This paper considers the problem of self-driving algorithm based on deep learning. This is a hot topic because self-driving is the most important application field of artificial intelligence. Existing work focused on deep learning which has the ability to learn end-to-end self-driving control directly from raw sensory data, but this method is just a mapping between images and driving. We prefer deep reinforcement learning to train a self-driving car in a virtual simulation environment created by Unity and then migrate to reality. Deep reinforcement learning makes the machine own the driving descision-making ability like human. The virtual to realistic training method can efficiently handle the problem that reinforcement learning requires reward from the environment which probably cause cars damge. We have derived a theoretical model and analysis on how to use Deep Q-learning to control a car to drive. We have carried out simulations in the Unity virtual environment for evaluating the performance. Finally, we successfully migrate te model to the real world and realize self-driving.

Freezing of gait (FoG) is a common gait disability in Parkinson's disease, that usually appears in its advanced stage. Freeze episodes are associated with falls, injuries, and psychological consequences, negatively affecting the patients' quality of life. For detecting FoG episodes automatically, a highly accurate detection method is necessary. This paper presents an approach for detecting FoG episodes utilizing a deep recurrent neural network (RNN) on 3D-accelerometer measurements. We investigate suitable features and feature combinations extracted from the sensors' time series data. Specifically, for detecting FoG episodes, we apply a deep RNN with Long Short-Term Memory cells. In our experiments, we perform both user dependent and user independent experiments, to detect freeze episodes. Our experimental results show that the frequency domain features extracted from the trunk sensor are the most informative feature group in the subject independent method, achieving an average AUC score of 93%, Specificity of 90% and Sensitivity of 81%. Moreover, frequency and statistical features of all the sensors are identified as the best single input for the subject dependent method, achieving an average AUC score of 97%, Specificity of 96% and Sensitivity of 87%. Overall, in a comparison to state-of-the-art approaches from literature as baseline methods, our proposed approach outperforms these significantly.

When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures Gil Fidel Ron Bitton Asaf Shabtai Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Abstract --State-of-the-art deep neural networks (DNNs) are highly effective in solving many complex real-world problems. However, these models are vulnerable to adversarial perturbation attacks, and despite the plethora of research in this domain, to this day, adversaries still have the upper hand in the cat and mouse game of adversarial example generation methods vs. detection and prevention methods. In this research, we present a novel detection method that uses Shapley Additive Explanations (SHAP) values computed for the internal layers of a DNN classifier to discriminate between normal and adversarial inputs. We evaluate our method by building an extensive dataset of adversarial examples over the popular CIF AR-10 and MNIST datasets, and training a neural network-based detector to distinguish between normal and adversarial inputs. We evaluate our detector against adversarial examples generated by diverse state-of-the-art attacks and demonstrate its high detection accuracy and strong generalization ability to adversarial inputs generated with different attack methods. I NTRODUCTION In recent years, deep neural network (DNN) learning algorithms have been widely used to solve a variety of complex problems. Their greatest impact has been seen in fields such as image classification, object recognition, natural language processing, and malware detection.

In recent years, with the development of deep learning technology, text generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text generation technology, that is the conditional text generation, including emotional text generation, personalized text generation, and so on. Conditional text generation (c-TextGen) has thus become a research hotspot. As a promising research field, we find that many efforts have been paid to researches of c-TextGen. Therefore, we aim to give a comprehensive review of the new research trends of c-TextGen. We first give a brief literature review of text generation technology, based on which we formalize the concept model of c-TextGen. We further make an investigation of several different c-TextGen techniques, and illustrate the advantages and disadvantages of commonly used neural network models. Finally, we discuss the open issues and promising research directions of c-TextGen.

Generative Adversarial Networks (GANs) have been a hot topic in Deep Learning ever since their initial invention and publication at NIPS 2014. There's a good reason for it all: GANs are able to create totally new content based on only a small bit of guidance. It's that sort of creativity which makes them so powerful. Just to name a few there. With all of this, massive resources are being poured into GAN research to figure out both how they work and how to design the absolute best GAN networks.

Rowel Atienza: Keras provides APIs for rapidly building, training, validating and deploying deep learning algorithms. It is suitable for someone who is starting in the field and also for advanced users. Keras is characterized by ease of use yet flexible enough to build complex networks especially with TensorFlow. Its tight integration with TensorFlow makes it a good choice for deep learning projects that could be deployed on production scale operations.

Artificial Intelligence (AI) is now playing a significant role in helping to managing cyber risk. This was recently evident in the aftermath of the Nordsk Hydro ransomware attack where AI was utilised to identify further vulnerabilities. This form of automative technology would seem to be a good match for managing the constant threats posed by hackers where new cyber attacks relentlessly attack computer systems and constant monitoring is required. Despite the automation basis there however still needs to be human involvement in this process. Billions amounts of data is consumed by AI via machine learning and deep learning techniques.

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Modern machine translation, search engines, mobile applications, and computer assistants are all equipped with deep learning technology. There are many classic machine learning algorithms and statistical algorithms which can be applied to data. By mimicking the human brain, deep learning models can work wonders when it comes to finding and creating patters from data. As deep learning reaches into a plethora of industries, it's becoming essential for software engineers to develop a work knowledge of its principles. We'll take an in-depth look at feedforward neural networks, an important part of the core neural network architecture.

Biotechnology, pharmaceutical, and life sciences industries are where applied artificial intelligence (AI) can greatly accelerate innovation and shorten the product development life-cycle. Developing a drug typically takes 10 to 15 years on average, with only approximately 12 percent of drugs in clinical trials ultimately gaining U.S. Food and Drug Administration (FDA) approval. In an AI milestone in life sciences, Insilico Medicine announced a new machine learning tool for drug discovery that can generate a novel molecule in days instead of years and published their findings in Nature Biotechnology on September 2, 2019. Insilico Medicine is a venture-backed start-up with multiple investors that include WuXi AppTec, Juvenescence, Peter Diamandis' BOLD Capital Partners, and Pavilion Capital. Led by CEO and Founder Alex Zhavoronkov, the company's mission is to extend longevity by applied AI solutions for drug discovery and aging research.