Reviewerly/exHarmony huggingface dataset:
Edge Detection of Motion-Blurred Images Aided by Inertial Sensors

Edge detection serves as the foundation for advanced image processing tasks. The accuracy of edge detection is significantly reduced when applied to motion-blurred images. In this paper, we propose an effective deblurring method adapted to the edge detection task, utilizing inertial sensors to aid in the deblurring process. To account for measurement errors of the inertial sensors, we transform them into blur kernel errors and apply a total-least-squares (TLS) based iterative optimization scheme to handle the image deblurring problem involving blur kernel errors, whose relating priors are learned by neural networks. We apply the Canny edge detection algorithm to each intermediate output of the iterative process and use all the edge detection results to calculate the network's total loss function, enabling a closer coupling between the edge detection task and the deblurring iterative process. Based on the BSDS500 edge detection dataset and an independent inertial sensor dataset, we have constructed a synthetic dataset for training and evaluating the network. Results on the synthetic dataset indicate that, compared to existing representative deblurring methods, our proposed approach demonstrates higher accuracy and robustness in edge detection of motion-blurred images.
The evaluation of a semi-automatic authoring tool for knowledge extraction in the AC&amp;amp;NL Tutor

This paper describes and evaluates the performance of a semi-automatic authoring tool (SAAT) for knowledge extraction in the AC&amp;NL Tutor, highlighting its strengths and weaknesses. We assessed the accuracy of automatic annotation tasks (Part-of-Speech tagging, Name Entity Recognition, Dependency parsing, and Coreference Resolution) performed on a dataset of 160 sentences from unstructured Wikipedia text on a computer. We compared the automatic annotations to the gold standard, created after human post-editing and validation. Human-error analysis included 3769 words, 582 subsentences, 1129 questions, 917 propositions, 1020 concepts, and 667 relations. It resulted in the error type classification and the set of custom rules further used for automatic error identification and correction. The results showed that an average of 68.7% of the error corrections referred to CoreNLP performance and 31.3% to the SAAT extraction algorithms. Our main contributions include an integrated approach to the comprehensive pre-processing of the text, knowledge extraction and visualization; the consolidated evaluation of natural language processing tasks and knowledge extraction output (sentences, subsentences, questions, concept maps) and the newly developed reference dataset.&amp;nbsp;
Analyzing Emotional Impact of Speech Balloons on Comic Characters: A Study of Arousal Levels

The dialogue between the protagonists in comics, where much of the information is communicated, usually comprises speech balloons. Thus, identifying the speech balloons and connecting them to the speaker is a crucial step. The speech balloon's shape also reveals information about the speaker's emotions. The main aim of this study is to recognize the emotional arousal of the comic character with the shape of the speech balloon. To achieve this new model has been designed with a Convolutional Neural Network and SVM, which helps to detect the features and categorize them into an emotional state associated with the shape of the speech balloon. With the proposed approach the authors have achieved 92% accuracy which is comparable accuracy with the previous work. As a result of the feature selection, it was determined that the final characteristics were more useful for estimating the shape of the speech balloon. From the experimental results, it can be observed that a higher precision rate is achieved for the surprising class i.e. 92.60%, and a higher recall rate is achieved for the anger emotion class i.e. 93.10 and a higher Fl score is achieved for the polite emotion class i.e. 93%.
Snapshot 3D profilometry using a RGB polarization camera

We propose a snapshot uniaxial 3D profilometry system that illuminates a sample with a color structured polarization pattern with axial chromatic aberration. We have previously shown that real-time 3D shape measurement can be performed without the occlusion problem using a RGB polarization camera and a monochromatic polarized pattern. Here we show that it is possible to double the depth range of the system, with no tradeoff in depth resolution, measurement speed, or accuracy, by using color pattern projection and an RGB polarization camera. We present the measurement principles of the system and show the results of a real-time 3D profilometry experiment.
Decoding the Autoencoder

Cross-sectional training of auto-encoder neural networks suggests that yield-curve dynamics are well described by two to three factors and are dynamically arbitrage-free. In this article, we discuss the evidence and identify stochastic model parameters that support the auto-encoders.
Stochastic Differential Equations in Finance

Harnessing the power of Mathematica and the Wolfram language for stochastic or probabilistic models
Yoked Learning in Molecular Data Science

Active machine learning is an established and increasingly popular experimental design technique where the machine learning model can request additional data to improve the model’s predictive performance. It is generally assumed that this data is optimal for the machine learning model since it relies on the model’s predictions or model architecture and therefore cannot be transferred to other models. Inspired by research in pedagogy, we here introduce the concept of yoked machine learning where a second machine learning model learns from the data selected by another model. We found that in 48% of the benchmarked combinations, yoked learning performed similar or better than active learning. We analyze distinct cases in which yoked learning can improve active learning performance. In particular, we prototype Yoked Deep Learning (YoDeL) where a classic machine learning model provides data to a deep neural network, thereby mitigating challenges of active deep learning such as slow refitting time per learning iteration and poor performance on small datasets. In summary, we expect the new concept of yoked (deep) learning to provide a competitive option to boost the performance of active learning and benefit from distinct capabilities of multiple machine learning models during data acquisition, training, and deployment.
Medical equipment engineering standards for design and risk management of immersive virtual reality for in-clinic and home used

Virtual reality (VR) refers to a computer-generated three-dimensional environment that can create a virtual world, tricking the human brain through visual, auditory, and tactile stimuli. It provides users with a sense of mental and physical immersion by responding to their movements and interactions in the real world. In recent years, the use of immersive VR technology in medical rehabilitation has become increasingly prevalent. Consequently, it is crucial to examine the ergonomic design of VR systems tailored for different target populations, whether for in-clinic or home use. Additionally, conducting risk analyses and implementing effective management strategies are vital to ensure the safe and reliable use of VR as a medical device.
Two-step differential phase-shifting phase retrieval using generative adversarial network

Phase-shifting interferometry (PSI) is a high-accuracy optical measurement technique which requires accurate phase-shift estimation or sufficient phase-shifting interferograms to ensure phase-retrieval accuracy. Therefore, obtaining the highest phase retrieval accuracy from fewer interferograms remains a challenge. Based on this, we propose a two-step differential phase-shifting phase retrieval method using generative adversarial network, which can achieve higher phase-retrieval accuracy from two arbitrary phase-shifting interferograms, compared to representative two-step arbitrary phase-shifting phase retrieval methods. A phase retrieval generative adversarial network (PR-GAN) which can be trained with artificial training data for phase retrieval from experimental interferograms is employed. Experimental results demonstrate that accurate phase results can be obtained, even when the phase shift is approximately π or the fringe number is relatively small.
ESA: Excitation-Switchable Attention for convolutional neural networks

Although various attention mechanisms can boost the representational power of convolutional neural networks (CNNs) and improve their performance, selecting an appropriate attention module becomes challenging when backbones or datasets change. Besides, as different CNN layers can learn distinct semantic features, applying the same attention module across all layers may not yield optimal results for enhancing the performance of a deep neural network. To address the above issues, we propose a novel excitation-switchable attention (ESA) to automatically select and integrate different excitation modules to compute attention maps, enabling a DNN to apply different attention modules in different layers for better feature learning and performance improvement. Extensive experiments on three widely-used image classification benchmarks demonstrate the superiority of our ESA over several well-known and widely-adopted attention modules.
The Recognition of Remote Sensing Image based on Federated Generation Adversarial Network

Recently, the processing of Synthetic Aperture Radar(SAR) has been gradually transitioning from traditional electromagnetic simulation to intelligent detection and recognition. As an intelligent architecture, Federated Learning(FL) has attracted increasing attention for the processing of SAR images due to its capacity for data protection and cooperative gain. In this paper, a new FL scheme named Fed-CWGP is proposed, where Wasserstein Generation Adversarial Network with gradient penalty(WGAN-gp) is considered to expand samples locally for the issue of few-shot learning. Additionally, the Conditional Generation Adversarial Network(CGAN) is used to control the direction of sample generation, solving the problem of non-independent and non-identical distribution of data distribution between nodes. This proposed scheme allows local classifiers and cloud servers to exchange and collaboratively learn model parameters, which improves the performance of global and node classification models and reduces the transmission delay between models. To enhance the privacy of model uploading, differential privacy is introduced on the gradient of each node model. Simulation results state that the proposed Fed-CWGP outperforms the classical schemes.
Functional Analysis And Performance Evaluation Of Decoder Decision Tree Generation Algorithms

Instruction decoders are indispensable components of instruction set simulators and processor toolchains. The lengthy and elaborate manual implementation of decoders can be greatly alleviated by decoder generation tools. These need to handle the rising complexity of modern instruction sets, notably irregularities such as non-uniform opcodes and logic propositions on bit fields. In addition, decoder generators need to provide cost-optimized solutions, as the efficiency of the decoding module can have a substantial effect on the overall performance. This paper analyzes five published algorithms for decoder generators from two perspectives: First, in terms of functionality, we systematically assess how each tool handles different properties of modern instruction sets, highlighting properties that are challenging, unhandled by current algorithms or even result in functionally erroneous decoders. Second, we challenge seemingly intuitive definitions of decoder optimality using a sophisticated model of decision tree cost. We validate this analytical model against an experimental performance evaluation of generated decoders for SPARC, MIPS32 and ARMv7 platforms. For our analysis, we implemented all five algorithms after correcting the discovered conceptual errors and extending them to handle the above-mentioned ISAs. Our work reveals that state-of-the-art decoder generation tools are unable to fully and correctly handle complex ISAs and adopt an erroneous notion of optimality.
Knowledge Integration Inside Multitask Network for Analysis of Unseen ID Types

Identity Document recognition is a key step in Know Your Customer applications where identity documents (IDs) are verified. IDs belonging to the same type share the same field structure called template. Traditional ID pipelines leverage this template to guide the localisation of the fields and then the text recognition. However, they have to be tuned to the different templates to correctly perform on those. Thus, such pipelines can not be directly used on new types of IDs. In this work, we address the task of text localisation and recognition in the context of new document types, where only the template is available with no labeled samples from the new ID type. To that end, we propose the use of Context Blocks (CB) performing template self-attention to guide the features of the network by the template. We propose three ways to leverage CB in a multitask architecture. To evaluate our approach, we design a new public task for the MIDV2020 database from rectified in-the-wild photos. Our method achieves the best results for two datasets including an industrial one composed of real examples.
Research on Citation Tones and Disyllabic Tone Sandhi in Maoping Town Dialect Based on Deep Learning

The paper focus on the application of deep learning techniques in the domain of speech processing. Chinese is a tonal language. In a tonal language, pitch information is used to discriminate the meaning of different words. A word spoken with different tones corresponds to different meanings. Chinese
Randomness of Sequences of Numbers Using Permutation Polynomials over Prime Finite Fields

The randomness of a sequence plays an important role in cryptography like deciding the key, encryption and decryption algorithms. The sequences obtained by permutation polynomials over the finite fields have different properties which are used as random number generators. In the present paper, we have checked the randomness of sequence of numbers obtained from permutation polynomials over prime finite fields using different non-parametric statistical tests.
PENGEMBANGAN MEDIA PEMBELAJARAN FLIPBOOK PADA MATERI MENYIMAK TEKS CERITA RAKYAT PADA SISWA KELAS IV SEKOLAH DASAR

This study aims to produce a flipbook learning media product on listening to folklore texts in grade IV elementary school students that is valid and practical. This research uses research and development methods (Research and Development) using the ADDIE development model which consists of 5 (five) stages, namely the analysis stage, planning stage, development stage, implementation stage and evaluation stage. The subjects in this study were grade IV students of SD Negeri 3 Semende Darat Laut. The results of data analysis based on the validation of flipbook learning media conducted by expert experts consisting of media experts, linguists and material experts obtained an average validation score with a percentage of 86.7% showing that flipbook learning media in the material listening to folklore texts developed was included in the "very valid" category. Based on the results of the questionnaire, student responses that have been tested obtained an average value of practicality with a percentage of 85.7%, indicating that the flipbook learning media developed is included in the category of "very practical". The results of students' scores on the postest obtained an average score of 96% with the category "very decent". Based on the results of validation by expert experts, the results of practicality trials by students and posttest results, it can be concluded that the flipbook learning media on the material for listening to folk terita texts in grade IV elementary school students is suitable for use in learning on theme 8 of the area where I live subtheme 1 the environment where I live learning 1.
The use of the quick response code in the teaching of the human anatomy of the locomotor apparatus

The study of human anatomy is a relevant part of the curriculum of health course graduation students. Given the constant reduction of hours destinated to the study of Anatomy and the debates regarding new teaching methods, the use of the Quick Response Code (QR code) has shown to be promising. Therefore, the Anatomy Department at the State University of Londrina (UEL) has developed a cataloging process concerning anatomical structures with the application of QR code, and this paper has the purpose of reporting on such experience. In the process, the structures dissected in UEL’s Anatomy Laboratory were cataloged based on a spreadsheet which contained the correlation between these structures and numbers; the information regarding each structure was then transcribed into a QR code using a digital generator, with posterior printing, lamination and attachment to the body part. The labels were made by sewing the numbered tags onto the structures. Within the discussion regarding alternative teaching methods, examples can be given regarding lamination, three- dimension projection and prospection. The use of the QR code has proven to be a valid alternative in aggregating knowledge to academic curriculum. Through the activities performed in the process of cataloging, it was possible not only to dedicate more time to the theorical and practical learning of human anatomy, but also to increase the independence in studying and developing research. Furthermore, there is an opportunity to expand the analysis directed toward human anatomy teaching and toward new learning methods.
PENGEMBANGAN MEDIA PEMBELAJARAN VIDEO ANIMASI MENGGUNAKAN APLIKASI CAPCUT PADA MUATAN IPS DIKELAS V SDN 44/VIII TELUK SINGKAWANG KABUPATEN TEBO

The problem in this study is that there are students who are still difficult to read and watch in front of the class and some students still learn a lot to smooth their reading word for word and have difficulty understanding the meaning or meaning of the reading. Students also find it difficult to use good and correct Indonesian. This can be seen from the large number of students who still use their mother tongue. From these problems, the purpose of this research is to develop an E-LKPD on the subject of Indonesian in the independent curriculum in grade IV of elementary school. This type of research is the ADDIE Research and Development (R&amp;D) model. This model has 5 parts, namely: Analysis, Design, Development, Implementation, Evaluation. The type of data used in this research is quantitative data. The instruments used are validation sheet instruments, practicality and effectiveness instruments. Data collection techniques used questionnaires, and documentation. The analysis technique used is validity, practicality and effectiveness analysis. So based on the effectiveness test of 18 students, the number of students who passed was 16 students. So, the average effectiveness is 88.88%. Then there were 2 students who did not complete it so that it could be concluded that the E-LKPD learning media was included in the effective category for use by students of SD N 147/VIII Bogo Rejo in the learning process.&#x0D; &#x0D; Keywords: ADDIE, E-LKPD, Reading and Viewing.
Perspectives of Teachers on the Teaching of Learners with Dyslexia at Intermediate Phase in Mainstream Primary Schools

The inclusion of learners with dyslexia in mainstream primary schools requires the provision of a least-restrictive teaching and learning environment which celebrates the uniqueness of all individuals. In view of this, the purpose of this study was to explore the perspectives of teachers in teaching dyslexic learners at the intermediate phase in mainstream primary schools. A qualitative research approach was utilised in the study. This offered participants an opportunity to freely reveal their opinions on the phenomenon under investigation. The population for this study comprised teachers who taught classes with both dyslexic and non-dyslexic learners at the intermediate phase. Purposive sampling was used to select twenty-four teachers with a minimum of three years of teaching experience from the eight schools under study. Semi-structured interviews were used to gather data from the research participants. Data were analysed thematically. The study established that dyslexic learners face diverse reading, writing and arithmetic challenges and these affect their overall academic performance. The study recommends that teachers should consider the preferred learning styles of individuals with dyslexia in their planning and delivery of academic content.
Online Change of Grade Attack Using SQL Injection Case Study: FUTA Cyber Security Department

Advancement in technology has made many universities to adopt the system of storing students' results in online databases in both developed and developing countries for easy accessibility and to eliminate redundancy. However, these are subject to different types of attacks ranging from hardware to software attacks. In Federal University of Technology Akure (FUTA), Cyber Security department is not left out because attackers contribute a potential threat to the institution's information security. There are various methods attackers can use to carry out this malicious act, but this research considered SQL injection attacks. The researcher reviews related works, designs and implements student database applications for FUTA Cyber Security department. The research demonstrates how SQL injection can be used to change student grades and preventive measures to minimize SQL injection attacks, such as avoiding sharing database accounts using different sites or applications, regular updates of database software, regular update of security patches and passwords and regular scanning of web applications with a web vulnerability scanner.
Model Komunikasi Guru dan Tenaga Kependidikan Terhadap Siswa Tunarungu di SLBN Konda

ABSTRACTThis study aims to describe the communication process that occurs at Konda State Special School (SLBN), namely between teachers and education staff towards students with disabilities who are deaf. The deaf are circumstances in which one cannot hear, so one cannot speak. The method of research used in this study is qualitative descriptive, where data is gathered through observation, interview, and document study. Then, the data is analyzed by doing data reduction, data presentation, and a deduction. Informant in this study are teachers and education staff who have had direct contact with deaf student. Studies have shown that teachers and education staff implement S-R model, circular and secondary communication models, where deaf students are active when communicating with communicators both in the learning process and outside the classroom. A student with his ignorance and confusion about something will ask him directly. Then, when a communication process is challenging, a second alternative is that of using a handphone or paper as a text. It can also be seen that teachers and education staff use both verbal (oral) and nonverbal (SIBI) forms of communication simultaneously in conveying messages. In addition, the obstacles to communicating with deaf students are due to physical disabilities so as not to speak like other humans and poor language mastery, and in the learning process the deaf students forgets easly, gets bored, gets tired quickly and has difficulty concentrating. Keywords : Communication Model, Deaf Students, Education Staff, SLB, Teacher