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关键词： Medical research   Computer science  

摘要： In recent years, there have been several solutions to medical image segmentation, such as U-shaped structure, transformer-based network, and multi-scale feature learning method. However, their network parameters and real-time performance are often neglected and cannot segment boundary regions well. The main reason is that such networks have deep encoders, a large number of channels, and excessive attention to local information rather than global information, which is crucial to the accuracy of image segmentation. Therefore, we propose a novel multi-branch medical image segmentation network MBSNet. We first design two branches using a parallel residual mixer (PRM) module and dilate convolution block to capture the local and global information of the image. At the same time, a SE-Block and a new spatial attention module enhance the output features. Considering the different output features of the two branches, we adopt a cross-fusion method to effectively combine and complement the features between different layers. MBSNet was tested on five datasets ISIC2018, Kvasir, BUSI, COVID-19, and LGG. The combined results show that MBSNet is lighter, faster, and more accurate. Specifically, for a 320 x 320 input, MBSNet's FLOPs is 10.68G, with an F1-Score of 85.29% on the Kvasir test dataset, well above 78.73% for UNet++ with FLOPs of 216.55G. We also use the multi-criteria decision making method TOPSIS based on F1-Score, IOU and Geometric-Mean (G-mean) for overall analysis. The proposed MBSNet model performs better than other competitive methods. Code is available at https://***/YuLionel/MBSNet.

关键词： Applied mathematics   Computer science   Information technology  

摘要： Graph contrastive learning has been developed to learn discriminative node representations on homogeneous graphs. However, it is not clear how to augment the heterogeneous graphs without substantially altering the underlying semantics or how to design appropriate pretext tasks to fully capture the rich semantics preserved in heterogeneous information networks (HINs). Moreover, early investigations demonstrate that contrastive learning suffer from sampling bias, whereas conventional debiasing techniques (e.g., hard negative mining) are empirically shown to be inadequate for graph contrastive learning. How to mitigate the sampling bias on heterogeneous graphs is another important yet neglected problem. To address the aforementioned challenges, we propose a novel multi-view heterogeneous graph contrastive learning framework in this paper. We use metapaths, each of which depicts a complementary element of HINs, as the augmentation to generate multiple subgraphs (i.e., multi-views), and propose a novel pretext task to maximize the coherence between each pair of metapath-induced views. Furthermore, we employ a positive sampling strategy to explicitly select hard positives by jointly considering semantics and structures preserved on each metapath view to alleviate the sampling bias. Extensive experiments demonstrate MCL consistently outperforms state-of-the-art baselines on five real-world benchmark datasets and even its supervised counterparts in some settings.

关键词： Education   Project management   Software   Computer science   Task analysis   Visualization   Production systems   Engineering education   Lean in higher education   Lean principles and techniques   value chain   waste removal  

摘要： This article describes how different Lean principles and techniques have been adapted and applied to identify, analyze, and improve the value chain of a Software Project Management module in a computer science curriculum. This research is part of an initiative to improve the processes related to higher education teaching management, at a university, with the main goal of maximizing the value that is provided to students when taking a module. To represent and visualize the whole value chain of the module, the value stream mapping Lean management tool has been used. The essential module activities and the necessary elements to teach each one of these activities, from the first session to the final mark, were detected, collected, and represented using diagrams. The value chain analysis and review were performed by applying Lean principles and techniques. This article details several improvements to be applied to the module to eliminate the waste that did not add value to students.

关键词： Computer science   Scientific data   Statistics  

摘要： The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose a great challenge for effectively predicting COVID-19 cases. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in the training procedure. We demonstrate the favorable performance of the hybrid model over its two single composing models as well as other popular predictive models through comprehensive numerical studies on two data sources under multiple evaluation metrics. Specifically, in county-level data of 8 California counties, our hybrid model achieves 4.173% MAPE, outperforming the composing AR (5.629%) and LSTM (4.934%) alone on average. In country-level datasets, our hybrid model outperforms the widely-used predictive models such as AR, LSTM, Support Vector Machines, Gradient Boosting, and Random Forest, in predicting the COVID-19 cases in Japan, Canada, Brazil, Argentina, Singapore, Italy, and the United Kingdom. In addition to the predictive performance, we illustrate the interpretability of our proposed hybrid model using the estimated AR component, which is a key feature that is not shared by most black-box predictive models for COVID-19 cases. Our study provides a new and promising direction for building effective and interpretable data-driven models for COVID-19 cases, which could have significant implications for public health policy making and control of the current COVID-19 an

关键词： Computer science   Artificial intelligence   Machine learning  

摘要： Automatic identification system (AIS) is a vessel-based system for the automatic broadcast and reception of vessel information, and it also supports data for trajectory prediction. Since the vessel's sailing route is flexible and changeable and the AIS broadcast is unconfirmed, the trajectory varies greatly and the original AIS data contains some noisy trajectory, which leads to low prediction accuracy and stability. Therefore, to solve the above problem, this paper proposes a trajectory prediction method based on bidirectional gate recurrent unit (BiGRU) and trajectory direction vector (TDV) with attention mechanism. This paper firstly proposes a TDV to associate latitude and longitude with the course and speed. Then the paper proposes an attention mechanism to self-adaptively update weight to the TDV in different stages to eliminate unreasonable predicted trajectory points. Finally, this paper combines the TDV attention mechanism and the BiGRU network to train a vessel trajectory prediction model.

摘要： We have analyzed all preprints on ChatGPT (N=501) and selected the most influential preprints (according to Altmetric) about ChatGPT across scientific disciplines to provide the most discussed research results about ChatGPT. We prompted ChatGPT to create a structured review article based on them. The results are surprisingly promising, suggesting that the future of creating review articles can lie in ChatGPT. Copyright © 2023, The Authors. All rights reserved.

关键词： Engineering education  

摘要： ChatGPT has become the most popular regenerative AI application, used for obtaining responses for queries in different domains. Some responses of ChatGPT reported in the internet are accurate, others are funny, and some are fictitious. This article focuses on the application of ChatGPT for academic instructional purposes in the field of Computer engineering and related majors. The capability of ChatGPT for instructional purposes is evaluated by providing responses to different questions pertaining to these engineering streams. This article explores different opportunities with use cases, ChatGPT can provide in augmenting the learning experience. It also provides scenarios of limitations and modifying the evaluation process to prevent the use of ChatGPT for unethical means of learning. © 2023, CC BY.

关键词： Data structures  

摘要： The application of Artificial intelligence for teaching and learning in the academic sphere is a trending subject of interest in the computing education. ChatGPT, as an AI-based tool, provides various advantages, such as heightened student involvement, cooperation, accessibility and availability. This paper addresses the prospects and obstacles associated with utilizing ChatGPT as a tool for learning and assessment in undergraduate Computer Science curriculum in particular to teaching and learning fundamental programming courses. Students having completed the course work for a Data Structures and Algorithms (a sophomore level course) participated in this study. Two groups of students were given programming challenges to solve within a short period of time. The control group (group A) had access to text books and notes of programming courses, however no Internet access was provided. Group B students were given access to ChatGPT and were encouraged to use it to help solve the programming challenges. The challenge was conducted in a computer lab environment using Programming Contest Control (PC2) environment which is widely used in ACM International Collegiate Programming Contest (ICPC). Each team of students address the problem by writing executable code that satisfies certain number of test cases. Student teams were scored based on their performance in terms of number of successful passed testcases. Results show that students using ChatGPT had an advantage in terms of earned scores, however there were inconsistencies and inaccuracies in the submitted code consequently affecting the overall performance. After a thorough analysis, the paper's findings indicate that incorporating AI in higher education brings about various opportunities and challenges. Nonetheless, universities can efficiently manage these apprehensions by adopting a proactive and ethical stance towards the implementation of such tools. Copyright © 2023, The Authors. All rights reserved.

摘要： As systems dazzle, researchers worry about lack of safeguards and regulation.

摘要： No abstract available.