Reviewerly/exHarmony huggingface dataset:
Frequency of use of online assessment by higher level teachers during confinement due to the COVID-19 pandemic

The purpose of this research was to review how often mathematics teachers use technological tools to assess their students and, the relationship established with the reliability, ease of use, and usefulness that teachers find in them; in order to offer them courses or workshops that allow them to use different tools that help them in the evaluation process, as well as, to show them different options of use for the different moments in their class, such as in diagnostic, formative or summative assessment. Under the paradigm of quantitative research, a questionnaire was applied to a random sample of 12 teachers from a public institution of higher education in Mexico City. All teachers were invited (N=14), but only 12 were able to participate; therefore, there was an 8% margin of error. The application of the questionnaire was during the period of confinement. The reliability of the questionnaire has been validated and Pearson correlation analyzes have been carried out, based on the variables: frequency and ease of use, usefulness, and reliability. The results showed that in the category of medium use, more than half of the teachers in the sample have shown a frequency of regular use of the online assessment tools. A comparison was made with the authors of the reviewed literature and coincidences were found with the answers given by the teachers. For example, there are authors who showed that teachers use ICT more frequently in their classes when they have all the necessary technological skills. Due to the above, various alternatives were proposed that encourage teachers to use it. The higher the frequency of use of online platforms, the greater the utility they find in them. With the results obtained, it is concluded that the frequency of use of online platforms positively influences the development of confidence that teachers must work with these tools.
An Exploration students' motivations in improving their English skill: Karawang Junior Islamic Boarding School Case

One of the important factors in supporting the achievement of the following characteristics of successful learning in English is motivation in learning English. The definition of motivation itself is the combined effect of effort and a desire to accomplish the goal. However, Indonesia that has a lot of diversity is not easy in realizing equal learning success in each region. The diversity of learning systems in Indonesia is also one of the interesting things to study. One of the researchers' concerns in this study is the learning system in Islamic boarding schools. In order that, tThis study aims at Exploring Secondary Students’ Motivation in Accomplish Successful English Language Learning. The student's behaviour, cognitive, and emotional will be carefully investigated. As this research concerns on students' motivations, this research tends to use a research case study at junior high school students. A qualitative method trough questionnaire is used in this research. The result shows that students' interest in learning English in schools based on Islamic boarding schools is not too high, because it is proven in the next question which shows results that most students find it difficult to learn English. It is also proven that they feel that the environment in which they live, in other words, the pesantren has not been able to increase their students' interest in learning English. Teachers must be able to become facilitators to increase students' interest in learning English
First Request First Service Entanglement Routing Scheme for Quantum Networks

Quantum networks enable many applications beyond the reach of classical networks by supporting the establishment of long-distance entanglement connections, and are already stepped into the entanglement distribution network stage. The entanglement routing with active wavelength multiplexing schemes is urgently required for satisfying the dynamic connection demands of paired users in large-scale quantum networks. In this article, the entanglement distribution network is modeled into a directed graph, where the internal connection loss among all ports within a node is considered for each supported wavelength channel, which is quite different to classical network graphs. Afterwards, we propose a novel first request first service (FRFS) entanglement routing scheme, which performs the modified Dijkstra algorithm to find out the lowest loss path from the entangled photon source to each paired user in order. Evaluation results show that the proposed FRFS entanglement routing scheme can be applied to large-scale and dynamic topology quantum networks.
Rhetorical structure and persuasive features of advertising: An intercultural analysis of English and Arabic online advertisements

Nowadays, the bulk of e-commerce is carried out in English. At the same time, however, the emergence and growth of Arab e-commerce has attracted attention from e-commerce giants, such as AliBaba and Amazon. Acquiring a deeper understanding of culture-specific advertising practices is thus quickly becoming necessary for digital advertisers and marketers seeking to tap into both English- and Arabic-speaking consumer markets. Despite this, little has been written on online advertising discourse within the particular contexts. To address this, the present study reports on a pragmatic two-level rhetorical analysis of the macro-structure of the sub-genre of online advertisements in English and Arabic and focuses on the identification of the persuasive linguistic features employed to signal constituent moves. Drawing upon Swales’ move-step model of rhetorical analysis, the exploration of a comparable corpus of 100 online advertisements of skin care products per language introduced new layers in the rhetorical organisation of [online] advertisements. The results revealed that English and Arabic advertisements consisted of both common and culture-specific rhetorical moves. The persuasive strategies employed common lexico-grammatical features at syntactic, semantic, and discoursal level, albeit to a different extent. Overall, the results of this study illustrate that online advertisements of skin care products could be considered as a particular sub-genre with context-specific macro- and micro-structures. Through the investigation of the specific datasets, this study expands upon the existing analytical frameworks used for the study of advertising discourse and adds to the intercultural exploration of the rhetorical structures and persuasive strategies used in online advertisements.
Information-motivational environment: quality improvement and self-organization development in the conditions of education informatization

Problem and goal. The information technologies influence features and the educational environment on the school education quality are considered. The results of studies of the Unified State Exam, State Russian Exam, Russian Verification Work, international studies (PISA) in national regions (on the example of the Republic of Buryatia) in the conditions of education informatization were studied. The information infrastructure was explored, which includes information resources, information systems of various classes, as a motivation system and students’ self-organization, especially during distance learning. Methodology. The data of these studies were grouped and combined, and then analyzed for problems of the general education quality in the conditions of the information environment using. The data sample was made for 2011-2020, according to such criteria as popular subjects, the general trend in the general education of students, the rating of countries in reading, mathematical and natural science literacy. Results. The information-motivational environment is singled out and its levels (macro, meso, micro) are determined. The technical interaction principle of the subject with the hierarchy of levels in the information-motivational environment is revealed. Conclusion. The information and motivation interaction contributes to the improvement of self-organization and the quality of education. The development of the information and motivational environment and its management can become a necessary technology for the development of a digital-centric person, a criterion for the quality of educational results, a set of measures aimed at creating conditions for obtaining high-quality general education in state and municipal educational organizations in the national regions of the Russian Federation.
Robust Actor Recognition in Entertainment Multimedia at Scale

Actor identification and localization in movies and TV series seasons can enable deeper engagement with the content. Manual actor identification and tagging at every time-instance in a video is error prone as it is a highly repetitive, decision intensive and time-consuming task. The goal of this paper is to accurately label as many faces as possible in the video with actor names. We solve this problem using a multi-step clustering process followed by a selection of face-instances that are: (a) representative of their member clusters and (b) aesthetically pleasing for visual identification. These face-instances can be matched with the actor names by automated or manual techniques to complete actor tagging. This solution is further optimized for seasons with repeating cast members which constitutes majority of the entertainment multimedia content. In such titles, the face labels from the previous episodes are efficiently used to pre-label faces in the subsequent episode. We guarantee the same level of accuracy even after scaling the solution to TV series seasons. This novel solution works in a completely realistic setup where the input to the solution is just the raw video. This is the first known work which has proved its robustness on more than 5000 TV episodes and movies across different genres, languages and runtimes with actors of diverse ethnicity, race, gender identity, age, etc. The proposed solution establishes a new state-of-the-art for cluster purity in both movies and TV series seasons by achieving near-perfect cluster homogeneity.
Unsupervised Multi-object Tracking via Dynamical VAE and Variational Inference

In this paper, we address the problem of motion modeling in the Multi-Object Tracking (MOT) task. We present an unsupervised probabilistic motion model and associated estimation algorithm based on a dynamical variational autoencoder (DVAE), called DVAE-UMOT. The DVAE is a latent-variable deep generative model used to model long-term and non-linear temporal dependencies in sequential data. Thanks to the powerful sequential modeling capacity of the DVAE, DVAE-UMOT is able to keep long-term tracks purely based on motion cues under the tracking-by-detection paradigm and generate reasonable bounding boxes when there is detection absence. Experimental results show that our model is particularly good at handling object disappearance and identity switch problems caused by long-term occlusion and unstable detections. Finally, DVAE-UMOT is shown experimentally to compete well with and even surpass the performance of two state-of-the-art probabilistic MOT motion models. Code and data are publicly available.
A secure framework of blockchain technology using CNN long short-term memory hybrid deep learning model

Generation Z is embracing blockchain technology, which is appropriate for the digital age. Internet of things (IoT) can benefit from blockchain technology IoT. The proliferation of IoT technology has led to breakthroughs in distributed system architecture. For the blockchain network to store, communicate, and exchange data, it needs a randomized data management system. This shows how difficult it may be to provide consistent and safe data replication in a distributed system, an issue blockchain technology may overcome. We need a solid prediction model that improves results. This article describes an innovative way to overcome the limitations of third-party transactions using Bitcoin. In this article, convolutional neural networks-long short term memory (CNN-LSTM) deep learning forecasting models are introduced. Convolutional layers help extract relevant data from instances. It has an long short-term memory (LSTM) layer, which lets it find long-and short term dependencies. The experiment's goal was to test the multivariate statistical model we suggested and compare its performance to well-established models. The addition of convolutional layers to a forecasting model may improve its accuracy, according to an experiment. The research shows that this strategy has a better chance of success and is more trustworthy than others.
Application Research of Partial Differential Equation Optimization Image Analysis in National Traditional Physical Education Teaching

In this paper, the initial parameters of C-V level set image recognition are optimized by using the global optimization characteristics of cultural algorithm, and a cultural algorithm C-V level set image recognition model is proposed, which is abbreviated as the CC-V model. The initial population space of the cultural algorithm is used to set the initial recognition parameters in a large range, and the population evolution is continuously optimized and guided by the situation knowledge and normative knowledge of the reliability space, so as to realize the global optimization of the recognition parameters and to timely terminate by judging the change of the image entropy fitness value. Through the analysis and comparison of the experimental results, the CC-V model has a better recognition effect than the C–V model. The partial differential equation image recognition model is applied to the video image sequence for moving target recognition. The background model is constructed by the block statistical histogram. The background difference method is used to locate the video moving target, and the minimum circumscribed rectangle of the multitarget positioning is used as the initial outline of the model recognition. The research results show that the nonintellectual factors of each group of ethnic traditional sports have an obvious effect on the application value of people, and the application effect of nonintellectual factors produced by different groups will also vary. All ethnic traditional sports groups show higher application value to nonintellectual character factors. The comprehensive application value of nonintelligence factors of the combat confrontation item group, accuracy item group, and endurance item group is relatively higher than the comprehensive application value of nonintelligence factors of other item groups.
Seismic Signal Adaptive Endpoint Detection Based on Human-Computer Interaction

With the development of artificial intelligence technology, robot technology has become more mature, and human-computer interaction technology has become more widely used.China is a country with frequent earthquakes.The application of robot technology to seismic signal detection is of great significance to solve various problems in earthquake disasters.The research purpose of this paper is to study adaptive endpoint detection of seismic signals based on human-computer interaction technology.Based on the analysis of adaptive noise reduction of signal autocorrelation function, this paper references the concept of autocorrelation similarity distance, and proposes human-machine interaction technology Seismic signal adaptive endpoint detection, discusses the description of noise and noisy signals based on autocorrelation similarity distance, and summarizes the adaptive endpoint detection method of seismic signals based on human-computer interaction technology, and gives the specific implementation of the algorithm Compared with the experimental detection results obtained by this method and the time-domain waveform envelope and the method of manually detecting the time-table seismic signal endpoint detection, the research results in this paper show that the accuracy of this algorithm is as high as 96.11%,Under the condition of noise ratio, the end position of the seismic signal can still be detected more accurately by using this algorithm.
The Cross-Postgraduate Training Mode Driven by Intelligent Signal Processing Technology: Taking the Major of Finance as an Example

At present, various economic and social problems have become more complex, requiring a multidisciplinary perspective to seek solutions, and thus more complex and innovative talents are needed. Deepening the reform of postgraduate interdisciplinary education, improving the quality of postgraduate interdisciplinary education, and cultivating high-level technical talents with a multidisciplinary background, innovation ability and comprehensive quality have also become the core issues to be solved urgently in postgraduate education. This paper takes the interdisciplinary training mode of postgraduates in finance as the research object and uses interdisciplinary education theory, systems science theory, and higher education theory to explore the components and main characteristics of the postgraduate interdisciplinary training mode. On this basis, this paper investigates the current situation of interdisciplinary training of postgraduates in finance in Chinese universities and then analyzes and discusses the problems existing in the current mode of interdisciplinary cultivation of postgraduates in finance in my country. Finally, based on the above research results, relevant suggestions for improving the interdisciplinary training of postgraduates in finance in my country are put forward. The research results show that postgraduate interdisciplinary training is a new form of talent training that readjusts the source and composition of each training link after introducing the concept of interdisciplinary education. The components of the postgraduate interdisciplinary training model include interdisciplinary support elements and postgraduate training elements. The former provides interdisciplinary external support for postgraduate training, and the latter constitutes the main content of postgraduate interdisciplinary training. The research results can be used to guide the interdisciplinary educational practice of other types of postgraduates and provide certain theoretical and empirical references for cultivating more high-level compound talents urgently needed by society.
An Application of the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>t</mi> </math>-Extension of the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>p</mi> </math>-Fibonacci Pascal Matrix in Coding Theory

We consider the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>t</mi> </math> -extension of the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>p</mi> </math> -Fibonacci Pascal matrix. First, we study the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>k</mi> </math> -th power of the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>t</mi> </math> -extension of the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>p</mi> </math> -Fibonacci lower and upper triangular Pascal matrix. Then, we obtain a new code which is named the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi>t</mi> </math> -extension of the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mi>p</mi> </math> -Fibonacci Pascal matrix coding/decoding by using them.
Sustainability of Malay Traditional Silverware in the East Coast region of Malaysia

The traditional Malay silverware industries in Malaysia that have existed for more than 200 years began to deteriorate from time to time. Silverware is a craft field with its uniqueness and almost extinction hereditary since 1948 on the East Coast of Malaysia. In this context, innovation and sustainability play a vital role in the design and product development. This study is to investigate and identify the current development of Malay traditional silverware and its relation to Sustainable Design Goals parallel with the Shared Prosperity Vision 2030. The silverware industry growth is significant among the local community to sustain for gloomy future.
Image Classification Using Quantum Machine Learning

When quantum algorithms are used in machine learning systems, it is referred to as “quantum machine learning.” An approach known as “quantum-enhanced machine learning” utilizes a quantum computer to evaluate classical data to boost machine learning. Data may be processed and stored more quickly and efficiently with the help of quantum machine learning. Using neural networks as analogies for physical systems is an important part of quantum machine learning. This paper summarizes the CIFAR-10 dataset. For the dataset, “five training batches and one testing batch” are used to divide the ten thousand photographs. One thousand images from each class are randomly selected for inclusion in the test batch. Even though each batch comprises all of the remaining photographs, some batches have a greater number of images from a particular category. It is estimated that each training batch contains around 5000 photographs. This section includes an evaluation of the classifier’s overall performance. Quantum neural networks describe “a parameterized quantum computational model best” implemented “on a quantum computer” (QNN). Third-party libraries such as PyTorch, Qiskit, and matplotlib are frequently loaded into the program. PyTorch is a popular option for GPU and CPU-based Deep Learning applications because it is built on Torch rather than merely Python. All of your quantum computing needs may be met by Qiskit, a Python library. Importing it will be necessary after the system is installed. Creating static, animated, and interactive graphics is easy with Matplotlib, a Python toolkit. To begin, we need to identify the quantum layers that will make up the circuit’s structure.
MEMBERSHIP AUTOMATION SYSTEM AND LIBRARY USING RFID AT FACULTY OF ENGINEERING UNTAG SEMARANG

This study aims to build a library system application that can be used in the library of the Faculty of Engineering UNTAG Semarang based on an RFID card. From the background of the problem, namely, the process of borrowing and returning books is quite long, because to search for borrowed books the librarian must go to the bookshelf and look for the title of the book to be borrowed. The difficulty in making reports, because it is done after the library activities are finished, does not automatically coincide with the process of borrowing and returning services. Starting from the problems above, and paying attention to the number of daily visitors in the library that must be served, the authors propose to create a library service application using an RFID tag that can help the process of searching for member data and library books quickly.
A Unified Encoder-Decoder Framework with Entity Memory

Entities, as important carriers of real-world knowledge, play a key role in many NLP tasks. We focus on incorporating entity knowledge into an encoder-decoder framework for informative text generation. Existing approaches tried to index, retrieve, and read external documents as evidence, but they suffered from a large computational overhead. In this work, we propose an encoder-decoder framework with an entity memory, namely EDMem. The entity knowledge is stored in the memory as latent representations, and the memory is pre-trained on Wikipedia along with encoder-decoder parameters. To precisely generate entity names, we design three decoding methods to constrain entity generation by linking entities in the memory. EDMem is a unified framework that can be used on various entity-intensive question answering and generation tasks. Extensive experimental results show that EDMem outperforms both memory-based auto-encoder models and non-memory encoder-decoder models.