Reviewerly/exHarmony huggingface dataset:
Breast Cancer Detection and Prevention Using Machine Learning

Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate.
A distributed deep neural network model for credit card fraud detection

This paper develops a distributed neural network model (DDNN) for detecting credit card fraud to federate credit card transaction data among different financial institutions. In addition, the convergence of the DDNN model is achieved by introducing a model optimization algorithm. The results demonstrate that (1) The use of a distributed model can avoid privacy leakage and data handling costs; (2) The DDNN model accelerates the convergence of the model through simultaneous computation of multiple clients; (3) The DDNN model detects credit card fraud better than multiple types of centralized models.
A Robust Deep Learning Approach for Accurate Segmentation of Cytoplasm and Nucleus in Noisy Pap Smear Images

Cervical cancer poses a significant global health burden, affecting women worldwide. Timely and accurate detection is crucial for effective treatment and improved patient outcomes. The Pap smear test has long been a standard cytology screening method, enabling early cancer diagnosis. However, to enhance quantitative analysis and refine diagnostic capabilities, precise segmentation of the cervical cytoplasm and nucleus using deep learning techniques holds immense promise. This research focuses on addressing the primary challenge of achieving accurate segmentation in the presence of noisy data commonly encountered in Pap smear images. Poisson noise, a prevalent type of noise, corrupts these images, impairing the precise delineation of the cytoplasm and nucleus. Consequently, segmentation boundaries become indistinct, leading to compromised overall accuracy. To overcome these limitations, the utilization of U-Net, a deep learning architecture specifically designed for automatic segmentation, has been proposed. This approach aims to mitigate the adverse effects of Poisson noise on the digitized Pap smear slides. The evaluation of the proposed methodology involved a dataset of 110 Pap smear slides. The experimental results demonstrate that the proposed approach successfully achieves precise segmentation of the nucleus and cytoplasm in noise-free images. By preserving the boundaries of both cellular components, the method facilitates accurate feature extraction, thus contributing to improved diagnostic capabilities. Comparative analysis between noisy and noise-free images reveals the superiority of the presented approach in terms of segmentation accuracy, as measured by various metrics, including the Dice coefficient, specificity, sensitivity, and intersection over union (IoU). The findings of this study underline the potential of deep-learning-based segmentation techniques to enhance cervical cancer diagnosis and pave the way for improved quantitative analysis in this critical field of women’s health.
Extremal quantum correlation generation using a hybrid channel

The preservation of quantum correlations requires optimal procedures and the proper design of the transmitting channels. In this regard, we address designing a hybrid channel comprising a single-mode cavity accompanied by a super-Gaussian beam and local dephasing parts based on the dynamics of quantum characteristics. We choose two-level atoms and various functions such as traced-distance discord, concurrence, and local-quantum uncertainty to analyze the effectiveness of the hybrid channel to preserve quantum correlations along with entropy suppression discussed using linear entropy. The joint configuration of the considered fields is found to not only preserve but also generate quantum correlations even in the presence of local dephasing. Most importantly, within certain limits, the proposed channel can be readily regulated to generate maximal quantum correlations and complete suppression of the disorder. Besides, compared to the individual parts, mixing the Fock state cavity, super-Gaussian beam, and local dephasing remains a resourceful choice for the prolonged quantum correlations' preservation. Finally, we present an interrelationship between the considered two-qubit correlations' functions, showing the deviation between each two correlations and of the considered state from maximal entanglement under the influence of the assumed hybrid channel.
Artificial Neural Networks

To many, artificial neural networks (ANNs) are the archetypal machine learning algorithm; they have a power and generality, together with a long pedigree, which makes them difficult to displace from this position. As one of the earliest general machine learners, ANNs were one of the first to be adopted for addressing data-based engineering problems. This chapter will discuss the historical development of ANNs in the context of engineering usage; in that context, it will prove useful to divide the history into three main periods: pre-history, the first (MLP) age, and the second (deep) age.
Augmented reality technologies in construction project assembly phases

Implementing augmented reality (AR) technology in the construction industry, particularly during the assembly stage of projects, presents a significant opportunity for accelerating the sector's digitalization process and enhancing labour productivity. Research, as the established literature shows, grows every year, however, further research is needed in order to achieve widespread adoption. This study aims to provide researchers with a systematic review of all the studies in the literature related to augmented reality technology's role in facilitating assembly adaptation within the construction industry. Utilizing the PRISMA method, a systematic review was conducted. Through thorough examinations, 42 articles directly relevant to the topic were identified, and the full texts consequently meticulously examined. The research suggests that a strong relationship between Building Information Modelling (BIM) technology and augmented reality concerning construction management issues exists. Additionally, the studies indicated that Head-Mounted Displays (HMDs) are frequently used as visualization tools, and the Unity game engine is commonly employed as software.
MQTT and blockchain sharding: An approach to user-controlled data access with improved security and efficiency

The rapid growth of the Internet of Things (IoT) has raised security concerns, including MQTT protocol-based applications that lack built-in security features and rely on resource-intensive Transport Layer Security (TLS) protocol. This paper presents an approach that utilizes blockchain technology to enhance the security of MQTT communication while maintaining efficiency. This approach involves using blockchain sharding, which enables higher scalability, improved performance, and reduced computational overhead compared to traditional blockchain approaches, making it well-suited for resource-constrained IoT environments. This approach leverages Ethereum blockchain's smart contract mechanism to ensure trust, accountability, and user privacy. Specifically, we introduce a shard-based consensus mechanism that enables improved security while minimizing computational overhead. We also provide a user-controlled and secured algorithm using Proof-of-Access implementation to decentralize user access control to data stored in the blockchain network. The proposed approach is analyzed for usability, including metrics such as bandwidth consumption, CPU usage, memory usage, delay, access time, storage time, and jitter, which are essential for IoT application requirements. The analysis demonstrated that the approach reduces resource consumption, and the proposed system outperforms TLS and existing blockchain approaches in these metrics, regardless of the choice of the MQTT broker. Additionally, thoroughly addressing future research directions, including issues and challenges, ensures careful consideration of potential advancements in this domain.
Synthetic minority oversampling using edited displacement-based <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si42.svg" display="inline" id="d1e3258"><mml:mi>k</mml:mi></mml:math>-nearest neighbors

Skewed class proportions in real-world datasets present a challenge for machine learning algorithms, as they have a tendency to correctly categorize the majority class while incorrectly classifying the minority class. Such classification disparities hold significant implications, particularly in predictive scenarios involving minority groups, where misclassifying minority instances could lead to adverse outcomes. To tackle this, class imbalance learning has gained attention, with the Synthetic Minority Oversampling Technique (SMOTE) being a notable approach that addresses class imbalance by generating synthetic instances for the minority class based on their feature space neighbors. Despite its effectiveness and simplicity, SMOTE is known to suffer from a noise propagation issue where noisy and uninformative samples are introduced. While various SMOTE variants, including hybrids with undersampling, have been developed to tackle this problem, identifying noisy samples in complex real-world datasets remains a challenge. To address this, our study introduces a new SMOTE-based hybrid approach called SMOTE-centroid displacement-based k-NN (SMOTE-CDNN). SMOTE-CDNN employs centroid displacement for class prediction, which is more robust against noisy data. After SMOTE is applied, noise instances are detected and removed for clearer decision boundaries if their labels predicted by our centroid displacement-based k-NN algorithm are different from the real ones. While our experiments on 24 imbalance datasets demonstrate the resilience and efficiency of our proposed algorithm, which outperforms state-of-art resampling algorithms with various classification models, we acknowledge the need for further investigation into specific dataset characteristics and classification scenarios to determine the generalizability of our approach.
Lessons in linguistics with ChatGPT: Metapragmatics, metacommunication, metadiscourse and metalanguage in human-AI interactions

This paper discusses metalanguage, metadiscourse, metacommunication and metapragmatics testifying to users' conscious awareness enacted in human-AI interactions, based on a corpus of posts sent to Reddit's r/ChatGPT. The emphasis falls on users' foci of attention as they perform linguistic tests on ChatGPT and on how the "meta" practices manifest themselves interactionally on the selected subreddit, where human-AI interactions are showcased for entertainment purposes. The findings suggest that engaging with AI reflects and, potentially, enhances language users' metalinguistic, metadiscursive, metacommunicative and metapragmatic awareness. This awareness is mirrored in ChatGPT's output, indicative of its previous human-assisted training. Additionally, this investigation demonstrates that, when acknowledged as one subject of study, the four "meta" concepts are intricately intertwined as they may co-occur and overlap.
DACP: Enforcing a dynamic access control policy in cross-domain environments

Enabling hybrid authorisations to enforce dynamic access control policy from single-domain to cross-domain environments (CDEs) is important for distributed services. However, traditional Attribute-Based Access Control (ABAC) models are incompatible with CDEs. To fill this gap, approaches that apply cryptographic primitives, e.g., attribute-based encryption (ABE), have been proposed. The computation and storage overhead in most ABE constructions is non-negligible and increases with the complexity of the associated policies. In addition, most access control policy systems enforce authorisation policies in a centralized way, raising serious security and privacy issues. In this paper, we introduce DACP – a practical Dynamic Access Control Policy system supporting dynamic cross-domain authorisation. DACP combines traditional ABAC approach and a novel cryptographic primitive Attribute-based group signature (ABGS). ABAC is used for the access control decision and policy enforcement according to the user’s attributes whereas ABGS is used for managing the user’s attributes between users and authorities. Thus, the user’s attributes are securely distributed along with the access structure in CDEs while preserving the user’s privacy. We present the concrete design and implementation of DACP, and evaluate it in real-world settings. The evaluation shows that DACP is practical and efficient in CDEs.
Transformer and Graph Convolutional Network for Text Classification

Abstract Graph convolutional network (GCN) is an effective tool for feature clustering. However, in the text classification task, the traditional TextGCN (GCN for Text Classification) ignores the context word order of the text. In addition, TextGCN constructs the text graph only according to the context relationship, so it is difficult for the word nodes to learn an effective semantic representation. Based on this, this paper proposes a text classification method that combines Transformer and GCN. To improve the semantic accuracy of word node features, we add a part of speech (POS) to the word-document graph and build edges between words based on POS. In the layer-to-layer of GCN, the Transformer is used to extract the contextual and sequential information of the text. We conducted the experiment on five representative datasets. The results show that our method can effectively improve the accuracy of text classification and is better than the comparison method.
The impact of high-immersion virtual reality on foreign language anxiety

Abstract Public speaking, especially in a foreign language, is associated with increased anxiety. Research has shown the potential of virtual reality (VR) for simulating real-life experiences, allowing for public speaking practice in an ecological and safe environment. This between-subjects study investigated the effect of VR on foreign language anxiety (FLA) in public speaking practice. Intermediate learners of English participated in eight public speaking sessions over a three-month period, yielding 160 research observations. The experimental intervention took place in high-immersion VR with subjects wearing a VR headset and speaking in front of virtual audience. In the control intervention, subjects used a videoconferencing platform (Zoom) to speak in front of a real-life audience. Multivariate regression analysis revealed that practicing speaking in VR was associated with statistically significant lower FLA scores, compared with speaking practice using Zoom. The study found that VR technology had a positive effect on practicing public speaking in a foreign language. The research findings have practical implications for professionals and curriculum designers in various domains where public speaking skills are essential. For example, incorporating VR-based public speaking practice can benefit professionals preparing for a job interview, an elevator pitch, or a conference presentation. Curriculum designers can consider integrating VR simulations into language courses to provide students with realistic public speaking experiences. This approach can help students overcome language barriers, reduce anxiety, and develop their communication skills in a controlled and supportive environment.
CtFPPN: A coarse-to-fine pattern parser for dealing with distribution imbalance of pixels

Unbalanced pixel distribution has always plagued pattern parsing tasks. The consequence of this is that the saliency of tiny semantic components is overshadowed by large components, resulting in insufficient graph attentions from the model. Recent attempts typically crop tiny patches and predict masks for each semantic part. However, those strategies consist of separate stages and lack interactions, thus cannot be jointly optimized for collaborative perception. To compensate for this flaw, a coarse-to-fine pattern parsing network (CtFPPN) is proposed based on capsule network (CapsNet). Its coarse-grained parser submodel predicts and binaries coarse-scaled parsing masks for large components. Given the coarse contexts as references, fine-grained parser submodel conducts fine-scaled parsing for tiny components. To connect two parsing phases, the discretization attention fragmentation mechanism (DAFM) and multi-head attention expectation-maximum routing agreement (MhAEMRA) are customized. DAFM balances the model's attention to large and small semantic components. MhAEMRA receives attention tendencies from DAFM and updates learnable parameters. With DAFM and MhAEMRA, CtFPPN gradually deconstruct patterns by clustering highly associated secondary entities in a bottom-up "part backtracking" manner. Quantitative and ablation experiments of face and human parsing demonstrate the superiority of CtFPPN over the state-of-the-arts, especially for the definition of fine-grained semantic boundaries of components.
Interaction graph-based characterization of quantum benchmarks for improving quantum circuit mapping techniques

Abstract To execute quantum circuits on a quantum processor, they must be modified to meet the physical constraints of the quantum device. This process, called quantum circuit mapping , results in a gate/circuit depth overhead that depends on both the circuit properties and the hardware constraints, being the limited qubit connectivity a crucial restriction. In this paper, we propose to extend the characterization of quantum circuits by including qubit interaction graph properties using graph theory-based metrics in addition to previously used circuit-describing parameters. This approach allows for an in-depth analysis and clustering of quantum circuits and a comparison of performance when run on different quantum processors, aiding in developing better mapping techniques. Our study reveals a correlation between interaction graph-based parameters and mapping performance metrics for various existing configurations of quantum devices. We also provide a comprehensive collection of quantum circuits and algorithms for benchmarking future compilation techniques and quantum devices.
CB-YOLOv5: Enhancing drone detection with BottleneckCSP and cross convolution for improved performance

With the advancement of technology, drones/unmanned aerial vehicles (UAVs) are widely used in various fields. Uncontrolled flights of UAVs pose serious security issues due to their potential use in illegal activities. Therefore, drone monitoring and automatic detection are crucial. In this study, a YOLOv5-based Cross convolution with BottleneckCSP (CB-YOLOv5) model is proposed for drone detection. The Bottleneck CSP module is employed for higher accuracy in multi-scale targets, while the cross convolution module is used to reduce the number of parameters and increase the inference speed of the model. The images are preprocessed with auto-orientation and resize. Then, the processed images are fed into CB-YOLOv5 to extract their deep features. Meanwhile, the proposed method is compared with state-of-the-art techniques to validate its effectiveness. The proposed method improves the mAP performance of the drone detection model by at least 5.08%.
Netnographic research ethics in applied linguistics: A systematic review of data collection and reporting practices

Ethnography – a methodological staple of applied linguistics research since the field's inception – has well-established ethical guidelines. Although ethnographic research has traditionally been subject to institutional ethics review board protocols, the expansion of ethnography into online spaces, which has been recharacterized as netnography, has presented novel ethical challenges (e.g., determining what constitutes 'public' versus 'private' data, protecting participants' identities, and more). To better understand how researchers have handled such ethical challenges, this study systematically reviews the data collection and reporting practices of peer-reviewed netnographic research in applied linguistics. High-impact journals were searched using specific criteria, resulting in 60 studies published in 14 journals during the span of 2000–2022. These studies were coded to examine how common issues were handled, such as: gaining informed consent, obtaining permissions (from companies and their representatives to use data), and protecting participants' identities. Data analyses revealed that, while such ethical issues are a consideration for many researchers, there is still ample room for improvement when it comes to ethical decision-making. Based on our review, in the discussion, we provide suggestions for those who intend to conduct netnographic research in the future.
Digital currency and blockchain security in accelerating financial stability: A mediating role of credit supply

This study examines how blockchain and digital currency have affected the supply of credit and financial stability. It pays particular attention to industry-based analyses and options presented by cryptocurrencies, stablecoins, and digital currencies for the credit supply and financial stability. A positivistic or quantitative research design is employed. The method of data collection is a survey-based questionnaire, as well as the time interval data method, from December 2021 to December 2022. The study sample comprises of five industrial zones of Punjab. The respondents are businessmen, managers, and employees (N = 449). The study finds that the use of various digital currencies quickly transforms business. The study shows that most industries do not require central banks and, instead, concentrate on modern digital currency and blockchain systems for monetary transfers. The private and public models of physical money will likely fail in the future. Rather, central banks should adopt digital currency and blockchain with an online technological payment strategy in order to enhance domestic financial stability and payment systems.