unnoticeable way, the provided result will be an image report that appears to be vague from the principal picture anyway that has upheaval instances of typical, decoded data. The proposed system provides technique to encode the internet of things networks information by cryptography strategy and conceal the scrambled message inside a picture document involving steganography technique also expands the quantity of pieces that can be saved inside a pixel of a picture. The use of convolutional networks in customary steganography strategy to radically build the payload that can be sent through a picture is applied. Subsequently, the convolutional networks calculation is created and prepared to build the payload of information to be encoded as well as securely decoded to see the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> message.
The program of the entrance examination for postgraduate studies: for candidates for the degree of doctor of philosophy (phd), field of knowledge: 01 education / pedagogy, specialty: 011 educational, pedagogical sciences

Program for passing the entrance exam to graduate school in the specialty 011 Educational, pedagogical sciences for graduate students of Kyiv National Linguistic University.
Development of an Auto-detection and Quantification Algorithm Of Malaria Infection Using Image Processing

Most of the malarial diagnostic methods either depend on manual counting of infected red blood cells or requires complex laboratory facilities. In both cases, the diagnostic is time-consuming, expensive, requires trained personnel, sometimes produce erroneous results due to manual intervention, and hinders rapid diagnostics of malarial infection. Malaria is mostly fatal if not diagnosed and treated promptly, therefore, it is imperative to devise a methodology that provides a rapid, cost-effective, and accurate malarial diagnosis with proper quantification. Here, we propose an image processing-based malaria detection methodology using support vector machine (SVM) that can detect and quantify malarial infection with up to 96&#x0025; accuracy. The image processing algorithm is implemented on a range of images and the outcomes are in good agreement with the actual diagnostic results thereby, validating the methodology.
Internet Language Study in China

Research on Chinese netspeak took off just before the turn of the century. After almost thirty years, it is now a robust field of study that engages the next generation of Chinese linguistic researchers and draws attention from multiple disciplines. With an objective to identify research trends and relevant research interests for future Chinese netspeak studies, this chapter revisits previous research published in Chinese academic periodicals and sourced for the Chinese Social Science Citation Index (CSSCI) from 1994 – the first year China had access to the global Internet – to 2019. An analysis of 504 studies published in major journals under the keyword “netspeak” (wǎngluò yǔyán, 网络语言) reveals a clear trajectory in research development. The earliest research, published before the year 2000, is characterized by inquisitiveness about this new way of speaking and uncertainty over its implications on the Chinese language. In the first decade of the twenty-first century, the field shifted its focus to how netspeak as a social language interacts with netizens’ psychology as well as Chinese society and culture. Between 2011 and 2019, the scope of research further expanded as netspeak became increasingly prominent in both official and public spheres. In the last decade, it has become an interdisciplinary subject that spans different fields of knowledge, including linguistics, education, law, communication, and cultural studies. This chapter concludes by calling for Chinese netspeak research to focus on strengthening its theoretical foundation, broadening its research scope, and enhancing its research focus to promote the future development of the field.
A Preliminary Study on the Cultivating Modes of Chinese Language and Literature Talents in Application-Oriented Universities

With the development of the society, the education methods in China are also constantly improving with the advance of the times.As a great cultural power in the world, China's language and literature accumulation is very rich.However, influenced by the traditional training modes, the mode of cultivation is rather monotonous and lacks application practice in cultivating Chinese language and literature professionals in application-oriented universities.Hence, the paper deviates from the diversified and complex needs of society in cultivating Chinese language and literature talents.In application-oriented universities, only by meeting social demands and closely integrating with the social economy and cultural development needs should the training objectives of Chinese language and literature professionals be established.To adjust the theoretical courses for talent training, and to conduct practical training in combination with the context of the reality is more in line with the cultivation of Chinese language and literature professionals in application-oriented universities.To highlight the educational characteristics of such universities and comprehensively improve the professionalism of students in these universities, enhance their innovative spirit and practical application ability, therefore, this paper explores the cultivating modes of Chinese language and literature talents in application-oriented universities.
Disciplinary Layout and Industrial Competitiveness Analysis in Colleges

Through the industrial agglomeration status, including the evolution of industrial structure, industrial layout and industrial restructuring, and higher education disciplines, including discipline structure, professional layout and curriculum system information, the paper through data analysis, determines the development of relations between them, the relevance and adaptability.It explores the industrial cluster and higher education disciplines layout of the fit between the degree of discovery of existing problems and deficiencies in both industry and education to offer solutions.
Education - from transmissive teaching to transformative learning

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Improving Transformer with Dynamic Convolution and Shortcut for Video-Text Retrieval

Recently, Transformer has made great progress in video retrieval tasks due to its high representation capability.For the structure of a Transformer, the cascaded self-attention modules are capable of capturing long-distance feature dependencies.However, the local feature details are likely to have deteriorated.In addition, increasing the depth of the structure is likely to produce learning bias in the learned features.In this paper, an improved Transformer structure named TransDCS (Transformer with Dynamic Convolution and Shortcut) is proposed.A Multi-head Conv-Self-Attention module is introduced to model the local dependencies and improve the efficiency of local features extraction.Meanwhile, the augmented shortcuts module based on a dual identity matrix is applied to enhance the conduction of input features, and mitigate the learning bias.The proposed model is tested on MSRVTT, LSMDC and Activity-Net benchmarks, and it surpasses all previous solutions for the video-text retrieval task.For example, on the LSMDC benchmark, a gain of about 2.3% MdR and 6.1% MnR is obtained over recently proposed multimodal-based methods.
Ensuring the Presence of a Person During Virtual Classes Using Histogram of Oriented Gradients (HOG) Algorithm

As of now, in the pandemic situation, all the learners, particularly students, participate in various virtual educational activities like attending classes, uploading assignments, watching tutorials, online exams and meetings. A major issue in online learning is to improve students participation and their attention in educational activities. Students can engage themselves in various activities during the online classes. It is necessary for the teachers to ensure that the students are physically present during the virtual class. In order to overcome this issue, we proposed a system, which will check for the presence of students through various activities. It will generate a report regarding their presence and intimate to the host (teacher) which will be helpful to monitor the students and avoids them to do malpractices. This will make the students to be physically present, attentive in the class and beneficial by gaining knowledge.