Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Unified Informetrics with Scopus and Web of Science

Numerous bibliographic databases track the number of publications, citations, and h-index to maintain the progress of an individual. However, the choice of journals varies among these databases, hence they produce different numbers of publications, citations and h-index for the same author. Various literatures are available on the comparative analysis of such bibliographic databases stating the fact that there are the differences between bibliometrics generated by different bibliographic databases but none of the literature provides any comprehensive or complete solution to overcome such differences. At present, there is no common platform that can provide a single count of number of publications, citations and h-index across multiple bibliographic databases. To overcome this limitation, we propose a new method in academic research publication to calculate weighted unified (single) informetrics for an author. With the proposed solution, one can view a single article, citation, and h-index count computed from multiple indexing databases. In this study, the data from Scopus and Web of Science is used to generate a new database named “conflate”. Further, a comparative analysis of the proposed model is performed with Scopus and Web of Science at three levels: author, organization, and journal. The proposed model can be observed as a new single indicator to determine the research influence of the author, organization, and journal. Aim: We propose a new method in academic research publication to calculate weighted unified informetrics. With the proposed solution, one can view a single article, citation, and h-index count computed from multiple indexing databases. The proposed model can be observed as a new indicator to determine the research influence of author, organization, and journal.
Deep-Transfer-Learning-Based Abnormal Behavior Recognition Using Internet of Drones for Crowded Scenes

Intelligent identification of abnormal behaviors in crowd scenes enables far more efficient development of smart cities. In recent approaches, abnormalities are detected using an autonomous unmanned aerial vehicle (UAV) by observing activity and representing crowd features such as density, direction, and anomalous behavior from acquired video frames. Thus, integrating fast monitoring via an autonomous UAV with automated procedures would significantly improve the effectiveness of detecting abnormal activities. Our work presents a real-time surveillance strategy for detecting anomalous behavior in crowds that is based on deep transfer learning and the Internet of Drones,and can be used to distinguish dynamic crowds. The deep structure is a simplified and lightweight representation of the ResNet learning structure. The proposed model that detects the abnormal behavior in this study processes the data analyzed from UAVs and intendS to increase its efficiency, robust-ness, and accuracy. For several UAV videos, the average accuracy is more than 90 percent. The experimental results reveal that the suggested method robustly detects abnormal behaviors in crowd scenarios using frames from UAV recordings with demanding conditions.
Improved Domain Generalization for Cell Detection in Histopathology Images via Test-Time Stain Augmentation

Automated cell detection in histopathology images can provide a valuable tool for cancer diagnosis and prognosis, and cell detectors based on deep learning have achieved promising detection performance. However, the stain color variation of histopathology images acquired at different sites can deteriorate the performance of cell detection, where a cell detector trained on a source dataset may not perform well on a different target dataset. Existing methods that address this domain generalization problem perform stain normalization or augmentation during network training. However, such stain transformation performed during network training may still not be optimally representative of the test images from the target domain. Therefore, in this work, given a cell detector that may be trained with or without consideration of domain generalization, we seek to improve domain generalization for cell detection in histopathology images via test-time stain augmentation. Specifically, a histopathology image can be decomposed into the stain color matrix and stain density map, and we transform the test images by mixing their stain color with that of the source domain, so that the mixed images may better resemble the source images or their stain-transformed versions used for training. Since it is difficult to determine the optimal amount of the mixing, we choose to generate a number of transformed test images where the stain color mixing varies. The generated images are fed into the given detector, and the outputs are fused with a robust strategy that suppresses improper stain color mixing. The proposed method was validated on a publicly available dataset that comprises histopathology images acquired at different sites, and the results show that our method can effectively improve the generalization of cell detectors to new domains.
Domain-Wall / Unary Encoding in QUBO for Permutation Problems

QUBO is the input language of quantum computers based on quantum annealing such as the D-Wave systems and of the "quantum-inspired" dedicated hardware such as Fujitsu’s Digital Annealing Unit. We propose a new way to represent integer variables in QUBO together with constraints on those variables by using unary/domain-wall encoding in order to solve constrained optimization problem by quantum annealing. We detail in particular how to encode permutation constraints on integers within the unary/domain-wall encoding scheme, as these constraints are omnipresent in complex combinatorial problems. This new encoding in QUBO is interesting in the context of quantum annealing because it minimizes interactions between variables, and therefore connections needed between qubits in hardware. When compared to the usual one-hot encoding of integers in QUBO, we show than the new encoding scheme divides by nearly a factor 2 the number of connections between qubits for complex problems involving permutation constraints and makes it possible to cope with larger problem instances on current quantum and quantum-inspired hardware.
A Classification Technique for English Teaching Resources and Merging Using Swarm Intelligence Algorithm

The evolution of social education has necessitated the optimization of various teaching approaches, and the classification of English teaching resources is one of the crucial factors. With the development of electronic computers and Big Data technologies, the classification of teaching resources that could not be realized before has become possible now. However, the traditional classification methods cannot meet the requirements of modern computing due to the limitations of implementation. The emergence of swarm intelligence algorithms makes the classification of teaching resources possible. Swarm intelligence algorithm is a swarm-based multipoint random search algorithm, which includes evolutionary algorithm, immune algorithm, particle swarm procedure (PSO), ant colony process, artificial fish swarm mechanism, and other typical intelligent techniques. The swarm intelligence algorithm has strong robustness and strong global and local search capabilities, as well as, implicit parallelism. Furthermore, it has no special requirements for objective functions and constraint functions. It has the function of “black box” and can overcome problems where traditional optimization methods are insufficient. The swarm intelligence algorithm has a large space for development and rich forms of expression, and there is an essential connection between them, so that they can be well integrated. The key goal of this study is to implement the swarm intelligence algorithm to the task of classifying English teaching resources and to provide a reference for optimizing the English teaching model. The experimental findings demonstrate that the suggested classification model for English teaching resources has excellent performance, is favorable to enhancing the utilization rate of teaching resources, and is applicable to other disciplines.
Domain-aware Intermediate Pretraining for Dementia Detection with Limited Data

Detecting dementia using human speech is promising but faces a limited data challenge. While recent research has shown general pretrained models (e.g., BERT) can be applied to improve dementia detection, the pretrained model can hardly be fine-tuned with the available small dementia dataset as that would raise the overfitting problem. In this paper, we propose a domain-aware intermediate pretraining to enable a pretraining process using a domain-similar dataset that is selected by incorporating the knowledge from the dementia dataset. Specifically, we use pseudo-perplexity to find an effective pretraining dataset, and then propose dataset-level and sample-level domain-aware intermediate pretraining techniques. We further employ information units (IU) from previous dementia research and define an IU-pseudo-perplexity to reduce calculation complexity. We confirm the effectiveness of perplexity by showing a strong correlation between perplexity and accuracy using 9 datasets and models from the GLUE benchmark. We show that our domain-aware intermediate pretraining improves detection accuracy in almost all cases. Our results suggested that the difference in text-based perplexity values between patients with Alzheimer's Disease (AD) and Healthy Control (HC) is still small, and the perplexity incorporating acoustic features (e.g., pause) may make the pretraining more effective.
Scenario Models for the Functioning of a Medical Cyber-Physical System in Normal and Emergency Situations

The existing solutions in the field of medical cyber-physical systems, considers their individual components and patient monitoring systems are studied in the paper. It has been established that there are separate developments in this area, but there is no comprehensive approach to organizing the cyber-physical environment of patients in hospitals and various other inpatient institutions. In the course of the study, some regular and emergency scenarios for the functioning of medical cyber-physical systems have been studied. Its are implemented when patients interact with the system while at home, as well as while in hospitals and various other inpatient institutions. These scenarios have been simulated at the logical level. To model processes, activity diagrams are constructed, data structures are described using class diagrams. The generalized architecture of the medical cyber-physical system is given. The obtained solutions can be used in the design and development of medical cyber-physical systems and their components that monitor the health status of patients, interact with them, collect and visualize health data, and solve auxiliary problems. The application of the proposed solutions will enable medical specialists to receive and evaluate information more quickly, which, in turn, will lead to an increase in the quality of medical services provided.
An Intelligent Recommendation Model for Health Culture Based on Short Video Content Analysis in the Mobile Internet Environment

The heart and soul of the Chinese people and the greatest cultural soft power of contemporary China are excellent Chinese healthy cultural environments. Short video platforms and short videos have a chance to develop as the Internet revolution continues to intensify and mobile clients gain popularity. This essay examines the challenges involved in producing brief videos that promote a healthy cultural environment in the new context of internet plus, as well as the means of transmitting such videos and creative methods for doing so. Excellent communication content is created in this paper from two perspectives of visual presentation and in-depth meaning, allowing users to fully understand the connotation of a healthy cultural environment. Through the theme of a healthy cultural environment, topic discussion and characteristic activities will be carried out to encourage users' spontaneous sharing of the interactive behavior of communication and the fission communication of brief videos of a healthy environment. This paper develops a model for intelligent recommendation and short video content analysis based on this. In order to further encourage the creative dissemination and development of short videos about healthy cultural environments, the model is used to choose high-quality videos and accurately recommend them to the appropriate users and to serve as an example of how a quick video can be used to communicate innovation and a healthy cultural environment.
Blended Educational Technologies in Higher Education: Systematic Review of Domestic Publications

The article presents the results of generalization and systematization of scientific and practical information on the variety of types of blended educational technologies used in Russian higher education institutions. This specific product of digitalization causes the greatest interest of researchers and practitioners in the field of education. This systematic review is based on national scientific publications related to the development and practical implementation of educational technologies in higher education. The authors have analyzed 180 papers published between 2016 and 2021 in peer-reviewed scientific journals approved by the Higher Attestation Commission. Methods of qualitative and quantitative content analysis, systems analysis and contextological analysis, as well as analytical grouping, have been used.Content analysis of publications showed that the main property of blended educational technologies is their variability, which manifests itself through the described system of indicators. However, the structural organization of blended educational technologies models remains the most debatable issue among researches.The authors have identified and described the main directions in the development of the theory and practical application of blended educational technologies, namely: the formation of the structure and boundaries of the conceptual and terminological field; elaboration of the methodological basis of blended education development and definition of theoretical basis for changing traditional learning systems by designing a learning process based on the mechanisms of pedagogical and digital technologies integration.It has been established that the empirical studies with the elements of experimental work focused on defining the most effective combinations of traditional and digital educational technologies dominate in the national scientific community. The most popular methods of such integration include rotation of forms and types of learning activity in traditional and digital learning environments, integration of digital and traditional educational technologies based on the Moodle learning management system, as well as inclusion of separate forms and means of e-learning in the educational process.
Unsupervised Site Adaptation by Intra-site Variability Alignment

A medical imaging network that was trained on a particular source domain usually suffers significant performance degradation when transferred to a different target domain. This is known as the domain-shift problem. In this study, we propose a general method for transfer knowledge from a source site with labeled data to a target site where only unlabeled data is available. We leverage the variability that is often present within each site, the intra-site variability, and propose an unsupervised site adaptation method that jointly aligns the intra-site data variability in the source and target sites while training the network on the labeled source site data. We applied our method to several medical MRI image segmentation tasks and show that it consistently outperforms state-of-the-art methods.
Review of electric vehicles' charging data anomaly detection based on deep learning

Anomalies in data may be caused by errors in the data, but sometimes the appearance of anomalous data often also suggests the emergence of a new domain or a new underlying process. Anomaly detection has therefore been an active area of research for decades. This paper is based on what anomaly detection is, the main areas of application for anomaly detection, and the challenges of anomaly detection at this stage. Deep learning-based anomaly detection is proposed based on the unpredictability, diversity, and sparsity that exist in anomaly detection, and deep learning anomaly detection algorithms are classified into unsupervised deep anomaly detection algorithms and semi-supervised deep anomaly detection algorithms based on the use of anomaly labels. This paper reviews and analyses the research on these two algorithms, and elaborates on the advantages and disadvantages of each type of anomaly detection algorithm when applied. Deep anomaly detection is also applied to the anomaly detection of charging data and is classified according to the different types of charging data, into deep anomaly detection for electric vehicles, deep anomaly detection for charging stations, and deep anomaly detection for charging poles. Finally, we provide an outlook on various algorithms for anomaly detection and future applications of anomaly detection in charging data.
What makes a home? Designing home personas to represent the homes of families caring for children with medical complexity

Personas are widely recognized as valuable design tools for communicating dimensions of individuals, yet they often lack critical contextual factors. For those people managing chronic health conditions, the home is a critical context of their patient work system (PWS). We propose the development of ‘home personas’ to convey essential aspects of the home context to those tasked with designing technologies and interventions to fit it. We used an iterative, multi-stakeholder design process to design ‘home personas’ for a model population, families caring for children with medical complexity. Each of the four resultant home personas—Multi-level, Customized, Ranch, and Rental—has a unique home layout, pain points, and are described on three dimensions that emerged from the data. This study builds on a foundation of work in the emerging field of Patient Ergonomics, describing a mechanism for distilling rich descriptions of the PWS into brief yet informative design tools.
Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background

With the development of Internet technology and the arrival of the knowledge-driven era, the breadth and depth of educational informatization are increasing day by day. Educational technology is not only a subject but also a career adapted to education and teaching. The growth speed of modern educational technology and the size of its benefits determine its management level to a large extent. With new technologies, new ideas, and new social needs, it is difficult for new ideas, new thoughts, and new methods to make the traditional e-learning management to accommodate the demands of the new era. At present, the work efficiency of modern educational technology visualization systems is generally not high, and modern distance teaching has an increasing demand for management informatization. However, there is a lack of a management platform for distance education that adapts to organizational characteristics such as openness, dynamics, flexibility, individualization, and decentralization. Therefore, this study introduces machine learning and BP neural network, establishes a visual modern distance teaching management system model, and uses machine learning algorithms to learn the visual process. The experimental results show that the system efficiency after learning is higher, and the time required for visualization of different groups in the experiment is 14.32 s, 13.18 s, 12.27 s, and 13.64 s, respectively, which effectively improves the efficiency of visualization and reduces the consumption of human resources.
Data Hunches: Incorporating Personal Knowledge into Visualizations

The trouble with data is that it frequently provides only an imperfect representation of a phenomenon of interest. Experts who are familiar with their datasets will often make implicit, mental corrections when analyzing a dataset, or will be cautious not to be overly confident about their findings if caveats are present. However, personal knowledge about the caveats of a dataset is typically not incorporated in a structured way, which is problematic if others who lack that knowledge interpret the data. In this work, we define such analysts' knowledge about datasets as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data hunches</i> . We differentiate data hunches from uncertainty and discuss types of hunches. We then explore ways of recording data hunches, and, based on a prototypical design, develop recommendations for designing visualizations that support data hunches. We conclude by discussing various challenges associated with data hunches, including the potential for harm and challenges for trust and privacy. We envision that data hunches will empower analysts to externalize their knowledge, facilitate collaboration and communication, and support the ability to learn from others' data hunches.
Reaching for upper bound ROUGE score of extractive summarization methods

The extractive text summarization (ETS) method for finding the salient information from a text automatically uses the exact sentences from the source text. In this article, we answer the question of what quality of a summary we can achieve with ETS methods? To maximize the ROUGE-1 score, we used five approaches: (1) adapted reduced variable neighborhood search (RVNS), (2) Greedy algorithm, (3) VNS initialized by Greedy algorithm results, (4) genetic algorithm, and (5) genetic algorithm initialized by the Greedy algorithm results. Furthermore, we ran experiments on articles from the arXive dataset. As a result, we found 0.59 and 0.25 scores for ROUGE-1 and ROUGE-2, respectively achievable by the approach, where the genetic algorithm initialized by the Greedy algorithm results, which happens to yield the best results out of the tested approaches. Moreover, those scores appear to be higher than scores obtained by the current state-of-the-art text summarization models: the best score in the literature for ROUGE-1 on the same data set is 0.46. Therefore, we have room for the development of ETS methods, which are now undeservedly forgotten.
FedMSA: A Model Selection and Adaptation System for Federated Learning

Federated Learning (FL) enables multiple clients to train a shared model collaboratively without sharing any personal data. However, selecting a model and adapting it quickly to meet user expectations in a large-scale FL application with heterogeneous devices is challenging. In this paper, we propose a model selection and adaptation system for Federated Learning (FedMSA), which includes a hardware-aware model selection algorithm that trades-off model training efficiency and model performance base on FL developers' expectation. Meanwhile, considering the expected model should be achieved by dynamic model adaptation, FedMSA supports full automation in building and deployment of the FL task to different hardware at scale. Experiments on benchmark and real-world datasets demonstrate the effectiveness of the model selection algorithm of FedMSA in real devices (e.g., Raspberry Pi and Jetson nano).