Reviewerly/exHarmony huggingface dataset:
Sistem Informasi Pendaftaran Praktek Kerja Nyata (PKN) dan Skripsi Fakultas Teknik Universitas Pancamarga

A website is a collection of pages containing certain information and can be accessed easily by anyone, anytime, and anywhere through the internet. In an educational institution, of course, it is very necessary for information systems in regulating academic activities that are fast, effective, efficient, accurate to improve the quality standards of education in these educational institutions.&#x0D; Faculty of Engineering, Panca Marga University is one of the private universities in Probolinggo Regency with 3 (three) study programs, namely electrical engineering, mechanical engineering and industrial engineering. Final projects such as Real Work Practice (PKN) and thesis are the final tasks that must be completed by a student who will graduate from the level of undergraduate education or diploma in college.&#x0D; Currently, the final project administration process at the Faculty of Engineering, Panca Marga University is still computerized simply where the final task implementation data is still stored in folders made by academic staff themselves, there is no final project reporting system. This is felt inappropriate so it takes a long time to process the final project data with a large number of students. Based on this, researchers created a pkn final project information system and web-based thesis. In this study, the authors used the Waterfall Method as an application development method. The Waterfall method is a sequential development model. In the process of creating the waterfall method, it starts with analysis, design, coding, testing and maintenance
Breaking Robust Data Hiding in Online Social Networks

Some robust data hiding approaches have been proposed to transmit secret data through online social networks (OSNs). Traditional steganalysis tools are inefficient in detecting these tailored steganographic methods. Although some algorithms have been developed to remove the hidden data of images, they are criticized for their low secret removal rate and poor image quality. Moreover, most of them are nongeneric methods, and multiple models must be trained to fit different algorithms. In this letter, we propose a general end-to-end data hiding break approach for OSNs, called the secret data remover (SDR). It is universal for algorithms of both robust steganography and robust watermarking, with which stego images are directly input and clean ones with the same appearances will then be generated. Moreover, we develop two novel techniques, namely, image fusion and latent renewal, to enhance the image quality and improve the overall performance. Experiments show that our proposed method achieves superior performance compared to state-of-the-art works. Hidden secret data are cleared while image quality is maintained or even slightly improved. At the same time, our work can be easily deployed in OSNs.
KSDSLD — A tool for keystroke dynamics synthesis &amp; liveness detection

Keystroke dynamics is a behavioral biometrics modality that employs the characteristic typing patterns of users to verify their identity, generally as a part of a multifactor authentication scheme. Naïve implementations of keystroke dynamics verification are susceptible to presentation attacks with synthesized samples, leveraging partial or complete knowledge of the legitimate users’ typing patterns to forge accurate imitations of their behavior. The companion article has presented several novel methods for the synthesis of forged keystroke dynamics samples given one or more authentic samples of free text by the legitimate user, and a liveness detection method that employs the latter as adversaries to train a classification model, which can distinguish human-written samples from synthetic forgeries. Here, we provide the implementation and its source code.
Active Learning for Neural Machine Translation

The machine translation mechanism translates texts automatically between different natural languages, and Neural Machine Translation (NMT) has gained attention for its rational context analysis and fluent translation accuracy. However, processing low-resource languages that lack relevant training attributes like supervised data is a current challenge for Natural Language Processing (NLP). We incorporated a technique known Active Learning with the NMT toolkit Joey NMT to reach sufficient accuracy and robust predictions of low-resource language transla- tion. With active learning, a semi-supervised machine learning strategy, the training algorithm determines which unlabeled data would be the most beneficial for obtaining labels using selected query techniques. We implemented two model-driven acquisition functions for selecting the samples to be validated. This work uses transformer-based NMT systems; baseline model (BM), fully trained model (FTM) , active learning least confidence based model (ALLCM), and active learning margin sampling based model (ALMSM) when translating English to Hindi. The Bilingual Evaluation Understudy (BLEU) metric has been used to evaluate system results. The BLEU scores of BM, FTM, ALLCM and ALMSM systems are 16.26, 22.56 , 24.54, and 24.20, respectively. The findings in this paper demonstrate that active learning techniques helps the model to converge early and improve the overall quality of the translation system.
A Study on the Importance and Practice of Animal Ethics Education in Moral Subject Education: Focusing on moral subject education in elementary school

Objectives The purpose of this study is to find the importance and practical measures of animal ethics education in moral subject education, focusing on elementary school moral subject education.&#x0D; Methods To this end, the time and ethical background of animal ethics education were examined. In addition, the contents related to animal ethics education presented in the moral curriculum and elementary school moral textbooks were analyzed. Based on this, it was intended to provide an example of how to apply animal ethics education in elementary school moral subject classes.&#x0D; Results In moral subject education, animal ethics education does not properly express the era and ethical background, questions about its sequence may be raised, and particularly, it does not present meaningful contents in the approach of elementary school moral subject education. The frequency of exposure to animal terms in elementary school moral subject education should be increased in accordance with the interest of the times, and the animal ethics theory of Singer, Regan, and Kant needs to be applied more actively.&#x0D; Conclusions In moral subject education, animal ethics education should be approached based on the times and ethical foundations, securing sequence. In particular, in elementary school moral subject education, animal ethics education should include various practical approaches that are insufficient.
Multi-level Cross-attention Siamese Network For Visual Object Tracking

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region.The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking.But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects.In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time.Specifically, a multilevel cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously.In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level crossattention module more efficient to fuse the information of the target and the search region.We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.
Student Group Division Algorithm based on Multi-view Attribute Heterogeneous Information Network

The student group division is benefit for universities to do the student management based on the group profile.With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location.Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system.However, it is challenge in feature representation due to diverse forms of the student data.To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective.Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN).The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information.Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books.Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups.Comparative experiments have been done on a real-life campus dataset collected from a university.The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.
Current Status and Prospect of Canadian Children’s Literature Research in Korea: Archiving Academic Journal Papers (1991-2022)

Canadian children’s literature has established itself as Canada’s national literature in a relatively short period of about 170 years. While Canada establishing its national identity, national writer and literature represented by Lucy Maud Montgomery’s Anne of Green Gables was born. The process of birth and growth of Canadian children’s literature has been in line with such context of the nation. Understanding the history and current status of Canadian children’s literature requires a macroscopic view of geographical, human, and cultural factors that have shaped the present Canada. Then, what about Canadian children’s literature ‘reserch’ in Korea? What development has Canadian children’s literature research made in Korea? What is the current status? These questions come from the awareness that Canadian children’s literature is still an unfamiliar area in domestic English literature research, and that the research itself is insufficient in quantity and quality. Responding to the questions, this study examines the development and current status of Canadian children’s literature research in Korea, focusing on research papers published in domestic academic journals from 1991 to 2022. Both quantitative analysis of the papers and qualitative analysis of the contents and timely topics covered by the papers will be carried out together. As a conclusion, this study will diagnose the challenges and future prospects left by the current Canadian children’s literature research in Korea.
A SPA Countermeasure for Hardware Implementation of SM2 Algorithm

SM2 algorithm is widely used in financial IC cards. It has the advantages of fast operation speed and short signature, but it may also contain security vulnerabilities. Attackers can crack the secret key via Simple Power Analysis (SPA), which is the inexpensive and extremely effective method, causing a great threat to the security of SM2 algorithm. In order to improve the safety of SM2 algorithm, this paper introduces atomic algorithm to implement point addition and point doubling operation, and proposes precomputed Non Adjacent Form (NAF) random window algorithm to achieve scalar multiplication. Based on experimental analysis with SAKURA-G FPGA board, the improved SM2 algorithm can resist successfully SPA. Compared with the original algorithm, the time of computation is reduced by 67.5%, and the number of slice registers has increased by less than 5%. The security and speed of SM2 algorithm has been significantly improved.
COMPUTATIONAL LINGUISTIC MATERIAL FOR VIETNAMESE SPEECH PROCESSING: APPLYING IN VIETNAMESE TEXT-TO-SPEECH

The motivation of this paper is to propose a set of best-quality linguistic materials for Vietnamese speech processing, which can be used for Vietnamese TTS and ASR problems. This proposed material includes: (1) a pronunciation dictionary, which adapts from X-SAMPA, (2) a rule-based grapheme to phoneme for Vietnamese. In order to test and evaluate, we have built a Vietnamese TTS system based on the Merlin engine, using the above materials, and evaluating the quality of speech and the accuracy of pronunciation. The results show that the applicability of these materials is favorable for further research and development on Vietnamese speech processing.
Research on Kindergarten Curriculum Strategy with Red Cultural Resources

The social culture of our country is gradually diversified, and the alienation between red education and children is intensifying.Preschool period is the key period for the formation of children's moral quality.The integration of red cultural resources into kindergarten curriculum is not only conducive to enriching kindergarten curriculum types, but also conducive to cultivating children's patriotic consciousness and enhancing children's cultural self-confidence.
PENGARUH PENGGUNAAN MEDIA SOSIAL YOUTUBE TENTAN ROASTING BEBERAPA PEJABAT DALAM STAND-UP COMEDY PADA PEMBELAJARAN MENULIS TEKS BIOGRAFI SISWA KELAS X SMK PGRI 2 BELITANG

The purpose of this research is to describe the effect of using YouTube social media about roasting several officials in stand-up comedy on the learnining of wriinge biographical texts for grade X students of SMK PGRI 2 Belitang. This reserch uses experimental method. Samples are selected by using a simple random sampling technique, the samples are students of grade X.1 and X.4 amounting 51 students. The data of this study are obtained through test techniques, questionnaires and interviews with Indonesian teachers who teach in grade X SMK PGRI 2 Belitang. Based on the results of the research description, the result of a significance value of 0.000 which means 0.000 &lt;0.05 so Ho is rejected and Ha is accepted, it means that YouTube social media about roasting some officials in stand-up comedy is effectively used in the learning of writing biographical texts. Thus, it is concluded that "the influence of using YouTube social media about roasting several officials in stand-up comedy on the of writing biographical texts for grade X students of SMK PGRI 2 Belitang" is influential and proven true.
Prediksi Performa Mahasiswa Menggunakan Model Regresi Logistik

Prediction of student performance is an important thing for a university. This is because it can help a university to prevent or treat students who are at risk of failing in their studies early. This study aims to predict student performance at Duta Bangsa Surakarta University (UDB). The model used in this study is a logistic regression model. Logistic regression is a mathematical modelling method used to determine the relationship between a binary dependent variable and one or more independent variables. The results showed that the logistic regression model could be used to predict student performance with MAPE by 8%. Keyword: Student Performace, Logistic Regression, UDB, MAPE
Development of A Portable Instrument for Detecting Partial Discharge by Ultrasonic Beamforming Technique

In this paper, we introduce partial discharge visualization technology. To monitor partial (external corona or creeping) discharge at exposed parts remotely, detection of ultrasound emitted from partial discharge is frequently carried out. Beamforming technique, in which ultrasonic waves measured by multiple microphones are analyzed, was adapted for determining an arrival direction of ultrasound. By superimposing arrival direction of ultrasound on video image of an objective apparatus recorded by a camera, we can promptly know position where partial discharge occurs in wide area to be monitored. A small and lightweight instrument called “Corona Discharge Viewer™ MK-760” was developed for on-site partial discharge detection. The beamforming computing is carried out repeatedly in real-time, and location of partial discharge is shown with visual image on the LCD display. Therefore, we can easily detect and locate partial discharge by just moving MK-760 facing towards to objective apparatus without any esoteric operations. Corona discharge occurred at an insulator and a bend section of a twisted bare wire of actual electric transmission facilities were satisfactorily detected and located by using MK-760.
Segformer-Mobilenetv3 fusion coordinate attention mechanism: A lite portrait segmentation network

Aiming at the problem of low accuracy of portrait segmentation, we proposes a lite mobile image segmentation algorithm, SegFormer MoibleNetv3(SMN), based on SegFormer and fusion coordinate attention mechanism MoblieNetv3 to achieve portrait segmentation. The improved MobileNetv3 is used as the backbone network for feature extraction, which can effectively reduce the size of the model and facilitate the migration and training of the model. SMN network structure can effectively learn portrait features, so as to separate portrait from background. The network structure can be selected to take portrait as input and output the corresponding image mask. Experiments were conducted on Matting, EG1800, and P3M-10K public datasets, The results show that the SMN algorithm outperforms the current mainstream lite portrait segmentation methods in terms of Mean Intersection over Union (MIoU), accuracy (Acc), model consistency (Kappa) and segmentation coefficient (Dice). MIoU index can reach 97.61% (Matting), 94.31%(EG1800), 95.64%(P3M-10K), accuracy (Acc) can reach 98.22%(Matting), 97.27%(EG1800), 97.97% (P3M-10K). The accuracy has been significantly improved, and the target person can be clearly separated from the background. The final model size is only 3.1M, which is smaller than SegFormer-B0 11.1M.The model in this paper can be applied to mobile phone and other mobile devices. Each image can reach more than 30 fps when processed on the test dataset on the experimental platform.
An IDE Support for Validating Machine Learning Applications in Bioengineering Text Corpora

Modeling in machine learning (ML) is critical for software systems in practice. ML applications are required to validate their models and implementations but quality validation is a challenging and time-consuming process for developers. To address this limitation, we present a novel validation technique for ML applications to help developers or researchers (e.g., bioengineering domain) inspect (1) software code (ML API usages) and (2) ML model (extracted features).
Grid Discrete Optimization Model Based on Ant Colony Algorithm

For the continuous optimal path planning problem where the optimal path is partially located in the obstacle area (unable to pass through the area), it cannot be solved by conventional analytical methods, so the grid optimization method is considered. As the computational complexity of the discretized problem is too large, this paper considers the use of ant colony algorithm for optimization search, which provides a feasible way to solve this kind of problem.
Remote Sensing Object Detection Based on Improved YOLOv4

Based on analyzing the YOLOv4 algorithm, aiming at the defect of low training accuracy in the application of YOLOv4 to detect remote sensing image objects, the CBAM module and Focal Loss function are introduced and inserted into the algorithm network structure, respectively and at the same time, so as to improve the detection accuracy of the network. Under the DOTA dataset, with the detection speed barely affected, mAP in the proposed algorithm increased by 4.09%, 3.87%, 3.16%, and 1.95%, respectively, compared with previous SSD, YOLOv3, YOLOv4, and YOLOv5. The optimization algorithm has the advantages of both efficiency and accuracy and provides an effective solution to the problem of object detection.
Utilization of Media GELIBO (Gemar Literasi dengan Flipbook) for Fourth Grader at Elementary School

The lack of motivation for students in fourth grader of SDN Dinoyo 3 Malang is the lack of motivation to read, the low understanding of students about the content of reading, and the lack of existing literacy reading books. GELIBO (Gemar Literasi dengan Flipbook) is one of the media to convey information in digital form, where digital information is the impact of technological advances based on the industrial revolution 4.0. This research purpose to analyze the reading and writing literacy ability of elementary school students. The type of research used in this study is qualitative descriptive research. The data were got from the headmaster and teacher by interview, observation, and documentation studies. This research used analysis techniques from Miles, and described Huberman that contained of Data Collection; Data Reduction; Data View; and conclusion drawing/verification. The results showed that the activity of making Mind Mapping is one of the activities that the author wants to do to overcome the lack of students' interest in reading the existing storybook. The effect of using GELIBO (Gemar Literasi with Flipbook) media can increase students' interest in learning, it caused of the factors: 1). student intelligence ability, 2). student learning motivation, 3). the influence of playmates, 4). the teacher's ability, 5). the facilities and infrastructure in the school are sufficient. The condition after the idea, using mind mapping activities written in the media GELIBO (Gemar Literasi dengan Flipbook) can foster students' interest in reading storybooks in learning at this time. So that this activity can be made a reference to further improve the quality of reading both reading storybooks and reading books reading existing learning materials.
A simulation methodology for superconducting qubit readout fidelity

Qubit readout is a critical part of any quantum computer including the superconducting-qubit-based one. The readout fidelity is affected by the readout pulse width, readout pulse energy, resonator design, qubit design, qubit-resonator coupling, and the noise generated along the readout path. It is thus important to model and predict the fidelity based on various design parameters along the readout path. In this work, a simulation methodology for superconducting qubit readout fidelity is proposed and implemented using Matlab and Ansys HFSS to allow co-optimization in the readout path. As an example, parameters are taken from an actual superconducting-qubit-based quantum computer. Without any calibrations, the model is able to predict the readout error of the system as a function of the readout pulse power. It is found that the system can still maintain high fidelity even if the input power is reduced by 7 dB. This can be used to guide the design and optimization of a superconducting qubit readout system.