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关键词： Computer science   Law   Policy  

摘要： The patent system assumes that inventors are human. Inventions devised by machines require their own intellectual property law and an international treaty.

关键词： Model-based learning   UML diagrams   Software design evaluation   Scenario modelling and tracing   Content analysis   Thematic analysis  

摘要： Evaluating a software design is an important practice of expert software designers. They spend significant time evaluating their solution, by developing an integrated mental model of the software design and the requirements. However, sufficient emphasis has not been given on teaching and learning of evaluation practices in software design courses, and hence, graduating students find it difficult to critically analyse an existing design and improve upon it. In this paper, we describe a model-based learning pedagogy for teaching-learning of software design evaluation. Model-based learning has been extensively used in science education and entails helping students construct, refine, revise, evaluate, and validate scientific models. We argue that modelling practices in software design evaluation are analogous to these practices. We adapted the model-based learning paradigm and operationalised it into a technology-enhanced learning environment (TELE) for fostering software design evaluation skills in computer science undergraduates. We conducted a research study with 22 undergraduate students to explore how the TELE and its features help students effectively evaluate a given software design. Students attempted a pre-test and post-test which asked them to identify defects in the design. We used the content analysis method to identify categories of defects from student responses in the pre-test and post-test. We also analysed student interaction logs and conducted focus group interviews to identify how features in the TELE contributed towards student learning. Findings from the study showed that students' understanding of evaluation improved, from merely adding new functionalities and requirements, to a process which involved identifying alternate scenarios in the design which violate the given requirements. Students perceived that pedagogical features of the TELE were useful in helping them effectively evaluate software designs. Findings from the study provide evidence fo

关键词： theoretical computer science   consciousness   artificial intelligence   global workspace   complexity theory  

摘要： This paper examines consciousness from the perspective of theoretical computer science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations. We propose a formal TCS model, the Conscious Turing Machine (CTM). The CTM is influenced by Alan Turing's simple yet powerful model of computation, the Turing machine (TM), and by the global workspace theory (GWT) of consciousness originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, Jean-Pierre Changeux, George Mashour, and others. Phenomena generally associated with consciousness, such as blindsight, inattentional blindness, change blindness, dream creation, and free will, are considered. Explanations derived from the model draw confirmation from consistencies at a high level, well above the level of neurons, with the cognitive neuroscience literature.

关键词： Computer science   Machine perception   Computational intelligence  

摘要： Achieving human-level performance on some of the machine reading comprehension (MRC) datasets is no longer challenging with the help of powerful pre-trained language models (PLMs). However, the internal mechanism of these artifacts remains unclear, placing an obstacle to further understand these models. This paper focuses on conducting a series of analytical experiments to examine the relations between the multi-head self-attention and the final MRC system performance, revealing the potential explainability in PLM-based MRC models. To ensure the robustness of the analyses, we perform our experiments in a multilingual way on top of various PLMs. We discover that passage-to-question and passage understanding attentions are the most important ones in the question answering process, showing strong correlations to the final performance than other parts. Through comprehensive visualizations and case studies, we also observe several general findings on the attention maps, which can be helpful to understand how these models solve the questions.

关键词： Computer science   Information technology   Scientific data  

摘要： Community detection is a vital task in many fields, such as social networks, and financial analysis, to name a few. The Louvain method, the main workhorse of community detection, is a popular heuristic method based on modularity. But it is difficult for the sequential Louvain method to deal with large-scale graphs. In order to overcome the drawback, researchers have proposed several parallel Louvain methods (Parallel Louvain Method, PLM), which suffer two challenges: (1) latency in the information synchronization and (2) communities swap. To tackle these two challenges, we propose a graph partition algorithm for the parallel Louvain method. Different from existing graph partition algorithms, our graph partition algorithm divides the graph into subgraphs called isolate sets, in which vertices are relatively decoupled from others, and the PLM computes and synchronizes information without delay and communities swap. We first describe concepts and properties of isolate sets. In the second place, we propose an algorithm to divide the graph into isolate sets, which enjoys the same computation complexity as the breadth-first search. Finally, we propose the isolate-set-based parallel Louvain method, which calculates and updates vertices information without latency and communities swap. We implement our method with OpenMP on an 8-cores PC. Experiments on 18 graphs show that our parallel method achieves a maximum 4.62 xspeedup compared with the sequential method, and outputs higher modularity on 14 graphs.

关键词： Computer science   Information technology   Quantum information  

摘要： In the field of network security, although there has been related work on software vulnerability detection based on classic machine learning, detection ability is directly proportional to the scale of training data. A quantum neural network has been proven to solve the memory bottleneck problem of classical machine learning, so it has far-reaching prospects in the field of vulnerability detection. To fill the gap in this field, we propose a quantum neural network structure named QDENN for software vulnerability detection. This work is the first attempt to implement word embedding of vulnerability codes based on a quantum neural network, which proves the feasibility of a quantum neural network in the field of vulnerability detection. Experiments demonstrate that our proposed QDENN can effectively solve the inconsistent input length problem of quantum neural networks and the problem of batch processing with long sentences. Furthermore, it can give full play to the advantages of quantum computing and realize a vulnerability detection model at the cost of a small amount of measurement. Compared to other quantum neural networks, our proposed QDENN can achieve higher vulnerability detection accuracy. On the sub dataset with a small-scale interval, the model accuracy rate reaches 99%. On each subinterval data, the best average vulnerability detection accuracy of the model reaches 86.3%.

关键词： Computer science   Risk factors  

摘要： Misinformation about the COVID-19 pandemic proliferated widely on social media platforms during the course of the health crisis. Experts have speculated that consuming misinformation online can potentially worsen the mental health of individuals, by causing heightened anxiety, stress, and even suicidal ideation. The present study aims to quantify the causal relationship between sharing misinformation, a strong indicator of consuming misinformation, and experiencing exacerbated anxiety. We conduct a large-scale observational study spanning over 80 million Twitter posts made by 76,985 Twitter users during an 18.5 month period. The results from this study demonstrate that users who shared COVID-19 misinformation experienced approximately two times additional increase in anxiety when compared to similar users who did not share misinformation. Socio-demographic analysis reveals that women, racial minorities, and individuals with lower levels of education in the United States experienced a disproportionately higher increase in anxiety when compared to the other users. These findings shed light on the mental health costs of consuming online misinformation. The work bears practical implications for social media platforms in curbing the adverse psychological impacts of misinformation, while also upholding the ethos of an online public sphere.

关键词： Internet of Things   Routing   Sensors   Routing protocols   Delays   Computer science   Computational modeling   Adaptive ranking   energy efficient   fuzzy   Internet of Things (IoT)   opportunistic routing   sustainable  

摘要： Internet of Things (IoT) is a wireless network of various battery-powered sensing units. Due to the limited battery capacity, the nonaccessible/abandoned nodes demand more energy to be reached or reintegrated to keep the network connected. When a specific tree topology for routing, called destination-oriented directed acyclic graphs (DODAGs), is used, isolated nodes spend maximum energy on the assigned task during data transformation from the sensor field to the DODAG root. The nodes closer to the DODAG root need to rely on faraway nodes and resource-burden-constrained nodes to lead to the quick energy drain. It brings an idea of IoT network nodes in which an extra amount of energy provide to the longer time alive nodes. This article proposes an ARFOR-adaptive ranking fuzzy-based energy-efficient opportunistic routing protocol for sustainable IoT applications. The proposed protocol consists of a parent node (PN) that acts as a head node in a cluster to aggregate the packets to DODAG root;and a volunteer node (VN) acts as a forwarder to transfer the packets to PN with threshold energy limits to increase network lifetime during the transmission cycle. The proposed VN selection is based on fuzzy parameters, such as Canberra distance, residual energy, and threshold. The simulation outcomes depict that the ARFOR fairly justifies the network timeline requirement with maximum percentage area coverage. The percentage gain in terms of network lifetime is comparatively significant for a lower number of VN.

关键词： adolescence   air pollution   early life stress   fine particulate matter   PM2   5  

摘要： Air pollution is a major environmental threat to public health;we know little, however, about its effects on adolescent brain development. Exposure to air pollution co-occurs, and may interact, with social factors that also affect brain development, such as early life stress (ELS). Here, we show that severity of ELS and fine particulate air pollution (PM2.5) are associated with volumetric changes in distinct brain regions, but also uncover regions in which ELS moderates the effects of PM2.5. We interviewed adolescents about ELS events, used satellite-derived estimates of ambient PM2.5 concentrations, and conducted longitudinal tensor-based morphometry to assess regional changes in brain volume over an approximately 2-year period (N = 115, ages 9-13 years at Time 1). For adolescents who had experienced less severe ELS, PM2.5 was associated with volumetric changes across several gray and white matter regions. Fewer effects of PM2.5 were observed for adolescents who had experienced more severe ELS, although occasionally they were in the opposite direction. This pattern of results suggests that for many brain regions, moderate to severe ELS largely constrains the effects of PM2.5 on structural development. Further theory and research is needed on the joint effects of ELS and air pollution on the brain.

关键词： Decision making  

摘要： The paper offers a contribution to the interdisciplinary constructs of analyzing fairness issues in automatic algorithmic decisions. Section 1 shows that technical choices in supervised learning have social implications that need to be considered. Section 2 proposes a contextual approach to the issue of unintended group discrimination, i.e. decision rules that are facially neutral but generate disproportionate impacts across social groups (e.g., gender, race or ethnicity). The contextualization will focus on the legal systems of the United States on the one hand and Europe on the other. In particular, legislation and case law tend to promote different standards of fairness on both sides of the Atlantic. Section 3 is devoted to the explainability of algorithmic decisions;it will confront and attempt to cross-reference legal concepts (in European and French law) with technical concepts and will highlight the plurality, even polysemy, of European and French legal texts relating to the explicability of algorithmic decisions. The conclusion proposes directions for further research. © 2022, CC BY.