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关键词： Safety   Software   Task analysis   Training   Computer science   Software architecture   IEC Standards   Experimental research   safety-critical systems   software architecture   software engineering education  

摘要： Contribution: This article details the conduction of an experiment to investigate the knowledge that computer science graduate students have about safety-critical systems development, in particular, safety requirements specifications. Future research directions are also discussed. Background: Safety-critical systems have been increasingly used in many critical domains, requiring a rigorous development process as well as certification by regulatory agencies. However, it is not clear whether computer science courses deliver adequate education for students to develop such systems. Experimental research with these students could provide evidence on whether they have the minimum knowledge necessary to develop safety-critical systems. This type of research presents several advantages, such as reproducibility, high-level control, and specific conclusions, but has not been widely adopted to verify learning issues. Research Question: How to measure the understanding of students on safety-critical systems development?

关键词： Model   Study of models and modelling   More  

摘要： Models are a universal instrument in science, technology, and daily life. They function as instruments in almost every scenario. Any human activity can be (and is) supported by models, e.g. reason, explain, design, act, predict, explore, communicate, collaborate, interact, orient, direct, guide, socialises, perceive, reflect, develop, making sense, teach, learn, imagine, etc. This universal suitability is also the basis for a wide use of models and modelling in Computer Science and Engineering. We claim that models form the fourth dimension in Computer Science. This paper sketches and systematises the main ingredients of the study model and modelling.

摘要： In diesem Artikel behandeln wir unser Lehrbaummodell mit den Lehr- und Lernmethoden, welches zum effektiven Lehren und Lernen in der Informatik, Wirtschaftsinformatik und verwandten Fachgebieten hinführt. Das Lehrbaummodell hat in seinen Stamm Planung und Motivation als wichtigste Lehrmethodiken auf dem effektives Lehren und Lernen basiert. Die Blätter des Lehrbaummodells wie Kommunikation, Material, Gedächtnis, Aufmerksamkeit, Lernstörung und Aktivierung im Unterricht stellen notwendige Teile des effektiven Lehren und Lernens dar und schließlich folgt der Gipfel: die Überprüfung auf Effektivität des Unterrichts und der Lehrmethoden. Wir zeigen Wege auf, wie der Wandel von traditionellen Lehrmethoden wie des Haltens eines Vortrags oder einer Vorlesung mit wenig Interaktion zu moderneren, effektiveren Methoden durch die Verwendung des Lehrbaummodells stattfinden kann. Der Artikel zeigt auf, wie Frustration im Lehren und Lernen in der Informatik, Wirtschaftsinformatik und verwandten Fachgebieten vermieden und effektiver gelehrt und gelernt werden kann. Wir versuchen, mit diesen Artikel einen Wandel von traditionellen Lehrmethoden zu psychologisch moderneren Lehrmethoden zu ermöglichen.

关键词： Mutual information   Entropy   Random variables   Optimization   Machine learning   Correlation   Computer science   Optimization   information theory   submodular optimization   combinatorial optimization   machine learning  

摘要： Information-theoretic quantities like entropy and mutual information have found numerous uses in machine learning. It is well known that there is a strong connection between these entropic quantities and submodularity since entropy over a set of random variables is submodular. In this paper, we study combinatorial information measures that generalize independence, (conditional) entropy, (conditional) mutual information, and total correlation defined over sets of (not necessarily random) variables. These measures strictly generalize the corresponding entropic measures since they are all parameterized via submodular functions that themselves strictly generalize entropy. Critically, we show that, unlike entropic mutual information in general, the submodular mutual information is actually submodular in one argument, holding the other fixed, for a large class of submodular functions whose third-order partial derivatives satisfy a non-negativity property. This turns out to include a number of practically useful cases such as the facility location and set-cover functions. We study specific instantiations of the submodular information measures on these, as well as the probabilistic coverage, graph-cut, log-determinants, and saturated coverage functions and see that they all have mathematically intuitive and practically useful expressions. Finally, we also study generalized independence between subsets of datapoints (random variables in the entropic case), and connect the independence characterizations to independence in log-submodular distributions. Regarding applications, we connect the maximization of submodular (conditional) mutual information to problems such as mutual-information-based, query-based, and privacy preserving summarization-and we connect optimizing the multi-set submodular mutual information to clustering and robust partitioning. We perform real world as well as synthetic experiments on various data summarization tasks.

关键词： Research conferences   Virtual conferences   Computer Science   COVID-19   Disruption  

摘要： The disruption from COVID-19 has been felt deeply across all walks of life. Similarly, academic conferences as one key pillar of dissemination and interaction around research and development have taken a hit. We analyse an interesting focal point as to how conferences in the area of Computer Science have reacted to this disruption with respect to their mode of offering and registration prices, and whether their response is contingent upon specific factors such as where the conference was to be hosted, its ranking, its publisher or its original scheduled date. To achieve this, we collected metadata associated with 170 conferences in the area of Computer Science and as a means of comparison;25 Psychology conferences. We show that conferences in the area of Computer Science have demonstrated agility and resilience by progressing to an online mode due to COVID-19 (approximately 76% of Computer Science conferences moved to an online mode), many with no changes in their schedule, particularly those in North America and those with a higher ranking. Whilst registration fees have lowered by an average of 42% due to the onset of COVID-19, conferences still have to facilitate attendance on a large scale due to the logistics and costs involved. In conclusion, we discuss the implications of our findings and speculate what they mean for conferences, including those in Computer Science, in the post-COVID-19 world.

关键词： Computer science   Computing methodologies and applications   Computer graphics   Computational geometry  

摘要： We present a new approach for path finding in weighted graphs using pre-computed minimal distance fields. By selecting the most promising minimal distance field at any given node and switching between them, our algorithm aims to find the shortest path possible. As we show, this approach scales excellently for various topologies, graph sizes and hardware specifications while maintaining a mean length error below 1% and reasonable memory consumption. By utilizing a simplified structure and keeping backtracking to a minimum, we are able to leverage the same approach on the massively parallel GPUs or any other shared memory parallel architecture, reducing the run time even further. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Gray-scale   Correlation   Feature extraction   Complexity theory   Morphology   Computer science   Toy manufacturing industry   Band selection   dilation   feature selection   hyperspectral images   mathematical morphology (MM)  

摘要： In this letter, we adapt the dilation operator from mathematical morphology to propose dilation distances. These dilation distances are then used for band selection in hyperspectral images. It is shown that dilation distances between bands can capture the spatial distance between the objects. Hence, using dilation-based distances would select those bands which identify spatially separated objects. This is illustrated using both toy and real data sets. Furthermore, we compare the proposed approach with existing methods and show empirically that dilation-distance-based band selection provided competitive results outperforming several methods.

关键词： Reinforcement learning   Feature extraction   Learning systems   Benchmark testing   Computer science   Generators   Games   Adversarial learning   partial domain adaptation   reinforcement learning  

摘要： Partial domain adaptation aims to transfer knowledge from a label-rich source domain to a label-scarce target domain (i.e., the target categories are a subset of the source ones), which relaxes the common assumption in traditional domain adaptation that the label space is fully shared across different domains. In this more general and practical scenario on partial domain adaptation, a major challenge is how to select source instances from the shared categories to ensure positive transfer for the target domain. To address this problem, we propose a domain adversarial reinforcement learning (DARL) framework to progressively select source instances to learn transferable features between domains by reducing the domain shift. Specifically, we employ a deep Q-learning to learn policies for an agent to make selection decisions by approximating the action-value function. Moreover, domain adversarial learning is introduced to learn a common feature subspace for the selected source instances and the target instances, and also to contribute to the reward calculation for the agent that is based on the relevance of the selected source instances with respect to the target domain. Extensive experiments on several benchmark data sets clearly demonstrate the superior performance of our proposed DARL over existing state-of-the-art methods for partial domain adaptation.

关键词： Training   Bagging   Diversity reception   Cybernetics   Cancer   Computer science   Sun   Bagging   hashing   imbalanced classification problems   undersampling  

摘要： Undersampling is a popular method to solve imbalanced classification problems. However, sometimes it may remove too many majority samples which may lead to loss of informative samples. In this article, the hashing-based undersampling ensemble (HUE) is proposed to deal with this problem by constructing diversified training subspaces for undersampling. Samples in the majority class are divided into many subspaces by a hashing method. Each subspace corresponds to a training subset which consists of most of the samples from this subspace and a few samples from surrounding subspaces. These training subsets are used to train an ensemble of classification and regression tree classifiers with all minority class samples. The proposed method is tested on 25 UCI datasets against state-of-the-art methods. Experimental results show that the HUE outperforms other methods and yields good results on highly imbalanced datasets.

关键词： artificial intelligence   big data   computer science   psychological assessment  

摘要： Psychological science can benefit from and contribute to emerging approaches from the computing and information sciences driven by the availability of real-world data and advances in sensing and computing. We focus on one such approach, machine-learned computational models (MLCMs)-computer programs learned from data, typically with human supervision. We introduce MLCMs and discuss how they contrast with traditional computational models and assessment in the psychological sciences. Examples of MLCMs from cognitive and affective science, neuroscience, education, organizational psychology, and personality and social psychology are provided. We consider the accuracy and generalizability of MLCM-based measures, cautioning researchers to consider the underlying context and intended use when interpreting their performance. We conclude that in addition to known data privacy and security concerns, the use of MLCMs entails a reconceptualization of fairness, bias, interpretability, and responsible use.