3,898 research outputs found
Enriching the Syntactic Annotation of Korean Treebanks for Higher-level Processing: A Comparative Study of the Penn Korean Treebank and the 21st Sejong Korean Treebank
Improving Performance in Recommender System: Collaborative Filtering Algorithm and User’s Rating Pattern
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Long-Term Monitoring and Analysis of a Curved Concrete Box-Girder Bridge
Capital investment in national infrastructure is significant. The need to maintain and protect critical infrastructure links has led in recent years to significant developments in the area of structural health monitoring. The objective is to track a structure’s long-term performance, typically using sensors, and to successively compare the most recently measured responses with prior response history. During construction of the West Street On-Ramp, a curved concrete box girder bridge, located in the city of Anaheim (California), eleven accelerometers were permanently installed on its bridge deck. The associated data acquisition system was configured to record once a specified threshold acceleration response was exceeded; during the period 2002–2010 a total of 1350 datasets including six earthquakes, for each of the eleven sensors, were acquired. This automatically acquired data was supplemented, during the summer of 2009, with responses measured during controlled vehicle tests. Six accelerometers were additionally installed on the frame of the weighed test vehicle. This paper presents the findings of the analyses of these measured data sets and serves to inform owners and managers as to the potential feedback from their instrumentation investment. All response histories were analyzed using frequency domain techniques for system identification. Extraction of the modal characteristics revealed a continuous reduction, of approximately 5%, in the first three natural frequencies over the period of the study. The measured responses from the vehicle sensors are discussed in the context of identifying the potential for bridge frequency measurement using instrumented vehicles
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization
The vulnerability of smartphones to cyberattacks has been a severe concern to
users arising from the integrity of installed applications (\textit{apps}).
Although applications are to provide legitimate and diversified on-the-go
services, harmful and dangerous ones have also uncovered the feasible way to
penetrate smartphones for malicious behaviors. Thorough application analysis is
key to revealing malicious intent and providing more insights into the
application behavior for security risk assessments. Such in-depth analysis
motivates employing deep neural networks (DNNs) for a set of features and
patterns extracted from applications to facilitate detecting potentially
dangerous applications independently. This paper presents an Analytic-based
deep neural network, Android Malware detection (ADAM), that employs a
fine-grained set of features to train feature-specific DNNs to have consensus
on the application labels when their ground truth is unknown. In addition, ADAM
leverages the transfer learning technique to obtain its adjustability to new
applications across smartphones for recycling the pre-trained model(s) and
making them more adaptable by model personalization and federated learning
techniques. This adjustability is also assisted by federated learning guards,
which protect ADAM against poisoning attacks through model analysis. ADAM
relies on a diverse dataset containing more than 153000 applications with over
41000 extracted features for DNNs training. The ADAM's feature-specific DNNs,
on average, achieved more than 98% accuracy, resulting in an outstanding
performance against data manipulation attacks
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