Reviewerly/exHarmony huggingface dataset:
Efficient Uncertainty Propagation in Model-Based Reinforcement Learning Unmanned Surface Vehicle Using Unscented Kalman Filter

This article tackles the computational burden of propagating uncertainties in the model predictive controller-based policy of the probabilistic model-based reinforcement learning (MBRL) system for an unmanned surface vehicles system (USV). We proposed filtered probabilistic model predictive control using the unscented Kalman filter (FPMPC-UKF) that introduces the unscented Kalman filter (UKF) for a more efficient uncertainty propagation in MBRL. A USV control system based on FPMPC-UKF is developed and evaluated by position-keeping and target-reaching tasks in a real USV data-driven simulation. The experimental results demonstrate a significant superiority of the proposed method in balancing the control performance and computational burdens under different levels of disturbances compared with the related works of USV, and therefore indicate its potential in more challenging USV scenarios with limited computational resources.
State-Aware High-Order Diffusion Method for Edge Detection in the Wavelet Domain

This paper addresses how to use high-order diffusion to restore the wavelet coefficients in the wavelet domain. To avoid image distortion, wavelets with symmetry are used for image decomposition to obtain the wavelet coefficients of each sub-band. Due to the influence of noise, it is particularly important to obtain the wavelet coefficients, which can accurately reflect the image information. According to the characteristics of wavelet threshold shrinkage and the advantages of the high-order variational method in denoising, a wavelet coefficient restoration scheme is proposed. The theoretical basis of our proposed method is established through the analysis of wavelet threshold theory. To keep the original structure of wavelet coefficients unchanged, we introduce the concept of state quantity of wavelet coefficients and obtain the corresponding state quantity of wavelet coefficients using normalization. The denoising wavelet coefficient is obtained by performing a fourth-order anisotropic diffusion of the state quantities. This paper takes image edge feature extraction as the experimental content and image edges are detected by the module of the wavelet coefficients. The effectiveness of the proposed algorithm is objectively verified from three aspects: denoising effect, edge continuity, and accuracy. The experimental results show that the proposed algorithm can obtain continuous and precise image edges. The algorithm presented in this paper also applies to texture images. Compared with other algorithms, the edges image obtained by this scheme shows advantages in terms of noise removal and edge protection.
Vectorized Representation of Commodities by Fusing Multisource Heterogeneous User-Generated Content with Multiple Models

In the field of personalized recommendation, user-generated content (UGC) such as videos, images, and product comments are becoming increasingly important, since they implicitly represent the preferences of users. The vectorized representation of a commodity with multisource and heterogeneous UGC is the key for sufficiently mining the preference information to make a recommendation. Existing studies have mostly focused on using one type of UGC, e.g., images, to enrich the representation of a commodity, ignoring other contents. When more UGC are fused, complicated models with heavy computation cost are often designed. Motivated by this, we proposed a low-computational-power model for vectorizing multisource and recommendation UGC to achieve accurate commodity representations. In our method, video description keyframes, commodities’ attribute text, and user comments were selected as the model’s input. A multi-model fusion framework including feature extraction, vectorization, fusion, and classification based on MobileNet and multilayer perceptrons was developed. In this UGC fusion framework, feature correlations between images and product comments were extracted to design the loss function to improve the precision of vectorized representation. The proposed algorithm was applied to an actual representation of a commodity described by UGC, and the effectiveness of the proposed algorithm was demonstrated by the classification accuracy of the commodity represented.
A Symphony of Sound Images: Spatial composition strategies in Zhang Xiaofu’s<i>Yarlung Zangbo</i>

Yarlung Zangbo is a multimedia symphony combining orchestra, visual images and electroacoustic music by one of China’s most internationally influential electroacoustic music composers, Zhang Xiaofu. This article focuses on the spatial aspects of Yarlung Zangbo . First, we discuss two different spatial composition preferences in electroacoustic music: image delivery and creation. We then divide the development of electroacoustic orchestral music into three different periods and examine the general spatial intention of the works in these periods. Finally, we analyse and identify four spatial composition strategies of Yarlung Zangbo : equity, virtuosity, diversity and unity. The successful utilisation of these strategies has not only made the piece a milestone in Chinese electroacoustic music, but also a unique and meaningful contribution to the electroacoustic music repertoire worldwide. We believe this article will provide valuable knowledge and insight for future research and practice of spatial composition.
Automatic Attendance Management System using Face Recognition

Face recognition is among the most productive image processing applications and has a pivotal role in the technical field. In the recent time automated face recognition has become a trend and has been developed very much, this is mainly due to two reasons first it is due to availability of modern technologies and second is due to the ability to save time using face recognition in the process of taking attendance of students. Its usage will grow vast in the future as it saves a lot of time. It consumes a lot of time to take attendance manually and few might also fake the attendance, in order to prevent time consumption and avoid faking the attendance. Face recognition is used to identify the person present in the class and mark their attendance , this is done with the help of image or video frame. We proposed an automatic attendance management system using techniques such as PCA algorithm. The face detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person. The traditional process of making attendance and present biometric systems are vulnerable to proxies. This paper is therefore proposed to tackle all these problems. The proposed system makes the use of Haar classifiers, KNN, CNN, SVM, Generative adversarial networks, and Gabor filters. After face recognition attendance reports will be generated and stored in excel format. The system is tested under various conditions like illumination, head movements, the variation of distance between the student and cameras. After vigorous testing overall complexity and accuracy are calculated. The Proposed system proved to be an efficient and robust device for taking attendance in a classroom without any time consumption and manual work. The system developed is cost-efficient and need less installation
A search optimized blockchain‐based verifiable searchable symmetric encryption framework

Summary Outsourcing storage and computation to cloud servers have become a trend. Although searchable symmetric encryption (SSE) had handled the data privacy issue caused by honest‐but‐curious servers, a semi‐honest server may return incomplete or incorrect results when users search for their encrypted data. To against such servers, scholars have recently used blockchain/Ethereum‐based SSE schemes which utilize the public, that is, active nodes, to verify the search process. However, the search operation in existing schemes is very expansive in terms of fee and time. In this paper, we propose a new blockchain‐based searchable encryption framework with search optimized, that is, free of charge, quicker, and more private, at the cost of some extra storage. Besides, we design a general and efficient verification algorithm for our framework, which makes the search verifiable. In addition, we deploy an instance of our framework on an official Ethereum test network, and the experimental results and evaluations demonstrate the advantage of our framework.
Let’s face it: When and how facial emojis increase the persuasiveness of electronic word of mouth

Abstract Facial emojis have increasingly permeated electronic word of mouth (eWOM), but the persuasive consequences of this phenomenon remain unclear. Drawing on emotions as social information (EASI) theory, this research reveals that facial emojis influence persuasion (e.g., product choice) by affecting emotional arousal and perceived ambiguity. While the effect through emotional arousal is generally positive, the effect through ambiguity depends on the emojis’ function in eWOM: facial emojis that replace a verbal expression increase ambiguity and therefore reduce persuasion, whereas those that reiterate a verbal expression decrease ambiguity and therefore enhance persuasion. Both the emotional-arousal and ambiguity pathways determine the net persuasive effect. This research also explores two situations (high verbal context richness and eWOM from strong ties) where replacing facial emojis can increase persuasion. Finally, the authors show that facial emojis’ persuasive power is generalizable to online brand communications, influencing key management outcomes such as click-through rates for digital ads.
Device Orientation Independent Human Activity Recognition Model for Patient Monitoring Based on Triaxial Acceleration

Tracking a person’s activities is relevant in a variety of contexts, from health and group-specific assessments, such as elderly care, to fitness tracking and human–computer interaction. In a clinical context, sensor-based activity tracking could help monitor patients’ progress or deterioration during their hospitalization time. However, during routine hospital care, devices could face displacements in their position and orientation caused by incorrect device application, patients’ physical peculiarities, or patients’ day-to-day free movement. These aspects can significantly reduce algorithms’ performances. In this work, we investigated how shifts in orientation could impact Human Activity Recognition (HAR) classification. To reach this purpose, we propose an HAR model based on a single three-axis accelerometer that can be located anywhere on the participant’s trunk, capable of recognizing activities from multiple movement patterns, and, thanks to data augmentation, can deal with device displacement. Developed models were trained and validated using acceleration measurements acquired in fifteen participants, and tested on twenty-four participants, of which twenty were from a different study protocol for external validation. The obtained results highlight the impact of changes in device orientation on a HAR algorithm and the potential of simple wearable sensor data augmentation for tackling this challenge. When applying small rotations (&lt;20 degrees), the error of the baseline non-augmented model steeply increased. On the contrary, even when considering rotations ranging from 0 to 180 along the frontal axis, our model reached a f1-score of 0.85±0.11 against a baseline model f1-score equal to 0.49±0.12.
MAKING EDUCATIONAL TECHNOLOGY ENHANCE THE COLLABORATIVE WORK IN DIFFERENT LEARNING SPACES

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 4867-4876ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1269Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
Local Differential Privacy-Based Federated Learning under Personalized Settings

Federated learning is a distributed machine learning paradigm, which utilizes multiple clients’ data to train a model. Although federated learning does not require clients to disclose their original data, studies have shown that attackers can infer clients’ privacy by analyzing the local models shared by clients. Local differential privacy (LDP) can help to solve the above privacy issue. However, most of the existing federated learning studies based on LDP, rarely consider the diverse privacy requirements of clients. In this paper, we propose an LDP-based federated learning framework, that can meet the personalized privacy requirements of clients. We consider both independent identically distributed (IID) datasets and non-independent identically distributed (non-IID) datasets, and design model perturbation methods, respectively. Moreover, we propose two model aggregation methods, namely weighted average method and probability-based selection method. The main idea, is to weaken the impact of those privacy-conscious clients, who choose relatively small privacy budgets, on the federated model. Experiments on three commonly used datasets, namely MNIST, Fashion-MNIST, and forest cover-types, show that the proposed aggregation methods perform better than the classic arithmetic average method, in the personalized privacy preserving scenario.
When Naïve Pedagogy Breaks Down: Adults Rationally Decide How to Teach, but Misrepresent Learners’ Beliefs

From early in childhood, humans exhibit sophisticated intuitions about how to share knowledge efficiently in simple controlled studies. Yet, untrained adults often fail to teach effectively in real-world situations. Here, we explored what causes adults to struggle in informal pedagogical exchanges. In Experiment 1, we first showed evidence of this effect, finding that adult participants failed to communicate their knowledge to naïve learners in a simple teaching task, despite reporting high confidence that they taught effectively. Using a computational model of rational teaching, we found that adults assigned to our teaching condition provided highly informative examples but failed to teach effectively because their examples were tailored to learners who were only considering a small set of possible explanations. In Experiment 2, we then found experimental evidence for this possibility, showing that knowledgeable participants systematically misunderstand the beliefs of naïve participants. Specifically, knowledgeable participants assumed naïve agents would primarily consider hypotheses close to the correct one. Finally, in Experiment 3, we aligned learners' beliefs to knowledgeable agents' expectations and showed learners the same examples selected by participants assigned to teach in Experiment 1. We found that these same examples were significantly more informative once learners' hypothesis spaces were constrained to match teachers' expectations. Our findings show that, in informal settings, adult pedagogical failures result from an inaccurate representation of what naïve learners believe is plausible and not an inability to select informative data in a rational way.
Undirected Structural Markov Property for Bayesian Model Determination

This paper generalizes the structural Markov properties for undirected decomposable graphs to arbitrary ones. This helps us to exploit the conditional independence properties of joint prior laws to analyze and compare multiple graphical structures, while being able to take advantage of the common conditional independence constraints. This work provides a theoretical support for full Bayesian posterior updating about the structure of a graph using data from a certain distribution. We further investigate the ratio of graph law so as to simplify the acceptance probability of the Metropolis–Hastings sampling algorithms.
EDUCATIONAL OPPORTUNITIES IN CLOUD COMPUTING BY AN ENVIRONMENTAL MONITORING SOLUTION USING SELF-PROPELLED ROBOTS

EDUCATIONAL OPPORTUNITIES IN CLOUD COMPUTING BY AN ENVIRONMENTAL MONITORING SOLUTION USING SELF-PROPELLED ROBOTS
Deep Reinforcement Learning for Articulatory Synthesis in a Vowel-to-Vowel Imitation Task

Articulatory synthesis is one of the approaches used for modeling human speech production. In this study, we propose a model-based algorithm for learning the policy to control the vocal tract of the articulatory synthesizer in a vowel-to-vowel imitation task. Our method does not require external training data, since the policy is learned through interactions with the vocal tract model. To improve the sample efficiency of the learning, we trained the model of speech production dynamics simultaneously with the policy. The policy was trained in a supervised way using predictions of the model of speech production dynamics. To stabilize the training, early stopping was incorporated into the algorithm. Additionally, we extracted acoustic features using an acoustic word embedding (AWE) model. This model was trained to discriminate between different words and to enable compact encoding of acoustics while preserving contextual information of the input. Our preliminary experiments showed that introducing this AWE model was crucial to guide the policy toward a near-optimal solution. The acoustic embeddings, obtained using the proposed approach, were revealed to be useful when applied as inputs to the policy and the model of speech production dynamics.
Foreign Object Shading Detection in Photovoltaic Modules Based on Transfer Learning

As a representative new energy source, solar energy has the advantages of easy access to resources and low pollution. However, due to the uncertainty of the external environment, photovoltaic (PV) modules that collect solar energy are often covered by foreign objects in the environment such as leaves and bird droppings, resulting in a decrease in photoelectric conversion efficiency, power losses, and even the “hot spot” phenomenon, resulting in damage to the modules. Existing methods mostly inspect foreign objects manually, which not only incurs high labor costs but also hinders real-time monitoring. To address these problems, this paper proposes an IDETR deep learning target detection model based on Deformable DETR combined with transfer learning and a convolutional block attention module, which can identify foreign object shading on the surfaces of PV modules in actual operating environments. This study contributes to the optimal operation and maintenance of PV systems. In addition, this paper collects data in the field and constructs a dataset of foreign objects of PV modules. The results show that the advanced model can significantly improve the target detection AP values.
A novel technique for texture description and image classification based in RGB compositions

Abstract At present, facial recognition entertains great importance in performing authentication processes, because it prevents unauthorized access to devices and places. Additionally, it allows for the identification of persons. Henceforth, this paper proposes a novel texture descriptor called Cyclical Chroma and a new classification technique, which takes in consideration the sub‐pixel values of 0–255 for each RGB (Red, Green, Blue) channel that conforms the image. To verify the effectiveness of the proposed techniques, tests were performed with a database of images in a controlled environment and in one under uncontrolled conditions; additionally, Cyclical Chroma was tested with a different classifier, denominated the Multiclass Classifier, and the results were compared against other descriptors, including GLCM, SHDH, LQP, and CCR, demonstrating the effectiveness of the proposed techniques with 100% efficiency with controlled images and 78% effectiveness under uncontrolled conditions prior to the application of an equalization technique, increasing the efficiency to 100%.
Improved multi-target tracking crossing paths in MIMO FMCW 8 × 16 radar system using a new hybrid AMC-JPDAF algorithm

This research paper deals with multi-target tracking in a MIMO radar system, which presents complex data that can result in correlation problems and create technical difficulties. The objective is to resolve these issues and prevent divergence in object-tracking scenarios. However, when the cross-path phenomenon occurs, the process of assigning target measurements in MIMO radar systems becomes more complicated, and the interference phenomenon can disturb the received signal and disrupt the state estimation process. We have created a new algorithm called AMC-JPDAF that is a combination of the particle filter with the adaptive Monte Carlo (AMC) method and the joint probabilistic data association filter (JPDAF). This replaces the conventional extended KALMAN filter (EKF) used in EKF-JPDAF. We incorporated an entropy calculation and resampling sub-algorithm to overcome the limitations of EKF-JPDAF, which resulted in a more accurate estimation of two crossing targets and reduced trajectory loss in various tracking scenarios. Our experiments demonstrate that AMC-JPDAF is effective in preventing possible divergence phenomena when simulating two intersecting drones tracking scenarios. We report that the coherent measurement ambiguity is resolved at the crossover point of the trajectories corresponding to each target, giving us a low trajectory loss rate of 3.9%, which is significantly better than the 18.7% and 10.8% reported by simulations that do not affect the trajectory estimation process. We employed the MATLAB software development framework to design a system that satisfies the goals initially established by AMC-JPDAF. We then validated the system's performance by using an experimental database collected from the MIMO-FMCW 8 × 16 radar system.
THEORETICAL ASPECTS OF INTRODUCING BLENDED LEARNING TECHNOLOGY IN PRIMARY EDUCATION

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 6668-6672ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1791Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
Partial least squares regression with multiple domains

Abstract This paper introduces the multiple domain‐invariant partial least squares (mdi‐PLS) method, which generalizes the recently introduced domain‐invariant partial least squares method (di‐PLS). In contrast to di‐PLS which solely allows transferring of knowledge from a single source to a single target domain, the proposed approach enables the incorporation of data from an arbitrary number of domains. Additionally, mdi‐PLS offers a high level of flexibility by accepting labeled (supervised) and unlabeled (unsupervised) data to cope with dataset shifts. We demonstrate the application of the mdi‐PLS method on a simulated and one real‐world dataset. Our results show a clear outperformance of both PLS and di‐PLS when data from multiple related domains are available for training multivariate calibration models underpinning the benefit of mdi‐PLS.