Reviewerly/exHarmony huggingface dataset:
Analysis on Advanced Science Technology Contents in the 2015 Revised Science National Curriculum

Objectives In modern society, which is continuously changing due to advances in science and technology, the 4th industrial revolution has accelerated the changes in human life. In this study, in order to draw implications for the reflection of advanced science technology in the future revised curriculum, we investigate how the topic of advanced science technology appears in the 2015 revised curriculum.&#x0D; Methods In this study the advanced science technology were defined base on the findings of the recent science and technology prediction survey and advanced science technology were divided into six groups and fourteen major issues depending on their importance. Also, advanced science and technology are classified into five descriptive categories: basic, principle, usage, influence, and profession. The achievement goals and learning contents of the 2015 revised curriculum were analyzed using this classification framework for middle school science, high school science.&#x0D; Results In the 2015 revised science curriculum the ecology/eco friendly group showed the highest percentage among the advanced science technology issue. The topic groups with the lowest frequency were the transportation/robot group and the information/communication group. When examining topic groupings, the high school elective courses were cited the most, particularly life science subjects with 8 cases and science history subjects with 3 cases. As a result of the frequency according to the advanced science technology content description category, the proportion of use, principle, and social impact was high, whereas the proportion of basic and profession description categories was small, confirming that the description method of advanced science and technology was biased.&#x0D; Conclusions First it is vital to diversify the advanced science technology categories given in curricula and textbooks. It is necessary to provide the non-biased contents of diverse sectors of the advanced science technology. Second, it is necessary to expose openly major advanced science technology topics. Third, it is required to deliver the class in a manner that may be adjusted in a variety of ways so that students can truly investigate or experiment with the advanced science technology.
Image Captioning for Thai Cultures

Before each trip, tourists generally gather information or photos from different places. This work aims at providing additional information about touristic sites in Thailand via automatic image captioning. Image captioning is the process of generating a textual description for given images. In recent years, the development of Artificial Intelligence in combining image processing and natural language processing has gained attention worldwide. Image captioning can be regarded as a sequence-to-sequence modeling problem, as it converts images, which are considered a sequence of pixels, to a sequence of words. This work proposed a finetuned model that combined CNNs and LSTM to generate the image description. In the experiment part, we use BLEU to evaluate the model.
Source Code Plagiarism Detection Based on Abstract Syntax Tree Fingerprintings

Syntax Tree (AST) is an abstract logical structure of source code represented as a tree. This research utilizes information of fingerprinting with AST to locate the similarities between source codes. The proposed method can detect plagiarism in source codes using the number of duplicated logical structures. The structural information of program is stored in the fingerprints format. Then, the fingerprints of source codes are compared to identify number of similar nodes. The final output is calculated from number of similar nodes known as similarities scores. The result shows that the proposed method accurately captures the common modification techniques from basic to advance.
RAS-E2E: The SincNet end-to-end with RawNet loss for text-independent speaker verification

Despite reaching satisfactory verification performance, variousness utterance duration and phonemes and the robustness of the system remain a challenge in speaker ver-ification tasks. To deal with this challenge, we propose RAS-E2E, a novel fully cross-lingual speaker verification system that discovers meaningful information from input raw waveforms of various duration utterances, including short utterance duration, to determine whether an utterance matches the target speaker by merging two powerful paradigms: SincNet and Rawnet training scheme with Bi-RNN. The conducted experiments on Voxceleb, Gowajee and internal call-center datasets demonstrate that RAS-E2E achieves better performance compared to the recent verification systems on waveforms.
Correlational analysis of the learning process of higher-level students in times of covid-19

As of the start of COVID-19 pandemic, the need to change the modality in which classes were being taught arose in the administrative computer systems undergraduate program of the faculty of accounting and administration of the Universidad Veracruzana, which brought within the problem to ensure that the learning process was equally effective under dual modality. The main goal of the presented research is to carry out a correlational analysis to determine if certain variables had a positive impact in the students learning process under the face-to-face and virtual modalities. An exploratory and descriptive research was carried under a quantitative data collection to test a hypothesis based on a statistical and inferential analysis. The research shows, it can be stated that the learning delivery model for the selected sample was better under the face-to-face modality. And that the use of technological platforms for online classes was a factor that positively contributed to online learning.
Enhancing Thai Keyphrase Extraction Using Syntactic Relations: An Adoption of Universal Dependencies Framework

Topical phrases representing the document and used in various fields are called keyphrases. Various methods are proposed to extract keyphrases automatically. However, most methods rely on candidate selection using linguistic heuristics in the English language. In this work for Thai keyphrases extraction, the candidate selection based on Universal Dependencies (UD) is proposed rather than using only POS sequence to make this step language independent. To enhance candidate selection, tree-based keyphrases extraction is also adapted to keep only logical candidates based on the cohesiveness index (CI). Besides that, the score filtering is proposed to combine linguistic heuristics, like stop words and the phrase's position. In the experiments, our method gained the double averaged F1 score of the state-of-the-art method, even though the UD was trained by only 1,781 EDUs and achieved 84% labeled attachment score. In addition, ablation studies on each factor in score filtering revealed which factor is important for keyphrase extraction.
A study of violent criminal behaviour analysis based on deep learning

We propose a deep learning model-based algorithm for the analysis of abnormal behaviour of violent crime video targets, for violent crime videos, to determine whether there are abnormalities in the targets. 2D-net, 3D-net and MIL are used to construct the model. A sparse sampling strategy is used to extract features and a continuous MIL is used to achieve the final classification. The model achieves satisfactory results on the UCF_Crimes dataset and performs well in comparison with similar algorithms in the field.
The condition of reading in a digital media culture

Digital media now play a role in defining our assumptions about all forms of reading (and writing), even when we are consuming traditional printed materials. The digital reading condition extends or intensifies qualities that we can identify as beginning in the age of print or even earlier eras. Processes of extension, intensification, and innovation in digital reading can be classified under four rubrics: hypertextual, multimodal, collective, and locative. Each of these can be identified as a creative response to technological innovations during the period from the 1980s to the present. The responses were both practical and theoretical. Writers and readers began producing and consuming texts with these characteristics, and researchers and critics began to promote or debate the value of these digital practices. In each case, the material conditions and sense ratios of reading changed as a result.
Research on the remote interactive technology of network education resources based on discrete cosine transform

Online educational resources are a new type of learning mode with information and knowledge as the core under modern Internet technology. It includes various elements such as computers and multimedia equipment, and uses modern information technology such as computers and multimedia equipment to collect and process information, and realize the simulation of various knowledge and behavioral skills needed in the learning process of the taught person. In this environment, people can access and interact with teaching resources remotely via the Internet. Due to some problems and shortcomings in the process of practical application, its development is limited to a certain extent. Therefore, this paper focuses on building a semantic model of online educational resources based on the discrete cosine transform, using ontological means, and using it to improve the quality of communication services of the whole learning community and provide users with better and more intelligent educational resources.
Analysis of English deep learning paths in the era of artificial intelligence

The position of artificial intelligence in human society has gained widespread attention, and the impact it has brought cannot be underestimated. The so-called deep learning is through the existing knowledge, experience or information to organize, and will be used in real life, it is the use of computer and artificial intelligence principles to achieve interactive learning process between the human brain and the machine and a new system model, now the depth of knowledge mining and acquisition has become the current trend of development. This paper will put forward relevant suggestions and give specific path analysis for future research in the field of artificial intelligence in China at the present stage for the deep learning mode of English in the digital background.
Application of artificial intelligence technology in computer aided translation software

In order to further improve the efficiency and accuracy of translation, based on the existing computer-aided translation software modules, artificial intelligence technology is introduced to gradually design its architecture, functional modules and databases and other components, and build a test environment, and test the function of the translation system software by taking Chinese to English as an example. The test results show that the translation system software can complete the translation task, and all the indicators meet the standards, which proves that the computer-aided translation software based on artificial intelligence technology designed this time has achieved initial success.
Kunraja Media Development (Aksara Jawa Uno Card) On Javanese Script writing Materials In Elementary Schools

This study aims to develop KUNRAJA media on Javanese script writing material in elementary schools in grade III UPT SD Negeri 43 Gresik for the 2020/2021 school year. The process of developing learning media uses the modified and simplified ADDIE model, namely, analysis (Analysis), design (Design), development (Development), implementation (Implementation) and evaluation (Evaluation). The results of the development of learning media in the form of KUNRAJA media contain material for writing Javanese script. Sources of data in this development research are material experts and media experts totaling 4 validators and class III students with a total of 28. The data collection used is the validation of learning media and student learning outcomes tests. With data analysis, namely, analysis of the validity of learning media, analysis of the practicality of learning media and the effectiveness of learning media.&#x0D; Based on the results of the descriptive analysis of the validation data, it was obtained that the development of the KUNRAJA media on the Javanese script writing material in elementary schools met the good criteria, as follows: a) The results of the validation of learning media conducted by 4 validators obtained a percentage of 100% by material experts 1 and a percentage of 100% by material expert 2 with a total average rating of 100%, and a percentage of 100% by media expert 1 and a percentage of 92.5% by media expert 2 with a total average rating of 96.25% which is categorized as valid and can be used in the learning process, b) Categorized as practical from the results of the analysis in the practicality of learning media by the validators indicating that the media can be used with little or no revision. c) while in the learning outcomes test, students who scored 70 were 28 students with a percentage of 93.1% which was categorized as effective for use in the learning process. &#x0D; Keywords: ADDIE Model, KUNRAJA, Javanese Script.
Electrocardiogram-based dual watermarking scheme for healthcare applications

The strong need for a copy-protection scheme for healthcare data has been well recognized. Research studies show that invisibility, embedding capacity, and robustness are some of the most common watermarking features, which makes it difficult to balance the contradictions between them. Inspired by dual watermarking where multimarks are concealed inside the carrier media to offer a robust and secure model. In this chapter, we propose a watermarking scheme which embeds dual marks into a host electrocardiogram (ECG) signal using a transform-domain approach. First, we consider the redundant discrete wavelet transform (RDWT) to decompose the 2D signal into non-overlapping sub-bands, and then the selected sub-bands are decomposed by fast Walsh Hadamard transform (FWHT) followed by QR decomposition to compute the coefficient for data embedding purposes. Second, we apply the sequence of RDWT-FHWT-QR to decompose both marks in a similar manner. Third, we modify the QR coefficients of the host signal with the coefficients of both marks. The QR code of the more robust mark (patient report) is generated to ensure its security against attack. Extensive analysis shows good performance of the proposed algorithm. Our experiments on ECG signals demonstrate that the robustness of our algorithm is significantly improved up to 63.94% when compared with traditional schemes.
Reservoir computing vs. neural networks in financial forecasting

Stock market prediction techniques are a major research area, thus, extracting time-dependent patterns for the existing predictive models is of major significance. In this work, we compare forecasting performance of the nonlinear model of recurrent neural networks (RNN) in two implementations, LSTM and CNN-LSTM, to the relatively novel approach of reservoir computing (RC), and in specific, the particular class of the echo state networks (ESN). This comparison focuses on exploiting data latent dynamics, in performing efficient training and high quality predictions of the evolution of real-world financial data. Applying a multivariate scheme to a stock market index without any stationarity techniques, a definite precedence of the ESN-RC over both types of RNN's in computational efficiency as well as prediction quality, emerges. Finally, the implemented approach is friendly to the trader, since specific values of a stock market timeseries provide with a frame allowing for in time forecasting, under real-world circumstances.
A Study of the Impact of Callbacks in Staged Static+Dynamic Partial Analysis

Partial analysis is a program analysis technique used in compilation systems when the whole program is not available. Many recent promising approaches perform partial analysis statically that involves identifying the interprocedural dependencies across program elements. These generated dependencies further get evaluated during runtime while generating the final analysis result. However, as the application and library methods are analyzed independently during static analysis, these approaches do not account for the effect of dynamic features such as callbacks. Consequently, in such scenarios, the runtime (say the Java Virtual Machine) needs to discard the static-analysis results and use the existing imprecise builtin analyses. The primary goal of this work is to find out the percentage of objects and methods that may get affected by callbacks, and to propose possible techniques to enhance the generation of dependencies in their presence.
DETEKSI SEL DARAH SECARA OTOMATIS DENGAN EXTREME LEARNING MACHINE

Blood tests in humans play an important role not only to determine a person's blood type but also to detect diseases. Blood tests are carried out by composing blood preparations and checking them in the laboratory. With the development of technology in computer vision, the examination of blood preparations can be done through digital images of the microscope preparat. This study was conducted to detect blood cells automatically from digital images using the Extreme Learning Machine (ELM) method. The most obvious difference between ELM and other Neural Networks is in the number of its hidden layer. The ELM architecture only consists of 1 layer, while the hidden layer architecture in other Neural Networks may contain more than 1 layer. In the learning process, ELM utilizes the Moore Penrose Pseudoinverse matrix inverse theory which has the best generalization results with faster computation time. Experiments were carried out by digitizing the image of the blood cells. These images consisted of two types, the stained images and the unstained images. In the stained microscopic images, each component of the blood cells can be clearly seen and easily identified. Meanwhile, in the image of the unstained preparations, the components of the blood cells cannot be distinguished or seen directly by the human eye. The experimental results show that the developed method can achieve 95.83% accuracy for the stained image and 68.6% accuracy for the unstained image. Furthermore, the average testing accuracy of varied white blood cell types has an accuracy of 81.36%.
Factors affecting the personal knowledge management amongst sales officers in Indonesian leading automotive companies

A capable and knowledgeable team is critical to an organisation's success, particularly with the transition in economic activity toward a knowledge-based economy. With the growing demand for knowledge workers, it is vital to implement personal knowledge management for each of the individuals. However, effective personal knowledge management is complicated to implement. Therefore, it is critical to understand the factors that could affect successful personal knowledge management practices, especially after the massive deployment of technology during the Industrial 4.0. This study aims to ascertain which factors affect the implementation of effective personal knowledge management, and their relative importance. This study found that leadership significantly affects trust, while trust and extrinsic rewards impact knowledge sharing activities. Technology application was found to affecting learning behaviour, while knowledge sharing and learning behaviour significantly impact one's ability to handle personal knowledge effectively. This study revealed that intrinsic rewards are more amenable to knowledge sharing.
Hybrid Attention Spatial-Temporal Network for Video Saliency Prediction

Video saliency prediction (VSP) is to understand and model a human’s visual attention in a dynamic scene. Current methods tend to generate visual representations on a fixed local spacetime, neglecting the inherent long-range spatiotemporal relations. To remedy this deficiency, in this paper, we propose a new Hybrid Attention Spatial-Temporal Network (HAST-Net) for VSP. First, we design a novel Local Perception Module (LPM) to combine a dynamic position encoding block and a channel attention block together for a more effective extraction of position relations and channel information. Second, we use a Spatial-Temporal Multi-head Self-attention (STMSA) module, followed by a Convolutional Residual Feed Forward Network (CRFFN) module, to capture the long-range spatiotemporal dependencies. Third, we add above modules to a 3D convolutional backbone, place them in sequence to integrate channel attention with self-attention. Experiment results show that our model outperforms the state-of-the-art models, increasing the NSS and CC on DHF1K dataset by 2.8% and 2.7% respectively.
Policy Analysis of the Allocation of Educational Resources for Student Resource Optimization in Tianjin

This chapter offers a policy analysis on the allocation of educational resources in Tianjin student resource optimization analysis in Tianjin. Education is the fundamental way for mankind to pass on civilization and knowledge, train the younger generation, and create a better life. As an economic center and an important participant in the coordinated development strategy of Beijing, Tianjin, and Hebei, Tianjin's educational resources are of self-evident importance, and student resources are an important link in education. This paper focuses on the analysis of the characteristics and development trend of student resources in general education and vocational education in Tianjin and explores the role of government and society in optimizing the allocation of student resources.
CHOOSECOLS and CHOOSEROWS: Less Is More

Much of the time a dataset warehouses more information than you currently need. That’s why data are so often filtered, after all; and the decision to filter can cut back the data from one of two vantage points: you may need to filter the fields you need and shun the others, but at other times you may leave all the fields intact and apply the filter to selected records. Whichever route you take, the new CHOOSECOLS and CHOOSEROWS dynamic array functions will help point you in the right direction.
TAKE and DROP: Selecting and Rejecting the Data

As with CHOOSECOLS and CHOOSEROWS, the new dynamic array TAKE and DROP functions trim datasets by temporarily removing unwanted records and keeping others. But TAKE and DROP do the trimming in ways that are less flexible than the options offered by the two CHOOSE functions – but at the same time, we’ll see that they can carry out certain tasks more efficiently.
The Effect of Stress on Taekwondo Athletes due to Infectious Diseases on Exercise Continuation and Dropouts

This study examined the effects of stress in taekwondo athletes due to infectious diseases on exercise sustaining and dropout, and a survey of 201 taekwondo athletes drew the following conclusions:BRIt was used as an SPSS 27.0 program for data processing on the survey results of this study, and was analyzed with exploratory factor analysis and Cronbach