Reviewerly/exHarmony huggingface dataset:
A Hybrid Approach to Blind Video Quality Prediction of User Generated Content

Increased use of social media platforms has resulted in a vast amount of user-generated video content being released to the internet daily. Measuring and monitoring the perceptual quality of these videos is vital for efficient network and storage management. However, these videos do not have a pristine reference, posing challenges for accurate quality monitoring. In this paper, we introduce a hybrid metric to measure the perceptual quality of user-generated video content using both pixel-level and compression-level features. Our experiments on large-scale databases of user-generated content show that the proposed method performs comparably in predicting the perceptual quality when compared with state-of-the-art metrics.
Neuro-fuzzy ensembles: A systematic mapping study

In the last decade, neurofuzzy networks have received considerable attention from academia. These systems strike a tradeoff between the performance of artificial neural networks and the interpretability of fuzzy inference systems expressed through fuzzy rules. Many researchers, however, are still dissatisfied with the performance of single neuro-fuzzy systems and are constantly working to improve them using ensemble techniques. This study conducts a systematic mapping of relevant studies on the application of ensemble techniques to neuro-fuzzy systems. Many aspects of the study are highlighted, including publication years, sources, contribution types, and application domains. As result, 48 articles published from 2000 to Mars 2022 were selected from six digital libraries (Science Direct, IEEE Xplore, ACM Digital Library, PubMed, Wiley, and Google Scholar. The findings revealed that the number of studies employing ensemble techniques with neuro-fuzzy systems is low and unstable. As the most used neuro-fuzzy system, ANFIS, from the Takagi-Sugeno-Kang category, is being studied in a wide range of application domains, most notably finance, medicine, and ecology.
Privacy-Preserving Feature Coding for Machines

Automated machine vision pipelines do not need the exact visual content to perform their tasks. Therefore, there is a potential to remove private information from the data without significantly affecting the machine vision accuracy. We present a novel method to create a privacy-preserving latent representation of an image that could be used by a downstream machine vision model. This latent representation is constructed using adversarial training to prevent accurate reconstruction of the input while preserving the task accuracy. Specifically, we split a Deep Neural Network (DNN) model and insert an autoencoder whose purpose is to both reduce the dimensionality as well as remove information relevant to input reconstruction while minimizing the impact on task accuracy. Our results show that input reconstruction ability can be reduced by about 0.8 dB at the equivalent task accuracy, with degradation concentrated near the edges, which is important for privacy. At the same time, 30% bit savings are achieved compared to coding the features directly.
Natural Language Inference for Arabic using Recurrent Neural Network and Word Embedding

The Natural Language Inference (NLI) task is a field of Natural Language Processing (NLP) in which researchers try to find ways to decide about the inference relation between two sentences. In this paper, we present an early version of an Arabic-oriented NLI system based on the word embedding technique and a deep learning model, namely Bi-LSTM. In this paper, we provide a general description of the system's various components as well as results from the parts that have already been implemented. Our primary experiments yielded positive results.
Learned Image Compression with Inception Residual Blocks and Multi-Scale Attention Module

Recently, deep learning-based image compression methods have achieved superior performance compared to traditional methods. However, the complexity of the leading scheme is still quite high in both the core network and the entropy coding. In this paper, we propose two efficient modules. First, we adopt an inception residual block (IRB) in the core network, which has lower complexity than previous non-local attention module and concatenated residual blocks. Second, we employ a multi-scale attention module (MSAM), which aggregates features from three different scales to capture the global information. The output of MSAM is used as importance map to guide bits allocation. In addition, the simple Gaussian mixture model is used in the entropy coding, instead of more complicated models. Experimental results demonstrate that the encoding and decoding of our method are about 17 times faster than the state-of-the-art method. Although the R-D performance drops slightly, the performance is still better than H.266/VVC (4:4:4) and other recent learning-based methods.
A Deep Learning post-processor with a perceptual loss function for video compression artifact removal

While video compression is necessary for large scale video streaming services, compression at low bitrate can degrade the original video and negatively affect the end user’s quality of experience. Deep Neural Networks (DNNs) are actively researched with respect to artifact removal, however the loss functions that are typically employed follows a derivation of a pixel-wise L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> norm. In this paper we consider a DNN as a post-processor for video compression artifact removal. The DNN is trained using a composite perceptual loss that combines a traditional L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> norm loss and a VMAF proxy network based on the Video Multimethod Assessment Function (VMAF). Results show an improvement in VMAF score over both the training and testing sets.
Using Self-labeling and Co-Training to Enhance Bots Labeling in Twitter

The rapid evolution in social bots have required efficient solutions to detect them in real-time. In fact, obtaining labeled stream datasets that contains variety of bots is essential for this classification task. Despite that, it is one of the challenging issues for this problem. Accordingly, finding appropriate techniques to label unlabeled data is vital to enhance bot detection. In this paper, we investigate two labeling techniques for semi-supervised learning to evaluate different performances for bot detection. We examine self-training and co-training. Our results show that self-training with maximum confidence outperformed by achieving a score of 0.856 for F1 measure and 0.84 for AUC. Random Forest classifier in both techniques performed better compared to other classifiers. In co-training, using single view approach with random forest classifier using less features achieved slightly better compared to single view with more features. Using multi-view of features in co-training in general achieved similar results for different splits.
Perceptual Video Coding Based on Semantic-Guided Texture Detection and Synthesis

Visually insensitive texture regions consume a large number of bitrate in hybrid video coding, leading to the waste of bandwidth resources. For this, we propose a semantic-guided texture synthesis framework (STSF). At encoder, high-level semantic information is adopted as texture features to detect texture regions and is sent to the decoder. Detected texture regions are coarsely encoded by hybrid codec. To generate realistic texture patterns, we design a multi-model semantic-guided texture synthesis generative adversarial network (STSGAN) at decoder, which works in a divide-and-conquer manner that semantically different texture regions are synthesized by different submodels in it. Experimental results show that STSF can achieve a −17.2% MOS BD-rate under the lowdelay_P configuration, compared with VVC.
Light Field Compression Based on Implicit Neural Representation

Light field, as a new data representation format in multimedia, has the ability to capture both intensity and direction of light rays. However, the additional angular information also brings a large volume of data. Classical coding methods are not effective to describe the relationship between different views, leading to redundancy left. To address this problem, we propose a novel light field compression scheme based on implicit neural representation to reduce redundancies between views. We store the information of a light field image implicitly in an neural network and adopt model compression methods to further compress the implicit representation. Extensive experiments have demonstrated the effectiveness of our proposed method, which achieves comparable rate-distortion performance as well as superior perceptual quality over traditional methods.
The Effect of In-Game Advertising on Non-Immersive Game Experience

Virtual Reality (VR) games, either in their Immersive or Non-Immersive form, are the cornerstone of the entertainment industry. Integrating advertisements inside game content is one of the most profitable ways of games monetization. In-game advertisements are either integrated into a game's content as diegetic game elements or as non-diegetic, simply placed in designated game-world areas to solely demonstrate a product or brand. This work presents preliminary evaluation results on the impact diegetic and non-diegetic in-game advertisements have on the non-immersive game experience.
As the End Drew Near: Exploring Volumetric Video in Videogames (Demo)

VVV: Volumetric Video in Videogames is a research-creation project focused on advancing experimental development using volumetric video in expressive videogames. It aimed to push deeper into interaction design paradigms for hybrid image forms like volumetric video, and offer a viable channel for engaging captured content in videogames. As the End Drew Near is the first experimental game prototype for VVV, informed by design strategies adapted from FMV games.
A security testing mechanism for detecting attacks in distributed software applications using blockchain

Distributed software applications are one of the most important applications currently used. Rising demand has led to a rapid increase in the number and complexity of distributed software applications. Such applications are also more vulnerable to different types of attacks due to their distributed nature. Detecting and addressing attacks is an open issue concerning distributed software applications. This paper proposes a new mechanism that uses blockchain technology to devise a security testing mechanism to detect attacks on distributed software applications. The proposed mechanism can detect several categories of attacks, such as denial-of-service attacks, malware and others. The process starts by creating a static blockchain (Blockchain Level 1) that stores the software application sequence obtained using software testing techniques. This sequence information exposes weaknesses in the application code. When the application is executed, a dynamic blockchain (Blockchain Level 2) helps create a static blockchain for recording the responses expected from the application. Every response should be validated using the proposed consensus mechanism associated with static and dynamic blockchains. Valid responses indicate the absence of attacks, while invalid responses denote attacks.
Introducing Preregistration of Research Design to Archaeology

Archaeology has an issue with "just-in-time" research, where insufficient attention is paid to articulating a research design before fieldwork begins. Data collection, management, and analysis approaches are under-planned and, often, evolved during fieldwork. While reducing the amount of preparation time for busy researchers, these tendencies reduce the reliability of research by exacerbating the effects of cognitive biases and perverse professional incentives. They cost time later through the accrual of technical debt. Worse, these practices hinder research transparency and scalability by undermining the quality, consistency, and compatibility of data. Archaeologists would benefit from embracing the "preregistration revolution" sweeping other disciplines. By publicly committing to research design and methodology ahead of time, researchers can produce more robust research, generate useful and reusable datasets, and reduce the time spent correcting problems with data. Preregistration can accommodate the diversity of archaeological research, including quantitative and qualitative approaches, hypothesis-testing and hypothesis-generating research paradigms, and place-specific and generalizing aims. It is appropriate regardless of the technical approach to data collection and analysis. More broadly, it encourages a more considered, thoughtful approach to research design. Preregistration templates for the social sciences can be adopted for use by archaeologists.
Proposing System to Recognize Emotions in Public Network Using Phobert Deep Learning Model

People can receive information faster especially in the 4.0 revolution with the continuous development of revolutions. The information can affect our emotional, psychological and spiritual well-being, especially in the recent high school graduation exam across the country. Therefore, we propose to build a user emotion analysis system in a public network with the PhoBert training model in the paper. Besides, we built our dataset aggregated from social networks, articles, blogs, etc. We next use the PhoBert model to solve the processing data problems. The simulation results have shown an accuracy of 86.5% on the training and 81.32% on the validation dataset with a training time of 3 hours (about 180 minutes). The results also show that we can build a warning system to avoid health and psychological effects with great emotion.
Comparison of Kernel Support Vector Machines in Conducting Sentiment Analysis Review of Buying Chips on the Shopee E- Marketplace in Indonesian

Small and Medium Enterprises (SMEs) have an important and strategic role because they have advantages in fields that utilize natural resources and are labor intensive. Therefore, E-Marketplace's utilization positively influences improving marketing and sales performance in SMEs. Chips are one of the popular SMEs processed products and vary from cassava chips, taro chips, banana chips, spinach chips, etc. In addition to stores, we can find this product on the Shopee e-marketplace. To improve and maintain the quality of chips as an SME product, it is necessary to analyze sentiment. This study conducted a sentiment analysis of the review of buying chips on the Shopee E-marketplace using the NLP approach, which was then carried out by classification modeling using SVM. This research tries to find the best model of the SVM kernel among Linear, Polynomial, Gaussian RBF, and Sigmoid kernels. Based on the experiments conducted, the Linear kernels provide the best performance. The result of linear kernel accuracy and recall is 89.60%. While the linear precision kernel value is 88.8%, then the Fl-Score value is 88.60%. In the end, the linear kernels in the SVM Algorithm have good potential to be used in the development of various systems related to detecting user reviews on the Indonesian-language Shopee E-Marketplace.
A Linguistic-based Transfer Learning Approach for Low-resource Bahnar Text-to-Speech

The Text-to-Speech (TTS) model often requires a large number of recorded utterances in standard quality for a high-fidelity synthesized speech. For low-resource languages, lacking data becomes a big challenge. In this work, we address this problem in the Bahnar Kriem language, a rare language used by Bahnar people living in Binh Dinh county, Vietnam. We propose the linguistic approach to process a poor-quality dataset of 720 utterances of Bahnar Kriem language, along with some preprocessing steps. We also analyze the Bahnar Kriem language and figure out a mixture between Bahnar and Vietnamese due to the historical development between the two races. Therefore, we propose the transfer learning approach to integrate the Vietnamese pronunciation into the Bahnar TTS synthesizer. The experiments show significant improvement in the performance of the TTS model for a low-resource language. Our model can also generate long Bahnar sentences with a short inference time. The subjective and objective evaluations suggest promising results and some potential improvements based on our approach. We also provide audio samples generated from our model <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Sentiment Analysis based on word vector representation for short comments in Vietnamese language

Word vector representation is a major stage in Natural Language Processing (NLP). It can be applied in various application such as sentiment analysis, text mining, topic detection, document summarization, information retrieval and has an impact to the performance. In literature, different proposed method focus on enhancing word representation model by N-gram, TF-IDF, and word embedding. This paper investigates several word vector representation for Vietnamese sentiment analysis including TF-IDF, Word2Vec, GloVe, and Doc2Vec. The experiment is evaluated on the five common classifiers and two Vietnamese sentiment analysis dataset.
Data Sharing and Electronic Medical Record Privacy Protection of Out-Patient-Department Using Blockchain

According to Thai national policies and laws, public health care is not allowed to share information about patients. The patient has to provide information and frequent repetition of histories such as congenital disease, or drug allergy. Since the system is suitable for managing databases or ledgers, blockchain technology can be used as an electronic database system for information sharing. As a result, 20 users, including patients and doctors from a local hospital in Chiang Mai, were involved. These studies revealed that patients and doctors can now obtain more treatment information more quickly than ever before, without requiring permission from data protection service providers. It costs money, is time-consuming, and can also share patient health information across clinics, or can share patient health information instantly between medical professionals without breaking any law. Because the information provider is the real owner of the information.
Systematic Literature Review of Augmented Reality and Virtual Reality Development in Education

Technology has been growing fast and influencing many different aspects of life. Recent studies on AR/VR have revealed that both technologies can potentially improve general education system. Implementing AR/VR for education can occupy further learning experience in such an innovative and efficient manner. This research was dedicated to students who studied at the tertiary level of education and all teachers using AR/VR as a learning method. This study concluded that STEM fields dominate the utilization of AR/VR compared to other fields. The second position was education field which utilize the novelty of AR/VR to increase learners' motivation. The third place is medical field which use AR/VR for simulations. The common outcome of the utilization of AR/VR is increased learning efficiency and motivation for the learner. The general response of people who use AR/VR technology is positive with some negative opinions, especially regarding the cost and delivery.
A Multi-scale Approach for Vietnamese Image Captioning in Healthcare Domain

The image caption generator is a task that aims to automatically generate a natural language with syntactically and semantically meaningful sentences to describe the visual content of a given image. This problem is attractive because it is a combination of two fields Computer Vision and Natural Language Processing. Despite some research on this problem, most of this research only focuses on generating English captions. In this paper, we present a Transformer-based model for this problem based on the VieCap4H dataset - the first grand dataset for the Healthcare domain in Vietnamese. In detail, we first propose the TG2F module to enhance visual representations and the BERT-based language model to obtain language presentation. Through experiments on the VieCap4H dataset, our approach achieves competitive results on the public test and private test without using any data augmentation method.
Training Siamese Neural Network Using Triplet Loss with Augmented Facial Alignment Dataset

In recent years, deep learning methods, especially CNN, have been gaining huge progress in the development of technologies and humanity. Despite this progress, face recognition challenges are still hindering it. In this paper, we investigate the improvement in the performance of face recognition models by applying a Siamese neural network with triplet loss function and train with an augmented facial dataset. Furthermore, this dataset is collected, cropped, aligned, and augmented with various adjustments in which fill the facial recognition challenges requirements. Moreover, we compare the proposed model with the two best public models using two proposed algorithms. Experimental results display good improvement, and we discuss the possible usage as in checking attendance or biotechnique.
Application of Genetic Algorithm K-Means Clustering of Villagers Characteristics For Smart Economy

One of the solutions carried out in helping rural economic growth is to implement a smart economy in smart village ecosystem. The purpose of this study is to apply the K-Means Genetic Algorithm grouping technique to analyze the readiness of villagers of Kabandungan Sub-regency Sukabumi Regency for the smart economy. In research, there is also a concept that is useful in the course of the research process, namely KDD. Tests were carried out on 350 respondents and 221 variables. Three methods are applied to the data namely GA, K-Means and GA K-Means. The optimal cluster value obtained at the elbow is 3 / K=3. The Davies Bouldin index (DBI) value is 14,511 for the GA method, next DBI value is 2,608 for the K-Means method, and the last value is 2,472 for the GA K-Means method. This makes the selection of the GA K-means method further analyzed, then the ANOV A approach is carried out to see the significance of the method results. The clustering results show that there are 23% of residents who are not ready for the smart economy, 57% of citizens who are sufficiently prepared for the smart economy, and 20% of residents who are very prepared for the smart economy. and the population of Cianaga Village who occupy the readiness is very well prepared & quite well prepared. The researchers' findings show that there are 3 levels of citizen participation, namely where cluster 1 is the crowdsourcing level, then cluster 2 distributed intelligence, and cluster 3 participatory science.
Page Object Detection in Vietnamese Document Images with Novel Approach

We witnessed the rising popularity of Vietnamese documents on online platforms. Digitized Vietnamese documents (e.g., administrative text, scientific papers, textbooks, etc.) are available online. As a result, we need algorithms that can understand documents. Vietnamese is one of the most difficult languages with the Latin alphabet with additional accent symbols and derivative characters. Moreover, we still struggle with challenges arising from external and internal factors. This requires a good enough detector model as the foundation for extracting information tasks. In this research, we address page object detection in Vietnamese document images. We explore the performance of the UIT-DODV-Ext dataset, the largest Vietnamese document image dataset that includes scientific papers and textbooks. Additionally, we leverage the state-of-the-art object detector and then propose CasGRoIENet to improve the performance of the UIT-DODV-Ext dataset. CasGRoIENet achieves 75.9% mAP which is 2.3% higher than state-of-the-art results.