Reviewerly/exHarmony huggingface dataset:
Ontology Matching Using Multi-head Attention Graph Isomorphism Network

Ontology matching is a widely used solution to the semantic heterogeneity problem in data integration or sharing. It consists of establishing mappings between entities that belong to different ontologies. However, as the number of ontologies is increasing for a given domain and the overlap between ontologies grows proportionally, it becomes crucial to develop more reliable and accurate techniques for the automation of this task. While traditional ontology mapping approaches are based on string metrics and structure analysis, some recent methods are using deep neural networks. In this article, we propose a novel approach for ontology matching based on Graph Neural Networks (GNN) as graph representations are helpful for entity and graph comparisons. Our approach is more precisely based on Multi-Head Attention Graph Isomorphism Network (MHAGIN). The results of experiments demonstrate the effectiveness of our approach compared with existing methods.
A Domain Knowledge Transformer Model for Occupation Profiling

Abstract Occupation profiling is a subtask of authorship profiling that is broadly defined as an analysis of individuals’ writing styles. Although the problem has been widely explored, no previous studies have attempted to identify Chinese classical poetry. Inspired by Trudgill’s seminal work on stylistic variation as a function of occupation, we present a novel Domain-Knowledge Transformer model to predict a poet’s occupation through their poems’ writing styles. Different from other Indo-European languages, Chinese has rarely used characters and two types of writing forms: traditional Chinese and simplified Chinese. To tackle these problems, we use the language-related component to standardize our input. We also use alphabetization to satisfy the restrictions on rhyming rules and tonal styles. As a special literal form, traditional domain knowledge, for example, named entities, themes, ages and the official career path, is valuable for poet occupation profiling. However, due to the lack of appropriate annotation datasets, it is difficult to recognize these features. Therefore, we proposed the domain knowledge component employing the latent Dirichletal location model to capture the extra theme information and establish named entity dictionaries to recognize the named entity of the datasets in this study. Finally, in the deep learning component, we combine Transformer with a convolutional neural network (CNN) model to perform occupation profiling. The experimental results suggest that our model is effective in this task. Moreover, the results demonstrate an account of other social attribution features of poetry style that are predictive of occupation in this domain.
Federated Learning in Medical Image Analysis: A Systematic Survey

Medical image analysis is crucial for the efficient diagnosis of many diseases. Typically, hospitals maintain vast repositories of images, which can be leveraged for various purposes, including research. However, access to such image collections is largely restricted to safeguard the privacy of the individuals whose images are being stored, as data protection concerns come into play. Recently, the development of solutions for Automated Medical Image Analysis has gained significant attention, with Deep Learning being one solution that has achieved remarkable results in this area. One promising approach for medical image analysis is Federated Learning (FL), which enables the use of a set of physically distributed data repositories, usually known as nodes, satisfying the restriction that the data do not leave the repository. Under these conditions, FL can build high-quality, accurate deep-learning models using a lot of available data wherever it is. Therefore, FL can help researchers and clinicians diagnose diseases and support medical decisions more efficiently and robustly. This article provides a systematic survey of FL in medical image analysis, specifically based on Magnetic Resonance Imaging, Computed Tomography, X-radiography, and histology images. Hence, it discusses applications, contributions, limitations, and challenges and is, therefore, suitable for those who want to understand how FL can contribute to the medical imaging domain.
Eye for Blind: A Deep Learning-Based Sensory Navigation System for the Blind

The research proposes a new system to assist visually impaired individuals in navigating their surroundings by introducing an “Eye for Blind” system that employs Convolutional Neural Networks (CNN). Our approach utilizes a CNN model to recognize images and generates audio feedback with descriptive labels. This innovative system has the potential to help visually impaired individuals detect and recognize photos, enhancing their independence and quality of life. We evaluated our proposed system using publicly available datasets, and the outcomes suggest that our approach has a high level of accuracy in recognizing social media posts, demonstrating its potential as a solution for the visually impaired community. By using the Eye for Blind system, visually impaired individuals will be able to navigate their surroundings more effectively and efficiently, eliminating their dependence on assistance from others. This technology has enormous potential to improve the quality of life for visually impaired individuals and can contribute to the development of other similar systems that can enhance their independence and mobility. In conclusion, our proposed system has significant potential to make a positive impact on the lives of visually impaired individuals, and we hope that it can be further developed and implemented in the future.
Asymptotic formula for large eigenvalues of the two-photon quantum Rabi model

We prove that the spectrum of the two-photon quantum Rabi Hamiltonian consists of two eigenvalue sequences (E m + ) m=0 ∞ , (E m - ) m=0 ∞ satisfying a three-term asymptotic formula with the remainder estimate O(m -1 lnm) when m tends to infinity. By analogy to the one-photon quantum Rabi model, the leading three terms of this asymptotic formula, describe a generalized rotating-wave approximation for large eigenvalues of the two-photon quantum Rabi model.
Developing intercultural communication strategies through Facebook: A longitudinal investigation of VELF exchanges

Open AccessDeveloping intercultural communication strategies through Facebook: A longitudinal investigation of VELF exchangesRino BossoRino BossoSearch for more papers by this authorhttps://doi.org/10.14220/9783737016391.103SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book cover 1. Auflage ISBN: 978-3-8471-1639-4 eISBN: 978-3-7370-1639-1 History Published online:December 2023 Information© 2023 V&R unipress, Robert-Bosch-Breite 10, D-37079, ein Imprint der Brill-GruppeUnless otherwise stated, this publication is licensed under the Creative Commons License Attribution-Non Commercial-No Derivatives 4.0 (see https://creativecommons.org/licenses/by-nc-nd/4.0/) and can be accessed under DOI 10.14220/9783737016391. Any use in cases other than those permitted by this license requires the prior written permission from the publisher.Unless otherwise stated, this publication is licensed under the Creative Commons License Attribution-Non Commercial-No Derivatives 4.0 (see https://creativecommons.org/licenses/by-nc-nd/4.0/) and can be accessed under DOI 10.14220/9783737016391. Any use in cases other than those permitted by this license requires the prior written permission from the publisher.PDF download
ELM-KL-LSTM: a robust and general incremental learning method for efficient classification of time series data

Efficiently analyzing and classifying dynamically changing time series data remains a challenge. The main issue lies in the significant differences in feature distribution that occur between old and new datasets generated constantly due to varying degrees of concept drift, anomalous data, erroneous data, high noise, and other factors. Taking into account the need to balance accuracy and efficiency when the distribution of the dataset changes, we proposed a new robust, generalized incremental learning (IL) model ELM-KL-LSTM. Extreme learning machine (ELM) is used as a lightweight pre-processing model which is updated using the new designed evaluation metrics based on Kullback-Leibler (KL) divergence values to measure the difference in feature distribution within sliding windows. Finally, we implemented efficient processing and classification analysis of dynamically changing time series data based on ELM lightweight pre-processing model, model update strategy and long short-term memory networks (LSTM) classification model. We conducted extensive experiments and comparation analysis based on the proposed method and benchmark methods in several different real application scenarios. Experimental results show that, compared with the benchmark methods, the proposed method exhibits good robustness and generalization in a number of different real-world application scenarios, and can successfully perform model updates and efficient classification analysis of incremental data with varying degrees improvement of classification accuracy. This provides and extends a new means for efficient analysis of dynamically changing time-series data.
An intelligent human‐centric systems to diagnose breast cancer using machine learning and optimized feature selection techniques

Abstract All over the world, breast cancer (BC) is the leading cause of cancer mortality among women. Computer‐aided methods can assist in early diagnosis. The proposed approach used SMOTE filter with Ch 2 test techniques for class balance and feature section using eight different ML models Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) with Linear and Radial Basis Function (RBF), Logistic Regression (LR), K‐nearest neighbor (KNN) and eXtreme Gradient Boosting (XGBoost). A Ch 2 test determines the top five features—glucose, HOMA, resistin, BMI, and insulin. Metrics such as accuracy, precision, recall, and F1‐Score are used to compare the performance of models. More than 99% accuracy was achieved by the proposed XGBoost model. Compared to the other breast cancer prediction models, the proposed model had an average accuracy improvement of 9.30%. As a result of our proposed model, breast cancer diagnosis will be more efficient based on risk factors. The proposed prediction model can also predict various breast cancer features. In addition to improving diagnostic decision‐support systems, the proposed model should be able to predict breast cancer disease accurately.
ExaPRR: A Framework for Support Dynamic and Interactive Events on Distributed Published Resource Repositories Mechanism in Distributed Exascale Computing Systems

Abstract This paper introduces the ExaPRR Framework, which is capable of managing and controlling dynamic and interactive events in addition to activities related to distributed Published Resources Repository, managing and controlling dynamic and interactive events of the said resource discovery (RD) by redefining the concept of request and response based on wave model. For this purpose, while analyzing the function of the Distributed Published Resource Repository RD and defining the concept of the next element participating in the global activity in this RD, it redefines the concept of request in the Distributed Exascale systems. This framework uses the concept of revising the state of implementation of dynamic and interactive events in the system and redefining the concept of request at every moment of the process of implementation of resource discovery activities based on system status, demanding requests, and dynamic and interactive event, manages the resource's discovery activities.
Recognition to weightlifting postures using convolutional neural networks with evaluation mechanism

For modern sport training, critical posture recognition of athletes can be helpful for athlete training. This paper proposes convolutional neural networks using a two-stage evaluation mechanism to recognize four critical postures of a weightlifter, that is, force releasing, knee flexion, knee extension and highest point. Using the proposed convolutional neural networks classify images and extract image features. Meanwhile, a two-stage evaluation mechanism is adopted to calculate the scores of image features, based on the calculated scores, the four critical postures can be accurately recognized. Experimental results show that the accuracy of our method is 92.85% in the recognition of the four critical postures, which defeats the competitive methods in critical posture recognition. Moreover, the training time of the proposed method linearly augments along with the increasing of data volume, that is, non-exponential growth, consequently, our method can be applied to large-scale image datasets. We demonstrate that the two-stage mechanism can calculate the scores of image features independently of specific scenarios, which assist neural networks improve classification capabilities. Moreover, using the two-stage mechanism can simplify the designed complexity of neural network architectures, thus reducing the training parameter of neural networks in the process of critical posture recognition.
Finite-dimensional estimation algebra on arbitrary state dimension with nonmaximal rank: linear structure of Wong matrix

Ever since Brockett, Clark and Mitter introduced the estimation algebra method, it becomes a powerful tool to classify the finite-dimensional filtering system. In this paper, we investigate finite-dimensional estimation algebra with non-maximal rank. The structure of Wong matrix Ω will be focused on since it plays a critical role in the classification of finite-dimensional estimation algebras. In this paper, we first consider general estimation algebra with non-maximal rank and determine the linear structure of the submatrix of Ω by using rank condition and property of Euler operator. In the second part, we proceed to consider the case of linear rank n−1 and prove the linear structure of Ω. Finally, we give the structure of finite-dimensional filters which implies the drift term must be a quadratic function plus a gradient of a smooth function.
Mobile Money and Banking Development in Sub-Saharan Africa

The study investigated the relationship between mobile money growth and banking development in Sub-Saharan Africa. The question of whether mobile money threatens or supports traditional banks is contentious. Therefore, the motivation was to comprehensively examine the extent of mobile money's influence on banking development. The study used a quantitative research design with aggregated quarterly panel data from the four regions of Sub-Saharan Africa. The Panel ARDL estimation was applied to quantify the nature of the relationship between mobile money and banking development variables. Study findings showed that an increase in active mobile money accounts and volumes was associated with a decline in bank accounts, bank branches, and ATMs in the long run. At the same time, this trend was offset by positive impacts on private sector credit and total bank assets again in the long run. The findings align and extend the technology acceptance models and show that increased use of mobile money technology has substitution and complementary effects on banking development. Policymakers and financial institutions should carefully consider the potential trade-offs and synergies between mobile money adoption and traditional banking services, leveraging the positives while addressing challenges arising from the disruptive forces of technological innovation.
Solitonic Neural Network Acting as an Episodic Memory

This concluding chapter introduces complex solitonic neural networks. Starting from the results obtained for the single X-Junction neuron, the connection between multiple units is described. The ability of these systems to achieve episodic recognition through the construction of refractive index mapping in nonlinear lithium niobate crystals is shown. The chapter concludes with an overview of the current limitations of these systems and a description of future prospects.
The order-<b>K</b>-ification monads

Abstract Monads prove to be useful mathematical tools in theoretical computer science, notably in denoting different effects of programming languages. In this paper, we investigate a type of monads which arise naturally from Keimel and Lawson’s $\mathbf{K}$ -ification. A subcategory of $\mathbf{TOP}_{\mathbf{0}}$ is called of type $\mathrm{K}^{*}$ if it consists of monotone convergence spaces and is of type $\mathrm K$ in the sense of Keimel and Lawson. Each such category induces a canonical monad $\mathcal K$ on the category $\mathbf{DCPO}$ of dcpos and Scott-continuous maps, which is called the order- $\mathbf{K}$ -ification monad in this paper. First, for each category of type $\mathrm{K}^{*}$ , we characterize the algebras of the corresponding monad $\mathcal K$ as k -complete posets and algebraic homomorphisms as k -continuous maps, from which we obtain that the order- $\mathbf{K}$ -ification monad gives the free k -complete poset construction over the category $\mathbf{POS}_{\mathbf{d}}$ of posets and Scott-continuous maps. In addition, we show that all k -complete posets and Scott-continuous maps form a Cartesian closed category. Moreover, we consider the strongness of the order- K -ification monad and conclude with the fact that each order- K -ification monad is always commutative.
Combining Fuzzy Partitioning and Incremental Methods to Construct a Scalable Decision Tree on Large Datasets

The Decision tree algorithm is a very popular classifier for reasoning through recursive partitioning of the data space. To choose the best attributes for splitting, the range of each continuous attribute should be split into two or more intervals. Then partitioning criteria are calculated for each value. Fuzzy partitioning can be used to reduce sensitivity to noise and increase tree stability. Also, tree-building algorithms face memory limitations as they need to keep the entire training dataset in the main memory. In this paper, we introduced a fuzzy decision tree approach based on fuzzy sets. To avoid storing the entire training dataset in the main memory and overcome the memory limitations, the algorithm incrementally builds FDTs. Membership functions are automatically generated. The Fuzzy Information Gain (FIG) is then used as the fast split attribute selection criterion, and leaf expansion is performed only on the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm can overcome memory limitations and balance accuracy and complexity while reducing the complexity of the tree.
Academic Job Satisfaction Research: Conceptual Framework for Russia

Digital transformation, revising the principles of the Bologna system, new directions of academic contacts – this is not even the full list of challenges faced by Russian universities. University teachers have to respond to them, which might lead to increased stress levels and calls for more careful monitoring of the dynamics of overall job satisfaction in academia. Despite research on academic job satisfaction being available, its mutual verification is difficult, since there is no general theoretical and methodological framework for this kind of research, and the set of common indicators is not adapted to Russian realities. In addition, a serious problem when it comes to academic job satisfaction surveys is the administrative divide between whoever initiated the research and teaching staffers, who becomes an object of research with reduced agency. The authors of the article propose an approach based on experience in monitoring the teaching staff’s attitude towards current challenges, which would combine quantitative and qualitative survey methods and focus on universal job satisfaction domains at the university. Choosing relevant academic job satisfaction domains that comprehensively characterize the working conditions of a university teacher should be justified during a series of testing studies, and most importantly — an internal sociological discussion.
Quantum Random Access Codes Implementation for Resource Allocation and Coexistence with Classical Telecommunication

In a world where Quantum Networks are rapidly becoming a reality, the development of the Quantum Internet is gaining increasing interest. Nevertheless, modern quantum networks are still in the early stages of development and have limited capacity to distribute resources among different users -- a constraint that needs to be taken into account. In this work we aim to investigate these constraints, using a novel setup for implementing Quantum Random Access Codes (QRACs), communication protocols known for their quantum advantage over their classical counterparts and semi-device-independent self-testing applications. Our QRAC states, made for the first time using weak coherent pulses instead of entangled single photons, allow us to experimentally test our encoding and decoding strategy from the resource allocation perspective. Moreover, by emulating a coexistent classical communication, we test the resilience of our implementation in presence of noise. The achieved results represent a significant milestone both for theoretical studies of quantum resource allocation and for the implementation of quantum infrastructures capable of coexisting with regular telecommunication networks.
Wearable systems without experiential disruptions: exploring the impact of device feedback changes on explicit awareness, physiological synchrony, sense of agency, and device-body ownership

Technologies on the body that require explicit awareness to be operated or monitored often risk disrupting human awareness and induce stress and excessive cognitive load. With the increasing interest in body-centric technologies, it is thus essential to understand how to build technologies that interface with human awareness without disrupting or requiring too many cognitive resources. In this paper, we build and evaluate a wearable system that uses different feedback types to alter human awareness (of the device). We further demonstrate how this awareness impacts cognitive load, sense of body-ownership, and sense of agency, which are often essential antecedents to successful and continued use. Moreover, we further investigate physiological signals, such as physiological synchrony, as well as qualitative reports in a multimodal analysis. Our results show that devices that provide feedback that deviate from expected behavior tend to generate higher amounts of explicit awareness, and that such increased awareness correlates with increased cognitive load, lower sense of agency and lower sense of body-ownership. Moreover, we find that interoceptive acuity correlates with diminished sense of agency. We discuss their implications for designing wearable body-centric systems that induce or disrupt different levels of awareness to deliver or diminish a sense of body-ownership and agency over the system.