In multi-label learning, each instance is associated with a set of labels simultaneously. Most existing studies assume that the set of labels for each instance is complete. However, it is generally difficult to obtain...
In multi-label learning, each instance is associated with a set of labels simultaneously. Most existing studies assume that the set of labels for each instance is complete. However, it is generally difficult to obtain all the relevant labels of each instance, and only a partial or even empty set of relevant labels is available, which is called semi-supervised multi-label learning with missing labels. To tackle this problem, we propose a novel framework that considers label correlations and instance correlations to recover the missing labels and utilizes a large amount of unlabeled data simultaneously to improve the classification performance. Specifically, a new supplementary label matrix is firstly obtained by learning the label correlation. Secondly, considering each class label may be decided by some specific characteristics of its own, a label-specific data representation is hence learned for each class label. Thirdly, instance correlations are utilized not only to recover the missing labels, but also to propagate the supervision information from labeled instances to unlabeled ones. In addition, a united objective function is designed to facilitate the above processing and an accelerated proximal gradient method is adopted to solve the optimization problem. Finally, extensive experimental results conducted on several benchmark datasets demonstrate the effectiveness of the proposed method compared to competing ones.
With the increasing demand of electric power for more-electric aircraft, current breaking and protection in intermediate frequency (360–800 Hz) AC power system becomes more and more difficult. Different from the...
详细信息
作者：
Xu, JieZhou, JiantaoCollege of Computer Science
Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China
Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performi...
详细信息
This presents a significant challenge for detecting and combating malicious software. Users often grant software permissions unknowingly, exposing their devices to risks such as unauthorized access, file manipulation,...
This presents a significant challenge for detecting and combating malicious software. Users often grant software permissions unknowingly, exposing their devices to risks such as unauthorized access, file manipulation, and malware propagation. Traditional detection algorithms relying on limited permission-based strategies fall short in addressing this issue. To overcome this, we propose PVitNet (Network based On Pyramid Feature processing and Vision Transformer), an Android malware detection method. PVitNet incorporates pyramid feature processing, attention mechanisms, and an automatic feature extraction tool. By leveraging semantic information from feature pyramid models and learning shared characteristics among similar software, we successfully identify Android malware families. Our experiments on the CICMalDroid 2020 dataset demonstrate the effectiveness of our approach, with a 14.96% increase in accuracy and an F1 score of 98.31%.
As a kind of microalgae, Spirulina plays an important role in fish culture, food processing industry, medical treatment and bioenergetic development due to its reasonable nutritional composition and high hydrogenase a...
详细信息
As an important energy in modern society, the stable operation of electricity is the guarantee of social and economic operation, and the loss caused by the damage of power facilities is immeasurable, so the regular mo...
详细信息
The Zr-2.5Nb alloy with a fine microstructure consisting ofαlaths was successfully prepared by electron beam melting(EBM).The thermal oxidation behaviors and kinetics of the as-built,and the EBM-built and hot isostat...
详细信息
The Zr-2.5Nb alloy with a fine microstructure consisting ofαlaths was successfully prepared by electron beam melting(EBM).The thermal oxidation behaviors and kinetics of the as-built,and the EBM-built and hot isostatically pressed(HIPed)Zr-2.5Nb materials in a temperature range of 450-600°C were in-vestigated and compared with those of the alloy prepared by conventional casting and *** was found that the oxidation kinetics of the as-built and the forged materials followed the parabolic rate law during isothermal oxidation at 550°C,but the HIPed materials exhibited a parabolic-to-linear kinetic transition,suggesting that the larger grain sizes enhanced the *** oxide layers of all materials were composed of a large fraction of monoclinic zirconia phase(m-ZrO_(2))and a small fraction of tetrago-nal zirconia phase(t-ZrO_(2)),and transformed from t-ZrO_(2)to m-ZrO_(2)with increasing oxidation *** surface hardness of the as-built,the forged and the HIPed materials increased from 215,204,and 188 HV before oxidation to 902,1070,and 1137 HV after oxidation,*** cross-sections of the materi-als showed the presence of micropores and microcracks inside the oxide layers with thicknesses ranging from 4 to 8μ*** the oxidation temperature of 600°C and oxidation time duration of 3 h,a dense black m-ZrO_(2)oxide layer with smooth surface and 902 HV hardness was obtained on the EBM as-built Zr-2.5Nb materials.
Monitoring renewable energy devices is crucial for the timely detection of faults and the improvement of system stability, making it a key component of the Internet of Things (IoT) ecosystem. However, existing intelli...
详细信息
Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful ...
详细信息
With the advancement of technology, unexposed spaces have emerged as a new type of strategic area, attracting significant attention from researchers. These spaces often present complex environments, such as extreme li...
详细信息
暂无评论