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Using Artificial Intelligence in SAP ABAP Development
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Research conducted on Brain-Computer Interfaces (BCIs) has grown considerably during the last decades. With the help of BCIs, users can (re)gain a wide range of functions. Our aim in this paper is to analyze the impact of BCIs on autonomy. To this end, we introduce three abilities that most accounts of autonomy take to be essential: (1) the ability to use information and knowledge to produce reasons; (2) the ability to ensure that intended actions are effectively realized (control); and (3) the ability to enact intentions within concrete relationships and contexts. We then consider the impact of BCI technology on each of these abilities. Although on first glance, BCIs solely enhance self-determination because they restore or improve abilities, we will show that there are other positive, but also negative impacts on user autonomy, which require further philosophical and ethical discussions.
The full text is available at https://arxiv.org/abs/1801.01586. .
Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these variables is also important, since performance tends to decline as the input dimensionality increases, hence the interest in using feature fusion techniques, able to produce feature sets that are more compact and higher level. A plethora of procedures to fuse original variables for producing new ones has been developed in the past decades. The most basic ones use linear combinations of the original variables, such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), while others find manifold embeddings of lower dimensionality based on non-linear combinations, such as Isomap or LLE (Linear Locally Embedding) techniques. More recently, autoencoders (AEs) have emerged as an alternative to manifold learning for conducting nonlinear feature fusion. Dozens of AE models have been proposed lately, each with its own specific traits. Although many of them can be used to generate reduced feature sets through the fusion of the original ones, there also AEs designed with other applications in mind. The goal of this paper is to provide the reader with a broad view of what an AE is, how they are used for feature fusion, a taxonomy gathering a broad range of models, and how they relate to other classical techniques. In addition, a set of didactic guidelines on how to choose the proper AE for a given task is supplied, together with a discussion of the software tools available. Finally, two case studies illustrate the usage of AEs with datasets of handwritten digits and breast cancer.
Distributed Denial of Service (DDoS) is one of the most prevalent attacks that an organizational network infrastructure comes across nowadays. We propose a deep learning based multi-vector DDoS detection system in a software-defined network (SDN) environment. SDN provides flexibility to program network devices for different objectives and eliminates the need for third-party vendor-specific hardware. We implement our system as a network application on top of an SDN controller. We use deep learning for feature reduction of a large set of features derived from network traffic headers. We evaluate our system based on different performance metrics by applying it on traffic traces collected from different scenarios. We observe high accuracy with a low false-positive for attack detection in our proposed system.
The impact of the industrial and digital (information) revolutions has, undoubtedly, been substantial on practically all aspects of our society, life, firms and employment. Will the forthcoming AI revolution produce similar, far-reaching effects? By examining analogous inventions of the industrial, digital and AI revolutions, this article claims that the latter is on target and that it would bring extensive changes that will also affect all aspects of our society and life. In addition, its impact on firms and employment will be considerable, resulting in richly interconnected organizations with decision making based on the analysis and exploitation of “big” data and intensified, global competition among firms. People will be capable of buying goods and obtaining services from anywhere in the world using the Internet, and exploiting the unlimited, additional benefits that will open through the widespread usage of AI inventions. The paper concludes that significant competitive advantages will continue to accrue to those utilizing the Internet widely and willing to take entrepreneurial risks in order to turn innovative products/services into worldwide commercial success stories. The greatest challenge facing societies and firms would be utilising the benefits of availing AI technologies, providing vast opportunities for both new products/services and immense productivity improvements while avoiding the dangers and disadvantages in terms of increased unemployment and greater wealth inequalities.
Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSLKDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the fortyone features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.
Will Democracy Survive Big Data and Artificial Intelligence?
- B S Frey
- BS Frey
Software 2.0, Blog Contribution in Medium Programming of
- A Karpathy