Reviewerly/exHarmony huggingface dataset:
Learning Steering Kernels for Guided Depth Completion

This paper addresses the guided depth completion task in which the goal is to predict a dense depth map given a guidance RGB image and sparse depth measurements. Recent advances on this problem nurture hopes that one day we can acquire accurate and dense depth at a very low cost. A major challenge of guided depth completion is to effectively make use of extremely sparse measurements, e.g., measurements covering less than 1% of the image pixels. In this paper, we propose a fully differentiable model that avoids convolving on sparse tensors by jointly learning depth interpolation and refinement. More specifically, we propose a differentiable kernel regression layer that interpolates the sparse depth measurements via learned kernels. We further refine the interpolated depth map using a residual depth refinement layer which leads to improved performance compared to learning absolute depth prediction using a vanilla network. We provide experimental evidence that our differentiable kernel regression layer not only enables end-to-end training from very sparse measurements using standard convolutional network architectures, but also leads to better depth interpolation results compared to existing heuristically motivated methods. We demonstrate that our method outperforms many state-of-the-art guided depth completion techniques on both NYUv2 and KITTI. We further show the generalization ability of our method with respect to the density and spatial statistics of the sparse depth measurements.
An adversarial human pose estimation network injected with graph structure

Because of the invisible human keypoints in images caused by illumination, occlusion and overlap, it is likely to produce unreasonable human pose prediction for most of the current human pose estimation methods. In this paper, we design a novel generative adversarial network (GAN) to improve the localization accuracy of visible joints when some joints are invisible. The network consists of two simple but efficient modules, i.e., Cascade Feature Network (CFN) and Graph Structure Network (GSN). First, the CFN utilizes the prediction maps from the previous stages to guide the prediction maps in the next stage to produce accurate human pose. Second, the GSN is designed to contribute to the localization of invisible joints by passing message among different joints. According to GAN, if the prediction pose produced by the generator G cannot be distinguished by the discriminator D, the generator network G has successfully obtained the underlying dependence of human joints. We conduct experiments on three widely used human pose estimation benchmark datasets, i.e., LSP, MPII and COCO, whose results show the effectiveness of our proposed framework.
Multi-objective genetic programming for feature learning in face recognition

Face recognition is a challenging task due to high variations of pose, expression, ageing, and illumination. As an effective approach to face recognition, feature learning can be formulated as a multi-objective optimisation task of maximising classification accuracy and minimising the number of learned features. However, most of the existing algorithms focus on improving classification accuracy without considering the number of learned features. In this paper, we propose new multi-objective genetic programming (GP) algorithms for feature learning in face recognition. To achieve effective face feature learning, a new individual representation is developed to allow GP to select informative regions from the input image, extract features using various descriptors, and combine the extracted features for classification. Then two new multi-objective genetic programming (GP) algorithms, one with the idea of non-dominated sorting (NSGPFL) and the other with the idea of Strength Pareto (SPGPFL), are proposed to simultaneously optimise these two objectives. NSGPFL and SPGPFL are compared with a single-objective GP for feature learning (GPFL), a single-objective GP for weighting two objectives (GPFLW), and a large number of baseline methods. The experimental results show the effectiveness of the NSGPFL and SPGPFL algorithms by achieving better or comparable classification performance and learning a small number of features.
A survey on information hiding using video steganography

In the last few decades, information security has gained huge importance owing to the massive growth in digital communication; hence, driving steganography to the forefront for secure communication. Steganography is a practice of concealing information or message in covert communication which involves hiding the information in any multimedia file such as text, image, or video. Many contributions have been made in the domain of image steganography; however, due to the low embedding capacity and robustness of images; videos are gaining more attention of academic researchers. This paper aims to provide a qualitative as well as quantitative analysis of various video steganography techniques by highlighting their properties, challenges, pros, and cons. Moreover, different quality metrics for the evaluation of distinct steganography techniques have also been discussed. The paper also provides an overview of steganalysis attacks which are commonly employed to test the security of the steganography techniques. The experimental analysis of some of the prominent techniques using different quality metrics has also been done. This paper also presented a critical analysis driven from the literature and the experimental results. The primary objective of this paper is to help the beginners to understand the basic concepts of this research domain to initiate their research in this field. Further, the paper highlighted the real-life applications of video steganography and also suggested some future directions which require the attention of the research community.
Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Most biomedical datasets, including those of 'omics, population studies, and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms. Machine learning (ML) algorithms have been used to classify outcomes in biomedical datasets, including random forests (RF), decision tree (DT), artificial neural networks (ANN), and support vector machine (SVM). However, their performance and efficiency in classifying multi-category outcomes of rectangular data are poorly understood. Therefore, we compared these metrics among the 4 ML algorithms. As an example, we created a large rectangular dataset using the female breast cancers in the surveillance, epidemiology, and end results-18 database, which were diagnosed in 2004 and followed up until December 2016. The outcome was the five-category cause of death, namely alive, non-breast cancer, breast cancer, cardiovascular disease, and other cause. We analyzed the 54 dichotomized features from ~45,000 patients using MatLab (version 2018a) and the tenfold cross-validation approach. The accuracy in classifying five-category cause of death with DT, RF, ANN, and SVM was 69.21%, 70.23%, 70.16%, and 69.06%, respectively, which was higher than the accuracy of 68.12% with multinomial logistic regression. Based on the features' information entropy, we optimized dimension reduction (i.e., reduce the number of features in models). We found 32 or more features were required to maintain similar accuracy, while the running time decreased from 55.57 s for 54 features to 25.99 s for 32 features in RF, from 12.92 s to 10.48 s in ANN, and from 175.50 s to 67.81 s in SVM. In summary, we here show that RF, DT, ANN, and SVM had similar accuracy for classifying multi-category outcomes in this large rectangular dataset. Dimension reduction based on information gain will increase the model's efficiency while maintaining classification accuracy.
University Teacher Training During the COVID-19 Emergency: The Role of Online Teaching-Learning Tools

This research summarizes the collaborative training experience of university professors acting as tutors of their peers. A three-phase training process was planned and taught: the goal was to train teachers to design, practice and use online teaching-learning ICT tools. The workshop also aimed to motivate and improve teachers' emotional state, given the adverse conditions generated from isolation due to the COVID-19 lockdown. Communication and planning in collaborative spaces, and activities conducted with and independent of the tutor group (a team with interdisciplinary, pedagogical and technical capacities) generated clear, progressive achievements in professor participants. A training space was created through the collaborative work of heterogeneous groups of professors via tools such as Zoom, WhatsApp, an institution platform based on Moodle and e-mail. Through these spaces knowledge was constructed collectively; professors were encouraged to continually practice new tools and were challenged through continuous training sessions. The process progressively strengthened teachers' ability to use ICT tools while simultaneously opening new spaces for genuine communication that helped teachers begin the new online academic period in a positive way.
Blockchain-based medical and water waste management conception

The revolutionary technology blockchain began with cryptocurrencies like bitcoin but has since expanded beyond the worlds of finance and banking. One relatively unexplored application domain is medical and water waste. The production of medical/water waste is an integral part of healthcare operations. However, Health care and water waste management methods can also pose a health and environment risk if the various steps in the management process are not carried out correctly. The objective of this paper is to propose conception of a system, based on the Blockchain and IoT, that ensures the control of these wastes in order to effectively manage, coordinate and monitor their disposal. An initial design of this system and an evaluation of the system's performance are also presented.
A Nighttime Vehicle Detection Method Based on YOLO v3

For the past several decades, the number of vehicles has been increased for many times, leading to a lot of traffic issues. The nighttime accident rate is much higher than that in daytime because of the weak light and the uneven distribution of nighttime brightness. Thus, in this paper an effective nighttime vehicle detection approach is designed. First, the original nighttime images were enhanced by an optimal MSR algorithm. Then, a pretrained YOLO v3 network was selected and fine-tuned by the enhanced images. Finally, the detection network was used to detect vehicles from the nighttime images and outperformed two widely used object detection methods, namely the Faster R-CNN and SSD, on the precision and detection efficiency. The average precision of the proposed method reaches 93.66%, which is 6.14% and 3.21% higher than that of the Faster R-CNN and SSD, respectively.
On the construction of a post-quantum blockchain for smart city

Owing to some special characteristics and features, blockchain is a very useful technique that can securely organize diverse devices in a smart city. It finds wide applications, especially in distributed environments, where entities such as wireless sensors need to be certain of the authenticity of the server. As contemporary blockchain techniques that address post-quantum concerns have not been designed, in this study, we investigate a blockchain in the post-quantum setting and seek to discover how it can resist attacks from quantum computing. In addition, traditional proof of work (PoW)-based consensus protocols such as Bitcoin cannot supply memory mining, and the transaction capacity of each block in a blockchain is limited and needs to be expanded. Thus, a new post-quantum proof of work (post-quantum PoW) consensus algorithm for security and privacy of smart city applications is proposed. It can be used to not only protect a blockchain under a quantum computing attack compared to existing classical hash-based PoW algorithms but also to supply memory mining. Meanwhile, an identity-based post-quantum signature is embedded into a transaction process to construct lightweight transactions. Subsequently, we provide a detailed description on the execution of the post-quantum lightweight transaction in a blockchain. Overall, this work can help enrich the research on future post-quantum blockchain and support the construction or architecture of emerging blockchain-based smart cities.
Proof of service power: A blockchain consensus for cloud manufacturing

With the gradual promotion and in-depth application of cloud manufacturing platform, some deep-seated problems restricting enterprises to go to the cloud are gradually exposed. Lack of trust and security of cloud manufacturing platforms are the bottleneck problems. How to ensure the credit of both trading sides and reduce the transaction risk and transaction cost are still urgent problems to be solved. In recent years, with the continuous development and maturity of blockchain technology, it has attracted widespread attention in the manufacturing field. Its characteristics of trustlessness, anti-tampering, traceability, and high transparency provide new ideas for the development of cloud manufacturing platforms and solving the above problems. However, the traditional consensus protocol has problems such as token dependence and high energy consumption, which is not suitable for the special scenario of cloud manufacturing with a large number of big deals. This paper proposes a blockchain consensus protocol of "Proof of Service Power" (PoSP) for application scenarios of cloud manufacturing. The consensus mechanism uses historical transaction information accumulated by member nodes in a cloud manufacturing system to calculate the service power of member nodes, which greatly reduces energy consumption in the Proof of Work (PoW) process. An introduction of the credit incentive mechanism makes PoSP consensus protocol get rid of token dependence while providing a quantitative basis for trust evaluation for each participant in the cloud manufacturing system. The experimental results show that the consensus mechanism for proof of service power has a good incentive effect, and it has lower energy consumption and higher transaction per second (TPS) than the proof of work and proof of stake (PoS). This study provides new ideas and a reference pattern for the application of blockchain technology in cloud manufacturing platform.
Detail preserving image denoising with patch-based structure similarity via sparse representation and SVD

The key problem of image denoising methods is to smooth noise while retaining the details of original image. The human vision system is more sensitive to the details (or the high frequency components) of original image, hence the restoration of image details ensures the good quality of denoised image. Different from denoising the image as a whole, this paper proposes a novel denoising method that reconstructs the high and low frequency components respectively. The sparse representation using patch-based structure similarity is proposed to reconstruct the high frequency parts. And the low frequency parts are reconstructed by singular value decomposition (SVD). Finally an energy minimization function that contains high and low frequency parts are presented. Experimental results illustrate that the proposed method is outstanding in both numerical precision and visual performance.
Enhanced Reinforcement Learning Method Combining One-Hot Encoding-Based Vectors for CNN-Based Alternative High-Level Decisions

Gomoku is a two-player board game that originated in ancient China. There are various cases of developing Gomoku using artificial intelligence, such as a genetic algorithm and a tree search algorithm. Alpha-Gomoku, Gomoku AI built with Alpha-Go’s algorithm, defines all possible situations in the Gomoku board using Monte-Carlo tree search (MCTS), and minimizes the probability of learning other correct answers in the duplicated Gomoku board situation. However, in the tree search algorithm, the accuracy drops, because the classification criteria are manually set. In this paper, we propose an improved reinforcement learning-based high-level decision approach using convolutional neural networks (CNN). The proposed algorithm expresses each state as One-Hot Encoding based vectors and determines the state of the Gomoku board by combining the similar state of One-Hot Encoding based vectors. Thus, in a case where a stone that is determined by CNN has already been placed or cannot be placed, we suggest a method for selecting an alternative. We verify the proposed method of Gomoku AI in GuPyEngine, a Python-based 3D simulation platform.
A blockchain-enabled platform for VAT settlement

Governments may unintentionally impose heavy administrative burdens on companies as they want to ensure the flow of tax revenue. Drawing on an engagement involving the Danish Business Authority and following a design science research (DSR) approach, this paper develops a prototype of a platform for value-added tax (VAT) settlement that is enabled by distributed ledger technology (DLT) and design principles for designing DLT platforms. The proposed prototype and design principles demonstrate how accounting information systems, DLT, and public governance may be interrelated to enhance social welfare. Regarding its practical implications, this paper provides a use case for governments seeking to reduce administrative burdens on small and medium-sized enterprises (SMEs) while still ensuring the flow of tax revenue.
Multi-hop reasoning over paths in temporal knowledge graphs using reinforcement learning

Knowledge graphs (KGs) are usually incomplete—many new facts can be inferred from KGs with existing information. In some traditional reasoning methods, temporal information is not taken into consideration, meaning that only triplets (head, relation, tail) are trained. In current dynamic knowledge graphs, it is a challenge to consider the temporal aspects of facts. Recent temporal reasoning methods embed temporal information into low-dimensional spaces. These methods mainly support implicitly reasoning, which means they cannot get the specific reasoning paths. These methods limit the accuracy of reasoning paths and ignore multiple explainable reasoning paths in temporal knowledge graphs (TKGs). To overcome this limitation, we propose a multi-hop reasoning model TPath in this paper. It is a reinforcement learning (RL) framework which can learn multi-hop reasoning paths and continuously adjust the reasoning paths in TKGs. More importantly, we add time vectors in reasoning paths, which further improve the accuracy of reasoning paths. Meanwhile, considering the diversity of temporal reasoning paths, we propose a new reward function. In TPath, the agent employs the Long Short-Term Memory networks (LSTM) to capture current observations from the environment, and it outputs action vectors (relation vectors and time vectors) to the environment through activation functions. Experimentally, our model outperforms other state-of-the-art reasoning methods in several aspects over two public temporal knowledge graph datasets.
FeedRec: News Feed Recommendation with Various User Feedbacks

Accurate user interest modeling is important for news recommendation. Most existing methods for news recommendation rely on implicit feedbacks like click for inferring user interests and model training. However, click behaviors usually contain heavy noise, and cannot help infer complicated user interest such as dislike. Besides, the feed recommendation models trained solely on click behaviors cannot optimize other objectives such as user engagement. In this paper, we present a news feed recommendation method that can exploit various kinds of user feedbacks to enhance both user interest modeling and model training. We propose a unified user modeling framework to incorporate various explicit and implicit user feedbacks to infer both positive and negative user interests. In addition, we propose a strong-to-weak attention network that uses the representations of stronger feedbacks to distill positive and negative user interests from implicit weak feedbacks for accurate user interest modeling. Besides, we propose a multi-feedback model training framework to learn an engagement-aware feed recommendation model. Extensive experiments on a real-world dataset show that our approach can effectively improve the model performance in terms of both news clicks and user engagement.
ACEnet: Anatomical context-encoding network for neuroanatomy segmentation

Segmentation of brain structures from magnetic resonance (MR) scans plays an important role in the quantification of brain morphology. Since 3D deep learning models suffer from high computational cost, 2D deep learning methods are favored for their computational efficiency. However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation. In order to overcome this limitation, we develop an Anatomical Context-Encoding Network (ACEnet) to incorporate 3D spatial and anatomical contexts in 2D convolutional neural networks (CNNs) for efficient and accurate segmentation of brain structures from MR scans, consisting of 1) an anatomical context encoding module to incorporate anatomical information in 2D CNNs and 2) a spatial context encoding module to integrate 3D image information in 2D CNNs. In addition, a skull stripping module is adopted to guide the 2D CNNs to attend to the brain. Extensive experiments on three benchmark datasets have demonstrated that our method achieves promising performance compared with state-of-the-art alternative methods for brain structure segmentation in terms of both computational efficiency and segmentation accuracy.
Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews

Sentiment analysis is an important task in the field of Nature Language Processing (NLP), in which users' feedback data on a specific issue are evaluated and analyzed. Many deep learning models have been proposed to tackle this task, including the recently-introduced Bidirectional Encoder Representations from Transformers (BERT) model. In this paper, we experiment with two BERT fine-tuning methods for the sentiment analysis task on datasets of Vietnamese reviews: 1) a method that uses only the [CLS] token as the input for an attached feed-forward neural network, and 2) another method in which all BERT output vectors are used as the input for classification. Experimental results on two datasets show that models using BERT slightly outperform other models using GloVe and FastText. Also, regarding the datasets employed in this study, our proposed BERT fine-tuning method produces a model with better performance than the original BERT fine-tuning method.