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ISBN: (纸本)9780472077182;9780472904808

摘要： Starting in 1966, African American activist Stokely Carmichael and other political leaders adopted the phrase "Black Power!" The slogan captured a mil...

关键词： 地方院校   计算机类专业课程   课程思政   教学方法  

摘要： 围绕地方院校计算机类专业课程中课程思政资源匮乏及创新不足的问题，以计算机类课程思政的根本目标为总体导向，从拟定人才培养方案为顶层设计，在实践教学详细设计中通过对思政元素的深入挖掘，从而对课程思政资源进行整合，以四维实践模式探索进行设计教育教学这一主线，最后由线及面完成整体设计，达到课程目标重构，从而形成一个思政体。经过教学案例的数量积累和思政元素的不断沉淀，为计算机类专业课程教学思政提供一些思考和探索。

关键词： Remote sensing   Superresolution   Image enhancement   Spatial resolution   Image quality   Image color analysis   Computer science   Accuracy   Colored noise   Classification algorithms   Image classification   image enhancement   image processing   image recognition   remote sensing  

摘要： Oceans and seas cover more than 70% of the Earth's surface. If compared with the land mass there are a lot of unexplored locations, a wealth of natural resources, and diverse ocean creatures that are inaccessible to us humans. Underwater rovers and vehicles play a vital role in discovering these resources, yet limited visibility in deep waters and technological constraints impede underwater exploration. To address these issues, advanced image super-resolution and enhancement techniques are crucial for reliable resource identification, species recognition, and underwater ecosystem study. This will ultimately bridge the current gap in environmental monitoring by facilitating resource tracking and underwater waste assessment. This article proposes a novel multistage fusion algorithm for underwater image super-resolution, designed to enhance the quality and spatial resolution of low-resolution underwater images toward a more accurate object characterization. The effectiveness of the proposed super-resolution technique is demonstrated using multiple performance metrics including accuracy, f1-score, recall, and precision. By enhancing the spatial resolution of underwater images, our approach meets the increasing demand for detailed and accurate information in underwater earth observation applications.

关键词： Prototypes   Semantics   Legged locomotion   Electronic mail   Training   Predictive models   Heavily-tailed distribution   Adaptation models   Context modeling   Computer science   Scene graph generation   debasing   concept learning   memory-based learning  

摘要： Existing Unbiased Scene Graph Generation (USGG) methods only focus on addressing the predicate-level imbalance that high-frequency classes dominate predictions of rare ones, while overlooking the concept-level imbalance. Actually, even if predicates themselves are balanced, there is still a significant concept-imbalance within them due to the long-tailed distribution of contexts (i.e., subject-object combinations). This concept-level imbalance poses a more pervasive and challenging issue compared to the predicate-level imbalance since subject-object pairs are inherently complex in combinations. To address the issue, we propose Multi-Concept Learning (MCL), a novel concept-level balanced learning framework orthogonal to existing SGG methods. MCL first quantifies the concept-level imbalance across predicates in terms of different amounts of concepts, representing as multiple concept-prototypes within the same class. Then, to achieve balanced learning across different concepts (i.e., concept-prototypes), we introduce the Concept-based Balanced Memory (CBM), which guides SGG models in generating balanced representations for concept-prototypes. Furthermore, the Concept Regularization (CR) technique is proposed to effectively help models in aligning relation features to their corresponding concept-prototypes, thereby generating concept-level compact and predicate-level distinctive representations for robust relation recognition. Finally, we introduce a novel metric, mean Context Recall (mCR@K), as a complement to mean Recall (mR@K), to evaluate the model's performance across concepts (determined by contexts) within the same predicate. Extensive experiments demonstrate the remarkable efficacy of our model-agnostic strategy in enhancing the performance of benchmark models on both VG-SGG and OI-SGG datasets, leading to new state-of-the-art achievements in two key aspects: predicate-level unbiased relation recognition and concept-level compositional generability. Code is avai

关键词： Anomaly detection   Feature extraction   Protocols   Filling   Vectors   Image edge detection   Graph neural networks   Decoding   Computer science   Deep learning   attributed network   graph convolutional network   reconstruction   mask-filling   neighborhood association  

摘要： As a widely adopted protocol for anomaly detection in attributed networks, reconstruction error prioritizes comprehensive feature extraction to detect anomalies over interrogating the differential representation between normal and abnormal nodes. Intuitively, in attributed networks, normal nodes and their neighbors often exhibit similarities, whereas abnormal nodes demonstrate behaviors distinct from their neighbors. Hence, normal nodes can be accurately represented through their neighbors and effectively reconstructed. As opposed to normal nodes, abnormal nodes represented by their neighbors may be erroneously reconstructed as normal, resulting in increased reconstruction error. Leveraging from this observation, we propose a novel anomaly detection protocol called Neighbor-aware Mask-Filling Anomaly Detection (NMFAD) for attributed networks, aiming to maximize the variability between original and reconstructed features of abnormal nodes filled with information from their neighbors. Specifically, we utilize random-mask on nodes and integrate them into the backbone Graph Neural Networks (GNNs) to map nodes into a latent space. Subsequently, we fill the masked nodes with embeddings from their neighbors and smooth the abnormal nodes closer to the distribution of normal nodes. This optimization improves the likelihood of the decoder to reconstructing abnormal nodes as normal, thereby maximizing the reconstruction error of abnormal nodes. Experimental results demonstrate that, compared to the existing models, NMFAD exhibits superior *** attributed networks.

关键词： extended reality technologies (XRs)   XR camp curriculum design   cognitive development   learning outcomes  

摘要： This study focused on an XR camp designed to integrate multiple subjects into one course, providing students with an immersive, creative, and engaging interdisciplinary learning experience. This study explored the distribution of cognitive development stages among computer science students (CSSs) and engineering students (ESs). Additionally, we compared differences in learning styles, levels of participation and contributions, and learning outcomes between the two groups and verified whether the degree of participation and contributions could predict learning outcomes. The subjects were 54 students (39 = CSSs, 15 = ESs) who participated in the XR camp course from 2020-2021. The students completed the active experiment scale in the 1st week;rated team members using a peer assessment rubric and projects using a work rubric in the 6th, 11th, and 16th weeks;completed the learning ability scales;and participated in interviews in the 18th week. Following their participation in the XR camp, 87% of the students developed high-level cognitive abilities in the XR camp. For the CSSs, course participation positively predicted cognitive development stages, work performance, and collaborative problem-solving abilities, with peer assessments positively influencing work performance. The ESs demonstrated greater interdisciplinary competence, and course participation also positively predicted work performance. These students could also apply XR knowledge and technology to other situations, confirming that the XR camp is conducive to learning for students from diverse backgrounds.

关键词： Blockchains   Consensus protocol   Security   Safety   Reliability   Bitcoin   Scalability   Formal verification   Model checking   Computer science   Ethereum 2.0   beacon chain   rewards and penalties   model checking   SPIN  

摘要： Ethereum 2.0 stands out as a progressive decentralized blockchain platform, drawing attention for its security, scalability, and flexibility. Central to Ethereum 2.0 is the Beacon Chain, serving as the cornerstone managing validator rewards, penalties, attestations, and slashing mechanisms. Rewards and Penalties Mechanism (RPM) is of particular importance within the Beacon Chain as it includes validator balances based on their attestation behavior. Despite the critical role of RPM in maintaining the reliability and security of the Beacon Chain, the absence of formal verification work employing model checking is notable. Therefore, this research endeavors to fill this gap by employing a formal verification technique to assess the RPM's behavior concerning Friendly Finality Gadget (FFG) attestations. Utilizing Process Meta Language (PROMELA), a formal model of the RPM is specified, encompassing safety and liveness properties crucial for its robust functioning. The properties, including invalid attestation, integrity, fairness, availability, failure to attest, and inactivity imposition, are formalized through Linear Temporal Logic (LTL). Subsequently, the formal model alongside the specified properties is subjected to verification using the SPIN model checker. The properties are analyzed with respect to verification time, states visited, and memory usage. The outcome of this research contributes to a rigorous analysis of the RPM's behavior. This work not only enhances an understanding of Beacon Chain's operational dynamics but also underscores the importance of formal verification in ensuring the reliability and security of blockchain protocols.

关键词： Prototypes   Semantics   Object detection   Noise   Visualization   Uncertainty   Fuses   Frequency modulation   Computer science   Training   Multimodal fusion   underwater perception   expert learning   uncertainty guidance  

摘要： Existing underwater salient object detection (USOD) methods design fusion strategies to integrate multimodal information, but lack exploration of modal characteristics. To address this, we separately leverage the RGB and depth branches to learn disentangled representations, formulating the heterogeneous experts and hierarchical perception network (HEHP). Specifically, to reduce modal discrepancies, we propose the hierarchical prototype guided interaction (HPI), which achieves fine-grained alignment guided by the semantic prototypes, and then refines with complementary modalities. We further design the mixture of frequency experts (MoFE), where experts focus on modeling high- and low-frequency respectively, collaborating to explicitly obtain hierarchical representations. To efficiently integrate diverse spatial and frequency information, we formulate the four-way fusion experts (FFE), which dynamically selects optimal experts for fusion while being sensitive to scale and orientation. Since depth maps with poor quality inevitably introduce noises, we design the uncertainty injection (UI) to explore high uncertainty regions by establishing pixel-level probability distributions. We further formulate the holistic prototype contrastive (HPC) loss based on semantics and patches to learn compact and general representations across modalities and images. Finally, we employ varying supervision based on branch distinctions to implicitly construct difference modeling. Extensive experiments on two USOD datasets and four relevant underwater scene benchmarks validate the effect of the proposed method, surpassing state-of-the-art binary detection models. Impressive results on seven natural scene benchmarks further demonstrate the scalability.

关键词： Perturbation methods   Rate-distortion   Distortion measurement   Adversarial machine learning   Robustness   Noise   Iterative methods   Dogs   Contrastive learning   Computer science   Adversarial learning   domain generalization   domain invariance learning   domain shift  

摘要： Deep learning methods often struggle with the domain shift problem, leading to poor generalization on out-of-domain (OOD) data. To address the problem, domain generalization (DG) has been proposed to leverage the source domains to train a model that can generalize to OOD data. Existing domain generalization methods primarily focus on learning domain invariance, but they fail to ensure proximity among samples within the same category when domains are aligned for domain-invariant learning. Consequently, their generalization performance remains suboptimal. In this paper, we propose a novel approach to address this issue by iteratively approximating the category domain-invariant distribution from all domains. Our method involves an iterative loop where we initially estimate the domain-invariant distribution for each category by averaging the statistical characteristics across all domains. Then the adversarial perturbation alignment is adopted to keep each sample close to its corresponding category domain-invariant distribution. With the iterative loop, the deep network is optimized for robust domain invariance learning. Extensive experiments demonstrate that our proposed method consistently outperforms state-of-the-art approaches across various scenarios.

关键词： cancer informatics   genomics   bioinformatics   computational biology   computer science   statistics  

摘要： Over a 20 year period, the journal Cancer Informatics has played an important role defining and forging a bridge between bioinformations and translational cancer research. The main focus of the journal has been to advance the prevention, diagnosis, and treatment of cancer. This involves the specialized intersection of genomics, molecular biology, data science, computer programing, statistics, communication theory, and the clinical sciences to answer important questions in the field of cancer research.