Reviewerly/exHarmony huggingface dataset:
Engagement Classification in E-Learning Environments Through Eye Features Using Scaled Dot-Product Attention Convolutional Neural Network

Engagement can be defined as how individuals are involved and interact with a task that requires their attention and emotional state. Analyzing a student's emotional reactions to four affective states, namely boredom, engagement, confusion, and frustration in e-learning environment can identify their learning behavior. By measuring student engagement and implementing the right prevention, such as rearranging the learning design or building an effective feedback system, students' learning intake can be increased. A person's actions and intentions can be identified using eye movement without knowing what the person is looking at. Therefore, this study utilized eye landmarks and gaze features extracted with OpenFace as the input to a Convolutional Neural Network (CNN)-based classifier that enhances the state-of-the-art model with the Scaled Dot-Product Attention mechanism to capture important part of the data. The performance of our model was compared with the state-of-the-art model and machine learning models on three different landmark points amounts, namely 56, 20, and 10 landmark points. The proposed model obtained accuracies of 96.89% on students' affective states classification using 20 eye landmark points and 95.81% on engagement levels classification using 56 eye landmark points, while the Support Vector Machine obtained 81.4% and 78.73% accuracy on both objectives respectively, and the Dense Neural Network obtained 92.66% and 93.65% accuracy on both objectives respectively. It can be concluded that adding an attention mechanism can boost the model's performance. Besides, similar accuracy was obtained using eye landmarks of 56 points and 20 points, but reduced more significantly on 10 points.
Comparison of Deep Learning Methods in Detecting Hate Speech in Indonesian Tweets

Hate speech has negative effects on both the targeted victims and the listeners. The dissemination of hate speech can occur not only physically or verbally, but also in writing on social media. The emergence of hate speech on social media platforms can be difficult to identify in written communication. Currently, hate speech detection relies on machine learning. This study generates a vector representation of words using three pre-trained word insertion models: Global Vectors (GloVe), FastText, and Bidirectional Encoder Representations from Transformers (BERT). Synthetic Minority Oversampling Technique (SMOTE) and Random Over Sampling (ROS) were utilized as balancing methods to rectify data imbalance between classes. In addition, three distinct deep learning architectures were used to identify sentence-level hate speech in Indonesian tweets: Bidirectional Long Sort-Term Memory (BiLSTM), Convolution Neural Network (CNN), and Recurrent Neural Network (RNN). The dataset was collected by crawling the data via the Twitter API. After data underwent preprocessing, characteristics were extracted. Based on experimental results, classifiers employing RNN and BERT embedding and utilizing SMOTE produced the most accurate results (95.5%).
Comparing Sparse and Dense Optical Flow Methods to Detect Traffic Anomalies, Based on Orientation

In intelligent traffic systems, it is often important to detect anomalous events in order to facilitate the avoidance of accidents and the improvement of traffic safety. Automatic anomaly detection helps human operators in detecting anomalous traffic events. For this study vehicle orientation is proposed as an approach to recognize anomalous events in traffic situations, by analyzing traffic surveillance video images. The study also compares the use of sparse optical flow and dense optical flow methods to obtain orientation features. A One-Class Support Vector Machine model is built to classify feature points into anomalous or "usual" events. Experiments conducted have demonstrated that the proposed method could reliably detect recorded anomalous events, such as vehicles driving against the traffic direction or committing illegal lane crossings.
Design of Application Framework for Vital Sign Monitoring and Remote Doctor Consultation

Health services entail face-to-face interactions between patients and physicians, whereby the patient reports the symptoms they are experiencing, and the doctor examines the patient's vital signs. In Indonesia, a doctor consultation system is presently accessible to the public. However, a vital sign monitoring system, which is linked to medical devices used in measuring patient vital signs, is yet to be established in the country. To address this, our research endeavours to establish an application framework for telehealth that encompasses doctor consultation features and the ability to remotely check a patient's vital signs. The application framework serves as a software foundation that can be transformed into finished software. Additionally, it ensures consistency in the produced product model during development. The analysis and design of the application framework employ the Feature-Oriented Domain Analysis (FODA). Typically, FODA is used to analyze a single domain, but this study applied it to two different domains. Our research findings reveal 14 primary features and 45 sub-features that must be incorporated into the application framework. The framework comprises 16 frozen and six hot spots, where the Strategy, Observer, and Filter design patterns are implemented. These outcomes can guide software developers in developing web-based Telehealth remote doctor consultations and vital sign monitoring.
A CNN-Based Arabic Diacritic Symbol Recognition System Using Domain Adaptation

The recognition of Arabic diacritic symbols is essential for accurate comprehension of Arabic texts, yet most existing Optical Character Recognition (OCR) systems lack this capability. This paper proposes a novel approach for Arabic diacritic recognition using a domain adaptation method applied to the AlexNet deep convolutional neural network architecture. A custom dataset of synthetic Arabic diacritic images was generated, incorporating variations in font styles, sizes, slant angles, and perspective distortions. The model was trained using this dataset, and a domain adaptation technique was employed to improve its generalization across diverse diacritic styles and handwriting variations. We evaluated the proposed model on the testing dataset consisting of a variety of fonts and distortions; the result was an overall accuracy of 97%, indicating that the model can achieve a high accuracy for most diacritic categories, with only a few misclassifications.
Speaker Recognition on Low Power Device Using Fully Convolutional QuartzNet

The need for a small and lightweight algorithm used for speaker recognition that can run on low-power devices is on the rise. This is mainly caused by security and privacy concerns of users with the use of their personal and biometric data. The speaker recognition task is mainly used as a biometric authentication, so an accurate model is also needed. The previous method uses feature engineering to extract features from raw audio files with heavy reliance on the training data and a dissimilarity between the training data and real-world implementation causes a significant decrease in its accuracy. We propose a Fully Convolutional QuartzNet as a deep learning approach to this problem. We achieved 84.6% accuracy when testing on a small subset DR-VCTK dataset with 30 classes and 56.40% accuracy on a small subset of the VoxCeleb dataset with fewer files for each of the 125 classes. The proposed model was also tested for binary speaker recognition, achieving 5.07% EER. We also achieve a small parameter count of only 33K parameters without sacrificing significant performance, and the proposed method can achieve its highest accuracy with only 53K parameters.
Implementation of Domain Adaptation for Keyword Determination of Scientific Articles Based on Multilabel BERT

Keywords that are not properly determined make scientific articles unreachable by readers. The process of determining keywords done manually can cause errors and can waste time. To automate the determination of keywords can be done by using classification. Different word arrangements in a sentence can change the context of the sentence. Also, two or more words can have the same meaning. Both factors can affect the performance of the classification model. The speed of the classification process depends on the number of words in the input data. On the other hand, the larger the dataset used to build the model, the better the performance of the classification model. Therefore, this study applies the concept of domain adaptation to the BERT Model which is an implementation of context-based classification. The evaluation results show an accuracy value of 92%, precision of 92%, recall of 92%, error of 8%, and F1 score of 92%. These results increased by 43% for the F1 Score value and there was a slight difference of 5% for the accuracy value when compared to the Baseline Method, namely the BERT Model. This research shows that applying the concept of domain adaptation can solve the problem of classifying target domains that are not part of the source domain while maintaining the performance of the BERT Model.
Investigating ResNet Variants in U-Net to Obtain High-Quality Depth Maps in Indoor Scenes

Depth estimation has an important role in various applications. Generally, active sensors such as Light Detector and Ranging (LiDAR) or stereo cameras are used in depth estimation. However, they have various drawbacks, primarily the computational cost required. In order to provide lower costs, monocular cameras became the solution. Images captured by monocular cameras, however, do not have depth information, making it impossible to estimate depth values. The success of deep learning in solving various computer vision tasks has been proven and is growing rapidly, one of them is the monocular depth estimation. There are various scenes solved in the case of monocular depth estimation, including indoor and outdoor. In this research, we will focus on indoor scenes due to the fact that the information in indoor scenes is more varied compared to outdoor scenes. This makes indoor scenes more challenging than outdoor scenes. In this research, we use the U-Net architecture by using a pre-trained ResNet at the encoder side. ResNet was chosen because of its proven ability to prevent vanishing and exploding gradients. In this work, we examine ResNet's ability to perform depth estimation and we investigate the impact of using weights in the loss function on the depth map obtained both quantitatively and qualitatively. We use the DIODE dataset: A Dense Indoor and Outdoor Depth Dataset as a benchmark. We achieved quantitative improvement with RMSE of 1.7673, REL of 0.3281, δ<1.25 of 57.38, δ<1.25² of 77.18, and δ<1.25³ of 86.71.
Performance Analysis of 3D Rendering Method on Web-Based Augmented Reality Application Using WebGL and OpenGL Shading Language

In recent years, web programming has increased to the extent where it is capable of rendering 3D objects on the web. Its rendering performance varied depending on where it ran and what library was used to render the objects. This study aims to investigate the rendering performance of several 3D renderer libraries that render 3D objects in an augmented reality application. WebGL is one of the APIs that uses the Javascript programming language to render 3D objects in web applications. To create augmented reality on the web, a JavaScript library called AR.js is needed, which allows developers to create augmented reality on the web without the need to use additional plugins. AR.js supports various types of augmented reality, i.e., bookmarks, images, and location-based types. It also allows developers to incorporate visual and audio effects into augmented reality. This study aims to investigate the rendering performance of WebGL raw shaders and GLSL shaders in an HTML-based augmented reality application. The results suggested that both WebGL and GLSL could render low-poly and medium-poly 3D objects well. However, according to the test, WebGL performance drops significantly when rendering high-polygon 3D objects. Nonetheless, the GLSL library could render the same high-poly 3D objects at a similar level with native library rendering performance. Thus, suggest GLSL shaders as the recommended library to render 3D objects on the web.
Parallelizing Maximal Clique Enumeration on GPUs

We present a GPU solution for exact maximal clique enumeration (MCE) that performs a search tree traversal following the Bron-Kerbosch algorithm. Prior works on parallelizing MCE on GPUs perform a breadth-first traversal of the tree, which has limited scalability because of the explosion in the number of tree nodes at deep levels. We propose to parallelize MCE on GPUs by performing depth-first traversal of independent subtrees in parallel. Since MCE suffers from high load imbalance and memory capacity requirements, we propose a worker list for dynamic load balancing, as well as partial induced subgraphs and a compact representation of excluded vertex sets to regulate memory consumption. Our evaluation shows that our GPU implementation on a single GPU outperforms the state-of-the-art parallel CPU implementation by a geometric mean of 4.9× (up to 16.7×), and scales efficiently to multiple GPUs. Our code has been open-sourced to enable further research on accelerating MCE.
A Comparative Study of Deep Transfer Learning Algorithm for Cheating Detection in the Exam Based on Surveillance Camera Recording

Academic cheating is a serious problem in education. One of the cheating that often happens is cheating during exams. This study uses surveillance camera recordings to present a novel approach for detecting cheating and suspicious activities during exams. This system allows monitoring of students during exams by observing suspicious activity that indicates cheating, such as cheating from a book, notes, or any text found on paper; talking to a person in the room; using the Internet; asking a friend a question over the phone; and using a phone. Leveraging the power of deep transfer learning architecture effectively captures intricate surveillance footage features. Additionally, the Long short-term memory (LSTM) network is utilized to model temporal dependencies in the sequence of frames, enhancing the accuracy of cheating detection. Experimental results show the efficacy of the proposed approach, achieving a commendable accuracy level of 0.964 and MCC 0.971. Furthermore, this study undertakes a comparative analysis against alternative architectures VGG-16, VGG-19, Resnet50V2, and InceptionResNetV2. The ResNet50V2-LSTM approach outperforms these models in cheating detection performance. This accomplishment underscores the potential of deep learning techniques in addressing the intricate task of detecting cheating during exams. Transfer learning with ResNet50V2 demonstrates the model's ability to learn and generalize features relevant to cheating behaviors, while the integration of LSTM effectively captures the temporal context within the video sequences. The study contributes to the broader discourse on utilizing advanced technologies to uphold the integrity of academic evaluations and promotes further research in refining cheating detection methodologies.
Phase Space Representation

Here we introduce the phase space representation of many-body quantum systems. We consider specifically Gaussian states and their characteristic function. We show how to compute expected values by derivation and define the covariance matrix in terms of different variables. We introduce a general method for linear transformations and gate as building blocks for quantum circuits and processors. First examples are gates with random coefficients.
Gaussian Boson Sampling

We introduce the fundamental models for Gaussian boson sampling and the link with the computation of the Hafnian. We show how to compute and train boson sampling patterns by machine learning.
Quantum Mechanics and Data-Driven Physics

Why quantum machine learning? Quantum mechanics and data-driven physics are similar. We describe their connections by focusing on their mathematical paradigms. We introduce quantum feature maps, quantum kernels, and examples of quantum classifiers. We discuss the basics of kernel methods with coding examples.
Uncertainties and Entanglement

We study advanced layers for computing uncertainties and entanglement. These layers require higher-order derivatives of the characteristic functions by the tf.GradientTape method. We show the use of machine learning for maximizing entanglement with beam splitters.
Kernelizing Quantum Mechanics

We show how to use quantum states, as coherent and squeezed states for a kernel machine.
Two-Qubit Transverse-Field Ising Model and Entanglement

We discuss the two-qubit transverse-field Ising model. We introduce entanglement measures. We show how to compute entanglement and unveil its significance in combinatorial optimization and variational quantum states.
Squeezing, Beam Splitters, and Detection

We introduce the squeezing layer by generalizing the symplectic operator. We study squeezed states including the vacuum and the coherent states. We detail multi-mode squeezing operators and beam splitters. We introduce the photon counting layer and homodyne detection.
STUDI PENDAHULUAN PENGEMBANGAN MEDIA FLIPBOOK BERBASIS AJARAN TRI-NGA DALAM MENINGKATKAN KEGIATAN LITERASI NUMERASI SD

In order for the learning process to be successful and run well, the teacher must have the right initiatives and steps in terms of encouraging students to be active in learning and providing adequate learning experiences to students. One of the efforts that can be made in the field of education is through the use of learning resources, books and Student Worksheets, which are converted into digital learning resources that are more attractive, interactive and cost-effective without reducing their functions, benefits and uses. Numerical literacy is knowledge and skills in using numbers and symbols related to basic mathematics to solve contextual problems and analyze information presented in various forms, then interpret the results of the analysis to predict and make decisions. Research and development data analysis uses qualitative data which requires a different analysis to interpret the data that has been collected. The development of Flipbook media based on tri-nga teachings can increase elementary school numeracy literacy activities with significant results.
States as a Neural Networks and Gates as Pullbacks

We represent the characteristic function of a state as a neural network. We detail the case of Gaussian states. We deepen linear transformations and show how to map cascaded gates in neural network models. We introduce the bug train representation and the concept of “pullback” for concatenating transformations. We present layered models in the phase space.
Variational Circuits for Quantum Solitons

We use quantum neural networks in the phase space as a Gaussian variational ansatz for the ground state of many-body Hamiltonians. Specifically, we consider a Bose-Hubbard model or quantum discrete nonlinear Schrodinger equation. In specific regimes, the model supports localized states and collections of localized states determined by the particle number and interaction strength. We study entanglement in the trained quantum network and perform boson sampling to outline the generation of particle pairs.
A novel multi-viewpoints based cosine similarity visual technique for an effective assessment of clustering tendency

Data clustering is an unsupervised technique that can be used to partition the data into groups based on the similarities of the retrieved objects using different distance metrics like Euclidean, cosine, etc. In contrast to Euclidean, the cosine computes the object's similarity by considering both the magnitude and direction of the data vectors. As a result, it performed far better than a standard Euclidean distance metric in applications involving real-time data clustering. The initial k-value (clustering tendency) is required by top clustering techniques like k-means and hierarchical approaches to determine the clusters' quality. Users with knowledge can assign the k-value. However, sometimes the right k-value in such algorithms may need to be assigned. After a thorough review of the work, it was discovered that the visual technique known as visual assessment of (cluster) tendency (VAT) effectively addresses the clustering tendency issue. It uses the Euclidean metric to find the similarity features in its algorithm. Another enhanced visual technique, cosinebased VAT (cVAT), outperformed the VAT for text data and speech clustering applications. However, the similarity features are extracted about a single viewpoint in cVAT. This paper develops the multi-viewpoints-based cosine similarity measure (MVPCSM) for a more informative assessment. Instead of using a single reference point like a typical cosine measure, the MVPCSM generates precise similarity characteristics using several views. The performance of the existing and proposed technique (MVPCSM-VAT) is evaluated using clustering accuracy (CA) and normalized mutual information (NMI). It has been demonstrated that the proposed MVPCSM-VAT is 15-25% more efficient than VAT and cVAT in terms of the parameters of CA and NMI. The proposed method successfully obtains more quality data clusters than MVS-VAT.
Exploration of Digital Transformation Schema for Professional Degree Postgraduate Training

Professional degree postgraduate education is the strategic focus of the reform and development of postgraduate education in China, and the digital transformation of education is also the strategic theme of the reform and development of education in China. This paper analyzes the current situation of informatization of professional degree postgraduate training in China, and puts forward the construction scheme of digital comprehensive service platform for professional degree postgraduate education and teaching based on the actual situation of China University of Geosciences (Beijing). It focuses on three aspects: postgraduate education information management, intelligent online teaching and teachers’ digital literacy improvement. The article also discusses the significance of the digital ecology of the education industry and the entire country for the evaluation of the cultivation of professional degree postgraduates in colleges and universities.
Multimodal prediction of profanity based on speech analysis

With increasing multimedia content and social activities, moderation problems increase. There are different approaches to moderation and automation. However, they have limitations in terms of usage in real-time. The analysis of scientific papers revealed that most of the more common approaches solve the task of detection instead of prediction by considering the final utterance. For this reason, calls are unprotected in toxic languages, and online broadcasts can be unpredictable. In this work, a new way for automatic speech moderation in terms of dynamic word prediction was suggested. The considered task involves the analysis of the auditory and textual channels of speech. Words can have different meanings depending on the context, so in solving the problem it is planned to consider profanity, which is socially unacceptable regardless of the context. In this paper approaches for working with speech stream in the task of profanity prediction were proposed. It can be possible to have smaller latency with usage of audio features. We also suggest the pipeline for real-time (with the ability to predict the sequence with a higher duration than the latency of the processing) prediction for multimodal prediction, which compensates the latency of ASR systems. As a result, in this paper, we compared different solutions for the next color prediction task for English speech and reached the F1 score of 86.6 for 3 class prediction.