The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampl...
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The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis aiming to ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health.
作者：
Wotawa, Franz
Institute of Software Technology Inffeldgasse 16b/2 GrazA-8010 Austria
Allocating tasks to computing nodes in a network is an important configuration problem. In the case of fail-safe networks, such configuration must be changed during operation if a computing node fails. Hence, a fast c...
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Answer Set programming (ASP) is a well-known AI formalism. Traditional ASP systems, that follow the "ground&solve" approach, are intrinsically limited by the so-called grounding bottleneck. Basically, th...
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In order to ease the early verification of uniprocessor real-time systems, the tool Cheddar provides a service that guarantees the applicability of a schedulability analysis method for a given architecture model. This...
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In order to ease the early verification of uniprocessor real-time systems, the tool Cheddar provides a service that guarantees the applicability of a schedulability analysis method for a given architecture model. This verification service uses a catalog of design patterns. In this article, we propose to extend these patterns to multiprocessor architectures. Designing such extension is a challenge because the knowledge of both the software and the hardware architectures are essential to decide on the schedulability of a task set in that context. Indeed, parallel execution of tasks involves hardware resource sharing, that has in turn an effect on the task execution times. Currently, no general method is able to assess the schedulability of a high-performance multicore system with a limited level of pessimism, except if assumptions or usage restrictions are set to simplify the system analysis. So, the research community is developing multiple schedulability tests based on various assumptions which constrain the task models and their execution platforms. In this article, we propose a framework based on Prolog that allows engineers to verify the conditions to apply a test are met. Prolog facts model the software and hardware architecture, and the inference engine checks whether these facts conform to a design pattern associated to a given verification method. The design pattern compliance framework is integrated with the Cheddar tool. Three examples of multiprocessor analyses illustrate the proposal. A scalability analysis shows the tool is able to verify the compliance of architectures composed of 600 tasks and 60 cores, in less than 140 s on a desktop computer.
The fully connected topology, which coordinates the connection of each neuron with all other neurons, remains the most commonly used structure in Hopfield-type neural networks. However, fully connected neurons may for...
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The fully connected topology, which coordinates the connection of each neuron with all other neurons, remains the most commonly used structure in Hopfield-type neural networks. However, fully connected neurons may form a highly complex network, resulting in a high training cost and making the network biologically unrealistic. Biologists have observed a small-world topology with sparse connections in the actual brain cortex. The bionic small-world neural network structure has inspired various application scenarios. However, in previous studies, the long-range wirings in the small-world network have been found to cause network instability. In this study, we investigate the influence of neural network training on the small-world topology. The role of the path length and clustering coefficient of neurons is expounded in the neural network training process. We employ Watt and Strogatz's small-world model as the topology for the Hopfield neural network and conduct computer simulations. We observe that the random existence of neuron connections may cause unstable network energies and generate oscillations during the training process. A new method is proposed to mitigate the instability of small-world networks. The proposed method starts with a neuron as the pattern centroid along the radial, which arranges its wirings in compliance with the Gaussian distribution. The new method is tested on the MNIST handwritten digit dataset. The simulation confirms that the new small-world series has higher stability in terms of the learning accuracy and a higher convergence speed compared with Watt and Strogatz's small-world model.
Modern applications combine information from a great variety of sources. Oftentimes, some of these sources, like machine-learning systems, are not strictly binary but associated with some degree of (lack of) confidenc...
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Modern applications combine information from a great variety of sources. Oftentimes, some of these sources, like machine-learning systems, are not strictly binary but associated with some degree of (lack of) confidence in the observation. We propose MV-Datalog and MV-Datalog(+/-) as extensions of Datalog and Datalog(+/-), respectively, to the fuzzy semantics of infinite-valued Lukasiewicz logic L as languages for effectively reasoning in scenarios where such uncertain observations occur. We show that the semantics of MV-Datalog exhibits similar model theoretic properties as Datalog. In particular, we show that (fuzzy) entailment can be decided via minimal fuzzy models. We show that when they exist, such minimal fuzzy models are unique and can be characterised in terms of a linear optimisation problem over the output of a fixed-point procedure. On the basis of this characterisation, we propose similar many-valued semantics for rules with existential quantification in the head, extending Datalog(+/-).
Answer set programming (ASP) is a declarative programming language suited to solve complex combinatorial search problems. Prioritized ASP is the subdiscipline of ASP which aims at prioritizing the models (answer sets)...
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Developing and releasing multiservice applications rely upon a pipeline of automation tools known as Continuous Integration/Continuous Deployment. Among those tools, continuous reasoning is exploited by large companie...
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Developing and releasing multiservice applications rely upon a pipeline of automation tools known as Continuous Integration/Continuous Deployment. Among those tools, continuous reasoning is exploited by large companies to perform incremental static analyses on their code commits as soon as they are integrated into a shared codebase. In this article, we extend continuous reasoning towards the continuous QoS- and context-aware management of multiservice applications in Cloud-IoT scenarios. We propose a novel continuous reasoning methodology that supports runtime decision on service placement by reacting both to changes in the infrastructure and in the application requirements, and capable of suggesting migrations only for services affected by such changes. The methodology is prototyped in Prolog and assessed through simulations over a realistic use case and over a lifelike motivating scenario at increasing infrastructure sizes. Experimental results show that our approach brings considerable speed-up in comparison with an exhaustive search employing non-incremental reasoning.
Diabetic neuropathy is a prevalent consequence of diabetes, impacting as many as half of those diagnosed with the condition. This ailment results from nerve damage, manifesting as sensory and motor symptoms such as ti...
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Female under-representation in Computing Sciences is a structural problem and hence, solving it requires a profound social change. Indeed, our millenary cultures' collective unconscious contains ingrained position...
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