1,987 research outputs found
Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection
The advance of smartphones and cellular networks boosts the need of mobile
advertising and targeted marketing. However, it also triggers the unseen
security threats. We found that the phone scams with fake calling numbers of
very short lifetime are increasingly popular and have been used to trick the
users. The harm is worldwide. On the other hand, deceptive advertising
(deceptive ads), the fake ads that tricks users to install unnecessary apps via
either alluring or daunting texts and pictures, is an emerging threat that
seriously harms the reputation of the advertiser. To counter against these two
new threats, the conventional blacklist (or whitelist) approach and the machine
learning approach with predefined features have been proven useless.
Nevertheless, due to the success of deep learning in developing the highly
intelligent program, our system can efficiently and effectively detect phone
scams and deceptive ads by taking advantage of our unified framework on deep
neural network (DNN) and convolutional neural network (CNN). The proposed
system has been deployed for operational use and the experimental results
proved the effectiveness of our proposed system. Furthermore, we keep our
research results and release experiment material on
http://DeceptiveAds.TWMAN.ORG and http://PhoneScams.TWMAN.ORG if there is any
update.Comment: 6 pages, TAAI 2017 versio
Intramuscular Hemangioma of the Temporalis Muscle With Incidental Finding of Bilateral Symmetric Calcification of the Basal Ganglia: A Case Report
We report an 11-year-old boy whose brain computed tomography findings incidentally revealed bilateral basal ganglia calcification. He was symptom-free and had no abnormal neurological findings. He was diagnosed with Fahr's disease based on radiological findings and after excluding other etiologies such as infection, metabolic disorders, congenital malformation and malignancies. Most of the reported cases display an autosomal dominant mode of inheritance. Although Fahr's disease is a rare cause of basal ganglia calcification in children, this disease should be considered in children with a family history of neuropsychiatric disorders
A Deep Learning Architecture For Histology Image Classification
Over the past decade, a machine learning technique called deep-learning has gained prominence in computer vision because of its ability to extract semantics from natural images. However, in contrast to the natural images, deep learning methods have been less effective for analyzing medical histology images. Analyzing histology images involves the classification of tissue according to cell types and states, where the differences in texture and structure are often subtle between states. These qualitative differences between histology and natural images make transfer learning difficult and limit the use of deep learning methods for histology image analysis.
This dissertation introduces two novel deep learning architectures, that address these limitations. Both provide intermediate hints to aid deep learning models. The first deep learning architecture is constructed based on stacked autoencoders with an additional layer, called a hyperlayer. The hyperlayer is an intermediate hint that captures image features at different scales. The second architecture is a two-tiered Convolutional Neural Networks (CNN), with an intermediate representation, called a pixel/region labeling. The pixel/region labels provide a normalized semantic description that can be used as an input to a subsequent image classifier.
The experiments show that by adding the hyperlayer, the architecture substantially outperforms fine-tuned CNN models trained without an intermediate target. In addition, the experiments suggest that the advantages of the labeling classifier are threefold. First, it generalizes to other related vision tasks. Second, image classification does not require extremely accurate pixel labeling. The architecture is robust and not susceptible to the noise. Lastly, labeling model captures low-level texture information and converts them to valuable hints.Doctor of Philosoph
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated Learning
Federated Learning (FL) offers a collaborative training framework, allowing
multiple clients to contribute to a shared model without compromising data
privacy. Due to the heterogeneous nature of local datasets, updated client
models may overfit and diverge from one another, commonly known as the problem
of client drift. In this paper, we propose FedBug (Federated Learning with
Bottom-Up Gradual Unfreezing), a novel FL framework designed to effectively
mitigate client drift. FedBug adaptively leverages the client model parameters,
distributed by the server at each global round, as the reference points for
cross-client alignment. Specifically, on the client side, FedBug begins by
freezing the entire model, then gradually unfreezes the layers, from the input
layer to the output layer. This bottom-up approach allows models to train the
newly thawed layers to project data into a latent space, wherein the separating
hyperplanes remain consistent across all clients. We theoretically analyze
FedBug in a novel over-parameterization FL setup, revealing its superior
convergence rate compared to FedAvg. Through comprehensive experiments,
spanning various datasets, training conditions, and network architectures, we
validate the efficacy of FedBug. Our contributions encompass a novel FL
framework, theoretical analysis, and empirical validation, demonstrating the
wide potential and applicability of FedBug.Comment: Submitted to NeurIPS'2
Applying Sequential Pattern Mining to Generate Block for Scheduling Problems
The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems
Applying Sequential Pattern Mining to Generate Block for Scheduling Problems
The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems
Applying Sequential Pattern Mining to Generate Block for Scheduling Problems
The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems
Applying Sequential Pattern Mining to Generate Block for Scheduling Problems
The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems
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