Reviewerly/exHarmony huggingface dataset:
Insights in Hierarchical Clustering of Variables for Compositional Data

Abstract R-mode hierarchical clustering is a method for forming hierarchical groups of mutually exclusive subsets of variables. This R-mode cluster method identifies interrelationships between variables which are useful for variable selection and dimension reduction. Importantly, the method is based on metric elements defined on the sample space of variables. Consequently, hierarchical clustering of compositional parts should respect the particular geometry of the simplex. In this work, the connections between concepts such as distance, cluster representative, compositional biplot, and log-ratio basis are explored within the framework of the most popular R-mode agglomerative hierarchical clustering methods. The approach is illustrated in a paleoecological study to identify groups of species sharing similar behavior.
What is worth retaining from the COVID-19 experience on remote online education in the prospective teacher education?

The COVID-19 pandemic reshaped the educational landscape, including the training of prospective teachers. This study explores prospective teachers’ experiences of transitioning to online distance education (ODE) and their intentions regarding the preferred teaching scenarios they wish to use in their future in-service practice. In the sample of 281 prospective teachers, we found interesting results: (a) smartphones offer very limited opportunities for active participation in ODE; (b) connectivity is the weakest point in ODE; (c) personal digital infrastructure and study conditions influence intention to choose teaching scenarios in ODE, when in the role of teacher, while group size does not. We can safely assume that online distance education will survive in some educational pockets but due to numerous limitations, it will not become a dominant current in teacher education.
Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion

There are many problems with remote sensing images, such as large data scales, complex illumination conditions, occlusion, and dense targets. The existing semantic segmentation methods for remote sensing images are not accurate enough for small and irregular target segmentation results, and the edge extraction results are poor. The authors propose a remote sensing image segmentation method based on a DCNN and multiscale feature fusion. Firstly, an end-to-end remote sensing image segmentation model using complete residual connection and multiscale feature fusion was designed based on a deep convolutional encoder–decoder network. Secondly, weighted high-level features were obtained using an attention mechanism, which better preserved the edges, texture, and other information of remote sensing images. The experimental results on ISPRS Potsdam and Urban Drone datasets show that compared with the comparison methods, this method has better segmentation effect on small and irregular objects and achieves the best segmentation performance while ensuring the computation speed.
Rise of Bitcoin, Economic Inequality and the Ecology

What do we know about the interrelations between economic inequality, ecology and the increased use of Bitcoin? The aim of the paper was to empirically test the relationship between economic and ecological effects related to the increase in Bitcoin’s network hashrate in a selection of countries that have the highest influx of crypto-mining. To test these three types of relationships, I collected a dataset concerning Bitcoin indicators, economic indicators and ecological indicators that were obtained from multiple trustworthy sources: OECD, World Bank, Fred Data, World Inequality Database (WID). Handling the data challenges, I used this unique panel dataset to explore the relationship between Bitcoin’s hashrate and two types of outcomes: (i) economic outcomes (such as the GDP which as we know relates to inequalities through the Kuznets curve) or direct measures of inequality (such as, income inequality (GINI) and the share of people with top 1% of income and 1% of wealth), and (ii) ecological outcomes (such as carbon emissions, carbon footprint and electronic waste). I found that the Bitcoin currency associates with certain redistribution of wealth, but the accumulation of crypto-currency-related wealth itself remains still concentrated in the wealth of the top 1%. Also, there is evidence for certain nonlinearities in the relationships with the ecological degradation, echoing the concept of the Kuznets curve.
Bisequent Calculus for Four-Valued Quasi-Relevant Logics: Cut Elimination and Interpolation

Abstract We present a uniform syntactical characterisation of the class of quasi-relevant logics which are four-valued extensions of the basic relevant logic B of Meyer and Routley. All these logics are obtained by the addition of suitable quasi-relevant implications to the four-valued logic of First Degree Entailment FDE. So far they were characterised axiomatically and semantically in several ways but did not obtain a special proof-theoretic treatment. To this aim a generalised form of sequent calculus called bisequent calculus (BSC) is applied. In BSC rules operate on the ordered pairs of ordinary sequents. It may be treated as the weakest kind of system in the rich family of generalised sequent calculi operating on items which are some collections of ordinary sequents, like hypersequents or nested sequents. It is shown that all logics under consideration have cut-free characterisation in BSC which satisfies the subformula property and yields decidability. It is also shown that the interpolation theorem holds for these logics if their language is enriched with additional negation.
Obstacle Avoidance for Unicycle-Modelled Mobile Robots with Time-Varying Control Barrier Functions

In this paper, we propose a safety-critical controller based on time-varying control barrier functions (CBFs) for a robot with an unicycle model in the continuous-time domain to achieve navigation and dynamic collision avoidance. Unlike previous works, our proposed approach can control both linear and angular velocity to avoid collision with obstacles, overcoming the limitation of confined control performance due to the lack of control variable. To ensure that the robot reaches its destination, we also design a control Lyapunov function (CLF). Our safety-critical controller is formulated as a quadratic program (QP) optimization problem that incorporates CLF and CBFs as constraints, enabling real-time application for navigation and dynamic collision avoidance. Numerical simulations are conducted to verify the effectiveness of our proposed approach.
Investigating Continual Learning Strategies in Neural Networks

This paper explores the role of continual learning strategies when neural networks are confronted with learning tasks sequentially.We analyze the stability-plasticity dilemma with three factors in mind: the type of network architecture used, the continual learning scenario defined and the continual learning strategy implemented.Our results show that complementary learning systems and neural volume significantly contribute towards memory retrieval and consolidation in neural networks.Finally, we demonstrate how regularization strategies such as elastic weight consolidation are more well-suited for larger neural networks whereas rehearsal strategies such as gradient episodic memory are better suited for smaller neural networks.
AnimeTransGAN: Animation Image Super-Resolution Transformer via Deep Generative Adversarial Network

To achieve better visual experiences for watching classic animations displayed on high-end displays, such as UHDTV (ultra high-definition television), a novel deep learning framework designed for animation image super-resolution (SR) is proposed in this paper. To overcome the possible drawbacks that GAN (generative adversarial network)-based SR models may not recover sufficient image details while transformer-based SR models may produce over-blurred/over-sharpened images, we propose a novel GAN-based model for animation image SR, where we integrate the superior detail recovery capability of transformer models for image SR, and the discriminative ability of the U-Net-based discriminator for determining the reality of generated images, denoted by AnimeTransGAN. Our experimental results demonstrate that the proposed AnimeTransGAN model quantitatively and qualitatively achieves better SR performances for animation images, compared with the state-of-the-art methods.
Facial Recognition in Celebrity Fan Art with Multi-Task Learning and Image Data Augmentation

In this paper, we address facial recognition in celebrity fan art images, which is of importance for portrait rights protection. Celebrity fan art images are collected from the Internet, and generative adversarial networks are utilized to augment the dataset. We adopt pre-trained deep neural networks including ResNet and EfficientNetV2 for classifying images and propose a multi-task learning-based approach to improve recognition performance. The experimental results show that the proposed method can achieve a high accuracy in facial recognition of fan art images.
Vibration Patterns for Body-Braille Using a Foot Sole Tactile Sensation

Herein, we consider a Body-Braille (BB) system that uses the sole of the foot as a sensory site. To determine the vibration pattern to be used for the BB system, we conducted an experiment to investigate the correct answer rate and reaction time for multiple vibration patterns. The experimental results revealed that four types of vibration are suitable for the BB system: short, 120 Hz, alternate, and concave vibrations. In the future, we aim to develop a BB system using these vibration patterns.
Acquisition of Highly Independent Latent Space for Feature-based Data Distribution Control Using DenseNet

The Transformer architecture has been applied i n many natural language processing tasks such as BERT(Bidirectional Encoder Representations from Transformers) and GP T(Generative Pretrained Transformer). In image recognition, Vision Transformer has garnered much attention for outperforming convolution-based convolution neural network structures . In the attention mechanism, focus is placed on the important information in the input data and more weight is assigned to such information to improve the output accuracy. In this research, we first acquire various latent variables using DenseNet. Multiple encoders are then used in an encoder-decoder structure to obtain a non-redundant latent space using the attention mechanism. Furthermore, multi-objective learning is employed in multiple stages to achieve intuitive feature map acquisition based on the data characteristics.
A Pixel-Based Character Detection Scheme for Texts with Arbitrary Orientations in Natural Scenes

In recent years, there has been a significant focus on deep learning-based research for detecting texts in natural scenes. While many studies have achieved promising results by targeting word detection, challenges remain in detecting and recognizing texts with arbitrary orientations. Complex image backgrounds, text occlusion, and variations in text styles easily affect the detection process of words. This paper introduces a pixel-based character detection scheme for extracting individual characters within words. The objective is to locate characters in irregular text orientations or shapes, thereby achieving better alignment of detection bounding boxes with character edges. Since existing datasets only provide word-level annotations and lack character-level ground truths, we generate realistically synthesized artificial data to address this limitation. We employ weakly supervised learning, utilizing partially annotated data for training, and subsequently enhance performance by incorporating actual data. Experimental results demonstrate that our scheme outperforms other character-level detection models regarding text recognition accuracy, as evidenced by comparisons on datasets such as ICDAR2017, TotalText, and CTW-1500.
Real-time Identification System Using YOLO and Isomorphic Object Identification for Visually Impaired People

In this study, we first design and implement a real-time object identification system for visually impaired people using a lightweight deep learning YOLO. Then, we verify the identification performance of our system for three types of milk boxes, which are objects with almost the same shape but different patterns. As a result of testing various images, an average confidence score of 96% was achieved at a distance of up to 30 cm. In addition, images with large changes in brightness and contrast can be correctly identified.
DAdAE: Domain Adversarial Autoencoder Based In-Vehicle CAN Anomaly Detection

Modern vehicles have multiple electronic control units (ECUs) that are connected as part of a complex cyber-physical system (CPS). The controller area network (CAN) is a well-known communication protocol that connects these ECUs because of its reliability and efficiency. However, adversaries can easily inject abnormal messages into the CAN bus remotely to affect vehicle driving safety. Existing anomaly detection methods only focus on specific vehicle models and have a limited range of applications across different vehicles. To address this challenge, this paper proposes a Domain Adversarial training-based AutoEncoder (DAdAE) for unsupervised CAN anomaly detection. The advantages of our approach are: 1) detect variant attack scenarios on different car models 2) does not require labeled data 3) works well even with a limited dataset. The effectiveness of the proposed model is evaluated on the survival dataset, and the experiment results show that the DAdAE model improves the overall f1 score significantly, compared to other unsupervised models.
Virtual Kitakyushu Revitalization Design Based on the Case Study of Immersive Legacies Exhibition in Wellington Museum

The article explores the use of virtual technologies to revitalize Kitakyushu, Japan, by drawing on the “Immersive Legacies” exhibition at Wellington Museum, New Zealand, which utilized virtual technologies to great effect. There the Reality-Virtuality Continuum was used to analyze the digital heritage of the “Gordon Wilson Flats”. These methods were then applied to address issues that Kitakyushu is facing, such as a declining population, outflow of young migration, and lower gross product. To this end, the study designed four virtual locations: Chugin Street, Kokura Castle, Tanga Market, and Katsuyama Park. These locations were envisioned as virtual commercial streets, VR scenic spots, Virtual Seafood Museum, and virtual gym, respectively. The aim was to use virtual technologies not only to revitalize Kitakyushu but also to offer diverse shopping, touring, educating, and exercising experiences. The study finds that virtual technologies successfully address the challenges faced by Kitakyushu and contribute to its revitalization. Moreover, the research extends the meaning of the Reality-Virtuality Continuum in design for city development and revitalizations.
Towards Real-Time Motion Planning for Industrial Robots in Collaborative Environments

In collaborative environments, real-time motion planning is crucial for industrial robots to navigate safely and efficiently. Traditional planning algorithms, such as Rapidly-exploring Random Trees (RRT) or Probabilistic Roadmaps (PRM), often face challenges in coping with dynamic environments due to their inherent computational complexity. To address this issue, we propose an approach based on Deep Reinforcement Learning (DRL) for real-time motion planning of industrial robots. Our method leverages the power of machine learning and neural networks to enable robots to make intelligent decisions in real-time, ensuring prompt and adaptive navigation. However, applying DRL to industrial robots poses unique challenges, as vision-based training is difficult and distance sensors commonly used in mobile robots are unavailable. To overcome these challenges, we employ depth cameras to generate distance information and convert the obtained point cloud into voxels using the Open3D library. The obstacles are then loaded into the simulation environment in real-time, allowing the agent to perceive and react to the dynamic environment. To achieve a low simulation-to-real-gap, we propose a hardware-in-the-loop (HIL) approach, where the real robot mimics the movements of the simulated robot. We demonstrate the effectiveness of our system through real-world experiments. Our code is available on GitHub [1].
VVC Extension Scheme for Object Detection Using Contrast Reduction

In recent years, video analysis using AI has been widely used, due to the remarkable development of image recognition technology. In 2019, MPEG has started standardization of Video Coding for Machines (VCM) as a video coding for image recognition. In the framework of VCM, both higher image recognition accuracy and video compression performance are required. In this paper, we propose an extention scheme of video coding for object detection using Versatile Video Coding (VVC). Unlike video for human vision, video used for object detection does not require a large image size or high contrast. Downsampling of the image can reduce the amount of information. Decreasing the image contrast can suppress the entropy of the image. Therefore, in our proposed scheme, the original image is reduced in size and contrast, then coded with VVC encoder. Then, the output image from the VVC decoder is restored to its original image size using the bicubic method. Experimental results show that the proposed video coding scheme achieves better coding performance than regular VVC in terms of object detection accuracy.
Real-Time Clustering and Classification of Physiological Big Data for Healthcare Monitoring

In recent years, there has been a significant increase in interest in medical health applications using Wireless Body Sensor Networks (WBSN). Monitoring people health conditions is crucial activity of today's healthcare system. As the future state of health is a consequence of past state, we induce data mining approaches on the window of past signals to predict worst health condition coming in the future. We propose the combinatorial real-time clustering and classification module, in which the future health risk would be predicted in real time. Also to enable continuous monitoring of the patient in real time, we are introducing mobile healthcare system by which the patient analysis and future predictions are carried out and informed to the patient by physician. The proposed Online Distribution Resource Aware (ODRA) algorithm has demonstrated superior accuracy and a decreased rate of False Positives (FPR) in predicting the risk status of 64 patients with varying illnesses using their Heart Rate (HR), Oxygen saturation (SpO2), and Blood Pressure signals. This performance is in comparison to the Resource Aware High-Quality Clustering (RAH) algorithm. This indicates that ODRA can be a promising algorithm for healthcare professionals to better assess the risk status of their patients and provide more accurate diagnoses and treatment plans.
Trajectory Prediction of Airport Cargo Tractor with Multi Trailer Based on Single Sensor

With the layer-by-layer addition of epidemic prevention and control, as well as the long-term low efficiency and insufficient control of man-drive luggage/cargo tractors, unmanned tractor has become the first landing application scene of autonomous vehicle in the background of the construction of smart airport. Apart from other vehicles, multiple trailers are towed behind the tractor, so the path planning of the tractor needs to consider the driving path of the rear trailers at the same time. In this paper, a two-dimensional mathematical model of tractor trailer group is established based on the Ackerman principle. And the pose of trailer is predicted by coordinate transformation and rotation displacement matrix. The calculation process of trailer drawbar angle is optimized by Fourier series and Gaussian filtering, and the relevant parameters are determined by comparing with the simulation results. A trajectory is designed for simulation to obtain the key data of the driving characteristic of the tractor trailer group, and the validity of the calculation result is verified.
A Concept and Functional Design of AR Cards

We propose card games in which various information is projected onto real cards by using Augmented Reality (AR) technology with an optical see-through head mounted display (HMD). In this paper, based on the results of preliminary experiments using a prototype of the initial implementation of this system, we discuss the requirements of the AR cards used in this system and the functional design of the AR card games based on these requirements.
A Company Recommendation Method Based on Characteristic Analysis for Job Hunting Support

Job-hunting websites and applications have become popular tools for job seekers, but they often lack differentiation, making it difficult to find companies that match their preferences. A survey of graduates in 2020 revealed a strong emphasis on positive company culture and growth opportunities. To address this, we propose a method that identifies key characteristics desired by graduates and ranks companies accordingly. Our approach involves assigning a total score to each company based on these characteristics and providing company recommendations to job seekers. This paper presents our proposed company recommendation method for ranking companies and its effectiveness through subject evaluations.
Color Improvement in Translucent 3D Printing using Color-Layer Embedding Technique

Full-color three-dimensional (3D) printing technology has been a challenge to maximize color fidelity while preserving advantages of depth appearance of printed objects. Especially for the 3D printer that uses translucent materials, such as the Polylactic Acid (PLA) polymers, colors printed on PLA materials usually appear dull and faint, even with a usual surface-painted technique, due to the complex subsurface light diffusion in translucent objects. In this work, we perform experiments to investigate the color appearance on PLA products printed with various polymer-color structures. Our results show that a double-layer color embedding, with a gap of about 0.4 mm PLA layers, can significantly expand the color gamut of the printed products, showing improvement in comparison to that made with the surface-painted technique. We thus propose this double color-layer embedding technique to enhance the quality of color appearance, while keeping the translucency and depth perception of the 3D printed objects, which can be beneficial in a wide range of applications in the new era of innovation and creativity of consumers.
Neighborhood Graph Filters Based Graph Convolutional Neural Networks for Multi-Agent Deep Reinforcement Learning

Multi-agent deep reinforcement learning (MADRL), where a group of agents inside multi-agent systems cooperate to achieve a common goal, has been shown useful in many applications such as collaborative robots, autonomous driving or video games involving teams. In this paper, we propose two multi-agent deep reinforcement learning (MADRL) frameworks for value function factorization built by using Graph Convolutional Neural Networks (GCNN) based on neighborhood graph filters (NGFs). These MADRL frameworks are based on the paradigm of centralized training with decentralized execution (CTDE). In this work, we show that the superior stability of the NGFs as compared to standard graph filters leads also to superior performance for the MADRL algorithms. In the first MADRL framework, the NGF-based GCNN is used to predict the local q-value at each of the agents, while in the other one, the NGF-based GCNN is used to mix the local q-values to generate a global Q-value. We have compared the performance of NGF-based GCNNs over state-of-the-art graph neural networks for value function factorization in the MADRL framework for the StarCraft II and Coalition Structure Generation problems. The results show that the proposed MARL frameworks outperform the existing state-of-art architectures.