Contemporary Computer Research Articles

Implementation of Parallel Applications on Linear Systolic and Star Topologies by Using Multistage Omega Network

Contemporary computer architecture comprises multicomputer settings. Multiple computers provide the facility of high performance to implement multiple threads that are faster and concurrently with different processors that can execute tasks simultaneously. The challenges are as follows: cost and high performance. Certain firms have to provide funding to establish data centers that need locations, hardware, and technicians. After the passage of a period of time, such equipment requires maintenance (updating and upgrading) to take place on it. This paper tackles these challenges by passing the drawbacks of data centers. It provides an algorithm that simulates a virtual machine, as one client is to be connected with eight servers to implement two applications namely convolution and mathematical operations. To represent different topologies, the algorithm simulates supercomputer systems by applying multistage omega network topology and parallel processing. Two types of topologies execute linear systolic and star, and is implemented on transmission control protocol (TCP) and user datagram protocol (UDP) protocols. The goal of this paper is to provide a kernel that draws near a cloud computing system by exploiting the JAVA programming language with its techniques (threading, socket, socket server, Datagram socket, and datagram packet). The results offer the connection between the client and servers to be successfully simulated by using multistage omega network. The linear systolic and star topologies are simulated in virtual parallel processing.

Streamlining Task Planning Systems for Improved Enactment in Contemporary Computing Surroundings

Streamlining Task Planning Systems for Improved Enactment in Contemporary Computing Surroundings

ZnO-based artificial synaptic diodes with zero-read voltage for neural network computing

Brain-inspired neuromorphic sensory devices play a crucial role in addressing the limitations of von Neumann systems in contemporary computing. Currently, synaptic devices rely on memristors and thin-film transistors, requiring the establishment of a read voltage. A built-in electric field exists within the p–n junction, enabling the operation of zero-read-voltage synaptic devices. In this study, we propose an artificial synapse utilizing a ZnO diode. Typical rectification curves characterize the formation of ZnO diodes. ZnO diodes demonstrate distinct synaptic properties, including paired-pulse facilitation, paired-pulse depression, long-term potentiation, and long-term depression modulations, with a read voltage of 0 V. An artificial neural network is constructed to simulate recognition tasks using MNIST and Fashion-MNIST databases, achieving test accuracy values of 92.36% and 76.71%, respectively. This research will pave the way for advancing zero-read-voltage artificial synaptic diodes for neural network computing.

Correlation and Agreement of Quantitative Flow Ratio With Fractional Flow Reserve in Saphenous Vein Grafts.

The applicability of quantitative flow ratio (QFR), a nonhyperemic, invasive coronary angiography-derived computation of fractional flow reserve (FFR), has not been studied in coronary artery bypass grafts. We sought to explore the correlation and diagnostic agreement between QFR and FFR in saphenous vein grafts (SVGs). A total of 129 prospectively included patients (mean age 73±8 years, 84% male) with prior coronary artery bypass grafting underwent invasive coronary angiography and pressure-derived functional assessment in 150 nonoccluded SVGs. QFR dedicated angiography images of the SVGs were acquired and used for offline QFR computation. The diagnostic performance of QFR was compared with 2-dimensional quantitative coronary angiography, using FFR as a reference. A threshold of ≤0.80 was used to define functional significance. QFR was successfully computed in 140 (93%) SVGs. We found a significant correlation between QFR and FFR (r=0.72, P<0.001). FFR indicated significant disease in 43 (31%) SVGs, whereas QFR analysis showed significant lesions in 53 (38%) bypass grafts. QFR exhibited a higher sensitivity and diagnostic accuracy compared with angiographic lesion assessment (84% versus 63%, P=0.030 and 83% versus 74%, P=0.036, respectively), whereas specificity did not differ (82% versus 79%, P=0.466). Lastly, QFR demonstrated a higher area under the receiver operating curve than quantitative coronary angiography (0.90 versus 0.82, P=0.008) for the detection of FFR-defined significant vein graft disease. This study shows the potential applicability of contemporary QFR computation in venous bypass grafts with a moderate correlation and good diagnostic accuracy compared with functional assessment using FFR.

Neuroscientific insights about computer vision models: a concise review.

The development of biologically-inspired computational models has been the focus of study ever since the artificial neuron was introduced by McCulloch and Pitts in 1943. However, a scrutiny of literature reveals that most attempts to replicate the highly efficient and complex biological visual system have been futile or have met with limited success. The recent state-of the-art computer vision models, such as pre-trained deep neural networks and vision transformers, may not be biologically inspired per se. Nevertheless, certain aspects of biological vision are still found embedded, knowingly or unknowingly, in the architecture and functioning of these models. This paper explores several principles related to visual neuroscience and the biological visual pathway that resonate, in some manner, in the architectural design and functioning of contemporary computer vision models. The findings of this survey can provide useful insights for building futuristic bio-inspired computer vision models. The survey is conducted from a historical perspective, tracing the biological connections of computer vision models starting with the basic artificial neuron to modern technologies such as deep convolutional neural network (CNN) and spiking neural networks (SNN). One spotlight of the survey is a discussion on biologically plausible neural networks and bio-inspired unsupervised learning mechanisms adapted for computer vision tasks in recent times.

Enhancing Automative Safety System for Driver Fatigue Detection by Using Mediapipe’s Framework

Traffic accidents caused by tiredness, exhaustion, and distracted driving are a big concern on a global scale. As per global Road Safety reports drowsy driving was reason for 91,000 road accidents in Tamil Nadu in the year 2023. In this study, I suggest a collective deep learning architecture for automatic driver sleepiness detection while some earlier works are suggested extracting the lips and eyes movements to identify sleepiness, more contemporary computer vision-based systems have performed only somewhat well. This is because they either use extremely large deep learning models with still-poor performance or they use hand-crafted features with traditional methods like K-NN Algotithm and openCV. To overcome such issues in this project I am planing to create a Driver Drowsiness Detection and Alerting System using Mediapipe‘s Face Mesh solution in Python. These systems assess the driver‘s alertness and warn the driver if needed. Continuous driving can be tedious and exhausting. A motorist may get droopy and perhaps nod off due to inactivity. we will create a drowsy driver detection system to address such an issue. For this, we will use Mediapipe‘s Face Mesh solution in python and the Eye Aspect ratio formula using by Navie Bayes Algorithm. My goal is to create a robust and easy-to-use application that detects and alerts users if their eyes are closed for a long time. Keyword: Navie Baye’s, Mediapipe Framework.

Log-Based Fault Localization with Unsupervised Log Segmentation

Localizing faults in a software is a tedious process. The manual approach is becoming impractical because of the large size and complexity of contemporary computer systems as well as their logs, which are often the primary source of information about the fault. Log-based Fault Localization (LBFL) is a popular method applied for this purpose. However, in real-world scenarios, this method is vulnerable to a large number of previously unseen log lines. In this paper, we propose a novel method that can guide programmers to the location of a fault by creating a hierarchy of log lines with the highest rank, selected by the traditional LBFL method. We use the intuition that the symptoms of faults are in the context of normal behavior, whereas suspicious log lines grouped together are from new or additional functionalities turned on during faulty execution. To obtain this context, we used unsupervised log sequence segmentation, which has been previously used to segment log sequences into meaningful segments. Experiments on real-life examples show that our method reduces the effort to find the most crucial logs by up to 64% compared with the traditional timestamp approach. We demonstrate that context is highly useful in advancing fault localization, showing the possibility of further speeding up the process.

Internal fit and marginal adaptation of all-ceramic implant-supported hybrid abutment crowns with custom-milled screw-channels on Titanium-base: in-vitro study

BackgroundAdvancements in digital dentistry helped in custom-milling screw-channels in implant-supported restorations; however, the fit of these restorations is still unclear especially for contemporary computer aided designing/computer aided manufacturing (CAD/CAM) materials. This study aimed to compare the internal and marginal fit of Ultra translucent multilayered zirconia versus lithium disilicate implant-supported hybrid abutment crowns (HACs) constructed with custom-milled screw-channels on Titanium-base.Materials and methodsA total of 24 HACs with custom-milled screw-channels were constructed from lithium disilicate (Group LDS) and Ultra translucent multilayered zirconia (Group UT) using digital workflow (n = 12). The internal and marginal gaps of HACs on their corresponding Titanium-bases were assessed using replica technique and stereomicroscope, respectively. After testing for normality, quantitative data were expressed as mean and standard deviation and compared using independent t-test at a level of significance (P ≤ 0.05).ResultsThere was no statistically significant difference between Group LDS and Group UT in terms of marginal and internal fit. The internal and marginal gaps in both groups were within the accepted values reported in literature.ConclusionsUT and LDS HACs with custom-milled screw-channels demonstrated comparable and acceptable internal fit and marginal adaptations to Ti-base, which lied within the range reported in literature.

A Multiparty Quantum Private Equality Comparison Scheme Relying on |GHZ3⟩ States

In this work, we present a new protocol that accomplishes multiparty quantum private comparison leveraging maximally entangled |GHZ3⟩ triplets. Our intention was to develop a protocol that can be readily executed by contemporary quantum computers. This is possible because the protocol uses only |GHZ3⟩ triplets, irrespective of the number n of millionaires. Although it is feasible to prepare multiparticle entangled states of high complexity, this is overly demanding on a contemporary quantum apparatus, especially in situations involving multiple entities. By relying exclusively on |GHZ3⟩ states, we avoid these drawbacks and take a decisive step toward the practical implementation of the protocol. An important quantitative characteristic of the protocol is that the required quantum resources are linear both in the number of millionaires and the amount of information to be compared. Additionally, our protocol is suitable for both parallel and sequential execution. Ideally, its execution is envisioned to take place in parallel. Nonetheless, it is also possible to be implemented sequentially if the quantum resources are insufficient. Notably, our protocol involves two third parties, as opposed to a single third party in the majority of similar protocols. Trent, commonly featured in previous multiparty protocols, is now accompanied by Sophia. This dual setup allows for the simultaneous processing of all n millionaires’ fortunes. The new protocol does not rely on a quantum signature scheme or pre-shared keys, reducing complexity and cost. Implementation wise, uniformity is ensured as all millionaires use similar private circuits composed of Hadamard and CNOT gates. Lastly, the protocol is information-theoretically secure, preventing outside parties from learning about fortunes or inside players from knowing each other’s secret numbers.

Computing marginal and conditional divergences between decomposable models with applications in quantum computing and earth observation

AbstractThe ability to compute the exact divergence between two high-dimensional distributions is useful in many applications, but doing so naively is intractable. Computing the $$\alpha \beta $$ α β -divergence—a family of divergences that includes the Kullback–Leibler divergence and Hellinger distance—between the joint distribution of two decomposable models, i.e., chordal Markov networks, can be done in time exponential in the treewidth of these models. Extending this result, we propose an approach to compute the exact $$\alpha \beta $$ α β -divergence between any marginal or conditional distribution of two decomposable models. In order to do so tractably, we provide a decomposition over the marginal and conditional distributions of decomposable models. We then show how our method can be used to analyze distributional changes by first applying it to the benchmark image dataset QMNIST and a dataset containing observations from various areas at the Roosevelt Nation Forest and their cover type. Finally, based on our framework, we propose a novel way to quantify the error in contemporary superconducting quantum computers.

Advances in manufacturing of carbon-based molecular nanomaterials based on rice husk/hull waste

This review highlights potential application areas for carbon-based molecular nanoparticles, such as carbon dots, carbon nanotubes, graphene quantum dots, and carbon quantum dots. The success of nano-manufacturing hinges on robust collaboration between academia and industry to advance applicable manufacturing techniques. Choosing the right approach is crucial, one that integrates the carbon base of nanomaterials with the required properties and impurities, as well as the scalability of the process. Molecular, in this context, refers to the nanoscale carbon structures that form the basis of these materials, including their arrangement, bonding, and properties at the molecular level. The article also explores the characterization of different types of molecular nanomaterials. Nanomaterials are increasingly used in almost every contemporary industry, including construction, textiles, manufacturing, and computing. This article reviews the most prominent sectors globally that employ nanomaterials. Biomasses containing lignin, cellulose, and hemicellulose have become some of the most extensively studied. Initially, rice waste was utilized for bulk materials, but lately, the production of multifunctional materials has surged in interest. Carbon nanostructures derived from rice waste offer a broad spectrum of applications and enhanced biocompatibility. Recent advancements, challenges, and trends in the development of multifunctional carbon-based nanomaterials from renewable rice waste resources are considered.

Video and Audio Deepfake Datasets and Open Issues in Deepfake Technology: Being Ahead of the Curve

The revolutionary breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) are extensively being harnessed across a diverse range of domains, e.g., forensic science, healthcare, virtual assistants, cybersecurity, and robotics. On the flip side, they can also be exploited for negative purposes, like producing authentic-looking fake news that propagates misinformation and diminishes public trust. Deepfakes pertain to audio or visual multimedia contents that have been artificially synthesized or digitally modified through the application of deep neural networks. Deepfakes can be employed for benign purposes (e.g., refinement of face pictures for optimal magazine cover quality) or malicious intentions (e.g., superimposing faces onto explicit image/video to harm individuals producing fake audio recordings of public figures making inflammatory statements to damage their reputation). With mobile devices and user-friendly audio and visual editing tools at hand, even non-experts can effortlessly craft intricate deepfakes and digitally altered audio and facial features. This presents challenges to contemporary computer forensic tools and human examiners, including common individuals and digital forensic investigators. There is a perpetual battle between attackers armed with deepfake generators and defenders utilizing deepfake detectors. This paper first comprehensively reviews existing image, video, and audio deepfake databases with the aim of propelling next-generation deepfake detectors for enhanced accuracy, generalization, robustness, and explainability. Then, the paper delves deeply into open challenges and potential avenues for research in the audio and video deepfake generation and mitigation field. The aspiration for this article is to complement prior studies and assist newcomers, researchers, engineers, and practitioners in gaining a deeper understanding and in the development of innovative deepfake technologies.

Facial Frontiers: Unveiling the Potential of LBPHs and Haar Cascades in Facial Recognition for Enhanced School Security

The article aims at introducing a Facial Recognition School Security System as one solution to improve security while speeding up administrative processes in educational institutions. The need for such a system emanates from concerns over the safety of schools as well as inadequacies inherent in conventional attendance and access control methods. Inadequate methods can be manual or rely on an older technology that leads to inefficiencies, inaccuracies and breaches of security. This proposed solution exploits contemporary artificial intelligence algorithms and computer vision techniques to facilitate the reliable identification and validation of entrants thereby providing contactless approach during Pandemic times in compliance with the COVID-19 safety protocols. At this period of COVID-19 it also acts as an avenue where physical touchpoints are reduced with consideration to social distancing measures. Our method is novel in the fact that it only detects and recognizes human faces as opposed to general object detection systems. We use Local Binary Pattern Histograms (LBPH) for face recognition and Haar Cascades for face detection. The Haar Cascade algorithm employs simple rectangular features to detect faces, using a cascade of weak classifiers to achieve high detection rates. The LBPH algorithm captures local texture patterns of facial features, calculating LBP values for each pixel. Our project demonstrates variable performance across different classes, with precision ranging from 0.50 to 1.00, recall from 0.33 to 1.00, and F1 scores from 0.33 to 0.94, while achieving an overall accuracy of 0.75, indicating robust performance in certain scenarios but room for improvement in others.

Analysis of High-Performance Parallel Computing using TOPSIS Method

High-performance parallel computing involves the simultaneous execution of multiple tasks or processes, orchestrated to achieve improved computational speed and efficiency. This approach leverages the power of parallelism, exploiting both multi-core CPUs and GPUs, distributed computing clusters, and specialized hardware accelerators. The fundamental idea is to divide a task into smaller sub-tasks that can be executed concurrently, thereby reducing processing time and enhancing overall performance. High-performance parallel computing is a transformative approach that enables us to tackle computationally intensive tasks efficiently. This abstract highlight its significance in contemporary computing and sets the stage for further exploration of the intricacies and innovations within this dynamic field. Researchers and practitioners continue to push the boundaries of what is achievable, making high-performance parallel computing a cornerstone of modern computational science and technology. High-performance parallel computing research is of paramount significance due to its transformative impact across diverse fields. It empowers scientists to tackle complex problems that were once computationally intractable, unlocking new frontiers in scientific discovery. It drives innovation in engineering and design, optimizing product development and manufacturing processes across industries. In healthcare, it accelerates genomics research and drug discovery, offering hope for improved medical treatments. Financial institutions rely on it for data analysis and risk assessment, shaping the global economy. Weather forecasting and environmental modelling are enhanced, aiding disaster preparedness and conservation efforts. In the digital age, parallel computing underpins artificial intelligence, enabling advancements in natural language processing and machine learning. Furthermore, it has vital applications in national security, space exploration, and materials science. In essence, high-performance parallel computing research serves as the backbone of technological progress, fostering innovation, efficiency, and problem-solving across a wide spectrum of disciplines, ultimately shaping the future of our world. TOPSIS, this method involves evaluating the geometric distance between each alternative solution and two reference solutions: the positive ideal solution and the negative ideal solution. The underlying principle of TOPSIS assumes that the criteria being assessed are of an ascending nature, where larger values represent better performance. To account for disparate dimensions or scales among the criteria, normalization is often employed within the TOPSIS framework. From the result Scatter- free imaging is got the first rank and object-scatter imaging is having the lowest rank.

Principles of Cloud Computing Infrastructure IaaS

Infrastructure as a Service (IaaS) represents a cornerstone in contemporary cloud computing, providing essential on-demand computing resources, including servers, storage, and networking. This paper explores the foundational principles of IaaS, highlighting its ability to facilitate dynamic server management through practices like auto-scaling and ephemeral server utilization. The use of custom images in IaaS ensures minimal downtime and scalable deployment, while loose coupling enhances fault tolerance and adaptability. Emphasizing high availability through auto-scaling, IaaS supports continuous operations even in geographically vulnerable areas. The security framework of IaaS, encompassing a shared responsibility model, ensures robust data protection and compliance with industry standards. The paper also addresses cost optimization strategies and the integration of hybrid cloud architectures to maximize resource efficiency and maintain data integrity. Overall, IaaS is integral to modern IT infrastructure, offering flexible, scalable, and cost-effective solutions that align with the evolving demands of the digital landscape.

High-Performance Computing Storage Performance and Design Patterns—Btrfs and ZFS Performance for Different Use Cases

Filesystems are essential components in contemporary computer systems that organize and manage data. Their performance is crucial in various applications, from web servers to data storage systems. This paper helps to pick the suitable filesystem by comparing btrfs with ZFS by considering multiple situations and applications, ranging from sequential and random performance in the most common use cases to extreme use cases like high-performance computing (HPC). It showcases each option’s benefits and drawbacks, considering different usage scenarios. The performance of btrfs and ZFS will be evaluated through rigorous testing. They will assess their capabilities in handling huge files, managing numerous small files, and the speed of data read and write across varied usage levels. The analysis indicates no definitive answer; the selection of the optimal filesystem is contingent upon individual data-access requirements.

Leveraging YOLO Algorithm and PaddleOCR in Machine Learning Applications

Abstract: This research is about implementation of the YOLO algorithm in PaddleOCR and several aspect of machine learning. The mention discusses this technology integration and way they have worked to accomplish the tasks and the expected use on the real world scenarios. The paper accomplishes this by extensively analysing the literature and doing deliberate experimentation. Insights that will give on algorithm effectiveness and challenges are also captured in this paper. Contemporary computer vision systems that leverage efficient machine learning approaches like YOLO (You Only Look Once) and PaddleOCR have expanded in almost every industrial sector. The paper is concerned with the integration of these algorithms in the wide programs and consequential effect on the practical field. A systemic reading of up to date literature and experimental analysis is done by this paper to bring out this vital aspect of their usage, the challenges as well as their prospects in the future

Multi-scale image recognition strategy based on convolutional neural network

The accurate recognition and interpretation of multi-scale visual information is a critical focus within contemporary computer vision research. To this end, this study explores and innovatively constructs a multi-scale image recognition strategy based on a Convolutional Neural Network (CNN) with a multi-level and multi-resolution perception domain. This strategy is embedded with an advanced multi-level convolutional operation mechanism, which enables the model to intelligently explore and learn the multi-scale feature representation space of images from tiny texture to grand structure, from shallow simple features to deep semantic abstraction. The core technology path of this paper is to design a deep separable convolutional architecture and combine pyramid pool technology to form a unique network module. This modular design not only ensures the computational efficiency of the model but also improves the ability of extracting and integrating multi-scale image features. Following intensive experimentation on an array of extensively recognized and substantial image datasets, the multi-scale image recognition approach introduced in our study has demonstrated marked enhancements in both recognition capability and stability, manifesting clear superiority compared to conventional, single-scale image recognition methodologies. This research not only enriches the theoretical framework of image recognition, but also provides a new and efficient solution for dealing with complex multi-scale image recognition challenges in practical applications, and further promotes the development of image understanding and recognition technology.

Modeling of Some Classes of Extended Oscillators: Simulations, Algorithms, Generating Chaos, and Open Problems

In this article, we propose some extended oscillator models. Various experiments are performed. The models are studied using the Melnikov approach. We show some integral units for researching the behavior of these hypothetical oscillators. These will be implemented as add-on sections of a thoughtful main web-based application for researching computations. One of the main goals of the study is to share the difficulties that researchers (who are not necessarily professional mathematicians) encounter in using contemporary computer algebraic systems (CASs) for scientific research to examine in detail the dynamics of modifications of classical and newer models that are emerging in the literature (for the large values of the parameters of the models). The present article is a natural continuation of the research in the direction that has been indicated and discussed in our previous investigations. One possible application that the Melnikov function may find in the modeling of a radiating antenna diagram is also discussed. Some probability-based constructions are also presented. We hope that some of these notes will be reflected in upcoming registered rectifications of the CAS. The aim of studying the design realization (scheme, manufacture, output, etc.) of the explored differential models can be viewed as not yet being met.

A Novel Scalable Quantum Protocol for the Dining Cryptographers Problem

This paper presents an innovative entanglement-based protocol to address the Dining Cryptographers problem, utilizing maximally entangled |GHZn⟩ tuples as its core. This protocol aims to provide scalability in terms of both the number of cryptographers n and the amount of anonymous information conveyed, represented by the number of qubits m within each quantum register. The protocol supports an arbitrary number of cryptographers n, enabling scalability in both participant count and the volume of anonymous information transmitted. While the original Dining Cryptographers problem focused on a single bit of information—whether a cryptographer paid for dinner—the proposed protocol allows m, the number of qubits in each register, to be any arbitrarily large positive integer. This flexibility allows the transmission of additional information, such as the cost of the dinner or the timing of the arrangement. Another noteworthy aspect of the introduced protocol is its versatility in accommodating both localized and distributed versions of the Dining Cryptographers problem. The localized scenario involves all cryptographers gathering physically at the same location, such as a local restaurant, simultaneously. In contrast, the distributed scenario accommodates cryptographers situated in different places, engaging in a virtual dinner at the same time. Finally, in terms of implementation, the protocol accomplishes uniformity by requiring that all cryptographers utilize identical private quantum circuits. This design establishes a completely modular quantum system where all modules are identical. Furthermore, each private quantum circuit exclusively employs the widely used Hadamard and CNOT quantum gates, facilitating straightforward implementation on contemporary quantum computers.