Reviewerly/exHarmony huggingface dataset:
Blockchain for Credibility in Educational Development: Key Technology, Application Potential, and Performance Evaluation

Blockchain proposes many innovative technologies to establish credible mechanisms in an open environment and therefore, it becomes a promising solution to the problem of credibility in educational development. To better understand the role of the blockchain, we aim to provide an extensive survey focusing on its key technology, application potential, and performance evaluation. First, from the perspective of blockchain characteristics, we summarize its application architecture in educational credibility. Next, we extensively discuss application potential of the blockchain, such as data storage, data sharing, achievement certification, and activity evaluation. Moreover, we investigate the performance evaluation, including basic performance metrics and specialized metrics for credibility. Finally, we analyze the challenges and research trends of blockchain in educational credibility and provide useful insights for future research.
C2RL: Convolutional-Contrastive Learning for Reinforcement Learning Based on Self-Pretraining for Strong Augmentation

Reinforcement learning agents that have not been seen during training must be robust in test environments. However, the generalization problem is challenging to solve in reinforcement learning using high-dimensional images as the input. The addition of a self-supervised learning framework with data augmentation in the reinforcement learning architecture can promote generalization to a certain extent. However, excessively large changes in the input images may disturb reinforcement learning. Therefore, we propose a contrastive learning method that can help manage the trade-off relationship between the performance of reinforcement learning and auxiliary tasks against the data augmentation strength. In this framework, strong augmentation does not disturb reinforcement learning and instead maximizes the auxiliary effect for generalization. Results of experiments on the DeepMind Control suite demonstrate that the proposed method effectively uses strong data augmentation and achieves a higher generalization than the existing methods.
Designing a Technological Pathway to Empower Vocational Education and Training in the Circular Wood and Furniture Sector through Extended Reality

Extended Reality (XR) is a term that refers to virtual, augmented, and, more recently, mixed reality (VR/AR//MR), which are key enabling technologies of the Industry 4.0 (I4.0) and the simulated digital environment of the metaverse. XR enables the simulation of workplace scenarios, providing workers with training in a risk-free environment, resulting in cost savings, improved occupational risk prevention, and enhanced decision-making processes. XR is ideal for supporting digital transformation for organisations in fields such as production, occupational risk prevention, maintenance, and marketing. XR is also a key driver for training initiatives aimed at promoting good practices in the circular economy in specific sectors such as woodworking and furniture (W&amp;F). The European Commission has recognised the potential of XR for the W&amp;F sector, funding initiatives such as the European project, Allview, which seeks to identify the most appropriate and beneficial technologies of I4.0 with a green and digital transition focus from the perspective of vocational education and training (VET). This paper presents the work carried out within the framework of Allview, including the research and comparison of current software and hardware of XR tools suitable for VET in the W&amp;F field, a review of successful examples of XR applied to W&amp;F training actions, and an analysis of the opinions gathered from European students, teachers, and training organisations regarding the use of XR in education. As a result, the authors present a training pathway aimed at the development and implementation of a XR training scenario/lab/environment focused on VR, 360° videos, and MR, as a guideline for developing immersive XR training contents, contributing to the digital and green transformation of VET in the W&amp;F sector.
Smooth Metric Adjusted Skew Information Rates

Metric adjusted skew information, induced from quantum Fisher information, is a well-known family of resource measures in the resource theory of asymmetry. However, its asymptotic rates are not valid asymmetry monotone since it has an asymptotic discontinuity. We here introduce a new class of asymmetry measures with the smoothing technique, which we term smooth metric adjusted skew information. We prove that its asymptotic sup- and inf-rates are valid asymptotic measures in the resource theory of asymmetry. Furthermore, it is proven that the smooth metric adjusted skew information rates provide a lower bound for the coherence cost and an upper bound for the distillable coherence.
Smooth Coverage Path Planning for UAVs with Model Predictive Control Trajectory Tracking

Within the Industry 4.0 ecosystem, Inspection Robotics is one fundamental technology to speed up monitoring processes and obtain good accuracy and performance of the inspections while avoiding possible safety issues for human personnel. This manuscript investigates the robotics inspection of areas and surfaces employing Unmanned Aerial Vehicles (UAVs). The contribution starts by addressing the problem of coverage path planning and proposes a smoothing approach intended to reduce both flight time and memory consumption to store the target navigation path. Evaluation tests are conducted on a quadrotor equipped with a Model Predictive Control (MPC) policy and a Simultaneous Localization and Mapping (SLAM) algorithm to localize the UAV in the environment.
A web framework for information aggregation and management of multilingual hate speech

Social media platforms have led to the creation of a vast amount of information produced by users and published publicly, facilitating participation in the public sphere, but also giving the opportunity for certain users to publish hateful content. This content mainly involves offensive/discriminative speech towards social groups or individuals (based on racial, religious, gender or other characteristics) and could possibly lead into subsequent hate actions/crimes due to persistent escalation. Content management and moderation in big data volumes can no longer be supported manually. In the current research, a web framework is presented and evaluated for the collection, analysis, and aggregation of multilingual textual content from various online sources. The framework is designed to address the needs of human users, journalists, academics, and the public to collect and analyze content from social media and the web in Spanish, Italian, Greek, and English, without prior training or a background in Computer Science. The backend functionality provides content collection and monitoring, semantic analysis including hate speech detection and sentiment analysis using machine learning models and rule-based algorithms, storing, querying, and retrieving such content along with the relevant metadata in a database. This functionality is assessed through a graphic user interface that is accessed using a web browser. An evaluation procedure was held through online questionnaires, including journalists and students, proving the feasibility of the use of the proposed framework by non-experts for the defined use-case scenarios.
SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images

Medical images are usually degraded by numerous noises during acquisition or transmission, which often causes low contrast leading to deterioration of image quality. As such, medical image denoising and enhancement has become a paramount routine task. To overcome this problem, we propose a cutting-edge joint statistical and morphological model for the denoising and enhancement operation. Firstly, we propose a statistical model in formulating the marginal distribution of the wavelet coefficients. This model is integrated into a Bayesian inference framework to develop a maximum a posterior (MAP) estimator of the noise-free coefficient. Based on the statistical model, we eliminate the need for noise level estimation, and allows the model to automatically adapts to the observed image data. Secondly, we propose an adjustable morphological reconstruction model to eliminate known and unknown noises associated with medical images, while preserving the image details. After these operations, the image is decomposed into several wavelet subbands to extract the illumination and detail components. The image is then reconstructed based on the inverse wavelet to generate the enhanced noise-free image. Experimental results show that the proposed framework obtained high EME values of 41.04, 48.81, 47.81, and 45.75 for OCTA, FFA, CT, and X-ray imaging modalities, and performs better than the state-of-the-art methods. The proposed algorithm can effectively and efficiently enhance medical images, which will assist the clinicians in disease diagnosis, monitoring, and treatment.
Toward the Automatic Generation of an Objective Function for Extractive Text Summarization

A fitness function is a type of objective function that quantifies the optimality of a solution; the correct formulation of this function is relevant, in evolutionary-based ATS systems, because it must indicate the quality of the summaries.Several unsupervised evolutionary methods for the automatic text summarization (ATS) task proposed in current standards require authors to manually construct an objective function that guides the algorithms to create good-quality summaries.In this sense, it is necessary to test each fitness function created to measure its performance; however, this process is time consuming and only a few functions are analyzed.This study proposes the automatic generation of heuristic functions, through genetic programming (GP), to be applied in the ATS task.Therefore, our proposed method for ATS provides an automatically generated fitness function for cluster-based unsupervised approaches.The results of this study, using two standard collections, demonstrate to automatically obtain an orientation function that leads to good quality abstracts.
Multichromatic quantum superpositions in entangled two-photon absorption spectroscopy

Quantum information science is driving progress in a vast number of scientific and technological areas that cover molecular spectroscopy and matter-light interactions in general. In these fields, the ability to generate quantum mechanically-entangled photons is opening avenues to explore the interaction of molecules with quantum light. This work considers an alternative way of correlating photons by including energy superpositions. We study how the multichromatic quantum superposition, or color superposition of photon-pair states, influences the optical properties of organic chromophores. This work uses electronic structure calculations based on time-dependent density functional theory, and a simple modification of the standard entangled two-photon absorption theory. Our calculations show that it is possible to substantially modify the optical absorption cross section of molecules, where constructive and destructive interferences are computed. The quantum interference effects are more pronounced than the constructive ones. These quantum effects, or related ones, could be observed in quantum spectroscopic experiments where qudit photon states are generated.
A point cloud denoising network based on manifold in an unknown noisy environment

Acquiring original point cloud data from 3D sensors is easily affected by the environment and inherent limitations of the sensor, which inevitably contain noise and outliers. The irregularity and disorder of point clouds make denoising challenging. Traditional point cloud denoising methods heavily rely on geometric or noise priors. Recently, a deep learning denoising method based on gradient estimation has been proposed. Although it achieves the best denoising performance under Gaussian noise, the gradient can fluctuate under unknown noise, leading to decreased denoising performance. To address the problem that the above two algorithms have reduced denoising ability when facing unknown scenes, this paper proposes a denoising network based on manifold features of point clouds for unknown scenes. Specifically, the neural network consists of two key components: one is the manifold feature extraction module, which extracts manifold features and ensures accurate estimation of noise offset the other is the manifold denoising module, which can predict the noise offset of different disturbance points in the manifold by taking manifold features as input. Experiments show that our proposed method outperforms existing denoising methods on various noise model datasets and real outdoor datasets.
Object Detection and Recognition System Using Deep Learning Method

Object detection has been studied by many researchers for important applications in the industry like detecting a road object for self-driving cars, medical research for detecting particular diseases, gesture control, etc. Object detection and recognition is incredibly very important wrt security purposes. As computers and models can work 24/7 it can watch for video surveillance in secure areas. Humans can quickly detect or make out what items are there in photos and photographs, where these images and pictures are located, and how they interact with systems when they see them. [1]. Object identification and tracking is a key challenge in CV systems and interactions, such as visual surveillance and human computer vision systems. Human visual systems are quick and precise, allowing them to handle complicated activities such as driving. Computers will be able to drive automobiles using improvised and speedy errorfree object identification algorithms, yet they will require specialized sensors and auxiliary gadgets to relay real-time scenarios. [1]Using exact object recognition and picture classification approaches, strategies, and methodologies, it is critical and essential for deciding autonomous driving in metropolitan situations. Many big companies are currently working on this and achieving their goals day by day. In this report a object detection system has been proposed which can detect various objects, in fact it can detect almost any object wrt. training given to the model. Proposed methodology for object detection in the report is You Look Only Once (YOLO).
Character and Word Level Gesture Recognition of Indian Sign Language

Communication is essential to humans because it allows the dissemination of knowledge and the formation of interpersonal connections. We communicate through speaking, facial expressions, hand gestures, reading, writing, and sketching, among other things. However, speaking is the most often utilized means of communication. People having speech and hearing disabilities can only communicate using hand gestures, making them extremely reliant on nonverbal modes of communication. Hearing-impaired persons can communicate via sign language. Globally, around 1 percent(5 million) of the Indian population falls into this group. ISL is a complete language with its own vocabulary, semantics, lexicon, and a variety of other distinctive linguistic features. In our work, we present the methods for Indian sign language recognition at the character and word levels. The Bag of Visual Words(BoVW) technique recognizes ISL at character level(A-Z, 0-9) with an accuracy of 99 percent. Indian Lexicon Sign Language Dataset - INCLUDE-50 dataset is used for word-level sign language recognition. Inception model, a deep Convolutional Neural Network(CNN) is used to train the spatial features and LSTM RNN(Recurrent Neural Network) is used to train the temporal features of the video. Using CNN predictions as input to RNN, we achieved an accuracy of 86.7 %. In order to optimize the training process, only 60 % of the dataset is trained using the Meta-Learning model along with LSTM RNN and obtained an accuracy of 84.4 %, thus reducing the training time by 70 % and reaching nearly as close accuracy as the previous pre-trained model.
Human Activity Recognition and Prediction: Overview and Research Gaps

With several applications in the disciplines of healthcare, human-computer interaction, assistive learning, and many others, Human Actions Recognition, or HAR, is a trending research area. Despite the fact that this field has seen a lot of development over the past ten years, there is still a need to develop strong hybrid machine learning models for human actions recognition that must meet the objectives of the application, must have solid prediction and high recognition rate. A little work has been done in anomaly detection while performing an action. This paper presents a detailed literature survey in this area. Future directions are also presented based on the literature which shows that there is still a need of hybrid optimization technique to enhance the model's functionality.
Image Denoising using Autoencoders : Denoising noisy imgaes by removing noisy pixels/grains from natural images using Deep learning and autoencoders techniques

Denoising images is widely used in applications from critical medical systems to software based image enhancement in our cell phones. The natural noise is simulated by adding noise to an image in random pixels. Currently the denoising problem can be solved in either with greedy algorithm or by deep learning techniques. In this paper we are discussing the usage of Autoencoders a deep learning technique to solve the problem of denoising. Autoencoders use down-sampling and up-sampling techniques to remove the unwanted noise from an image.By using Encoders we compress the image gradually to remove the tiny details like noise using convolution layers and ReLU as activation function. The compressed images are fed to Decoders, the decoder up-samples the image bit-by-bit till the image reaches the original resolution, while up-sampling the tiny details like human hair or tiny speckles on face are restored without trying to bring back the noisy pixels. To train the proposed model we used FFHQ-Face dataset which consists of 70 thousand images of 128x128 resolution, each image is a face of and unique individual. The output of the neural network is a denoised image with PSNR of above 35, which maximum achieved compared to other pre-trained models.
Blockchain Enabled Architecture for Secure Authentication in the Metaverse Environment

Metaverse is the new virtual world that runs in parallel to the physical world. Multiple verses come together to form a large metaverse where various users, avatars, and devices communicate with one another using various technologies like AR, VR, XR, and MR. The traditional way of implementing these solutions is in a centralized way. Consequently, the authentication mechanisms used in the Metaverse are also centralized. The centralized nature of authentication has many limitations and is prone to authentication security threats. As users and avatars move across various verses in the metaverse, it becomes critical to have a decentralized mechanism for authentication. There are certain decentralized solutions proposed in the literature. But they come with their own limitations. Blockchain is a decentralized ledger technology that can provide an anonymous identity mechanism. In this paper, we propose a Blockchain-enabled architecture for the authentication of Avatars and users. This framework will ensure the decentralized authentication and traceability of the Avatar across the Metaverse. It will help in resolving security issues like impersonation, sever spoofing, identity interoperability, mutual authentication issues, replay, etc.
Blockchain Based Adaptive Non-Cooperative Game Strategy For Smart Power Contracts

The peer-to-peer energy trading on the decentralized platform among microgrids requires new approaches to be developed. The heterogeneous microgrids may be surplus or deficit power due to uncertainty in power generation and electric power demand. This paper proposes the blockchain-based adaptive non-cooperative game strategy among the owners of microgrids. The power token-based smart contracts have been created for energy trading among the networked microgrids. The smart contracts are made secure, reliable, and trustworthy with blockchain technology. The distributed identity-based microgrid (DIBM) scheme is designed to avoid the distributed denial of service (DDoS) attack. For the smart power contracts, the hash key has been obtained with python programming and other simulation work has been done in MATLAB.
Strategies for Improving Object Detection in Real-Time Projects that use Deep Learning Technology

The popularity and prevalence of devices equipped with object detection technology and controllable via the Internet of Things (IoT) have increased, especially in the post-Corona era. The development of neural networks and artificial intelligence by combining them with IoT systems has achieved acceptable satisfaction among users in adverse conditions by reducing the need for manpower and increasing productivity. Therefore, the scope of using such mechanisms has expanded in most fields, from self-driving vehicles to agricultural crops. Beginners will be confronted with a massive amount of complex information as a result of the design and application of such technologies in interdisciplinary fields. Due to the popularity of using the You Only Look Once (YOLO) object detection algorithm, this article provided a guideline as a traffic light subject classification and, offers suggested solutions and exclusive approches to increase the accuracy of object detection in real-time projects with a practical application attitude for the enthusiasts and developers particularly in object detection scenarios by employing YOLO.
An Effective Mechanism for Virtual Machine Placement using Cuckoo Search

Virtualization has been proved as a boon for computing in today’s scenario in the cloud. One of the major challenges faced while computing user applications relates to the placement of resources of the data centre especially virtual machines to the physical hosts optimally for successfully completion of users computational tasks. Sustainable use of resources eminently energy efficient and renewable resources contribute to the economies of scale In this paper we apply a KCS algorithm which integrates bio inspired cuckoo search with unsupervised K clustering machine learning algorithm for resolving the VMP problem. The integration of these two algorithms can result in near optimal solution.
Comparison of Face Recognition Accuracy of ArcFace, Facenet and Facenet512 Models on Deepface Framework

Face recognition is one of the biometric-based authentication methods known for its reliability. In addition, face recognition is also currently very concerning, especially with the growing use and available technology. Many frameworks can be used for the face recognition process, one of which is DeepFace. DeepFace has many models and detectors that can be used for face recognition with an accuracy above 93%. However, the accuracy obtained needs to be tested, especially when faced with a dataset of Indonesian faces. This research will discuss the accuracy comparison of the Facenet model, Facenet512, from ArcFace, available in the DeepFace framework. From the comparison results, it is obtained that Facenet512 has a high value in accuracy calculation which is 0.974 or 97.4%, compared to Facenet, which has an accuracy of 0.921 or 92.1%, and ArcFace, which has an accuracy of 0.878 or 87.8%. The benefit of this research is to test how high the accuracy of the existing model in DeepFace is if tested with the Indonesian dataset. In this test, Facenet512 is the model that has the highest accuracy when compared to ArcFace and Facenet. This research is expected to help DeepFace users determine the best model to use and provide references to DeepFace developers for future development.