Reviewerly/exHarmony huggingface dataset:
An Intelligent Human Age Prediction from Face Image Framework Based on Deep Learning Algorithms

Age prediction is the task of extracting features from the human face image. Human aging factors can be expressed as multifactorial, gradual, time-dependent, physical, and biological damage. Attributes are extracted from a face image, and the aging factor depends on cells, tissues, and all living organisms. Human age prediction is distinct from chronological age prediction. Each human’s biological identity has unique characteristics. Age prediction depends on the maturity process of organs, other tissues, and cells. Many research works have been done on age classification using various techniques from human face images. It is a difficult task to the analysis of facial appearance. Issues in the existing algorithm are inefficient and require more computation time and storage space. To address these issues, this paper proposed a Deep convolutional neural network (DCNN) with a Cuckoo search algorithm (DCNN-CS). In this proposed work, DCNN-CS produces an effective age prediction from the human face image within a minimum execution time, handling a large dataset. The accuracy rate of the convolutional neural network (CNN) got 81.32, the Deep Neural Network (DNN) got 82.34, the Long short-term memory (LSTM) got 88.12, and the proposed work SLSTM-DNN got 91.45.
Use of Data Mining for The Analysis of Consumer Purchase Patterns with The Fpgrowth Algorithm on Motor Spare Part Sales Transactions Data

This study aims to analyze consumer purchasing patterns for motorcycle parts using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data. This research aims to obtain helpful information for companies in planning marketing strategies and increasing sales. The data used in this study are motorcycle parts sales transaction data from motorcycle parts stores for one year. The data is then processed using the FP-Growth algorithm to find significant purchasing patterns. The results of this study show that the FP-Growth algorithm can be used to identify substantial consumer purchasing patterns. Some purchase patterns found include a combination of often purchased products, the most active purchase time, and the most purchased product category. Using data mining and the FP-Growth algorithm can assist companies in understanding significant consumer purchasing patterns to improve the effectiveness of marketing strategies and increase sales of motorcycle parts. The novelty of this research lies in using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data to analyze consumer purchasing patterns. This research also provides valuable information for companies in planning marketing strategies and increasing sales by identifying significant consumer purchasing patterns, such as product combinations often purchased together and the most purchased product categories.
Machine Learning Approaches for Predicting Psoriatic Arthritis Risk Using Electronic Medical Records: Population-Based Study

Psoriasis (PsO) is a chronic, systemic, immune-mediated disease with multiorgan involvement. Psoriatic arthritis (PsA) is an inflammatory arthritis that is present in 6%-42% of patients with PsO. Approximately 15% of patients with PsO have undiagnosed PsA. Predicting patients with a risk of PsA is crucial for providing them with early examination and treatment that can prevent irreversible disease progression and function loss.The aim of this study was to develop and validate a prediction model for PsA based on chronological large-scale and multidimensional electronic medical records using a machine learning algorithm.This case-control study used Taiwan's National Health Insurance Research Database from January 1, 1999, to December 31, 2013. The original data set was split into training and holdout data sets in an 80:20 ratio. A convolutional neural network was used to develop a prediction model. This model used 2.5-year diagnostic and medical records (inpatient and outpatient) with temporal-sequential information to predict the risk of PsA for a given patient within the next 6 months. The model was developed and cross-validated using the training data and was tested using the holdout data. An occlusion sensitivity analysis was performed to identify the important features of the model.The prediction model included a total of 443 patients with PsA with earlier diagnosis of PsO and 1772 patients with PsO without PsA for the control group. The 6-month PsA risk prediction model that uses sequential diagnostic and drug prescription information as a temporal phenomic map yielded an area under the receiver operating characteristic curve of 0.70 (95% CI 0.559-0.833), a mean sensitivity of 0.80 (SD 0.11), a mean specificity of 0.60 (SD 0.04), and a mean negative predictive value of 0.93 (SD 0.04).The findings of this study suggest that the risk prediction model can identify patients with PsO at a high risk of PsA. This model may help health care professionals to prioritize treatment for target high-risk populations and prevent irreversible disease progression and functional loss.
Cryptocurrency as a method of payment in the tourism sector

Abstract The purpose of this study is to look into the capability of cryptocurrencies as a viable alternative to traditional payment methods in the travel and tourism industry. Given the increasing popularity of digital currencies, the tourism industry must consider alternative payment methods. The study's goal is to determine whether cryptocurrencies can be a safe and practical payment option for the travel and tourism industry. The benefits and drawbacks of cryptocurrency adoption in the travel and tourism industry were examined using a case study method. The study focused on customer preferences, security, and regulatory compliance. In-depth research was conducted on a select group of companies that have already implemented cryptocurrency as a payment option, as well as on interviews of business leaders. According to the study's findings, there is a high demand for the adoption of cryptocurrencies in the travel and tourism sector. Customers seeking secure and convenient payment methods may benefit from the use of cryptocurrencies. The study emphasizes the importance of addressing security concerns and regulatory compliance, as these are significant barriers to cryptocurrency adoption in the travel industry. The study provides a road map for cryptocurrency adoption in the travel industry, recommending that the industry focus on developing secure and user-friendly cryptocurrency payment methods, as well as actively collaborate with regulatory bodies to ensure compliance. This case study offers valuable insights to industry participants on how to effectively embrace and use cryptocurrencies in day-to-day business operations. Finally, this study offers a thorough examination of the potential of cryptocurrencies as a new form of payment in the travel and tourism industry. The findings of the study provide a clear understanding of the benefits and drawbacks of using cryptocurrencies, as well as valuable insights for industry participants on how to effectively adopt and use cryptocurrencies in their operations.
The Impact of The Covid-19 Pandemic on Interior Design Students&amp;rsquo; Performance in Jordanian Universities

This research study compared the perceived performance of interior design students participating in Classroom Learning Versus Online Learning due to the COVID-19 pandemic lockdown in Jordan.The survey results suggest that first-year students had higher satisfaction ratings than third-year students.Second-year students had a medium level of expectations and perceptions regarding both online and classroom learning.The reasons for this difference in perceived performance could be attributed to a variety of factors, such as the novelty of online learning, the more complex coursework of third-year students, and the difficulty of conveying concepts in an online learning environment.In order to ensure that all students are receiving an equitable education, regardless of their year level, it is important to understand the root causes of the difference in satisfaction between first-and third-year students and to develop strategies to address any issues that arise.The findings of this study provide insight into the factors affecting student satisfaction with online learning and can inform the development of strategies to support students in their learning during the pandemic.
Trajectory Optimization of High-Speed Robotic Positioning with Suppressed Motion Jerk via Improved Chicken Swarm Algorithm

For the trajectory optimization of the time–jerk of robotic arms with a chicken swarm optimization algorithm, using five-order B-spline interpolation can ensure smooth and continuous acceleration, but, due to the performance problems of the algorithm, the low solution accuracy and the slow convergence speed, the ideal trajectory curve cannot be obtained. To address these problems, an improved chicken swarm algorithm based on a parallel strategy and dynamic constraints (PDCSO) is proposed, where the rooster update method is employed with a parallel strategy using X-best guidance and a Levy flight step. Dynamic constraints for the rooster are given, followed by the hens, and the optimal rooster position that improved the convergence accuracy while preventing the local optimum was determined. Simulation experiments using 18 classical test functions showed that the PDCSO algorithm outperformed other comparative algorithms in terms of convergence speed, solution accuracy and solution stability. Simulation validation in ADAMS and real machine tests proved that PDCSO can effectively reduce the running time and motion shock for robotic arms and improve the execution efficiency of such arms.
Current Development, Challenges, and Future Trends in Cloud Computing: A Survey

Cloud computing is a new paradigm in information and communication technologies (ICTs) that provides the ability to access shared pools of different computing resources that are related to many cloud users within a pay-per-use or on-demand approach.It has transformed the delivery model of ICT from a product to a service.This provides several different advantages for institutions, companies and users based on savings and reduced capital expenditure through lower operating expenses.This paper provides a comprehensive survey of cloud computing.It first develops an understanding of cloud computing in general and discusses its advantages, current development, challenges and future trends.Subsequently, a detailed discussion on the cloud computing architectures, services models, fault tolerance mechanisms, services selection methods, adoption by industry, and scheduling of cloud-based resources is also presented.Nonetheless, cloud computing has many obstacles which expose it to a number of limitations.Some of these challenges include security of data, fault tolerance, and load balancing.A number of techniques in literature are proposed to cope with these challenges which are discussed and analyzed.Experimental data and usage drift validates the popularity of cloud computing and its adoption in recent years.Future trends in cloud computing support the use of intelligent machine learning (ML) techniques and new technologies to cope with some of the challenges and making cloud computing more efficient, secure and commercially viable to be widely accepted.
Building Applications and Developing Digital Signature Devices based on the Falcon Post-Quantum Digital Signature Scheme

Falcon is an efficient and secure postquantum signature scheme for services based on quantum computing. It employs the hash-and-sign approach in conjunction with the Gentry, Peikert, and Vaikuntanathan (GPV) framework on Number Theory Research Unit (NTRU) lattices. This study evaluated the operation procedure and the capacity to run the Falcon scheme using a key length of 1024 bits on different hardware and software platforms, such as personal computers and Raspberry Pi 4 and Windows, Ubuntu, and Android operating systems. The following results were obtained: file sizes ranged from 30 to 5449268 KB, digital signature times ranged from 50 to 19500ms, and signature verification times ranged from 14 to 19000ms. The results show that the Falcon post-quantum signature scheme works stably and ensures execution speed on different platforms, similar to current digital signature schemes.
Quantum walks in spaces with applied potentials

Abstract Discrete quantum walks are a universal model of quantum computation equivalent to the quantum circuit model and can be mapped onto quantum circuits and executed using quantum computers. Quantum walks can model and simulate many physical systems and several quantum algorithms are based on them. Discrete quantum walks have been extensively studied, but quantum walks that evolve in spaces in which potentials are applied received little or no attention. Here, we formulate the discrete quantum walk model in one and two-dimensional spaces in which potentials are applied. In this formulation the quantum walker carries a “charge” affected by the potentials and the walk evolution is driven by both constant and time-varying potentials. We reproduce the tunneling through a barrier phenomenon and study the quantum walk evolution in one and two-dimensional spaces with various potential distributions. We demonstrate that our formulation can serve as a basis for applied quantum computing by studying maze running and the motion of vehicles in urban spaces. In these spaces curbs and buildings are modeled as impenetrable potential barriers and traffic lights as time-varying potential barriers. Quantum walks in spaces with applied potentials may open the way for the development of novel quantum algorithms in which inputs are introduced as potential profiles.
Machine translation from signed to spoken languages: state of the art and challenges

Abstract Automatic translation from signed to spoken languages is an interdisciplinary research domain on the intersection of computer vision, machine translation (MT), and linguistics. While the domain is growing in terms of popularity—the majority of scientific papers on sign language (SL) translation have been published in the past five years—research in this domain is performed mostly by computer scientists in isolation. This article presents an extensive and cross-domain overview of the work on SL translation. We first give a high level introduction to SL linguistics and MT to illustrate the requirements of automatic SL translation. Then, we present a systematic literature review of the state of the art in the domain. Finally, we outline important challenges for future research. We find that significant advances have been made on the shoulders of spoken language MT research. However, current approaches often lack linguistic motivation or are not adapted to the different characteristics of SLs. We explore challenges related to the representation of SL data, the collection of datasets and the evaluation of SL translation models. We advocate for interdisciplinary research and for grounding future research in linguistic analysis of SLs. Furthermore, the inclusion of deaf and hearing end users of SL translation applications in use case identification, data collection, and evaluation, is of utmost importance in the creation of useful SL translation models.
Direct torque control and dynamic performance of induction motor using fractional order fuzzy logic controller

&lt;p&gt;&lt;span lang="EN-US"&gt;Conventional direct torque control (DTC) is one of the best control systems for regulating the torque of an induction motor (IM). However, the DTC’s enormous waves in flux and torque cause acoustic noise that degrades control performance, especially at low speeds due to the DTC’s low switching frequency. Direct torque control systems, which focus just on torque and flux, have been proposed as a solution to these problems. In order to improve DTC control performance, this work introduces a fractional-order fuzzy logic controller method. The objective is to analyze this technique critically with regard to its efficacy in reducing ripple, its tracking speed, its switching loss, its algorithm complexity, and its sensitivity to its parameters. Simulation in MATLAB/Simulink verifies the anticipated control approach’s performance.&lt;/span&gt;&lt;/p&gt;
Review: Verification Process of Academic Certificates Using Blockchain Technology.

The rapid advances in Blockchain technology have impacted various areas, including financial, healthcare, and supply chain systems.Due to its unique characteristics, such as decentralization, trustworthiness, and security, this technology has recently been used in education, particularly in the verification of academic certificates.A certificate from an academic institution is a crucial document that could give someone access to new prospects.It frequently serves as a great starting point for choosing candidates during the recruitment process.Due to the lack of an effective anti-forging mechanism, academic certificates are vulnerable to fraud, forgery, and imitation.To combat this trend, educational institutions have implemented methods that often entail a third party verifying the legitimacy of academic certificates.A review of reference studies was conducted for this purpose.Many databases and papers that focused on blockchain in academic certificate verification and made important contributions rather than just making general statements about the topic were chosen.This paper investigates the benefits and challenges of blockchain technology in academic certificate verification.In the results obtained, it has been suggested that while blockchain technology in academic degree verification is still a new field, it holds a lot of promise for the academic certificate verification process as a whole.Finally, to avoid fake documents, this review paper focuses on papers related to the Academic Certificate Authenticity System (ACAS) using blockchain technology.
LAHM : Large Annotated Dataset for Multi-Domain and Multilingual Hate Speech Identification

Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks. In this paper, we present a new multilingual hate speech analysis dataset for English, Hindi, Arabic, French, German and Spanish languages for multiple domains across hate speech - Abuse, Racism, Sexism, Religious Hate and Extremism. To the best of our knowledge, this paper is the first to address the problem of identifying various types of hate speech in these five wide domains in these six languages. In this work, we describe how we created the dataset, created annotations at high level and low level for different domains and how we use it to test the current state-of-the-art multilingual and multitask learning approaches. We evaluate our dataset in various monolingual, cross-lingual and machine translation classification settings and compare it against open source English datasets that we aggregated and merged for this task. Then we discuss how this approach can be used to create large scale hate-speech datasets and how to leverage our annotations in order to improve hate speech detection and classification in general.
Source Acquisition Device Identification from Recorded Audio Based on Spatiotemporal Representation Learning with Multi-Attention Mechanisms

Source acquisition device identification from recorded audio aims to identify the source recording device by analyzing the intrinsic characteristics of audio, which is a challenging problem in audio forensics. In this paper, we propose a spatiotemporal representation learning framework with multi-attention mechanisms to tackle this problem. In the deep feature extraction stage of recording devices, a two-branch network based on residual dense temporal convolution networks (RD-TCNs) and convolutional neural networks (CNNs) is constructed. The spatial probability distribution features of audio signals are employed as inputs to the branch of the CNN for spatial representation learning, and the temporal spectral features of audio signals are fed into the branch of the RD-TCN network for temporal representation learning. This achieves simultaneous learning of long-term and short-term features to obtain an accurate representation of device-related information. In the spatiotemporal feature fusion stage, three attention mechanisms-temporal, spatial, and branch attention mechanisms-are designed to capture spatiotemporal weights and achieve effective deep feature fusion. The proposed framework achieves state-of-the-art performance on the benchmark CCNU_Mobile dataset, reaching an accuracy of 97.6% for the identification of 45 recording devices, with a significant reduction in training time compared to other models.
Proof of Work vs. Proof of Stake in Cryptocurrency

This research document demonstrates the understanding of two key elements: Proof of Work (PoW) and Proof of Stake (PoS) within cryptocurrency. Cryptocurrency is often misunderstood as just volatile and risky but many investors do not understand what it even is. What is proof of work (BTC)? What is proof of stake (ETH)? How are they similar and how are they different? Cryptocurrency is actually more relatable that originally imagined once a person understands digital currency, money, it’s origins, and how they hold similar monetary value within their own wallets. Cryptocurrency in PoW involves complicated mathematical equation solving via mining from a large growing pool of miners. These miners receive rewards such as bitcoin/tokens/etc. from their mined hash blocks verified, and blocks added to the blockchain. Challenges are that there are many miners and there may be a computational limit per core of each PC to be able to mine since Bitcoin has increased its difficulty 70 billion times since it started. BTC or Bitcoin is still widely trusted due to its increasing difficulty to race to the finish line to finish the advanced math computations. Whereas within PoS this is not a race between the masses and advanced math computations, but a validator that generally has more put in their stake vs. gained by receiving the processing fees associated. ETH or Ethereum has shifted from PoW to PoS for its better energy efficiency in resources. It has employed many other additional checks from Casper to Gasper which is the combination of Casper (fork-choice algorithm), LMD-GHOST (heavest observed subtree), and finality which requires 2/3rd agreement as well as once a block is justified it is upgraded to a finalized block. the similarities between the two PoW and PoS are that they are consensus-driven algorithms. They are designed to reach an agreement between the systems in place before each block is placed in the blockchain. Additionally, they also have a shared public ledger that operates on a global if not international scale. With blockchains, there is always only one true version and this is relying on a network rather than a governing authority like a bank/government entity to provide security and to prevent fraudulent transactions. It is still widely contested which is better than these two well-known consensus algorithms and it is still widely contested. The best answer is suited on a per investor per business basis in terms of the willingness of taking a risk just like any investment. This document serves to help provide guidance in the understanding of cryptocurrency of two main consensus algorithms, its similarities, differences, and help identify see what a potential investor the reader may be.