Microwave tomography is a promising imaging modality in which the dielectric properties of an unknown object are reconstructed quantitatively. Microwave tomography requires solving the non-linear and ill-posed inverse...
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ISBN:
(纸本)9788831299091;9798350394436
Microwave tomography is a promising imaging modality in which the dielectric properties of an unknown object are reconstructed quantitatively. Microwave tomography requires solving the non-linear and ill-posed inverse scattering problem. A priori information is typically required to regularize the problem and generate useful images. In this work, electromagnetic power balance is introduced as a physics-informed regularizer. Electromagnetic power balance is incorporated withthe conventional data mismatch cost function to produce a new multiplicative regularizer. the technique is validated with low loss dielectric cylinders and a simplified forearm model in simulation.
the paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. the BSNN model integrates diverse neural...
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there are considered theoretical approaches for the implementation of a web-based learning environment of higher education institutions as a type of online-based learning environment and network surroundings, which pr...
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Secondary studies, such as systematic literature reviews (SLR) and systematic mapping studies (SMS), are commonly conducted in any discipline to search, appraise and collate all relevant empirical evidence in order to...
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ISBN:
(数字)9783031641824
ISBN:
(纸本)9783031641817;9783031641824
Secondary studies, such as systematic literature reviews (SLR) and systematic mapping studies (SMS), are commonly conducted in any discipline to search, appraise and collate all relevant empirical evidence in order to provide a complete interpretation of research results to answer research questions. these also help to provide insight into future research work, which is typically achieved by observing gaps in past works and hinting at the possibility of future research in those gaps. By combining NLP and time series, we propose a meta-analysis to extend current systematic methodologies of literature reviews and mapping studies. Our work uses a Word2Vec model, pre-trained in the softwareengineering domain, and is combined with an autoregressive integrated moving average (ARIMA) time series model. Our aim is to forecast future trajectories of research outlined in systematic studies, rather than just describing them, specifically in the field of software traceability. Using the same dataset from our own previous mapping study, we were able to go beyond descriptively analysing the data that we gathered. In this paper, we continue and expand on our previous work to show the emerging importance of terms linked to requirements and design in software traceability and the forecast for the following periods based on ARIMA time series modelling. Our proposed methodology is an exemplar for exploring the potential of language models coupled with time series in the context of systematically reviewing the literature.
Due to the COVID-19 pandemic, the global supply chain is disrupted at an unprecedented scale under uncertain and unknown trends of labor shortage, high material prices, and changing travel or trade regulations. To sta...
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ISBN:
(数字)9781665490429
ISBN:
(纸本)9781665490429
Due to the COVID-19 pandemic, the global supply chain is disrupted at an unprecedented scale under uncertain and unknown trends of labor shortage, high material prices, and changing travel or trade regulations. To stay competitive, enterprises desire agile and dynamic response strategies to quickly react to disruptions and recover supply-chain functions. Although both centralized and multi-agent approaches have been studied, their implementation requires prior knowledge of disruptions and agent-rule-based reasoning. In this paper, we introduce a model-based multi-agent framework that enables agent coordination and dynamic agent decision-making to respond to supply chain disruptions in an agile and effective manner. through a small-scale simulated case study, we showcase the feasibility of the proposed approach under several disruption scenarios that affect a supply chain network differently, and analyze performance trade-offs between the proposed distributed and centralized methods.
the ubiquitous adoption of container images to virtualize the software contents bring significant attention in its security configuration due to intricate and evolving security issues. Early security assessment of con...
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ISBN:
(纸本)9789897586477
the ubiquitous adoption of container images to virtualize the software contents bring significant attention in its security configuration due to intricate and evolving security issues. Early security assessment of container images can prevent and mitigate security attacks on containers, and enabling practitioners to realize the secured configuration. Using security tools, which operate in intrusive manner in the early assessment, raise critical concern in its applicability where the container image contents are considered as highly sensitive. Moreover, the sequential steps and manual intervention required for using the security tools negatively impact the development and deployment of container images. In this regard, we aim to empirically investigate the effectiveness of Open Container Initiative (OCI) properties withthe Machine Learning (ML) models to assess the security without peeking inside the container images. We extracted OCI properties from 1,137 real-world container images and investigated six traditional ML models with different OCI properties to identify the optimal ML model and its generalizability. Our empirical results show that the ensemble ML models provide the optimal performance to assess the container image security when the model is built with all the OCI properties. Our empirical evidence will guide practitioners in the early security assessment of container images in non-intrusive way as well as reducing the manual intervention required for using security tools to assess the security of container images.
the integration of Large Language Models (LLMs) into edge devices such as smartphones represents a significant leap in mobile technology, promising enhanced user experiences and novel functionalities. this paper prese...
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this research investigates the intricate relationship between sustainability adoption and the financial performance of startups in the Indian business landscape. Utilizing data from a survey conducted among 152 startu...
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Text-to-speech conversion involves the transformation of written text into speech. the proposed system, which does the conversion of text to speech, is implemented by using deep learning algorithms. Planned algorithms...
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Rice consumption has been a major primary food in India for 70% of its population. Nowadays, many crops along with paddy are being affected by several kinds of diseases due to varied environmental changes. Many farmer...
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