Reviewerly/exHarmony huggingface dataset:
Bayesian Network Structural Learning Using Adaptive Genetic Algorithm with Varying Population Size

A Bayesian network (BN) is a probabilistic graphical model that can model complex and nonlinear relationships. Its structural learning from data is an NP-hard problem because of its search-space size. One method to perform structural learning is a search and score approach, which uses a search algorithm and structural score. A study comparing 15 algorithms showed that hill climbing (HC) and tabu search (TABU) performed the best overall on the tests. This work performs a deeper analysis of the application of the adaptive genetic algorithm with varying population size (AGAVaPS) on the BN structural learning problem, which a preliminary test showed that it had the potential to perform well on. AGAVaPS is a genetic algorithm that uses the concept of life, where each solution is in the population for a number of iterations. Each individual also has its own mutation rate, and there is a small probability of undergoing mutation twice. Parameter analysis of AGAVaPS in BN structural leaning was performed. Also, AGAVaPS was compared to HC and TABU for six literature datasets considering F1 score, structural Hamming distance (SHD), balanced scoring function (BSF), Bayesian information criterion (BIC), and execution time. HC and TABU performed basically the same for all the tests made. AGAVaPS performed better than the other algorithms for F1 score, SHD, and BIC, showing that it can perform well and is a good choice for BN structural learning.
The innovative competence of university teachers to improve learning: A comparative study between Bolivia, Spain and Mexico

The innovative competence of university teaching staff is a fundamental component in improving the quality of academic provision in higher education. The originality of the study lies in the contribution of a valid and reliable tool for the assessment of the innovative competence of university teaching staff. As a result, higher education institutions in different countries will be able to determine the training needs of their teachers in order to improve their innovative performance and, ultimately, student learning. The aim of the study is to identify, analyse and differentiate the innovative profile of a sample of university teachers working in Bolivia, Spain and Mexico. The research methodology (non-experimental and ‘ex-post-facto’) consists of applying a questionnaire to 1404 teachers from various universities in the countries involved. The results obtained suggest the need to structure specific teacher training strategies to remedy the deficiencies found in innovative competence in the three participating countries.
Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization Designs

To address the shortcomings of the sine cosine algorithm such as the low search accuracy, slow convergence speed, and easily falling into local optimality, a sine cosine algorithm for elite individual collaborative search was proposed. Firstly, tent chaotic mapping was used to initialize the population and the hyperbolic tangent function was applied non-linearly to adjust the parameters of the sine cosine algorithm, which enhanced the uniformity of population distribution and balanced the global exploration and local exploitation ability. Secondly, the search method of the sine cosine algorithm was improved by combining the search strategy of the sine cosine algorithm, the m-neighborhood locally optimal individual-guided search strategy, and the global optimal individual-guided search strategy, and, then, the three search strategies were executed alternately, which achieved collaboration, improved the convergence accuracy, and prevented the algorithm from falling into local optima. Finally, a greedy selection strategy was employed to select the best individuals for the population, which accelerated the convergence speed of the sine cosine algorithm. The simulation results illustrated that the sine cosine algorithm for elite individual collaborative search demonstrated a better optimization performance than the sine cosine algorithm, the other improved sine cosine algorithms, the other chaos-based algorithms, and other intelligent optimization algorithms. In addition, the feasibility and applicability of the sine cosine algorithm for elite individual collaborative search were further demonstrated by two mechanical optimization design experiments.
Variational Bayesian Approximation (VBA) with Exponential Families and Covariance Estimation

Variational Bayesian Approximation (VBA) is a fast technique for approximating Bayesian computation. The main idea is to assess the joint posterior distribution of all the unknown variables with a simple expression. Mean–Field Variational Bayesian Approximation (MFVBA) is a particular case developed for large–scale problems where the approximated probability law is separable in all variables. A well–known drawback of MFVBA is that it tends to underestimate the variances in the variables, even though it estimates the means well. It can lead to poor inference results. We can obtain a fixed point algorithm to evaluate the means in exponential families for the approximating distribution. However, this does not solve the problem of underestimating the variances. In this paper, we propose a modified method of VBA with exponential families to first estimate the posterior mean and then improve the estimation of the posterior covariance. We demonstrate the performance of the procedure with an example.
Proximal Nested Sampling with Data-Driven Priors for Physical Scientists

Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for models with a log-convex likelihood, which are ubiquitous in the imaging sciences. The purpose of this article is two-fold. First, we review proximal nested sampling in a pedagogical manner in an attempt to elucidate the framework for physical scientists. Second, we show how proximal nested sampling can be extended in an empirical Bayes setting to support data-driven priors, such as deep neural networks learned from training data.
Using Machine Learning to Identify Product Styles

The Waikato Environment for Knowledge Analysis (WEKA), a machine learning tool, was used to develop a model to identify product styles, and the style of classic chairs was determined using the model. Data used to develop the model consisted of 100 images of four styles of chairs such as Windsor, Shaker, Thonet, and Ming. After pre-processing the images using the image filters of WEKA, the images were used to train the model to classify chair styles. The accuracy of the model ranged from 96 to 98%. This validated the performance of the proposed method in classifying the styles of chairs, which helps the design of new chairs.
A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation

Recognizing human actions can help in numerous ways, such as health monitoring, intelligent surveillance, virtual reality and human–computer interaction. A quick and accurate detection algorithm is required for daily real-time detection. This paper first proposes to generate a lightweight graph neural network by self-distillation for human action recognition tasks. The lightweight graph neural network was evaluated on the NTU-RGB+D dataset. The results demonstrate that, with competitive accuracy, the heavyweight graph neural network can be compressed by up to 80%. Furthermore, the learned representations have denser clusters, estimated by the Davies–Bouldin index, the Dunn index and silhouette coefficients. The ideal input data and algorithm capacity are also discussed.
Effectiveness of Machine and Deep Learning for Blockchain Technology in Fraud Detection and Prevention

Blockchain was formerly originated to prevent fraud in digital currency exchanges. Blockchain refers to a collective decentralised ledger that is unaffected by tinkering. It provides the confirmed contributor access to the store, views, and shares the digital information in a situation that is rich in safety, which in turn supports the development of trust, liability, and transparent business associations. Identity theft and fraud safety are endless challenges for everyone in buying and selling. With each novelty in security technology, hackers and fraudsters learn how to outsmart the technology and breach these networks. The first section of the chapter describes the structure of the blockchain, its framework, the pros and cons of combining these technologies, and the role and importance of machine and deep learning algorithms in fraud detection and prevention in the blockchain. The next section focuses on the reported work, highlighting different researchers’ work for fraud detection and prevention using Blockchain technology. The chapter’s final section comprises a comparative analysis based on various performance parameters such as accuracy, the area under the curve, confidence, true negative, false positive, and truly positive for a different type of fraud detection using blockchain technology.
Medical Imaging Systems Using Blockchain

It has been discovered that in the old electronic health record platform, each health service unit managed its health records, making sharing difficult on the different medical platforms. However, it has been discovered that blockchain technology is a panacea that makes it possible to enable medical service units that are based on different platforms to share electronic health record data. However, one major challenge with this approach is the difficulty in storing whole electronic health record data in the blockchain, given the price and size of the blockchain. As a way out, cloud computing was ticked as a potential solution. Cloud computing affords a unique opportunity, including storage of scalability and availability. But again, the electronic health record with cloud computing advantage may be susceptible to attacks because sensitive data is transferred through a public channel. The task of sharing and disseminating medical information and records electronically is inevitable as far as medical management and treatment combination are concerned. Unfortunately, the old cloud-based electronic medical record storage platform is hard to achieve data security sharing. Given the tamper resistance and traceability, blockchain technology makes it possible for highly sensitive health data to be shared. Therefore, this chapter intends to provide detailed information on the application of medical imaging systems using blockchain.
Blockchain Technology: A Vehicle for Efficient and Comprehensive Medical Application Solutions

Blockchain technology is the technology of the twenty-first century. Web 3.0 is the name given to the next digital revolution. It was first developed to provide a secure payment mechanism, but it was later adapted and utilized in various industries. Healthcare is one of the domains where this technology has been deployed in the last few years and has yielded considerable results. The fast adoption of digital technology in the clinical sector has led to the construction of massive health records. As a result of this growth, there are never-before-seen requirements for clinical information security throughout usage and exchange. This adoption of the blockchain method as a reasonable and effective data storage and handling platform is opening up new opportunities in healthcare for tackling key data privacy, security, and integrity issues. Patients and healthcare providers have difficulty accessing, storing, integrating, and transmitting health information. Individuals are likely to access and control their medical information from everywhere on the planet, process and report their clinical findings, grant permissions, and even securely exchange that data with certain medical professionals. Straightforward health information access and an even more complete digital information architecture might help the medical industry better prepare for public health threats like COVID-19. We will examine several of the numerous ways that blockchain methodology has been employed in the clinical area, as well as its prospective scopes and possibilities for a better tomorrow, in this research.
Advancing Health Research Data Analysis with Blockchain Technology

It has been discovered that the storage problem of complex health records and data has been addressed, but there were still several issues with data security sharing with cloud technology. The decentralization attribute of blockchain will help in solving the problem. In electronic health records, blockchain can assist in reducing data sharing and interoperability in the industry by creating an overarching mechanism connecting different personal forms. It can also mimic data sharing by directly bridging the gap between owners and buyers. Therefore, this chapter will provide detailed information on Advancing health research data analysis with blockchain.
Securing Clinical Trials Data with Blockchain

Discovering and developing new drugs/ medicines is very crucial for the pharmaceutical industry. The increasing number of drugs approved in recent years demonstrates the impact of modern drug discovery approaches, digital technologies, and automated drug development methodologies. Drug development is a systematic and methodological process of developing a new pharmaceutical drug once the process of Drug discovery has identified the prime pharmacological component. The structured sequence of steps followed for drug development aims to ensure the safety and efficacy of the drug being developed. It includes pre-clinical research on microorganisms and animals, preparation of detailed data with respect to pharmacology, pharmacokinetics and toxicology details, application and approval by regulatory authorities and conduction of clinical trials. The conduction of clinical trials is an expensive affair as it needs a collaborative effort by multiple stakeholders along with a high level of monitoring and regulation. The data generated during the lifecycle of clinical trials is very critical for pharmacological scientific publications, regulatory approval for the target drug and post-marketing surveillance that ultimately leads to the development of better decision support systems for drug development. Hence, the data integrity of such data is of prime importance. Several Clinical data management (CDM) systems have been developed to ensure seamless collection and management of clinical trial data. These CDM systems enable useful analysis and decisions supported by authentic data. However, such systems face several security challenges with respect to privacy, integrity and authenticity of the clinical data. Another major challenge in conducting the clinical trials is finding the appropriate willing candidate who is physically and clinically suitable for the study. In view of the above, it is highly desirable to have a technology component that can address the above-mentioned issues. In this chapter, the technologies like blockchain and cloud computing have been introduced to address the challenges posed by clinical trial data management. The paper also proposes a blockchain based secure clinical data management system. The proposed system intends to help the data security issues like data integrity, privacy, ease and quick access to immutable clinical trial data with thorough access control enabling greater transparency and accountability.
Virtual Laboratory Works in Technology Education: Advantages and “Pitfalls”

The improvement of methods and software for modelling devices, processes and phenomena leads to a rapid extension of their application area not only in engineering but also in education. Replacing a real lab works with their tools, installations and equipment by their virtual substitutions, draws students away from the world of technology; this does not contribute to the formation of the skills necessary for organizing and conducting a real live experiment. The article discusses a way to reduce such damage by modeling not only the functions of devices and equipment, but also the process of assembling the correct installation. Unfortunately, the Lab VIEW graphic capabilities are insufficient for developing such kind of virtual laboratory work, especially with reference to the disciplines of the "Microwave technologies and antennas" course. The considered laboratory work developed by authors in Delphi7 differs from the typical virtual labs primarily in the way the student should perform it. He must assemble a proper test stand on his own: take all the needed parts from the warehouse, connect them correctly using coaxial, cords or waveguides, and also set up instruments and equipment, i.e. adjust the polarization of the radiation field, tune in the generators and amplifiers. And, of course, all this is being done with a mouse, rotating the simulated knobs, pressing the simulated switches and so on. Such a lab work becomes actually a kind of training apparatus, strengthening not only the student’s knowledge of a specific topic, but also his/her practical skills in organizing and conducting experiments. Finally, some additional improvements are mentioned.
A Case Study on Personalized Art Education for Learners with Developmental Disabilities

Personalized learning is increasingly in demand to meet the diversity of learners in education field. Therefore, this study aimed to explore and analyze personalized art education plans for learners with devel-opmental disabilities, through cases that respects their diversity in terms of characteristics, levels, needs, and aptitudes. The following research results were derived from the case of this study, which applied a cyclical process of learner understanding, learner-centered curriculum design, learner-led lesson im-plementation, process-centered assessment, and communication and reflection of learning outcomes, cen-tering learners as the subject who lead the lesson and as a source of information. First, personalized art education, which prioritizes understanding of learners, can be useful for disabled learners with a wide range of individual diversity as well as disability types and characteristics. Second, art education can en-able meaningful learning by respecting the world they express with their own unique visual language. Third, various and continuous process-centered assessment methods can prevent disabled learners from receiv-ing unreasonable grades. Personalized art education for disabled learners requires a system and support to design and utilize level-based and overall curriculum, beyond the concerns and efforts of individual teachers. Future research should focus on the methodology of art education that respects the diversity of learners and ways to materialize personalized art education.
Design‐for‐testability for reversible logic circuits based on bit‐swapping

Abstract The emerging technology of reversible circuits offers a potential solution to the synthesis of ultra low‐power quantum computing systems. A reversible circuit can be envisaged as a cascade of reversible gates only, such as Toffoli gate, which has two components: k control bits and a target bit ( k ‐CNOT), k ≥ 1. While analysing testability issues in a reversible circuit, the missing‐gate fault model is often used for modelling physical defects in k ‐CNOT gates. A new design‐for‐testability (DFT) technique is proposed for reversible circuits that deploys bit‐swapping using Fredkin reversible gates. It is shown that in an ( n × n ) circuit implemented with k ‐CNOT gates, addition of only two extra inputs along with a few Fredkin gates yields easy testability in the circuit. The modified design admits a universal test set of maximum size 2 n + 1 that detects all detectable missing gate faults in the original circuit, where n is the number of input/output lines in the circuit. The DFT overhead in terms of quantum cost is much less compared to that of previous approaches. The method is more advantageous for large circuits.
Residue-guided phase unwrapping in fringe projection measurements using second differences

Abstract This paper presents an innovative algorithm for unwrapping 2D phase maps with discontinuities. The method employs residue detection for identifying affected areas at a coarse scale. Unlike traditional techniques relying on subjective assessment, this algorithm automates threshold determination, ensuring precision without manual intervention. At the pixel level, it utilizes a bitmap mask based on second differences and the geometric mean formula to locate inconsistencies within the wrapped map precisely. This coarse-to-fine process establishes an optimal threshold for the second difference mask, resulting in highly accurate unwrapped outcomes while maintaining computational efficiency. Compared to conventional methods, this approach delivers superior unwrapped results, making it suitable for diverse applications. Experimental validation includes computer-simulated surfaces and practical fringe projection systems, accompanied by a thorough error analysis.
Vibration analysis of drum membrane using computer vision

The real time experience of what one sees and what one hears is enriching but if one can visualize what is audible will be fascinating. We suggest a simpler approach for sound localization that directly collects motion signals based on visual movements. In this study, frequency has been detected at various location of drum using computer vision method. A smartphone camera set up on a tripod next to the drum has captured these vibrations at approximately 240 frames per second. To determine the frequency, a Fourier analysis was performed on the data collected from the frames. We detected frequency of ~94 Hz at multiple spots by analyzing the motions of a point or region of interest (ROI) at membrane of drum. The frequency of drum membrane detected by a mobile microphone was found to be equivalent to the frequency observed using the proposed computer-vision method. Similar concepts can be employed in understanding the fundamentals of sounds generated by various musical instruments.
A New Similarity Metric for Deformable Registration of MALDI–MS and MRI Images

Multimodal imaging is a prominent strategy for biomedical research. For instance, Mass Spectrometry Imaging (MSI) can reveal the chemical composition of tissues with high specificity, helping to elucidate their metabolic pathways. However, this technique is not necessarily informative about the structural organization of a tissue. Other modalities, such as Magnetic Resonance Imaging (MRI), reveal functional areas in tissue. Images are analyzed jointly using several computational methods. Registration is a pivotal step that estimates a transformation to spatially align two images. Automatic methods usually rely on similarity metrics. Similarity metrics are used as optimization functions to find the transformation parameters. MALDI–MS and MR images have different intensity distributions that cannot be accounted for by traditional similarity metrics. In this article, we propose a novel similarity metric for deformable registration, based on the update of distance transformation values. We show that our method limits the intensity distortions while providing precisely registered images, on both synthetic and mouse brain images.