Reviewerly/exHarmony huggingface dataset:
An Audit of Diagnostic Mammography Examination among Women Presenting with Breast Symptoms

Background: Mammography is one of the imaging modalities that has been in existence for a long period of time in investigating the breast for pathologies. Its use in imaging the breast can be for the purpose of screening and diagnosis. Breast screening allows for early detection of breast cancer. Early detection of breast can save lives. Despite its important imaging modality for breast, mammography is not readily available in this part of the world. Our institution just acquired one recently. Objectives: Therefore, the aim of our study is to document the pattern of mammographic findings and to determine the prevalence of breast pathologies (benign and malignant) among symptomatic patients referred for mammography. Methodology: This descriptive cross-sectional study comprises 121 symptomatic women who presented for mammographic examination at the Radiology Department of the University of Abuja Teaching Hospital, Gwagwalada from January 2016 to December 2018. Two basic views (craniocaudal and mediolateral views) of the breast were obtained using an EXR-650 mammographic machine. Some of the patients had complimentary ultrasound scans. Results: The mean age of the study population was 47.23± 8.25 with an age range of 32–77 years. The majority of the patients were in the age group 40–49 years. Most women in the study were premenopausal and the majority had no family history of breast cancer. Breast pain was the most common symptom patients presented with. Heterogenous fibro glandular tissue accounted for the majority of mammographic breast density. Age correlated positively with a pattern of breast density among the study population. Abnormal mammographic findings were seen in 67 (55.4%) patients among which benign mass was the most prevalent. Among patients who presented with breast pain majority had normal mammographic findings while for those with breast lump, benign breast mass accounted for 35.7% representing the majority and malignant mass accounted for 19.1%. The relationship between mammographic outcome and indication was statistically significant P = 0.0001. Conclusion: Mammography plays a pivotal role in the evaluation of the breast in patients who presented with symptoms as it can differentiate benign and malignant lesions of the breast. In this study, the use of mammography reveals various pathologies in which benign breast mass accounted for the majority of breast pathologies detected.
Time series quantum classifiers with amplitude embedding

Abstract Quantum Machine Learning was born during the past decade as the intersection of Quantum Computing and Machine Learning. Today, advances in quantum computer hardware and the design of simulation frameworks able to run quantum algorithms in classic computers make it possible to extend classic artificial intelligence models to a quantum environment. Despite these achievements, several questions regarding the whole quantum machine learning pipeline remain unanswered, for instance the problem of classical data representation on quantum hardware, or the methodologies for designing and evaluating quantum models for common learning tasks such as classification, function approximation, clustering, etc. These problems become even more difficult to solve in the case of Time Series processing, where the context of past historical data may influence the behavior of the decision-making model. In this piece of research, we address the problem of Time Series classification using quantum models, and propose an efficient and compact representation of time series in quantum data using amplitude embedding. The proposal is capable of representing a time series of length n in $$log_2(n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>l</mml:mi> <mml:mi>o</mml:mi> <mml:msub> <mml:mi>g</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> computational units, and experiments conducted on benchmark time series classification problems show that quantum models designed for classification can also outperform the accuracy of classic methods.
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation

It is common to model a deterministic response function, such as the output of a computer experiment, as a Gaussian process with a Matérn covariance kernel. The smoothness parameter of a Matérn kernel determines many important properties of the model in the large data limit, including the rate of convergence of the conditional mean to the response function. We prove that the maximum likelihood estimate of the smoothness parameter cannot asymptotically undersmooth the truth when the data are obtained on a fixed bounded subset of . That is, if the data-generating response function has Sobolev smoothness , then the smoothness parameter estimate cannot be asymptotically less than . The lower bound is sharp. Additionally, we show that maximum likelihood estimation recovers the true smoothness for a class of compactly supported self-similar functions. For cross-validation we prove an asymptotic lower bound , which, however, is unlikely to be sharp. The results are based on approximation theory in Sobolev spaces and some general theorems that restrict the set of values that the parameter estimators can take.
REKP: Refined External Knowledge into Prompt-Tuning for Few-Shot Text Classification

Text classification is a machine learning technique employed to assign a given text to predefined categories, facilitating the automatic analysis and processing of textual data. However, an important problem is that the number of new text categories is growing faster than that of human annotation data, which makes many new categories of text data lack a lot of annotation data. As a result, the conventional deep neural network is forced to over-fit, which damages the application in the real world. As a solution to this problem, academics recommend addressing data scarcity through few-shot learning. One of the efficient methods is prompt-tuning, which transforms the input text into a mask prediction problem featuring [MASK]. By utilizing descriptors, the model maps output words to labels, enabling accurate prediction. Nevertheless, the previous prompt-based adaption approaches often relied on manually produced verbalizers or a single label to represent the entire label vocabulary, which makes the mapping granularity low, resulting in words not being accurately mapped to their label. To address these issues, we propose to enhance the verbalizer and construct the refined external knowledge into a prompt-tuning (REKP) model. We employ the external knowledge bases to increase the mapping space of tagged terms and design three refinement methods to remove noise data. We conduct comprehensive experiments on four benchmark datasets, namely AG’s News, Yahoo, IMDB, and Amazon. The results demonstrate that REKP can outperform the state-of-the-art baselines in terms of Micro-F1 on knowledge-enhanced text classification. In addition, we conduct an ablation study to ascertain the functionality of each module in our model, revealing that the refinement module significantly contributes to enhancing classification accuracy.
Distributed, Dynamic and Recursive Planning for Holonic Multi-Agent Systems: A Behavioural Model-Based Approach

In this work, we propose a new distributed, dynamic, and recursive planning approach able to consider the hierarchical nature of the holonic agent and the unpredictable evolution of its behaviour. For each new version of the holonic agent, introduced because of the agent members obtaining new roles to achieve new goals and adapt to the changing environment, the approach generates a new plan that can solve the new planning problem associated with this new version against which the plans, executed by the holonic agent, become obsolete. To do this, the approach starts by generating sub-plans capable of solving the planning subproblems associated with the groups of the holonic agent at its different levels. It then recursively links the sub-plans, according to their hierarchical and behavioural dependency, to obtain a global plan. To generate the sub-plans, the approach exploits the behavioural model of the holonic agent’s groups, thereby minimising the computation rate imposed by other multi-agent planning methods. In our work, we have used a concrete case to show and illustrate the usefulness of our approach.
Entanglement-based approach to quantum coherence, path information, and uncertainty

The seminal multibeam Young's interference experiment established the wave-particle duality principle, connecting quantum properties (interference) and path information. To quantify this connection, we propose a framework in which quantum measurement is applied to a subsystem of an entangled state, thus producing a quantum ensemble in the other part. The applied observable is characterized by its uncertainty quality arising from entropic uncertainty relations. In the proposed approach initial entanglement appears as a resource that can be transformed into classical information or an ensemble's coherence. Our framework provides a connection between such fundamental quantum information concepts as entanglement, coherence, and uncertainty.
Integrating Augmented Reality (AR) and Virtual Reality (VR) in Transformation of Teaching and Learning Pedagogy in Education 4.0

Education 4.0 is an evolving paradigm that leverages technology to revolutionize teaching and learning methods, by giving augmented reality (AR) and virtual reality (VR) technologies that provide a more engaging, personalized, and immersive educational experience. This book chapter explores the integration of AR and VR in the transformation of teaching and learning pedagogy by bridging the gap between the physical and digital worlds within the framework of Education 4.0. Integrating AR and VR has led the traditional pedagogical approaches be augmented with interactive and dynamic elements, facilitating deeper knowledge retention and application. These technologies are particularly beneficial in STEM fields. It has proven successful in the early childhood, language education, and special needs education also. The chapter also explains a case study on how AR and VR can be used to promote learning in the subject of chemistry. AR and VR promise to redefine the future of education, preparing learners to thrive in an ever-changing global landscape.
Education 4.0 Using AI With Reference to Edtech Industry

Education 4.0 is a new paradigm shift in education driven by advancements in technology such as artificial intelligence (AI), virtual and augmented reality, and the IOT (Internet of Things). AI is a key driver of this transformation and has the potential to revolutionize the way we teach and learn. This chapter explores the potential of AI-based edtech in the context of Education 4.0, its implications, and challenges. One way that the wave to audio framework can be used in educational technology is through the development of interactive learning materials. By incorporating audio waveforms into online textbooks, e-learning modules, and other digital learning resources, students can interact with the materials in new and engaging ways. For example, students could listen to an audio waveform of a teacher speaking about a particular topic while simultaneously viewing related visual content on a screen. This approach provides a more immersive and engaging learning experience that could enhance student engagement and knowledge retention.
Ontology and Description Logic for E-Learning

A semantic web is a web environment in which users and computer agents interact semantically. Users need to put their queries exactly as they want; its goal is to make accurate content available. When the machine is capable of answering all questions, it will provide the precise answer as well as a list of hyperlinks. In the semantic web, ontologies serve as a data model for describing the concepts and relationships that exist between them. In this the content is annotated with meta tags to provide useful meaning and allow humans and machines to collaborate. This chapter describes the semantic web and how it can be useful in the case of e-learning. It gives the architecture of a semantic-based conceptual e-learning portal and highlights the importance of ontology in the field of education. Description logic is an integral part of the generation of well-formed datasets. This chapter integrates DL to ontologies.
Human Face Mask Detection Using YOLOv7+CBAM in Deep Learning

COVID-19 and its variants have affected millions of people around the world. Wearing a mask is an effective way to reduce the spread of the epidemic. While wearing masks is a proven strategy to mitigate the spread, monitoring compliance remains a challenge. In this chapter, the authors propose a mask detection method based on deep learning and convolutional block attention module (CBAM). In this chapter, they extract representative features from input images through supervised learning. In order to improve the recognition accuracy under limited computing resources. They choose YOLOv7 network model and incorporate CBAM into its network structure. Compared with the original version of YOLOv7, the proposed network model improves the mean average precision (mAP) up to 0.3% in face mask detection process. Meanwhile, the method improves the detection speed of each frame 73ms. These advancements have significant implications for real-time, large-scale monitoring systems, thereby contributing to public health and safety.
The Role of AI in Improving Interaction With Cultural Heritage

Over the years, artificial intelligence techniques have been applied in several application fields, exploiting data to execute different tasks and achieve disparate objectives. Therefore, the cultural heritage field can utilise AI techniques to improve the interaction between visitors and cultural assets. Then, this work aims to present the background related to the principal AI techniques and provides an overview of the literature aimed at improving the user cultural experience. This overview focuses on AI integration with tools aimed to enhance the interaction among visitors and cultural sites, such as recommender systems, context-aware recommender systems, and chatbots. Finally, the most common measure used for estimating the accuracy of the AI methodologies will be introduced.
Enabling Privacy-Preserving Data Sharing with Bilateral Access Control for Cloud

Cloud computing plays an essential role in various fields. However, the existing cloud services face a severe challenge, which is how to share the data among a large scale of devices securely. In this paper, we introduce a cloud-based privacy-preserving data sharing scheme, derived from identity-based matchmaking encryption. In our scheme, the access policies are designed by both the sender and receiver simultaneously, to support bilateral access control. To improve efficiency, we delegate the match algorithm to the cloud server, reducing the computation cost and communication overhead on end devices without revealing the users’ privacy. Through formal security analysis, we show that our scheme holds security, authenticity, and privacy. Finally, we evaluate our scheme by conducting extensive experiments, indicating that our scheme is more efficient than the other data-sharing schemes in ME-based services in a real-world dataset.
Forecasting Cyber Crime in the Metaverse Era

One of the digital transformation features of the new generation of cyber world is the meta verse. It is expected to establish a self-sustainable virtual ecosystem of fully immersive environments, real-time experiences with numerous chances to interact with the world for users and industrial businesses. With the introduction of new networks and enabling technological tools, the meta verse will succeed in the long term. However, with increasing interaction using new meta verse and a lot of third-party services, there would be an arena for many possibilities of cybercrime threats. Hence, combating cybercrimes is critical for the meta verse, and they should be investigated since the commercial adoption of the meta verse is imminent. Therefore, this chapter discusses many cybercrime methods and techniques which are expected in the meta verse environment, (AI) related cyber-attacks are expected, and solutions to the meta verse is possible.
Deep Modular Co-Attention Shifting Network for Multimodal Sentiment Analysis

Human Multimodal Sentiment Analysis (MSA) is an attractive research that studies sentiment expressed from multiple heterogeneous modalities. While transformer-based methods have achieved great success, designing an effective ”co-attention” model to associate text modality with nonverbal modalities remains challenging. There are two main problems: 1) the dominant role of the text in modalities is underutilization, and 2) the interaction between modalities is not sufficiently explored. This paper proposes a deep modular Co-Attention Shifting Network (CoASN) for MSA. A Cross-modal Modulation Module based on Co-attention (CMMC) and an Advanced Modality-mixing Adaptation Gate (AMAG) are constructed. The CMMC consists of the Text-guided Co-Attention (TCA) and Interior Transformer Encoder (ITE) units to capture inter-modal features and intra-modal features. With text modality as the core, the CMMC module aims to guide and promote the expression of emotion in nonverbal modalities, and the nonverbal modalities increase the richness of the text-based multimodal sentiment information. In addition, the AMAG module is introduced to explore the dynamical correlations among all modalities. Particularly, this efficient module first captures the nonverbal shifted representations and then combines them to calculate the shifted word embedding representations for the final MSA tasks. Extensive experiments on two commonly used datasets, CMU-MOSI and CMU-MOSEI, demonstrate that our proposed method is superior to the state-of-the-art performance.
Grammatical Features in Indonesia English: A Study of Indonesian College Students

This study aims to expose detailed descriptions of the grammatical features of Indonesian English. Indonesian English refers to a newly recognised linguistic variety of English as proposed by Coleman (2017). The grammatical characteristics as the result of acquiring a new language lead to the presence of a new variety of Indonesian English. Applying descriptive qualitative research, the data were collected based on recordings of the speech of 24 college students on three different topics. Following the principle proposed by Cogo and Dewey (2012), this study reveals twelve grammatical features as the characteristics of Indonesian English. They are namely the over-generalisation and omission of the copula BE (i.e am, is, are), the use of Indonesian collocation and literal translation, subject–verb agreement, the omission of the plural marker 's', assigning different lexical meanings, inappropriate prepositions, the omission of possessive markers, the omission and addition of articles, the application of verb tenses, the use of Indonesian acronyms and abbreviations, the incorrect construction of passive forms, and the use of Indonesian lexical items. It is shown that one of the main factors in the emergence of this new English variant is the contact of the multiple languages used by the speakers.
Closing loopholes of measurement-device-independent nonlinear entanglement witnesses

The concept of entanglement witnesses forms a useful technique to detect entanglement in realistic quantum devices. Measurement-device-independent nonlinear entanglement witnesses (MDI-NEWs) are a kind of entanglement witness that eliminates dependence on the correct alignments of measurement devices for guaranteeing the existence of entanglement and also detects more entangled states than its linear counterparts. While this method guarantees entanglement independent of measurement alignments, it is still prone to serving wrong results due to other loopholes. Here, we study the response of MDI-NEWs to two categories of faults occurring in experiments. In the first category, the detection loophole, characterized by lost and additional events of outcomes of measurements, is investigated, and bounds which guarantee entanglement are obtained in terms of the efficiency of measurement being performed. In the second category, we study noise associated with the sets of additional quantum inputs required in MDI-NEW scenarios. In this case, a type of noise is identified which still allows the MDI-NEWs to guarantee entanglement. We also show that MDI-NEWs are less or equally robust in comparison to their linear counterparts under the same noise in additional quantum inputs, although the former group detects a larger volume of entangled states in the noiseless scenario than their linear cousins.
Human Activity Tracker and Recognition

Human Activity Recognition (or, HAR) is a piece of software that uses AI algorithms to recognize and categories human physical activity. By analyzing signal data from multiple sensors such as accelerometers, gyroscopes, and magnetometers, the system is meant to recognize and categorize physical activities such as walking, running, leaping, ascending stairs, and others. To recognize human activity patterns, the HAR system employs signal preprocessing, feature extraction, and classification algorithms. The use of simulated intelligence techniques such as deep learning computations, convolutional brain organizations, and supporting vector machines has improved the display of HAR frameworks. The system may be utilized for a variety of purposes, including security, sports, fitness, and healthcare. In general, the HAR framework provides a beneficial value to robotized human activities. Man-made reasoning (Artificial Intelligence) plays an important role in Human Activity Recognition by allowing frameworks to learn and adapt to new conditions. In general, HAR framework is beneficial asset to robotized human movement recognition, working with the advancement of clever frameworks that can research human behaviour and work on personal fulfilment. Overall, Human Activity Recognition Using Computerized Reasoning is promising innovation that enables intelligent frameworks to perceive and group human activities gradually. This breakthrough has the potential to disrupt several businesses and improve people's personal pleasure by enabling personalized medical treatment, improving game execution, and improving street safety. The creation of this software sets the path for more study into themes such as the relationship between individual health status and physical activity. Overall, creating a fruitful Human Action Acknowledgement project utilizing recordings necessitates a broad understanding of AI and Profound Learning methods. As a result, success of this project highlights the value of creativity and perseverance in learning. Finally, it is the initial step towards developing more advanced systems that will improve people's lives in the future.