教学课程资源库 更多>>

关键词： Computer Science Jobs   Skillsets Required   Hard and Soft Skills   Social Network Analysis   Community analysis  

摘要： The rapid growth of technology and computer science, which has led to a surge in demand for skilled professionals in this field. The skill set required for computer science jobs has evolved rapidly, creating challenges for those already in the workforce who need to adapt their skills quickly to meet industry demands. To stay ahead of the curve, it is essential to understand the hottest skills needed in the field. The article introduces a new method for analyzing job advertisements using social network analysis to identify the most critical skills required by employers in the *** this research, to form the communication network of skills, first 5763 skills were collected from the LinkedIn social network, then the relationship between skills was collected and searched in 7777 computer science job advertisements, and finally, the balanced communication network of skills was formed. The study analyzes the formed communication network of skills in the computer science job market and identifies four distinct communities of skills: Generalists, Infrastructure and Security, Software Development, and Embedded Systems. The findings reveal that employers value both hard and soft skills, such as programming languages and teamwork. Communication skills were found to be the most important skill in the labor market. Additionally, certain skills were highlighted based on their centrality indices, including communication, English, SQL, Git, and business skills, among others. The study provides valuable insights into the current state of the computer science job market and can help guide individuals and organizations in making informed decisions about skills acquisition and hiring practices.

关键词： Aggregates   Data integration   Soft sensors   Data models   Computer science   Scalability   Fuses   Authentication   cloud computing   data fusion   multi-source data   range-aggregate queries  

摘要： The huge values created by Big Data and the recent advances in cloud computing have been driving data from different sources into cloud repositories for comprehensive query services. However, cloud-based data fusion makes it challenging to verify if an untrusted server faithfully integrates data and executes queries or not. This is even harder for range-aggregate queries that apply aggregate operations on data within given ranges. In this article, we propose a query authentication scheme, named MARS, enabling a user to efficiently authenticate range-aggregate queries on multi-source data. Specifically, MARS creates a VG-tree by subtly integrating Expressive Set Accumulator into a multi-dimensional G-tree while signing the root digest with a multi-source aggregate signature scheme. Compared with previous solutions, MARS has the following merits: (1) Practicality. Instead of treating range and aggregate queries separately, the user can directly verify the statistical result of selected data. (2) Scalability. Instead of authenticating the individual result from each source, the user can perform an aggregative validation on the integrated result from multiple sources. The experimental results demonstrate the effectiveness of MARS. For large-scale data fusion, the user-side verification time increases by only 103 ms as the amount of data sources increases by five times.

关键词： Testing   Quantum algorithm   Complexity theory   Probability distribution   Computer science   Aerospace electronics   Measurement   Quantum computing   quantum algorithm   property testing   probability distribution  

摘要： We explore potential quantum speedups for the fundamental problem of testing the properties of closeness and k-wise uniformity of probability distributions: 1) Closeness testing is the problem of distinguishing whether two n-dimensional distributions are identical or at least epsilon -far in & ell;(1) - or & ell;(2) -distance. We show that the quantum query complexities for & ell;(1 )- and & ell;(2) -closeness testing are O(root n/epsilon) and O(1/epsilon) , respectively, both of which achieve optimal dependence on epsilon , improving the prior best results of Gily & eacute;n and Li (2019) and 2) k-wise uniformity testing is the problem of distinguishing whether a distribution over {0,1}(n) is uniform when restricted to any k coordinates or epsilon -far from any such distribution. We propose the first quantum algorithm for this problem with query complexity O(root n(k)/epsilon) , achieving a quadratic speedup over the state-of-the-art classical algorithm with sample complexity O(n(k)/epsilon(2)) by O'Donnell and Zhao (2018). Moreover, when k=2 our quantum algorithm outperforms any classical one because of the classical lower bound Omega(n/epsilon(2)) . All our quantum algorithms are fairly simple and time-efficient, using only basic quantum subroutines such as amplitude estimation.

关键词： Diseases   Semantics   Heterogeneous networks   Predictive models   Representation learning   Computer science   Bioinformatics   Semantic feature learning based on vari- ous meta-paths   higher-order neighbor topological struc- ture integration   pairwise local feature learning   disease-related miRNA prediction   deep learning  

摘要： Dysregulation of miRNAs is closely related to the progression of various diseases, so identifying disease-related miRNAs is crucial. Most recently proposed methods are based on graph reasoning, while they did not completely exploit the topological structure composed of the higher-order neighbor nodes and the global and local features of miRNA and disease nodes. We proposed a prediction method, MDAP, to learn semantic features of miRNA and disease nodes based on various meta-paths, as well as node features from the entire heterogeneous network perspective, and node pair attributes. Firstly, for both the miRNA and disease nodes, node category-wise meta-paths were constructed to integrate the similarity and association connection relationships. Each target node has its specific neighbor nodes for each meta-path, and the neighbors of longer meta-paths constitute its higher-order neighbor topological structure. Secondly, we constructed a meta-path specific graph convolutional network module to integrate the features of higher-order neighbors and their topology, and then learned the semantic representations of nodes. Thirdly, for the entire miRNA-disease heterogeneous network, a global-aware graph convolutional autoencoder was built to learn the network-view feature representations of nodes. We also designed semantic-level and representation-level attentions to obtain informative semantic features and node representations. Finally, the strategy based on the parallel convolutional-deconvolutional neural networks were designed to enhance the local feature learning for a pair of miRNA and disease nodes. The experiment results showed that MDAP outperformed other state-of-the-art methods, and the ablation experiments demonstrated the effectiveness of MDAP's major innovations. MDAP's ability in discovering potential disease-related miRNAs was further analyzed by the case studies over three diseases.

关键词： Computer science   Translational research  

摘要： Medical imaging research is often limited by data scarcity and availability. Governance, privacy concerns and the cost of acquisition all restrict access to medical imaging data, which, compounded by the data-hungry nature of deep learning algorithms, limits progress in the field of healthcare AI. Generative models have recently been used to synthesize photorealistic natural images, presenting a potential solution to the data scarcity problem. But are current generative models synthesizing morphologically correct samples? In this work we present a three-dimensional generative model of the human brain that is trained at the necessary scale to generate diverse, realistic-looking, high-resolution and morphologically preserving samples and conditioned on patient characteristics (for example, age and pathology). We show that the synthetic samples generated by the model preserve biological and disease phenotypes and are realistic enough to permit use downstream in well-established image analysis tools. While the proposed model has broad future applicability, such as anomaly detection and learning under limited data, its generative capabilities can be used to directly mitigate data scarcity, limited data availability and algorithmic fairness. Medical imaging research is limited by data availability. To address this challenge, Tudosiu and colleagues develop a 3D generative model of the human brain that can generate high-resolution morphologically correct brains conditioned on patient characteristics.

关键词： Scalability   Visualization   Computational modeling   Encoding   Psychology   Human computer interaction   Computer science   Conceptual framework   scalability   structured literature analysis   visualization  

摘要： We introduce a conceptual model for scalability designed for visualization research. With this model, we systematically analyze over 120 visualization publications from 1990 to 2020 to characterize the different notions of scalability in these works. While many article have addressed scalability issues, our survey identifies a lack of consistency in the use of the term in the visualization research community. We address this issue by introducing a consistent terminology meant to help visualization researchers better characterize the scalability aspects in their research. It also helps in providing multiple methods for supporting the claim that a work is "scalable." Our model is centered around an effort function with inputs and outputs. The inputs are the problem size and resources, whereas the outputs are the actual efforts, for instance, in terms of computational run time or visual clutter. We select representative examples to illustrate different approaches and facets of what scalability can mean in visualization literature. Finally, targeting the diverse crowd of visualization researchers without a scalability tradition, we provide a set of recommendations for how scalability can be presented in a clear and consistent way to improve fair comparison between visualization techniques and systems and foster reproducibility.

关键词： Knowledge representation   Ontology   Deep learning   Reasoning   Occupational health and safety  

摘要： Reports of fatal and nonfatal workplace injuries depict severe circumstances involving health and safety in industries. This leads to workers staying away from work in U.S. for an average of 12 days in 2020, implicating in managerial, financial, and organizational losses. In this context, Vision-Based Deep Learning (VBDL) and Knowledge Representation and Reasoning (KRR) allow real-time data retrieval of situations along with the semantic modeling and expressivity of the real world to mitigate injuries. This article presents a framework that interoperates vision-based deep learning and ontology reasoning to identify adverse working situations, introducing a novel ontology composed of a holistic perspective of workers' health and safety. Moreover, the article provides multi-agent framework modeling to orchestrate the components' interoperability, describing the framework's architecture and deployment in workrooms. As a result, a practical evaluation over five days produced 8395 axioms constituted by 1210 individuals in the ontology, which allowed a temporal analysis of harmful conditions and their multiple overlapping using SPARQL and reasoning rules, particularly relevant to understanding explanations of overexertion, physical and environmental injuries. Therefore, the proposed ontology-based framework corroborates the long-term support in identifying, assessing, and controlling risks in the industries due to a well-defined knowledge model.

关键词： Training   Noise measurement   Data models   Computational modeling   Estimation   Predictive models   Computer science   Instance-dependent noisy labels   time-consistent curriculum learning   image classification  

摘要： Many machine learning algorithms are known to be fragile on simple instance-independent noisy labels. However, noisy labels in real-world data are more devastating since they are produced by more complicated mechanisms in an instance-dependent manner. In this paper, we target this practical challenge of Instance-Dependent Noisy Labels by jointly training (1) a model reversely engineering the noise generating mechanism, which produces an instance-dependent mapping between the clean label posterior and the observed noisy label and (2) a robust classifier that produces clean label posteriors. Compared to previous methods, the former model is novel and enables end-to-end learning of the latter directly from noisy labels. An extensive empirical study indicates that the time-consistency of data is critical to the success of training both models and motivates us to develop a curriculum selecting training data based on their dynamics on the two models' outputs over the course of training. We show that the curriculum-selected data provide both clean labels and high-quality input-output pairs for training the two models. Therefore, it leads to promising and robust classification performance even in notably challenging settings of instance-dependent noisy labels where many SoTA methods could easily fail. Extensive experimental comparisons and ablation studies further demonstrate the advantages and significance of the time-consistency curriculum in learning from instance-dependent noisy labels on multiple benchmark datasets.

关键词： Deep spatio-temporal network (DSTN)   information fusion   interactive attention mechanism   remaining useful life (RUL)  

摘要： Emerging multisource data provide a promising way to make breakthroughs in remaining useful life prediction. Due to the diversity in industrial sites and the complexity of the engineering systems, a large amount of degradation information of machinery is hidden in multitype data, which poses a challenge to adequately capture the complex features that jointly affect remaining useful life. To this end, we propose an interactive attention-based deep spatio-temporal network to effectively fuse vibration waveforms and time-varying operating signals. Specifically, the spatio-temporal structure in the proposed model has the ability to mine long-term dependence and local spatial information from raw multisource data simultaneously. An interactive attention mechanism is used to weight the extracted feature contributions from different source dynamically. Furthermore, a modified mean absolute percentage error criterion is designed in the training process for the inherent properties of the remaining useful prediction. For illustration, a case study of a rotating machinery in an oil refinery and a public dataset of an aircraft engine are investigated. The extensive experiments have demonstrated that, compared to relying solely on either vibrational or operating signals and different fusion strategies, the proposed model can effectively integrate multisource data to reduce prediction loss with an acceptable performance.

ISBN: (纸本)9787309175431