Reviewerly/exHarmony huggingface dataset:
Chaotic Agent Navigation: Achieving Uniform Exploration Through Area Segmentation

A chaotic navigation algorithm is designed for an autonomous agent, that has the objective of exploring a given area, whilst moving unpredictably. The algorithm uses the Renyi map as a randomness source to generate the orientation of the robot and the direction it can move. There are eight possible directions, but the motion is limited to only three directions each time, which are determined by its orientation, so as to make the motion smoother and applicable to a real robot. To improve coverage, the area is segmented into sixteen equal subareas and the agent distributes its motion equally in each one, moving sequentially in each subarea. Two approaches were considered for the threshold of moving from one subarea to another. In the first, a move to an adjacent subarea is performed based on the number of executed steps. In the second, the move was performed after a certain coverage percentage is achieved. The simulations for the segmented area showed a slight rise in the coverage percentage, and a more consistent coverage across the whole area, when compared with the unsegmented technique.
Ultrasound-Based 3-D Gesture Recognition: Signal Optimization, Trajectory, and Feature Classification

Gesture recognition is an emerging technology of human–computer interaction. Compared to the conventional technologies such as wearable devices, cameras, and radars, ultrasound-based gesture recognition has the advantages of low cost, low power consumption, and appropriates to fog or dark environment. In this study, we propose a gesture trajectory recognition device via ultrasonic signal, which is successfully applied in recognition and classification for gestures in 3-D space. First, the sensing hardware and software configurations for transmitting and receiving ultrasonic signals are designed, which can accurately obtain ultrasonic echo. Effects of different structures on the ultrasound scattering are explored. An adaptive filter based on wavelet packet decomposition is utilized to remove the noise in the ultrasonic echo. Second, the vibration principle of the ultrasonic sensor is modeled, and the rising envelope of ultrasound is fit by the quadratic curve to accurately extract the time of flight (TOF). Then, the error analysis and compensation are performed. An active 3-D positioning model is constructed and the positioning and tracking algorithm of gesture motion is theoretically established. A binomial fitting algorithm-based sliding template is proposed. To process the abnormal value and locate the start position of the gesture, the data association criterion and the maximum displacement threshold are introduced. Projection, dimensionality reduction, and reconstruction are performed on the trajectory. Then, the distance feature and the direction feature are extracted, and the hidden Markov model (HMM) is applied to realize the classification and recognition of gesture trajectory. The experiment shows that the 3-D gesture recognition system based on ultrasound can recognize a total of 36 character gestures from 0 to 9 and A to Z, with a recognition rate of 90.53%.
Unsupervised Clustering of Microseismic Signals Using a Contrastive Learning Model

Distinguishing useful microseismic signals is a critical step in microseismic monitoring. Here, we present the time series contrastive clustering (TSCC) method, an end-to-end unsupervised model for clustering microseismic signals that uses a contrastive learning network and a centroidal-based clustering model. The TSCC framework consists of two successive phases: pretraining and fine-tuning. In the pretraining phase, two random cropping augmentations are used to transform the time series microseismic data into two distinct but correlated views. Then, the multiscale temporal and instance contrasting learning are used to discriminate between negative and positive views, thus motivating the encoder to capture microseismic signal contextual information from multiple perspectives and generate distinct representations from unlabeled data. During the fine-tuning phase, the encoder weights are iteratively fine-tuned by simultaneously performing contrast learning and clustering. The corresponding loss is a weighted combination of the contrastive and clustering loss functions, which induces the encoder to learn representations that improve the clustering performance. The test results demonstrate that the proposed method can achieve better clustering accuracy (ACC) than popular clustering methods, including <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means, deep embedding clustering (DEC), unsupervised clustering with deep convolutional autoencoders (DCAs), and deep clustering with self-supervision (DCSS). Moreover, the TSCC model can produce results comparable to supervised deep learning approaches while requiring no labeled data, manual feature extraction, or large training datasets. In practice, the TSCC model has a clustering ACC of 98.07% and a normalized mutual information (NMI) of 86.26%.
Designing a new deep convolutional neural network for content-based image retrieval with relevance feedback

Content-based image retrieval (CBIR) is a hot research topic in computer vision. Relevance Feedback (RF) is a powerful technique that can help to increase quality of the CBIR. In this study, a new method for addressing the problem of the CBIR is proposed. To this end, a novel Convolutional Neural Network (CNN) and a new framework of applying RF have been investigated. Also, in order to obtain more efficient image features and reduce the dimensionality of feature vectors, we applied Generalized Discriminant Analysis (GDA) on the extracted features. The proposed method was tested on three benchmark datasets including the OT, Corel-1000 and Caltech-101. The experimental results illustrate that the proposed method performs better compared to the state-of-the-art in the field. Our approach achieved remarkable performances in image retrieval by demonstrating mean Average Precisions (mAPs) of 96.8%, 95.62% and 98.78% on the OT, Corel-1000 and Caltech-101, respectively.
Cooperative Particle Swarm Optimization Decomposition Methods for Large-scale Optimization

Many practical optimization problems can be classified as large-scale global optimization problems (LSOP). Various cooperative co-evolutionary (CC) algorithms have been proposed to combat the challenges of LSOPs. When CC algorithms are applied to large scale optimization problems, the effects of inter-connected variables, known as variable dependencies, can cause major performance degradation. Current literature provides different approaches to decomposing large-scale problems with variable dependencies during optimization using a wide range of base optimizers. In this paper, a cooperative particle swarm optimization (CPSO) algorithm is used as the base optimizer in a scalability study with a range of decomposition methods to determine ideal divide-and-conquer approaches when using a CPSO. Experimental results demonstrate that a variety of dynamic regrouping of variables, seen in the merging CPSO (MCPSO) and decomposition CPSO (DCPSO), as well varying total fitness evaluations per dimension, results in high-quality solutions when compared to six state-of-the-art decomposition approaches.
Path of Destruction: Learning an Iterative Level Generator Using a Small Dataset

We propose a new procedural content generation method which learns iterative level generators from a dataset of existing levels. The Path of Destruction method, as we call it, views level generation as repair; levels are created by iteratively repairing from a random starting level. The first step is to generate an artificial dataset from the original set of levels by introducing many different sequences of mutations to existing levels. In the generated dataset, features are observations of destroyed levels and targets are the specific actions that repair the mutated tile in the middle of the observations. Using this dataset, a convolutional network is trained to map from observations to their respective appropriate repair actions. The trained network is then used to iteratively produce levels from random starting maps. We demonstrate this method by applying it to generate unique and playable tile-based levels for several 2D games (Zelda, Danger Dave, and Sokoban) and vary key hyperparameters.
Universal Simulation Platform, a VR Simulator for IED Neutralization Training

Improvised explosive devices are mainly used to cause harm and terror. Explosive ordnance disposal technicians detect and safely neutralize improvised explosive devices. Due to their nature, improvised explosive devices pose an inherent risk to explosive ordnance disposal technicians. To minimize the risk associated with improvised explosive devices, explosive ordnance disposal technicians receive extensive training in working with explosive ordnances in various forms. Training often involves mock scenarios designed to simulate real-world conditions and can involve specialized equipment such as “disruptors” that fire live ammunition against a simulated improvised explosive device. While this training format is valuable, it still involves the expenses associated with the environment setup and equipment used during the training. Because of that, only a limited number of training sessions can be conducted. In this paper, we propose Universal Simulation Platform, a software based on virtual reality technology that can be used for improvised explosive device neutralization training. This simulation software allows for customized scenarios implemented by participants or any organization intending to deploy the software. Participants in each simulation can interact with the virtual environment while trying to neutralize or mitigate the impact of the improvised explosive device under various conditions and settings.
Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations

Despite being considered as the next frontier in computation, Quantum Computing is still in an early stage of development. Indeed, current commercial quantum computers suffer from some critical restraints, such as noisy processes and a limited amount of qubits, among others, that affect the performance of quantum algorithms. Despite these limitations, researchers have devoted much effort to propose different frameworks for efficiently using these Noisy Intermediate-Scale Quantum (NISQ) devices. One of these procedures is D'WAVE Systems' quantum-annealer, which can be used to solve optimization problems by translating them into an energy minimization problem. In this context, this work is focused on providing useful insights and information into the behaviour of the quantum-annealer when addressing real-world combinatorial optimization problems. Our main motivation with this study is to open some quantum computing frontiers to non-expert stakeholders. To this end, we perform an extensive experimentation, in the form of a parameter sensitive analysis. This experimentation has been conducted using the Traveling Salesman Problem as benchmarking problem, and adopting two QUBOs: state-of-the-art and a heuristically generated. Our analysis has been performed on a single 7-noded instance, and it is based on more than 200 different parameter configurations, comprising more than 3700 unitary runs and 7 million of quantum reads. Thanks to this study, findings related to the energy distribution and most appropriate parameter settings have been obtained. Finally, an additional study has been performed, aiming to determine the efficiency of the heuristically built QUBO in further TSP instances.
Direct Interaction Word-Gesture Text Input in Virtual Reality

As Virtual Reality (VR) devices become more affordable and experience a more widespread adoption, applications for this immersive technology become more diverse, increasingly also including text-based uses, such as messaging and text processing. While text input methods for VR have existed for a long time, none have reached the ease-of-use or performance of conventional, physical keyboards. In the absence of physical keyboards, touch-screen devices have adopted word-gesture keyboards, that allow users to input text through smooth motions. In this work, we introduce a word-gesture keyboard for VR that uses direct interaction with a virtual keyboard, to allow a more direct transfer of hand motion. We present a preliminary evaluation with promising results, which suggest usability improvements over more well established methods of text input in VR.
Comparison of Data Encodings and Machine Learning Architectures for User Identification on Arbitrary Motion Sequences

Reliable and robust user identification and authentication are important and often necessary requirements for many digital services. It becomes paramount in social virtual reality (VR) to ensure trust, specifically in digital encounters with lifelike realistic-looking avatars as faithful replications of real persons. Recent research has shown that the movements of users in extended reality (XR) systems carry user-specific information and can thus be used to verify their identities. This article compares three different potential encodings of the motion data from head and hands (scene-relative, body-relative, and body-relative velocities), and the performances of five different machine learning architectures (random forest, multilayer perceptron, fully recurrent neural network, long-short term memory, gated recurrent unit). We use the publicly available dataset "Talking with Hands" and publish all code to allow reproducibility and to provide baselines for future work. After hyperparameter optimization, the combination of a long-short term memory architecture and body-relative data outperformed competing combinations: the model correctly identifies any of the 34 subjects with an accuracy of 100% within 150 seconds. Altogether, our approach provides an effective foundation for behaviometric-based identification and authentication to guide researchers and practitioners. Data and code are published under https://go.uniwue.de/58w1r.
Secure Medical Blockchain Model

The Internet of Medical Things (IoMT) uses wireless networks to help patients to communicate with healthcare professionals. Therefore, IoMT devices suffer from a lack of security controls, just like many Internet of Things (IoT) gadgets. Thus, in this paper, we develop a system that uses a blockchain to secure medical data for each transaction between physicians and patients. This system also helps the physician to send the treatment to the blockchain. The blockchain creates a new block for the treatment and connects it with the previous block. This system also helps patients to access their treatment through the blockchain. SHA-256 is used to hash the new block using some information about the last block. We modify SHA-256 using the LZ4 algorithm to compress data. We also prevent a new block hash code starting with a specific number of zeros, which made the proposed system give a time complexity better than all related work. In this paper, we also develop a party-authentication technique that ensures the two parties of the transaction. The proposed system makes a transaction with O(n) time complexity. Thus, our system takes 1 s to create a block for the transaction. We also make a green computing algorithm comparison between our proposed system and the blockchain version. This comparison proves that our proposed method consumes less energy to create a new block. This paper proves that our method performs better than all previous blockchain versions.
Humour as an online safety issue: Exploring solutions to help platforms better address this form of expression

This paper makes a case for addressing humour as an online safety issue so that social media platforms can include it in their risk assessments and harm mitigation strategies.We take the ' online safety' regulation debate, especially as it is taking place in the UK and the European Union, as an opportunity to reconsider how and when humour targeted at historically marginalised groups can cause harm.Drawing on sociolegal literature, we argue that in their online safety efforts, platforms should address lawful humour targeted at historically marginalised groups because it can cause individual harm via its cumulative effects and contribute to broader social harms.We also demonstrate how principles and concepts from critical humour studies and Feminist Standpoint Theory can help platforms assess the differential impacts of humour.
Active Polarization Imaging for Cross-Linear Image Histogram Equalization and Noise Suppression in Highly Turbid Water

The absorption and scattering of impurity particles in turbid water cause the target signal light to be attenuated and to produce backscattered light, resulting in the reduced quality of underwater polarimetric imaging. As water turbidity increases, the effect of backscattered light becomes greater, making polarization imaging in highly turbid water a challenge. Theory and experiment show that the increase in the intensity of backscattered light leads to high noise gain in the underwater active polarization imaging model. In order to enhance image contrast and suppress noise gain in highly turbid water, we propose an underwater imaging enhancement method that appropriately combines the non-physical and physical models. The method uses contrast limited adaptive histogram equalization (CLAHE) for a certain number of cross-linear images (Imin) before calculating their polarization enhancement images, and it constructs joint filtering (multi-frame averaging and bilateral filtering) to suppress the high noise gain introduced by the imaging model and CLAHE. The experimental results in highly turbid water validate the rationality and feasibility of the proposed method, and the comparative processing results (52.7~98.6 NTU) outperform those of the conventional non-physical and physical model methods. The method maintains the complexity of the system and facilitates the application of conventional polarimetric imaging in harsher underwater environments.
Federated learning with hyper-network—a case study on whole slide image analysis

Federated learning(FL) is a new kind of Artificial Intelligence(AI) aimed at data privacy preservation that builds on decentralizing the training data for the deep learning model. This new technique of data security and privacy sheds light on many critical domains with highly sensitive data, including medical image analysis. Developing a strong, scalable, and precise deep learning model has proven to count on a variety of high-quality data from different centers. However, data holders may not willing to share their data considering the restriction of privacy. In this paper, we approach this challenge with a federated learning paradigm. Specifically, we present a case study on the whole slide image classification problem. At each local client center, a multiple-instance learning classifier is developed to conduct whole slide image classification. We introduce a privacy-preserving federated learning framework based on hyper-network to update the global model. Hyper-network is deployed at the global center that produces the weights of the local network conditioned on its input. In this way, hyper-networks can simultaneously learn a family of the local client networks. Instead of communicating raw data with the local client, only model parameters injected with noise are transferred between the local client and the global model. By using a large scale of whole slide images with only slide-level labels, we mensurated our way on two different whole slide image classification problems. The results demonstrate that our proposed federated learning model based on hyper-network can effectively leverage multi-center data to develop a more accurate model which can be used to classify a whole slide image. Its improvements in terms of over the isolated local centers and the commonly used federated averaging baseline are significant. Code will be available.
A study on relationship between prediction uncertainty and robustness to noisy data

The presence of noise results in the decrease of the data quality, and thus affects seriously the learning performance. In this paper, we investigate theoretically and empirically how adjusting the temperature value of the softmax function to control the prediction uncertainty can help improve the model robustness to noise. Moreover, we design a modified loss function, where a regularisation term based on the uncertainty of distinguishing those non-ground truth labels is incorporated in the loss function to simultaneously improve the training accuracy and the model robustness to noise. This study theoretically and experimentally validates that the temperature parameter of the softmax function significantly impacts the classification accuracy and the proposed loss function is effective in improving the performance in the presence of noise. The study provides some practical guidelines for improving classifier performance through clearly expressing and understanding the relationship between uncertainty and noise robustness.
Exploring relationship of soil PTE geochemical and “VIS-NIR spectroscopy” patterns near Cu–Mo mine (Armenia)

PTE contamination of soils remains one of the global environmental concerns. The ways of detecting and monitoring PTE concentrations in soils varies including traditional field sampling accompanied by sample preparation and chemical analysis and state of the art visible and near-infrared (Vis-NIR) spectroscopic approaches. Among the different Machine Learning (ML) to extract soil information from spectra and to explore the relationship between spectral reflectance data and soil PTE content PLSR method is a well-established one to construct a soil PTE estimation model. This study aimed to explore the relationship of soil PTE geochemical and VIS-NIR spectroscopy characteristics in agricultural soils near Cu-Mo mine area in Armenia. PLSR method is applied to identify the links between the spectra and agricultural soil Ti, V, Cr, Mn, Fe, Co, Ba, Pb, Zn, Cu, Sr, Zr and Mo contents to reveal the potential of VIS-NIR spectroscopy in ex-situ monitoring of Kajaran soils. The results show that different portions of VIS-NIR spectra are responsible for Ti (1100-1200 nm, 2350-2500 nm), V (350-500 nm, 700-750 nm, 1000-1100 nm, 1400-2500 nm), Cr (1300-1400 nm, 1900-2100 nm) and Ba (450-500 nm, 600-800 nm, 1050-1700 nm, 2000-2100 nm, 2350-2400 nm) estimations through PLSR correspondingly. However, among the studied PTEs Ti and V, which shows significant negative correlations in VIS-NIR spectra registered at around 400-600 nm and 850-1150 nm regions, are remarkable and promising with the PLSR estimation results using VIS-NIR spectra Ti (R2Test = 0.74), V (R2Test = 0.71). This study shows that VIS-NIR spectroscopy has a high potential for the estimation of at least several PTE in soils and PLSR modelis reliable for deriving information from there.