With the evolvement of the Internet of things(IoT), mobile edge computing(MEC) has emerged as a promising computing paradigm to support IoT data analysis and processing. In MEC for IoT, the differentiated requirements...
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With the evolvement of the Internet of things(IoT), mobile edge computing(MEC) has emerged as a promising computing paradigm to support IoT data analysis and processing. In MEC for IoT, the differentiated requirements on quality of service(QoS) have been growing rapidly, making QoS a multi-dimensional concept including several attributes, such as performance, dependability, energy efficiency, and economic factors. To guarantee the QoS of IoT applications, theories and techniques of multi-dimensional QoS evaluation and optimization have become important theoretical foundations and supporting technologies for the research and application of MEC for IoT,which have attracted significant attention from both academia and industry. This paper aims to survey the existing studies on multi-dimensional QoS evaluation and optimization of MEC for IoT, and provide insights and guidance for future research in this field. This paper summarizes the multi-dimensional and multi-attribute QoS metrics in Io T scenarios, and then several QoS evaluation methods are presented. For QoS optimization, the main research problems in this field are summarized, and optimization models as well as their corresponding solutions are elaborated. We take notice of the booming of edge intelligence in artificial intelligence-empowered Io T scenarios, and illustrate the new research topics and the state-of-the-art approaches related to QoS evaluation and optimization. We discuss the challenges and future research directions.
The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have be...
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The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have been developed to tackle these ***,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional *** fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within *** traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of *** selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)*** this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious *** classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable *** the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive *** experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different *** outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%*** results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.
Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and t...
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Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.
Rough set theory places great importance on approximation accuracy,which is used to gauge how well a rough set model describes a target ***,traditional approximation accuracy has limitations since it varies with chang...
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Rough set theory places great importance on approximation accuracy,which is used to gauge how well a rough set model describes a target ***,traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set *** overcome this,two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is *** first is the relative average approximation accuracy,which is based on all sets in the universe and has several basic *** second is the absolute average approximation accuracy,which is based on undefinable sets and has yielded significant *** also explore the relationship between these two types of average approximation ***,the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables.
A multi-secret image sharing (MSIS) scheme facilitates the secure distribution of multiple images among a group of participants. Several MSIS schemes have been proposed with a (n, n) structure that encodes secret...
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Autism spectrum disorder (ASD) affects 1 in 100 children globally. Early detection and intervention can enhance life quality for individuals diagnosed with ASD. This research utilizes the support vector machine-recurs...
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Autism spectrum disorder (ASD) affects 1 in 100 children globally. Early detection and intervention can enhance life quality for individuals diagnosed with ASD. This research utilizes the support vector machine-recursive feature elimination (SVM-RFE) method in its approach for ASD classification using the phenotypic and Automated Anatomical Labeling (AAL) Brain Atlas datasets of the Autism Brain Imaging data Exchange preprocessed dataset. The functional connectivity matrix (FCM) is computed for the AAL data, generating 6670 features representing pair-wise brain region activity. The SVM-RFE feature selection method was applied five times to the FCM data, thus determining the optimal number of features to be 750 for the best performing support vector machine (SVM) model, corresponding to a dimensionality reduction of 88.76%. Pertinent phenotypic data features were manually selected and processed. Subsequently, five experiments were conducted, each representing a different combination of the features used for training and testing the linear SVM, deep neural networks, one-dimensional convolutional neural networks, and random forest machine learning models. These models are fine-tuned using grid search cross-validation (CV). The models are evaluated on various metrics using 5-fold CV. The most relevant brain regions from the optimal feature set are identified by ranking the SVM-RFE feature weights. The SVM-RFE approach achieved a state-of-the-art accuracy of 90.33% on the linear SVM model using the data Processing Assistant for Resting-State Functional Magnetic Resonance Imaging pipeline. The SVM model’s ability to rank the features used based on their importance provides clarity into the factors contributing to the diagnosis. The thalamus right, rectus right, and temporal middle left AAL brain regions, among others, were identified as having the highest number of connections to other brain regions. These results highlight the importance of using traditional ML models fo
In the enormous field of Natural Language Processing (NLP), deciphering the intended significance of a word among a multitude of possibilities is referred to as word sense disambiguation. This process is essential for...
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In the times of advanced generative artificial intelligence, distinguishing truth from fallacy and deception has become a critical societal challenge. This research attempts to analyze the capabilities of large langua...
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Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs) is not only constitute an encouraging research domain but also represent a promising industrial trend that permits the development of various IoT-based ...
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Standard empirical risk minimization (ERM) models may prioritize learning spurious correlations between spurious features and true labels, leading to poor accuracy on groups where these correlations do not hold. Mitig...
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Standard empirical risk minimization (ERM) models may prioritize learning spurious correlations between spurious features and true labels, leading to poor accuracy on groups where these correlations do not hold. Mitigating this issue often requires expensive spurious attribute (group) labels or relies on trained ERM models to infer group labels when group information is unavailable. However, the significant performance gap in worst-group accuracy between using pseudo group labels and using oracle group labels inspires us to consider further improving group robustness through preciser group inference. Therefore, we propose GIC, a novel method that accurately infers group labels, resulting in improved worst-group performance. GIC trains a spurious attribute classifier based on two key properties of spurious correlations: (1) high correlation between spurious attributes and true labels, and (2) variability in this correlation between datasets with different group distributions. Empirical studies on multiple datasets demonstrate the effectiveness of GIC in inferring group labels, and combining GIC with various downstream invariant learning methods improves worst-group accuracy, showcasing its powerful flexibility. Additionally, through analyzing the misclassifications in GIC, we identify an interesting phenomenon called semantic consistency, which may contribute to better decoupling the association between spurious attributes and labels, thereby mitigating spurious correlation. The code for GIC is available at https://***/yujinhanml/GIC. Copyright 2024 by the author(s)
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