Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Digital Contact-Tracing Technologies for Informing Public Health Policies

Contact tracing is the cornerstone of epidemic control of infectious diseases, especially in the era of COVID-19. This labor-intensive task calls for the use of digital technology to help identify individuals who have potentially been exposed to the infection to deliver necessary interventions and treatment. Mobile applications based on different technologies and system architectures have been developed and widely used in concert with public health policies and regulations. Three main types of digital contact-tracing technologies, namely Bluetooth low energy (BLE), location tracking, and check-in, were adopted in contact-tracing apps and implemented with a centralized or decentralized system architecture to protect privacy and facilitate spatiotemporal co-occurrence matching. Passive data collection methods, including BLE and location tracking, could be contrasted with check-in-based apps, which require users’ effort to record their whereabouts. Persuasive computing with a centralized system for collecting contact-tracing data from users might require additional legislation for authorization and privacy protection. Technology options should therefore be taken into account when designing enforceable public health policies on the use of contact-tracing apps. At the same time, public health policies also inform the information system’s design. This paper aims to delineate and contrast current technologies and system architectures used for developing contact-tracing apps and examine the intertwined relationship between the design and implementation of public health policies and the design of digital contact-tracing systems.
Factor Analysis of Croatian Secondary School Teachers’ Readiness for Digital Transformation

Based on the fact that digitization of education and culture is one of the fundamental strategic objectives of the European Commission and based on the analysis of key documents published by the European Commission in recent years, it can be said that infrastructure, digital competences, and the use of digital content in the educational process are fundamental guidelines that will guide the transformation of educational systems in all EU Member States in the coming years. The aim of this article is to identify the factors, based on a survey of the respondents’ attitudes, that could drive digital transformation in secondary educational institutions in Croatia. Within the theoretical background of the technology acceptance model (TAM), the results of the survey of 185 teachers and subsequent factor analysis show that the material support of institutions is mandatory as a base for change, while individual factors such as fear of technology and digital enthusiasm could govern the teachers’ response to acceptance of the new technologies.
Optimizing measurements sequences for quantum state verification

Abstract We consider the problem of deciding whether a given state preparation, i.e., a source of quantum states, is accurate; namely, it produces states close to a target one within a prescribed threshold. While most of the result in the literature considers the case in which the measurement operators can be arbitrarily chosen depending on the target state, obtaining favorable (Heisenberg) scaling, we focus on the case in which the measurements can be only chosen from a given set. We show that, in this case, the order of measurements is critical for quickly assessing accuracy. We propose and compare different strategies to compute optimal or suboptimal measurement sequences either relying solely on a priori information, i.e., the target state for state preparation, or actively adapting the sequence to the previously obtained measurements. Numerical simulations show that the proposed algorithms reduce significantly the number of measurements needed for verification and indicate an advantage for the adaptive protocol especially assessing faulty preparations.
The Muddlescape of Time

Abstract There is a well-known clash between the subjective sensation of time as flowing or passing, and the physicist’s description of ‘block time’ in which all of time is laid out as a timescape, the temporal analogue of a spatial landscape. A common mistake is to conflate the physical arrow of time — the asymmetry of sequences of physical states under time reversal — with the psychological flow of time. A clearer picture is to replace the language of time’s passage with one in which states of the world at various moments are correlated with states of the brain at those same moments. Time’s flow is a purely psychological phenomenon and not an aspect of fundamental spacetime physics.
Visually-Rich Document Understanding: Concepts, Taxonomy and Challenges

The increasing prevalence of Visually-rich Documents (VRDs) in diverse domains has led to a growing interest in Visually-rich Document Understanding (VrDU). Researchers have been developing sophisticated systems to extract explicit and implicit information from such documents. However, a comprehensive literature review on this field still missing. In this paper, we present a comprehensive overview of VrDU systems, focusing on their applications in Key Information Extraction (KIE), Visual Question Answering (VQA) and document classification tasks. We delve into the fundamental concepts and techniques used in VrDU, providing a high-level abstraction of these approaches. To facilitate understanding and comparison, we propose a taxonomy that categorizes VrDU systems based on four key aspects: modality, geometric approach, OCR integration type and task. Additionally, we present the benchmarks commonly used in the field and discuss the current challenges faced by VrDU systems. We believe that this paper advances the research in the field of VrDU by providing a comprehensive resource for researchers and practitioners
Real-Time Multi-Lingual Hate and Offensive Speech Detection in Social Networks Using Meta-Learning

A rapid increase in users on social media has given rise to a vast amount of user-generated content, including hate speech and offensive language. Such content can have serious negative consequences, ranging from psychological harm to inciting violence and discrimination. Existing studies have explored different deep learning and Natural language processing (NLP) methods to perform hate speech detection, and these solutions have yielded significant performance. Most existing solutions are limited to detecting hate speech only in English with less focus on content generated in other languages, particularly in low-resource or regional languages. The goal of this paper is to address this challenge of hate speech detection for low-resource languages and propose a tool that could provide a real-time prediction for social media posts. In this study, the main focus was on English, Hindi, Hinglish, Bengali, and Marathi languages which are commonly used in social media platforms in India. A meta-learning-based model was employed to perform hate speech detection in these languages. The proposed method helps to overcome the limitation of data scarcity and provides fast adaptation to an unseen target language. Extensive experiments were conducted on datasets comprised of different regional languages spoken in India. Accuracy, Precision, recall, and F1-score metrics are used to evaluate the model's performance. The results show that when the dataset size is small, meta-learning-based models perform better than traditional fine-tuned language models.
Benchmarking Database for a Case Charging of Data Sponsor in Telecom

Database plays an important role in software architecture and greatly influences system performance. In this paper, we evaluate how two databases, Cassandra and Aerospike, adapt the data sponsor concurrency problem in the telecom sector. To obtain the evaluation result, we provide a method by building the test cases in different scenarios.
Performance Analysis of Private Blockchain in Mobile Ad-hoc Networks (MANETs)

This research paper explores and examines the performance of a private blockchain in a mobile ad hoc network (MANET). The adoption of blockchain in a mobile ad hoc network (MANET) presents distinct challenges due to the inherent mobility of the network. The dynamic nature of node connectivity and continuous changes due to mobility make the consensus process difficult. Due to their mobility, nodes in a MANET have limited time for rendezvous in order to exchange and verify new blocks while they are on the move. Previous studies have not thoroughly discussed the measurement of blockchain performance in a MANET. To address this gap, this research proposes an analysis that evaluates the performance of a private blockchain in a MANET environment using key metrics like throughput, latency, and scalability. The analysis considers different factors such as data rate, packet size, area coverage, and hashing ability.
Combined Channel and Spatial Attention-Based Stereo Endoscopic Image Super-Resolution

Stereo Imaging technology integration into medical diagnostics and surgeries brings a great revolution in the field of medical sciences. Now, surgeons and physicians have better insight into the anatomy of patients' organs. Like other technologies, stereo cameras have limitations, e.g., low resolution (LR) and blurry output images. Currently, most of the proposed techniques for super-resolution focus on developing complex blocks and complicated loss functions, which cause high system complexity. We proposed a combined channel and spatial attention block to extract features incorporated with a specific but very strong parallax attention module (PAM) for endoscopic image super-resolution. The proposed model is trained using the da Vinci dataset on scales 2 and 4. Our proposed model has improved PSNR up to 2.12 dB for scale 2 and 1.29 dB for scale 4, while SSIM is improved by 0.03 for scale 2 and 0.0008 for scale 4. By incorporating this method, diagnosis and treatment for endoscopic images can be more accurate and effective.
Enhancing Hadoop Performance with Q-Learning for Optimal Parameter Tuning

This paper presents an approach to enhance Hadoop performance by leveraging deep Q-Learning, a form of Reinforcement Learning, to optimize parameter settings. The performance of Hadoop, a widely adopted distributed computing framework, relies heavily on configuring numerous parameters. However, the complex and extensive search space poses challenges in determining the optimal settings. In response, we propose an innovative deep Q-Learning algorithm that iterative discovers and applies the most effective parameter configurations, thereby improving Hadoop's performance. Our approach involves defining state and action spaces, learning from performance feedback, and identifying the optimal configuration to maximize Hadoop's efficiency. Our proposed model consistently outperforms existing solutions by benchmarking with popular jobs such as TeraSort, WordCount, Hive Aggregate, and WordCooccur, demonstrating significant reductions in execution times and improved computational efficiency. This research accelerates big data processing in Hadoop and provides a scalable and efficient solution applicable to similar distributed computing frameworks. Furthermore, our paper reinforces the effectiveness and applicability of Reinforcement Learning in addressing complex optimization problems. By harnessing the power of deep Q-Learning, we showcase its capability to navigate the intricate parameter space of Hadoop, resulting in enhanced performance and streamlined data processing. This research contributes to advancing distributed computing frameworks, offering a novel and efficient approach to optimize parameter settings and improve overall system performance.
Multi-Stage Hybrid-CNN Transformer Model for Human Intent-Prediction

Human intention prediction (HIP) is one aspect of Human-Robot Interaction (HRI) that could facilitate understanding and improving how humans interact with robots and computers. However, current gaze-based intent prediction models that perform well often require invasive methods using specialized equipment. In this paper we present a non-invasive, contactless method for predicting human intentions using a multi-stage hybrid CNN-Transformer framework. The model consists of a depth estimator and two key components: a gazed object predictor and a human intent classifier. The gazed object predictor is a modified Detection-Transformer (DETR) and used a ResNet50 backbone for feature extraction and obtained an accuracy of 32.15% in the custom dataset. Meanwhile, the human intent classifier is a transformer-based classifier that achieved a 98% accuracy when predicting human intention based on a series of gazed objects. The resulting cascaded HIP system attained an accuracy of 54%.
Automatic Modulation Classification for NLOS 5G Signals with Deep Learning Approaches

The Automatic Modulation Classification (AMC) technique identifies the modulation scheme used in a received signal automatically without any prior knowledge or manual intervention. When multiple signals with different modulation schemes coexist in wireless communication systems, it’s a crucial task. In AMC, features and patterns are typically analyzed to classify signals into one of several predefined modulation schemes based on machine learning algorithms. It has been shown that deep learning (DL) algorithms are effective in AMC. Using deep neural networks for modulation classification is an excellent choice since they can learn complex patterns and hierarchical representations from data. In this paper, first we produce a dataset for non-line-of-sight signals with digital modulation of Binary Phase-shift keying (BPSK), Quadrature Phase Shift Keying (QPSK), and Quadrature Amplitude Modulation (QAM) which are widely used in modern telecommunications to transmit information. We also use three popular DL approaches in many applications for classification and recognition based on DNN, CNN and LSTM for classification. The experimental results show that the CNN approach outperforms the others significantly.
Study of Machine Vision System for Automated Pineapple's Eyes Detection

This study aimed to improve the production process of canned pineapple by introducing an algorithm that utilizes machine vision and image processing techniques to detect and locate pineapple eyes accurately after the peeling process. The result can be subsequently transmitted the coordinates to the automatic pineapple eyes removal machine. The algorithm's primary objective is to improve productivity, especially by reducing waste due to human error. Therefore, the prototype has been made. Including the image acquisition process and the rotating system to capture pineapple surface features, the system was operated by a stepper motor along with an inspection camera for data collection. The algorithm was created based on thresholding method, with the calibration, pineapple eyes can be located in term of position(x) and angle (φ) of rotation axis. In summary, from our design and experiment. Our algorithm results in a maximum error of position (x) at 4.871 mm and a maximum error of angle (φ) at 3.415 degree at the processing time of 0.68 millisecond, with the entire process taking 20.92 seconds.
Enhancing Facial Expression Synthesis through GAN with Multi-Scale Dilated Feature Extraction and Edge-Enhanced Facial Features<sup>*</sup>

Affective computing aims to facilitate effective communication between humans and machines. Many affective computing systems use machine learning models trained on labeled data, like images and videos, to recognize emotions. Among these, the Generative Adversarial Network (GAN)-based expression GAN (ExprGAN) stands out as it can generate faces displaying various expressions. However, the generated faces often lack clarity in crucial facial features, such as the eyes and lips, which are essential for defining facial expressions. To address this issue, a novel feature extraction block is proposed. This module incorporates two parallel channels with multi-scale dilated convolution to mimic the human visual system and extract multi-scale facial features from the facial images. Additionally, an unsharp masking filter is integrated into the pre-processing stage to enhance the quality of facial expression features, making them sharper and clearer. The proposed model can generate faces with six distinct expressions: Anger, Disgust, Fear, Happiness, Sadness, and Surprise. To evaluate, a Convolutional Neural Network (CNN)-based expression classifier, a Principal Component Analysis (PCA)-based face classifier, and the FID (Frechet Inception Distance) score are used for comparison. The results demonstrate that the proposed model outperforms the existing ExprGAN, providing better quality and more expressive faces, which can be beneficial for improving human-machine interaction.
Exploring Bystanders' Roles in Labeled Cyberbullying Threads on Twitter: A preliminary analysis

This study presents findings from an anal-ysis of a newly developed corpus, CYBY23, focused on cyberbullying, aiming to comprehensively examine labeled cyberbullying threads on the social media platform Twitter, with a specific emphasis on the role of bystanders. Previous corpora used for automatic cyberbullying detection have primarily focused on the main posts, disregarding the threaded responses. Con-sequently, these studies have overlooked valuable infor-mation regarding the involvement of bystanders, which is crucial for enhancing the accuracy of cyberbullying detection. This study addresses this gap by incorpo-rating bystander roles within the corpus, resulting in significant impact on annotators' perception and classification of cyberbullying instances. The findings suggest promising prospects for improved automated cyberbullying detection. Notably, the most frequently observed bystander roles align with the content of the main post. Surprisingly, impartial bystanders are most prevalent in cyberbullying threads characterized by high levels of aggression. This article provides a detailed analysis of the annotation process and examines the influence of bystanders roles in greater depth.
Task Assignment and Path Planning of Multiple Unmanned Aerial Vehicles Using Integer Linear Programming

In this paper, we propose a strategy to enhance the performance of task assignment and path planning in applications of distributed multiple unmanned aerial vehicles (multi-UAV). Multi-UAVs are made up of many small UAVs with limited mission resources. They can operate autonomously, appropriately, and universally. UAV swarm task coordination and resource allocation can be achieved with reasonable allocation of UAV tasks and resources according to the UAV region and its own performance. Based on the in-depth research of the traditional auction algorithms, this work proposes a method that can improve multi-UAV task allocation efficiency, namely the Integer Linear Programming (ILP). We are proposing ILP formulation for numerous drones and tasks in an environment that enables the completion of tasks. We also measure the computational time requirements of the proposed ILP approach with various number of drones and tasks, so that the limit of this approach can be identified for practical application. This research overcomes the difficulties between task allocation and path planning. Compared with auction-based algorithms this technique can better complete allocation results and reduce resource consumption. It's expected that the simulation results will show that the algorithm is effective in computing and task execution efficiency.
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data

In this paper, we investigate Federated Learning (FL), a paradigm of machine learning that allows for decentralized model training on devices without sharing raw data, thereby preserving data privacy. In particular, we compare two strategies within this paradigm: Federated Averaging (FedAvg) and Personalized Federated Averaging (Per-FedAvg), focusing on their performance with Non-Identically and Independently Distributed (Non-IID) data. Our analysis shows that the level of data heterogeneity, modeled using a Dirichlet distribution, significantly affects the performance of both strategies, with Per-FedAvg showing superior robustness in conditions of high heterogeneity. Our results provide insights into the development of more effective and efficient machine learning strategies in a decentralized setting.
FedVANET-TP: Federated Trajectory Prediction Model for VANETs

In recent years, deep learning techniques have been employed within Intelligent Transportation Systems (ITS) to outperform classical trajectory prediction models, aiming for greater precision and efficiency. Federated learning (FL) has attracted substantial attention for its ability to safeguard sensitive vehicle data while reducing communication overhead. However, FL-based trajectory prediction models face a prioritization dilemma. Some prioritize security, leading to longer computation times and higher resource consumption, while others trade off data privacy for increased accuracy, incurring additional computational costs. In this study, we introduce an approach named FedVANET-TP for predicting trajectories in Vehicular Ad hoc Networks (VANETs). Our method ensures a balanced utilization of computational resources, attains high accuracy and offers optimal privacy. The model is constructed within a FL framework and utilizes an encoder-decoder architecture. This architecture incorporates Convolutional Neural Network (CNN) layers to capture crucial spatial features and Long Short-Term Memory (LSTM) layers for capturing temporal dependencies from historical trajectories. FedVANET-TP has been trained and validated using the NGSIM US-101 dataset. The simulation results show that the model’s average root mean squared error (RMSE) and average overall accuracy are superior to those of the comparison models. This model offers higher accuracy than centralized benchmarks and lower resource consumption than federated-based benchmarks, all while ensuring optimal data confidentiality.
360-GAN: Cycle-Consistent GAN for Extrapolating 360-Degree Field-of-View

360-degree images, also known as panoramic, have become increasingly popular in the field of Extended Reality (XR). They offer an immersive experience to users, allowing them to explore images in a more engaging and dynamic manner. However, the visual quality associated with 360 images in XR can vary greatly depending on factors, such as image resolution with crisp details and vibrant colors. Thus, complex camera systems are required to shoot 360-degree environments. Generative adversarial networks (GANs), which have already been successfully applied to out-painting tasks and for the generation of masked regions in images, have the potential to solve the need for complex infrastructures. As such, from only a small RGB crop, the full environment could be generated. However, traditional GANs can fail to blend the input crop with the generated extrapolated region by introducing sharp vertical edges that disrupt the overall visual coherence. Another challenge in generating 360-degree images is the representation and handling of the spherical geometry of the panorama. In this work, we present 360-GAN, a cycle-consistent GAN model to generate 360-degree omnidirectional images from small RGB crops. Moreover, to maintain the spherical consistency of the generated 360 panoramic images, our method uses Structural Similarity Index (SSIM) as an added loss function. We evaluate our approach through quantitative measurements, benchmarking them against other state-of-the-art approaches. Our method generates realistic results maintaining the spherical consistency of the omnidirectional images with a Frechet Inception Distance (FID) of 46.59, nearly 6 points better than the most current state-of-the-art methods.