Reviewerly/exHarmony huggingface dataset:
Blending of Contrast Enhancement Techniques for Chest X-Ray Pneumonia Images

Pneumonia, Lung cancer, Atelectasis are common lung diseases that cause notable deaths all over the world. These diseases can be treated well if identified through X-ray images of chest. The Chest X-rays images usually comprise of various discrete values of intensity and elevated contrast, and hence improper contrast and less brightness is observed. In this paper to confront the improper contrast of X-ray images, various contrast enhancement techniques are used such as DSIHE, BBHE, RMSHE along with PSO technique to enhance the X-ray images. Different parameters such as Entropy, PIQE, BRISQUE are used for objective computation results.
Comparing the Effectiveness of Instructional Video Types: An In-Depth Analysis on Pre-Service Teachers for Online Learning

This mixed method study investigated comparatively the effects of instructional video (IV) types used in online learning (OL). A total of 394 pre-service teachers (PTs) participated from two universities in Turkey. Results indicated that the IV type which includes interactive elements and a combination of the human embodiment and instructional contents on screen by using lightboard or green screen technologies are more effective. Not only the details of instructor’s position, visibility, narrative style, facial movements, gestures, cues but also real-like learning settings and video duration enhance the effectiveness of IVs. The opinions of PTs were reported under six themes: video length, interaction elements, instructor’s gestures and facial expressions, naturality and reality, immersion and flow, dynamic drawing. Implications on IV types, suggestions for future studies were also discussed.
The relationship between leadership style and turnover intention of nurses in the public hospitals of Jordan

This study aimed to empirically determine the significant relationships between transformational leadership, authentic leadership and turnover intention among registered nurses (RNs) working in Jordanian public hospitals. This study deployed a quantitative cross-sectional survey using a questionnaire approach. From the 398 questionnaires distributed to the nine public hospitals in three regions in Jordan, there were 354 responses representing a response rate of 88.5%. The findings show that transformational leadership has a negative relationship with turnover intention. In addition, there was a negative relationship between authentic leadership and turnover intention among RNs in public hospitals in Jordan. The study results emphasised the roles of both leadership types in mitigating the turnover intention of RNs in Jordanian public hospitals. The stakeholders involved in the decision-making process should take note of these findings to develop effective strategies to retain nurses and other healthcare staff.
Design methodology for immersive educational resources

Based on the analysis of sources on the problem of research, the article defines types of electronic educational resources (EER), general requirements for them, focuses on didactic and methodological requirements and existing holistic systems of psychological and pedagogical requirements for EER, presents the main types of parameters that can be used to evaluate the quality of EER, analyzes the typical structure of EER and characterizes its components. The analysis of approaches to defining the concepts of “immersiveness”, “immersive educational environment”, “immersive approach in education”, “immersive educational resources” (IER) was carried out, on the basis of which the relationship between the virtual environment and immersive learning environment was determined, the pedagogical aspects of learning in immersive environments were considered, the classification of IER was made. The main components of IER design and implementation are analyzed, and the essence of IER design and IER design methodology is determined. In the main part of the proposed general model of the methodology of IOR design and analyzed the goals, subjects of methods, stages, and content of training, forms of organization of the educational process, methods, teaching tools, and predicted results of the methodology. In the conclusion the elements of the methodology of IЕR design are presented, in particular, a complex IЕR in the form of a distance learning course, examples of designing prototypes of immersive teaching materials, etc. are considered.
Multi-Modal Sarcasm Detection in Social Networks: A Comparative Review

Sentiment Analysis (SA) has become an extremely sought after area of research especially post COVID-19 when people used to spent a lot of time on the social media to interact with each other. This interaction was done through posts having both textual and visual cues and also by participating in online discussions forums. Some of the inherent challenges encountered in the process of SA include discernment of sarcasm, irony, humor, negation, multi-polarity or Aspect-Level Sentiment Analysis (ASA) etc. Researchers are now gradually shifting their focus to the identification and detection of sarcasm and how it can empower SA. Sarcasm expresses a person&#x2019;s downside feelings by using positive words in an implicit way. It also has an overall impact on increasing the efficiency of the SA models. Eliciting sarcastic statements is tough for humans as well as for machines without the knowledge of the context or background in which it is expressed, body language and/or facial expression of the speaker and his voice modulation. This review paper studies some of the approaches used for sarcasm detection and also guides researchers in exploring the different modalities of data for developing applications like a virtual chat-bot or assistant, depression analysis, stress management system at workplace etc.
Improved Fuzzy Algorithm for College Students’ Academic Early Warning

The existing fuzzy clustering algorithms are mostly fuzzy comprehensive evaluation algorithms based on specific elements, but the main problem of such fuzzy algorithms is the lack of overall research on the responsible individuals and the lack of hierarchy in the algorithms. It is suitable for data mining of academic early warning systems. Therefore, an improved fuzzy algorithm based on fuzzy performance evaluation based on composite elements is proposed, and it is applied to the performance evaluation system to solve the complex problems in performance evaluation. In the process of building smart campuses in colleges and universities, academic prewarning, as the main part of smart campuses, mainly uses data mining technology to ensure students complete their studies smoothly and at the same time provides certain decision-making support for colleges and universities. Based on the research topic of the relevant departments of a certain school, this paper aims to build an academic early warning system suitable for the school to ensure that students can successfully complete their studies. The main research contents are divided into two parts: “study early warning model research” and “design and implementation of an academic early warning system.” Through analysis and experiments, it is proved that the model evaluation effect based on the algorithm improvement is the best, with recall reaching 85%, precision reaching 78.96%, and AUC reaching 80.25%.
Sentimental analysis on user’s reviews using BERT

In the current E-Commerce and M−Commerce evolved Internet world, people are expressing their sentiments on the product or service in the form of reviews and ratings. Sentiment analysis finds a contextual inference in the user sentiment which helps the E-Commerce sector and other end-users to realize the opinion of the product or service. People give their opinion in a variety of formats like reviews, blogs, news, or comments. Sentiment Analysis is used quick gaining insights using large volumes of text data which can be helpful in improving the quality of service and can make companies to earn huge profits. In sentimental analysis, modeling of sentimental relations such as word negation and word intensification are great challenge. In the proposed work, Bidirectional Encoder Representations from Transformers (BERT) is constructed for classifying user sentiments on IMDB Movie Review and Amazon Fine Food Review datasets. Word negations and intensifications are also considered for deriving the sentiment of reviews. The experimental results shows that the BERT can capture the sentimental relations effectively than basic ML, DL models and other recent literatures.
FocusNet: Classifying better by focusing on confusing classes

Nowadays, most classification networks use one-hot encoding to represent categorical data because of its simplicity. However, one-hot encoding may affect the generalization ability as it neglects inter-class correlations. We observe that, even when a neural network trained with one-hot labels produces incorrect predictions, it still pays attention to the target image region and reveals which classes confuse the network. Inspired by this observation, we propose a confusion-focusing mechanism to address the class-confusion issue. Our confusion-focusing mechanism is implemented by a two-branch network architecture. Its baseline branch generates confusing classes, and its FocusNet branch, whose architecture is flexible, discriminates correct labels from these confusing classes. We also introduce a novel focus-picking loss function to improve classification accuracy by encouraging FocusNet to focus on the most confusing classes. The experimental results validate that our FocusNet is effective for image classification on common datasets, and that our focus-picking loss function can also benefit the current neural networks in improving their classification accuracy.
Evolutionary Multitasking AUC Optimization [Research Frontier]

Learning to optimize the area under the receiver operating characteristics curve (AUC) performance for imbalanced data has attracted much attention in recent years. Although there have been several methods of AUC optimization, scaling up AUC optimization is still an open issue due to its pairwise learning style. Maximizing AUC in the large-scale dataset can be considered as a non-convex and expensive problem. Inspired by the characteristic of pairwise learning, the cheap AUC optimization task with a small-scale dataset sampled from the large-scale dataset is constructed to promote the AUC accuracy of the original, large-scale, and expensive AUC optimization task. This paper develops an evolutionary multitasking framework (termed EMTAUC) to make full use of information among the constructed cheap and expensive tasks to obtain higher performance. In EMTAUC, one mission is to optimize AUC from the sampled dataset, and the other is to maximize AUC from the original dataset. Moreover, due to the cheap task containing limited knowledge, a strategy for dynamically adjusting the data structure of inexpensive tasks is proposed to introduce more knowledge into the multitasking AUC optimization environment. The performance of the proposed method is evaluated on a series of binary classification datasets. The experimental results demonstrate that EMTAUC is highly competitive to single task methods and online methods. Supplementary materials and source code implementation of EMTAUC can be accessed at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xiaofangxd/EMTAUC</uri> .
Visible–infrared person re-identification based on key-point feature extraction and optimization

Feature extraction for visible–infrared person re-identification (VI-ReID) is challenging because of the cross-modality discrepancy in the images taken by different spectral cameras. Most of the existing VI-ReID methods often ignore the potential relationship between features. In this paper, we intend to transform low-order person features into high-order graph features, and make full use of the hidden information between person features. Therefore, we propose a multi-hop attention graph convolution network (MAGC) to extract robust person joint feature information using residual attention mechanism while reducing the impact of environmental noise. The transfer of higher order graph features within MAGC enables the network to learn the hidden relationship between features. We also introduce the self-attention semantic perception layer (SSPL) which can adaptively select more discriminant features to further promote the transmission of useful information. The experiments on VI-ReID datasets demonstrate its effectiveness.
A <scp>blockchain‐based</scp> approach to secure electronic health records using fuzzy commitment scheme

Abstract The electronic health record is the most sensitive data which needs a secure framework to protect it from data breaching. Recently, blockchain‐based platforms are used in healthcare services for data security, data sharing and availability of immutable data during medico‐legal verification. In this paper, a blockchain‐based approach for securing electronic health records using elliptic curve cryptography (ECC) and a biometric‐based fuzzy commitment scheme is proposed. The proposed approach addresses common issues of blockchain, namely scalability, integrity, confidentiality and decentralization of the ledger. The security of the proposed scheme was verified using the Random Oracle model. The block time for a variable number of transactions per block was measured and compared with the block time of the existing approaches. Also, the proposed approach was compared with the existing approaches with reference to various security features.
Whale optimization algorithm based on Levy flight and memory for static smooth path planning

In this paper, a whale optimization algorithm based on Levy flight and memory (WOALFM) is proposed to solve the curvature discontinuity problem in the global path planning of unmanned vehicles. Levy flight and chaotic mapping are introduced to disturb the solutions of each generation and enhance the diversity of solutions. A memory strategy based on fractional-order expansion is presented to remember the influence of the positions of individuals in previous generations on the positions of current generation. This strategy based on Levey flight and memory can enhance searching ability and avoid falling into local optimum. Furthermore, the ratio of global searching to local searching can be adjusted to achieve desired results in WOALFM algorithm. The proposed WOALFM algorithm is tested and compared with five algorithms including whale optimization algorithm (WOA), Moth-Flame Optimization (MFO), particle swarm optimization (PSO), Fractional-Order Velocity based Particle Swarm Optimization (FOPSO) and Grey Wolf Optimizer (GWO) on 23 standard benchmark functions. The experimental results show the effectiveness of WOALFM. The proposed algorithm is applied to smooth path planning problem of unmanned vehicles. Three factors, including the length, curvature and curvature derivative of a path are considered in order to obtain the shortest smooth path without collisions. The experimental results show that more collision-free paths can be obtained in lower computational cost by the presented method.
Knowledge Graphs: Introduction, History and, Perspectives

Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central role in representing information extracted by AI systems, and for improving the predictions of AI systems by giving them knowledge expressed in KGs as input. The goals of this article are to (a) introduce KGs and discuss important areas of application that have gained recent prominence; (b) situate KGs in the context of the prior work in AI; and (c) present a few contrasting perspectives that help in better understanding KGs in relation to related technologies.
A Novel Stripe Noise Removal Model for Infrared Images

Infrared images often carry obvious streak noises due to the non-uniformity of the infrared detector and the readout circuit. These streak noises greatly affect the image quality, adding difficulty to subsequent image processing. Compared with current elimination algorithms for infrared stripe noises, our approach fully utilizes the difference between the stripe noise components and the actual information components, takes the gradient sparsity along the stripe direction and the global sparsity of the stripe noises as regular terms, and treats the sparsity of the components across the stripe direction as a fidelity term. On this basis, an adaptive edge-preserving operator (AEPO) based on edge contrast was proposed to protect the image edge and, thus, prevent the loss of edge details. The final solution was obtained by the alternating direction method of multipliers (ADMM). To verify the effectiveness of our approach, many real experiments were carried out to compare it with state-of-the-art methods in two aspects: subjective judgment and objective indices. Experimental results demonstrate the superiority of our approach.
A multi-input multi-label claims channeling system using insurance-based language models

Servicing claims, a time consuming and labor-intensive task, plays a pivotal role in how insurance companies serve their policyholders. Claims may not get routed early enough in the process to the correct team, leading to dissatisfied customers because of inefficient claim’s management. Claims departments need to process substantial amount of structured and unstructured data to successfully route claims — a process referred to as channeling. The scope of the present work is limited to the auto insurance claims with a focus on four different downstream classification tasks including claims’ fraud and bodily injuries. We propose a system that utilizes claims’ notes and structured data to build machine learning models, which employ an insurance-based language model built by enhancing Google’s BERT, to route claims to domain experts. The proposed channeling system successfully routes important claims to domain experts for additional review, which can substantially improve claims management and customer satisfaction.
Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning

Molecular subtypes of breast cancer are important references to personalized clinical treatment. For cost and labor savings, only one of the patient's paraffin blocks is usually selected for subsequent immunohistochemistry (IHC) to obtain molecular subtypes. Inevitable block sampling error is risky due to the tumor heterogeneity and could result in a delay in treatment. Molecular subtype prediction from conventional H&E pathological whole slide images (WSI) using the AI method is useful and critical to assist pathologists to pre-screen proper paraffin block for IHC. It is a challenging task since only WSI-level labels of molecular subtypes from IHC can be obtained without detailed local region information. Gigapixel WSIs are divided into a huge amount of patches to be computationally feasible for deep learning, while with coarse slide-level labels, patch-based methods may suffer from abundant noise patches, such as folds, overstained regions, or non-tumor tissues. A weakly supervised learning framework based on discriminative patch selection and multi-instance learning was proposed for breast cancer molecular subtype prediction from H&E WSIs. Firstly, co-teaching strategy using two networks was adopted to learn molecular subtype representations and filter out some noise patches. Then, a balanced sampling strategy was used to handle the imbalance in subtypes in the dataset. In addition, a noise patch filtering algorithm that used local outlier factor based on cluster centers was proposed to further select discriminative patches. Finally, a loss function integrating local patch with global slide constraint information was used to fine-tune MIL framework on obtained discriminative patches and further improve the prediction performance of molecular subtyping. The experimental results confirmed the effectiveness of the proposed AI method and our models outperformed even senior pathologists, which has the potential to assist pathologists to pre-screen paraffin blocks for IHC in clinic.
Uncharted Aspects of Human Intelligence in Knowledge-Based “Intelligent” Systems

This paper briefly surveys several prominent modeling approaches to knowledge-based intelligent systems (KBIS) design and, especially, expert systems and the breakthroughs that have most broadened and improved their applications. We argue that the implementation of technology that aims to emulate rudimentary aspects of human intelligence has enhanced KBIS design, but that weaknesses remain that could be addressed with existing research in cognitive science. For example, we propose that systems based on representational plasticity, functional dynamism, domain specificity, creativity, and concept learning, with their theoretical and experimental rigor, can best characterize the problem-solving capabilities of humans and can best overcome five key limitations currently exhibited by knowledge-based intelligent systems. We begin with a brief survey of the relevant work related to KBIS design and then discuss these five shortcomings with new suggestions for how to integrate results from cognitive science to resolve each of them. Our ultimate goal is to increase awareness and direct attention to areas of theoretical and experimental cognitive research that are fundamentally relevant to the goals underlying KBISes.
Applying Hybrid Deep Neural Network for the Recognition of Sign Language Words Used by the Deaf COVID-19 Patients

The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare centers have taken extreme care to minimize the risk of exposure to the virus by restricting the visitors and relatives of the patients. The dramatic changes happened in the healthcare norms have made it hard for the deaf patients to communicate and receive appropriate care. This paper reports a work on automatic sign language recognition that can mitigate the communication barrier between the deaf patients and the healthcare workers in India. Since hand gestures are the most expressive components of a sign language vocabulary, a novel dataset of dynamic hand gestures for the Indian sign language (ISL) words commonly used for emergency communication by deaf COVID-19 positive patients is proposed. A hybrid model of deep convolutional long short-term memory network has been utilized for the recognition of the proposed hand gestures and achieved an average accuracy of 83.36%. The model performance has been further validated on an alternative ISL dataset as well as a benchmarking hand gesture dataset and obtained average accuracies of 97% and 99.34±0.66% , respectively.