Reviewerly/exHarmony huggingface dataset:
Automatic Speaker Positioning in Meetings Based on YOLO and TDOA

In recent years, many things have been held via video conferences due to the impact of the COVID-19 epidemic around the world. A webcam will be used in conjunction with a computer and the Internet. However, the network camera cannot automatically turn and cannot lock the screen to the speaker. Therefore, this study uses the objection detector YOLO to capture the upper body of all people on the screen and judge whether each person opens or closes their mouth. At the same time, the Time Difference of Arrival (TDOA) is used to detect the angle of the sound source. Finally, the person's position obtained by YOLO is reversed to the person's position in the spatial coordinates through the distance between the person and the camera. Then, the spatial coordinates are used to calculate the angle between the person and the camera through inverse trigonometric functions. Finally, the angle obtained by the camera, and the angle of the sound source obtained by the microphone array, are matched for positioning. The experimental results show that the recall rate of positioning through YOLOX-Tiny reached 85.2%, and the recall rate of TDOA alone reached 88%. Integrating YOLOX-Tiny and TDOA for positioning, the recall rate reached 86.7%, the precision rate reached 100%, and the accuracy reached 94.5%. Therefore, the method proposed in this study can locate the speaker, and it has a better effect than using only one source.
Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance

Motion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, among others, by sampling-based methods and convex optimization strategies. Although they have been investigated from various perspectives in the existing literature, recent developments aimed at integrating robots into social, healthcare, industrial, and educational contexts have attributed greater importance to additional concepts that would allow them to communicate, cooperate, and collaborate with each other, as well as with human beings, in a meaningful and efficient manner. Therefore, in this survey, in addition to a brief overview of some of the essential aspects of motion planning algorithms, a few vital considerations required for assimilating robots into real-world applications, including certain instances of social, urban, and industrial environments, are introduced, followed by a critical discussion of a set of outstanding issues worthy of further investigation and development in future scientific studies.
DreamWalk: Dynamic remapping and multiperspectivity for large‐scale redirected walking

Summary Redirected walking (RDW) provides an immersive user experience in virtual reality applications. In RDW, the size of the physical play area is limited, which makes it challenging to design the virtual path in a larger virtual space. Mainstream RDW approaches rigidly manipulate gains to guide the user to follow predetermined rules. However, these methods may cause simulator sickness, boundary collision, and reset. Static mapping approaches warp the virtual path through expensive vertex replacement in the stage of model pre‐processing. They are restricted to narrow spaces with non‐looping pathways, partition walls, and planar surfaces. These methods fail to provide a smooth walking experience for large‐scale open scenes. To tackle these problems, we propose a novel approach that dynamically redirects the user to walk in a non‐linear virtual space. More specifically, we propose a Bezier‐curve‐based mapping algorithm to warp the virtual space dynamically and apply multiperspective fusion for visualization augmentation. We conduct comparable experiments to show its superiority over state‐of‐the‐art large‐scale redirected walking approaches on our self‐collected photogrammetry dataset.
Fog Density Evaluation by Combining Image Grayscale Entropy and Directional Entropy

The fog density level, as one of the indicators of weather conditions, will affect the management decisions of transportation management agencies. This paper proposes an image-based method to estimate fog density levels to improve the accuracy and efficiency of analyzing fine meteorological conditions and validating fog density predictions. The method involves two types of image entropy: a two-dimensional directional entropy derived from four-direction Sobel operators, and a combined entropy that integrates the image directional entropy and grayscale entropy. For evaluating the performance of the proposed method, an image test set and an image training set are constructed; and each image is labeled as heavy fog, moderate fog, light fog, or fog-free according to the fog density level of the image based on a user study. Using our method, the average accuracy rates of image fog level estimation were 77.27% and 79.39% on the training set using the five-fold cross-validation and the test set, respectively. Our experimental results demonstrate the effectiveness of the proposed combined entropy for image-based fog density level estimation.
Findings on Ad Hoc Contractions

Abbreviations are often overlooked, since their frequency and acceptance are almost second nature in everyday communication. Business names, handwritten notes, online messaging, professional domains, and different languages all have their own set of abbreviations. The abundance and frequent introduction of new abbreviations cause multiple areas of overlaps and ambiguity, which mean documents often lose their clarity. We reverse engineered the process of creating these ad hoc abbreviations and revealed some preliminary statistics on what makes them easier or harder to define. In addition, we generated candidate definitions for which it proved difficult for a word sense disambiguation model to select the correct definition.
Enabling Efficient and Privacy-Preserving Task Allocation with Temporal Access Control for Mobile Crowdsensing

The increasing proliferation of GPS-enabled mobile devices, including Unmanned Aerial Vehicles (UAVs), smartphones, and laptops, has resulted in a significant upsurge in the outsourcing of spatial data to cloud servers for storage and computation purposes, such as task allocation and location-based services. However, the reliance on untrusted cloud servers introduces the risk of privacy breaches, as these servers possess the ability to deduce and access users’ private information based on task content and query requirements. Existing privacy-preserving task-allocation schemes offer only coarse-grained and non-temporal access control, which restricts their applicability in scenarios involving multiple users and time-series data, such as trajectory and time-related routes. To overcome these challenges, this paper proposes an Efficient and Privacy-Preserving Task Allocation with Temporal Access Control (EPTA-T) scheme for mobile crowdsensing. By leveraging the techniques of Gray code and randomizable matrix multiplication, EPTA-T achieves efficient and privacy-preserving task allocation in mobile crowdsensing. Specifically, EPTA-T supports fine-grained and temporal access control through the utilization of an attribute-based access tree and function integration. The formal security analysis demonstrated that EPTA-T effectively guarantees data privacy and query privacy throughout the task allocation process. Extensive experiments conducted using a real-world dataset indicated that the EPTA-T scheme surpassed the performance of the state-of-the-art scheme.
A Gaussian Process Decoder with Spectral Mixtures and a Locally Estimated Manifold for Data Visualization

Dimensionality reduction plays an important role in interpreting and visualizing high-dimensional data. Previous methods for data visualization overestimate the local structure and lack the consideration of global preservation. In this study, we develop a Gaussian process latent variable model (GP-LVM) for data visualization. GP-LVMs are one of the frameworks of principal component analysis and preserve the global structure effectively. The drawbacks of GP-LVMs are the absence of local structure preservation and the use of low-expressive kernel functions. Therefore, we introduce regularization for local preservation and an expressive kernel function into GP-LVMs to overcome these limitations. As a result, we reflect the global and local structures in low-dimensional representations, improving the reliability and visibility of embeddings. We conduct qualitative and quantitative experiments comparing baselines and state-of-the-art methods on image and text datasets.
FusionTrack: Multiple Object Tracking with Enhanced Information Utilization

Multi-object tracking (MOT) is one of the significant directions of computer vision. Though existing methods can solve simple tasks like pedestrian tracking well, some complex downstream tasks featuring uniform appearance and diverse motion remain difficult. Inspired by DETR, the tracking-by-attention (TBA) method uses transformers to accomplish multi-object tracking tasks. However, there are still issues with existing TBA methods within the TBA paradigm, such as difficulty detecting and tracking objects due to gradient conflict in shared parameters, and insufficient use of features to distinguish similar objects. We introduce FusionTrack to address these issues. It utilizes a joint track-detection decoder and a score-guided multi-level query fuser to enhance the usage of information within and between frames. With these improvements, FusionTrack achieves 11.1% higher by HOTA metric on the DanceTrack dataset compared with the baseline model MOTR.
Deep Reinforcement Learning Method for 3D-CT Nasopharyngeal Cancer Localization with Prior Knowledge

Fast and accurate lesion localization is an important step in medical image analysis. The current supervised deep learning methods have obvious limitations in the application of radiology, as they require a large number of manually annotated images. In response to the above issues, we introduced a deep reinforcement learning (DRL)-based method to locate nasopharyngeal carcinoma lesions in 3D-CT scans. The proposed method uses prior knowledge to guide the agent to reasonably reduce the search space and promote the convergence rate of the model. Furthermore, the multi-scale processing technique is also used to promote the localization of small objects. We trained the proposed model with 3D-CT scans of 50 patients and evaluated it with 3D-CT scans of 30 patients. The experimental results showed that the proposed model has strong robustness, and its accuracy was improved by more than 1 mm on average under the premise of using a smaller dataset compared with the DQN models in recent studies. The proposed model could effectively locate the lesion area of nasopharyngeal carcinoma in 3D-CT scans.
Perceptual Relevance of Haptic Feedback during Virtual Plucking, Bowing and Rubbing of Physically-Based Musical Resonators

The physics-based design and realization of a digital musical interface asks for the modeling and implementation of the contact-point interaction with the performer. Musical instruments always include a resonator that converts the input energy into sound, meanwhile feeding part of it back to the performer through the same point. Specifically during plucking or bowing interactions, musicians receive a handful of information from the force feedback and vibrations coming from the contact points. This paper focuses on the design and realization of digital music interfaces realizing two physical interactions along with a musically unconventional one, rubbing, rarely encountered in assimilable forms across the centuries on a few instruments. Therefore, it aims to highlight the significance of haptic rendering in improving quality during a musical experience as opposed to interfaces provided with a passive contact point. Current challenges are posed by the specific requirements of the haptic device, as well as the computational effort needed for realizing such interactions without occurrence during the performance of typical digital artifacts such as latency and model instability. Both are however seemingly transitory due to the constant evolution of computer systems for virtual reality and the progressive popularization of haptic interfaces in the sonic interaction design community. In summary, our results speak in favor of adopting nowadays haptic technologies as an essential component for digital musical interfaces affording point-wise contact interactions in the personal performance space.
DEVELOPMENT OF EXPERIENTIAL LEARNING PROJECTS IN THE ONLINE EDUCATIONAL ENVIRONMENT

Appears in: EDULEARN23 Proceedings Publication year: 2023Page: 5416 (abstract only)ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.1423Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
A Remote Quantum Error-Correcting Code Preparation Protocol on Cluster States

The blind quantum computation (BQC) protocol allows for privacy-preserving remote quantum computations. In this paper, we introduce a remote quantum error correction code preparation protocol for BQC using a cluster state and analyze its blindness in the measurement-based quantum computation model. Our protocol requires fewer quantum resources than previous methods, as it only needs weak coherent pulses, eliminating the need for quantum memory and limited quantum computing. The results of our theoretical analysis and simulations show that our protocol requires fewer quantum resources compared to non-coding methods with the same qubit error rate.
SGGTSO: A Spherical Vector-Based Optimization Algorithm for 3D UAV Path Planning

The application of 3D UAV path planning algorithms in smart cities and smart buildings can improve logistics efficiency, enhance emergency response capabilities as well as provide services such as indoor navigation, thus bringing more convenience and safety to people’s lives and work. The main idea of the 3D UAV path planning problem is how to plan to get an optimal flight path while ensuring that the UAV does not collide with obstacles during flight. This paper transforms the 3D UAV path planning problem into a multi-constrained optimization problem by formulating the path length cost function, the safety cost function, the flight altitude cost function and the smoothness cost function. This paper encodes each feasible flight path as a set of vectors consisting of magnitude, elevation and azimuth angles and searches for the optimal flight path in the configuration space by means of a metaheuristic algorithm. Subsequently, this paper proposes an improved tuna swarm optimization algorithm based on a sigmoid nonlinear weighting strategy, multi-subgroup Gaussian mutation operator and elite individual genetic strategy, called SGGTSO. Finally, the SGGTSO algorithm is compared with some other classical and novel metaheuristics in a 3D UAV path planning problem with nine different terrain scenarios and in the CEC2017 test function set. The comparison results show that the flight path planned by the SGGTSO algorithm significantly outperforms other comparison algorithms in nine different terrain scenarios, and the optimization performance of SGGTSO outperforms other comparison algorithms in 24 CEC2017 test functions.
Asymptotic UMVUE: Asymptotic Moments Matching the UMVUE under the Ewens Sampling Formula

The Ewens sampling formula is a distribution related to the random partition of a positive integer. In this study, we investigate the issue of non-existent solutions in parameter estimation under the distribution. We derive the first and second moments matching estimators to the uniformly minimum variance unbiased estimator (UMVUE) in the asymptotic sense (hereafter referred to as Asymptotic UMUVE) using the Ewens sampling formula. A Monte Carlo simulation study is performed to evaluate the efficiency of the resulting estimators. AMS Subject Classification: 62F12, 62P25
Operational Matrix of New Shifted Wavelet Functions for Solving Optimal Control Problem

This paper is devoted to proposing an approximate numerical algorithm based on the use of the state parameterization technique in order to find the solution to the optimal control problem (OCP). An explicit formula for new shifted wavelet (NSW) functions is constructed. A new formula that expresses the first-order derivative of the NSW in terms of their original NSW is established. The development of our suggested numerical algorithms begins with the extraction of a new operational matrix of derivative from this derivative formula. The expansion’s convergence study is performed in detail, and some illustrative examples of OCP are displayed. The proposed algorithm is compared with the exact one and some other methods in the literature. This confirms the accuracy and the high efficiency of the presented algorithm.
Super-resolution GANs for upscaling unplanned urban settlements from remote sensing satellite imagery – the case of Chinese urban village detection

The semantic segmentation of informal urban settlements represents an essential contribution towards renovation strategies and reconstruction plans. In this context, however, a big challenge remains unsolved when dealing with incomplete data acquisitions from multiple sensing devices, especially when study areas are depicted by images of different resolutions. In practice, traditional methodologies are directed to downgrade the higher-resolution data to the lowest-resolution measure, to define an overall homogeneous dataset, which is however ineffective in downstream segmentation activities of such crowded unplanned urban environments. To this purpose, we hereby tackle the problem in the opposite direction, namely upscaling the lower-resolution data to the highest-resolution measure, contributing to assess the use of cutting-edge super-resolution generative adversarial network (SR-GAN) architectures. The experimental novelty targets the particular case involving the automatic detection of ‘urban villages’, sign of the quick transformation of Chinese urban environments. By aligning image resolutions from two different data sources (Gaofen-2 and Sentinel-2 data), we evaluated the degree of improvement with regard to pixel-based landcover segmentation, achieving, on a 1 m resolution target, classification accuracies up to 83%, 67% and 56% for 4x, 8x, and 10x resolution upgrades respectively, disclosing the advantages of artificially-upscaled images for segmenting detailed characteristics of informal settlements.
Joint Deployment and Coverage Path Planning for Capsule Airports with Multiple Drones

Due to the advantages of low cost and high flexibility, drones have been applied to urban surveillance, vegetation monitoring, and other fields with the need for coverage of regions. To expand UAVs’ coverage, we designed the Capsule Airport (CA) to recharge and restore drones and provide take-off and landing services. Meanwhile, the combination of drones’ coverage path planning (CPP) and the deployment of CAs is a crucial problem with few relevant studies. We propose a solution approach to the CPP problem based on selecting scanning patterns and trapezoidal decomposition. In addition, we construct a 0–1 integer programming model to minimize the cost of the distance between CAs and the scanning missions. Specifically, a solution approach based on greedy and clustering heuristics is designed to solve this problem. Furthermore, we then develop a local-search-based algorithm with the operators of CA location exchange and drone scanning mission exchange to further optimize the solution. Random instances at different sizes are used to validate the performance of proposed algorithms, through which the sensitivity analysis is conducted with some factors. Finally, a case study based on the Maolichong forest park in Changsha, China, is presented to illustrate the application of the proposed method.
Feeling of hand deformation as a monkey's hand: an experiment on a visual body with discomfort and its algebraic analysis

While there are many studies in which body ownership can be transferred to a virtual body, there are few experimental studies of how subjects feel about their own bodies being deformed since a real body cannot be deformed. Here, we propose such an experimental setup, in which a twisted hand is diagonally viewed from behind, which is called a "monkey's hand." Although the subject cannot see the thumb hidden behind his or her arm, he or she feels that the monkey's hand has an ambiguous thumb that functionally never exists but structurally exists. This ambiguity is consistent with experimental results on proprioceptive drift, by which the deformation of the hand is measured. The ambiguity of the presence and absence of the thumb is finally analyzed with a specific algebraic structure called a lattice. This can help us understand disownership as being different from the absence of ownership.
Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels

Abstract Artificial Intelligence plays a main role in supporting and improving smart manufacturing and Industry 4.0, by enabling the automation of different types of tasks manually performed by domain experts. In particular, assessing the compliance of a product with the relative schematic is a time-consuming and prone-to-error process. In this paper, we address this problem in a specific industrial scenario. In particular, we define a Neuro-Symbolic approach for automating the compliance verification of the electrical control panels. Our approach is based on the combination of Deep Learning techniques with Answer Set Programming (ASP), and allows for identifying possible anomalies and errors in the final product even when a very limited amount of training data is available. The experiments conducted on a real test case provided by an Italian Company operating in electrical control panel production demonstrate the effectiveness of the proposed approach.
Development of Android-Based Multimedia Learning Media in Recognition 0f Human Body Anatomy (SI-MANTAN)

The COVID-19 pandemic in the world since the beginning of 2020 until now is still a challenge for every as pect of life, including the world of education in the health sector. The use of technology is the main choice in the education system which is falsified in learning methods. There are many alternative learning methods by utilizing technology, one of which is multimedia-based learning. The use of multimedia today is more in demand than other methods. Health students have the opportunity to take advantage of the use of multimedia in the learning process to achieve competence while participating in learning in each course. One of the basic course study materials studied by health students is generally about the anatomy and physiology of the human body. Students experience obstacles in understanding the anatomy and physiology of the human body during the COVID-19 pandemic due to limited access to the laboratory as usual before the pandemic occurred.