ServeNet: A Deep Neural Network for Web Services Classification

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.Comment: Accepted by ICWS'2

Deep Learning Algorithms for Cardiac Image Classification and Landmark Detection

With the increase in computational power, deep learning algorithms have become an active field of research over the last 7 years. These data-driven machine learning algorithms have produced good results in many applications, image analysis being one of them. Today, many image analysis tasks in the medical field are done manually, taking valuable time and effort from professionals. Automating some of these tasks could relieve work-load and speed up the healthcare process. In this thesis, the potential use of deep learning algorithms for medical image analysis will be evaluated. Two problems will be investigated, cardiac image classification and landmark detection in cardiac images. The algorithms used will be based on two existing deep learning algorithms, the U-net and AlexNet. The deep learning algorithms will be implemented in the deep learning software Caffe, and all training and testing will be ran on an Amazon Web Services instance. The data used for training, testing and validation are provided by the Department of Clinical Physiology at Lund University Hospital. This data is augmented by scaling and rotation to provide a larger and more representative data set for training. The trained algorithm for image classification achieved 98.8% accuracy on validation data, while the algorithm for landmark detection achieved approximately 95 % accuracy on validation data. The image classification algorithms worked well, and it serves as a proof of concept with the potential of being able to solved more clinically difficult problems. With the high accuracy of the landmark detection algorithm largely being due to an imbalance in class distribution, this algorithm, while showing some promise, needs more work to be clinically useful.Artificiell intelligens (AI) har under de senaste åren används framgångsrikt inom många olika områden. Ett område där dessa AI algoritmer ännu inte slagit igenom är sjukvården, där denna typ av automatisering utgör en potentiell lösning på de långa vårdköerna som finns idag. I denna rapport utvärderas den potential artificiell intelligens har för att lösa problem inom medicinsk bildanalys

Deep Learning in the Automotive Industry: Applications and Tools

Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.Comment: 10 page

Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning

With the rapid growth in smartphone usage, more organizations begin to focus on providing better services for mobile users. User identification can help these organizations to identify their customers and then cater services that have been customized for them. Currently, the use of cookies is the most common form to identify users. However, cookies are not easily transportable (e.g., when a user uses a different login account, cookies do not follow the user). This limitation motivates the need to use behavior biometric for user identification. In this paper, we propose DEEPSERVICE, a new technique that can identify mobile users based on user's keystroke information captured by a special keyboard or web browser. Our evaluation results indicate that DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy). The technique is also efficient and only takes less than 1 ms to perform identification.Comment: 2017 Joint European Conference on Machine Learning and Knowledge Discovery in Database

Crisis Event Extraction Service (CREES) - Automatic Detection and Classification of Crisis-related Content on Social Media

Social media posts tend to provide valuable reports during crises. However, this information can be hidden in large amounts of unrelated documents. Providing tools that automatically identify relevant posts, event types (e.g., hurricane, floods, etc.) and information categories (e.g., reports on affected individuals, donations and volunteering, etc.) in social media posts is vital for their efficient handling and consumption. We introduce the Crisis Event Extraction Service (CREES), an open-source web API that automatically classifies posts during crisis situations. The API provides annotations for crisis-related documents, event types and information categories through an easily deployable and accessible web API that can be integrated into multiple platform and tools. The annotation service is backed by Convolutional Neural Networks (CNNs) and validated against traditional machine learning models. Results show that the CNN-based API results can be relied upon when dealing with specific crises with the benefits associated with the usage word embeddings