Reviewerly/exHarmony huggingface dataset:
Fake Profiles Identification in Online Social Networks Using Random Forest Algorithm

Every day several people are creating their profiles on the social network platforms. The social network sites not only providing advantages to the users and also provide security issues to the users as well their information. To analyze, who are encouraging threats in social network need to classify the social networks profiles of the users. From the classification, can get the genuine profiles and fake profiles on the social networks. Different classification methods for detecting the fake profiles on the social networks. In this paper proposing Machine learning and Natural language Processing (NLP) techniques to improve the accuracy rate of the fake profiles detection using the Random forest algorithm.
Implementasi Pencarian Rute Terpendek Tour Wisata di Banyuwangi pada Agen Travel Menggunakan Algoritma Floyd Warshall

Tourism is travel aim to visit tourist attractions. Holidays to tourist attractions can eliminate boredom, improve the brain's work system, and foster a feeling of happiness. Holidays to tourist attractions can be assisted by travel agents. This research was carried out by looking for the shortest route from tour packages the travel agent in Banyuwangi. Search for the shortest route is aim to save time on travel package rundown tour, the distance traveled, and the fuel used. Search for the shortest route can be calculated using Floyd Warshall Algorithm. This algorithm was chosen because it can evaluate each pair vertex at each iteration to find the shortest distance. Search for the shortest distance is checking each pair vertex and choosing the smallest distance between the actual distance and the initial distance plus the final distance. The results of this research are found in the last iteration of each tour package. The total distance of the tour package is calculated using the Floyd Warshall Algorithm to get smaller distance than the total distance of the tour package with the route taken normally. Tour package distance using Floyd Warshall Algorithm experienced distance savings seen from the difference between the two.
Efektivitas Penggunaan Multimedia Pembelajaran Powerpoint terhadap Peningkatan Motivasi dan Hasil Belajar Siswa

This study aims to determine the effectiveness of the use of PowerPoint learning multimedia on increasing motivation and student learning outcomes in the Indonesian language subject in class XII IPS 2 MA Daarul Abroor Tasikmalaya. The method used is pre-Experimental Design. With the form of One-Group Pretest-Postest Designs, which compares the results of the pre-test and post-test. The sample of this research was 32 students, namely class XII IPS 2 MA Daarul Abroor Tasikmalaya. Researchers used data collection techniques in the form of pre-test and post-test. From the results of this study, the following conclusions were obtained: (1) The use of PowerPoint learning multimedia was effective in increasing student motivation in Indonesian language subjects in Class XII IPS 2 MA Daarul Abroor Tasikmalaya. This is proven by the Wilcoxon Test that z = -4.940 and the value of Asym.Sig (2-tailed) -0.000 because the resulting value of Asym.Sig (2-tailed) is smaller than the alpha used, which is 0.05, then Ho is rejected or Ha accepted; and (2) The use of PowerPoint learning multimedia is effective in improving student learning outcomes in the Indonesian language subject in Class XIl IPS 2 MA Daarul Abroor Tasikmalaya. This is proven by the Wilcoxon Test, the value of z = -4.939 and the value of Asym.Sig (2-tailed) = 0.000 because the resulting value of Asym.Sig (2-tailed) is smaller than the alpha used, which is 0.05, then Ho is rejected or Haha accepted. This study concluded that the use of PowerPoint learning multimedia effectively had a significant influence on the motivation and learning outcomes of class XII IPS 2 MA Daarul Abroor Tasikmalaya.&#x0D; Keywords: Multimedia, Learning, Powerpoint, Motivation, Learning Outcomes
The Development and Application of an Intelligent PAD Class for Linear Algebra Teaching in Universities

PAD (Presentation-Assimilation-Discussion) Class is an innovative teaching mode, and Mosotech is an intelligent teaching tool adapted to mobile Internet and big data. This paper integrates the two to construct an intelligent PAD Class that injects vitality into linear algebra teaching and arouses students’ initiative and autonomy, thereby improving the efficiency of both learning and teaching. Keywords: Intelligent Teaching, Linear Algebra, Mosotech , PAD Class DOI: 10.7176/JEP/14-24-08 Publication date: August 31 st 2023
A Study on ‘Writing Self-introduction for Employment(Startup)’ in University Writing Education for Freshmen: Focusing on the Case of Long-term Project Using Online Space

This class plan provides conditions for use in actual classes by linking the university writing curriculum, which is centered on academic writing, and the four items of Self-introduction for Employment(Startup). The writing activity for each week in the self-introduction writing class using the 'long-term project method' and 'online space' is a 'preparation' process for preparing writing material, and include short but complete writing activities as a practice assignment for the subject itself. It is also 'Genre-centered learning' that produces 'results' tailored to the style in consideration of the communication context, and 'Dialogism learning' that requires learners' active and continuous activities. In the case study, the actual class design and process were explained. In addition, through the activity results and survey responses, the implications of the activity for writing classes and self-introduction education were dealt with.
Noise Types in Correlation-Based Target Detection

Quantum illumination (QI) [1] is a general name for a class of target detection protocols, where an entangled state is used to illuminate a region of space where a reflecting target may be present. Storing part of the state and sending the other part to the target, QI can suppress the sources of uncorrelated noise by using the correlations in frequency, phase, and arrival time between the stored state and the target return state [2], [3]. This alleviates the task of detecting a weakly reflecting target against a strong background [4]. After initial proposal based on the entangled photon pairs, QI has been extended [5] to the case of Gaussian states, which are easier to prepare and manipulate.
Quantum State Tomography of Qudits via Hong-Ou-Mandel Interference

Hong-Ou-Mandel (HOM) interference [1] is a bunching phenomenon based on the bosonic nature of photons. When two photons that are perfectly indistinguishable from each other are mixed using a half beam splitter (HBS), they always bunch and exhibit the absence of coincidence resulting in a specific interferogram called HOM dip. The important property of the HOM interference is that the decrease in coincidence reflects the degree of indistinguishability of the input photons, i.e., it provides information regarding the probability of overlap of the input photons. Thus, this property should enable the estimation of a target photon in an unknown quantum state using a probe photon in a known state. In this work, we realize the above concept as the quantum state tomography (QST) applicable to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d\text{-dimensional}$</tex> photonic qudits, which we call HOM-QST [2]. Interestingly, we observe that HOM-QST is robust against various imperfections and that the probe photon can be replaced by classical light. Furthermore, we confirm this by demonstrating a proof-of-principle experiment using two-dimensional polarization qubits.
On-Chip Multi-Wavelength Lasers for All-Optical THz Signal Processing

The behavior of semiconductor lasers has been extensively explored for all-optical information processing, ranging from all-optical flip-flop memories [1] to neuro-inspired computing [2]. When it comes to processing of THz signals, so-called P1 dynamics have been exploited for microwave generation, frequency conversion, and amplification [3]. This approach, however, decreases in efficiency beyond 100 GHz due to the weakened interaction between the injected signal and the laser process. Here, we propose a novel approach for THz processing based on feedback-controlled multi-wavelength lasers (MWL), whose mode spacings and thus beatnote frequencies can be tailored and are, in principle, only limited by the gain bandwidth of the active medium; for instance, reaching up to 10 THz in InP. As a first proof-of-concept demonstration, using a dual-mode laser, we show selective filtering and amplification of two CW optical signals separated by 1.3 THz. We obtain power gain values above 15 dB and suppression ratios of more than 18 dB. By adjusting the MWL operation conditions or design, additional offset frequencies can be obtained, thus enabling the processing of broadband THz signals.
Real-Time GAN-Based Model for Underwater Image Enhancement

Enhancing image quality is crucial for achieving an accurate and reliable image analysis in vision-based automated tasks. Underwater imaging encounters several challenges that can negatively impact image quality, including limited visibility, color distortion, contrast sensitivity issues, and blurriness. Among these, depending on how the water filters out the different light colors at different depths, the color distortion results in a loss of color information and a blue or green tint to the overall image, making it difficult to identify different underwater organisms or structures accurately. Improved underwater image quality can be crucial in marine biology, oceanography, and oceanic exploration. Therefore, this paper proposes a novel Generative Adversarial Network (GAN) architecture for underwater image enhancement, restoring good perceptual quality to obtain a more precise and detailed image. The effectiveness of the proposed method is evaluated on the EUVP dataset, which comprises underwater image samples of various visibility conditions, achieving remarkable results. Moreover, the trained network is run on the RPi4B as an embedded system to measure the time required to enhance the images with limited computational resources, simulating a practical underwater investigation setting. The outcome demonstrates the presented method applicability in real-world underwater exploration scenarios.
The effects of Team-based Learning Activities on the Creative Talent Competency and Personality of Pre-Service Child Care Teachers

본 연구 목적은 팀기반 학습활동이 예비보육교사의 창의인재역량과 인성에 미치는 영향력을 검증하고, 예비보육교사의 창의인재역량 증진과 인성 함양에 대한 시사점을 제시하며, 교수-학습의 지원과 교수설계에 대한 기초자료를 제공하는 데 있다. 연구 대상은 부산시 소재 4년제 D 대학교 아동학전공 학습자 122명이다. 자료 분석은 창의인재역량과 인성 검사 사전점수를 이용하여 집단의 동질성을 확인하고, 사전-사후 점수의 평균 차이를 확인하기 위해 대응 표본 t 검정을 실시 하였다. 연구 결과는 다음과 같다. 첫째, 팀 기반 학습활동은 예비보육교사의 창의인재역량에 긍정적인 효과가 있으며, 하위요인 중 정의적 차원과 사회적 차원에서 효과가 있는 것으로 나타났다. 특히 과제집착력과 사회 가치 추구에서 긍정적인 효과가 있는 것으로 나타났다. 둘째, 팀 기반 학습활동은 예비보육교사의 인성 강화에 효과적이며, 하위요인 중 도덕성과 정서성에서 효과가 있는 것으로 나타났다. 특히 배려 및 봉사, 정직 및 정의, 긍정적 자기 이해 요인에서 의미 있는 효과가 있는 것으로 나타났다.The purpose of this study is to verify the impact on the creative talent competency and personality of pre-service child care teachers, to present implications for the enhancement of creative talent competency and personality of pre-service child care teachers, and to provide basic data on teaching-learning support and instructional design. The subjects of the study were 122 learners of child studies at 4-year D University in Busan. In the data analysis, the homogeneity of the group was confirmed using the creative talent competence and the personality test pre-score, and the paired sample t test was performed to confirm the mean difference between the pre-post scores. The results of the study are as follows. First, team-based learning activities have a positive effect on the creative talent competency of pre-service child care teachers, and among the subfactors, it has been shown to be effective at the definitional level and at the social level. In particular, it has been shown to have a positive effect on task obsession and the pursuit of social values. Second, team-based learning activities were found to be effective in strengthening the personality of pre-service child care teachers, and among the subfactors, morality and emotionality were effective. In particular, team-based learning activities have been shown to have significant effects on caring and service, honesty and justice, and positive self-understanding factors.
A Study on Education Satisfaction of Students in Tourism Specialized High Schools Using Revised-IPA

This study aims to find out the education satisfaction of students at tourism-specialized high schools using Revised-IPA. Therefore, a survey was conducted on students enrolled in specialized tourism high schools. As a result of the study, first, special lectures inviting experts, teacher competencies, friendship satisfaction, and participation in class activities were considered important. Second, satisfaction with major selection and school meals was considered important, but the satisfaction was low. Third, both importance and satisfaction were low in satisfaction with educational expectations and improvement of confidence in the major. Fourth, providing chances for major experience activities, major clubs, and basic learning ability improvement led to high satisfaction, but the importance was low. Fifth, gender differences in education satisfaction were small. Sixth, education satisfaction by grade was similar to the overall Revised-IPA results. Based on the results, we suggest follow-up research on the operation of curricula and programs, and the establishment of an educational environment.
Performance Comparison for Exercise Motion classification using Deep Learing-based OpenPose

Recently, research on behavior analysis tracking human posture and movement has been actively conducted. In particular, OpenPose, an open-source software developed by CMU in 2017, is a representative method for estimating human appearance and behavior. OpenPose can detect and estimate various body parts of a person, such as height, face, and hands in real-time, making it applicable to various fields such as smart healthcare, exercise training, security systems, and medical fields. In this paper, we propose a method for classifying four exercise movements - Squat, Walk, Wave, and Fall-down - which are most commonly performed by users in the gym, using OpenPose-based deep learning models, DNN and CNN. The training data is collected by capturing the user's movements through recorded videos and real-time camera captures. The colle cted dataset undergoes preprocessing using OpenPose. The preprocessed dataset is then used to train the proposed DNN and CNN models for exercise movement classification. The performance errors of the proposed models are evaluated using MSE, RMSE, and MAE. The performance evaluation results showed that the proposed DNN model outperformed the proposed CNN model.
Study on the optimization of the quality management path of university physical education teaching in the context of multimedia network technology

Abstract Physical education is the mainstream of physical education in general higher education institutions and is an important part of cultivating talents with comprehensive development. In this paper, to optimize the problem of video acquisition in physical education under multimedia, we adopt the tracking algorithm based on color information - CamShift algorithm to track the teacher’s target so that the teacher’s target is within the field of view of the camera to accomplish a better recording effect. Furthermore, for the problem that the CamShift algorithm is interfered with by class skin color, occlusion, and other problems that cause the algorithm to track incorrectly and the algorithm tracking is not robust, the optical flow tracking algorithm and Kalman filter method are combined to optimize the CamShift algorithm. Finally, a comparison is made between teaching with multimedia technology and previous teaching formats. The results show that three people in the test panel scored above 90 in the final assessment testing, while only one person in the comparison panel, 15 students (65.21%), scored between 85 and 90 in the experimental group, while 9 students (39.13%) scored between 85 and 90 in the control group. The grade point average of students’ theoretical knowledge in the experimental panel was 85.94, which exceeded the grade point average of 81.62 in the control group by 4.32 points; therefore, the optimized media recording effect based on the improved CamShift algorithm improved the teaching experience and the teaching quality of physical education by using multimedia teaching tools, and the research indexes achieved the expected results.
Vision Based Object Dimension Detection

This paper presents a method for real-time object dimension detection of commonly used objects: pens, wallets, and keys. This system is ported on Jetson Nano. The results are translated as an audio output which helps especially the visually impaired individuals to identify the objects. This methodology involves data collection of the objects, preprocessing of the dataset. YOLO (You Only Look Once) model is trained and tested using the collected dataset. The deployment of low power embedded system is a novel approach of object detection and dimension detection in real-time with the use of Convolutional neural network algorithm. The system is efficient in detecting dimension with an accuracy of 84.35% for pen, 82.07% for key and 87.84% with dimensional variance of ±0.29 cm in length and ± 0.18 cm in width.
Time-Multiplexed Photonic Reservoir Computer for Recognition of Human Actions in Videos

Artificial Neural Networks (ANN) are a powerful computational concept used to classify large datasets [1]. The most popular ANNs are composed of large number of neurons organized in stacked layers: the complexity of these network ensures high-performance, at the cost of high power and time-consuming training procedures.
VSNet: Vehicle State Classification for Drone Image with Mosaic Augmentation and Soft-Label Assignment

Numerous architectures are under development to comprehend object information and background in images by analyzing features extracted through Convolutional Neural Networks (CNNs). Autonomous driving requires understanding diverse information and collecting data from heterogeneous environments to generalize classification models. However, the patterns of feature maps extracted through convolution layers in drone image data, which encompass assorted types of vehicles and road shapes, tend to be simple, leading to overfitting during model training. To prevent overfitting, this study applies Mosaic Augmentation to increase data diversity and brings generalization to the data. This data augmentation method randomly combines four selected images to create a new mosaic image. Soft-label Assignment is used to determine the labels of the mosaic images. The dataset is collected using a drone flying along roads, and approximately 4,000 images are used for training. In the experiment, the classification performance of vehicle status is listed based on the weight of the loss function of the soft label and hard label. Having achieved an accuracy of 83.41%, the effectiveness of the proposed method is compared with dilated residual networks in terms of improving model performance.
Archive management information system design for higher education accreditation instruments in Indonesia

Presently Faculty of economics and business Jenderal Sudirman university , there were no facilities that could be used in the preparation of university accreditation instruments, while the need for accreditation data was greatly needed by the academics of the yellowish university in the preparation of accreditation instruments for universities and study programs so that they were still having difficulty filing and presenting form data information, plans strategic and operational and self-evaluation plan along with the documents needed during the visitation activity by the assessor of the National Accreditation Board of Higher Education (BAN-PT). With the use of information technology in the application of computerized based applications for filing and presenting accreditation data needs, it is expected to help the academic community more quickly and precisely in the data processing instrument for accreditation. It is expected that with the development of archiving applications and the presentation of accreditation instruments with the completion of the System Development Lyfe Cycle (SDLC) method in the problem analysis phase, the applications built can improve the quality of accreditation instruments in data processing that are well integrated and can be utilized at any time by the community the Jenderal Soedirman University.
Sparse Double Descent in Vision Transformers: Real or Phantom Threat?

Vision transformers (ViT) have been of broad interest in recent theoretical and empirical works. They are state-of-the-art thanks to their attention-based approach, which boosts the identification of key features and patterns within images thanks to the capability of avoiding inductive bias, resulting in highly accurate image analysis. Meanwhile, neoteric studies have reported a "sparse double descent" phenomenon that can occur in modern deep-learning models, where extremely over-parametrized models can generalize well. This raises practical questions about the optimal size of the model and the quest over finding the best trade-off between sparsity and performance is launched: are Vision Transformers also prone to sparse double descent? Can we find a way to avoid such a phenomenon? Our work tackles the occurrence of sparse double descent on ViTs. Despite some works that have shown that traditional architectures, like Resnet, are condemned to the sparse double descent phenomenon, for ViTs we observe that an optimally-tuned $$\ell _2$$ regularization relieves such a phenomenon. However, everything comes at a cost: optimal lambda will sacrifice the potential compression of the ViT.