Reviewerly/exHarmony huggingface dataset:
Evaluation of breast cytology by applying modified Masood’s scoring system

Background: The occurrence of breast cancer is increasing worldwide with peak incidence occurring above the age of 50 years in developed countries, whereas in india, it is above the age of 40. Fine-needle aspiration cytology (FNAC) is a quick, easy, and cost-effective diagnostic tool for breast disease, and also helpful to differentiate between various benign and malignant lesions of breast. Aims and Objectives: (1) to assess the usefulness of Modified Masood’s Scoring Index (MMSI) in breast cytology. (2) To study cytohistopathological correlation in breast lesions. After the FNAC of breast lesion, classify the breast lesion based on MMSI and histopathology. Materials and Methods: A hospital-based prospective study was conducted between March 2021 and September 2022 on 183 patients who were referred for FNAC from the surgery department to the cytology section of the Pathology Department at Netaji Subhash Chandra Bose Medical College Hospital in Jabalpur, Madhya Pradesh. Results: In our study, the largest group of cases (45.90%) was in the 20–39 age range, with a mean age of 37±14.65. The majority of cases (60.6%) belonged to category II (proliferative breast disease [PBD] without atypia) with a mean MMSI score of 11.35±5.55. The cytological findings showed a correlation of 98.20% with MMSI in category II and 88.68% in category IV, while the histopathological findings showed a correlation of 89.52% with MMSI category II and 98.15% with MMSI category IV. Fibroadenoma (FA) was the most common finding on histopathological examination, accounting for 120 cases (65.6%). Conclusion: The MMSI is an effective tool to complement cytomorphological diagnosis in breast lesions, including PBD with or without atypia and carcinomas. It is particularly valuable in the early management and prognosis of patients, as treatment options can vary based on the MMSI score.
Türkiye'de Mobil Destekli Dil Öğrenimi: 2009-2022 Döneminde Üretilen Lisansüstü Tezler Üzerine Sistematik Bir İnceleme Çalışması

The present study reviewed the mobile-assisted language learning postgraduate theses (MALL-PGTS) produced in Türkiye during the period 2009-2022. The aim was to provide a broad insight into the general research trend of Turkish MALL-PGTs. In this regard, seventy-nine MALL-PGTs (60 master's theses, 19 doctoral theses) were collected and analyzed based on the author's coding scheme that was made up of six key categories. The results revealed that an increasing trend of producing MALL-PGTs in Türkiye continued steadily in the thirteen-year interval, and MALL was perceived as complementary method to conventional learning. In addition, most MALL-PGTs aimed to investigate the affective domains of learners and focused on general perceptions of learners in MALL environments. However, most MALL-PGTs did not specify the instructional approach adopted. The most common adopted research method was mixed-method research. MALL implementations mostly conducted with the preparatory class students at the universities and the sample size of most MALL-PGTs corresponded to the number of participants that ranged from 50 to 100. The most common target language was also English as a foreign language. Perhaps the most important findings of the present study were that an increasing trend of not specifying the types of mobile devices used and the learning contexts regarding MALL designs continued steadily in the last five years. Overall, the results indicated that most Turkish MALL-PGTs suffered from the principles of MALL design and thus did not meet the minimum requirements for generalizability of research outcomes.
3D Human Motion Capture Method Based on Computer Vision

Introduction. The analysis of approaches to tracking the human body identified problems when capturing movements in a three-dimensional coordinate system. The prospects of motion capture systems based on computer vision are noted. In existing studies on markerless motion capture systems, positioning is considered only in two-dimensional space. Therefore, the research objective is to increase the accuracy of determining the coordinates of the human body in three-dimensional coordinates through developing a motion capture method based on computer vision and triangulation algorithms. Materials and Methods . A method of motion capture was presented, including calibration of several cameras and formalization of procedures for detecting a person in a frame using a convolutional neural network. Based on the skeletal points obtained from the neural network, a three-dimensional reconstruction of the human body model was carried out using various triangulation algorithms. Results. Experimental studies have been carried out comparing four triangulation algorithms: direct linear transfer, linear least squares method, L2 triangulation, and polynomial methods. The optimal triangulation algorithm (polynomial) was determined, providing an error of no more than 2.5 pixels or 1.67 centimeters. Discussion and Conclusion . The shortcomings of existing motion capture systems were revealed. The proposed method was aimed at improving the accuracy of motion capture in three-dimensional coordinates using computer vision. The results obtained were integrated into the human body positioning software in three-dimensional coordinates for use in virtual simulators, motion capture systems and remote monitoring.
Comparative study on pose estimators such as MoveNet Lighting, MoveNet Thunder, and OpenPose (MobileNet) model for Human Pose Estimation over Real-Time Feed

Human pose estimation is a crucial task in various domains, including fitness and motion analysis, and sports performance evaluation. Existing technologies have limitations in terms of accuracy and real-time performance, which highlights the need for advanced solutions. The aim of this paper is to display the comparison between models for human pose estimation over real-time feed, with high accuracy and real-time performance. The problem statement involves developing a model and testing it to check if it can handle multiple subjects, different poses, and various lighting conditions. The proposed methodology involves using state- of-the-art models, such as MoveNet Lightning and MoveNet Thunder, along with the comparison to pre-existing models such as OpenPose model, which are slow and inaccurate. The key findings of this work include real-time performance comparison, high accuracy, and applicability in various domains. Nevertheless, it is imperative to acknowledge and confront specific constraints, such the issue of pose ambiguity and the challenge of effectively controlling occlusion. The suggested study presents a potentially effective approach for real-time human pose assessment, which has the capacity to yield advantages for humans. Keywords: Human pose estimation, real-time feed, MoveNet Lightning, MoveNet Thunder, OpenPose.
Searching for Self

A significant corpus of work exists exploring the information seeking practices of researchers, including academic scientists (Ellis et al., 1993; Hemminger et al., 2007), social scientists (Ellis, 1993; Meho and Tibbo, 2003), historians (Duff and Johnson, 2002; Rhee, 2011), and humanities scholars more broadly (Buchanan et al., 2005; Given &amp; Wilson, 2015). However, very little work has been done on research-as-information seeking. An activity that can be done by laypeople as well as students and academics, research differentiates itself from other kinds of information seeking in several ways: the information sought is often used to produce outputs, the duration of the activity is often longer (ranging from days to decades) than more casual information seeking, and the methods employed in research are diverse and systematic. This paper introduces the new framework of the Research Self. The Research Self is a holistic and flexible model, with seven interrelated dimensions, designed to deepen our understanding of research practice.
A data privacy-preserving model driven on differential approach in cloud environment

With the wide range of cloud services, including flexibility, high scalability, and the least upfront capital investment, it also offers machine learning services that are utilized in various applications, such as medical diagnosis, risk analysis, and product recommendation. Since it has many advantages, many organizations move their data to the cloud platform for storage, processing, and distribution intentions. The classification service is offered by the cloud service provider so that owners can assess their data. It gathers the data from the owners&apos; side for training the machine learning model and accomplishing the classification task. The cloud servers handled by a third party might not be totally trusted by the data owners. However, data privacy is a significant challenge among the critical barriers to preserving data while distributing it. A novel data protection model is proposed, driven on machine learning and differential approaches that carry out classification tasks while protecting the owners&apos; data through noise addition. The model enables the different owners to store, share, and utilize data on the cloud platform. The experiments are carried out on Cleveland, New-Thyroid, and SA-Heart datasets to determine the effectiveness of the model in terms of accuracy, precision, recall, and F1-score. The results show that it achieves high accuracy, precision, recall, and F1-score up to 88.37%, 83.28%, 98.41%, and 82.91%, respectively.
Synthesis rule-based classification approach for malicious websites identification

A phishing attack is a form of cyber-attack where the attacker uses various social engineering techniques to obtain personal or sensitive data from a common man or an organization. Moreover, with the introduction of web 2.0, the internet has become a crucial part of everyone&apos;s life because of a wide variety of utilities it provides. With this increase in the popularity, attackers have increased the misuse of this technology through various forms of phishing. In spite of the great efforts by the cyber security teams throughout the world to neutralize such attacks, they were unsuccessful. In addition, there is a lot of research currently being done to identify such attacks, but they are not able to reduce the false-negative rate in the identification of phishing. In this paper, we will be using the classification- based approach using a hybrid machine learning-based model with specified rules to tackle such crimes, especially phishing attacks. In this research, the use of a stack of models which has an accuracy of around 98.5% with the use of the proposed rule-based system has been successful in the identification of phishing attacks.
Classification of acute myeloid leukemia using convolutional neural network

Acute myeloid leukemia is a life-threatening disease of white blood cells. It is a cancer of the blood and bone marrow. AML is considered &apos;acute&apos; by physicians because the disease can progress rapidly. It is the most common type of acute leukemia in adults. The diagnosis of AML is done by a hematologist, which is time-consuming and more error-prone. Computer-aided diagnosis is more efficient and detects malignant cells more accurately. To classify and detect subtypes of this disease in an automated manner, we have proposed a convolutional neural network ANCOM, that can identify all subtypes of acute myeloid leukemia as well as healthy leukocytes. The experimental analysis of our study shows that the proposed algorithm provides better results for blast cell detection. We have used a public dataset from Cancer Imaging Archive to train our model. It has fifteen classes and a severe class imbalance problem. We have used random sampling techniques and data augmentation to balance the dataset. The model achieved an overall accuracy of 91.7% for detection and 92.5% for classification on multi-class classification. For binary classification, whether a white blood cell is malignant or not, it got 95% and 95.45% for the said dataset. A comparative analysis with VGG16, Resnet50, and Inception V3 was also done. Our model has achieved better accuracy without any need for segmentation of the microscopic images.
Retraction: Research on the Construction of Early Warning Index System of Network Public Opinion Emergency Based on Computer Simulation (IOP Conf. Ser.: Mater. Sci. Eng. 592 012101)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity and contains similarities to an earlier work. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organizers. The authors of the article have been given the opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication for this notice, IOP Publishing has not received any response. We would welcome hearing the authors account of events. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. [1] Hongbing Ge 2019 IOP Conf. Ser.: Mater. Sci. Eng. 592 012101 [2] https://iopscience.iop.org/article/10.1088/1755-1315/310/5/052044 Retraction published: 02 October 2023
Visual Indicators Representing Avatars' Authenticity in Social Virtual Reality and Their Impacts on Perceived Trustworthiness

Photorealistic avatars show great potential in social VR and VR collaboration. However, identity and privacy issues are threatening avatars' authenticity in social VR. In addition to the necessary authentication and protection, effective solutions are needed to convey avatars' authenticity status to users and thereby enhance the overall trustworthiness. We designed several visual indicators (VIs) using static or dynamic visual effects on photorealistic avatars and evaluated their effectiveness in visualizing avatars' authenticity status. In this study we explored suitable attributes and designs for conveying the authenticity of photorealistic avatars and influencing their perceived trustworthiness. Furthermore, we investigated how different interactivity levels influence their effectiveness (the avatar was either presented in a static image, an animated video clip, or an immersive virtual environment). Our findings showed that using a full name can increase trust, while most other VIs could decrease users' trust. We also found that interactivity levels significantly impacted users' trust and the effectiveness of VIs. Based on our results, we developed design guidelines for visual indicators as effective tools to convey authenticity, as a first step towards the improvement of trustworthiness in social VR with identity management
Exploration on the Application of Knowledge Graph in Modern Chinese Teaching

A knowledge graph is a structured semantic knowledge base that provides organized descriptions of the entities, properties, and interactions between entities in the real world. In course teaching, knowledge graph provides learners with an effective way of course learning. Knowledge graph presents the knowledge system of a course to learners in the form of visualization, which promotes the construction of the knowledge framework of the course and improves the learning efficiency.
Model of an Intelligent and Automated Negotiation Agent for the Service Level Agreement Negotiation Process in Cloud Computing

With the help of an intelligent agent, a cloud service provider (CSP) offers various services and resources to their customers over the internet. Consumers and CSPs in the cloud market are in a constant state of negotiation over their individual needs. Competition among CSPs is fierce because of the enormous number of CSPs in the market. Intelligent negotiation agents are highly demanding as a timesaving and success rate-enhancing tool. While negotiating, multiple attributes such as price, resources, reliability, CSP performance, utility value, and many more are considered. There’s increased false negotiations, faulty servers, and exorbitant fees are all issues arising because of the forever changing CSP market. An intelligent and automated negotiation agent (IANA) understands the conduct of the opponent and the needs of both parties. This need of users and CSP information is utilized in negotiations to find the best possible delivery of services between the customer and the CSP. As a result of the proposed design, negotiations are expedited, and there is a higher success rate and better oversight of service delivery.
Continuous-variable quantum computation with optical quantum entanglement and quantum teleportation in time domain

Enabling large-scale and high-speed quantum computation is a key to practical quantum computation. Continuous-variable approach in optical systems offer advantages in scalability and speed by leveraging their temporal degree of freedom and inherent large carrier frequency. In this paper, we investigate the generation and manipulation of quantum entanglement through a time-domain multiplexing approach. By employing time-domain multiplexing, we generate a two-dimensional cluster state—a universal resource for large-scale quantum computation—and perform quantum operations in the time domain with cluster state. Additionally, our ongoing research focuses on the generation and measurement of broadband optical quantum entanglement through an optical parametric oscillator, which holds potential as a foundation for high-speed quantum computing surpassing limitations of existing systems. By further engineering the quantum entanglement, we have also theoretically formulated a practical teleportation-based architectures for quantum computation in time domain. These advancements form the groundwork for the development of practical optical quantum computation.
Method for testing freeform surfaces based on a Shack-Hartmann sensor with plane wavefront scanning and stitching

Currently, the surface error measurement technology for freeform faces a significant contradiction between measurement accuracy and dynamic range. The study proposes a non-null testing method for measuring freeform surfaces by utilizing a Shack-Hartmann wavefront sensor to emit a small aperture parallel beam and scan along the normal direction at the center of subapertures for stitching (SHPSS). A mathematical model based on ray tracing and the reflection theorem is established to calculate the sampling points on an ideal freeform surface, the reference spot array on CCD, and the corresponding relationship between microlens array and spots. An algorithm is proposed to iteratively calculate the wavefront aberration and gradually approach the actual sampling points using the established model. Theoretical analysis and numerical simulation results indicate that SHPSS can increase the dynamic range and improve the accuracy of wavefront reconstruction. The error analysis of the SHPSS method is carried out, the measurement accuracy of full aperture freeform surface is 11.45 nm. A testing system is set up and experiments are conducted on a 100 mm aperture freeform reflective mirror. The RMS of the SHPSS test results is less than λ/30 (λ=635 nm) compared to the interferometric test results. By analyzing five groups of repeated measurement experiments, the repeatability accuracy of SHPSS method is less than 1/80 λ (RMS). This demonstrates the feasibility and measurement capabilities of the method for freeform surface testing.
THE REASONS FOR USING EMOJIS ON WHATSAPP CHATS: A CASE STUDY OF INDONESIAN STUDENTS’ TRANSLINGUAL PRACTICE

&lt;p&gt;&lt;span&gt;Translingual practice recognizes language repertoire in which both linguistics and non-linguistics sources are put together in one’s brain and used when necessary (Canagarajah, 2013). Since one’s language repertoire is different from others, the language produced might also be different. The offline and online contexts might also differentiate the production of the language. In online communication, such as communication through WhatsApp, ones can interpret the linguistics and non-linguistic codes differently, including the emojis which are available in WhatsApp. Due to the different language repertoire, there must be reasons why emojis are used differently by WhatsApp users. This present study aims to find the reasons why emojis are used on WhatsApp chats which are written by students in academic contexts. The data is emojis used in WhatsApp chats used by five university students in a private university in Indonesia. Even though only five participants are joining this study, it aims to examine in-depth reasons why the emojis are used in WhatsApp chats. The data is gained by asking the participants to convert the WhatsApp chats to Word files. In addition, to know the detailed information about the interpretations of the use of emojis in WhatsApp, this study applied MIMI (The Mobile Instant Messaging Interview) (Kaufmann &amp;amp; Peil, 2020; Gibson, 2020; Enochsonn, 2011).It is analyzed by using Netnography (Kozinets, 2015). The results show that there are six reasons why the emojis are used on WhatsApp chats. It can be concluded that in Translingual Practice, language is not fixed; instead, the meaning emerges in the language contact zone.&lt;/span&gt;&lt;/p&gt;
Bibliography Counselor: A Citation Recommendation Tool

Including citations of appropriate references is an important part of writing scholarly articles. Recommender systems for bibliographic citation suggest possible references to authors easing the tedious task of finding bibliographic details of the desired references. With a surge in the number of research documents being published or archived, it is becoming increasingly challenging getting up to date with the literature has become challenging. In this work, we propose a recommendation algorithm for bibliographic citation based on a set of diverse digital libraries. Based on the proposed model, further, we present Bibliography Counselor, a recommender system for writing bibliographic articles in LATEX. The implementation can be found in this GitHub repository.
Recommendation Algorithms Based on User Reviews and Scoring

This study offers a recommendation algorithm based on user reviews and ratings to overcome the sparsity issue of the rating matrix and the problem of incorrect suggestions resulting from utilizing rating data alone. Two parallel convolutional neural network models are used to extract the text features in user and item reviews simultaneously by setting up shared layers to obtain two matrices of user preferences and item attributes about the reviews, which are then combined to generate the rating matrix for review prediction. The rating matrix is dissected using a hidden semantic model approach to create two matrices of user preferences and item characteristics about ratings. These are then merged to obtain the rating matrix for rating prediction. For the feature fusion portion of reviews and ratings, the predicted rating matrices for reviews and ratings are combined using a multilayer perceptron architecture to generate the final rating prediction matrix. The algorithmic model suggested in this research fuses review and rating data and is thus able to rectify inaccurate data during feature fusion and has some improvement on the cold start issue, when fresh users or objects are available with inadequate data for modeling. Experiments demonstrate that the algorithmic model suggested in this research is superior to the original recommended algorithmic model for ratings or reviews regarding rating prediction accuracy.