Reviewerly/exHarmony huggingface dataset:
The psychophysics of human three-dimensional active visuospatial problem-solving

Our understanding of how visual systems detect, analyze and interpret visual stimuli has advanced greatly. However, the visual systems of all animals do much more; they enable visual behaviours. How well the visual system performs while interacting with the visual environment and how vision is used in the real world is far from fully understood, especially in humans. It has been suggested that comparison is the most primitive of psychophysical tasks. Thus, as a probe into these active visual behaviours, we use a same-different task: Are two physical 3D objects visually the same? This task is a fundamental cognitive ability. We pose this question to human subjects who are free to move about and examine two real objects in a physical 3D space. The experimental design is such that all behaviours are directed to viewpoint change. Without any training, our participants achieved a mean accuracy of 93.82%. No learning effect was observed on accuracy after many trials, but some effect was seen for response time, number of fixations and extent of head movement. Our probe task, even though easily executed at high-performance levels, uncovered a surprising variety of complex strategies for viewpoint control, suggesting that solutions were developed dynamically and deployed in a seemingly directed hypothesize-and-test manner tailored to the specific task. Subjects need not acquire task-specific knowledge; instead, they formulate effective solutions right from the outset, and as they engage in a series of attempts, those solutions progressively refine, becoming more efficient without compromising accuracy.
Monotonicity Reasoning in the Age of Neural Foundation Models

Abstract The recent advance of large language models (LLMs) demonstrates that these large-scale foundation models achieve remarkable capabilities across a wide range of language tasks and domains. The success of the statistical learning approach challenges our understanding of traditional symbolic and logical reasoning. The first part of this paper summarizes several works concerning the progress of monotonicity reasoning through neural networks and deep learning. We demonstrate different methods for solving the monotonicity reasoning task using neural and symbolic approaches and also discuss their advantages and limitations. The second part of this paper focuses on analyzing the capability of large-scale general-purpose language models to reason with monotonicity.
INNOVATIVE POTENTIAL AND READINESS FOR CHANGE AMONG COLLEGE TEACHERS

The article examines the relationship between readiness for change and the manifestation of innovative potential among college teachers. The factor analysis used to identify and describe three latent factors that influence on the manifestation of the components of readiness for change and innovative potential as interrelated and mutually influencing phenomena. In general, teachers have a predominant average level of expression of innovative potential.
Mutual entity authentication of quantum key distribution network system using authentication qubits

Abstract Entity authentication is crucial for ensuring secure quantum communication as it helps confirm the identity of participants before transmitting any confidential information. We propose a practical entity authentication protocol for quantum key distribution (QKD) network systems that utilizes authentication qubits. In this protocol, authentication qubits that are encoded with pre-shared information are generated and exchanged to verify the legitimacy of each entity. By using the authentication qubit, participants can identify each other with enhanced security level through the quantum channel. The proposed protocol can be easily integrated with existing QKD systems without the need for additional hardware. In this study, we demonstrated the efficacy of the proposed scheme using a 1xN QKD network system and verified its stable operation over a deployed fiber network. Additionally, a security analysis of the proposed entity authentication protocol and architecture is provided.
WORKSHOP MANAJEMEN PUBLIKASI PENELITIAN DAN PENGABDIAN BERBASIS OJS DI ST3B

In this workshop, the publication of scientific writing for research and service based on OJS uses OJS V.3.3 from PKP. The OJS that have been owned by ST3B are OJS Tabgha, Imparta and Beatitudes, these OJS are the objects of this service. In this activity, the devotees collaborate on two different institutions, between Karimun University and ST3B, this was done as a forum for detecting the detailed needs of ST3B for publications. The service methods used are interview, role play and demonstrative as well as direct practice using a personal account on OJS that has been registered at every OJS ST3B. The results obtained are participants are able to register, submit articles, then the editorial board plays a role in the process of managing OJS on a regular basis and arrangements. Limited time and instruments used during service can be minimized with the enthusiasm of the workshop participants and a family atmosphere to jointly advance ST3B in research publications and service using OJS that already exists in ST3B
Image inpainting based on double joint predictive filtering and Wasserstein generative adversarial networks

Image inpainting is promising but challenging in computer vision tasks; it aims to fill in missing regions of corrupted images with semantically sensible content. By utilizing generative adversarial networks (GAN), state-of-the-art methods have achieved great improvements, but the ordinary GAN generally suffers from difficulties in training and unstable gradients, leading to unsatisfactory inpainting results. Image-level predictive filtering is a widely used restoration method that adaptively predicts the weights of pixels around a target pixel and then linearly combines these pixels to generate the image, but it cannot fill larger missing regions. Thus, we extend image-level predictive filtering to the deep feature level through an encoder–decoder network and embed adaptive channel attention and spatial attention modules in the encoder network. We use Wasserstein GAN instead of normal GAN due to its superior properties and then combine it with image-level predictive filtering and deep feature-level predictive filtering, which ultimately leads to a significant improvement in image inpainting. We validate our method on two public datasets: CelebA-HQ and Places2. Our method demonstrates good performance across four metrics: peak signal-to-noise ratio, L1, structural similarity index measure, and learned perceptual image patch similarity.
Robust coverless video steganography based on inter-frame keypoint matching

Existing coverless video steganography schemes primarily focus on single-frame features, which manifest poor robustness. To enhance resistance against adversarial attacks, this paper proposes a coverless video steganography method based on inter-frame keypoint matching, leveraging the movement orientation of keypoints within the video for information hiding. Initially, the method divides video frames into blocks, and then constructs an inter-frame keypoint matching network by using the keypoint detection method combined with deep learning. By narrowing down the matching scope and filtering out mismatches, the network effectively discerns the keypoint matching relationships. For keypoints successfully matched within the blocks, a coordinate system is established based on the original keypoint, determining the position of the matched keypoints. The most frequently occurring orientation within the blocks is designated as the orientation of the block. Subsequently, we introduce a mapping rule based on quadrant labels, generating robust hash sequences according to the quadrant labels corresponding to the orientation of the blocks. Compared to the latest coverless video steganography schemes, this method shows superior robustness in resisting 14 common traditional attacks and video attacks. Concurrently, it exhibits commendable hiding capacity and hiding success rate.
Human Activity Recognition System Using Smartphone

Recognition of human activity has a wide range of applications in medical research and human survey systems. We present a powerful activity recognition system based on a Smartphone in this paper. The system collects time series signals with a 3- dimensional Smartphone accelerometer as the only sensor, from which 31 features in the time domain and frequency domain are created. The quadratic classifier, k-nearest neighbor algorithm, support vector machine, and artificial neural networks are used to classify activities. Feature extraction and subset selection are used to reduce dimensionality. In addition to passive learning, we use active learning techniques to lower the cost of data tagging. The findings of the experiment demonstrate that the categorization rate of passive learning is 84.4 percent and that it is resistant to common cell phone postures and poses.
Machine Learning Techniques in Image Segmentation

Image is an important medium to express information easily. This paper deals with the content of image segmentation with machine learning. Segmentation is the process of extracting the information required from the image. Machine learning is the process that helps to classify to obtain good results. A number of algorithms are designed for the segmentation process. The algorithms are selected based on the application. Quality segmentation can be applied if the algorithm is fixed at the application level. Standalone methods can be used for real-time applications. Schematic segmentation is one of the best techniques used for segmenting images. Machine learning combines basic techniques to produce good results. The algorithms vary for different input images like MRI, CT Scans, Colour images, etc. Algorithms like k-mean clustering are mostly used in processing. Many problems occur in segmentation which can be removed by Bayesian architectures. The usage of machine learning improves accuracy and efficiency. Labeling, training and testing are some of the methods used in segmentation through machine learning.
Real-Time Network Video Data Streaming in Digital Medicine

Today, the use of digital medicine is becoming more and more common in medicine. With the use of digital medicine, health data can be shared, processed, and visualized using computer algorithms. One of the problems currently facing digital medicine is the rapid transmission of large amounts of data and their appropriate visualization, even in 3D. Advances in technology offer the possibility to use new image processing, networking, and visualization solutions for the evaluation of medical samples. Because of the resolution of the samples, it is not uncommon that it takes a long time for them to be analyzed, processed, and shared. This is no different for 3D visualization. In order to be able to display digitalized medical samples in 3D at high resolution, a computer with computing power that is not necessarily available to doctors and researchers is needed. COVID-19 has shown that everyday work must continue even when there is a physical distance between the participants. Real-time network streaming can provide a solution to this, by creating a 3D environment that can be shared between doctors/researchers in which the sample being examined can be visualized. In order for this 3D environment to be available to everyone, it must also be usable on devices that do not have high computing capacity. Our goal was to design a general-purpose solution that would allow users to visualize large amounts of medical imaging data in 3D, regardless of the computational capacity of the device they are using. With the solution presented in this paper, our goal was to create a 3D environment for physicians and researchers to collaboratively evaluate 3D medical samples in an interdisciplinary way.
An examination of the challenges and awareness presented by the transfer of born-digital records in the public sector: A case study of Türkiye

In the Turkish public sector, official policy, and practice on the transfer of digital records to the Turkish State Archives has yet to be implemented, and the consequences of this have yet to be fully identified or comprehended. As such there is a need for research and recommendations on this problem. The purpose of this study is to provide a situational analysis of the transfer process of born-digital records to the national archives in Türkiye, by identifying the challenges faced in the digital transfer process, with the aim of raising awareness among public bodies. The survey method was used to collect data about the digital transfer process in Türkiye. A total of 120 public bodies participated in the survey. The originality value of this study can be said to be significant as of the target population, 120 Turkish public sector bodies were surveyed to determine their challenges and level of awareness regarding the born-digital transfer process. In addition to the high number of Turkish public bodies surveyed, this is the first study of its kind in Türkiye on digital transfer and its processes. The findings of the research revealed that public bodies in Türkiye are not currently ready for digital transfer. The findings also highlighted that the legal and technical infrastructure required for digital transfer in the public sector was inadequate. The research findings also concluded that staff working in the public sector require training in the processes related to the transfer of digital records. In line with the research data recommendations have been made regarding the digital transfer process in Türkiye.
Machine Learning Algorithms for Health Care Data Analytics Handling Imbalanced Datasets

In Machine Learning, classification is considered a supervised learning technique to predict class samples based on labeled data. Classification techniques have been applied to various domains such as intrusion detection, credit card fraud detection, etc. However, classification techniques on all these domains have been applied to balanced datasets. Balanced datasets are those which contain equal proportion of majority and minority examples. However, in real-time, obtaining balanced datasets is difficult because majority of the datasets tend to be imbalanced. Developing a model for classifying imbalanced datasets is a challenge, particularly in the medical domain. Accurate identification of a disease-affected patient within time is critical as any misclassification leads to severe consequences. However the imbalanced nature of most of the real-time datasets presents a challenge for most of the conventional machine learning algorithms. For the past few years, researchers have developed models using Conventional machine learning algorithms (linear and nonlinear) are stating unsatisfactory performance in classifying imbalanced datasets. To address this problem of skewed datasets several statistics techniques &amp;amp; robust machine Learning techniques have been developed by the researchers. The discussion on handling imbalanced datasets in the healthcare domain using machine learning techniques is a primary focus of this chapter.
Quantum-inspired Probabilistic Database Corruption Detection

A crucial problem of machine learning is the management of unclean probabilistic databases. We approach this problem by applying recent results and methods from quantum information to detect a specific class of database corruption. We present a quantifier of the global corruption of the probabilistic database and show its relationship with detection protocols based on generalized Bell inequalities. Furthermore, we show a relation between the noise generating the corruption and information encoded in the database schema. Finally, we discuss how our work indicates a way to export quantum information results to study noise in probabilistic databases.
Using Machine Learning Techniques in Predicting Auditor Opinion: Empirical Study

For auditors, a key concern is the reliability and quality of the auditing opinion’s final decision. The emerging topic of machine learning in auditing is being increasingly explored by developing trusted and efficient algorithms for classifying auditing opinions. The prediction of audit opinions of Egyptian listed companies is crucial to the security market risk mitigation process. Numerous innovations might be put into practice to raise audit efficiency, improve audit quality, and enhance auditor insight by using machine learning techniques. The aim of this paper is to provide a new audit opinion prediction model for financial statements. To this end, a sample of a group of listed Egyptian companies was selected. The model was trained with the aid of auditor opinion labels using two widely used supervised machine learning classifiers (SVM—Support Vector Machine and NV—Naive Bayes). The obtained results were then compared with the trained model that uses the clustering outcomes as a new relative auditor opinion. The results show that the developed method managed to predict the audit opinion with accuracy rates of 83.7 and 83.9 %, respectively. The performance evaluated in terms of overall prediction accuracy, and the Type I and Type II error rates show that the SVM models have higher results than the Naïve Bayes models. This study indicates that traditional methods have a poor performance using two traditional techniques (logistic and probit regressions).
Effective E-commerce Based on Predicting the Level of Consumer Satisfaction

COVID-19 has dramatically accelerated the growth of e-commerce, and this has led to increased competition between online sellers. To provide a competitive service, businesses need to achieve a high level of customer satisfaction. This paper tries to enhance the e-commerce’s services through the analysis of earlier customer feedback and reviews to predict the level of customer satisfaction. Five classification algorithms—Decision Tree (DT), Random Forest (RF), XGBoost, support vector machine (SVM), and K-Nearest Neighbors (KNN)—were applied to predict the rating before the user gave the rating or review score, which represents customer satisfaction. Data preprocessing was conducted, and new features were developed. The different techniques were evaluated. (RF) model can predict the review score by F1-score 0.67 better than the other models. The analysis highlights the important features that affect customer satisfaction, which include location, delivery time, product value, and freight ratio.
Blockchain Technology to Enhance Performance of Drugs Supply Chain in the Era of Digital Transformation in Egypt

Medicine is one of the products that must be delivered to the patient safely, undamaged, and during its validity period. However, the risks of the drug supply chain have become increasingly apparent. To address these risks and due to the rapid advances in the technology field, various digital transformation trials around the globe start to be considered in order to replace the traditional drug supply chain until its delivery to the patient. Therefore, the aim of this paper is to contribute in the digital transformation goal of the drug supply chain in Egypt. Thus, this paper proposes a trusted automatic system, based on the blockchain and Internet of Things (IoT) technologies to enhance the performance of the drug supply chain and accordingly make the drug manufacturing and distribution system in Egypt more efficient and reliable.
Modeling Cloud Infrastructure Provisioning: A Software-as-a-Service Approach

Provisioning means making an infrastructure element, such as a server or network device, ready for use. DevOps community leverages the Infrastructure as Code (IaC) approach to supply tools for cloud infrastructure provision. However, each provisioning tool has its scripting language, and managing different tools for several cloud providers is time-consuming and error-prone. In previous work, we presented a model-driven infrastructure provisioning tool called ARGON, which leverages the IaC approach using Model-Driven Engineering. ARGON provides a modeling language to specify cloud infrastructure resources and generates scripts to support cloud infrastructure provisioning orchestration. Since ARGON runs in the Eclipse Desktop IDE, we propose to migrate from an ARGON Desktop to an ARGON Cloud as a Software-as-a-Service approach. On the one hand, we developed a domain-specific modeling language using JavaScript Frameworks. On the other hand, we used a Model-to-Text transformation engine through a REST web service to generate scripts. Finally, we carried out an example by modeling infrastructure resources for Amazon Web Services and then generating a script for the Ansible tool.
Fractional cuckoo search invasive weed optimized neural network for data classification

Summary The progression of organizing and classifying data components based on pre‐established criteria is known as data classification. In data classification problems, machine learning methods are frequently used. Although the ability of an algorithm to learn is essential, the recognition quality of algorithms is undoubtedly a key indicator. However, the error made in the beginning is significant, and if it is not rectified then, it causes chaos in data classification. In this research, a fractional hybrid optimization is created with a feed‐forward neural network (FFNN) for the data classification. The pre‐processing of the medical data involves the imputation of missing values and the log transformation. In the feature selection process, the chord distance and the Jaro‐Winkler distance are both employed to choose the important features. The FFNN classifier is used to classify the data, which is trained using the hybrid optimization method, namely the developed Fractional Cuckoo search Invasive Weed Optimization algorithm (FCSIWO). The fractional concept, Invasive Weed Optimization (IWO) algorithm, and Cuckoo search (CS) algorithm are combined to create the FCSIWO. When compared to existing data classification models, the developed technique achieved an optimal sensitivity of 0.9463, an optimal accuracy of 0.9440, and an optimal specificity of 0.9422.
Applications of Machine Learning

In this chapter, we briefly discuss various real-time applications of machine learning algorithms. Machine Learning Algorithms explain the following topics: Introduction to ML algorithms, Supervised Learning, Classification, Regression (Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, Support Vector Machine, Random Forest, AdaBoost, Gradient-Boosting Trees), and Unsupervised Learning (K-Means Clustering, Gaussian Mixture Model, Hierarchical Clustering, Recommender Systems, PCA/T-SNE). Application of Machine Learning explains various real-time applications like augmentation, automation, finance, government, healthcare, marketing, traffic alerts, image recognition, video surveillance, sentiment analysis, product recommendation, online support using chatbots, Google translate, online video streaming applications, virtual professional assistants, machine learning usage in social media, stock market signals using machine learning, auto-driven cars, and real-time dynamic pricing.
Immediacy as a better service: Analysis of limitations of the use of ChatGPT in library services

The paper examines ChatGPT's application in library services and assesses its shortcomings. The study emphasizes the immediateness that new technologies offer to library services and how ChatGPT falls short of satisfying this demand because it relies on pre-existing data and lacks real-time interaction. The paper suggests that while ChatGPT can assist in basic inquiries, it cannot replace human interaction and expertise in addressing more complex questions and concerns. The study suggests that libraries should give preference to efficient and individualized service to better meet the demands of their patrons. The study provides valuable insights for libraries and other service organizations considering the implementation of chatbots or similar AI technologies.