Reviewerly/exHarmony huggingface dataset:
LF-DWNet: Robust Depth Estimation Network for Light Field with Disparity Warping

Light field (LF) images can store multi-view geometry characteristics about the observed scene, which can be helpful in depth estimation. Depth estimation has attracted much attention in recent years for its widely use in the computer vision tasks. Many approaches have been proposed to estimate the depth of LF images, including conventional methods and learning-based methods. But most of them are hard to apply to different complex situations. We propose a robust depth estimation network for LF images with disparity warping (LF-DWNet), which is robust in large disparity pixels, occlusions, and noise areas. To reduce the effect of large disparity pixels, we introduce the disparity warping processing on EPI. To extract the depth feature from warped EPI and reduce the effect of occlusions and noise areas, we design a feature extraction module based on the attention mechanism. To make full use of the depth feature our attention-based module gets, we need to guide the depth estimation by the global structure information. Besides, our LF-DWNet can integrate the depth feature from multi streams of attention-based feature extraction modules and get more credible depth map. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of our method.
A General View of Normalizing Activations

As illustrated before, normalizing input, as a pre-processing, is usually performed over the full dataset, for improving the conditioning of optimization problem. It is straightforward to normalize the activations in deep neural networks over the full dataset using the population statistics, which is the main thoughts of normalization developed in machine learning communities. Besides, There are another line of work for normalizing the internal representation of deep neural network in computer vision communities to adjust the contrast information for images. In this chapter, we will first introduce the preliminary work of normalizing activations of DNNs, prior to the milestone normalization technique—batch normalization (BN) [1]. We then illustrate the algorithm of BN and how it is developed by exploiting the merits of the previous methods.
Indonesian Citizens’ Health Behavior in a Pandemics: Twitter Conversation Analysis using Latent Dirichlet Allocation

Health is an essential thing in carrying out human activities. The COVID-19 pandemic has made people aware of the importance of maintaining health and hygiene for individuals, families, and the surrounding community. All countries, including Indonesia, are impacted by the COVID-19 pandemic, which has undoubtedly changed health behavior in the community. This study aims to reveal changes in health behavior during the pandemic through conversations on social media such as Twitter. The study was conducted using the Latent Dirichlet Allocation (LDA) method to analyze changes in Indonesian citizens’ health behavior during the pandemic through social media analysis. The results of social media analysis using LDA on 495,740 tweet data indicate that it is true that there has been a change in public health behavior. At the beginning of the pandemic, many people still did not believe that various hoaxes were spread, and it was difficult to comply with health protocols. Hence, the government massively appealed to make regulations to break the chain of the spread of COVID-19. However, at a critical time with many victims falling, the public became more aware, maintained health protocols, followed the vaccination program, and finally, people got used to coexistence with COVID-19. These results indicate that the Indonesian people are wiser in dealing with the COVID-19 pandemic and following the applicable health protocols.
Prediction of Disease Progress Using Medical Visit Records with Irregular Time Intervals

Time irregularity is common in medical visit record data. For disease progress prediction, irregular time intervals occur in two places. One is in the input historical data for prediction (denoted as input intervals), the other is the interval between the last input data and the future prediction point (denoted as prediction interval). Most previous work only considered the irregularity in the first case. The irregularity in prediction interval is usually ignored. In this paper, a new prediction model TA2D-LSTM is proposed by studying the impact of different kinds of irregular time interval as well as the different contribution of each visit for disease progress prediction. The proposed framework consists of two stages, the input sequence modeling stage and the prediction point modeling stage. In the first stage, the time aware LSTM structure is used to consider the irregular input intervals and mainly focuses on the contribution of recent visits. Then the attention network is built to learn the different contribution of all input visits. After that, the decay structure DECAY is designed to adjust the output of the first stage based on the irregular prediction interval. The adjusted results from the time aware LSTM and the attention network are then merged together to produce the final predictions results. The experimental results on a large-scale real-world hyperthyroidism disease data set verify the effectiveness of the proposed TA2D-LSTM for disease prediction task.
Academic and industry training for data modelling

Finding a common ground between academic and industry training is a challenging task. However, despite all the differences between the two worlds, there is one important common aspect for the master students and industry trainees in the field of conceptual data modelling: the mistakes they make when creating data models. This idea paper describes the existing differences and similarities between two academic and industrial training approaches for teaching conceptual data modelling. We propose ideas for improvement of training quality on both sides by analysing and tackling the ground errors in conceptual data modelling made both by novices and professionals. Additionally, we propose the ideas for methodology exchanges between the two training types.
Synthesising Linear API Usage Examples for API Documentation

Code examples are essential resources for learning application programming interfaces (APIs). The shortage of such examples can be a major learning obstacle for API users. Writing and maintaining effective API usage examples can also be an effort-intensive and repetitive process for API developers because API users ideally want such examples to be simple, standalone, and linear. To address these challenges, several approaches have been proposed to automatically extract API code examples from various resources and embed them into official API documents; however, little emphasis has been placed on addressing the underlying issue directly and helping API developers write and maintain API usage examples. In this paper, we present a new approach for automatically synthesising linear code examples from less repetitive versions by in-lining reusable utility methods. The proposed approach aims to benefit API developers in terms of productivity and maintainability, as well as API users in terms of API learnability and comprehension. We have implemented the proposed approach in a prototype for the Java programming language, which we also discuss in this paper.
Implementation of design thinking in part 30 of the Qur’an augmented reality based learning application to improve user experience and cognitive ability

Due to the rapid development of technology, gadgets have become a necessity. 98% of parents in Southeast Asia allowed their children to use gadgets for educational purposes. Learning the Qur'an is necessary from an early age for every Muslim. However, conventional learning methods bore children easily. Quranic Augmented Reality (QAR) is a Qur'an Learning Application that applied Augmented Reality technology. This application can support the enhancement of children's cognitive ability. The User Experience (UX) of QAR application is developed using Design Thinking (DT) method. To test the application, the Cognitive Walkthrough method, and the System Usability Scale (SUS) were used as measurement tools. Based on the experiments, the new design UX of QAR was shown to increase the usability of the application of up to 75.33 SUS Score. It has improved from marginal to acceptable category. The results of the field implementation indicate that the application of QAR had a significant influence on the learning process. This can be seen from the significance value of p<0.05 and the average increase of 14.50 for the cognitive level of Remembering (C1) and 15.50 for Understanding (C2) categories in the Bloom Taxonomy.
Block Chain Application for E-Voting Process Using ML for South Asian Continent

A digital voting system which allows people to vote sitting at their home based on their face recognition. The votes will be counted and saved in a blockchain, based structure which is secure and immutable, thus giving availability with security in a system. The traditional voting system does not allow people to vote sitting at their home. Considering the situation of covid everything is going digital. Questions on EVM from losing parties regarding some malfunctioning. Digital, secure, reliable, user friendly and a low-cost system. Standing in long queues during voting is a tiring and time-consuming process.
Open-Set Domain Adaptation Classification Via Adversarial Learning

Domain adaptation has achieved great success in using labeled source domain samples to identify unlabeled target domain samples. Here, we aimed to solve the open-set domain adaptation, which is different from the closed-set domain adaptation in that it contains categories in target domain that do not appear in source domain. A new method based on open-set domain adaptation by back propagation (OSBP) is proposed. Known category samples in target domain are gradually selected, and domain invariant features are extracted through adversarial training. Singular value balance is utilized to preserve the discriminability of the network. In addition, a channel attention module is added to the feature generator. We conduct experiments on two benchmark datasets of domain adaptation datasets. Experimental results demonstrate that the proposed method has a significant improvement over some previous methods.
Rapid Earthquake Magnitude Estimation Using Deep Learning

Earthquake magnitude estimation is one of the critical parts of earthquake early warning systems. It uses the first few seconds of a waveform recorded by an earthquake detection station, which is required to be rapid and accurate. In this paper, we propose a novel framework to estimate magnitude integrated with deep learning, consisting of feature stage and regression stage. In the feature stage, we extract temporal & spatial features by deep learning methods, and combine them with hand-crafted features embedded expert domain knowledge. Then, each earthquake can be represented by a hybrid feature. Therefore, magnitude estimation can be modeled as a regression problem to solve. Our framework is evaluated on 5,503 earthquake records collected in Sichuan province, China. It is found that, learning the temporal & spatial features by deep neural networks is critical for magnitude estimation. The results demonstrate the state-of-the-art performance, compared with other approaches.
Digitalization Mining and Protection of Security Language Resources in Big Data Environment

The Baoan language is the language of the Chinese security people. It is also used in some parts of Monguor people. As an important carrier for the dissemination and inheritance of minority culture, it is of great practical significance to excavate and protect the resources of the security language. In the context of modern culture, the minority language, represented by the Baoan dialect, is facing a great survival crisis and communication pressure, and even is on the verge of being lost. Faced with the new big data environment, we should give full play to the technical advantages, use the digital way to excavate and protect the security language resources reasonably and scientifically, and ensure the security language to develop in an orderly way. By analyzing the current situation of security language resources and combining with big data environment, this paper puts forward the strategy of digitalization mining and protection of security language resources.
Research on visual art design of children’s picture books based on Augmented Reality Technology

With the progress and development of science and technology, the sales of traditional paper books will inevitably be affected, and because of the popularization of science and technology, traditional paper books began to add AR technology to supplement their shortcomings. AR technology is augmented reality technology, which integrates a large number of real scenes into virtual parts for interaction through the device screen. This topic studies the application of AR technology in the visual design of traditional children’s picture book art. Using the advantages of AR technology to change the limitations of traditional children’s picture book design, let children’s picture book design blend the virtual part and the real part, and present a three-dimensional story effect that fits the content of the picture book.
Multi-hop Multi-key Homomorphic Encryption with Less Noise Under CRS Model

The application of information technologies such as big data, artificial intelligence, and cloud computing in the Internet environment makes protecting personal privacy and preventing data leakage an important issue. Homomorphic encryption (HE) supports the calculation and processing on ciphertexts and provides a technical guarantee for the security of personal information. Multi-key homomorphic encryption (MKHE) can perform cooperative calculation on ciphertexts under different keys, and is one of the solutions for secure computing in multi-user scenario. But the homomorphic multiplication after ciphertext extension makes the ciphertext dimension increase quadratically with respect to the number of users, so relinearization with noise is needed to reduce the dimension. In this paper, we propose an efficient multi-hop MKHE scheme with less relinearization noise under the common reference string (CRS) model, which supports real-time joining of new users. First, we propose an auxiliary coding scheme by introducing a random matrix instead of the encryption with noise. On this basis, we further propose how to construct the relinearization key, which can be pre-computed before the ciphertext input. In our MKHE scheme, the preprocessing of the ciphertext is cancelled to reduce the dimension of the ciphertext and the number of non-linear keys after homomorphic multiplication. In addition, we prove the semantic security of the scheme under the RLWE assumption, noise analysis and storage analysis also show that our scheme has smaller noise growth and storage space.
Bayesian Networks in the English Language Proficiency Test

This paper uses Bayesian networks to construct a prediction model for the college English application proficiency test. The analysis of the factors affecting the passing of the English application proficiency test is carried out using a Bayesian network-based classifier, and the results are verified using real data. The model can be used to provide decision support for the education management and reform of higher education institutions.
A Non-Bottleneck Residual Approach for QR Code Printed Source Identification

With the rapid development of the internet and the rising popularity of smartphones, especially mobile phone cameras, QR code has gained popularity outside their original use, from inventory tracking in factories & logistics to advertisements, electronic tickets, and even mobile payments in the commercial field, making them readily available to everywhere. With the advancement of deep learning, neural networks, and computer vision, many important statistical features can be automatically extracted and learned to improve identification accuracy. This research studied many CNN models for QR code-based printed source identification through a series of experiments that involved color QR codes to observe whether the bottleneck residual method is necessary for printed source identification. Our simplified residual model could compete with all of the tested models in color printer source identification even without bottleneck residual blocks implemented.
Hybrid Method for Semantic Similarity Computation Using Weighted Components in Ontology

In this paper, the researchers propose an approach to measure the semantic similarity between two concepts in an ontology like WordNet and DBpedia. Some earlier semantic similarity approaches proposed concentrated on the ontology structure between concepts and some concentrated only on the information content of concepts. This paper proposes a semantic similarity approach with path length, information content, and semantic depth (i.e., PLICD) to combine both path length as well as information content-based approaches. This proposed approach uses weighted shortest path length and information content calculated using semantic depth and hyponyms of the concepts to measure semantic similarity between two concepts. Through experimentations performed on WordNet and DBpedia, the researchers note that the PLICD semantic similarity approach has delivered a statistically meaningful enhancement as compared to the other semantic similarity approaches concerning accuracy and F score.
A Recommendation Algorithm for Auto Parts Based on Knowledge Graph and Convolutional Neural Network

Due to the large number and complex types of auto parts, the ability that algorithm can accurately match the right auto parts is a major problem for buyers to solve. To address this problem, we propose a knowledge graph convolutional network with user history and item entity augmentation for auto parts recommendation system. First, the knowledge graph of auto parts and the knowledge graph of users to find a set of node examples. Second, collect contextual data about each instance. This data is to obtain information such as the hierarchical type, role, attribute value, and inferred neighbors of each neighbor instance from the knowledge graph. Finally, process this information into an array and use this array as the input to the neural network. After one or more aggregations and merging, as a vector of construction examples, the vector contains a wide range of information. The algorithm combines the advantages of knowledge graph and graph convolution, and combines with the idea of recommendation based on item content and recommendation based on user, so that the recommendation effect is improved.
Cluster analysis for cancer omics data using Neural Network with data augmentation

In recent years, with the popularity of machine learning, more and more cancer analysis based on machine learning has been rapidly researched and developed. Previous studies have shown that neural networks can not only detect cancer, but also classify gene expression subtypes. In addition to these supervised learning studies, unsupervised learning can also be exploited to study cancer subtypes based on patients' molecular profiling data. In this work, we propose a method using ResNet with virtual adversarial training and mutual information maximization for cluster analysis. The results show that our method achieves higher accuracy than other clustering methods. Next, we compared datasets with different numbers of features of same cancer, and showed that higher-dimensional data can produce better clustering performance.
Region Probability Map-Guided Fast Wide-Area Multiobject Detection

Detecting multiobjects in a wide-area scenario efficiently is a critical technology for industry and security applications. The detection performance has benefited enormously from the probability estimation of the unknown environment. Representative methods, such as particle filter (PF), construct the probability distribution model and iteratively locate target objects. However, randomly sampling and detecting image patches in the large covering field-of-view (FOV) make these methods inefficient and computation costly. To address these issues, we propose a region probability map (RPM)-guided fast wide-area detection system that can simultaneously detect multiobjects from a large FOV at 300 frames per second (fps) through a coarse-to-fine-grained detection paradigm. Specifically, a segmentation-based RPM generation module is introduced to assign probability measurements to different regions of the panoramic image, which models how likely the desired objects will occur in these regions. Then, based on the generated probability map of the whole scene, a novel RPM-guided PF framework is proposed to speed up the detection process by concentrating the detection on high-probability areas. Finally, a rapid and low-latency active detection system based on a wide-angle camera, a high-speed camera, and an ultrafast galvano mirror is implemented, which gains a 15.38% efficiency improvement while achieving more accurate detection compared with existing methods. Extensive experimental results verify the robustness and effectiveness of our proposed system.
Investigation Learning Rate Parameter for Keypoint-Based UAV Object Detection

Learning rate is one of the challenging hyper-parameters during training deep learning model. An optimal model requires proper configuration value of learning rate. Larger learning rate results in rapid changes and makes the model not optimal, whereas smaller learning rate makes the model train slower. Finding a good choice of learning rate can be essential to getting the network to learn at all. In this paper, we proposed to compare the CenterNet model with different backbones and different learning rates. Backbones that we use are ResNet-101 and Deep Layer Aggregation, moreover there are eight experiments in total using different learning rate. CenterNet is a model based on keypoint estimation. In other words, CenterNet tries to find the center point of the object which is then regressed into other object properties such us weight and height of bounding boxes. Furthermore, we focus on detecting aerial images, hence the dataset we use is the VisDrone-2019 dataset. Our result shows that training Deep Layer Aggregation backbone using learning rate of 0.000125 can achieve the highest performances around 18.2 percent.
Quantum Reinforcement Learning for Multi-Armed Bandits

This work focuses on the multi-armed bandits (MAB) problem and proposes a quantum reinforcement learning (RL) algorithm for action selection. Existing quantum RL algorithms generally assume that some prior information about the optimal action is known, and initial probability is set unequally. Our algorithm can be executed with equal initial probability on each action, and can greatly accelerate the learning process.