The Wendelstein 7-X steady state plasma control and data acquisition system has been successfully commissioned and well established to investigate the plasma startup and run the first complex physics programs during t...
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The Wendelstein 7-X steady state plasma control and data acquisition system has been successfully commissioned and well established to investigate the plasma startup and run the first complex physics programs during the initial operation phase of W7-X. The main components of W7-X and a set of operational diagnostics have been integrated into the CoDaC system. The various different requests from the experimenters could be satisfied using the well established set of tools and interfaces - demonstrating their qualification for continuous operation. This paper summarizes the workflow from the users' point of view while presenting the main tools and interfaces for W7-X Segment Control. (C) 2017 The Authors. Published by Elsevier B.V.
Digital transformation is accelerating due to the rapid emergence of open APIs. Using a curated dataset of nearly 15,000 APIs, the author visualizes the structure of the API ecosystem, revealing distinct clusters with...
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Digital transformation is accelerating due to the rapid emergence of open APIs. Using a curated dataset of nearly 15,000 APIs, the author visualizes the structure of the API ecosystem, revealing distinct clusters within it.
This paper addresses web services for Internet of Things (IoT) applications. The research focus is on IoT applications for mobile monitoring and third party's database access. Both web services design aspects and ...
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This paper addresses web services for Internet of Things (IoT) applications. The research focus is on IoT applications for mobile monitoring and third party's database access. Both web services design aspects and deployment issues are studied. By synthesis of functional abstraction for configuration management and performance monitoring, an approach to design RESTful web services for mobile monitoring is suggested. In addition to traditional operations supported by Database Management Systems, web services' operations for subscription management and notifications about occurrences of specific data-related events, as well as operations for trust and security management are proposed in the context of third party's database access. Web services deployment issues concern the access control aimed to prevent from overloading. A formal method for a model-based load evaluation of SOA (Service Oriented Architecture) application server is suggested. The method takes into account the distributed server architecture and the processing of heterogeneous traffic. The SOA application server's utilization is evaluated by simulation.
The use of RESTful Web services has gained momentum in the development of distributed applications based on traditional Web standards such as HTTP. In particular, these services can integrate easily into various appli...
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The use of RESTful Web services has gained momentum in the development of distributed applications based on traditional Web standards such as HTTP. In particular, these services can integrate easily into various applications, such as mashups. Composing RESTful services into Web-scale workflows requires a lightweight composition language that's capable of describing both the control and data flow that constitute a workflow. The authors address these issues with Bite, a lightweight and extensible composition language that enables the creation of Web-scale workflows and uses RESTful services as its main composable entities.
Programs draw significant parts of their functionality through the use of application programming interfaces (APIs). Apart from the way developers incorporate APIs in their software, the stability of these programs de...
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Programs draw significant parts of their functionality through the use of application programming interfaces (APIs). Apart from the way developers incorporate APIs in their software, the stability of these programs depends on the design and implementation of the APIs. In this work, we report how we used software telemetry data to analyze the causes of API failures in Android applications. Specifically, we got 4.9 gb worth of crash data that thousands of applications sent to a centralized crash report management service. We processed that data to extract approximately a million stack traces, stitching together parts of chained exceptions, and established heuristic rules to draw the border between applications and the API calls. We examined a set of more than a half million stack traces associated with risky API calls to map the space of the most common application failure reasons. Our findings show that the top ones can be attributed to memory exhaustion, race conditions or deadlocks, and missing or corrupt resources. Given the classes of the crash causes we identified, we recommend API design and implementation choices, such as specific exceptions, default resources, and non-blocking algorithms, that can eliminate common failures. In addition, we argue that development tools like memory analyzers, thread debuggers, and static analyzers can prevent crashes through early code testing and analysis. Finally, some execution platform and framework designs for process and memory management can also eliminate some application crashes.
Within the last two years, the Java technology has largely entered the world of element, network, and service management and has undoubtedly proven useful in combination with the SNMP framework for bath manager and ag...
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Within the last two years, the Java technology has largely entered the world of element, network, and service management and has undoubtedly proven useful in combination with the SNMP framework for bath manager and agent side developments. However, very few experiments have been made so far trying to provide an in depth integration of this technology within the TMN framework. In this paper we present a free software which offers a generic environment which provides a basis for reaching this integration. This environment, called J(TMN,) is composed of a set of Java software packages which enable both development and deployment of TMN compliant components. Not bound to any specific management platform and easily portable to any TMN system, its goal is to allow experimentation and deployment of more advanced features such as mobile agents, active network management, and advanced delegation in harmony with existing TMN approaches.
OpenMicroscopyEnvironment Remote Objects (OMERO) is an open-source client-server system for archiving, visualizing and analyzing image data and associated meta-data in a repository and making the data usable in a data...
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OpenMicroscopyEnvironment Remote Objects (OMERO) is an open-source client-server system for archiving, visualizing and analyzing image data and associated meta-data in a repository and making the data usable in a database [1]. For this paper, OMERO has been studied as an image data repository and was selected as a central data space for metallography. Based on use cases examples from the Fraunhofer Institute for Mechanics of Materials (IWM), various possibilities of automated microstructural analysis using OMERO will be presented and discussed.
As the number of Android malware has been increased rapidly over the years, various malware detection methods have been proposed so far. Existing methods can be classified into two categories: static analysis-based me...
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As the number of Android malware has been increased rapidly over the years, various malware detection methods have been proposed so far. Existing methods can be classified into two categories: static analysis-based methods and dynamic analysis-based methods. Both approaches have some limitations: static analysis-based methods are relatively easy to be avoided through transformation techniques such as junk instruction insertions, code reordering, and so on. However, dynamic analysis-based methods also have some limitations that analysis overheads are relatively high and kernel modification might be required to extract dynamic features. In this paper, we propose a dynamic analysis framework for Android malware detection that overcomes the aforementioned shortcomings. The framework uses a suffix tree that contains API (applicationprogramming Interface) subtraces and their probabilistic confidence values that are generated using HMMs (Hidden Markov Model) to reduce the malware detection overhead, and we designed the framework with the client-server architecture since the suffix tree is infeasible to be deployed in mobile devices. In addition, an application rewriting technique is used to trace API invocations without any modifications in the Android kernel. In our experiments, we measured the detection accuracy and the computational overheads to evaluate its effectiveness and efficiency of the proposed framework.
Reference documentation is an important source of information on API usage. However, information useful to programmers can be buried in irrelevant text, or attached to a non-intuitive API element, making it difficult ...
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Reference documentation is an important source of information on API usage. However, information useful to programmers can be buried in irrelevant text, or attached to a non-intuitive API element, making it difficult to discover. We propose to detect and recommend fragments of API documentation potentially important to a programmer who has already decided to use a certain API element. We categorize text fragments in API documentation based on whether they contain information that is indispensable, valuable, or neither. From the fragments that contain knowledge worthy of recommendation, we extract word patterns, and use these patterns to automatically find new fragments that contain similar knowledge in unseen documentation. We implemented our technique in a tool, Krec, that supports both information filtering and discovery. In an evaluation study with randomly-sampled method definitions from ten open source systems, we found that with a training set derived from about 1000 documentation units, we could issue recommendations with 90 % precision and 69 % recall. In a study involving ten independent assessors, indispensable knowledge items recommended for API types were judged useful 57 % of the time and potentially useful an additional 30 % of the time.
Compressed prompts aid instruction-tuned language models (LMs) in overcoming context window limitations and reducing computational costs. Existing methods, which are primarily based on training embeddings, face variou...
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Compressed prompts aid instruction-tuned language models (LMs) in overcoming context window limitations and reducing computational costs. Existing methods, which are primarily based on training embeddings, face various challenges associated with interpretability, the fixed number of embedding tokens, reusability across different LMs, and inapplicability when interacting with black-box APIs. This study proposes prompt compression with reinforcement learning (PCRL), which is a discrete prompt compression method that addresses these issues. The proposed PCRL method utilizes a computationally efficient policy network that edits prompts directly. The training approach employed in the proposed PCRLs can be applied flexibly to various types of LMs, including both decoder-only and encoder-decoder architecture and it can be trained without gradient access to the LMs or labeled data. The proposed PCRL achieves an average reduction of 24.6% in terms of the token count across various instruction prompts while maintaining sufficient performance. In addition, we demonstrate that the learned policy can be transferred to larger LMs, and through a comprehensive analysis, we explore the token importance within the prompts. The source code is available at https://***/nenomigami/PromptCompressor.
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