Reviewerly/exHarmony huggingface dataset:
Performance Comparison of Randomized and Non-Randomized Learning Algorithms based Recommender Systems

Recommender System (RS) is an information filtering software tool that provides relevant recommendations to users on various products. Finding the preference of users on products is the core component of RS. Most of the available RS datasets are complex enough to represent the user preferences and usually require a lot of processing before their utilization. On the other hand, selecting an appropriate learning algorithm for making relevant recommendations is a complex task. In this paper, we perform various pre-processing steps on a movie dataset to find the key features that help us to identify the actual user preferences. Moreover, we perform a comparative analysis of various randomized and non-randomized learning algorithms by utilizing these features to identify the best learning algorithm. Our study reinforces the superiority of randomized learning algorithms over non-randomized ones on MovieLens dataset.
Auto capture on drug text detection in social media through NLP from the heterogeneous data

Auto Capture Drug Text detects (ACDTD) that textual content has currently gained important activities in Pharmaceutical drugs research. The recent studies focused on the different resources of textual content-related messages including social networks. Where a huge quantity of consumers posted information about the usage of pharmaceutical drugs and it's combined and cleared neatly. Our textual content classification strategies depend on regenerating the features of a large set of designs; it represents the semantic rules from the short textual nuggets. Significantly use the detailed structured files and it is combined by instruction data from the various collections to regulate the classification approach. Social Media Online Natural Language Processing (SMONLP) with ACDTD class achieves the F-scores of 0.90, 0.738, and 0.878 for the three information files. Integrating the instruction messages from various compatible files enhances the ACDTD F-scores for the in-house information files to 0.797 and 0.854 respectively. The heterogeneous textual content training using the cases where information sets are maintained may reduce the cost and time in the future.
Pikachu

Blockchain systems based on a reusable resource, such as proof-of-stake (PoS), provide weaker security guarantees than those based on proof-of-work. Specifically, they are vulnerable to long-range attacks, where an adversary can corrupt prior participants in order to rewrite the full history of the chain. To prevent this attack on a PoS chain, we propose a protocol that checkpoints the state of the PoS chain to a proof-of-work blockchain such as Bitcoin. Our checkpointing protocol hence does not rely on any central authority. Our work uses Schnorr signatures and leverages Bitcoin recent Taproot upgrade, allowing us to create a checkpointing transaction of constant size. We argue for the security of our protocol and present an open-source implementation that was tested on the Bitcoin testnet.
Application of Spatial Data Mining to national mines inventories for exploration and land planning of high place-value mineral resources. The case of aggregates in Spain

Countries' mine inventories usually include information related to exploited rock type or mineral, amount of resources, owner, location, mining rights associated, etc. All this information is a vast dataset that in the last couple of decades has been included in digital cartography in official websites, some of them freely available. This information is usually exploited as individual registers classified by substance, location, etc., but when analyzed as a whole and combined with other economic, social, and environmental variables, valuable information can be obtained. If the variable time is included in the study, these databases can reveal the evolution of mining activity in recent decades. This study analyzes the distribution of aggregates quarries in Spain, as a case study, relating their location to different socio-geographical features such as urban areas, environmentally protected areas, or population density, by means of Spatial Data Mining (SDM) tools. Aggregates are used as construction materials and are the largest non-energy extractive industry, being the second most consumed product by human beings after water. Due to the low unit value and high volume of consumption, aggregates are considered a high place-value commodity. By combining the information of aggregates in the Spanish mines inventory with geological, social, demographic, environmental, and administrative information, and using SDM techniques, relevant conclusions can be obtained such as the delineation of areas in the land use planning to be dedicated to aggregates exploitation, or the identification of priority zones for exploration for mining companies. This information could serve policy makers and land use planners in making data-driven decisions and mining companies in determining areas with the highest profit potential to develop their operations. • Valuable information can be extracted from countries mines inventories. • Aggregates are construction materials and they represent the largest non-energy extractive industry. • Spatial Data Mining applied to aggregate mines inventories permit to extract geological, social, and economic information. • Location of aggregate quarries is determined by the presence of suitable materials and the population distribution.
The Privacy-Fairness-Accuracy Frontier

Both law and computer science are concerned with developing frameworks for protecting privacy and ensuring fairness. Both fields often consider these two values separately and develop legal doctrines and machine learning metrics in isolation from one another. Yet, privacy and fairness values can conflict, especially when considered alongside the accuracy of an algorithm. The computer science literature often treats this problem as an "impossibility theorem" - we can have privacy or fairness but not both. Legal doctrine is similarly constrained by a focus on the inputs to a decision - did the decisionmaker intend to use information about protected attributes. Despite these challenges, there is a way forward. The law has integrated economic frameworks to consider tradeoffs in other domains, and a similar approach can clarify policymakers' thinking around balancing accuracy, privacy, and fairnesss. This piece illustrates this idea by using a law & economics lens to formalize the notion of a Privacy-Fairness-Accuracy frontier, and demonstrating this framework on a consumer lending dataset. An open-source Python software library and GUI will be made available.
A Case Study －Korean University Students’ Experience with and Perceptions of an Academic English Writing Class

The purpose of this case study is to examine Korean university students’ perceptions about the academic English writing class they took during the Spring semester of 2022. More specifically, this study aims to understand how satisfied students were with their learning experiences in the academic English writing class, and how they perceived its appropriateness as a mandatory English general education offered in the Korean higher education context.A survey questionnaire consisting of Likert-scale, multiple choice, and open-ended questions was the main source of the data, and a total of 65 freshmen taking the course responded. To triangulate, in-depth interviews were also conducted with three focal participants via email or Zoom upon their choice. Descriptive statistics were calculated to analyze the responses to the Likert-scale and the multiple-choice questions, and the content analysis method was applied for open-ended survey questions and interviews.Analysis of data showed that students viewed their academic English writing experiences positively due to the content knowledge and sense of achievement gained through the class as well as the instructor’s teaching style. Some participants were dissatisfied with their learning experiences because of content difficulty, relatively heavy workload, and their insufficient English proficiency which hindered them from following the class. Most students self-evaluated that their writing skills after the class somewhat improved. Students chose writing an essay, learning about academic writing conventions, such as APA styles and in-text citations, and instructor’s feedback as class activities that helped them improve their academic English writing.To the question about the appropriateness of academic English writing as a mandatory general education subject, a vast majority of students hold a positive view. Their reasons include their belief of academic English writing as essential knowledge that they need to possess as a university student belonging to an academic discourse community as well as transferable knowledge needed not only in an English class but also in various disciplines. They also recognize its practicality for their academic activities and its rarity to learn anywhere else except university.The findings suggest that school administrators should allocate more credit hours for an academic English writing course and offer it for students in their 2nd or 3rd year of university. The findings also emphasize the importance of instructors’ effort to tailor their instruction according to their students and to provide supplementary learning materials, such as pre-recorded videos so as to effectively guide students to improve their skills for English writing for academic purposes.
Multi-Target State and Extent Estimation for High Resolution Automotive Sensor Detections

This paper discusses the perception and tracking of individual as well as group targets as applied to multi-lane public traffic. Target tracking problem is formulated as a two hierarchical layer problem-on the first layer, a multi-target tracking problem based on multiple detections is distinguished in the measurement space, and on the second (top) layer, group target tracking with birth and death as well as merging and splitting of group target tracks as they evolve in a dynamic scene is represented. This configuration enhances the multi-target tracking performance in situations including but not limited to target initialization(birth), target occlusion, missed detections, unresolved measurement, target maneuver, etc. In addition, group tracking exposes complex individual target interactions to help in situation assessment which is challenging to capture otherwise.
A 3D Hand Attitude Estimation Method for Fixed Hand Posture Based on Dual-View RGB Images

This work provides a 3D hand attitude estimation approach for fixed hand posture based on a CNN and LightGBM for dual-view RGB images to facilitate the application of hand posture teleoperation. First, using dual-view cameras and an IMU sensor, we provide a simple method for building 3D hand posture datasets. This method can quickly acquire dual-view 2D hand image sets and automatically append the appropriate three-axis attitude angle labels. Then, combining ensemble learning, which has strong regression fitting capabilities, with deep learning, which has excellent automatic feature extraction capabilities, we present an integrated hand attitude CNN regression model. This model uses a Bayesian optimization based LightGBM in the ensemble learning algorithm to produce 3D hand attitude regression and two CNNs to extract dual-view hand image features. Finally, a mapping from dual-view 2D images to 3D hand attitude angles is established using a training approach for feature integration, and a comparative experiment is run on the test set. The results of the experiments demonstrate that the suggested method may successfully solve the hand self-occlusion issue and accomplish 3D hand attitude estimation using only two normal RGB cameras.
An Efficient Clustering Technique for Big Data Mining

Data mining and big data analytics are approaches for analyzing data and extracting hidden information. Because big data is complicated and large in volume, traditional techniques to analysis and extraction do not function effectively. Data clustering is a common data mining approach that divides data into groups and makes it simple to extract information from them. Big data can include both organized and semi structured information, and it's becoming increasingly beneficial for companies. Examples include old organized database of inventory level, transactions, and consumer information, as well as non - structured comprehension from the internet, social media platforms, and embedded systems. Numerous schemes have been developed to reach the needed in relation to efficiency and effectiveness, and much study has been committed to Big Data analytics. Nevertheless, a few methodologies, such as clustering algorithms, require further research in regards to performance, usefulness, and other factors, leading to the development of a model which gives proper Big Data Analytics assessment and the impactful use of this methodology to retrieve relevant knowledge. We recorded and analyzed several big data sets in our proposed work, as well as discovered relevant current approaches. In this paper we proposed a new clustering technique using dimensionality reduction approach. For implementation of this work, we used real time streaming data in unstructured form and noisy sometimes. The proposed hybrid clustering techniques that improve the clustering accuracy as well as time for generate effectives clusters on large unstructured data. We confirm the findings by testing the suggested methodology on available information sets and comparing and analyzing the effectiveness of the developed system with that of current systems.
Cybersecurity Management in South African Universities

The study aimed to assess cyber security at South African universities. The researcher will use literature to assess the state of cybersecurity at South African universities. The results from the literature review revealed poor implementation and adherence of cyber security strategy and standards by employees and students; poor cyber security awareness relative to information communication technology (ICT) infrastructures and assets; and lack of strategy and framework to implement cyber security management. The study recommends continuous monitoring and evaluation of information management systems at various South African universities with the view to assess the state. A replica of the study may be studied in other part of the world.
MC-ViT: Multi-path cross-scale vision transformer for thymoma histopathology whole slide image typing

Accurate histological typing plays an important role in diagnosing thymoma or thymic carcinoma (TC) and predicting the corresponding prognosis. In this paper, we develop and validate a deep learning-based thymoma typing method for hematoxylin & eosin (H&E)-stained whole slide images (WSIs), which provides useful histopathology information from patients to assist doctors for better diagnosing thymoma or TC.We propose a multi-path cross-scale vision transformer (MC-ViT), which first uses the cross attentive scale-aware transformer (CAST) to classify the pathological information related to thymoma, and then uses such pathological information priors to assist the WSIs transformer (WT) for thymoma typing. To make full use of the multi-scale (10×, 20×, and 40×) information inherent in a WSI, CAST not only employs parallel multi-path to capture different receptive field features from multi-scale WSI inputs, but also introduces the cross-correlation attention module (CAM) to aggregate multi-scale features to achieve cross-scale spatial information complementarity. After that, WT can effectively convert full-scale WSIs into 1D feature matrices with pathological information labels to improve the efficiency and accuracy of thymoma typing.We construct a large-scale thymoma histopathology WSI (THW) dataset and annotate corresponding pathological information and thymoma typing labels. The proposed MC-ViT achieves the Top-1 accuracy of 0.939 and 0.951 in pathological information classification and thymoma typing, respectively. Moreover, the quantitative and statistical experiments on the THW dataset also demonstrate that our pipeline performs favorably against the existing classical convolutional neural networks, vision transformers, and deep learning-based medical image classification methods.This paper demonstrates that comprehensively utilizing the pathological information contained in multi-scale WSIs is feasible for thymoma typing and achieves clinically acceptable performance. Specifically, the proposed MC-ViT can well predict pathological information classes as well as thymoma types, which show the application potential to the diagnosis of thymoma and TC and may assist doctors in improving diagnosis efficiency and accuracy.
An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

The attention-based encoder-decoder technique, known as the trans-former, is used to enhance the performance of end-to-end automatic speech recognition (ASR).This research focuses on applying ASR end-toend transformer-based models for the Arabic language, as the researchers' community pays little attention to it.The Muslims Holy Qur'an book is written using Arabic diacritized text.In this paper, an end-to-end transformer model to building a robust Qur'an vs. recognition is proposed.The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework.A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic model.A Mel filter bank is used for feature extraction.To build a language model (LM), the Recurrent Neural Network (RNN) and Long short-term memory (LSTM) were used to train an n-gram word-based LM.As a part of this research, a new dataset of Qur'an verses and their associated transcripts were collected and processed for training and evaluating the proposed model, consisting of 10 h of .wavrecitations performed by 60 reciters.The experimental results showed that the proposed end-to-end transformer-based model achieved a significant low character error rate (CER) of 1.98% and a word error rate (WER) of 6.16%.We have achieved state-of-the-art end-to-end transformer-based recognition for Qur'an reciters.
Type-2 Neutrosophic Set and Their Applications in Medical Databases Deadlock Resolution

Electronic patient data gives many advantages, but also new difficulties.Deadlocks may delay procedures like acquiring patient information.Distributed deadlock resolution solutions introduce uncertainty due to inaccurate transaction properties.Soft computing-based solutions have been developed to solve this challenge.In a single framework, ambiguous, vague, incomplete, and inconsistent transaction attribute information has received minimal attention.The work presented in this paper employed type-2 neutrosophic logic, an extension of type-1 neutrosophic logic, to handle uncertainty in real-time deadlock-resolving systems.The proposed method is structured to reflect multiple types of knowledge and relations among transactions' features that include validation factor degree, slackness degree, degree of deadline-missed transaction based on the degree of membership of truthiness, degree of membership of indeterminacy, and degree of membership of falsity.Here, the footprint of uncertainty (FOU) for truth, indeterminacy, and falsity represents the level of uncertainty that exists in the value of a grade of membership.We employed a distributed real-time transaction processing simulator (DRTTPS) to conduct the simulations and conducted experiments using the benchmark Pima Indians diabetes dataset (PIDD).As the results showed, there is an increase in detection rate and a large drop in rollback rate when this new strategy is used.The performance of Type-2 neutrosophicbased resolution is better than the Type-1 neutrosophic-based approach on the execution ratio scale.The improvement rate has reached 10% to 20%, depending on the number of arrived transactions.
Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

Cervical Cancer (CC) is a rapidly growing disease among women throughout the world, especially in developed and developing countries.For this many women have died.Fortunately, it is curable if it can be diagnosed and detected at an early stage and taken proper treatment.But the high cost, awareness, highly equipped diagnosis environment, and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage.To solve this issue, the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell images.The system is designed using the YOLOv5 (You Only Look Once Version 5) model, which is a deep learning method.To build the model, cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and preprocessed.Then the YOLOv5 models were applied to the labeled dataset to train the model.Four versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early stage.All of the model's variations performed admirably.The model can effectively detect cervical cancerous cell, according to the findings of the experiments.In the medical field, our study will be quite useful.It can be a good option for radiologists and help them make the best selections possible.
Fault Tolerant Optical Mark Recognition

Optical Mark Recognition (OMR) systems have been studied since 1970.It is widely accepted as a data entry technique.OMR technology is used for surveys and multiple-choice questionnaires.Due to its ease of use, OMR technology has grown in popularity over the past two decades and is widely used in universities and colleges to automatically grade and grade student responses to questionnaires.The accuracy of OMR systems is very important due to the environment in which they are used.The OMR algorithm relies on pixel projection or Hough transform to determine the exact answer in the document.These techniques rely on majority voting to approximate a predetermined shape.The performance of these systems depends on precise input from dedicated hardware.Printing and scanning OMR tables introduces artifacts that make table processing error-prone.This observation is a fundamental limitation of traditional pixel projection and Hough transform techniques.Depending on the type of artifact introduced, accuracy is affected differently.We classified the types of errors and their frequency according to the artifacts in the OMR system.As a major contribution, we propose an improved algorithm that fixes errors due to skewness.Our proposal is based on the Hough transform for improving the accuracy of bias correction mechanisms in OMR documents.As a minor contribution, our proposal also improves the accuracy of detecting markers in OMR documents.The results show an improvement in accuracy over existing algorithms in each of the identified problems.This improvement increases confidence in OMR document processing and increases efficiency when using automated OMR document processing.