Reviewerly/exHarmony huggingface dataset:
A Generalization of the Chomsky-Halle Phonetic Representation using Real Numbers for Robust Speech Recognition in Noisy Environments

Speech recognition is difficult when the speech signal is weak or occurs in a noisy environment. This paper presents an efficient and robust method that can reconstruct the standard pronunciation of English phonemes and words given a weak or noisy signal. The reconstruction is based on a novel representation of the reconstruction task as a problem of data retrieval from a database in two different cases: (1) when the phonemes are represented in the database as binary tuples and the input is also a binary tuple from which deletion errors occur, and (2) when the phonemes are represented in the database and in the input as tuples of real values ranging between 0 and 1. In the latter case, the input phoneme could contain both a higher or lower value than the standard phoneme in the database that is intended by the speaker. For case (2) a theorem is proven regarding when the data retrieval can be expected to be reliable.
Motion recognition method of college football teaching based on convolution of spatio-temporal graph

In the past decade, people’s life is getting better and better, and the attention to sports competition is also increasing. In the current information age, sports and athletes’ data are very important, especially team football. In college, football coaches can use the data to analyze the situation of college football players and opposing players to better specify the corresponding tactics to win the game. However, at present, most of the data results need to be manually recorded and counted on the spot or after the game. In the process of statistics, Zhou Jing will inevitably have omissions and other problems. For this problem, a method based on space-time graph convolution. In the process, machine vision and motion recognition methods are combined, and the joint movements of different football players are extracted through the pose estimation method to obtain motion recognition results. To ented the methods on the KTH dataset. The results showed that the football motion recognition using the research method reached 98% on the dataset, which significantly improved the accuracy of nearly 5% over the existing state-of-the-art methods. At the same time, the accuracy rate of football movements was less than 5%. This means that the research method can effectively identify football sports, and can be widely used in other fields, and promote the development of human movement recognition in human-computer interaction and smart city and other fields.
Transfer learning based cascaded deep learning network and mask recognition for COVID-19

The COVID-19 is still spreading today, and it has caused great harm to human beings. The system at the entrance of public places such as shopping malls and stations should check whether pedestrians are wearing masks. However, pedestrians often pass the system inspection by wearing cotton masks, scarves, etc. Therefore, the detection system not only needs to check whether pedestrians are wearing masks, but also needs to detect the type of masks. Based on the lightweight network architecture MobilenetV3, this paper proposes a cascaded deep learning network based on transfer learning, and then designs a mask recognition system based on the cascaded deep learning network. By modifying the activation function of the MobilenetV3 output layer and the structure of the model, two MobilenetV3 networks suitable for cascading are obtained. By introducing transfer learning into the training process of two modified MobilenetV3 networks and a multi-task convolutional neural network, the ImagNet underlying parameters of the network models are obtained in advance, which reduces the computational load of the models. The cascaded deep learning network consists of a multi-task convolutional neural network cascaded with these two modified MobilenetV3 networks. A multi-task convolutional neural network is used to detect faces in images, and two modified MobilenetV3 networks are used as the backbone network to extract the features of masks. After comparing with the classification results of the modified MobilenetV3 neural network before cascading, the classification accuracy of the cascading learning network is improved by 7%, and the excellent performance of the cascading network can be seen.
Detecting genuine tripartite entanglement via general symmetric informationally complete measurements

We study multipartite entanglement and genuine tripartite entanglement based on general symmetric informationally complete positive operator valued measurements (GSIC-POVMs). By representing the bipartite density matrices in terms of GSIC-POVMs, we obtain the lower bound of the sum of squares of the corresponding probability. We then construct a special matrix with the correlation probability of GSIC-POVMs to derive useful and operational criteria to detect genuine tripartite entanglement. We also generalize the results to obtain a sufficient criterion to detect entanglement for multipartite quantum states in arbitrary dimensions. Detailed examples show that the new method can detect more entangled and genuine entangled states than previous criteria.
A Comparative Study of Secure and Efficient Data Duplication Mechanisms for Cloud-Based IoT Applications

Cloud-based processes store enormous amounts of data to support several daily applications, especially in intelligent systems and the Industrial Internet of Things (IIoT) applications. The advancement of hosted cloud storage services endorses the productivity of IIoT applications. It is critical to enabling businesses to increase data security, privacy, efficiency, accessibility, and flexibility. There are many concerns for cloud providers, including storage overhead, upload/download bandwidth, and data security and privacy. Utilizing secure data deduplication technology, cloud service providers may maximize their available storage capacity, resulting in effective and secure disk space use. This review focuses on a comparative study of secure data deduplication mechanisms for cloud-based applications, including MD5 and various versions of SHA. These techniques remove duplicate data in different storage levels and enhance users’ data integrity, confidentiality, and privacy protection. It is computationally more efficient than traditional compression methods, which may support IoT applications to manage the exponential growth of their data. We also present an overview and classification of the most recent research on data deduplication based on essential mechanisms.
Information Foraging in the Era of AI: Exploring the Effect of ChatGPT on Digital Q&amp;amp;A Platforms

Large Language Models (LLMs) such as ChatGPT and Bard have been widely recognized as one of the fastest-growing technologies in history. They have gained considerable attention in the news due to their potential to cause significant transformation in various industries, particularly in traditional online Q&A platforms. Knowing its emergence and great potential to reform how online users forage information, in this study, we explore the impact of this technology on content generation and consumption on online Q&A forums, taking the most well-known Q&A platform "Stack Exchange" as an example. Our initial findings demonstrate that since the launch of ChatGPT, there has been an upward trend in the quality and complexity of questions being asked on the platform. This suggests that users are now more inclined to seek answers to more challenging questions rather than easier ones on online Q&A forums. In addition, we also observed a corresponding rise in both the length and complexity of the answers provided on the platform. Our second set of findings indicates that there has been a notable decrease in the number of visits, posts, users, and active users on Stack Exchange following the introduction of the LLM, particularly within the Technology sector. We further validated these results by analyzing the behavior of users and posts on Stack Overflow. Our research suggests that the adoption of this AI technology has hurt the overall engagement of online Q&A platforms. Finally, we discuss the potential business implications of our study and highlight future research opportunities in this area.
Fake Product Identification Using Blockchain Technology

Abstract: The most significant problem affecting today's retail industry is product counterfeiting. Products that are counterfeited are just poor imitations of a legitimate brand. Due to the lack of transparency, supply chain management frequently experienced problems with service redundancy, ineffective coordination among departments, and lack of standardisation. Nowadays, it is very common for products to be counterfeited, and it is nearly impossible to tell if a product is fake just by looking at it. Various strategies, including RFID tags, artificial intelligence, machine learning, QR code-based systems, and many more, have been used from time to time to prevent product counterfeiting.
A geospatial platform for the tectonic interpretation of low-temperature thermochronology Big Data

Abstract Low-temperature thermochronology is a powerful tool for constraining the thermal evolution of rocks and minerals in relation to a breadth of tectonic, geodynamic, landscape evolution, and natural resource formation processes through deep time. However, complexities inherent to these analytical techniques can make interpreting the significance of results challenging, requiring them to be placed in their geological context in 4-dimensions (3D + time). We present a novel tool for the geospatial archival, analysis and dissemination of fission-track and (U-Th)/He data, built as an extension to the open-access AusGeochem platform ( https://ausgeochem.auscope.org.au ) and freely accessible to scientists from around the world. To demonstrate the power of the platform, three regional datasets from Kenya, Australia and the Red Sea are placed in their 4D geological, geochemical, and geographic contexts, revealing insights into the tectono-thermal evolutions of these areas. Beyond facilitating data interpretation, the archival of fission track and (U-Th)/He (meta-)data in relational schemas unlocks future potential for greater integration of thermochronology and numerical geoscience techniques. The power of formatting data to interface with external tools is demonstrated through the integration of GPlates Web Service with AusGeochem , enabling thermochronology data to be readily viewed in their paleogeographic context through deep time from within the platform.
K-Prototype Algorithm in Grouping Regency/City in South Sulawesi Province Based on 2020 People's Welfare

Clustering is something that is used to analyze data in machine learning, data mining, pattern engineering, image analysis, and bioinformatics. To produce the information needed for a data analysis using the clustering process, this is because the data has a large variety and amount. Researchers will use the K-Prototype method where this method becomes an efficient and effective algorithm in processing mixed-type data. The K-Prototype algorithm has problems in finding the best number of clusters. So, in this paper, researchers will conduct research by finding the best number of clusters in the K-Prototype method. There are many ways to determine this, one of which is the Elbow method. The determination of this method is seen from the SSE (Sum Square Error) graph of several number of clusters. The results of the clustering formed 2 clusters which were considered optimal based on the value of k that experienced the greatest decrease. The results showed that Cluster 1 is a cluster that has characteristics of people's welfare which is better than Cluster 2
HATE SPEECH DETECTION USING MACHINE LEARNING AND N-GRAM TECHNIQUES

Toxic online material has emerged as a significant issue in contemporary society as a result of the exponential increase in internet usage by individuals from all walks of life, including those with varied cultural and educational backgrounds. Automatic identification of damaging text offers a problem because it needs to differentiate between disrespectful language and hate speech. In this paper, we provide a technique for automatically categorizing literature into the categories of hateful and non-hateful. This study discusses the difficulty of automatically identifying hate speech. It is examined how machine learning and natural language processing may be combined in various ways. Following that, the experiment results are contrasted in terms of how well they apply to this project. When we examine the models under consideration and fine-tune the model that produces the greatest performance accuracy after testing it against test data, we get a 94% accuracy rate.
Identity-Guided Spatial Attention for Vehicle Re-Identification

In vehicle re-identification, identifying a specific vehicle from a large image dataset is challenging due to occlusion and complex backgrounds. Deep models struggle to identify vehicles accurately when critical details are occluded or the background is distracting. To mitigate the impact of these noisy factors, we propose Identity-guided Spatial Attention (ISA) to extract more beneficial details for vehicle re-identification. Our approach begins by visualizing the high activation regions of a strong baseline method and identifying noisy objects involved during training. ISA generates an attention map to mask most discriminative areas, without the need for manual annotation. Finally, the ISA map refines the embedding feature in an end-to-end manner to improve vehicle re-identification accuracy. Visualization experiments demonstrate ISA’s ability to capture nearly all vehicle details, while results on three vehicle re-identification datasets show that our method outperforms state-of-the-art approaches.
Minute-Scale Models for the Diffuse Fraction of Global Solar Radiation Balanced between Accuracy and Accessibility

The separation models are tools used in solar engineering to estimate direct normal (DNI) and diffuse horizontal (DHI) solar irradiances from measurements of global solar irradiance (GHI). This paper proposes two empirical separation models that stand out owing to their simple mathematical formulation: a rational polynomial equation. Validation of the new models was carried out against data from 36 locations, covering the four major climatic zones. Five current top minute-scale separation models were considered references. The tests were performed on the final products of the estimation: DNI and DHI. The first model (M1) operates with eight predictors (evaluated from GHI post-processed measurements and clear-sky counterpart estimates) and constantly outperforms the already established models. The second model (M2) operates with three predictors based only on GHI measurements, which gives it a high degree of accessibility. Based on a statistical linear ranking method according to the models’ performance at every station, M1 leads the hierarchy, ranking first in both DNI and DHI estimation. The high accessibility of the M2 does not compromise accuracy; it is proving to be a real competitor in the race with the best-performing current models.
Analysis of Expert System Implementation in Computer Damage Diagnosis with Forward Chaining Method

The necessity for computerization is currently growing quickly because computers are now necessary for all needs connected to business and daily life. Therefore, users' ability to quickly and easily access information seems to be hindered by maintenance and repairs. An expert system is a computer-based method that uses facts, knowledge, and reasoning to solve issues that are often best handled by a subject-matter expert. Expert systems are created for expertise that is close to human capabilities in a given sector. A computer expert currently needs a lot of time to diagnose computer damage, and even technicians frequently put off their work in order to come up with fixes. Forward chaining was used in the construction of this system. In order to derive conclusions, forward chaining is employed to test the given factors against the recorded rules in the system. As a result, this expert system was developed to assist users in dealing with the damage and early maintenance that frequently affect computer systems. using computers in everyday tasks.If we are familiar with the Forward Chaining method used by the expert system to trace computer damage, the known crash features to address frequent crashes on that PC, and the application in object-oriented programming languages such as Visual Basic 6.0, we can determine where the damage is located.
Software Development Effort Estimation Using Relational Database and Optimized Learning Mechanism

Accurately estimating the cost of software development is crucial for effective project planning and resource allocation.However, traditional cost estimation methods rely heavily on expert judgment and historical data, which can be time consuming and prone to errors.This study suggests a learning-based cost estimation model that leverages relational databases to improve accuracy.The proposed approach estimates project cost based on the effort required to complete software development, which is a key driver of the project cost.The proposed model is designed to address the challenges posed by the variability in open-source development, including variable team sizes, working hours, and expertise.The study collects and pre-processes data from open-source platforms and selects cost drivers and metrics based on logical rules and SQL queries.Moreover, we propose an optimized Artificial Neural Network (ANN) with augmented topology to automate the selection of neuron units, layers, and adjustment of learnable parameters according to the input variables.The proposed model is evaluated on a 100 open-source software repositories dataset and demonstrates its effectiveness in accurately estimating development cost.The system is implemented using Python and evaluated using performance parameters such as MSE, RMSE, MAE, and MMRE.Results indicate that our proposed model offers a more accurate and efficient approach to software cost estimation, especially for freelancers and outsourcing firms.The proposed model has the potential to save time and resources and improve the reliability and accuracy of software cost estimation.
Physics-Informed Long-Short-Term Memory Neural Network for Parameters Estimation of Nonlinear Systems

This article proposes a physics-informed long-short-term memory neural network optimisation method to estimate all parameters of an arbitrary nonlinear system initialised using any initial conditions. Although the technique is applicable for parameters' estimation of any nonlinear system, in this article, two well-known chaotic systems are considered with different initial conditions. The three-dimensional Lorenz chaotic system and the four-dimensional Rössler hyperchaotic system are considered. The Lorenz system exhibits stable, chaotic and periodic behaviours for some sets of parameters. Thus, different sets of parameters of the Lorenz system are individually estimated, leading to stable, periodic and chaotic behaviours. In 10000 epochs, the stable and periodic parameters' estimation accuracy is more than 99%; in 15000 epochs, chaotic parameters' estimation accuracy is more than 99%. Upto same epochs, physics-informed neural network is employed for parameter estimation of stable, periodic and chaotic behaviours of the Lorenz system. The comparison results of estimated parameters are shown with dataset obtained using ode45. Next, Circuit realisation of the Lorenz system is done and the dataset is obtained using multisim circuit simulation for stable, chaotic and periodic behaviour. The proposed physics-informed long-short-term memory neural network is used to obtain parameters from the practical data. Then, the estimation of the Lorenz system with a continuum variation of parameters' sets from stable to chaotic to stable behaviours is successfully achieved using the proposed physics-informed long-short-term memory neural network. Finally, the hyperchaotic Rössler system parameters are successfully estimated using the proposed technique with more than 99% accuracy in 20000 epochs.
AI-assisted diplomatic decision-making during crises—Challenges and opportunities

OPINION article Front. Big Data, 12 May 2023Sec. Cybersecurity and Privacy Volume 6 - 2023 | https://doi.org/10.3389/fdata.2023.1183313
Professional Mosque Management Through Application-Based Mosque Management

Considering how important the existence of the mosque is for the Imam community and how muchresponsibility the mosque manager carries, it certainly requires good mosque management. Managementitself has four main pillars that are the foundation in the field of management science, namely: planning,organizing, movement/direction (actuating), and supervision (controlling). The function of managementitself is to plan, organize, coordinate and control resources so that goals can be achieved effectively andefficiently.Application-based mosque management aims to provide more knowledge to mosque administratorsin managing mosques in a professional, accountable and transparent manner. In addition, applicationbased mosque management will educate mosque administrators to use technological and informationadvances in managing, recording and reporting all forms of important information in a professional,accountable and transparent manner. Therefore, application-based mosque management is essentiallytraining and developing the ability of human resources (HR) in managing mosques in accordance withthe progress and demands of the times. This is where the role of application-based mosque managementis needed in managing the mosque professionally, accountably and transparently.
Conflict Management And Problem-Solving Strategies

The article "Strategies for conflict management and problem solving" is based on the size of the application, on the art of strategies focused by the teaching staff, the instructive, educational and advisory activity in the management of conflict situations and in the development of resolution skills, taking into account the particularities of age, of preadolescents, by means of counselling methods, techniques and instruments regarding the adaptation and effective social integration of the educable, the development of the learning mechanism and the practice of some skills regarding the maintenance of physical, mental, emotional and social health well-being in order to ensure a state of good.The study also follows the way in which the teachers focus on the emotional regulation strategies focused on the antecedents of adolescents, thus advising them in the management of the control of situations and contexts with a dysfunctional emotional charge, which lead to undesirable effects on the process of self-knowledge and personal development, but they can also become risk factors for the deterioration of the quality of individual life and the quality of social relationships regardless of social rules, roles and assumed responsibilities.The study also aims to connect the learner's ability to develop these strategies and the way in which, at the level of the educational environment and social life, they promote prosocial behaviour.