The Personal data Protection Act (PDPA) was created to prevent the breach of personal information of users of computer systems without the data owner's consent. One type of data that frequently has problems with p...
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ISBN:
(纸本)9798350381771;9798350381764
The Personal data Protection Act (PDPA) was created to prevent the breach of personal information of users of computer systems without the data owner's consent. One type of data that frequently has problems with privacy violations is images and videos. Because of difficult control, as a result, there are often extraneous people in the frame instead of just the intended subject. If the person caught in the frame does not want this information published, there will be a problem with that video. This causes the identity of the person to be concealed so that they can be identified. Although doing this is an acceptable method, censorship is labor-intensive and time-consuming. For these reasons, we proposed the automated Face Selection and Censoring on Image and Video System using Multi-Task Cascaded Convolutional Neural Networks (MTCNN) Model to automatically detect the face and censor only unwanted persons. Furthermore, our proposed method also executes an automated system that can sense and ignore some frames that are not essential or redundant with other nearby frames to reduce complex processing and time-consumption.
The aim of this paper is to identify and understand bot activity in twitter discussion. The prevalence of Twitter bots have gained significant limelight recently due to their misuse in influencing public sentiment for...
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ISBN:
(纸本)9781450387910
The aim of this paper is to identify and understand bot activity in twitter discussion. The prevalence of Twitter bots have gained significant limelight recently due to their misuse in influencing public sentiment for political gains. For our analysis, we use Twitter data of 2019 Canadian Elections. We perform principal component analysis and K-means clustering on the data set. Using the results we isolate bots from human accounts.
The combination of machine learning techniques and signal analysis is a well-known solution for the fault diagnosis of industrial equipment. Efficient maintenance management, safer operation, and economic gains are th...
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ISBN:
(数字)9783030623623
ISBN:
(纸本)9783030623616;9783030623623
The combination of machine learning techniques and signal analysis is a well-known solution for the fault diagnosis of industrial equipment. Efficient maintenance management, safer operation, and economic gains are three examples of benefits achieved by using this combination to monitor the equipment condition. In this context, the selection of meaningful information to train machine learning models arises as an important issue, since it influences the model accuracy and complexity. Aware of this, we propose to use the ratio between the interclass and intraclass Kullback-Leibler divergence to identify promising data for training fault diagnosis models. We assessed the performance of this metric on compressor fault datasets. The results suggested a relation between the model accuracy and the ratio between the average interclass and intraclass divergences.
The average annual daily bicyclist volume (AADB) is a measure being used in non-motorized transportation studies such as exposure modeling. This metric can be estimated by averaging the daily bicyclist's volume me...
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ISBN:
(纸本)9781728103235
The average annual daily bicyclist volume (AADB) is a measure being used in non-motorized transportation studies such as exposure modeling. This metric can be estimated by averaging the daily bicyclist's volume measured throughout the year with a long-term automated counter. As continuous data for a whole year at many sites may not be available, a common practice is to collect short-term count data for a sample of locations and then apply an extrapolation method to convert short-term count data to yearly count data. To perform extrapolation, each short-term counter must be matched to one or more long-term counters with similar demand patterns. The matching procedure can significantly impact the accuracy of AADB estimation. This study proposes a matching approach based on a cluster analysis approach, Partitioning Around Medoids (PAM), and a supervised learning approach, K-Nearest Neighbor (KNN). It was found that the proposed approach using a combination of certain variables such as land use and a traffic distribution index resulted in low AADB estimation error rates, which enhanced existing approaches.
Scenario-based validation is a promising approach for the safety validation of highly automated driving systems. By modeling relevant driving scenarios, utilizing simulations and selecting insightful test cases, the t...
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ISBN:
(纸本)9781728103235
Scenario-based validation is a promising approach for the safety validation of highly automated driving systems. By modeling relevant driving scenarios, utilizing simulations and selecting insightful test cases, the testing effort is reduced. However, current methods can't automatically create intuitive models of the vehicle trajectories in a scenario. We propose to use unsupervised machine learning to train neural networks to solve the modeling problem. The models learn a small set of intuitive parameters without the need for labeled data and use them to generate new realistic trajectories. The neural networks, which base on the InfoGAN and beta-VAE architectures, are adapted from the image domain to the time series domain. Although our methods are generally applicable, our experiments focus on lane change maneuvers on highways. To train the networks, we use more than 5600 measured lane change trajectories extracted from the highD dataset. Our results show that the networks learn to describe lane change maneuvers by up to four intuitive parameters. Furthermore, the networks are able to map existing lane change trajectories to values of the learned parameters and generate new, previously unseen, realistic trajectories. We compare both architectures among themselves and to a polynomial model, and show respective advantages.
The volatility incorporated in cryptocurrency prices makes it difficult to earn a profit through day trading. Usually, the beststrategy is to buy a cryptocurrency and hold it until the price rises over a long period....
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ISBN:
(纸本)9783030623616;9783030623623
The volatility incorporated in cryptocurrency prices makes it difficult to earn a profit through day trading. Usually, the beststrategy is to buy a cryptocurrency and hold it until the price rises over a long period. This project aims to automate short term trading using Reinforcement learning (RL), predominantly using the Deep Deterministic Policy Gradient (DDPG) algorithm. The algorithm integrates with the BitMEX cryptocurrency exchange and uses Technical Indicators (TIs) to create an abundance of features. Training on these different features and using diverse environments proved to have mixed results, many of them being exceptionally interesting. The most peculiar model shows that it is possible to create a strategy that can beat a buy and hold strategy relatively effortlessly in terms of profit made.
This study investigates a resolution improvement method using the visible range and its band ratio of remote sensing data. In this paper, we examined the conditions of learningdata to improve the accuracy of the reso...
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ISBN:
(纸本)9781728197326
This study investigates a resolution improvement method using the visible range and its band ratio of remote sensing data. In this paper, we examined the conditions of learningdata to improve the accuracy of the resolution improvement method through the band ratio of unmanned aerial vehicle data. Results obtained using the dataset with a large standard deviation had greater accuracy than those using the dataset with a small standard deviation for 72.2 % of the band ratio in the testdata.
Automatic speaker assignment can enhance the efficiency of automated systems used for creating meeting minutes. Therefore, various methods of speaker identification have been widely utilized;however, in such methods, ...
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ISBN:
(纸本)9781728197326
Automatic speaker assignment can enhance the efficiency of automated systems used for creating meeting minutes. Therefore, various methods of speaker identification have been widely utilized;however, in such methods, it is necessary to optimally arrange the equipment. In this paper, we propose a method for identifying speakers using an omnidirectional camera and a microphone. In this process, tens of minutes of image and voice data were used for model training, and the speaker was successfully identified. In the speaker discrimination experiments, the proposed method was able to identify the speaker with an average success rate of more than 70.0% using the lip height and product of the lip height feature and lip width feature as the lip movement features.
Driving style (DS) classification and identification plays an increasingly important role in the development of advanced driver assistance systems and automated vehicles. Both the enhancement of driving safety and the...
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ISBN:
(纸本)9781728103235
Driving style (DS) classification and identification plays an increasingly important role in the development of advanced driver assistance systems and automated vehicles. Both the enhancement of driving safety and the improvement of fuel efficiency are essential goals of current research in driving style characterization. However, the comfort perspective has still hardly been investigated, despite its importance for the future of driving automation. This paper proposes a driving style classification method, focused on global comfort of the driver and the passengers, but which can also be integrated into the above safety-efficiency viewpoint. Although human comfort in vehicles is affected by different factors, the amplitude and frequency of accelerations are recognized as key signals for assessing driving comfort. The proposed DS classification approach is based on a hybrid machine learning method that combines an unsupervised clustering method with a data-driven extreme learning machine (ELM) algorithm. Hierarchical clustering is used to explore the relevance of the acceleration components in relation to ride comfort, while a single layer ELM topology is implemented to model the DS classifier. The method has been evaluated using experimental data obtained with an instrumented car equipped with in-vehicle sensors and measurement units. The obtained clustering results are consistent with comfort standard indicators, while the data driven algorithm provides encouraging results: more than 95% classification rate using unseen data.
To bring autonomous driving onto public roads, autonomous vehicles must be able to make safe driving decisions. In order to achieve this, they need to be self-aware, meaning they have to be aware of their current capa...
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ISBN:
(纸本)9781728103235
To bring autonomous driving onto public roads, autonomous vehicles must be able to make safe driving decisions. In order to achieve this, they need to be self-aware, meaning they have to be aware of their current capabilities at all times. One step towards self-awareness is the assessment of the quality of the available sensor data and the estimation of its impact on the processing chain. Knowing when sensor data is compromised will contribute to safer driving decisions of the vehicle. In this contribution, we present a novel, deep-learning approach for overexposure detection in camera images as one step towards sensor data quality monitoring.
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