Traditional unimodal biometric recognition technologies, wh-ile widely applied across various fields, still face limitations such as environmental interference, spoofing attacks, and individual differences, leading to...
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The sense of presence in virtual reality is a crucial factor for evaluating the quality and effectiveness of virtual reality products. In order to assess it accurately, an objective assessment method for virtual reali...
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The Internet of Things(loT)has grown rapidly due to artificial intelligence driven edge *** enabling many new functions,edge computing devices expand the vulnerability surface and have become the target of malware ***...
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The Internet of Things(loT)has grown rapidly due to artificial intelligence driven edge *** enabling many new functions,edge computing devices expand the vulnerability surface and have become the target of malware ***,attackers have used advanced techniques to evade defenses by transforming their malware into functionality-preserving *** systematically analyze such evasion attacks and conduct a large-scale empirical study in this paper to evaluate their impact on *** specifically,we focus on two forms of evasion attacks:obfuscation and adversarial *** the best of our knowledge,this paper is the first to investigate and contrast the two families of evasion attacks *** apply 10 obfuscation attacks and 9 adversarial attacks to 2870 malware *** obtained findings are as follows.(1)Commercial Off-The-Shelf(COTS)malware detectors are vulnerable to evasion attacks.(2)Adversarial attacks affect COTS malware detectors slightly more effectively than obfuscated malware examples.(3)Code similarity detection approaches can be affected by obfuscated examples and are barely affected by adversarial attacks.(4)These attacks can preserve the functionality of original malware examples.
Span-based methods have unique advantages for solving nested named entity recognition (NER) problems. As primary information, boundaries play a crucial role in span representation. However, auxiliary information, whic...
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In traditional visual SLAM systems, significant tracking inaccuracies arise in dynamic environments due to the foundational design tailored for static scenarios. To address this limitation, a semantic SLAM architectur...
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When clustering multi-target highlights in an underwater battlefield environment, the traditional K-means algorithm cannot be used because of unknowing the priori information on the number of targets. For problem with...
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Air pollution poses extensive threats to human health and the environment. Air quality forecasting helps people take preventive measures in advance, reducing unnecessary losses. This paper introduces a new Air Quality...
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Blood pressure is an important physiological signal of the human body and serves as a crucial reference standard for the prevention of cardiovascular diseases, making blood pressure monitoring particularly significant...
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Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elasti...
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Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elastic demand, climate and consumer response, load analysis, and sustainable energy generation predictive modelling are used. As both supply and demand fluctuate, and weather and power prices can rise significantly during peak periods, accurate load forecasting becomes critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in helping electric utilities make informed decisions related to power, load switching, voltage regulation, switching, and infrastructure development. Forecasting is a methodology used by electricity companies to forecast the amount of electricity or power production needed to maintain constant supply as well as load demand balance. It is required for the electrical industry to function properly. The smart grid is a new system that enables electricity providers and customers to communicate in real-time. The precise energy consumption sequence of the consumers is required to enhance the demand schedule. This is where predicting the future comes into play. Forecasting future power system load (electricity consumption) is a critical task in providing intelligence to the power grid. Accurate forecasting allows utility companies to allocate resources and assume system control in order to balance the same demand and availability for electricity. In this article, a study on load forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired techniques, and other techniques is carried out. Many other algorithms based on load forecasting are discussed in this study. Different methods of load forecasting were compared using three performance indices: RMSE (Root Mean Square Err
Stereo disparity is a major factor affecting visual comfort in stereoscopic video, and human eyes will feel uncomfortable when viewing stereoscopic scenes with large disparities. Therefore, in order to improve stereos...
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