Reviewerly/exHarmony huggingface dataset:
PhyLoNet: Physically-Constrained Long-Term Video Prediction

Motions in videos are often governed by physical and biological laws such as gravity, collisions, flocking, etc. Accounting for such natural properties is an appealing way to improve realism in future frame video prediction. Nevertheless, the definition and computation of intricate physical and biological properties in motion videos are challenging. In this work, we introduce PhyLoNet, a PhyDNet extension that learns long-term future frame prediction and manipulation. Similar to PhyDNet, our network consists of a two-branch deep architecture that explicitly disentangles physical dynamics from complementary information. It uses a recurrent physical cell (PhyCell) for performing physically-constrained prediction in latent space. In contrast to PhyDNet, PhyLoNet introduces a modified encoder-decoder architecture together with a novel relative flow loss. This enables a longer-term future frame prediction from a small input sequence with higher accuracy and quality. We have carried out extensive experiments, showing the ability of PhyLoNet to outperform PhyDNet on various challenging natural motion datasets such as ball collisions, flocking, and pool games. Ablation studies highlight the importance of our new components. Finally, we show an application of PhyLoNet for video manipulation and editing by a novel class label modification architecture.
A$$^{2}$$: Adaptive Augmentation for Effectively Mitigating Dataset Bias

Recently, deep neural networks (DNNs) have become the de facto standard to achieve outstanding performances and demonstrate significant impact on various computer vision tasks for real-world scenarios. However, the trained networks can often suffer from overfitting issues due to the unintended bias in a dataset causing inaccurate, unreliable, and untrustworthy results. Thus, recent studies have attempted to remove bias by augmenting the bias-conflict samples to address this challenge. Yet, it still remains a challenge since generating bias-conflict samples without human supervision is generally difficult. To tackle this problem, we propose a novel augmentation framework, Adaptive Augmentation (A $$^{2}$$ ), based on a generative model that help classifiers learn debiased representations. Our framework consists of three steps: 1) extracting bias-conflict samples from a biased dataset in an unsupervised manner, 2) training a generative model with the biased dataset and adapting the learned biased distribution to the extracted bias-conflict samples’ distribution, and 3) augmenting bias-conflict samples by translating bias-align samples. Therefore, our classifier can effectively learn the debiased representation without human supervision. Our extensive experimental results demonstrate that A $$^{2}$$ effectively augments bias-conflict samples, mitigating widespread bias issues. The code is available in here ( https://github.com/anjaeju/A2-Adaptive-Augmentation-for-Effectively-Mitigating-Dataset-Bias ).
Gated Cross Word-Visual Attention-Driven Generative Adversarial Networks for Text-to-Image Synthesis

The main objective of text-to-image (Txt2Img) synthesis is to generate realistic images from text descriptions. We propose to insert a gated cross word-visual attention unit (GCAU) into the conventional multiple-stage generative adversarial network Txt2Img framework. Our GCAU consists of two key components. First, a cross word-visual attention mechanism is proposed to draw fine-grained details at different subregions of the image by focusing on the relevant words (via the visual-to-word attention), and select important words by paying attention to the relevant synthesized subregions of the image (via the word-to-visual attention). Second, a gated refinement mechanism is proposed to dynamically select important word information for refining the generated image. Extensive experiments are conducted to demonstrate the superior image generation performance of the proposed approach on CUB and MS-COCO benchmark datasets.
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation

To further reduce the cost of semi-supervised domain adaptation (SSDA) labeling, a more effective way is to use active learning (AL) to annotate a selected subset with specific properties. However, domain adaptation tasks are always addressed in two interactive aspects: domain transfer and the enhancement of discrimination, which requires the selected data to be both uncertain under the model and diverse in feature space. Contrary to active learning in classification tasks, it is usually challenging to select pixels that contain both the above properties in segmentation tasks, leading to the complex design of pixel selection strategy. To address such an issue, we propose a novel Active Domain Adaptation scheme with Multi-level Contrastive Units (ADA-MCU) for semantic image segmentation. A simple pixel selection strategy followed with the construction of multi-level contrastive units is introduced to optimize the model for both domain adaptation and active supervised learning. In practice, MCUs are constructed from intra-image, cross-image, and cross-domain levels by using both labeled and unlabeled pixels. At each level, we define contrastive losses from center-to-center and pixel-to-pixel manners, with the aim of jointly aligning the category centers and reducing outliers near the decision boundaries. In addition, we also introduce a categories correlation matrix to implicitly describe the relationship between categories, which are used to adjust the weights of the losses for MCUs. Extensive experimental results on standard benchmarks show that the proposed method achieves competitive performance against state-of-the-art SSDA methods with 50% fewer labeled pixels and significantly outperforms state-of-the-art with a large margin by using the same level of annotation cost. Code will be in https://github.com/haoz19/ADA-MCU .
Deep Active Ensemble Sampling for Image Classification

Conventional active learning (AL) frameworks aim to reduce the cost of data annotation by actively requesting the labeling for the most informative data points. However, introducing AL to data hungry deep learning algorithms has been a challenge. Some proposed approaches include uncertainty-based techniques, geometric methods, implicit combination of uncertainty-based and geometric approaches, and more recently, frameworks based on semi/self supervised techniques. In this paper, we address two specific problems in this area. The first is the need for efficient exploitation/exploration trade-off in sample selection in AL. For this, we present an innovative integration of recent progress in both uncertainty-based and geometric frameworks to enable an efficient exploration/exploitation trade-off in sample selection strategy. To this end, we build on a computationally efficient approximate of Thompson sampling with key changes as a posterior estimator for uncertainty representation. Our framework provides two advantages: (1) accurate posterior estimation, and (2) tune-able trade-off between computational overhead and higher accuracy. The second problem is the need for improved training protocols in deep AL. For this, we use ideas from semi/self supervised learning to propose a general approach that is independent of the specific AL technique being used. Taken these together, our framework shows a significant improvement over the state-of-the-art, with results that are comparable to the performance of supervised-learning under the same setting. We show empirical results of our framework, and comparative performance with the state-of-the-art on four datasets, namely, MNIST, CIFAR10, CIFAR100 and ImageNet to establish a new baseline in two different settings.
Application of Multi-modal Fusion Attention Mechanism in Semantic Segmentation

The difficulty of semantic segmentation in computer vision has been reintroduced as a topic of interest for researchers thanks to the advancement of deep learning algorithms. This research aims into the logic of multi-modal semantic segmentation on images with two different modalities of RGB and Depth, which employs RGB-D images as input. For cross-modal calibration and fusion, this research presents a novel FFCA Module. It can achieve the goal of enhancing segmentation results by acquiring complementing information from several modalities. This module is plug-and-play compatible and can be used with existing neural networks. A multi-modal semantic segmentation network named FFCANet has been designed to test the validity, with a dual-branch encoder structure and a global context module developed using the classic combination of ResNet and DeepLabV3+ backbone. Compared with the baseline, the model used in this research has drastically improved the accuracy of the semantic segmentation task.
The Impact of Keypoints Normalization on SIBI Recognition using Modified Shift-GCN

Sign language is a form of non-verbal communication usually used by someone who is speech impaired or deaf to communicate with others. There have been many efforts to develop sign language recognition. However, the dataset is mainly limited to images containing the hand area with static poses. In a previous study, Modified Shift-Graph Convolutional Network (Modified Shift-GCN) performed well on American Sign Language Recognition (ASL) with a relatively simple graph dataset without tracking and normalization. In this study, we explore the implementation of Modified Shift-GCN to recognize the Indonesian Sign Language (SIBI) on a more complex graph dataset combining the hand and body graphs with dynamic poses. We investigate the effect of key-point tracking and normalization on the performance of the Modified Shift-GCN on sign language recognition. The experiments using 10-folds cross-validation show that the normalization yield an average accuracy of 97.9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , superior to that without normalization with an average accuracy of 55.3%.
Calibration-Free Monocular Distance Estimation Performance Assessment Under Influences of Environmental Conditions

Single-camera distance measurement can provide a low-cost and setup-free experience. Several techniques to estimate the distance of the object of interest from a single image require calibration for specific parameters and training for a model when machine learning approaches are implemented. This study introduces the use of a single calibration-free image to generate and evaluate a geometric model with a tilted-oriented object. ArUco fiducial marker is utilized since it can be detected easily using its pre-existing detection software. The perimeter, sum of diagonal lengths, and area of the pattern corners are the features for distance estimation. Our algorithm can determine the distance using only a single image in the training stage, which is considered well-balanced between complexity and accuracy in diverse low-cost, setup-free, or calibration-free applications.
Detection of Sarcasm Sentences in Indonesian Tweets using SentiStrength

Detection of sarcasm sentences in the Natural Language Processing (NLP) field has a fairly high level of difficulty because it ignores elements of facial expressions and intonation of speech style. SentiStrength works by way of unsupervised learning so it is easier to use to classify sentiments more quickly. This study uses SentiStrengthID which has been developed for Indonesian texts. The tweets used in this study are tweets in Indonesian. SentiStrength will classify sentences into 3 classes, namely positive, negative, and neutral sentiments to detect the spread of sarcasm sentences within each sentiment group. The results of this study indicate the SentiStrength accuracy value of 54.52% in detecting sarcasm sentences in Indonesian-language tweets. SentiStrength managed to divide the detected sarcasm sentences into as many as 13 sentences on positive sentiment, 24 sentences on negative sentiment, and 70 sentences on neutral sentiment.
Comparison of Kernels on Support Vector Machine (SVM) Methods for Analysis of Cyberbullying

Along with the development of social media, there are various abuses and deviations from the ethics of interaction by its users. One form of social media abuse is cyberbullying. Recently, cyberbullying has become more frequent and these events are still difficult to detect. Therefore, researchers conducted an analysis of cyberbullying on Twitter social media using the Support Vector Machine (SVM). The data used is the crawled data of Twitter in Indonesian. This is included in non-linear data so it requires the kernel to carry out the text mining process. However, to date there has been no specific research on what kernels are good to use in cases of cyberbullying. Therefore, researchers conducted an experiment to implement SVM on tweet classification. The researchers will also look for the best kernels among the four kernels, namely Polynomial, Radial Basis Function (RBF), Sigmoid, and Linear. From the results of the experiments conducted, the SVM method can be used for the classification of cyberbullying. In addition, it is known that the sigmoid kernel has the highest accuracy of 83.85%.
Facial Expression Classification Analysis Using Facial Images Based on Resnet-50V2

Emotion is one of the ways to express feelings, it can be shown through facial expressions or body movements. One of the easiest ways to tell someone's emotions is by looking at their facial expressions. But to detect emotions on human faces using computers and get high accuracy results is a challenge for computers to do. Facial emotional expression works with color segments and facial points that form patterns. Facial emotions work to describe the condition of the sender of the message to the recipient of the message. Emotions have a role as a supporting indicator in communicating in addition to the intonation of speech. The purpose of this research is to classify human emotions using Convolutional Neural Network (CNN) with ResNet-50v2 architecture. The best result is 83.16% using 200 epochs.
Characteristics of Blockchain-based Digital Asset Datasets: A Systematic Review

The widespread use of two types of digital assets derived from blockchain technology, namely cryptocurrencies and NFTs, is inextricably linked to the technology's high level of popularity. The transparency of the blockchain allows for quick access to a wealth of data. This work generates an in-depth review of online datasets for both sorts of digital assets. This study provides a thorough analysis of the platforms employed and the characteristics of the datasets. A meticulous analysis of 45 datasets and the 88 papers that used them reveals the use of more than seven different types of platforms and three different types of data characteristics across all datasets. Datasets with graph-based data type generally have low sparsity, while ordered data type can have various resolutions. Datasets with unstructured data type have specific properties to meet particular needs and can have high dimensionality.
Visualization of Front-End Data Logger Internet of Things Technology using Vue.Js Framework

The danger of spreading the new variant of Coronavirus Disease (COVID-19) still exists. It takes a new habit to prevent it. The World Health Organization (WHO)urges to minimize the spread of the COVID-19 variant, including by getting used to washing hands regularly. To support this call, the researchers carried out various innovations. One of them is through the application of Internet of Things (IoT) technology for routine hand washing. This research innovates by using an IoT tool that we named Amikom Purwokerto Hand Sanitizer (AMPUH). In addition to functioning as an automatic hand-washing device, this tool is also capable of recording temperature data, and the number of users, controlling hand sanitizer liquid and measuring saturation automatically. The recordings are stored through a data logger and processed through an IoT platform called ThingSpeak. ThingSpeak has the disadvantage of data visualization which is less attractive and informative. A website dashboard is needed to visualize the data contained on the ThingSpeak platform so that the display is more attractive and easier to understand. Therefore, this study aims to create a website dashboard that can provide information from the data stored on the ThingSpeak platform. The author focuses on the front-end by using the Vue.js framework. This framework has advantages such as being flexible (can be used in large-and small-scale websites), reducing repetitive code, and making the page display more dynamic. In building this front end, we apply the Extreme Programming development method. Based on the results of the study, showed that the implementation of the Vue.js framework on the AMPUH data logger front-end resulted in performance in accordance with expectations. Websites that are built to produce attractive and informative visualizations.
Use Case-Based Analytical Hierarchy Process Method for Software Requirements Prioritization

There are many factors that can cause the failure of a software project. One of them is the failure to identify and address the problems stakeholders face due to ineffective requirements engineering. Another factor is the failure to correctly determine the priorities of software requirements. Due to limited resources of software projects, it is essential to focus on the most important requirements to ensure software success. Therefore, requirement prioritization is a critical phase within the software development life cycle. This study proposes a method to prioritize software requirements based on the development of the Analytical Hierarchy Process (AHP) method. There are several limitations of the AHP method, namely suffering from scalability problems, time-consuming, and inconsistent due to the redundancy produced by the pairwise comparison. To address these limitations, we propose a method for requirements prioritization named the Use Case-Based Analytical Hierarchy Process (UC-Based-AHP), which aims to overcome the challenges faced by the AHP method by reducing the number of pairwise comparisons in the AHP method with the use of natural language processing (NLP) and previously created use cases. As a result, complexity will be reduced and the results obtained will have a 0.71 reliability value compared with the original AHP.
Comparison of Word2vec and Doc2vec Methods for Text Classification of Product Reviews

Word Embedding is the optimal tool for many Natural Language Processing (NLP) tasks, especially those that require native input as a text feature. In this study, we will try to compare the performance of Word2vec and Doc2vec on unbalanced review text data using XGBoost, which in the end will look for which combination is suitable for processing unbalanced data Word2vecProduces high-performance values. Doc2vec has a better advantage over Word2vec with the condition that we will know the closeness between the review sentences, not the closeness of words anymore. Based on experiments, the performance of Word2vec and Doc2vec paired with the XGBoost Algorithm was able to classify unbalanced datasets with an average F1 Score value of 0.9342 for Word2vec and 0.9344 for Doc2vec. The results of testing the vector dimensions for both Word2vec and Doc2vec show that the larger the size of the word vector dimensions used, the longer the training time required.
UML Design for Decision Support Model in Determining the Sustainability of Online Course Materials

The scenario of online learning is a very urgent need in the world of future knowledge. Since the Corona Virus Disease-19 pandemic, the world economy has started to plummet and caused many adults to lose their jobs. The advantage is the flexibility and rapid development of the internet. In 2020, the number of unemployed increased significantly. This reason makes people strive to improve their ability to meet job requirements by taking online courses. Online courses are a way that people can choose to improve their skills anywhere and anytime. The sustainability of online course material that is offered to the course user and issued by the company will be discussed in this study. The novelty of this research is to obtain a decision support model based on fuzzy logic for determining online courses. The method used is decision-making based on UML and fuzzy logic for the final decision. The fuzzy inference model process begins by determining the decision parameters then using fuzzification with absolute input then refracted with fuzzy criteria, and ends with defuzzification with absolute output. There are two groups of parameters in this study, company profits which consist of 5 parameters and user benefits, which consist of 9 parameters. Once the model is verified and valid, the final decision is useful for users looking for online course and also useful for the decision unit of online course companies in determining the sustainability of online course materials.
Comparative Analysis of Anomaly Classification Method for Flood Prediction in Smart City Platform

The term “smart city” refers to a city that addresses issues using information and communication technology. Through the utilization of this information technology, smart cities can become data producers as well as data consumers. All the data produced by the smart city is gathered onto a single data platform. The information that was gathered can be applied to the study of a variety of urban issues, including floods, which is one of them. The sensors used to forecast the weather can also be utilized to look for unusual patterns, which can help with flood prediction. This work presents a comparative analysis to evaluate which anomaly classification algorithm is the most effective in predicting flooding. For this investigation, we use data obtained from an actual weather radar installed in a city in Indonesia. In addition, we compared our findings to those of any other readily available datasets. Based on the results, we can choose several classification algorithm with the best selected evaluation metric.
Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data

Searching for health-related information online is becoming more difficult due to the proliferation of multiple-meaning phrases such as semantic words. This study examines the semantic process in medical terms using a collection of doctor's answer texts which requires finding an appropriate model for recognizing text in pairs of comparable Indonesian medical phrases or terminology synonyms. This study contributes to finding an automatic semantic text detection using a word embedding approach to identify text in pairs of similar Indonesian medical terms. We selected 108 pairs of annotated medical terms (Biomedical Named Entity Recognition (Bio-NER)) in Indonesian based on a collection of doctor texts with 60 pairs of similar words and 48 pairs of dissimilar words. Our dataset was processed with the word embedding approach of FastText and BioWordVec. There are two approaches of BioWordVec: with (BioWordVec-2) and without (BioWordVec) translation process. We compared the performance of FastText, BioWordVec, and BioWordVec-2 using measures like accuracy, specificity, and sensitivity. The results show that the BioWordVec-2 model performs better than other models in identifying similar pairs.