Reviewerly/exHarmony huggingface dataset:
Hate Speech Detection in Indonesian Twitter Texts using Bidirectional Gated Recurrent Unit

As the number of social media users rises, the probability of hate speech spread in social media also rises indirectly. Hate speech has become one of most common cases found on social media. The spread of hate speech can lead to a riot that might cause conflict, group extermination, and even human casualties. Some of the latest controversies in Indonesia related to hate speech was the hate speech uttered to the government that led to polemic and even demonstration in the country. Along with this, it is important to detect hate speech to avoid conflict to happen. As the spread of hate speech in social media increases, it requires significant human efforts and is costly to detect manually. Therefore, this experiment is built to detect hate speech detection in Indonesian twitter texts using several conventional machine learning and deep learning based, BiGRU, with various features. The machine learning approaches being used are SVM and RFDT, while deep learning based methods used are BiGRU and pre-trained IndoBERT with BiGRU. Several methods used are Word2vec and fastText. The experiment shows that BiGRU method with IndoBERT and no stop word removal achieves the best performance with 84.77% accuracy. BiGRU has advantages on storing important information from text, thus making a better result than conventional machine learning algorithm.
A Universal Bert-Based Front-End Model for Mandarin Text-To-Speech Synthesis

The front-end text processing module is considered as an essential part that influences the intelligibility and naturalness of a Mandarin text-to-speech system significantly. For commercial text-to-speech systems, the Mandarin front-end should meet the requirements of high accuracy and low time latency while also ensuring maintainability. In this paper, we propose a universal BERT-based model that can be used for various tasks in the Mandarin front-end without changing its architecture. The feature extractor and classifiers in the model are shared for several sub-tasks, which improves the expandability and maintainability. We trained and evaluated the model with polyphone disambiguation, text normalization, and prosodic boundary prediction for single task modules and multi-task learning. Results show that, the model maintains high performance for single task modules and shows higher accuracy and lower time latency for multi-task modules, indicating that the proposed universal front-end model is promising as a maintainable Mandarin front-end for commercial applications.
Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks

Reliably assessing model confidence in deep learning and predicting errors likely to be made are key elements in providing safety for model deployment, in particular for applications with dire consequences. In this paper, it is first shown that uncertainty-aware deep Dirichlet neural networks provide an improved separation between the confidence of correct and incorrect predictions in the true class probability (TCP) metric. Second, as the true class is unknown at test time, a new criterion is proposed for learning the true class probability by matching prediction confidence scores while taking imbalance and TCP constraints into account for correct predictions and failures. Experimental results show our method improves upon the maximum class probability (MCP) baseline and predicted TCP for standard networks on several image classification tasks with various network architectures.
Exploring Implicit Relationships in Social Network for Recommendation Systems

Online social platforms have provided a large amount of available information to recommendation systems. With this intuition, social recommendation systems emerged and have attracted increasing attention over the past years. Most existing social recommendation methods only use explicit social relationships among users. However, implicit social relationships can effectively improve the quality of recommendation when users only have few social relationships. To this end, the discovery of implicit relations among users plays a central role in advancing social recommendation. In this paper, we propose a novel approach to fuse direct and indirect friends toward discovering more accurate social recommendation method. We learn users' preferences by carefully integrating users' direct and indirect friends. In particular, we construct item rankings based on the feedback from users' direct and indirect friends on the item. Furthermore, to distinguish the impact of users' direct friends and indirect friends, we also extend the ranking assumption in item domain to user domain, so that information from user rankings can be leveraged to further improve the recommendation performance. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed method.
Blockchain technology in healthcare: making digital healthcare reliable, more accurate, and revolutionary

The growing pace of innovation and the changing face of healthcare are very fast, but still are not enough to meet today's challenges. The problem of interoperability among healthcare institutions, frequent cyberattacks on hospital centralized systems, and a massive amount of critical big data from clinical and biomedical research leave one to brainstorm and bring the focus to these areas. Blockchain is a distributed, decentralized, digitally shared ledger technology with the power of providing transparency, immutability, trust, and security. Blockchain technology in healthcare is being utilized in the hope to solve various issues of medical sciences. Donating organs, the supply chain in medicine, crowdfunding medical research, cyberattacks, and interoperability are some of the issues that blockchain can solve. This chapter aims at introducing the concept of blockchain, its origin, use cases in healthcare, and how various platforms like Ethereum, Linux Foundation Hyperledger, Quorum, and Corda can be useful to medical science.
High-Performance Mixed-Low-Precision CNN Inference Accelerator on FPGA

Low-precision techniques can effectively reduce the computational complexity and bandwidth requirements of a convolutional neural network (CNN) inference, but may lead to significant accuracy degradation. Mixed-low-precision techniques provide a superior approach for CNN inference since it can take the advantages of low precision while maintaining accuracy. In this article, we propose a high-performance, highly flexible W <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> W8A8 (INT8 weight and INT8 activation) and W <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> WTA2 (TERNARY weight and INT2 activation) mixed-precision CNN inference hardware architecture, DPUmxp, designed and implemented on Xilinx Virtex UltraScale+13P FPGA with peak performance up to 58.9 TOPS.
Comparison of Methods for Evaluating Complexity of Simplified Texts among Deaf and Hard-of-Hearing Adults at Different Literacy Levels

Research has explored using Automatic Text Simplification for reading assistance, with prior work identifying benefits and interests from Deaf and Hard-of-Hearing (DHH) adults. While the evaluation of these technologies remains a crucial aspect of research in the area, researchers lack guidance in terms of how to evaluate text complexity with DHH readers. Thus, in this work we conduct methodological research to evaluate metrics identified from prior work (including reading speed, comprehension questions, and subjective judgements of understandability and readability) in terms of their effectiveness for evaluating texts modified to be at various complexity levels with DHH adults at different literacy levels. Subjective metrics and low-linguistic-complexity comprehension questions distinguished certain text complexity levels with participants with lower literacy. Among participants with higher literacy, only subjective judgements of text readability distinguished certain text complexity levels. For all metrics, participants with higher literacy scored higher or provided more positive subjective judgements overall.
Domain Adaptive Meta-Learning for Dialogue State Tracking

Domain adaptation for low-resource dialogue state tracking (DST) is of significance due to the growing diversity of conversation scenarios. In this paper, we propose a novel domain adaptive model-agnostic meta-learning (DAMAML) framework. Under this framework, we equip the DST model with two domain adaptors and a unified parameter generator. The parameter generator takes a domain embedding as input to produce parameters of domain adaptors, which modulate domain-shared initial parameters to the subspace of each domain. In this way, we simultaneously model multiple individual meta-learners with each covering the distribution of one domain, allowing more efficient adaptation. Compared with the conventional MAML, this framework not only is able to seek domain-shared initial parameters that facilitate fast adaptation, but also has better capability to fit a diversified domain distribution. Experimental results and in-depth analysis demonstrate the effectiveness of the proposed framework.
Graph Structure Extraction from Floor Plan Images and Its Application to Similar Property Retrieval

Floor plan images describe the layout of real estates. Although they provide essential information on properties for evaluating their value, they are intractable for computers due to various drawing styles owing to lack of standard formats. The purpose of this paper is to automatically transform a floor plan image into a graph that is well-structured and mathematically well-defined representation. This approach facilitates comparing and evaluating floor plans and can be applied to many systems handling floor plans. Our method achieves it with 92% accuracy. Furthermore, we show similar property retrieval using the extracted graphs as an example of possible applications.
Content-Sensitive Superpixel Generation for SAR Images With Edge Penalty and Contraction–Expansion Search Strategy

In this article, we present a content-sensitive superpixel generation method with edge penalty and the contraction–expansion search strategy (EPCES) for synthetic aperture radar (SAR) images. Specifically, the edge information can be obtained by our previously proposed ratio-based edge detector with recurrent guidance filter, which has been proven to be robust to speckle noise and capable of detecting weak edges in low-contrast areas. The content-sensitive superpixel seeds’ initialization method is proposed with respect to the heterogeneous state of the SAR imagery, benefiting from which EPCES can generate an exact number of superpixels set by the user and the fine details can be preserved well. In EPCES, a new dissimilarity with edge penalty is defined to generate the superpixels with better edge adherence. Rather than adopting the conventional clustering method based on local <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means, we propose the contraction–expansion search strategy (CES), which explicitly utilizes the continuity information contained in neighboring pixels and enforces the connectivity of the superpixel without any postprocessing step. With the aid of the CES, our proposed method can attain superpixels with low computational cost and high edge adherence. Experimental results on both synthetic and real-world SAR images verify that the proposed method consistently performs favorably against several state-of-the-art methods in terms of both quality and efficiency.
Remote sensing image super‐resolution based on convolutional blind denoising adaptive dense connection

The current super-resolution (SR) deep network is mainly applied to the common image and pays little attention to the image with noise. The remote sensing image contains much noise, so that the SR reconstruction effect is not satisfactory. Therefore, a convolution blind denoising adaptive dense connection SR (CBD-ADCSR) network for the remote sensing image is proposed in this paper. The whole model is divided into a convolution blind denoising (CBD) network for denoising and an ADCSR network for reconstruction. Firstly, the components of the network are given in detail and are analysed. Secondly, a data set making method is designed combining motion blur, defocusing blur and Gaussian noise, which is used to generate low-resolution image data sets with complex degradation for the model training. Finally, through the detailed comparative experiment, it is proved that the reconstruction effect of the CBD-ADCSR model is better than that of the most state-of-the-art algorithms in objective criteria. In addition, compared with the original ADCSR network, CBD-ADCSR has a stronger ability for noise suppression.
Scattering-based Quality Measures

Various clustering algorithms use diverse settings, parameters, and initializations, generally result in different clustering solutions. Therefore, it is essential to compare and evaluate the clustering results and select the methods that best fits the “actual” data distribution. This can be achieved by using informative quality metrics that reflect the “goodness” of the resulting solutions compared to the ground truth. Different Extrinsic validation metrics have been provided in the literature, including F-measure, Entropy, Rand Index, and Purity. However, there is a gap in the literature in evaluating the level of divergence between multiple clusterings in an aggregate, especially in consensus clustering. In this paper, we propose three scattering measures that calculate the divergence level (i.e., scattering level) between two or more clustering algorithms. The proposed metrics are Scatter F-score, Scatter Entropy, and Scatter Purity. The proposed scattering measures are variants of the traditional F-measure, Entropy, and Purity quality measures. The scattering measures are used as pre-assessment criteria for deciding which clustering algorithms to combine in an aggregate. Experimental results on artificial, real, and text datasets show that the scattering measures play an important role in enhancing the clustering quality in consensus clustering and increasing the feasibility of the consensus.
Hyper-Convolution Networks for Biomedical Image Segmentation

The convolution operation is a central building block of neural network architectures widely used in computer vision. The size of the convolution kernels determines both the expressiveness of convolutional neural networks (CNN), as well as the number of learnable parameters. Increasing the network capacity to capture rich pixel relationships requires increasing the number of learnable parameters, often leading to overfitting and/or lack of robustness. In this paper, we propose a powerful novel building block, the hyper-convolution, which implicitly represents the convolution kernel as a function of kernel coordinates. Hyper-convolutions enable decoupling the kernel size, and hence its receptive field, from the number of learnable parameters. In our experiments, focused on challenging biomedical image segmentation tasks, we demonstrate that replacing regular convolutions with hyper-convolutions leads to more efficient architectures that achieve improved accuracy. Our analysis also shows that learned hyper-convolutions are naturally regularized, which can offer better generalization performance. We believe that hyper-convolutions can be a powerful building block in future neural network architectures for computer vision tasks. We provide all of our code here: https://github.com/tym002/Hyper-Convolution
A new system for analyzing contents of Computer Science courses

With the growth of technology and the changing of human behavior, the fields of Computer Science (CS), Information Technology (IT), and Data Science (DS) become more popular and should be learned by children, students, and people. Consequently, institutes widely initiate new online/offline courses to serve this need. However, this leads to educate different knowledge and skills in which some of them may not match the needs of companies for hiring employees. To address this issue, we introduce a new system for analyzing content on CS courses, called CSCDA system (Computer Science Course Description Analysis system). The system can identify similar and dissimilar contents belonging to two CS courses by applying text processing techniques and keyword similarity matching. These identified contents can help to set up a standard, to improve integrity and quality, and to reduce redundancy of the contents will be taught in the courses. Experimental studies are conducted on CS courses of Thai Universities to investigate the effectiveness of the CSCDA system based on four measures, i.e. percentage of similar contents, precision, recall, and F-measure, respectively. Last, a comparative study is performed and the result shows that our proposed method can effectively analyze course contents and outperforms the other keyword extraction methods.
Facebook Tells Me Your Gender: An Exploratory Study of Gender Prediction for Turkish Facebook Users

Online Social Networks (OSNs) are very popular platforms for social interaction. Data posted publicly over OSNs pose various threats against the individual privacy of OSN users. Adversaries can try to predict private attribute values, such as gender, as well as links/connections. Quantifying an adversary’s capacity in inferring the gender of an OSN user is an important first step towards privacy protection. Numerous studies have been made on the problem of predicting the gender of an author/user, especially in the context of the English language. Conversely, studies in this field are quite limited for the Turkish language and specifically in the domain of OSNs. Previous studies for gender prediction of Turkish OSN users have mostly been performed by using the content of tweets and Facebook comments. In this article, we propose using various features, not just user comments, for the gender prediction problem over the Facebook OSN. Unlike existing studies, we exploited features extracted from profile, wall content, and network structure, as well as wall interactions of the user. Therefore, our study differs from the existing work in the broadness of the features considered, machine learning and deep learning methods applied, and the size of the OSN dataset used in the experimental evaluation. Our results indicate that basic profile information provides better results; moreover, using this information together with wall interactions improves prediction quality. We measured the best accuracy value as 0.982, which was obtained by combining profile data and wall interactions of Turkish OSN users. In the wall interactions model, we introduced 34 different features that provide better results than the existing content-based studies for Turkish.
Data Reliability and Trustworthiness Through Digital Transmission Contracts

As decision-making is increasingly data-driven, trustworthiness and reliability of the underlying data, e.g., maintained in knowledge graphs or on the Web, are essential requirements for their usability in the industry. However, neither traditional solutions, such as paper-based data curation processes, nor state-of-the-art approaches, such as distributed ledger technologies, adequately scale to the complex requirements and high throughput of continuously evolving industrial data. Motivated by a practical use case with high demands towards data trustworthiness and reliability, we identify the need for digitally-verifiable data immutability as a still insufficiently addressed dimension of data quality. Based on our discussion of shortcomings in related work, we thus propose ReShare, our novel concept of digital transmission contracts with bilateral signatures, to address this open issue for both RDF knowledge graphs and arbitrary data on the Web. Our quantitative evaluation of ReShare’s performance and scalability reveals only moderate computation and communication overhead, indicating significant potential for cost-reductions compared to today’s approaches. By cleverly integrating digital transmission contracts with existing Web-based information systems, ReShare provides a promising foundation for data sharing and reuse in Industry 4.0 and beyond, enabling digital accountability through easily-adoptable digitally-verifiable data immutability and non-repudiation.
Efficient Binary Cnn For Medical Image Segmentation

In this work, we propose accurate binary Depthwise Separable Convolutional Neural Networks (DSCNNs) for medical image segmentation. The networks are binarized by learning the distribution of weights and activations, and by using parameter-free skip connections in their encoder and decoder structure. We design full precision DSCNNs based on a symmetric encoder-decoder, feature pyramid network with an asymmetric decoder, and spatial pyramid pooling with atrous convolutions strategies for image segmentation. The DSCNNs have 14 X and 8 X fewer number of model parameters and operations, respectively, than standard segmentation networks. The trained full precision DSCNNs are used as baselines to achieve accurate binary DSCNNs. The networks are trained on two medical ultrasound datasets, a public fetal skull dataset and a privileged bladder dataset. The accuracy of the binary DSCNNs are within a 3% drop from the full precision networks on both the medical datasets.
Improving Indonesian Text Classification Using Multilingual Language Model

Compared to English, the amount of labeled data for Indonesian text classification tasks is very small. Recently developed multilingual language models have shown its ability to create multilingual representations effectively. This paper investigates the effect of combining English and Indonesian data on building Indonesian text classification (e.g., sentiment analysis and hate speech) using multilingual language models. Using the feature-based approach, we observe its performance on various data sizes and total added English data. The experiment showed that the addition of English data, especially if the amount of Indonesian data is small, improves performance. Using the fine-tuning approach, we further showed its effectiveness in utilizing the English language to build Indonesian text classification models.
An Asymmetric Cycle-Consistency Loss For Dealing With Many-To-One Mappings In Image Translation: A Study On Thigh Mr Scans

Adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a high potential in medical applications. However, the fact that images in one domain potentially map to more than one image in another domain (e.g. in case of pathological changes) exhibits a major challenge for training the networks. We offer a solution to improve the training process in case of many-to-one mappings by modifying the cycle-consistency loss. We show formally and empirically that the proposed method improves the performance without radically changing the architecture and increasing the model complexity.