Reviewerly/exHarmony huggingface dataset:
Applying Cloud Computing and Internet of Things Technologies to Develop a Hydrological and Subsidence Monitoring Platform

As the Internet of Things (IoT) matures, large-scale groundwater flow data collection, transmission, and storage can be carried out through many sensors.There are significant resource requirements for data analysis and data storage platforms.Cloud computing has been the latest computing management system developed in recent years.It shows exponential growth in its application to groundwater modeling systems and subsidence monitoring work.The goal of this study is to apply cloud computing and IoT technologies to continuously collect real-time groundwater-related data and transmit it to a cloud platform.We developed a hydrological and subsidence monitoring platform that can process large-scale groundwater data and use database systems to store the data and ensure data integrity.Therefore, the efficiency of execution is discussed when data processing and storage are performed.The platform can monitor the state of the groundwater layer in the Choshui River Alluvial Fan in Taiwan.The platform displays the data of the groundwater layer visually, clearly understanding the situation of land subsidence in the Choshui River Alluvial Fan in Taiwan and providing managers, researchers, and users with accurate quantitative assessment methods.
An Intelligence Architecture for Grounded Language Communication with Field Robots

For humans and robots to collaborate effectively as teammates in unstructured environments, robots must be able to construct semantically rich models of the environment, communicate efficiently with teammates, and perform sequences of tasks robustly with minimal human intervention, as direct human guidance may be infrequent and/or intermittent. Contemporary architectures for human-robot interaction often rely on engineered human-interface devices or structured languages that require extensive prior training and inherently limit the kinds of information that humans and robots can communicate. Natural language, particularly when situated with a visual representation of the robot’s environment, allows humans and robots to exchange information about abstract goals, specific actions, and/or properties of the environment quickly and effectively. In addition, it serves as a mechanism to resolve inconsistencies in the mental models of the environment across the human-robot team. This article details a novel intelligence architecture that exploits a centralized representation of the environment to perform complex tasks in unstructured environments. The centralized environment model is informed by a visual perception pipeline, declarative knowledge, deliberate interactive estimation, and a multimodal interface. The language pipeline also exploits proactive symbol grounding to resolve uncertainty in ambiguous statements through inverse semantics. A series of experiments on three different, unmanned ground vehicles demonstrates the utility of this architecture through its robust ability to perform language-guided spatial navigation, mobile manipulation, and bidirectional communication with human operators. Experimental results give examples of component-level behaviors and overall system performance that guide a discussion on observed performance and opportunities for future innovation.
An End-to-End Method to Extract Information from Vietnamese ID Card Images

Information extraction from ID cards plays an important role in many daily activities, such as legal, banking, insurance, or health services.However, in many developing countries, such as Vietnam, it is mostly carried out manually, which is time-consuming, tedious, and may be prone to errors.Therefore, in this paper, we propose an end-to-end method to extract information from Vietnamese ID card images.The proposed method contains three steps with four neural networks and two image processing techniques, including U-Net, VGG16, Contour detection, and Hough transformation to pre-process input card images, CRAFT, and Rebia neural network for Optical Character Recognition, and Levenshtein distance and regular expression to post-process extracted information.In addition, a dataset, including 3.256 Vietnamese ID cards, 400k manual annotated text, and more than 500k synthetic text, was built for verifying our methods.The results of an empirical experiment conducted on our self-collected dataset indicate that the proposed method achieves a high accuracy of 94%, 99.5%, and 98.3% for card segmentation, classification, and text recognition.
Enhancing Students’ Speaking Skills by Using YouTube for Maritime English Learning in Akademi Maritim Nusantara

One of the tools that can help teachers and students in a classroom’s teaching and learning program is media. Visual and audio are two kinds of media that can be used as tools to facilitate activities in class, especially in language teaching and learning. One of the most common media to be used worldwide is YouTube. Not only that it is an entertaining media source, but also YouTube can play a great deal in aiding students’ learning process. The aims of this study are describing how YouTube videos can be used to enhance the students’ speaking skill, and describing the process of utilizing YouTube videos in language teaching-learning activity. The subject of this study is the students of the third semester in the Marine Engineering Program at Akademi Maritim Nusantara. The classroom action research design is used as the method to conduct this study. Data collections are obtained by speaking interview and assessment. The result of the study which was conducted in two cycles showed that, the average score of speaking indicators of the students increased from 60.8 to 66.3 in Cycle One. In Cycle Two, the improvement was progressing to 76.4 on the average score of speaking indicators. From those results, the study concluded that there was an improvement in the students’ speaking skill. The students’ fluency, pronunciation, vocabulary, context, and grammar can be seen as the improving aspects of the students’ speaking skill. In conclusion, the use of YouTube videos to improve students’ speaking skill is reviewed.
EAES: Effective Augmented Embedding Spaces for Text-Based Image Captioning

Text-based Image Captioning has been a novel problem since 2020. This topic remains challenging because it requires the model to comprehend not only the visual context but also the scene texts that appear in an image. Therefore, the ways image and scene texts are embedded into the main model for training is crucial. Based on the M4C-Captioner model, this paper proposes the simple but effective EAES embedding module for effectively embedding images and scene texts into the multimodal Transformer layers. In detail, our EAES module contains two significant sub-modules: Objects-augmented and Grid features augmentation. With the Objects-augmented module, we provide the relative geometry feature, representing the relation between objects and between OCR tokens. Furthermore, we extract the grid features for an image with the Grid features augmentation module and combine it with visual objects, which help the model focus on both salient objects and the general context of an image, leading to better performance. We use the TextCaps dataset as the benchmark to prove the effectiveness of our approach on five standard metrics: BLEU4, METEOR, ROUGE-L, SPICE and CIDEr. Without bells and whistles, our method achieves 20.21% on the BLEU4 metric and 85.78% on the CIDEr metric, 1.31% and 4.78% higher, respectively, than the baseline M4C-Captioner method. Furthermore, the results are incredibly competitive with other methods on METEOR, ROUGE-L and SPICE metrics. Source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/UIT-Together/EAES_m4c</uri> .
Embedding Tamper-Resistant, Publicly Verifiable Random Number Seeds in Permissionless Blockchain Systems

Many blockchain processes require pseudo-random numbers. This is especially true of blockchain consensus mechanisms that aim to fairly distribute the opportunity to propose new blocks between the participants in the system. The starting point for these processes is a source of randomness that participants cannot manipulate. This paper proposes two methods for embedding random number seeds in a blockchain data structure to serve as inputs to pseudo-random number generators. Because the output of a pseudo-random number generator depends deterministically on its seed, the properties of the seed are critical to the quality of the eventual pseudo-random number produced. Our protocol, B-Rand, embeds random number seeds that are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">confidential</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tamper-resistant</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unpredictable</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collision-resistant</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">publicly verifiable</i> as part of every transaction. These seeds may then be used by transaction owners to participate in processes in the blockchain system that require pseudo-random numbers. Both the Single Secret and Double Secret B-Rand protocols are highly scalable with low space and computational cost, and the worst case is linear in the number of transactions per block.
Active Learning Under Malicious Mislabeling and Poisoning Attacks

Deep neural networks usually require large labeled datasets for training to achieve state-of-the-art performance in many tasks, such as image classification and natural language processing. Although a lot of data is created each day by active Internet users, most of these data are unlabeled and are vulnerable to data poisoning attacks. In this paper, we develop an efficient active learning method that requires fewer labeled instances and incorporates the technique of adversarial retraining in which additional labeled artificial data are generated without increasing the budget of the labeling. The generated adversarial examples also provide a way to measure the vulnerability of the model. To check the performance of the proposed method under an adversarial setting, i.e., malicious mislabeling and data poisoning attacks, we perform an extensive evaluation on the reduced CIFAR-10 dataset, which contains only two classes: airplane and frog. Our experimental results demonstrate that the proposed active learning method is efficient for defending against malicious mislabeling and data poisoning attacks. Specifically, whereas the baseline active learning method based on the random sampling strategy performs poorly (about 50&#x0025;) under a malicious misla-beling attack, the proposed active learning method can achieve the desired accuracy of 89&#x0025; using only one-third of the dataset on average.
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Reinforcement Learning (RL) agents in the real world must satisfy safety constraints in addition to maximizing a reward objective. Model-based RL algorithms hold promise for reducing unsafe real-world actions: they may synthesize policies that obey all constraints using simulated samples from a learned model. However, imperfect models can result in real-world constraint violations even for actions that are predicted to satisfy all constraints. We propose Conservative and Adaptive Penalty (CAP), a model-based safe RL framework that accounts for potential modeling errors by capturing model uncertainty and adaptively exploiting it to balance the reward and the cost objectives. First, CAP inflates predicted costs using an uncertainty-based penalty. Theoretically, we show that policies that satisfy this conservative cost constraint are guaranteed to also be feasible in the true environment. We further show that this guarantees the safety of all intermediate solutions during RL training. Further, CAP adaptively tunes this penalty during training using true cost feedback from the environment. We evaluate this conservative and adaptive penalty-based approach for model-based safe RL extensively on state and image-based environments. Our results demonstrate substantial gains in sample-efficiency while incurring fewer violations than prior safe RL algorithms. Code is available at: https://github.com/Redrew/CAP
ON THE APPLICATION OF MATRIX SCALING AND POWERING METHODS OF SMALL STATE SPACES FOR SOLVING TRANSIENT DISTRIBUTION IN MARKOV CHAIN

The iterative solution methods for transient distribution in Markov chain is the computation of state probability distributions at an arbitrary point of time, which in the case of a discrete-time Markov chain means, finding the distribution at some arbitrary time step denoted , a row vector whose component is the probability that the Markov chain is in state at time step . In this study, the solutions of transient distribution in Markov chain using matrix scaling and powering methods for small state spaces which produce a significantly more accurate response in less time for some types of situations and also, tries to get to the end result as quickly as possible has been investigated, in order to provide some insight into the solutions of transient distribution of Markov chain. Our goal is to compute solutions and algorithms for tiny state spaces utilizing matrix scaling and powering approaches, which begin with an initial estimate of the solution vector and then comes closer and closer to the true solution with each step or iteration. With the help of several existing Markov chain laws, theorems, and formulas, matrices operations such as multiplication with one or more vectors, Padé variant of the matrix-powering and scaling technique are used. While the algorithms are explained, the transient distribution vector’s Padé approximants , and a backward error analysis of the Padé approximation are obtained for certain illustrative examples..
Off-Policy Meta-Reinforcement Learning With Belief-Based Task Inference

Meta-reinforcement learning (RL) addresses the problem of sample inefficiency in deep RL by using experience obtained in past tasks for solving a new task. However, most existing meta-RL methods require partially or fully on-policy data, which hinders the improvement of sample efficiency. To alleviate this problem, we propose a novel off-policy meta-RL method, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">embedding learning and uncertainty evaluation</i> (ELUE). An ELUE agent is characterized by the learning of what we call a task embedding space, an embedding space for representing the features of tasks. The agent learns a belief model over the task embedding space and trains a belief-conditional policy and Q-function. The belief model is designed to be agnostic to the order in which task information is obtained, thereby reducing the difficulty of task embedding learning. For a new task, the ELUE agent collects data by the pretrained policy, and updates its belief on the basis of the belief model. Thanks to the belief update, the performance of the agent improves with a small amount of data. In addition, the agent updates the parameters of its policy and Q-function so that it can adjust the pretrained relationships when there are enough data. We demonstrate that ELUE outperforms state-of-the-art meta RL methods through experiments on meta-RL benchmarks.
Interpreting Deep Neural Networks for Medical Imaging Using Concept Graphs

The black-box nature of deep learning models prevents them from being completely trusted in domains like biomedicine. Most explainability techniques do not capture the concept-based reasoning that human beings follow. In this work, we attempt to understand the behavior of trained models that perform image processing tasks in the medical domain by building a graphical representation of the concepts they learn. Extracting such a graphical representation of the model’s behavior on an abstract, higher conceptual level would help us to unravel the steps taken by the model for predictions. We show the application of our proposed implementation on two biomedical problems - brain tumor segmentation and fundus image classification. We provide an alternative graphical representation of the model by formulating a concept level graph as discussed above, and find active inference trails in the model. We work with radiologists and ophthalmologists to understand the obtained inference trails from a medical perspective and show that medically relevant concept trails are obtained which highlight the hierarchy of the decision-making process followed by the model. Our framework is available at https://github.com/koriavinash1/BioExp .
The Selection of Learning Platforms to Support Learning Using Fuzzy Multiple Attribute Decision Making

The utilization of information technology in learning has functioned as a tool in the teaching and learning process during the Covid-19 pandemic. The need for the availability of a learning platform using LMS (Learning Management System) or free e-learning that is easily obtained from the public network (internet) makes the utilization of the learning platform indispensable for the teaching and learning process. Learning platforms available on the internet can also be used independently by students. However, not all existing learning platforms can be used as the appropriate means to improve the quality of education. The educator policies are needed to utilize the existing learning platforms so that learning objectives can be achieved. This study will analyze how to choose the right learning platform for an educational institution using SAW (Simple Additive Weighting)-based Fuzzy Multiple Attribute Decision Making (FMADM) method. FMADM is a method used to find the optimal alternative from a number of alternatives with certain criteria. The purpose of this study is to assist educators in deciding the most appropriate learning platform that can be used to support the teaching and learning process during the Covid 19 pandemic.
Iterative adversarial removal of gender bias in pretrained word embeddings

Recent advances in Representation Learning have discovered a strong inclination for pre-trained word embeddings to demonstrate unfair and discriminatory gender stereotypes. These usually come in the shape of unjustified associations between representations of group words (e.g., male or female) and attribute words (e.g. driving, cooking, doctor, nurse, etc.) In this paper, we propose an iterative and adversarial procedure to reduce gender bias in word vectors. We aim to remove gender influence from word representations that should otherwise be free of it, while retaining meaningful gender information in words that are inherently charged with gender polarity (male or female). We confine these gender signals in a sub-vector of word embeddings to make them more interpretable. Quantitative and qualitative experiments confirm that our method successfully reduces gender bias in pre-trained word embeddings with minimal semantic offset.
Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy

Current interest in Fourier lightfield microscopy is increasing, due to its ability to acquire 3D images of thick dynamic samples. This technique is based on simultaneously capturing, in a single shot, and with a monocular setup, a number of orthographic perspective views of 3D microscopic samples. An essential feature of Fourier lightfield microscopy is that the number of acquired views is low, due to the trade-off relationship existing between the number of views and their corresponding lateral resolution. Therefore, it is important to have a tool for the generation of a high number of synthesized view images, without compromising their lateral resolution. In this context we investigate here the use of a neural radiance field view synthesis method, originally developed for its use with macroscopic scenes acquired with a moving (or an array of static) digital camera(s), for its application to the images acquired with a Fourier lightfield microscope. The results obtained and presented in this paper are analyzed in terms of lateral resolution and of continuous and realistic parallax. We show that, in terms of these requirements, the proposed technique works efficiently in the case of the epi-illumination microscopy mode.
Analysis of geochemical patterns in a soil profile over mineralized bedrock

Transported soils cause difficulties in the identification of geochemical anomalies. It has been demonstrated that the joint application of local singularity analysis (LSA) and principal component analysis (PCA) can identify geochemical anomalies effectively, especially in regolith-covered areas. However, more convincing evidence is needed to explain the reasons for this. In this study, a soil profile overlying several mineralized veins cutting through bedrock was analysed in situ using a portable X-ray fluorescence spectrometer. The patterns of two mineralization-related elements, Cu and Mo, were analysed. The results revealed that the element concentrations of the soil sharply decreased as the distance from the bedrock increased, and this relationship can be described by a power-law model. LSA enhanced several vein-like anomalies corresponding to the mineralization veins in the bedrock, and the presence of vertically elongated weak anomalies in the soil indicates the migration of ore elements originating from the underlying bedrock through the soil. The statistics show that the patterns of the local singularity index (LSI) are stable at different depths and in different media, whereas the concentration patterns are not. In addition, the mineralization-related elements have a higher correlation coefficient for the LSI than for the concentration. Since a previous simulation study determined that a mineralization-indicative first principal component prefers that the variables have a close relationship and that the variables have similar patterns in different geological objects, the patterns discovered in this study explain why LSA is effective in identifying geochemical anomalies, especially when combined with PCA. Supplementary material: the high-resolution photo, the element concentration data and the lithologic data of the profile are available at https://doi.org/10.6084/m9.figshare.c.5957122 Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis