Efficient issue assignment in software development relates to faster resolution time, resources optimization, and reduced development effort. To this end, numerous systems have been developed to automate issue assignm...
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In this paper, we introduce a novel algorithm for the initial placement of honeypots. Our method is grounded in a two-person, zero-sum game framework that rigorously considers various factors: the cost of deploying ho...
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ISBN:
(数字)9798350385328
ISBN:
(纸本)9798350385335
In this paper, we introduce a novel algorithm for the initial placement of honeypots. Our method is grounded in a two-person, zero-sum game framework that rigorously considers various factors: the cost of deploying honeypots, the effectiveness of defense strategies, the expenses borne by attackers, the consequences of successful attacks, and the value of network nodes. The cornerstone of our approach is a reward function designed to incentivize defenders to focus on securing high-value nodes, thereby enhancing overall network security. Through comprehensive simulations that account for changing target nodes and node weights, our algorithm demonstrates superior adaptability and efficacy in bolstering defenses against sophisticated cyber threats in dynamic networks such as the Internet of Battlefield Things (IoBT) networks, filling a critical void in existing security paradigms.
The issues of AI risk and AI safety are becoming critical as the prospect of artificial general intelligence (AGI) looms larger. The emergence of extremely large and capable generative models has led to alarming predi...
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This study delves into micro-mobility in Indonesia, focusing on legal, safety, and policy aspects of electric scooters, bicycles, and similar devices. The absence of specific regulations and safety concerns pose chall...
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In recent years, the technology sector has witnessed substantial disruptions, leading to major layoffs within toptier tech conglomerates. As the momentum of the digital age continues, there's an emerging concern r...
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The requirement for efficient and sustainable urban environments has made energy management in smart buildings a crucial topic of research and development in recent years. This study targets gaps in energy management ...
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ISBN:
(数字)9798331520090
ISBN:
(纸本)9798331520106
The requirement for efficient and sustainable urban environments has made energy management in smart buildings a crucial topic of research and development in recent years. This study targets gaps in energy management research by dynamically choosing the best ventilation devices (AC, fans, windows) based on the changeable environmental factors like temperature, humidity, and wind speed using reinforcement learning thus elevating energy management in smart buildings. The ML model was trained via reinforcement learning (RL), with a fuzzy control scoring system designed to take the consequent user satisfaction and power cost of the device selected by the model based on the weather conditions (temperature, humidity, and wind speed), to grade the selection during training. Moreover, verification of the model's decision-making mechanism was executed by an integrated simulation platform through case studies showing their applicability in typical and extreme conditions. The proposed system proved the capability of understanding the weather state to select the optimal device to turn on for the lowest power cost and highest user satisfaction in the building. This supports the United Nations (UN) sustainable development goals (SDGs) by promoting sustainable, cost-efficient energy use and improved living standards.
We created a scatterer database based on the modified MNIST data set. Using simple neural networks, we achieve a 90% accuracy in classifying objects. We investigated the accuracy as a function of antenna number and da...
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ISBN:
(数字)9781733509671
ISBN:
(纸本)9798350362978
We created a scatterer database based on the modified MNIST data set. Using simple neural networks, we achieve a 90% accuracy in classifying objects. We investigated the accuracy as a function of antenna number and data set size. For large data sets, neural networks exhibit a higher accuracy compared to other traditional machine learning methods.
Guided Growth is a machine learning system that supports parents and caregivers in providing age-specific recommendations for children aged 0-10 years. The system automates the process of recommending essential docume...
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ISBN:
(数字)9798331507244
ISBN:
(纸本)9798331507251
Guided Growth is a machine learning system that supports parents and caregivers in providing age-specific recommendations for children aged 0-10 years. The system automates the process of recommending essential documents, health requirements, and lifestyle changes based on the child’s age. Experimental results show that the platform’s machine learning engine successfully provides personalized recommendations, improving the accuracy and timeliness of health and developmental suggestions. These results demonstrate significant improvements over traditional methods, especially in minimizing missed milestones and enhancing user satisfaction. The system’s effectiveness in real-world applications proves its potential to revolutionize child care by providing automated, accurate, and relevant guidance for parents and caregivers.
Hyperspectral imaging (HSI) captures detailed spectral data across numerous contiguous bands, offering critical insights for applications such as environmental monitoring, agriculture, and urban planning. However, the...
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ISBN:
(数字)9798331536626
ISBN:
(纸本)9798331536633
Hyperspectral imaging (HSI) captures detailed spectral data across numerous contiguous bands, offering critical insights for applications such as environmental monitoring, agriculture, and urban planning. However, the high dimensionality of HSI data poses significant challenges for tra-ditional deep learning models, necessitating more efficient solutions. In this paper, we propose the Layer-Optimized Spatial-Spectral Transformer (LO-SST), a refined version of the Spatial-Spectral Transformer (SST) that incorporates structured layer pruning to reduce computational complexity while maintaining robust performance. LO-SST lever-ages self-supervised pretraining with a Masked Autoen-coder (MAE) framework, enabling the model to effectively learn spatial and spectral dependencies even in scenarios with limited labeled data. The use of separate spa-tial and spectral positional embeddings further enhances the model's ability to capture intricate relationships within hyperspectral data. Our experiments show that LO-SST achieves competitive segmentation accuracy while signifi-cantly reducing computational demands compared to traditional models. The effectiveness of random masking over alternative strategies during pretraining is also demonstrated, underscoring its ability to preserve critical image features. These results highlight the potential of LO-SST as an efficient and scalable solution for hyperspectral image segmen-tation, particularly in resource-constrained applications.
Programmable Wireless Environments (PWEs) leverage Reconfigurable Intelligent Surfaces (RIS) to convert the wireless propagation into a deterministic process. Recently, PWEs have shown promising results in boosting th...
Programmable Wireless Environments (PWEs) leverage Reconfigurable Intelligent Surfaces (RIS) to convert the wireless propagation into a deterministic process. Recently, PWEs have shown promising results in boosting the efficiency of Radio-Frequency (RF) imaging, creating a novel, lightweight object detection and visualization approach for Extended Reality (XR). As a first step towards optimizing the PWE-XR synergy, this work proposes and compares a set of four PWE configuration policies. The goal is to deduce which policy yields the optimal classification of a set of arbitrary 3D objects present within the PWE. The rationale is that the PWE configuration policy that yields optimal object classification, will also yield the best XR quality in subsequent studies. Evaluation results regarding the performance of the proposed policies, based on ray-tracing, are demonstrated and discussed.
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