Reviewerly/exHarmony huggingface dataset:
Doing Business using Cryptocurrency in Malaysia

As the society is becoming more digitised day by day, we are being constantly introduced with advanced smart technologies that transpire changes in our lives. The blockchain technology and cryptocurrencies came in at the appropriate time which has provided pathways and security for many vulnerable internet-connected devices as the technology uses distributed verification of transactions. Due to their unique features, cryptocurrencies carry value on their own and now can be used for trading and transactions. In many established markets, transactions and trading in cryptocurencies have been growing and many have seen them as potential assets and investment option. Nevertheless, in many other countries, cryptocurrencies are not very popular and due to lack of awareness, many of their citizens are yet to own any cryptocurrencies. This study assessed the awareness of cryptocurrencies among selected Malaysian public and tested certain determinants and found almost three quarter of the respondents were aware of them but none is owning even one digital currency. Among the determinants, age group, ethnicity and occupation status were found to have influenced respondents’ awareness about cryptocurrencies. If it is an interest of the government to promote the use of cryptocurrencies, necessary exposure campaigns as well as guarantee of control and security should be made a priority.
Interactive Portrait Bokeh Rendering System

Portrait bokeh rendering has become a hot topic in computer vision and graphics in recent years. Existing methods usually suffer from noticeable artifacts around foreground boundaries and unrealistic rendering effects. To tackle these problems, we design a brand new bokeh system in this paper. The system is comprised of three modules: depth estimation, portrait matting, and bokeh rendering. The introduction of the portrait matting module makes it possible to preserve the details of portraits in final rendering results. In bokeh rendering modules, we propose two pixelwise rendering methods which are based on light gathering and light scattering to render realistic bokeh effect. For flexibility and interactivity. We provide two parameter interfaces, i.e., aperture size and bokeh salience to adjust rendering details according to the preferences of different users. Finally, experimental results on our synthetic dataset and real images demonstrate the effectiveness of our proposed method.
SiCoDeF² Net: Siamese Convolution Deconvolution Feature Fusion Network for One-Shot Classification

Nowadays, deep convolutional neural networks (CNNs) for face recognition exhibit a performance comparable to human ability in the presence of the appropriate amount of labelled training data. However, training CNNs remains as an arduous task due to the lack of training samples. To overcome this drawback, applications demand one-shot learning to improve the obtained performances over traditional machine learning approaches by learning representative information about data categories from few training samples. In this context, Siamese convolutional network (SiConvNet) provides an interesting deep architecture to tackle the data limitation. In this regard, applying the convolution operation on real world images by using the trainable correlative Gaussian kernel adds correlations to the output images, which hinder the recognition process due to the blurring effects introduced by the convolution kernel application. As a result the pixel-wise and channel-wise correlations or redundancies could appear in both single and multiple feature maps obtained by a hidden layer. In this sense, convolution-based models fail to generalize the feature representation because of both the strong correlations presence in neighboring pixels and the channel-wise high redundancies between different channels of the feature maps, which hamper the effective training. Deconvolution operation helps to overcome the shortcomings that limit the conventional SiConvNet performance, learning successfully correlation-free features representation. In this paper, a simple but efficient Siamese convolution deconvolution feature fusion network (SiCoDeF2Net) is proposed to learn the invariant and discriminative complementary features generated from both the (i) sub-convolution (SCoNet) and (ii) sub deconvolutional (SDeNet) networks using a concatenation operation which significantly improves the one-shot unconstrained facial recognition task. Extensive experiments performed on several widely used benchmarks, provide promising results, where the proposed SiCoDeF2Net model significantly outperforms the current state-of-art in terms of classification accuracy, F1, precision and recall. The code will be available on: https://github.com/purbayankar/SiCoDeF2Net.
Research on Business English Translation Architecture Based on Artificial Intelligence Speech Recognition and Edge Computing

In today’s society, the continuous deepening of international cultural integration has become the background of the times. China has become more and more closely connected with the world, and many physical or online news media have become a platform for China to receive world information and spread Chinese culture. Business English translation is therefore valued by translation researchers and translators. Aiming at the shortcomings of current business English translation research, this paper designs and develops a business English translation architecture based on artificial intelligence speech recognition and edge computing. First of all, considering the relevance and complementarity between speech and text modalities, this paper uses the deep neural network feature fusion method to effectively fuse the extracted monomodal features and perform speech recognition. Secondly, adopt the edge computing method to establish the business English translation system architecture. Finally, the simulation test analysis verifies the efficiency of the business English translation framework established in this paper. Compared with the existing methods, our proposal improved the accuracy than others at least 10% and the time of model building also decreased obviously. The purpose of this research is to discuss how to deal with the many differences between the source language and the target language, and how to enhance the readability of the translation and meet the reader’s cultural cognition and needs.
Toward Unsupervised 3d Point Cloud Anomaly Detection Using Variational Autoencoder

In this paper, we present an end-to-end unsupervised anomaly detection framework for 3D point clouds. To the best of our knowledge, this is the first work to tackle the anomaly detection task on a general object represented by a 3D point cloud. We propose a deep variational autoencoder based unsupervised anomaly detection network adapted to the 3D point cloud and an anomaly score specifically for 3D point clouds. To verify the effectiveness of the model, we conducted extensive experiments on ShapeNet dataset. Through quantitative and qualitative evaluation, we demonstrate that the proposed method outperforms the baseline method.
Lightweight Monocular Depth with a Novel Neural Architecture Search Method

This paper presents a novel neural architecture search method, called LiDNAS, for generating lightweight monocular depth estimation models. Unlike previous neural architecture search (NAS) approaches, where finding optimized networks is computationally demanding, the introduced novel Assisted Tabu Search leads to efficient architecture exploration. Moreover, we construct the search space on a pre-defined backbone network to balance layer diversity and search space size. The LiDNAS method outperforms the state-of-the-art NAS approach, proposed for disparity and depth estimation, in terms of search efficiency and output model performance. The LiDNAS optimized models achieve result superior to compact depth estimation state-of-the-art on NYU-Depth-v2, KITTI, and ScanNet, while being 7%-500% more compact in size, i.e the number of model parameters.
KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction

Distantly supervised relation extraction is the most popular technique for identifying semantic relation between two entities. Most prior models only focus on the supervision information present in training sentences. In addition to training sentences, external lexical resource and knowledge graphs often contain other relevant prior knowledge. However, relation extraction models usually ignore such readily available information. Moreover, previous works only utilize a selective attention mechanism over sentences to alleviate the impact of noise, they lack the consideration of the implicit interaction between sentences with relation facts. In this paper, (1) a knowledge-guided graph convolutional network is proposed based on the word-level attention mechanism to encode the sentences. It can capture the key words and cue phrases to generate expressive sentence-level features by attending to the relation indicators obtained from the external lexical resource. (2) A knowledge-guided sentence selector is proposed, which explores the semantic and structural information of triples from knowledge graph as sentence-level knowledge attention to distinguish the importance of each individual sentence. Experimental results on two widely used datasets, NYT-FB and GDS, show that our approach is able to efficiently use the prior knowledge from the external lexical resource and knowledge graph to enhance the performance of distantly supervised relation extraction.
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data. For example, one client might have patient data with "healthy" pancreases only while datasets from other clients may contain cases with pancreatic tumors. The vanilla federated averaging algorithm makes it possible to obtain more generalizable deep learning-based segmentation models representing the training data from multiple institutions without centralizing datasets. However, it might be sub-optimal for the aforementioned multi-task scenarios. In this paper, we investigate heterogeneous optimization methods that show improvements for the automated segmentation of pancreas and pancreatic tumors in abdominal CT images with FL settings.
Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation

This paper tackles automatically discovering phone-like acoustic units (AUD) from unlabeled speech data.Past studies usually proposed single-step approaches.We propose a twostage approach: the first stage learns a subword-discriminative feature representation, and the second stage applies clustering to the learned representation and obtains phone-like clusters as the discovered acoustic units.In the first stage, a recently proposed method in the task of unsupervised subword modeling is improved by replacing a monolingual outof-domain (OOD) ASR system with a multilingual one to create a subword-discriminative representation that is more language-independent.In the second stage, segment-level kmeans is adopted, and two methods to represent the variablelength speech segments as fixed-dimension feature vectors are compared.Experiments on a very low-resource Mboshi language corpus show that our approach outperforms state-of-theart AUD in both normalized mutual information (NMI) and F-score.The multilingual ASR improved upon the monolingual ASR in providing OOD phone labels and in estimating the phone boundaries.A comparison of our systems with and without knowing the ground-truth phone boundaries showed a 16% NMI performance gap, suggesting that the current approach can significantly benefit from improved phone boundary estimation.
Fusion in dissimilarity space for RGB-D person re-identification

Person re-identification (Re-id) is the task of recognizing people across different non-overlapping sensors of a camera network. Despite the recent advances with deep learning (DL) models for multi-modal fusion, state-of-the-art Re-id approaches fail to leverage the depth guided contextual information to dynamically select the most discriminant convolutional filters for better feature embedding and inference. Thanks to low cost modern RGB-D sensors (e.g. Microsoft Kinect and Intel RealSense Depth camera) that avail us with different modalities such as illumination invariant high-quality depth images, RGB images and skeleton information can be obtained simultaneously. State-of-the-art Re-id approaches utilize multi-modal fusion in feature space where the chances of fused noisy features to dominate the final recognition process is high. In this paper, we address this issue by exploiting the advantage of using an effective fusion technique in dissimilarity space. Given a query RGB-D image of an individual, two CNNs are separately trained with 3-channel RGB and 4-channel RGB-D images to produce two different feature embeddings required for pair-wise matching with embeddings for reference images, where dissimilarity scores w.r.t reference images from both modalities are fused together for final ranking. Additionally, lack of a proper RGB-D Re-id dataset prompts us to contribute a new RGB-D Re-id dataset named SUCVL RGBD-ID, including RGB and depth images of 58 identities from three cameras where one camera was installed in poor illumination conditions and the remaining two cameras were installed in two different indoor locations with different indoor lighting environments. Extensive experimental analysis on our dataset and two publicly available datasets show the effectiveness of our proposed method. Moreover, our proposed method is general and can be applied to a multitude of different RGB-D based applications.
A Multi-Scale Spatial Attention Region Proposal Network for High-Resolution Optical Remote Sensing Imagery

Deep convolutional neural networks (DCNNs) are driving progress in object detection of high-resolution remote sensing images. Region proposal generation, as one of the key steps in object detection, has also become the focus of research. High-resolution remote sensing images usually contain various sizes of objects and complex background, small objects are easy to miss or be mis-identified in object detection. If the recall rate of region proposal of small objects and multi-scale objects can be improved, it will bring an improvement on the performance of the accuracy in object detection. Spatial attention is the ability to focus on local features in images and can improve the learning efficiency of DCNNs. This study proposes a multi-scale spatial attention region proposal network (MSA-RPN) for high-resolution optical remote sensing imagery. The MSA-RPN is an end-to-end deep learning network with a backbone network of ResNet. It deploys three novel modules to fulfill its task. First, the Scale-specific Feature Gate (SFG) focuses on features of objects by processing multi-scale features extracted from the backbone network. Second, the spatial attention-guided model (SAGM) obtains spatial information of objects from the multi-scale attention maps. Third, the Selective Strong Attention Maps Model (SSAMM) adaptively selects sliding windows according to the loss values from the system’s feedback, and sends the windowed samples to the spatial attention decoder. Finally, the candidate regions and their corresponding confidences can be obtained. We evaluate the proposed network in a public dataset LEVIR and compare with several state-of-the-art methods. The proposed MSA-RPN yields a higher recall rate of region proposal generation, especially for small targets in remote sensing images.
An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.
An efficient data hiding for ECG signals based on the integer wavelet transform and block standard deviation

Herein, we present an improved electrocardiogram (ECG) steganography for the protection of patients’ sensitive data, such as blood pressure, glucose level, lipid profile, and other personal health information. Based on the integer wavelet transform (IWT), we concealed secret bits in an ECG host using the standard deviation (SD) block, coefficient alignment technique, and least significant bit replacement. By computing the SD of a host block before bit embedding, data bits can be effectively embedded in the IWT coefficients. Simulations revealed that the payload of the proposed method is superior to that of existing techniques when the payload size is larger than 15.5 Kb, and our signal-to-noise ratio (SNR) is also better than theirs. In addition, our method is superior to other ECG steganographic methods in terms of the peak SNR and hiding capability. The proposed method not only provides good perceived quality but also protection against attacks. With its short implementation time, our proposed method can be applied in portable biometric devices or wearable electronics.
Technology-Mediated Experiences and Social Context: Relevant Needs in Private Vs. Public Interaction and the Importance of Others for Positive Affect

Technologies, such as smartphones or wearables, take a central role in our daily lives. Making their use meaningful and enjoyable requires a better understanding of the prerequisites and underpinnings of positive experiences with such technologies. So far, a focus had been on the users themselves, that is, their individual goals, desires, feelings, and acceptance. However, technology is often used in a social context, observed by others or even used in interaction with others, and thus shapes social dynamics considerably. In the present paper, we start from the notion that meaningful and/or enjoyable experiences (i.e., wellbeing) are a major outcome of technology use. We investigate how these experiences are further shaped by social context, such as potential spectators. More specifically, we gathered private (while being alone) and public (while other people are present) positive experiences with technology and compared need fulfillment and affective experience. In addition, we asked participants to imagine a change in context (from private to public or public to private) and to report the impact of this change on experience. Results support the idea of particular social needs, such as relatedness and popularity, which are especially relevant and better fulfilled in public than in private contexts. Moreover, our findings show that participants experience less positive affect when imaginatively removing the present others from a formerly public interaction, i.e., when they imagine performing the same interaction but without the other people present. Overall, this underlines the importance of social context for Human-Computer Interaction practice and research. Practical implications relate to product development, e.g., designing interactive technologies that can adapt to context (changes) or allow for context-sensitive interaction sets. We discuss limitations related to the experimental exploration of social context, such as the method of data collection, as well as potential alternatives to address those limitations, such as diary studies.
Target-Dependent Scalable Image Compression Using a Reconfigurable Recurrent Neural Network

Conventional human-centric image compression techniques are optimized for human visual perception, and are generally evaluated by metrics such as MSSSIM and PSNR. On the other hand, task-centric image compression techniques that target deep neural networks (DNN) inference focus on understanding images, and are measured by inference accuracy. As images should be encoded and decoded differently depending on the target metric, existing deep learning based compression techniques have focused on designing an independent neural network that is optimized for either one of the two targets. However, as these techniques require the target metric to be determined during a training phase, separate DNN models should be trained for different target applications, allowing no scalability between human- and task-centric compression. In this paper, we propose a target-dependent scalable image compression pipeline, where compressed images can be differently decoded in real time depending on the interpolated target between perceptual quality and DNN inference accuracy. This dynamic scalability is supported by a reconfigurable recurrent neural network (RNN) that can dynamically change its flow according to the interpolation parameter. The experimental results show that the proposed method achieves comparable MSSSIM and classification accuracy to the compression networks individually trained for human-centric and task-centric targets, respectively, while providing scalability between the two targets depending on the interpolation parameter.
The lineaments as one of the precursors of earthquakes: A case study of Tashkent geodynamical polygon in Uzbekistan

This work is to study earthquake precursors using lineament analysis of five MW3 earthquakes that occurred in 2018 on the territory of the Tashkent geodynamic polygon in Uzbekistan. The Landsat 8 satellite images were processed using the automated lineament detection method in the LEFA software. The processing steps included image preprocessing with graphic filters, edge detection using Canny's algorithm, the definition of a vector of linear elements based on the probabilistic Hough transform and combining collinear linear elements into lineaments. The analysis of the cyclicity of precursors before and after earthquakes was based on the study of the distribution of the lineaments trend in the study area using rose diagrams and lineaments density maps. The results showed a change in the dynamics of the lineament structure. The statistical analysis of lineaments revealed that the number and orientation of lineaments changed significantly about 2–4 months before an earthquake, and after 2–3 months, the system gradually returned to its initial state. The rapid increase in lineaments density began almost 20 days before the event, reaching a maximum about 4 days before the earthquake, and its decrease began 16 days after the earthquake. The main trends observed in the lineament map showed dominant trends in NS, EW, and the subdominant directions were in NW–SE, NE–SW, which coincided with main directions of the faults.