In current markets efforts for safety analyses and approval increase. Instead of review-based methods, recent publications demand for a shift of safety considerations to early stages and to bridge the gap between designers and safety experts. This paper develops a modelling method which helps to explicit safety knowledge and model safety functions. It unites existing methods of functional and structural modelling and extends the concept of safety functions. The resulting method, thus contributes to the requested shift and helps to bridge the gap between safety experts and product designers.
The LSDSem’17 shared task is the Story Cloze Test, a new evaluation for story understanding and script learning. This test provides a system with a four-sentence story and two possible endings, and the system must choose the correct ending to the story. Successful narrative understanding (getting closer to human performance of 100%) requires systems to link various levels of semantics to commonsense knowledge. A total of eight systems participated in the shared task, with a variety of approaches including.
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, Dan Roth. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.
For the certification of modern safety critical systems tree based failure models, like standardized fault trees (FTs), are frequently used methodologies. But when it comes to software-intensive systems these techniques have some crucial disadvantages, especially in modeling timing behavior. To deal with these weak points state/event fault trees (SEFTs) [6] were developed. However, these kind of fault trees can only be analyzed in a quantitative way. In this paper we propose an approach to analyze them qualitatively as well. This results in ordered event sequences which represent different ways for triggering a critical event of the underlying SEFTs, which can be seen as a time-dependent equivalent of the minimal cut set (MCS) analysis of standardized FTs. To evaluate our approach, we implemented the SEFTAnalyzer to apply it on a software-controlled fire alert system.
This Chapter explores Amazon Ech2, which provides you with environments called instances. Amazon Elastic Compute Cloud (Amazon Ech2) enables you to provision computing environments called instances. With Amazon Ech2, you have the flexibility to choose the hardware resources you need. With Amazon Ech2, you choose your hardware resources from a broad set of preconfigured options by selecting a specific instance type and instance size. For example, your instance has a number of virtual CPUs (vCPUs) and a specific amount of RAM. The instance type is rated for a certain level of network throughput. Some instance types also include other hardware resources such as high-performance local disks, graphics cards, or even field-programmable gate arrays (FPGAs). The details of how the instance accesses the host resources, such as the specific hypervisor in use, also depend on the instance type that you select.
This Chapter provides an overview of various methods for encrypting data at rest in AWS. AWS Key Management Service (AWS KMS) is a managed AWS service that makes it easy to create and manage encryption keys to encrypt your data across a wide range of AWS services and in your applications. As a secure, resilient service, AWS KMS uses FIPS 140-2 validated cryptographic modules, known as a hardware security module (HSM), to protect your master keys. The Federal Information Processing Standards (FIPS) are responsible for defining security requirements for cryptographic modules. AWS CloudHSM offers third-party, validated FIPS 140-2, level-three hardware security modules in the AWS Cloud. The hardware security module is a computing device that provides a dedicated infrastructure to support cryptographic operations.
We introduce MCScript2.0, a machine comprehension corpus for the end-to-end evaluation of script knowledge. MCScript2.0 contains approx. 20,000 questions on approx. 3,500 texts, crowdsourced based on a new collection process that results in challenging questions. Half of the questions cannot be answered from the reading texts, but require the use of commonsense and, in particular, script knowledge. We give a thorough analysis of our corpus and show that while the task is not challenging to humans, existing machine comprehension models fail to perform well on the data, even if they make use of a commonsense knowledge base. The dataset is available at http://www.sfb1102. uni-saarland.de/?page_id=2582
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