Node co-activations as a means of error detection—Towards fault-tolerant neural networks

Context: Machine learning has proved an efficient tool, but the systems need tools to mitigate risks during runtime. One approach is fault tolerance: detecting and handling errors before they cause harm. Objective: This paper investigates whether rare co-activations – pairs of usually segregated nodes activating together – are indicative of problems in neural networks (NN). These could be used to detect concept drift and flagging untrustworthy predictions. Methods: We trained four NNs. For each, we studied how often each pair of nodes activates together. In a separate test set, we counted how many rare co-activations occurred with each input, and grouped the inputs based on whether its classification was correct, incorrect, or whether its class was absent during training. Results: Rare co-activations are much more common in inputs from a class that was absent during training. Incorrectly classified inputs averaged a larger number of rare co-activations than correctly classified inputs, but the difference was smaller. Conclusions: As rare co-activations are more common in unprecedented inputs, they show potential for detecting concept drift. There is also some potential in detecting single inputs from untrained classes. The small difference between correctly and incorrectly predicted inputs is less promising and needs further research.Peer reviewe

Control of potato late blight by caraway oil in organic farming

Caraway (Carum carvi) seeds contain biologically active essential oils, which have shown potential in controlling Phytophthora infestans (P.i.). An attempt is being made to develop a P.i. control strategy for organic farming based on caraway oil

Testing the Robustness of AutoML Systems

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Robustness of AutoML for Time Series Forecasting in Sensor Networks

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Command Similarity Measurement Using NLP

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Quantum Software Engineering Challenges from Developers' Perspective: Mapping Research Challenges to the Proposed Workflow Model

Despite the increasing interest in quantum computing, the aspect of development to achieve cost-effective and reliable quantum software applications has been slow. One barrier is the software engineering of quantum programs, which can be approached from two directions. On the one hand, many software engineering practices, debugging in particular, are bound to classical computing. On the other hand, quantum programming is closely associated with the phenomena of quantum physics, and consequently, the way we express programs resembles the early days of programming. Moreover, much of the software engineering research today focuses on agile development, where computing cycles are cheap and new software can be rapidly deployed and tested, whereas in the quantum context, executions may consume lots of energy, and test runs may require lots of work to interpret. In this paper, we aim at bridging this gap by starting with the quantum computing workflow and by mapping existing software engineering research to this workflow. Based on the mapping, we then identify directions for software engineering research for quantum computing.Comment: 4 pages, 1 figur

Practices and Infrastructures for Machine Learning Systems: An Interview Study in Finnish Organizations

Using interviews, we investigated the practices and toolchains for machine learning (ML)-enabled systems from 16 organizations across various domains in Finland. We observed some well-established artificial intelligence engineering approaches, but practices and tools are still needed for the testing and monitoring of ML-enabled systems.Peer reviewe

Regression Test Selection Tool for Python in Continuous Integration Process

In this paper, we present a coverage-based regression test selection (RTS) approach and a developed tool for Python. The tool can be used either on a developer's machine or on build servers. A special characteristic of the tool is the attention to easy integration to continuous integration and deployment. To evaluate the performance of the proposed approach, mutation testing is applied to three open-source projects, and the results of the execution of full test suites are compared to the execution of a set of tests selected by the tool. The missed fault rate of the test selection varies between 0-2% at file-level granularity and 16-24% at line-level granularity. The high missed fault rate at the line-level granularity is related to the selected basic mutation approach and the result could be improved with advanced mutation techniques. Depending on the target optimization metric (time or precision) in DevOps/MLOps process the error rate could be acceptable or further improved by using file-level granularity based test selection.Peer reviewe

Software business : A short history and trends for the future

During its 70 years of existence, the software business has been following an evolution curve that can be considered typical for several fields of industrial businesses. Technological breakthroughs and innovations are typically seen as enablers for business evolution in the domain of technology and innovation management. Software, data collection, and data analysis represent a greater and greater part of the value of products and services, and today, their role is also becoming essential in more traditional fields. This, however, requires business and technology competences that traditional industries do not have. The transformation also enables new ways of doing business and opens the field for new kinds of players. Together, all this leads to transformation and new possibilities for the software industry. In this paper we study the overall trajectory of the software business, and then offer some viewpoints on the change in different elements of business models. Copyright © by the paper's authors. Copying permitted only for private and academic purposes.Peer reviewe