Reviewerly/exHarmony huggingface dataset:
Rapid Remote Online Evaluation in Endoscopic Diagnostics: An Analysis of Biopsy-Proven Respiratory Cytopathology

This prospective study assesses the use of rapid remote online cytological evaluation for diagnosing endoscopical achieved biopsies. It focuses on its effectiveness in identifying benign and malignant conditions using digital image processing.The study was conducted between April 2021 and September 2022 and involved analyses of 314 Rapid Remote Online Cytological Evaluations in total (154 imprint cytologies, 143 fine needle aspirations and 17 brush cytologies) performed on 239 patients at the LungenClinic Grosshansdorf. During on-site evaluation via telecytology, the time requirement was recorded and the findings were compared with the cyto-/histological and final diagnoses.By means of rapid remote online evaluation, findings of 86 cytological benign, 190 malignant and 38 unclear diagnoses were recorded (Ø assessment time, 100 s; range, 11-370 s). In 27 of the 37 specimens with unclear diagnoses, the final findings were malignant tumours and only 6 were benign changes. The diagnosis of another 4 of these 37 findings remained unclear. Excluding these 37 specimens, rapid remote online evaluation achieved a sensitivity of 90.5% with a specificity of 98.5% and a correct classification rate of 92.4% with regard to the final diagnosis of all cases. As expected, an increase in the sensitivity rate for the cytological detection of malignant tumours (76.1% vs. 92.5%) was found especially in fine-needle aspirations.Rapid remote online analysis allows the fast quantitative and qualitative evaluation of clinically obtained cytological specimens. With a correct classification rate of more than 93%, sampling deficiencies can be corrected promptly and diagnostic and therapeutic approaches can be derived.
Natural Language Processing: An Overview

Natural Language Processing (NLP) stands at the intersection of linguistics and artificial intelligence, aiming to facilitate meaningful interactions between computers and human languages. This paper provides a comprehensive overview of NLP, tracing its evolution from its inception to its current state-of-the-art methodologies. At its core, NLP seeks to enable machines to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. The significance of NLP has grown exponentially with the digital age, finding applications in diverse domains such as chatbots, search engines, content recommendations, and automated translation systems. <br/>Historically, NLP relied heavily on rule-based methods and basic statistical approaches. However, the last decade has witnessed a paradigm shift with the advent of machine learning, and more recently, deep learning techniques. These methods, powered by vast amounts of data and enhanced computational power, have led to significant advancements in various NLP tasks. For instance, sentiment analysis, once a challenging endeavor due to linguistic nuances like sarcasm and cultural context, has seen improved accuracy rates with the introduction of neural networks and transformer architectures. <br/>Yet, NLP is not without its challenges. Ambiguities inherent in human languages, polysemy (multiple meanings of a word), and the vast diversity of languages and dialects present hurdles that are yet to be fully overcome. Moreover, as NLP systems become more integrated into our daily lives, ethical considerations, such as bias in algorithms and the potential misuse of generated content, come to the forefront. <br/>Recent innovations, like zero-shot learning and multimodal NLP, which combines textual data with other modalities like images or sound, hint at the future trajectory of the field. As we stand on the cusp of a new era in NLP, it is imperative to reflect on its journey, acknowledge its challenges, and envision a future where machines not only understand human language but do so responsibly and ethically.
Meme Kanserinde Yapay Zekâ Kullanımı: Güncel Yaklaşım

Breast cancer is one of the most common types of cancer among women worldwide and is still a major cause of death. More than 2 million women are diagnosed with breast cancer annually. Artificial intelligence has great potential in breast cancer diagnosis, treatment, and management. In addition to traditional imaging techniques, the analysis of genetic data is used by artificial intelligence in the process of breast cancer diagnosis. In addition, artificial intelligence plays an important role in treatment planning and follow-up of patients. Data analytics and extensive data integration also contribute to developments in this field. However, the audit and ethical responsibilities of artificial intelligence applications should be considered.
A Study of the Impact of Metaverse Marketing Characteristics on Consumers’ Purchase Intention

This study explores the impact of metaverse marketing characteristics on purchase intention from a consumer perspective, aiming to reveal the mechanism of metaverse marketing from the perspective of mediation of flow experience. The study adopts the S-O-R model (stimulus-organism-response model) and the flow experience theory as the theoretical basis. By collecting data through a questionnaire survey among middle-aged and young people, and conducting empirical analysis using SPSS software, this study explores the impact of metaverse marketing characteristics on purchase intention and the mediating role of flow experience. A total of 119 valid questionnaires were collected, with a sample type of young and middle-aged people aged 18 to 35. Research has found that metaverse marketing has the characteristics of perception, interactivity, and interest, which positively affect consumers’ purchase intention; The flow experience plays a mediating role between the marketing characteristics of the metaverse and consumer purchase intention. The research on metaverse marketing enriches the relevant theories of the metaverse and provides specific guidance for enterprises to carry out metaverse marketing activities.
Development of client applications on Blockchain

Objective . The article discusses issues related to the process of developing client Web applications for IT projects, which are based on the principles of decentralized network technology - blockchain. The authors explore practical ways to implement the user authorization process and its interaction with the blockchain network through a client application. Method . The MetaMask browser extension was chosen as a research tool, which allows you to develop, test and run your own software modules. Result . In the course of the study, the analysis of MetaMask tools was carried out, the processes associated with the start of work were considered both from the point of view of the developer and from the point of view of the wallet user. Understanding the organization of these processes will allow users to form the necessary knowledge in the development of client applications built on blockchain. Conclusion . The program code templates developed by the authors can be used as standard ones in the development of client applications in the decentralized Ethereum network. The advantage of the proposed solutions lies in the use of simple software structures that allow the formation of basic principles.
Challenges of Metaverse in Education Digitalization

Education digitalization is the use of modern information technology into education for advanced teaching and learning experiences. It involves the use of digital tools, resources and platforms to support communication, teaching, learning and assessment. Metaverse is a new technology for education digitalization, where learners interact with each other in a virtual world. The aim of the paper is to explain the basics of the Metaverse and to point out its benefits and challenges in the education digitalization from a technical, economic and pedagogical points of view to define its potential for transformation of modern teaching and learning.
Enhancing Resource Allocation for Cloud Computation Platform with Priority Based Scheduling

Cloud computing platforms are clusters of high-end computers in Internet-based data centers that provide elastic software and hardware products at scale. In this paper, we propose a formula for predicting the number of resources needed for executing a job in a cloud render cluster. The users of the cloud computing platforms can use this formula to better balance production costs and deadlines, thus reducing costs and improving performance. According to the result of our experiments, our approach can accurately predict the system resource consumption. Thus, it can be used for setting parameters that maintain system performance.
Module of Axis-based Nexus Attention for weakly supervised object localization

Abstract Weakly supervised object localization tasks remain challenging to identify and segment an entire object rather than only discriminative parts of the object. To tackle this problem, corruption-based approaches have been devised, which involve the training of non-discriminative regions by corrupting (e.g., erasing) the input images or intermediate feature maps. However, this approach requires an additional hyperparameter, the corrupting threshold, to determine the degree of corruption and can unfavorably disrupt training. It also tends to localize object regions coarsely. In this paper, we propose a novel approach, Module of Axis-based Nexus Attention (MoANA), which helps to adaptively activate less discriminative regions along with the class-discriminative regions without an additional hyperparameter, and elaborately localizes an entire object. Specifically, MoANA consists of three mechanisms (1) triple-view attentions representation, (2) attentions expansion, and (3) features calibration mechanism. Unlike other attention-based methods that train a coarse attention map with the same values across elements in feature maps, MoANA trains fine-grained values in an attention map by assigning different attention values to each element. We validated MoANA by comparing it with various methods. We also analyzed the effect of each component in MoANA and visualized attention maps to provide insights into the calibration.
Faculty and Students’ Perspective of Health Informatics Programs in Saudi Arabia

Health Informatics is a discipline that deals with the acquisition, storage, retrieval and use of health data, information, and technologies for the purpose of improving healthcare quality. It involves the application of information science methods to analyse health information for improved problem solving and healthcare delivery. The use of information system applications in healthcare is increasingly popular and includes the use of applications like medical records, order entry, medical knowledge provision, imaging, as well as guidelines and other types of decision support.To examine health informatics undergraduate programs in Saudi Arabian government universities.This study follows a quantitative, descriptive cross-sectional design using a self-reported, electronic questionnaire. The data was collected using an online questionnaire via Google Forms. Probability stratified random sampling was utilized.Findings revealed a response rate at 79% (n=184) of students and 67% (n=38) of faculty members. Variation was detected in the level of agreement toward the examined six variables. Both students and faculty members express same level of agreement toward all variables. A significant correlation was determined between facilities and other variables such as students' involvement, research, admission and curriculum. Similarly, there is a significant relationship between students' involvement and curriculum with research, demission, faculty members role and outcomes.
An Empirical Analysis Towards Replacing Vocabulary-Rigid Embeddings by a Vocabulary-Free Mechanism

This paper addresses the limitations of subword based models in NLP by aligning the word embedding layer of a vocabulary-rigid transformer model to a vocabulary-free one. In order to do so, a CNN is trained to mimic the word embeddings layer of a BERT model, using a sequence of byte tokens as input. The study compares cosine-based and Euclidean-based loss functions for training the student network and finds better results with cosine-based metrics. The research contributes techniques for re-training transformer embedding layers and provides insights into loss function selection. The findings have implications for developing flexible and robust NLPmodels.
Enhancing Load Balancing in Cloud Computing Through Deadlock Prediction

Cloud computing has become a crucial aspect of Information Technology, offering solutions to many of the challenges faced by internet users. However, the increasing number of users worldwide has led to congestion at certain nodes, resulting in unbalanced loads or hanging systems, commonly known as deadlock. This paper proposes an algorithm using deadlock prediction to enhance the load balancer in Cloud environment. This algorithm leverages Machine Learning and prediction techniques, specifically the Linear Regression Model, to forecast the possibility of deadlock in VMs. By predicting deadlock, the available resources can be allocated to satisfy all requests. The algorithm was deployed in the CloudSim simulation environment, which was integrated with Weka library for the Machine Learning techniques. The results were compared to well-known algorithms such as FCFS, RoundRobin, MaxMin, and MinMin. The evaluation revealed that the proposed algorithm outperformed these popular algorithms, demonstrating its effectiveness in enhancing Cloud computing’s load balancing capabilities.
The least common multiple of a bivariate quadratic sequence

Let $F\in \mathbb Z[x,y]$ be some polynomial of degree 2. We find the asymptotic behaviour of the least common multiple of the values of $F$ up to $N$. More precisely, we consider $\psi_F(N) = \log(\text{LCM}_{0 \lt F(x,y)\leq N}\lbrace F(x,y) \rbrace)$ a
Implementing augmented reality to improve students’ biology learning outcomes: Gender-based effect

&lt;span lang="EN-US"&gt;The current study examined the difference in students’ biology learning outcomes based on gender following the implementation of Augmented reality (AR). A gender equality study is useful as a foundation for establishing gender-sensitive learning. Gender equality promotes higher productivity and development outcomes, therefore it has favorable effects on economic and technological growth, as well as social fairness. We administered a quasi-experimental non-equivalent pretest-posttest control group design. The research population is all of class X public high school in Pamekasan Regency, Indonesia. The research samples were 56 senior high school students in class X. The classes that will be used as research samples are first tested for equality. The equivalence test analysis will use analysis of variance (ANOVA) by utilizing the SPSS 23.00 for windows program. Participants’ learning outcomes were measured using an essay test. The revised Blooms taxonomy indicators were used as the reference to construct the tests. The test covered the indicators of applying (C3), analyzing (C4), and synthesizing (C5). The participants’ answered were evaluated using a 4-point scale rubric adopted from Hart. One-way ANCOVA (analysis of covariance) was performed, involving the pretest scores as the covariate. The results showed that there was no difference in biology learning outcomes between male and female students involved in this study (p&amp;gt;0.05). These findings demonstrate that the implementation of augmented reality can close the gender gap in biology education. Augmented reality can be a solution to improve the learning environment in biology classrooms and facilitate gender-based learning demands.&lt;/span&gt;
Indonesian students’ reading literacy ability in the cooperative integrated reading and composition learning: A meta-analysis

&lt;span lang="EN-US"&gt;A few of meta-analysis studies regarding the intervention of cooperative learning have been conducted. However, it has not been performed on the studies related to the intervention of cooperative integrated reading and composition (CIRC) learning for Indonesian students’ reading literacy. This study aims to approximate and examine the effect of CIRC learning on students’ reading literacy ability across group size of intervention, educational level, and geographical location. A meta-analysis was performed to conduct this study. The search of document using the database of Google Scholar found 97 documents. The selection of document established 10 documents published from 2015–2021 consisting of one conference paper and nine journal articles. Hedges’ equation was employed to measure the effect size. The data analysis used the Z test and Q Cochrane test. The results revealed that the CIRC learning had a strong positive effect (g=2.041; p&amp;lt;0.05) on students’ reading literacy ability. It indicates that the CIRC learning is effective for enhancing students’ reading literacy ability. Furthermore, some moderating factors such as group size of intervention, educational level, and geographical location did not affect students’ heterogeneous reading literacy ability. This study suggests language teachers to use the CIRC learning in enhancing students’ reading literacy ability.&lt;/span&gt;
Copula-based risk aggregation with trapped ion quantum computers

Abstract Copulas are mathematical tools for modeling joint probability distributions. In the past 60 years they have become an essential analysis tool on classical computers in various fields. The recent finding that copulas can be expressed as maximally entangled quantum states has revealed a promising approach to practical quantum advantages: performing tasks faster, requiring less memory, or, as we show, yielding better predictions. Studying the scalability of this quantum approach as both the precision and the number of modeled variables increase is crucial for its adoption in real-world applications. In this paper, we successfully apply a Quantum Circuit Born Machine (QCBM) based approach to modeling 3- and 4-variable copulas on trapped ion quantum computers. We study the training of QCBMs with different levels of precision and circuit design on a simulator and a state-of-the-art trapped ion quantum computer. We observe decreased training efficacy due to the increased complexity in parameter optimization as the models scale up. To address this challenge, we introduce an annealing-inspired strategy that dramatically improves the training results. In our end-to-end tests, various configurations of the quantum models make a comparable or better prediction in risk aggregation tasks than the standard classical models.
Seeing the whole elephant: integrated advanced data analytics in support of RWE for the development and use of innovative pharmaceuticals

Introduction The 21st century has brought about significant technological advancement, allowing the collection of new types of data from the real world on an unprecedented scale. The healthcare industry will benefit immensely from this abundance of patient data from electronic health records (EHR), patient-reported outcomes (PROs), laboratory, demographic, social media, digital, and even climate data.
False Correlation Reduction for Offline Reinforcement Learning

Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems. Most existing papers only discuss defending against out-of-distribution (OOD) actions while we investigate a broader issue, the false correlations between epistemic uncertainty and decision-making, an essential factor that causes suboptimality. In this paper, we propose falSe COrrelation REduction (SCORE) for offline RL, a practically effective and theoretically provable algorithm. We empirically show that SCORE achieves the SoTA performance with 3.1x acceleration on various tasks in a standard benchmark (D4RL). The proposed algorithm introduces an annealing behavior cloning regularizer to help produce a high-quality estimation of uncertainty which is critical for eliminating false correlations from suboptimality. Theoretically, we justify the rationality of the proposed method and prove its convergence to the optimal policy with a sublinear rate under mild assumptions.
Perceptually Weighted Rate Distortion Optimization for Video-Based Point Cloud Compression

Dynamic point cloud is a volumetric visual data representing realistic 3D scenes for virtual reality and augmented reality applications. However, its large data volume has been the bottleneck of data processing, transmission, and storage, which requires effective compression. In this paper, we propose a Perceptually Weighted Rate-Distortion Optimization (PWRDO) scheme for Video-based Point Cloud Compression (V-PCC), which aims to minimize the perceptual distortion of reconstructed point cloud at the given bit rate. Firstly, we propose a general framework of perceptually optimized V-PCC to exploit visual redundancies in point clouds. Secondly, a multi-scale Projection based Point Cloud quality Metric (PPCM) is proposed to measure the perceptual quality of 3D point cloud. The PPCM model comprises 3D-to-2D patch projection, multi-scale structural distortion measurement, and fusion model. Approximations and simplifications of the proposed PPCM are also presented for both V-PCC integration and low complexity. Thirdly, based on the simplified PPCM model, we propose a PWRDO scheme with Lagrange multiplier adaptation, which is incorporated into the V-PCC to enhance the coding efficiency. Experimental results show that the proposed PPCM models can be used as standalone quality metrics, and they are able to achieve higher consistency with the human subjective scores than the state-of-the-art objective visual quality metrics. Also, compared with the latest V-PCC reference model, the proposed PWRDO-based V-PCC scheme achieves an average bit rate reduction of 13.52%, 8.16%, 10.56% and 9.54%, respectively, in terms of four objective visual quality metrics for point clouds. It is significantly superior to the state-of-the-art coding algorithms. The computational complexity of the proposed PWRDO increases by 1.71% and 0.05% on average to the V-PCC encoder and decoder, respectively, which is negligible. The source codes of the PPCM and PWRDO schemes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/VVCodec/PPCM-PWRDO</uri> .
Use Case Point Methods For Analysis of Software Development Cost In Information Communication Technology Units of Khairun University

The Information and Communication Technology Unit (ICTU) is a technical implementation unit in the field of information and communication technology development and management. Currently, Khairun University's ICTU is undertaking a software development project, one of which involves creating an application called "Permata". In the process of developing this application, one of the common challenges faced is estimating the project costs for software development, as the developers do not yet have a standard method to determine the costs of software development. The objective of this research is to produce a Use Case Point application for estimating the costs of software development at ICTU, Khairun University. The Use Case Point (UCP) method is derived from the Function Point Analysis (FPA) method and aims to provide a simple estimation method oriented toward software project objects. The software objects involved in this method are Actor Types (UAW), Use Case Weight (UUCW), Technical System Development Complexity (TF), and Development Environment Complexity (EF). The estimated time value for developing the Permata application is 1,389.844855 man-hours, equivalent to approximately 1.904 months. The estimated cost for the Permata application is Rp129,099,127.