Reviewerly/exHarmony huggingface dataset:
Threshold Determination Method for Detecting the Gazing Areas on a <scp>PC</scp> Screen While Silent Reading Japanese Texts

This study aims to develop a method that can detect the areas of Japanese texts that are difficult to read on a PC screen by analyzing eye movements. An effective method for detecting the areas of text that are difficult to read is to create a heatmap and consider the areas where the eye stays for longer than a fixation duration threshold (threshold). Usually, the threshold is determined through a 50 ms step search. However, since the eye stay time (fixation duration) for difficult‐to‐read text is longer than the fixation duration when reading smoothly, it is expected that steps that last longer than 50 ms are more effective at detecting the threshold. Furthermore, there are individual differences in the speed at which we comprehend text. In this study, we proposed a method that considers both the efficient search and individual differences among each subject. The experimental results showed that the threshold determined by the proposed method is more useful than the thresholds of 1000 and 800 ms that were used in a previous study, as well as the usual fixation duration of 250 ms. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
CBIR-ANR: A content-based image retrieval with accuracy noise reduction

Due to the expansion of multimedia data leveraged by social networks and smartphone devices, large sets of information are available and increasing daily. In this context, information retrieval is crucial to open new opportunities to individuals, governments, and businesses. Therefore, we present the CBIR-ANR software in which the content-based image retrieval (CBIR) is followed by an accuracy noise reduction (ANR) strategy that adjusts query responses and increases assertiveness in image retrieval. Also, the software combines three low-level features to form a 187-dimensional feature vector, which is size efficient for large-scale data sets and competitive with related work.
Iteration-free digital quantum simulation of imaginary-time evolution based on the approximate unitary expansion

Abstract Imaginary-time evolution plays an important role in many areas of quantum physics and has been widely applied to the ground-state determination of various Hamiltonian in the quantum computation field. In this work, we propose an iteration-free quantum algorithm in a full gate-based frame using the approximate unitary expansion to simulate the imaginary-time evolution operator, avoiding the resource overhead caused by repeated measurement for state reconstruction or complex pre-calculations in the classical computers. We detail the algorithm and analyze the complexity and related characteristics including a lower bound for ancillary qubits at a given success probability. Then an application demonstration of the algorithm in quantum chemistry with hydrogen molecule under noiseless and noisy conditions is offered. In addition, we present another imaginary-time evolution simulation method based on similar construction schemes. Our algorithms can serve as the alternative proposals for the imaginary-time evolution realization in the future fault-tolerant quantum computers.
Evaluation Model of Physical Education Teaching Effect Based on AHP Algorithm

The multifaceted sources of physical education teaching factors and the uncertainty of evaluation have an impact on the qualitative and quantitative evaluation results of teaching effects. In order to improve the evaluation accuracy of the physical education teaching effect, the evaluation model of the physical education teaching effect was designed based on the AHP algorithm. The evaluation model is based on monitoring the whole process of teaching. Based on the multifaceted sources of physical education teaching factors and the uncertainty of evaluation, the overall objectives and selects three-level evaluation indicators were analyzed. The AHP algorithm was used to establish the hierarchical structure and obtained the total ranking and comprehensive score of the hierarchy. The test results show that the teaching evaluation model designed in this paper has an RMSE mean value of 1.923, which has higher evaluation accuracy and is conducive to the improvement of teaching quality.
New results on permutation binomials of finite fields

After a brief review of the existing results on permutation binomials of finite fields, we introduce the notion of equivalence among permutation binomials (PBs) and describe how to bring a PB to its canonical form under equivalence. We then focus on PBs of Fq2 of the form Xn(Xd(q−1)+a), where n and d are positive integers and a∈Fq2⁎. Our contributions include two nonexistence results: (1) If q is even and sufficiently large and aq+1≠1, then Xn(X3(q−1)+a) is not a PB of Fq2. (2) If 2≤d|q+1, q is sufficiently large and aq+1≠1, then Xn(Xd(q−1)+a) is not a PB of Fq2 under certain additional conditions. (1) partially confirms a recent conjecture by Tu et al. (2) is an extension of a previous result with n=1.
Performance Comparison between RSA and El-Gamal Algorithms for Speech Data Encryption and Decryption

This article presents a performance comparison of two known public key cryptography techniques namely RSA (Rivest–Shamir–Adleman) and El-Gamal algorithms to encrypt/decrypt the speech signals during transferring over open networks. Specifically, this work is divided into two stages. The first stage is enciphering-deciphering the input speech file by employing the RSA method. The second stage is enciphering-deciphering the same input speech file by employing the El-Gamal method. Then, a comparative analysis is performed to test the performance of both cryptosystems using diverse experimental and statistical analyses for the ciphering and deciphering procedures like some known speech quality measures: histogram, spectrogram, correlation, differential, speed performance and noise effect analyses. The analyses outcomes reveal that the RSA and El-Gamal approaches are efficient and adequate for providing high degree of security, confidentiality and reliability. Additionally, the outcomes indicate that the RSA speech cryptosystem outperforms its counterpart the El-Gamal speech cryptosystem in most of ciphering/deciphering speech performance metrics.
Breast Cancer Classification Model Using Principal Component Analysis and Deep Neural Network

Life-threatening cancer is prevalent over the globe. According to statistics, most people are diagnosed with cancer in the later stages, even though cancer can be prevented and cured in early stages. The goal of this research is to diagnose a breast cancer is either benign or malignant, as well as to forecast the likelihood of a cancer recurrence even after a course of therapy has been completed. Despite the fact that many machine learning algorithms have resulted in strong predictions, the accuracy in the early stages of categorization is not up to the expected level. Deep learning (DL), a higher degree of machine learning, can forecast breast cancer types and recurrences. Classifiers were built using a deep neural network (DNN) that used Principal Component Analysis (PCA) to choose features. Different machine learning techniques are compared with the proposed system’s accuracy. Early-stage breast cancer prediction is more accurate with the DNN-based method. The clinical management system will benefit from the proposed system since it will aid in identification of cancer at an early stage and the subsequent provision of appropriate therapy.
Design and Implementation of Machine Learning-Based Hybrid Model for Face Recognition System

Face recognition technologies must be able to recognize users’ faces in a chaotic environment. Facial detection is a different issue from facial recognition in that it requires reporting the position and size of every face in an image, whereas facial recognition does not allow for this. Due to their general similarity in look, the photographs of the same face have several alterations, which makes it a challenging challenge to solve. Face recognition is an extremely challenging process to do in an uncontrolled environment because the lighting, perspective, and quality of the image to be identified all have a significant impact on the process's output. The paper proposed a hybrid model for the face recognition using machine learning. Their performance is calculated on the basis of value derived for the FAR, FRR, TSR, ERR. At the same time their performance is compared with some existing machine learning model. It was found that the proposed hybrid model achieved the accuracy of almost 98%.
Knowledge Transfer in Software Development Teams—Systematic Mapping

The acquisition of new knowledge, by means of teaching programming languages, tools, methodologies, and development processes, is a need in the software industry to retain the experience of the programmer within the organization. This work aims analysing existing research on techniques, tools, approaches and experiences applied to the transmission and acquisition of new knowledge. Additionally, to analyse the learning process within an organizational environment, and how a company captures, maintains and transmits the knowledge. We conducted a survey to identify the software practitioner needs regarding experience and knowledge transfer. Once we could realize there is a challenge to manage the knowledge, a systematic mapping of the literature is carried out. We have considered works published between 2007 and 2019. 51 articles were selected, 71% are studies in industry, 29% are focused on university education. Our findings reveal the great challenge involved in studying the acquisition and exchange of new knowledge. Additionally, we were supported with an initial survey to validate results. Much of the literature focuses on analysing academic processes, more research is needed on the process of learning new knowledge with respect to programming languages, tools, methodologies, cycles and development processes in the software industry.
The Key Criteria for Predicting Unusual Behavior in the Elderly With Deep Learning Models Under 5G Technology

Deep learning algorithms and technology based on 5G networks may be able to help identify unusual behavior in elderly people. Because 5G networks have a lower latency and a greater bandwidth, it is possible to use more complex algorithms and larger data sets for training and detection in a real-time. On top of that, real-time analysis of the data gathered through in-home monitoring of the elderly can become much simpler to carry out with the help of 5G’s potential to simplify the process. However, the system needs to be developed in a way that it considers the preferences of the most important criteria of elderly people, who have more requirements in terms of their living situation and the environment in which they live. Therefore, this study advocated using “Decision Making Trial and Evaluation Laboratory” (DEMATEL) to analyze the most crucial criteria (feature) required for creating a model for identifying odd behavior in elderly persons. Convolutional Neural Networks (CNNs) and Long Short-Term Memories (LSTMs) are adopted in detecting unusual behavior in the elderly after the analysis key criteria for predicting unusual behavior in the elderly by DEMATEL. The research established a concept by linking the SIMADL dataset with the dimension of elderly people’s behavior and performed an experimental analysis using both CNN and LSTM. Performance evaluations show that the LSTM performs better in detecting unusual behavior in elderly persons with 96% accuracy. Depressive disorder is the most significant aspect of ageing that might lead to a typical unusual behavior in elderly persons, according to DEMATEL analysis.
Self-Training With Double Selectors for Low-Resource Named Entity Recognition

Named Entity Recognition (NER) is fundamental to multiple downstream natural language processing (NLP) tasks, but most advanced NER methods heavily rely on massive labeled data with high cost. In this paper, we explore the effectiveness of self-training for low-resource NER. It is one of the semi-supervised approaches to reduce the reliance on manual annotation. However, random pseudo sample selection in standard self-training framework may cause serious error propagation, especially for token-level tasks. To that end, this paper focuses on pseudo sample selection and proposes a new self-training framework with double selectors, namely auxiliary judge task and entropy-based confidence measurement. Specifically, the auxiliary judge task is proposed to filter out the pseudo samples with wrong predictions. The entropy-based confidence measurement is introduced to select pseudo samples with high quality. In addition, to make full use of all pseudo samples, we propose a cumulative function based on the idea of curriculum learning to prompt the model to learn from easy samples to hard ones. Samples with low quality are filtered out through the double selectors, which is more conducive to the training of student models. Experimental results on five NER benchmark datasets from different languages indicate the effectiveness of the proposed framework over several advanced baselines.
SketchNE: Embedding Billion-Scale Networks Accurately in One Hour

We study large-scale network embedding with the goal of generating high-quality embeddings for networks with more than 1 billion vertices and 100 billion edges. Recent attempts LightNE and NetSMF propose to sparsify and factorize the (dense) NetMF matrix for embedding large networks, where NetMF is a theoretically-grounded network embedding method. However, there is a trade-off between their embeddings’ quality and scalability due to their expensive memory requirements, making embeddings less effective under real-world memory constraints. Therefore, we present the SketchNE model, a scalable, effective, and memory-efficient network embedding solution developed for a single machine with CPU only. The main idea of SketchNE is to avoid the explicit construction and factorization of the NetMF matrix either sparsely or densely when producing the embeddings through the proposed sparse-sign randomized single-pass SVD algorithm. We conduct extensive experiments on nine datasets of various sizes for vertex classification and link prediction, demonstrating the consistent outperformance of SketchNE over state-of-the-art baselines in terms of both effectiveness and efficiency. SketchNE costs only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.0 hours</i> to embed the Hyperlink2012 network with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3.5 billion</i> vertices and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">225 billion</i> edges on a CPU-only single machine with embedding superiority (e.g., a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">282%</i> relative HITS@10 gain over LightNE).
Identification of Hate Speech and Offensive Content using BI-GRU-LSTM-CNN Model

The propagation of hateful speech on social media has increased in past few years, creating an urgent need for strong counter-measures. Governments, corporations, and scholars have all made considerable investments in these measurements. Hate speech on social media platforms can lead to cyber-conflict that can impact social life at the individual and national levels. It can make people feel isolated, anxious and fearful. It can also lead to hate crimes. However, social media platforms are not able to monitor all content posted by users. This is why there is a need for automated identification of hate speech. The English text is notorious for its difficulty, complexity and lack of resources. When examining each class individually, it should be noticed that a many hateful tweets have been misclassified. As a result, it is advised to further examine the forecasts and mistakes to obtain additional understanding on the misclassification. To automatically detect hate speech in social media data, we propose a NLP model that blends convolutional and recurrent layers. Using the proposed model, we were able to identify occurrences of hate on the test dataset. According to our research, doing so could considerably raise test scores. Proposed model uses a deep learning technique based on the Bi-GRU-LSTM-CNN classifier with an accuracy of 77.16%.
PoE: A Panel of Experts for Generalized Automatic Dialogue Assessment

Chatbots are expected to be knowledgeable across multiple domains, e.g. for daily chit-chat, exchange of information, and grounding in emotional situations. To effectively measure the quality of such conversational agents, a model-based automatic dialogue evaluation metric (ADEM) is expected to perform well across multiple domains. Despite significant progress, existing ADEMs tend to perform well only on data that are similar to its training data (overfit to its training domain). This calls for a domain-generalized metric that can assess dialogues of different characteristics. To this end, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Panel of Experts</i> (PoE), a multitask network that consists of a shared transformer encoder and a collection of lightweight adapters. The shared encoder captures the general knowledge of dialogues across domains, while each adapter specializes in one specific domain and serves as a domain expert. To validate the idea, we construct a high-quality multi-domain dialogue dataset leveraging data augmentation and pseudo-labeling. The PoE network is comprehensively assessed on 16 dialogue evaluation datasets spanning a wide range of dialogue domains. It achieves state-of-the-art performance in terms of mean Spearman correlation over all the evaluation datasets. It exhibits better zero-shot generalization than existing state-of-the-art ADEMs and the ability to easily adapt to new domains with few-shot transfer learning.
A Secure Data Sharing Framework with Blockchain in IoMT using Bald Eagle Search Optimization

In health information systems, present interoperability problems are solved by blockchain technology (BT). Moreover, blockchain technology offers the high-tech standard that assures a secure sharing of computerized healthcare information among healthcare providers, medical care organizations, individuals, and medical experts. In healthcare management, Internet of medical things (IoMT) devices can share sensory information from patients to be investigated and prepared. However, in IoMT devices the secrecy of patients’ health information transmission is still a major problem within a range of material regions. In order to overcome such challenges, an effective BES_SecDataSharing method is proposed for securely sharing the data with blockchain in IoMT. The entities involved in the proposed BES_SecDataSharing method are data owner, data user, cloud user, attribute authority, and blockchain. Six stages are involved in this proposed method are initialization phase, registration phase, key issuance phase, data preservation phase, authentication phase, and data sharing phase. The proposed BES_SecDataSharing methods has attained a minimum memory usage of 85 MB, validation time of 28.767 Sec, and normalized variance of 1.594. In addition, the method has obtained a maximum conditional privacy of 0.787.
Cuckoo Search Optimized Histogram Equalization for Low Contrast Image Enhancement

Image enhancement is a process to improve the visual standard of image so as to extract spatial features of image. Histogram Equalization is method by which image can be improved for better perception and interpretation by different automated image processing system and human beings. Although, for low-contrast images (with lower dynamic range) traditional histogram equalization deteriorate quality of the output image by introducing washed-out form caused by too much brightness change. This paper presents histogram equalization based image enhancement method which is optimized using cuckoo search algorithm. Foremost purpose of the proposed method is to control over and under enhancement by calculating clip limits of the image histogram before performing histogram equalization. This method uses cuckoo search algorithm to automatically control the clip limit according to the image nature using improved fitness function. Effectiveness of the proposed method in comparison to other methods is analyzed using well known image quality metrics.
A Blockchain Enabled Predictive, Analytical Model for Fraud Detection in Healthcare Data

With technological advancements, existing Electronic Health Record (EHR) management systems, along with Machine Learning (ML) algorithms, are geared up for a huge transformation in the healthcare business. Healthcare data is created every microsecond from a variety of sources and contains a wealth of information. However, there are a number of problems with the data in these systems, including security, dependability, and accessibility, to mention a few. Consequently, there is always a risk associated with such data. Conventional methods are ineffective in addressing these crucial issues because they lack a uniform structure for data security and availability strategies. To safeguard the integrity of the use of EHR system, a new solution is needed to manage government security regulations and improve data accessibility. Furthermore, past healthcare data (healthcare provider data, inpatient/outpatient data) must be analyzed using machine learning approaches to predict resource requirements and probable fraud, which is critical for driving better future decisions. This study intends to bridge the gap by offering a prototype as architecture for a secure and intelligent EHR management system that relies on blockchain technology and machine learning methods. Finally, the proposed system is evaluated by considering transaction latency and throughput using the Hyperledger Caliper tool. The accuracy, precisionrecall, and F1 score are the performance metrics used to evaluate prediction models.
Privacy Preserving Ensemble Learning Classification Model for Mental Healthcare

Privacy protection technology can effectively handle personal data to prevent the leakage of sensitive information during data publishing and data analysis. Countless research analysis on facilitating privacy-preserving data collection has been conducted through the years. Medical research represents an important application that is necessary to extract useful information and protect patient privacy One of the main techniques recently used is ensemble learning. In contrast to ordinary approaches, which try to learn one hypothesis from a training dataset, ensemble learning tries to construct a set of hypotheses and combine them. This strategy is primarily used to improve the performance of the model, but the major restraint of this approach is that multiple parties cannot share their data extracted from the ensemble learning model with a privacy guarantee; therefore, it is a great demand to develop privacy-preserving collaborative ensemble learning. In this paper, introducing a differential privacy-based ensemble learning classification model while maintaining its utility and accuracy, which will evaluate the medical health record and classify the patient’s medical illness from the given data in a way that requires very little human participation from doctors. The proposed model has lower time and money costs associated and will provide assistance to medical practitioners.