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关键词： Sun   Tensors   Vectors   Optimization   Computer science   Complexity theory   Bipartite graph   Artificial intelligence   Feature extraction   Surges   Dynamic anchor learning   incomplete multi-view clustering   bipartite graph learning   pseudo-label supervise learning   low-rank tensor  

摘要： Compared to static anchor selection, existing dynamic anchor learning could automatically learn more flexible anchors to improve the performance of large-scale multi-view clustering. Despite improving the flexibility of anchors, these methods do not pay sufficient attention to the alignment and fairness of learned anchors. Specifically, within each cluster, the positions and quantities of cross-view anchors may not align, or even anchor absence in some clusters, leading to severe anchor misalignment and imbalance issues. These issues result in inaccurate graph fusion and a reduction in clustering performance. Besides, in practical applications, missing information caused by sensor malfunctions or data losses could further exacerbate anchor misalignment and imbalance. To overcome such challenges, a novel Incomplete Multi-view Clustering with Paired and Balanced Dynamic Anchor Learning (PBDAL) is proposed to ensure the alignment and fairness of anchors. Unlike existing unsupervised anchor learning, we first design a paired and balanced dynamic anchor learning scheme to supervise dynamic anchors to be aligned and fair in each cluster. Meanwhile, we develop an enhanced bipartite graph tensor learning to refine paired and balanced anchors. Our superiority, effectiveness, and efficiency are all validated by performing extensive experiments on multiple public datasets.

关键词： Education   Computer science   Computer science education   Generative adversarial networks   Computational modeling   Indexes   Ethics   Chatbots   Training   Testing   Automated MCQ generation   conditional generative adversarial networks (cGANs)   computer science education   dataset curation   educational assessment  

摘要： This work presents a novel perspective towards generating automated multiple-choice questions (MCQs)-a task fundamentally different due to the highly dynamic nature of computer science education, which spans several sub-domains. Taking advantage of Conditional Generative Adversarial Networks (cGANs), our model provides a versatile approach to addressing the need for diversity and context in relevant MCQ generation across proficiency levels, topic areas. Resulting MCQs inspire implementations within a variety of educational environments - from classrooms, to online courses, and finally exams - equipping teachers with an instrument that could be easily adapted based on the specific needs o students. The model is trained on a carefully constructed dataset that includes material from more than 20 subareas in computer science, consisting of materials such as textbooks, online encyclopedias and Q&A websites. Through rigorous evaluation using comprehensive performance metrics, including Question Relevance Score (QRS), Diversity Index (DI), and Difficulty Alignment Accuracy (DAA), we demonstrate the efficacy and robustness of our framework in generating high-quality MCQs. Moreover, we address ethical considerations inherent in AI-driven educational assessment, ensuring fairness, transparency, and accountability in the MCQ generation process. The cGAN architecture facilitates the generation of contextually relevant MCQs across various proficiency levels and subject domains, enhancing the educational assessment process. The comprehensive dataset developed for this study encompasses diverse computer science topics curated from authoritative textbooks, online resources, question banks, and instructor-generated content. Additionally, a user-friendly QT application has been developed, enabling seamless integration of the cGAN model into educational environments. Through rigorous evaluation and ethical considerations, this framework demonstrates its efficacy, ensuring fairness, tr

关键词： Measurement   Bibliometrics   Blogs   Productivity   Heating systems   Diffusion models   Data models   Computer science   Computational modeling   Volume measurement   Scientometrics   institution research impact   influence diffusion   citation networks   graph theory  

摘要： Assessing an institution's research output has traditionally relied mostly on the opinion of peers, but as the field of scientometrics progressed dramatically in recent years, the evolution of quantitative indicators gained popularity and became essential. The present approaches for evaluating an institution's research output rely on citation data, that has been quantitatively analyzed and are predominantly data-centric models. It is observed that research productivity and impact are taken into account by many ranking systems, and the measurement of research impact is mostly dependent on citation-counting techniques and the volume of research produced. The recent ranking systems evaluate the reputation of academic institutions by employing network-based algorithms, like Page Rank which examine the citation network at the institution level. An Article's outreach and causal impact are not completely taken into consideration by the aforementioned approaches which leads to incomplete quantification of metrics. In this study, we propose a framework that uses a data-agnostic influence diffusion model to measure the academic impact of the institutions in the citation network. The diffusion model is inspired by the phenomenon of heat diffusion. The suggested framework investigates the citation trajectory for each article published by the university in the research area, analyses its influence diffusion in citation networks, and provides methods, metrics, and processes that others could apply to similar problems or datasets. By leveraging transfer entropy-based techniques, our research goes beyond citation mapping to investigate the causal link between academic influence and the reputation of publication venues employed by universities.

关键词： Feature extraction   Transformers   Convolution   Computational modeling   Data mining   Sun   Mixers   Computer science   Support vector machines   Redundancy   Hyperspectral image classification   hierarchical Transformer   multi-feature   cluster attention  

摘要： Vision Transformer (ViT), known for capturing non-local features, is an effective tool for hyperspectral image classification (HSIC). However, ViT's multi-head self-attention (MHSA) mechanism often struggles to balance local details and long-range relationships for complex high-dimensional data, leading to a loss in spectral-spatial information representation. To address this issue, we propose a deformable convolution-enhanced hierarchical Transformer with spectral-spatial cluster attention (SClusterFormer) for HSIC. The model incorporates a unique cluster attention mechanism that utilizes spectral angle similarity and Euclidean distance metrics to enhance the representation of fine-grained homogenous local details and improve discrimination of non-local structures in 3D HSI and 2D morphological data, respectively. Additionally, a dual-branch multiscale deformable convolution framework augmented with frequency-based spectral attention is designed to capture both the discrepancy patterns in high-frequency and overall trend of the spectral profile in low-frequency. Finally, we utilize a cross-feature pixel-level fusion module for collaborative cross-learning and fusion of the results from the dual-branch framework. Comprehensive experiments conducted on multiple HSIC datasets validate the superiority of our proposed SClusterFormer model, which outperforms existing methods. The source code of SClusterFormer is available at https://***/Fang666666/HSIC_SClusterFormer.

关键词： Software   Codes   Collaboration   Remote working   Project management   Software engineering   Scrum (Software development)   Organizations   Microservice architectures   Computer science   Agile software development   platform engineering   internal development platform   open-source   scrum  

摘要： Platform Engineering (PE) is a growing area of Software Engineering, with many facets, including the elusive concept of Internal Development Platforms (IDPs). They integrate various technologies and tools to support the realization of software projects, especially their managing and developing roles, fostering Agile collaboration and enhancing team productivity. In particular for the new trends of Agile remote working and for Agile applied to the Digital Transformation in the Public Sector. While proprietary IDPs abound, academic research that could naturally lead to open-source IDPs remain scarce. In particular for Scrum, a fundamental Agile methodology in the strategic area of Digital Transformation in the Public Sector, where IDP self-hosting is also appropriate. This paper addresses this gap by proposing the design and realization of the first, to the best of our knowledge, IDP supporting Scrum, being open-source and with self-hosting capabilities. The end-product of our research is the Compositional Agile System (CAS), an extendable microservice architecture-based IDP. Its design, as well as CAS, makes full use of hands-on previous successful experiences of IDP development by members of this team, specifically non-trivial tasks in Italian Public Administration and to foster remote working in University Software Engineering classes, a practice started during the pandemic but now a standard. We offer several architectural and example scenarios where CAS can be of use, concentrating on the Public Administration. Particularly relevant is the use of CAS in analyzing a pillar of Italian Digital Administration available to its citizens, i.e., AppIo. The software is available as an open resource to the research community.

关键词： Computational models   Computer science  

摘要： Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation methods used. In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis. Our review examines the existing literature across various dimensions, including disease types and associated clinical specialties, clinical data, LLM techniques, and evaluation methods. Additionally, we offer recommendations for applying and evaluating LLMs for diagnostic tasks. Furthermore, we assess the limitations of current research and discuss future directions. To our knowledge, this is the first comprehensive review for LLM-based disease diagnosis.

关键词： Phishing   Electronic mail   Incremental learning   Neural networks   Companies   Data models   Computational modeling   Predictive models   Frequency modulation   Computer science   Ensemble classifiers   incremental learning   on-line incremental learning   online incremental learning   online learning   neural networks   aggregation functions   uninorms   phishing link detection  

摘要： This contribution proposes a classifier designed to reduce the number of false positive detections. It is a self-tuning model, tested in the context of phishing link detection. In this prediction model diverse types of aggregation functions and time-series data periods are used. Aggregation functions are employed to integrate the prediction values of classification models applied in the email phishing problem. Dividing a dataset into subsets allows for incremental learning strategies. This makes it possible to gradually improve the model by using previously acquired knowledge when training on new data. The aim of the contribution is to discuss the problem of obtaining minimal value of FPR while simultaneously getting maximal value of TPR. We applied the proposed ensemble model and neural networks models which were adjusted to the incremental learning strategy (and as base models were applied typical examples of incremental learning models). The study analyzes the dataset provided by FreshMail company. The reason to consider this problem arose with the real-life problem of the Freshamil Company and the data provided by this Company. This dataset uniquely fulfills the criteria essential for our experiments. Unlike other phishing datasets, this data provides dates which is important for the incremental learning approach. The proposed approach of ensemble learning models, based on aggregation functions, is compared to the well-known neural network models which may be treated as state of the art models in recognizing phishing attacks. The main advantage of the proposed algorithm is achieving high numbers of true positives while simultaneously achieving relatively small number of false positives. According to the statistical tests, for some of the desired TPR levels, the proposed model obtained significantly better by a few percentage points results than neural network models. It mitigates the cost arising from the manual analysis of these cases by domain experts.

关键词： Oral communication   Visualization   Electronic mail   Sentiment analysis   Computer science   Annotations   Social networking (online)   Pipelines   Image retrieval   Emotion recognition   Conversation   intention   multi-modal learning   sticker retrieval  

摘要： Using stickers in online chatting is very prevalent on social media platforms, where the stickers used in the conversation can express someone's intention/emotion/attitude in a vivid, tactful, and intuitive way. Existing sticker retrieval research typically retrieves stickers based on context and the current utterance delivered by the user. That is, the stickers serve as a supplement to the current utterance. However, in the real-world scenario, using stickers to express what we want to say rather than as a supplement to our words only is also important. Therefore, in this paper, we create a new dataset for sticker retrieval in conversation, called StickerInt, where stickers are used to reply to previous conversations or supplement our words.(1) Based on the created dataset, we present a simple yet effective framework for sticker retrieval in conversation based on the learning of intention and the cross-modal relationships between conversation context and stickers, coined as Int-RA. Specifically, we first devise a knowledge-enhanced intention predictor to introduce the intention information into the conversation representations. Subsequently, a relation-aware sticker selector is devised to retrieve the response sticker via cross-modal relationships. Extensive experiments on two datasets show that the proposed model achieves state-of-the-art performance and generalization capability in sticker retrieval.

关键词： Lattices   Vectors   Data mining   Polynomials   Cloud computing   Servers   Security   Computer science   Training   Software   Proof of retrievability   lattice   implementation  

摘要： Proof of Retrievability (POR) is an important cryptographic primitive that has attracted considerable attention in the research community for its ability to enable users to audit the integrity of outsourced files on cloud servers without retrieving them. A POR scheme with public verifiability further enhances usability by allowing users to delegate the auditing task to a third party, making it highly desirable for a wide range of applications. However, most existing publicly verifiable POR schemes derive their security from the computational hardness of discrete logarithm or factoring, making them vulnerable to quantum attacks. Although it is possible to construct quantum-resistant POR schemes with public verifiability upon hash trees or general lattices, the resulting schemes often exhibit performance limitations when compared to existing constructions, thereby limiting their deployment in real-world applications. In this work, we address this gap by constructing a publicly verifiable POR scheme on structured lattices. We show that our scheme is provably secure in the random oracle model under the Ring-LWE and Ring-SIS assumptions. We provide an implementation of our scheme and the experimental results show that its performance is comparable to certain well-known constructions based on traditional assumptions.

关键词： 黑神话:悟空   中华优秀传统文化   传承传播   创新发展  

摘要： 《黑神话：悟空》一经亮相便在全球范围内引发高度关注，成功“出圈”。分析其“出圈”现象主要成因，应是对中华优秀传统文化的传承与创新性发展。《黑神话：悟空》的火爆“出圈”，让我们看到在传统文化经由现代游戏产业的传承与传播带来广阔发展前景的同时，也带来富有价值的启示：一是实现了跨媒介叙事与经典文本的现代重生；二是实现了视觉呈现与传统美学的新媒体重构；三是推动了思想表达与传统观念的功能性再现；四是推动了中华优秀传统文化的传承创新与跨文化传播。