教学课程资源库 更多>>

关键词： Ethical technology  

摘要： The field of computer science is perhaps uniquely connected with industry. For example, our main publication outlets (i.e. conferences) are regularly sponsored by large technology companies, and much of our research funding is either directly or indirectly provided by industry. In turn, this places potential limitations on academic freedom, which is a profound ethical concern, yet curiously is not directly addressed within existing ethical codes. A field that limits academic freedom presents the risk that the results of the work conducted within it cannot always be relied upon. In the context of a field that is perhaps unique in both its connection to industry and impact on society, special measures are needed to address this problem. This paper discusses the range of protections that could be provided. © 2022, CC BY.

关键词： Computer science   Information technology   Chemistry   Drug discovery  

摘要： A host of companies provide a remote, automated workforce for conducting experiments around the clock.

关键词： Tensors   Internet of Things   Knowledge engineering   Computer science   Semantics   Graph neural networks   Task analysis   Graph neural network   graph representation learning   Internet of Things (IoT)   knowledge graph   knowledge reasoning   link prediction   tensor   tensor decomposition   tensor operations  

摘要： Knowledge graph builds the bridge from massive data generated by the interaction and communication between various objects to intelligent applications and services in Internet of Things. The graph representation learning technology represented by graph neural networks plays an essential role in the understanding and reasoning of the knowledge graph with complicated internal structure. Although they are capable of assigning different attention weights to neighbors, the graph attention network (GAT) and its variants are inherently flawed and inadequate in modeling high-order knowledge graphs with high heterogeneity. Therefore, we propose a novel multirelational GAT framework in this article for knowledge reasoning over heterogeneous graphs by employing tensor and tensor operations. Specifically, we formulate the general high-order heterogeneous knowledge graph first. Then, the tensor GAT (TGAT), composed of three components: 1) heterogeneous information propagation;2) multimodal semantic-aware attention;and 3) knowledge aggregation, is developed to simulate rich interactions between mixed triples, entities, and relationships when aggregating local information. What is more, we utilize the Tucker model to compress the parameters of TGAT and further reduce the storage and calculation consumption of the intermediate calculation process on the premise of maintaining the expressive power. We conduct extensive experiments to solve the link prediction task on four real-world heterogeneous graphs, and the results demonstrate that the TGAT model proposed in this article remarkably outperforms state-of-the-art competitors and improves the hits@1 accuracy by up to 7.6%.

关键词： Computer science   Diseases   Evolution  

摘要： How a pandemic that devastated the medieval world began, and efforts to control monkeypox.

关键词： Watches  

摘要： Context: The retraction of research papers, for whatever reason, is a growing phenomenon. However, although retracted paper information is publicly available via publishers, it is somewhat distributed and inconsistent. Objective: The aim is to assess: (i) the extent and nature of retracted research in Computer Science (CS) (ii) the post-retraction citation behaviour of retracted works and (iii) the potential impact upon systematic reviews and mapping studies. Method: We analyse the Retraction Watch database and take citation information from the Web of Science and Google scholar. Results: We find that of the 33,955 entries in the Retraction watch database (16 May 2022), 2,816 are classified as CS, i.e., ≈ 8%. For CS, 56% of retracted papers provide little or no information as to the reasons. This contrasts with 26% for other disciplines. There is also a remarkable disparity between different publishers, a tendency for multiple versions of a retracted paper to be available beyond the Version of Record (VoR), and for new citations long after a paper is officially retracted (median=3;maximum=18). Systematic reviews are also impacted with ≈ 30% of the retracted papers having one or more citations from a review. Conclusions: Unfortunately retraction seems to be a sufficiently common outcome for a scientific paper that we as a research community need to take it more seriously, e.g., standardising procedures and taxonomies across publishers and the provision of appropriate research tools. Finally, we recommend particular caution when undertaking secondary analyses and meta-analyses which are at risk of becoming contaminated by these problem primary studies. © 2022, CC BY-NC-SA.

关键词： Computer science   Scientific data  

摘要： Around 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3 G/4 G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332 GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.

关键词： Computer science   Conservation biology  

摘要： Preserving maritime ecosystems is a major concern for governments and administrations. Additionally, improving fishing industry processes, as well as that of fish markets, to have a more precise evaluation of the captures, will lead to a better control on the fish stocks. Many automated fish species classification and size estimation proposals have appeared in recent years, however, they require data to train and evaluate their performance. Furthermore, this data needs to be organized and labelled. This paper presents a dataset of images of fish trays from a local wholesale fish market. It includes pixel-wise (mask) labelled specimens, along with species information, and different size measurements. A total of 1,291 labelled images were collected, including 7,339 specimens of 59 different species (in 60 different class labels). This dataset can be of interest to evaluate the performance of novel fish instance segmentation and/or size estimation methods, which are key for systems aimed at the automated control of stocks exploitation, and therefore have a beneficial impact on fish populations in the long run.

关键词： Noise measurement   Training   Training data   Data models   Search engines   Face recognition   Computer science  

摘要： The noise transition matrix $T$ , reflecting the probabilities that true labels flip into noisy ones, is of vital importance to model label noise and build statistically consistent classifiers. The traditional transition matrix is limited to model closed-set label noise, where noisy training data have true class labels within the noisy label set. It is unfitted to employ such a transition matrix to model open-set label noise, where some true class labels are outside the noisy label set. Therefore, when considering a more realistic situation, i.e., both closed-set and open-set label noises occur, prior works will give unbelievable solutions. Besides, the traditional transition matrix is mostly limited to model instance-independent label noise, which may not perform well in practice. In this paper, we focus on learning with the mixed closed-set and open-set noisy labels. We address the aforementioned issues by extending the traditional transition matrix to be able to model mixed label noise, and further to the cluster-dependent transition matrix to better combat the instance-dependent label noise in real-world applications. We term the proposed transition matrix as the cluster-dependent extended transition matrix. An unbiased estimator (i.e., extended $T$ -estimator) has been designed to estimate the cluster-dependent extended transition matrix by only exploiting the noisy data. Comprehensive experiments validate that our method can better cope with realistic label noise, following its more robust performance than the prior state-of-the-art label-noise learning methods.

关键词： Education computing  

摘要： Oxford-style debating is a well-known tool in social sciences. Such formal discussions on particular topics are widely used by historians and sociologists. However, when we try to go beyond standard thinking, it turns out that Oxford-style debating can be a great educational tool in telecommunication and computer science. This article presents this unusual method of education at technical universities and in the IT industry, and describes its features and challenges. Best practices and examples of debating are provided, taking into account emerging topics in telecommunications and computer science, such as cybersecurity. The article also contains feedback from IT engineers who participated in Oxford-style debates. All this aims to encourage this form of education in telecommunication and computer science. Copyright © 2022, The Authors. All rights reserved.

关键词： Computational science   Computer science   Learning algorithms  

摘要： The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI". Artificial intelligence approaches inspired by human cognitive function have usually single learned ability. The authors propose a multimodal foundation model that demonstrates the cross-domain learning and adaptation for broad range of downstream cognitive tasks.