Task Analysis Research Articles

Teachers Management of Everyday-Living Skills of Intellectually Challenged Learners

Intellectually Challenged Learners (ICLs) are a long-lasting medical condition that affects people for the rest of their lives and typically prevents them from reaching their full potential. This study, therefore, investigated teachers’ management of Everyday-Living Skills (ELS) of ICLs. The research is survey-based and primarily descriptive. The study’s population was made up of instructors in public inclusive basic schools in Lagos State, Nigeria. The study’s sample consisted of 100 inclusive basic school instructors in total. The researchers employed a well-structured study questionnaire they had created to gather information on the “Management of ELS of ICLs.” A reliability index of 0.72 was calculated with the aid of the Spearman-Brown Prophecy Formula. To address the questions posed, a mean rating was used. The t-test and Analysis of Variance (ANOVA) were used to evaluate the hypotheses at the 0.05 significance level. This study discovered that when teaching ELS to ICLs, the teacher-direct approach (TDA), generalization and application, task analysis (TA), and positive reinforcement and encouragement (PIE) were consistently used. It was recommended that to enable effective ELS instruction, the government should allot enough funds to equip instructors with the required instructional resources, assistive technology, and improved environments.

Evaluating the Usability of mHealth Apps: An Evaluation Model Based on Task Analysis Methods and Eye Movement Data.

Advancements in information technology have facilitated the emergence of mHealth apps as crucial tools for health management and chronic disease prevention. This research work focuses on mHealth apps for the management of diabetes by patients on their own. Given that China has the highest number of diabetes patients in the world, with 141 million people and a prevalence rate of 12.8% (mentioned in the Global Overview of Diabetes), the development of a usability research methodology to assess and validate the user-friendliness of apps is necessary. This study describes a usability evaluation model that combines task analysis methods and eye movement data. A blood glucose recording application was designed to be evaluated. The evaluation was designed based on the model, and the feasibility of the model was demonstrated by comparing the usability of the blood glucose logging application before and after a prototype modification based on the improvement suggestions derived from the evaluation. Tests showed that an improvement plan based on error logs and post-task questionnaires for task analysis improves interaction usability by about 24%, in addition to an improvement plan based on eye movement data analysis for hotspot movement acceleration that improves information access usability by about 15%. The results demonstrate that this study presents a usability evaluation model for mHealth apps that enables the effective evaluation of the usability of mHealth apps.

TEXTKNOCKOFF: KNOCKOFF NETS FOR STEALING FUNCTIONALITY OF TEXT SENTIMENT MODELS

Most commercial machine learning models today are designed to require significant amounts of time, money, and human effort. Therefore, intrinsic information about the model (such as architecture, hyperparameters, and training data) needs to be kept confidential. These models are referred to as black boxes, and there is an increasing amount of research focused on both attacking and protecting them. Recent publications have often concentrated on the field of computer vision; in contrast, there is still relatively little research on methods for attacking black box models with textual data. This article introduces a research method for extracting the functionality of a black box model in the task of text sentiment analysis. The method has been effectively tested based on random sampling techniques to reconstruct a new model with equivalent functionality to the original model, achieving high accuracy (94.46% compared to 94.92%) and high similarity (96.82%).

A Subject Review on Application of Analytical Chemistry in the Mitochondrial Medicine

Understanding energy metabolism and intracellular energy transmission requires knowledge of the function and structure of the mitochondria. Issues with mitochondrial morphology, structure, and function are the most prevalent symptoms. They can damage organs such as the heart, brain, and muscle due to a variety of factors, such as oxidative damage, incorrect metabolism of energy, or genetic conditions. The control of cell metabolism and physiology depends on functional connections between mitochondrial and biological surroundings. Therefore, it is essential to research mitochondria in situ or in vivo without isolating them from their surrounding biological environment. Finding and spotting abnormal alterations in mitochondria is the primary research technique for understanding mitochondrial illnesses. The purpose of this review is to collect original studies and papers describing a variety of analytical chemistry tasks carried out in mitochondria. Analytical chemistry is essential to the biological and medical sciences. Several analytical methods have been used in this field, such as chromatographic, spectroscopic, spectrophotometric, electrochemical analysis, and electrospray ionization mass spectrometry. While spectroscopic techniques in particular have yielded important information in certain cases, the nature of these techniques nevertheless limits the information that can be collected. Mass spectrometry may, however, produce incredibly detailed datasets.

A fused convolutional transformer for voltammetric electronic tongue analysis tasks

Electronic tongue and artificial intelligence have been evaluated as new rapid analysis technologies in complex solution research. The use of nanomaterials to modify the screen-printed carbon electrode (SPCE) increased the specific surface area and electrocatalytic active sites of the electrode. The voltammetric electronic tongue utilizes multiple cross sensitive sensors to describe the overall characteristics of complex samples, and the complementary information between sensors provides rich and comprehensive descriptions for analyzing various components. The use of electronic tongue for rapid analysis of water quality has gradually become a trend, but the overlapping phenomenon of peaks formed by the similar redox potential of each component in water and the interference of the substrate makes it difficult to obtain effective information of each component in water quality. Here, we propose a Fused Convolutional Transformer model (FCvT) that removes the limitation of overlapping peaks by fusing local features and global complementary features to quantify components in complex solutions. The performance of FCvT was evaluated by testing synthetic water samples consisting of four standardized substance solutions and compared with partial least squares (PLS), support vector regression (SVR), artificial neural networks (ANN), and convolutional neural networks (CNN). The results show that the FCvT obtains the lowest mean absolute percentage error in the set of independent tests.The average quantization error of FCvT is reduced by 34.4 % relative to other methods.

Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis Tasks

The availability of trajectory data combined with various real-life practical applications has sparked the interest of the research community to design a plethora of algorithms for various trajectory analysis techniques. However, there is an apparent lack of full-fledged systems that provide the infrastructure support for trajectory analysis techniques, which hinders the applicability of most of the designed algorithms. Inspired by the tremendous success of the Bidirectional Encoder Representations from Transformers (BERT) deep learning model in solving various Natural Language Processing tasks, our vision is to have a BERT-like system for trajectory analysis tasks. We envision that in a few years, we will have such system where no one needs to worry again about each specific trajectory analysis operation. Whether it is trajectory imputation, similarity, clustering, or whatever, it would be one system that researchers, developers, and practitioners can deploy to get high accuracy for their trajectory operations. Our vision stands on a solid ground that trajectories in a space are highly analogous to statements in a language. We outline the challenges and the road to our vision. Exploratory results confirm the promise and possibility of our vision.

Learning locality-sensitive bucketing functions.

Many tasks in sequence analysis ask to identify biologically related sequences in a large set. The edit distance, being a sensible model for both evolution and sequencing error, is widely used in these tasks as a measure. The resulting computational problem-to recognize all pairs of sequences within a small edit distance-turns out to be exceedingly difficult, since the edit distance is known to be notoriously expensive to compute and that all-versus-all comparison is simply not acceptable with millions or billions of sequences. Among many attempts, we recently proposed the locality-sensitive bucketing (LSB) functions to meet this challenge. Formally, a (d1,d2)-LSB function sends sequences into multiple buckets with the guarantee that pairs of sequences of edit distance at most d1 can be found within a same bucket while those of edit distance at least d2 do not share any. LSB functions generalize the locality-sensitive hashing (LSH) functions and admit favorable properties, with a notable highlight being that optimal LSB functions for certain (d1,d2) exist. LSB functions hold the potential of solving above problems optimally, but the existence of LSB functions for more general (d1,d2) remains unclear, let alone constructing them for practical use. In this work, we aim to utilize machine learning techniques to train LSB functions. With the development of a novel loss function and insights in the neural network structures that can potentially extend beyond this specific task, we obtained LSB functions that exhibit nearly perfect accuracy for certain (d1,d2), matching our theoretical results, and high accuracy for many others. Comparing to the state-of-the-art LSH method Order Min Hash, the trained LSB functions achieve a 2- to 5-fold improvement on the sensitivity of recognizing similar sequences. An experiment on analyzing erroneous cell barcode data is also included to demonstrate the application of the trained LSB functions. The code for the training process and the structure of trained models are freely available at https://github.com/Shao-Group/lsb-learn.

On some methods of determining proper frequencies torsional oscillations in metal-working machines

Forced torsional vibrations that occur in machine tool drives are one of the pressing problems of modern machine tool industry. The reasons for these fluctuations may be imbalances of rotating masses, structural imperfections of bearing units, operating or technological process features (for metal-cutting machines – eccentric clamping of the workpiece, processing of non-round workpieces, etc.). In this work, using the example of a real metal-cutting machine, the task of dynamic analysis of the drive of the main movement of the machine with determination of the natural frequencies of torsional vibrations is realized. The real object is replaced by a dynamic model (DM). When constructing the DM, the inertial and rigidity characteristics are determined from the conditions: – the kinetic energy of the object is equivalent to the kinetic energy of the DM (from this condition the axial moments of inertia of the concentrated masses of the row model are obtained); – the potential energy of elastic deformations of the object is equivalent to the potential energy of deformation of the DM (from this condition the rigidity coefficients of the DM are obtained). The natural frequencies of the row DM were determined in three ways: 1) by solving the frequency equation (exact method) in combination with the method of reducing the number of masses of A.P. Cherevkov; 2) partial systems method (fundamental frequency only); 3) method of residuals (Tolle). The following conclusions were made: 1) the main (smaller) frequency can be found by any of the methods considered (since the results of the three methods coincided); 2) when simplifying the DM using the method of partial systems, the criterion for the possibility of further simplifications over the specified frequency range should be taken into account; the transition from a three-mass DM to a two-mass one if the criterion in this calculation was not met led to an error in determining the value of the fundamental frequency of about 10%; 3) dynamic calculations of machines using DM and methods for determining natural frequencies make it possible, based on known frequencies of disturbing forces, to identify possible near-resonant operating modes and avoid them in advance by making changes to the design of the machine at the design stage

Successfully initiating an escalation of care in acute ward settings-A qualitative observational study.

To address knowledge gaps by (i) developing a theoretical understanding of escalation and (ii) identifying escalation success factors. Non-participant observations were used to examine deteriorating patient escalation events. Escalation event data were collected by a researcher who shadowed clinical staff, between February 16th 2021 and March 17th 2022 from two National Health Service Trusts. Events were analysed using Framework Analysis. Escalation tasks were mapped using a Hierarchical Task Analysis diagram and data presented as percentages, frequency and 95% CI. A total of 38 observation sessions were conducted, totaling 105 h, during which 151 escalation events were captured. Half of these were not early warning score-initiated and resulted from bleeding, infection, or chest pain. Four communication phenotypes were observed in the escalation events. The most common was Outcome Focused Escalation, where the referrer expected specific outcomes like blood cultures or antibiotic prescriptions. Informative Escalations were often used when a triggering patient's condition was of low clinical concern and ranked as the second most frequent escalation communication type. General Concern Escalations occurred when the referrer did not have predetermined expectations. Spontaneous Interaction Escalations were the least frequently observed, occurring opportunistically in communal workspaces. Half of the events were non-triggering escalations and understanding these can inform the design of systems to support staff better to undertake them. Escalation is not homogenous and differing escalation communication phenotypes exist. Informative Escalations represent an organizational requirement to report triggering warning scores and a targeted reduction of these may be organizationally advantageous. Increasing the frequency of Spontaneous Escalations, through hospital designs, may also be beneficial. Our work highlights that a significant proportion of escalation workload occurs without a triggering early warning score and there is scope to better support these with designed systems. Further examination of reducing Informative and increasing Spontaneous Escalations is also warranted. Extensive PPIE was completed throughout the lifecycle of this study. PPIE members validated the research questions and overarching aims of the overall study. PPIE members contributed to the design of the study reviewed documents and the final data generated.

Medical image registration via neural fields

Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images. Recent learning-based methods, trained to directly predict transformations between two images, run much faster, but suffer from performance deficiencies due to domain shift. Here we present a new neural network based image registration framework, called NIR (Neural Image Registration), which is based on optimization but utilizes deep neural networks to model deformations between image pairs. NIR represents the transformation between two images with a continuous function implemented via neural fields, receiving a 3D coordinate as input and outputting the corresponding deformation vector. NIR provides two ways of generating deformation field: directly output a displacement vector field for general deformable registration, or output a velocity vector field and integrate the velocity field to derive the deformation field for diffeomorphic image registration. The optimal registration is discovered by updating the parameters of the neural field via stochastic mini-batch gradient descent. We describe several design choices that facilitate model optimization, including coordinate encoding, sinusoidal activation, coordinate sampling, and intensity sampling. NIR is evaluated on two 3D MR brain scan datasets, demonstrating highly competitive performance in terms of both registration accuracy and regularity. Compared to traditional optimization-based methods, our approach achieves better results in shorter computation times. In addition, our methods exhibit performance on a cross-dataset registration task, compared to the pre-trained learning-based methods.

Towards multimodal sarcasm detection via label-aware graph contrastive learning with back-translation augmentation

Multimodal sarcasm detection, as a sentiment analysis task, has witnessed great strides owing to the rapid development of multimodal machine learning. However, existing graph-based studies mainly focus on capturing the atomic-aware relations between textual and visual graphs within individual instances, neglecting label-aware connections between different instances. To address this limitation, we propose a novel Label-aware Graph Contrastive Learning (LGCL) method that detects ironic cues from a label-aware perspective of multimodal data. We first construct unimodal graphs for each instance and fuse them into graph semantic space, to obtain the multimodal graphs. Then, we introduce two label-aware graph contrastive losses: Label-aware Unimodal Contrastive Loss (LUCL) and Label-aware Multimodal Contrastive Loss (LMCL), to make the model aware of the shared ironic cues related to sentiment labels within multimodal graph representations. Additionally, we propose Back-translation Data Augmentation (BTrA) for both textual and visual data to enhance contrastive learning, where different back-translation schemes are designed to generate a larger number of positive and negative samples. Experimental results on two public datasets demonstrate our method achieves state-of-the-art (SOTA) compared to previous methods.

Unveiling the Evolution of Virtual Reality in Medicine: A Bibliometric Analysis of Research Hotspots and Trends over the Past 12 Years.

Virtual reality (VR), widely used in the medical field, may affect future medical training and treatment. Therefore, this study examined VR's potential uses and research directions in medicine. Citation data were downloaded from the Web of Science Core Collection database (WoSCC) to evaluate VR in medicine in articles published between 1 January 2012 and 31 December 2023. These data were analyzed using CiteSpace 6.2. R2 software. Present limitations and future opportunities were summarized based on the data. A total of 2143 related publications from 86 countries and regions were analyzed. The country with the highest number of publications is the USA, with 461 articles. The University of London has the most publications among institutions, with 43 articles. The burst keywords represent the research frontier from 2020 to 2023, such as "task analysis", "deep learning", and "machine learning". The number of publications on VR applications in the medical field has been steadily increasing year by year. The USA is the leading country in this area, while the University of London stands out as the most published, and most influential institution. Currently, there is a strong focus on integrating VR and AI to address complex issues such as medical education and training, rehabilitation, and surgical navigation. Looking ahead, the future trend involves integrating VR, augmented reality (AR), and mixed reality (MR) with the Internet of Things (IoT), wireless sensor networks (WSNs), big data analysis (BDA), and cloud computing (CC) technologies to develop intelligent healthcare systems within hospitals or medical centers.

Reduction of environmental risk of industrially developed territory through the early warning system: the example of northern and arctic territories of The Russian Federation

Within early warning and technological hazards, the need to notify, and to identify, assess and provide the necessary information about the sources of potential hazards is of particular importance. Since there may be causal links between natural and technological disasters, there are increasing concerns that the available knowledge base does not allow for timely warning or relief measures for many hazards.Any early warning system aims to reduce risk as a measure of potential environmental and economic damage. Such reduction, as a rule, is achieved by continuous study of information on the state of functioning of the naturaltechnical system. Hence the task of systematic collection, accumulation, processing and analysis of information arises. The system of continuous observation, control and analysis of the environmental situation for subsequent diagnosis and forecasting of the state of natural objects is realized within the framework of monitoring, which is performed at three levels – global (planetary), regional and local.The Sakha Republic (Yakutia) is the largest region of the Russian Federation with a highly concentrated natural-resource potential, providing full-scale projects of national economic development of interregional and federal significance. Yakutia, the area of which is 20 % of Russia’s territory, is a giant natural enclave in geotechnical terms, the administrative boundaries of which cross many geographically heterogeneous ecosystems of seven neighboring territories (administratively independent subjects of ownership). The article considers and reveals the main directions of the early warning system to prevent socio-natural risk as applied to Yakutia, taking into account its natural-territorial uniqueness.

Atlas for the Lateralized Visuospatial Attention Networks (ALANs): Insights from fMRI and network analyses

Abstract Hemispheric specialization is central to human evolution and fundamental to human cognitive abilities. While being a defining feature of functional brain architecture, hemispheric specialization is overlooked to derive brain parcellations. Alongside language, which is typically lateralized in the left hemisphere, visuospatial attention is set to be its counterpart in the opposite hemisphere. However, it remains uncertain to what extent the anatomical and functional underpinnings of lateralized visuospatial attention mirror those supporting language. Building on our previous work, which established a lateralized brain atlas for language, we propose a comprehensive cerebral lateralized atlas delineating the anatomo-functional bases of visuospatial attention, Atlas for Lateralized visuospatial Attentional Networks (ALANs). Combining task and resting-state functional connectivity analyses, we identified 95 lateralized brain areas comprising three networks supporting visual (visu), motor (somato-motor), and spatial processing (posterior-medial) processes at work during a line judgment bisection task, and two large-scale networks related to integrated visuospatial attention processes, the parieto-frontal and temporo-frontal networks. We identify hubs playing a pivotal role in the intra-hemispheric interaction within visuospatial attentional networks. The rightward lateralized parieto-frontal encompasses one hub, the inferior frontal sulcus, while the temporo-frontal network encompasses two right hubs: the inferior frontal cortex (pars triangularis and the anterior insula) and the posterior part of the superior temporal sulcus. Compared with our language lateralized atlas, we demonstrated that specific regions within these networks encompass the homotope of the language network from the left hemisphere. This atlas of visuospatial attention provides valuable insights for future investigations into the variability of visuospatial attention and hemispheric specialization research. Additionally, it facilitates more effective comparisons among different studies, thereby enhancing the robustness and reliability of research in the field of attention.

Streamlined jet tagging network assisted by jet prong structure

Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computationally intensive and require substantial data for training. In this paper, we introduce a new jet classification network using an MLP mixer, where two subsequent MLP operations serve to transform particle and feature tokens over the jet constituents. The transformed particles are combined with subjet information using multi-head cross-attention so that the network is invariant under the permutation of the jet constituents. We utilize two clustering algorithms to identify subjets: the standard sequential recombination algorithms with fixed radius parameters and a new IRC-safe, density-based algorithm of dynamic radii based on HDBSCAN. The proposed network demonstrates comparable classification performance to state-of-the-art models while boosting computational efficiency drastically. Finally, we evaluate the network performance using various interpretable methods, including centred kernel alignment and attention maps, to highlight network efficacy in collider analysis tasks.

Relative effect size-based profiles as an alternative to differentiation analysis in multi-species single-cell transcriptional studies.

Combining data from experiments on multispecies studies provides invaluable contributions to the understanding of basic disease mechanisms and pathophysiology of pathogens crossing species boundaries. The task of multispecies gene expression analysis, however, is often challenging given annotation inconsistencies and in cases of small sample sizes due to bias caused by batch effects. In this work we aim to demonstrate that an alternative approach to standard differential expression analysis in single cell RNA-sequencing (scRNA-seq) based on effect size profiles is suitable for the fusion of data from small samples and multiple organisms. The analysis pipeline is based on effect size metric profiles of samples in specific cell clusters. The effect size substitutes standard differentiation analyses based on p-values and profiles identified based on these effect size metrics serve as a tool to link cell type clusters between the studied organisms. The algorithms were tested on published scRNA-seq data sets derived from several species and subsequently validated on own data from human and bovine peripheral blood mononuclear cells stimulated with Mycobacterium tuberculosis. Correlation of the effect size profiles between clusters allowed for the linkage of human and bovine cell types. Moreover, effect size ratios were used to identify differentially regulated genes in control and stimulated samples. The genes identified through effect size profiling were confirmed experimentally using qPCR. We demonstrate that in situations where batch effects dominate cell type variation in single cell small sample size multispecies studies, effect size profiling is a valid alternative to traditional statistical inference techniques.

TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease.

Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression. We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations. TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.

Multi-grained contrastive representation learning for label-efficient lesion segmentation and onset time classification of acute ischemic stroke

Ischemic lesion segmentation and the time since stroke (TSS) onset classification from paired multi-modal MRI imaging of unwitnessed acute ischemic stroke (AIS) patients is crucial, which supports tissue plasminogen activator (tPA) thrombolysis decision-making. Deep learning methods demonstrate superiority in TSS classification. However, they often overfit task-irrelevant features due to insufficient paired labeled data, resulting in poor generalization. We observed that unpaired data are readily available and inherently carry task-relevant cues, but are less often considered and explored. Based on this, in this paper, we propose to fully excavate the potential of unpaired unlabeled data and use them to facilitate the downstream AIS analysis task. We first analyze the utility of features at the varied grain and propose a multi-grained contrastive learning (MGCL) framework to learn task-related prior representations from both coarse-grained and fine-grained levels. The former can learn global prior representations to enhance the location ability for the ischemic lesions and perceive the healthy surroundings, while the latter can learn local prior representations to enhance the perception ability for semantic relation between the ischemic lesion and other health regions. To better transfer and utilize the learned task-related representation, we designed a novel multi-task framework to simultaneously achieve ischemic lesion segmentation and TSS classification with limited labeled data. In addition, a multi-modal region-related feature fusion module is proposed to enable the feature correlation and synergy between multi-modal deep image features for more accurate TSS decision-making. Extensive experiments on the large-scale multi-center MRI dataset demonstrate the superiority of the proposed framework. Therefore, it is promising that it helps better stroke evaluation and treatment decision-making.

Caching or not: An online cost optimization algorithm for geodistributed data analysis in cloud environments

With the wide application of big data technology, a large number of data geographically stored in data centers across various regions are generated everyday, waiting to be analyzed by big data tasks. Examples of such data analysis tasks include weather prediction and intelligent healthcare applications. Clouds are being used by more and more enterprises due to their nearly infinite resources, ease of scaling, and other characteristics. Organizations often rent StaaS (Storage-as-a-Service) storage products offered by cloud providers, such as OSS (Object Storage Service), for massive data storage, while building a big data cluster in cloud environments to analyze the collected data from various regions. However, when the data to be analyzed are not located in the rented cluster, how to efficiently and economically process the distributed input data stored in clouds becomes an urgent problem to be solved. A simple approach is to only cache frequently accessed data rather than all data from other regions into the cluster to reduce total traffic costs. However, it is generally very hard to predict future data access curves. Thus, a rash caching decision may incur more costs. To address this problem, in this paper we propose an online algorithm for guiding cloud users to make cost-effective caching decisions properly, while not requiring any future information. We prove theoretically that the competitive ratio of our online algorithm is less than 2. Finally we verify the effectiveness of our proposed algorithm through extensive experiments based on the real price of Alibaba’s public IaaS cloud products using both real-world Yahoo S2 data and synthesized datasets.

A Hybrid Deep Spatiotemporal Attention‐Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals

ABSTRACTParkinson's disease (PD), a severe and progressive neurological illness, affects millions of individuals worldwide. For effective treatment and management of PD, an accurate and early diagnosis is crucial. This study presents a deep learning‐based model for the diagnosis of PD using a resting state electroencephalogram (EEG) signal. The objective of the study is to develop an automated model that can extract complex hidden nonlinear features from EEG and demonstrate its generalizability on unseen data. The model is designed using a hybrid model, consisting of a convolutional neural network (CNN), bidirectional gated recurrent unit (Bi‐GRU), and attention mechanism. The proposed method is evaluated on three public datasets (UC San Diego, PRED‐CT, and University of Iowa [UI] dataset), with one dataset used for training and the other two for evaluation. The proposed model demonstrated remarkable performance, attaining high accuracy scores of 99.4%, 84%, and 73.2% using UC San Diego, PRED‐CT, and UI datasets, respectively. These results justify the effectiveness and robustness of the proposed model across diverse datasets, highlighting its potential for versatile applications in data analysis and prediction tasks. Our proposed hybrid spatiotemporal attention‐based model has been developed with 10‐fold cross‐validation (CV) for UC San Diego dataset and 10‐fold CV and leave‐one‐out cross‐validation (LOOCV) strategies for PRED‐CT and UI datasets. Our results indicate that the proposed PD detection system is accurate and robust. The developed prototype can be used for other neurodegenerative diseases such as Alzheimer's disease, Huntington's disease, and so forth.