Complex Processing Tasks Research Articles

ChatGPT for complex text evaluation tasks

AbstractChatGPT and other large language models (LLMs) have been successful at natural and computer language processing tasks with varying degrees of complexity. This brief communication summarizes the lessons learned from a series of investigations into its use for the complex text analysis task of research quality evaluation. In summary, ChatGPT is very good at understanding and carrying out complex text processing tasks in the sense of producing plausible responses with minimum input from the researcher. Nevertheless, its outputs require systematic testing to assess their value because they can be misleading. In contrast to simple tasks, the outputs from complex tasks are highly varied and better results can be obtained by repeating the prompts multiple times in different sessions and averaging the ChatGPT outputs. Varying ChatGPT's configuration parameters from their defaults does not seem to be useful, except for the length of the output requested.

An energy-saving joint resource allocation strategy for mobile edge computing

Mobile Cloud-Edge Collaboration (MCEC) views in the main of converting the site for user electronics. By naturally integrating mobile devices with cloud computing (CC) resources at the edge of the scheme, this mutual paradigm improves storage, processing, and communication capabilities. This cooperation increases the performance of user electronics, delivering users responsive and resource-efficient knowledge. Offloading in Mobile Cloud-Edge Collaboration (MCEC) is a strategic device that recovers computational efficiency and resource energy for mobile devices. By reasonably moving computation tasks from mobile devices to the edge or cloud servers, offloading declines the load on the limited processing and energy capabilities of mobile devices. This joint method influences the stable computing power and storage aptitude accessible in the cloud-edge structure, confirming that resource-intensive uses like complex data processing or machine learning (ML) tasks can be implemented professionally. Offloading not only increases the receptiveness and performance of mobile users but also contributes to energy conservation, extending the battery time of mobile devices. This study proposes an African Vultures Optimizer algorithm-based Offloading Strategy for Mobile Cloud-Edge Collaboration (AVOAOS-MCEC) approach for consumer electronics. The AVOAOS-MCEC technique is based on the nature of AVOA is a new nature-based system, which is inspired by the unusual behavior of African vultures in foraging and navigation. In addition, the AVOAOS-MCEC technique designs a task offloading process to reduce the total energy utilization with the fulfillment of capacity and delay requirements. The experimental validation of the AVOAOS-MCEC method is verified utilizing distinct measures. An extensive comparison study stated that the AVOAOS-MCEC technique outperforms the other models in terms of several performance measures.

Integration of Manifold Learning and Density Estimation for Fine-Tuned Face Recognition

With the rapid advancements in data analysis and the increasing complexity of high-dimensional datasets, traditional dimensionality reduction techniques like Local Linear Embedding (LLE) often face challenges in maintaining accuracy and efficiency. This research aims to overcome the limitations of LLE, specifically its reliance on the nearest neighbor concept, its inability to distinguish differences among manifold points, and its underutilization of data discrimination information. To address these issues, we propose an advanced LLE algorithm that integrates decision tree-based neighbor recognition with Gaussian kernel density estimation. Decision trees accurately determine neighboring relationships, which are then optimized using Gaussian kernel density estimation to better reflect the distribution of sample points on the manifold. The algorithm also incorporates data discrimination information to enhance classification accuracy and efficiency. Evaluations in facial recognition tasks using SVM classifiers demonstrate significant improvements. Integrating decision trees (LLE-DT) yielded accuracy gains, with LFW at 98.75%, CFP 96.10%, and Olivetti 92.18%. Gaussian density estimation (LLE-GDE) achieved further enhancements, especially in LFW (99.13%), with CFP at 96.85%, and Olivetti at 91.82%. Combining both methods (LLE-DT-GDE) led to substantial improvements: LFW 99.61%, CFP 97.23%, and Olivetti 93.56%, highlighting the synergy between decision trees and Gaussian estimation. This advanced LLE algorithm effectively addresses the limitations of traditional approaches, showing promising results in complex data processing tasks such as facial recognition. These findings suggest its potential for broader applications in fields requiring robust data analysis and classification.

Enhancing Regular Expression Processing through Field-Programmable Gate Array-Based Multi-Character Non-Deterministic Finite Automata

This work investigates the advantages of FPGA-based Multi-Character Non-Deterministic Finite Automata (MC-NFA) for enhancing regular expression processing over traditional software-based methods. By integrating Field-Programmable Gate Arrays (FPGAs) within a data processing framework, our study showcases significant improvements in processing efficiency, accuracy, and resource utilization for complex pattern matching tasks. We present a novel approach that not only accelerates database and network security applications, but also contributes to the evolving landscape of computational efficiency and hardware acceleration. The findings illustrate that FPGA’s coherent access to main memory and the efficient use of resources lead to considerable gains in processing times and throughput for handling regular expressions, unaffected by expression complexity and driven primarily by dataset size and match location. Our research further introduces a phase shift compensation technique that elevates match accuracy to optimal levels, highlighting FPGA’s potential for real-time, accurate data processing. The study confirms that the benefits of using FPGA for these tasks do not linearly correlate with an increase in resource consumption, underscoring the technology’s efficiency. This paper not only solidifies the case for adopting FPGA technology in complex data processing tasks, but also lays the groundwork for future explorations into optimizing hardware accelerators for broader applications.

Brain Tumor Detection Using Neural Classification in Machine Learning

Brain cancer classification is a difficult task due to the variety and complexity of tumors shown in magnetic resonance imaging (MRI) pictures. This research presents two neural network approaches for categorizing MRI brain images. The proposed neural network method consists of three steps: feature extraction, dimensionality reduction, and classification. First, we extracted features from MRI images using discrete wavelet transformation (DWT). In this second stage, we reduce the salient features of MRIs using principal component analysis (PCA). For the classification step, two supervised machine learning classifiers have been developed. Artificial neural networks are used by both classifiers; however, the second one employs back propagation (BPN) while the first one uses feed-forward (FF-ANN). Using the classifiers, MRI brain images of the subjects were classified as normal or abnormal. Artificial neural networks have numerous applications, including function approximation, feature extraction, optimization, and classification (ANNs). They are specifically intended to enhance photos, distinguish and categorize items, separate and register objects, and extract features. Among these, object and picture recognition is the most important for complex processing tasks such as classifying brain tumors. Radial basis function (RBF), cellular, multi-layer perceptron (MLP), hop field, and pulse-coupled neural networks have all been used in image segmentation. These networks can be categorized as feed-forward (associative) or feedback (auto-associative).

Variational sparse diffusion and its application in mesh processing

PurposeThe primary objective of this study is to tackle the enduring challenge of preserving feature integrity during the manipulation of geometric data in computer graphics. Our work aims to introduce and validate a variational sparse diffusion model that enhances the capability to maintain the definition of sharp features within meshes throughout complex processing tasks such as segmentation and repair.Design/methodology/approachWe developed a variational sparse diffusion model that integrates a high-order L1 regularization framework with Dirichlet boundary constraints, specifically designed to preserve edge definition. This model employs an innovative vertex updating strategy that optimizes the quality of mesh repairs. We leverage the augmented Lagrangian method to address the computational challenges inherent in this approach, enabling effective management of the trade-off between diffusion strength and feature preservation. Our methodology involves a detailed analysis of segmentation and repair processes, focusing on maintaining the acuity of features on triangulated surfaces.FindingsOur findings indicate that the proposed variational sparse diffusion model significantly outperforms traditional smooth diffusion methods in preserving sharp features during mesh processing. The model ensures the delineation of clear boundaries in mesh segmentation and achieves high-fidelity restoration of deteriorated meshes in repair tasks. The innovative vertex updating strategy within the model contributes to enhanced mesh quality post-repair. Empirical evaluations demonstrate that our approach maintains the integrity of original, sharp features more effectively, especially in complex geometries with intricate detail.Originality/valueThe originality of this research lies in the novel application of a high-order L1 regularization framework to the field of mesh processing, a method not conventionally applied in this context. The value of our work is in providing a robust solution to the problem of feature degradation during the mesh manipulation process. Our model’s unique vertex updating strategy and the use of the augmented Lagrangian method for optimization are distinctive contributions that enhance the state-of-the-art in geometry processing. The empirical success of our model in preserving features during mesh segmentation and repair presents an advancement in computer graphics, offering practical benefits to both academic research and industry applications.

Effective data visualization strategies in untargeted metabolomics.

Covering: 2014 to 2023 for metabolomics, 2002 to 2023 for information visualizationLC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results.

Pattern Matching in Python: Expanding the Horizons of Engineering Applications

Pattern matching is a powerful programming construct that simplifies code, enhances readability, and enables efficient handling of complex data structures. This paper introduces the pattern matching feature newly introduced in Python 3.10 and explores its applications in various engineering domains. The aim of this research is to showcase how Python's pattern matching capability can be leveraged for parsing and analyzing data, structural matching in data analysis, model and system validation, and signal processing. Through illustrative examples and case studies, we demonstrate the versatility and effectiveness of Python pattern matching in solving real-world engineering problems. By introducing pattern matching in Python, this research opens new avenues for engineers and scientists to tackle complex data processing tasks, enhance system validation techniques, and streamline algorithmic implementations. With the integration of pattern matching into Python's ecosystem, the language becomes even more powerful and expressive, empowering practitioners to write cleaner, more concise, and efficient code. This research lays the foundation for the adoption and exploration of pattern matching techniques in Python, highlighting its potential impact on engineering applications and providing a roadmap for future research and development in this field.

MaRDIFlow: A Workflow Framework for Documentation and Integration of FAIR Computational Experiments

Numerical algorithms and computational tools are essential for managing and analyzing complex data processing tasks. With ever increasing availability of meta-data and parameter-driven simulations, the demand and the need for reliable and automated workflow frameworks to reproduce computational experiments has grown. In this work, we aim to develop a novel computational workflow framework, namely MaRDIFlow, that describes the abstraction of multi-layered workflow components. Herein, we plan to enable and implement scientific computing data FAIRness into actionable guidelines for FAIR computational experiments.

Tamper-proof access control for IoT clouds using enclaves

Internet of Things (IoT) devices rely on cloud computing for processing user-sensitive data, like health recordings and geolocalization. In this case, security primitives like cryptography and certificate-based authentication does not prevent the cloud provider from acting against the privacy policy. This paper presents a framework for clouds to execute arbitrarily complex processing tasks over IoT data while maintaining the access control policies over the client’s control. We rely on a memory enclave to enforce that the cloud follows personal and customizable access policies and analyzed the security properties of our scheme. The performance evaluation reveals that these robust security improvements come with a latency overhead of just 0.1 ms, confirming the system’s viability. The system leverages multi-threaded processing inside an enclave to process thousands of client messages per second, achieving high scalability. This work also contributes with a microbenchmark that identifies how much each step of an enclave application influences the performance and evaluates the enclave viability for performing realistic IoT data processing.

Performance Evaluation of Deep Learning Algorithm Using High-End Media Processing Board in Real-Time Environment

Image processing-based artificial intelligence algorithm is a critical task, and the implementation requires a careful examination for the selection of the algorithm and the processing unit. With the advancement of technology, researchers have developed many algorithms to achieve high accuracy at minimum processing requirements. On the other hand, cost-effective high-end graphical processing units (GPUs) are now available to handle complex processing tasks. However, the optimum configurations of the various deep learning algorithms implemented on GPUs are yet to be investigated. In this proposed work, we have tested a Convolution Neural Network (CNN) based on You Only Look Once (YOLO) variants on NVIDIA Jetson Xavier to identify compatibility between the GPU and the YOLO models. Furthermore, the performance of the YOLOv3, YOLOv3-tiny, YOLOv4, and YOLOv5s models is evaluated during the training using our PowerEdge Dell R740 Server. We have successfully demonstrated that YOLOV5s is a good benchmark for object detection, classification, and traffic congestion using the Jetson Xavier GPU board. The YOLOv5s achieved an average precision of 95.9% among all YOLO variants and the highest success rate achieved is 98.89.

Mobile Device-Based Train Ride Comfort Measuring System

As an important train performance quality, comfort depends on vibration and noise data measured on a running train. Traditional vibration and noise measurement tools are facing challenges in terms of collecting big data, portability, and cost. With the continuous upgrade of mobile terminal hardware, the built-in sensors of mobile phones have the ability to undertake relatively complex data measurement and processing tasks. In this study, a new type of train comfort measurement system based on a mobile device is developed by using a built-in sensor to measure the vibration and noise. The functions of the developed system include the real-time display of three-way vibration acceleration, lateral and vertical Sperling indicators, sound pressure level, and train comfort-related data storage and processing. To verify the accuracy of the mobile device-based train ride comfort measuring system (DTRCMS), a comparison of test results from this system and from the traditional measuring system is conducted. The comparison results show that the DTRCMS is in good agreement with the traditional measuring system. The relative error in three-direction acceleration and Sperling values is 2~10%. The fluctuation range of the noise measured by DTRCMS is slightly lower than that of the professional noise meter, and the relative error is mainly between 1.5% and 4.5%. Overall, the study shows that using mobile devices to measure train comfort is feasible and practical and has great potential for big data-based train comfort evaluation in the future.

The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents.

Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate on unsupervised learning methodology. The two important models are as follows: the supervised and unsupervised models. The main difference is the method of training that they undergo. Supervised models are provided with training on a particular dataset and its outcome. In the case of unsupervised models, only input data is given, and there is no set outcome from which they can learn. The predicting/forecasting column is not present in an unsupervised model, unlike in the supervised model. Supervised models use regression to predict continuous quantities and classification to predict discrete class labels; unsupervised models use clustering to group similar models and association learning to find associations between items. Unsupervised migration is a combination of the unsupervised learning method and migration. In unsupervised learning, there is no need to supervise the models. Migration is an effective tool in processing and imaging data. Unsupervised learning allows the model to work independently to discover patterns and information that were previously undetected. It mainly works on unlabeled data. Unsupervised learning can achieve more complex processing tasks when compared to supervised learning. The unsupervised learning method is more unpredictable when compared with other types of learning methods. Some of the popular unsupervised learning algorithms include k-means clustering, hierarchal clustering, Apriori algorithm, clustering, anomaly detection, association mining, neural networks, etc. In this research article, we implement this particular deep learning model in the marketing oriented asset allocation of high level accounting talents. When the proposed unsupervised migration algorithm was compared to the existing Fractional Hausdorff Grey Model, it was discovered that the proposed system provided 99.12% accuracy by the high level accounting talented candidate in market-oriented asset allocation.

ENOS: Energy-Aware Network Operator Search in Deep Neural Networks

This work proposes a novel <i>Energy-aware Network Operator Search</i> (<i>ENOS</i>) approach to address the energy-accuracy trade-offs of a deep neural network (DNN) accelerator. In recent years, novel hardware-friendly inference operators such as binary-weight, multiplication-free, and deep-shift have been proposed to improve the computational efficiency of a DNN accelerator. However, simplifying DNN operators invariably comes at lower accuracy, especially on complex processing tasks. While prior works generally implement the same inference operator throughout the neural architecture, the proposed ENOS framework allows an <i>optimal layer-wise integration of inference operators with optimal precision</i> to maintain high prediction accuracy and high energy efficiency. The search in ENOS is formulated as a continuous optimization problem, solvable using gradient descent methods, thereby minimally increasing the training cost when learning <i>both</i> layer-wise inference operators and weights. Utilizing ENOS, we discuss multiply-accumulate (MAC) cores for digital spatial architectures that can be reconfigured to different operators and varying computing precision. ENOS training methods with single and bi-level optimization objectives are discussed and compared. We also discuss a sequential operator assignment strategy in ENOS that only learns the assignment for one layer in one training step. Furthermore, a stochastic mode of ENOS is also presented. ENOS is characterized on ShuffleNet and SqueezeNet using CIFAR10 and CIFAR100. Compared to the conventional uni-operator approaches, under the same energy budget, ENOS improves accuracy by 10&#x2013;20%. ENOS also outperforms the accuracy of comparable mixed-precision uni-operator implementations by 3-5% for the same energy budget.

Understanding Differences in Wayfinding Strategies.

Navigating to goal locations in a known environment (wayfinding) can be accomplished by different strategies, notably by taking habitual, well-learned routes (response strategy) or by inferring novel paths, such as shortcuts, from spatial knowledge of the environment's layout (place strategy). Human and animal neuroscience studies reveal that these strategies reflect different brain systems, with response strategies relying more on activation of the striatum and place strategies associated with activation of the hippocampus. In addition to individual differences in strategy, recent behavioral studies show sex differences such that men use place strategies more than women, and age differences such that older adults use more response strategies than younger adults. This paper takes a comprehensive multilevel approach to understanding these differences, characterizing wayfinding as a complex information processing task. This analysis reveals factors that affect navigation strategy, including availability of the relevant type of environmental knowledge, momentary access to this knowledge, trade-offs between physical and mental effort in different navigation contexts, and risk taking. We consider how strategies are influenced by the computational demands of a navigation task and by factors that affect the neural circuits underlying navigation. We also discuss limitations of laboratory studies to date and outline priorities for future research, including relating wayfinding strategies to independent measures of spatial knowledge, and studying wayfinding strategies in naturalistic environments.

Signal estimation and filtering from quantized observations via adaptive stochastic resonance.

Using a gradient-based algorithm, we investigate signal estimation and filtering in a large-scale summing network of single-bit quantizers. Besides adjusting weights, the proposed learning algorithm also adaptively updates the level of added noise components that are intentionally injected into quantizers. Experimental results show that minimization of the mean-squared error requires a nonzero optimal level of the added noise. The process adaptively achieves in this way a form of stochastic resonance or noise-aided signal processing. This adaptive optimization method of the level of added noise extends the application of adaptive stochastic resonance to some complex nonlinear signal processing tasks.

Using Vector Fields for Efficient Simulation of Macroscopic Molecular Communication

Molecular communication has been identified as a communication concept complementing radio communication in some areas and being the unique solution in others. This particularly includes communication between nano machines but, more recently, also macroscopic application domains such as in fluid systems in a chemical factory. We are interested in simulating such macroscopic molecular communication both accurately as well as on a large scale. In this work, we make use of the concept of vector fields for efficient simulation of particle movements in a fluid environment. Such vector fields can be pre-computed so that the simulation of the communication itself is very fast. We discuss both the concepts and the methodological approach to outline the advantages of this idea and validate the system compared to lab measurements. Going beyond previous work, we also integrated both on-off keying (OOK) and pulse position modulation (PPM) to demonstrate the feasibility of the simulation concept even for more complex signal processing tasks.

Learning Optimum Number of Bases for Indian Languages in Non-negative Matrix Factorization based Multilingual Speech Separation

Non-negative matrix factorization-based audio source separation separating a target source has shown significant performance improvement when the spectral bases attained after factorization exhibits latent structures in the mixed audio signal comprising multiple speaker sources. If all the sources are known, the spectral bases may be inferred on priority by using a training process on the database of isolated sources. The number of bases inferred for a source should not include bases matching spectral patterns of the interfering sources in the audio mixture; otherwise, the estimated target source after separation will be incorporated with undesirable spectral patterns. It is difficult to distinguish and separate similar audio sources in an overlapped speech, leading to a complex speech processing task. Therefore, this research attempts to learn an optimum number of bases for Indian languages leading to successful separation of target source in multi-lingual multiple speaker speech mixtures using non-negative matrix factorization. The languages used for utterances are Hindi, Marathi, Gujarati, and Bengali. The speaker combinations used are female-female, male-male, and female-male. The optimum number of bases which was determined by evaluating improvement in the separation performance was found to be 40 for all the languages considered.

Ultra-high field (7T) functional magnetic resonance imaging in amyotrophic lateral sclerosis: a pilot study

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease of the central nervous system that results in a progressive loss of motor function and ultimately death. It is critical, yet also challenging, to develop non-invasive biomarkers to identify, localize, measure and/or track biological mechanisms implicated in ALS. Such biomarkers may also provide clues to identify potential molecular targets for future therapeutic trials. Herein we report on a pilot study involving twelve participants with ALS and nine age-matched healthy controls who underwent high-resolution resting state functional magnetic resonance imaging at an ultra-high field of 7 Tesla. A group-level whole-brain analysis revealed a disruption in long-range functional connectivity between the superior sensorimotor cortex (in the precentral gyrus) and bilateral cerebellar lobule VI. Post hoc analyses using atlas-derived left and right cerebellar lobule VI revealed decreased functional connectivity in ALS participants that predominantly mapped to bilateral postcentral and precentral gyri. Cerebellar lobule VI is a transition zone between anterior motor networks and posterior non-motor networks in the cerebellum, and is associated with a wide range of key functions including complex motor and cognitive processing tasks. Our observation of the involvement of cerebellar lobule VI adds to the growing number of studies implicating the cerebellum in ALS. Future avenues of scientific investigation should consider how high-resolution imaging at 7T may be leveraged to visualize differences in functional connectivity disturbances in various genotypes and phenotypes of ALS along the ALS-frontotemporal dementia spectrum.

The Potential of Artificial Intelligence in Higher Education

Artificial Intelligence (AI) is seeping into many aspects of our everyday lives, with common internet applications, smartphones and even household appliances. Within education, AI is a rapidly emerging field and there is a strong potential for AI to greatly extend and enhance teaching and learning in higher education (Crompton et al., 2020). AI is defined as “computing systems that are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and use of data for complex processing tasks” (Popenici &amp; Kerr, 2017). In the Horizon Report 2020 report (Brown et al., 2020), AI is listed as one of six technologies with the potential for high impact in higher education. The Horizon Report (2020), is an annual publication that examines the major trends in educational technology that are shaping global higher education. This paper will highlight some of the ways AI is supporting both students and faculty members including bespoke learning, intelligent tutoring systems, facilitating collaboration, and automated grading. This is followed by a section on ethical implications.