Identifying objects is among the most crucial and complex problems in computer vision. The effectiveness of object detection tasks has significantly increased due to advancements in deep learning architectures. Object...
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Previous studies have shown that coronary artery occlusion can cause myocardial ischemia, which can induce ventricular tachycardia or fibrillation. According to the time sequence, myocardial ischemia can be divided in...
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Energy harvesting devices are rapidly evolving to rival battery-backed technologies. Batteries have a shorter life- time and need maintenance compared to capacitors. Moreover, the usage of batteries comes with undenia...
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The main aim of this work is to develop an Online Discussion platform for users. The platforms will become more mature while having more users. In the paper, a sophisticated strategy is suggested to tackle a critical ...
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Retailers need real-time insights to know customer behavior and preferences to enhance operations efficiency and administrative cost reduction. There is a need to interconnect various legacy devices within the retail ...
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Strong and flexible detection systems are vital to protect network infrastructures from Distributed Denial of Service (DDoS) attacks, which are becoming more common and sophisticated. To detect DDoS attacks, this stud...
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In response to the substantial wastage of food globally, this initiative proposes a solution called 'Feed the Needy.' This charitable application streamlines the redistribution of excess food from various even...
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To address the limitations of current methods in detecting small objects, such as pedestrians and cyclists, within autonomous driving scenarios, we propose a novel 3D object detection algorithm based on an improved Pi...
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Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities a...
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Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities at a single granularity (i.e., slice-level or function-level). In practice, slice-level vulnerability detection is fine-grained but may contain incomplete vulnerability details. Function-level vulnerability detection includes full vulnerability semantics but may contain vulnerability-unrelated statements. Meanwhile, they pay more attention to predicting whether the source code is vulnerable and cannot pinpoint which statements are more likely to be vulnerable. In this paper, we design mVulPreter, a multi-granularity vulnerability detector that can provide interpretations of detection results. Specifically, we propose a novel technique to effectively blend the advantages of function-level and slice-level vulnerability detection models and output the detection results' interpretation only by the model itself. We evaluate mVulPreter on a dataset containing 5,310 vulnerable functions and 7,601 non-vulnerable functions. The experimental results indicate that mVulPreter outperforms existing state-of-the-art vulnerability detection approaches (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, StatementLSTM, SySeVR, and Devign). IEEE
Crop pest damage accrues to fifty per cent loss in yield which leads to severe monetary losses and an overall rise in farming costs. Understanding the complex processes by which the host plants acquire resistance to i...
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