Reviewerly/exHarmony huggingface dataset:
Integrating 3Ds Max with Robot Technology: A Case Study on the Robot Palletizer System

This paper explores the integration of 3Ds Max, a powerful 3D modelling and animation software with robot technology using a robot palletizer system as a reference framework. The main problem addressed is the lack of effective methods to evaluate robotic systems' functionality and enable optimization before robot physical installation. To overcome this challenge, 3D Max simulations are proposed to enable virtual prototyping thus providing comprehensive evaluations of the performance of robotic systems. The research has developed a robot palletizer system simulation that can serve as a training platform for engineers and clients, allowing them to familiarize themselves with the robot's operation in a safe and controlled setting. The paper contributes to the field of robotics by demonstrating the effectiveness of 3Ds Max simulation for enhanced performance, efficiency, and safety in robotic systems. Furthermore, it emphasizes the importance of further research on leveraging simulation and virtual environments within the context of Industry 4.0 and 5.0.
Path Planning of Mobile Target Robot Based on RRT* Algorithm

In order to better train shooters of different levels, this paper focuses on path planning and evaluation of mobile target robots. Given the global map, multiple random points are generated in the accessible area of the map. The target robot moves between the points in different orders, where the paths between adjacent random points are obtained based on the RRT * algorithm. Afterwards, the final path is obtained by merging each short sub-path between each adjacent point pair. A path evaluation function that evaluates the shooting difficulty of a path is then proposed with regards to three aspects, obstacles, distance, and shooting angle. If the robot's movement speed, path difficulty and shooter's level can be matched properly, the shooter's shooting accuracy and agility can be improved more quickly. Experiments have been conducted based on both simulation and real robots, where the feasibility of the approach has been verified.
A SAR Denoising Network Based on Generative Adversarial Learning

Synthetic Aperture Radar (SAR) images are critical in microwave remote sensing and are used for resource exploration and target location. However, due to the principle of coherent imaging, SAR images are often interfered by speckle noise, which reduces image quality. It is necessary to remove noise but retain details. We are committed to improving the network structure to increase the diversity and authenticity of generated images. Inspired by Se-Res-Unet, we fuse generative adversarial networks for SAR image denoising and named the model SAR-RsuGan. In it, the generator and the discriminator compete with each other to generate high-quality images. We use synthetic SAR images for training and compare with other methods to obtain the best performance.
Consttuction and optimization of Higher Education Management System Based on Data Mining Technology

With the development of social informatization, educational informatization is also developing. At the stage of higher education, many colleges and universities have saved a lot of educational data, which poses new challenges to the educational management system of colleges and universities. Nowadays, traditional higher Education Management (abbreviated as EM for convenience) methods are still adopted, which neglect the shaping of students’ personalities and the satisfaction of emotional needs, and fail to comprehensively consider the differences in the problems encountered by different students in learning, growth, and social practice. This article was based on Data Mining (DM) technology to construct and optimize a higher EM system, aiming to use DM technology to construct and optimize an efficient EM system, so as to play its role in teaching, student management, employment, and other aspects, and promote the development of higher education. This article tested the satisfaction of higher EM systems using DM technology through experiments, and found that the average satisfaction score was 8.2 points. This indicated that the higher EM system based on DM technology could indeed have a good effect on the development of higher education.
A Study of Web 3.0 Blockchain and Natural Learning Process Programming Study: Hate Discourse Detection

As the use of social networking platforms such as Facebook, Twitter, and Instagram increases, so does the prevalence of hate discourse. Hate discourse can be defined as an offensive language addressed at any racial, caste-creed, religious faith, or cultural group. Now, with the open development of Web 3.0, it is difficult to control every element of hate discourse because there is no governing authority. Using Block Chain and Natural Language Programming, this research paper studies an innovative approach for detecting hate discourse on Web 3.0. The proposed study states about blockchain guarantees the rigid and lucidity of data, while NLP algorithms are employed to examine and categorize hateful content online. The study indicates there are models which can be used to detect hate discourse with high accuracy, and the use of blockchain enhances the system's reliability and confidentiality. Thus, the study will identify the models which can mitigate hate discourse on Web 3.0 and may serve as a helpful tool for promoting secure surfing and acceptance of the coexistence of different communities together.
Fortifying IoT Against Quantum Threats: Strategies for Securing Connected Devices in the Quantum Era

All data sent to and from IOT devices, (as well as any data held), must be encrypted in order to enhance the security of IOT devices. At the moment, elliptic curve cryptosystems (ECCs) are used as fundamental building blocks to secure communication in the Internet of Things. Yet, forthcoming quantum computers can quickly compromise public key systems like ECC. We should take action right away to get the IOT ready for the quantum future due to recent developments in quantum computing. This paper at first will give a brief overview on Crystal - Kyber, SIKE and BIKE algorithm which are among the NIST's third round candidate. Then the comparison of these three on the basis of key size, computational power and memory requirement. Finally a recommendation on the most suitable algorithm for making IoT secure in quantum world.
Study and Implementation of Quantum AI Algorithms and Comparison with Classical AI Algorithms

TensorFlow and TensorFlow Quantum are two frameworks developed by Google that provide powerful tools for machine learning and quantum machine learning, respectively. While both are based on the same foundational concepts of tensors and automatic differentiation, they differ in their approach to modeling data and computation.While TensorFlow and TensorFlow Quantum share some similarities in terms of their underlying principles, they are designed to address distinct problem domains. Understanding the differences between these two frameworks using the same dataset to compare the efficiency of both systems can help choose the right tool for specific needs, whether they are working on classical machine learning problems or exploring the potential of quantum computing.
Analysis of Blockchain Technology to Protect Data Access Using Intelligent Contract Mechanism for 5G Networks

With constantly changing internet models automating and digitising countless residential and industrial applications, the world of today has made significant progress in the sharing of healthcare data and the implementation of crucial actions. The two most crucial factors in the setting of healthcare data exchange today are privacy and data integrity. In order to prevent unauthorised individuals from viewing or accessing sensitive data, such as medical information, encryption is essential. The intermediary nodes shouldn't be given too much weight because conventional encryption techniques could not be deployable through them. The current security alternatives for IoT-driven healthcare monitoring frameworks are discussed in this article along with the numerous security challenges that exist in the current 5G network design and how blockchain technology may be used to encrypt data between nodes. The suggested approach outperformed the alternatives and the present strategies for IoT-driven healthcare monitoring frameworks' security solutions, according to a variety of performance factors utilised to test it. A number of performance parameters were used to test the suggested technique, and the results showed that it performed better than the alternatives.
Advanced Use of Blockchain And Deep Learning Technologies For Financial Forecasting

Blockchain technology dependent on cryptocurrency have lately excited the curiosity of capitalists. They focused on forecasting the financial item's risk and return ratios. As a result, financial items require an autonomous algorithm to anticipate the return percentage of cryptocurrency. Deep learning (DL) algorithms that were lately developed lay the path for the return percentage forecasting procedure. This paper proposes a blockchain financial product utilizing DL for a smart return rate prediction (RRP-DLBFP) method. The suggested RRP-DLBFP method entails creating a long short-term memory (LSTM) framework for return percentage forecasting. Furthermore, the Adam optimization is used to effectively change the LSTM algorithm's hyperparameters, resulting in improved forecasting accuracy. The Ethereum rate of return has been selected as the aim of guaranteeing the RRP-DLBFP method's superior performance, and its outcomes are studied in various metrics. In terms of several assessment variables, the model's results demonstrated the superiority of the RRP-DLBFP method over the present latest methods. The suggested RRP-DLBFP exhibits MSE values of 0.0435 & 0.0655, accordingly, contrasted with a mean of 0.6139 & 0.723 for comparing techniques in both training and evaluation.
Accurate Recasting of Giant Text into Charts Using Rapid Automatic Keyword Extraction Algorithm in Comparison with Bag of Words Algorithm

Aim: For contrasting the accuracy of understandability between the techniques Novel Rapid Automatic Keyword Extraction (RAKE) and BOW (Bag Of Words) in the NLP (Natural Language Processing) utilizing the corpus Donald trumph speeches for building squarified charts from big text, that is the central goal. Materials and Methods: After pulling off key phrases from the huge content in a document, the rate of accuracy is evaluated. The accuracy rate evaluated based upon the quantity of similar key phrases in Novel RAKE and BOW compared with manual allotted key phrases. The key phrases grabbing done utilizing the Novel RAKE and BOW with sample sizes each 28 in the NLP (Natural Language Processing) resulting in G-power value 80%. Results: The Novel Rapid Automatic Keyword Extraction (RAKE) shows a larger rate of accuracy of 73.04% than the BOW (Bag Of Words) of 69.04% in gathering key phrases from a text file. This indicates that there is no statistically significant difference between the Novel RAKE algorithm and the BOW method with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{p}=0.333(\mathrm{p} &gt; 0.05)$</tex> . Conclusion: The Novel RAKE has a larger rate of prediction accuracy of 73.04% when distinguished with the rate of prediction accuracy of 69.04% in BOW and subset of parameters or attributes in the marginal distribution.
Dactylology Interpretation Using Key-Points Detection and LSTM

The practice of finger spelling, or dactylology, is frequently used by deaf, hard of hearing, mute, or people working underwater. It is also used when speaking out loud is not an option or practical. In this paper, key-point detection and Long Short-Term Memory (LSTM) model are used to demonstrate a method for deciphering dactylology. A LSTM network trained to recognize and interpret finger spelling gestures is fed with key-points from a real-time video sequence to detect the faces, postures, and hands. On the basis of annotated dactylology video sequences, this method is evaluated for its accuracy in interpreting a wide variety of finger spelling gestures. In real-time, the key-point detection method with the LSTM approach shows promise for interpreting dactylology. By using key-point detection and LSTM networks, finger spelling can be made efficient and accurate through key-point detection and LSTM networks. Many applications are possible with this approach, such as translating sign language and developing assistive technology for the deaf.
Spam Email Classification by Deep Learning

Spam emails are a certain kind of email that are utilized extensively for daily chores and crucial communication at all levels of an organization. Emailing has accelerated economic development by raising international activity to a new level. Email protocols like SMTP and POP are prone to manipulation because they are so widely used and accessible. The presence of spam emails has an impact on the entire organization. To categorize emails as spam or ham, we used three separate research-based techniques and classification strategies. Tokenizer was modified to reduce the text's size to small bits, and encoder was used to turn our sequence of text into a 1D array. In order to achieve an accuracy of 97 percent, we next pad all the sequences to the same size. Finally, we implement RNN for deep learning using the spam base dataset.
Performance Analysis of Machine Learning Algorithms for Instagram Post Reach Analysis

Machine learning algorithms are mathematical models which makes computers to learn by itself using data from the user. In this work we are predicting the instagram reach analysis based on reach of a particular post. We used the Passive Aggressive Regressor, Linear Regression, decision tree algorithm to determine which of the posts or stories will get highest reach and engagement rates which are useful for business enterprises.
Detecting the Political Bias in News Articles and Similarity Using Word Embeddings in American Journalism

Political polarity is on the rise in the world and machine learning tasks are adapting to the same. No longer is it sufficient to simply classify something as positive or negative, for a subject can be polarly opposite like positive or negative, but at the same time, can be switched entirely depending on the perspective. This is why the study of political bias makes up for a very interesting study since the multi-classification task of determining the alignment of a paper is a different take as to the usual understanding of sentiment analysis. Sentiment analysis in general is a natural language processing technique that helps to contextualize whether the natural text has any sort of bias to it. For the purposes of this paper, this polarity will be more aligned to whether a given news article in question is left-leaning, right-leaning or centrist. The outcome is doing so is to gain a knowledge of reporting in the USA as to see whether press organizations are truly neutral in their reporting and exactly how severe is the bias when it comes to particular words along with the other words they are naturally associated with. For the crux of this study, the proposed model will be utilizing three unsupervised document modelling algorithm that utilize document embeddings using paragraph vectors after utilizing Latent Dirichlet Allocation for fuzzy clustering. All three of these models will be used to make a prediction and while their accuracies will be evaluated separately, these three models will be combined towards a hard voting ensemble classifier so that a better classification score can be obtained and to reduce doubts since subjectivity is a large problem when dealing with natural text and especially, articles.
Validation of Products and Eliminating Counterfeits Using Blockchain

Supply chain executives often battled assistance inefficiencies, poor collaboration among departments. Nowadays, it could be exceedingly difficult to tell whether a factor is rightful a mere by peering into it due to the fact objects constantly get falsified. Untrue marketing poses major challenges for credible businesses, but too handful of individuals are conscious of the catastrophic harm these items inflict on corporate image. In the past, an array of policies had been designed in order to get over the issue of illicit product imitation. Enthusiasm in the digital currency blockchain has elevated over the previous several decades. Although a particular instance regarding banking transactions has attracted the most scrutiny, it could possibly have a bearing on other business sectors. The feasibility of employing blockchain technology to identify fictitious resources is addressed in this paper. We have put together several different layouts and are nevertheless sampling to hone a known notion. It was successfully enacted that utilizing just technological approaches will not be adequate for mitigating knockoffs. Essential considerations involve employing impermeable wrapping it, having an impartial security mechanism, spreading the word, and suing fraudsters legitimately. The procurement histories gauging procedure applied to the current initiative allows for identifying faux possessions swifter. Blockchain software, it affirms the genuineness and keeps track of legitimate products throughout the supply chain, making this possible. The personal information is particularly secure and confidential and is spared from any vulnerabilities since alterations are unable to be done to it sans the consent of every involved party.
A robust audio watermarking algorithm based on graph Fourier transform and singular value decomposition

In order to solve the robustness problem of audio watermarking, this paper proposes a robust audio watermarking algorithm based on Graph Fourier transform and singular value decomposition. The Arnold transform is used to scramble and encrypt the image watermark, and then the watermark is embedded in the audio carrier by graph Fourier transform and singular value decomposition. Through experimental simulation, the audio carrier can still extract a complete watermark after various attacks such as noise, resampling, filtering, and compression, indicating that the algorithm has good robustness.
Real-time Personalized Ranking and Recommendation System for Linear TV: A Dual Dynamic Queue Approach

The increasing number of channels on linear TV has amplified the challenge of efficient content discovery for viewers. This paper presents an innovative, real-time personalized ranking and recommendation system designed to address this problem. The objective was to enhance viewer satisfaction and interaction through a personalized, dynamic channel surfing experience. Our approach uses a dual dynamic queue system - the Dynamic History Channel Queue (DHCQ) and the Dynamic Future Channel Queue (DFCQ), each serving a unique purpose in managing the viewer's channel interaction. — Advanced deep learning models reinforce these queues’ dynamism by generating ‘global’ and ‘local’ content embeddings and ‘user’ embeddings. These embeddings are used to provide real-time updates, considering both timestamps and the time-sensitivities of the content and viewer. A ‘look- ahead’ feature was integrated to account for future content on each channel, adding another layer of personalization. — Preliminary user feedback highlighted a strong interest in this new form of personalized channel surfing, with a majority of respondents indicating a preference for our system over traditional channel navigation methods. The results show that our proposed solution could potentially change the way users interact with the ever-growing number of channels on linear TV.
Cloud-based Internet Linear Video Delivery System Using Edge Computing on CDN

Internet users are increasingly exposed to linear streaming services. Unlike television (TV) broadcasting, there is no limit to the number of channels that can be delivered via linear streaming services. Considering regions and various viewer preferences, several linear delivery platforms on cloud are being developed to efficiently provide various channels. In such platforms that use HTTP adaptive streaming (HAS), programs are scheduled in a manipulated manifest file. However, when an urgent program is dynamically inserted, the aforementioned approach requires complex implementations on the server and player sides to ensure reliable and rapid program switching, especially in low latency delivery because all the players’ manifest files must be quickly updated. Thus, we propose a cloud-based HAS linear delivery system that utilizes edge computing within the content delivery network (CDN) to facilitate prompt schedule changes. By shifting away from manifest manipulation or baseband switching used in TV broadcasting systems, the proposed approach offers quick adaptations to urgent schedule changes and efficient delivery of addressable linear streaming channels. This study presents an end-to-end system that focuses on the efficient use of cloud resources for encoding, program switching at pre-scheduled times by leveraging the CDN's edge capabilities, and simple low-latency playback. Moreover, the prompt realization of program schedule changes, particularly in scenarios involving dynamic urgent program insertions, and the replacement of certain programs based on viewers’ attributes on CDN edge, are addressed. By utilizing CDN edge computing, the cloud-based system offers dependable program switching to efficiently deliver localized or personalized channels.
Testing 4K HDR-WCG professional video content for subjective quality using a remote testing approach

Modern content is rapidly shifting from the standard dynamic range (SDR) color volume defined in ITU-R BT.709 towards the high dynamic range and wide color gamut (HDR-WCG) color volume defined in ITU-R BT.2100. For optimizing delivery channels of this content it is crucial to have high performance video quality metrics. However, existing subjective databases used to design and qualify such video quality metrics do not provide adequate test coverage for professional HDR-WCG video content. In this paper we describe a subjective video quality study aimed to be relevant for existing as well as near future HDR-WCG. — Due to the Covid pandemic, the experiment was designed for remote testing, rather than a typical laboratory environment. We carefully analyzed the subjective results and concluded that it is possible to obtain comparable reliability of data in our remote testing environment with a controlled laboratory environment. This opens the door for large-scale video quality testing, enabling continual refinement of video quality metrics, reducing the turn-around time for subjective testing, and eliminating bias in the training data by accessing a much larger and more diverse set of users. — We applied the subjective data set to compare existing image/video quality metrics such as VMAF, HDR-VDP and HDR-VQM. Furthermore, we identified several ways the VMAF metric can be adjusted to improve its accuracy on HDR-WCG content.