The Internet of Things (IoT) bridges the physical and digital worlds by utilizing sensors, actuators, communication technologies, computing power, and data analytics to enable precise monitoring and control of the sur...
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Graph neural networks (GNNs) are powerful models for learning graph-structured data. In most graph neural networks, only the first-order neighbouring nodes, also known as positive samples, are utilized for message pas...
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AI ethics principles and guidelines are typically high-level and do not provide concrete guidance on how to develop responsible AI systems. To address this shortcoming, we perform an empirical study involving intervie...
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ISBN:
(纸本)9781665495905
AI ethics principles and guidelines are typically high-level and do not provide concrete guidance on how to develop responsible AI systems. To address this shortcoming, we perform an empirical study involving interviews with 21 scientists and engineers to understand the practitioners' views on AI ethics principles and their implementation. Our major findings are: (1) the current practice is often a done-once-and-forget type of ethical risk assessment at a particular development step, which is not sufficient for highly uncertain and continual learning AI systems;(2) ethical requirements are either omitted or mostly stated as high-level objectives, and not specified explicitly in verifiable way as system outputs or outcomes;(3) although ethical requirements have the characteristics of cross-cutting quality and non-functional requirements amenable to architecture and design analysis, system-level architecture and design are under-explored;(4) there is a strong desire for continuously monitoring and validating AI systems post deployment for ethical requirements but current operation practices provide limited guidance. To address these findings, we suggest a preliminary list of patterns to provide operationalised guidance for developing responsible AI systems.
—This paper focuses on the issue of information and knowledge management for master students in Chinese universities. According to some foreign learners’ statements, the information from official channels is fragmen...
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Online Health Community (OHC) is a platform that provides medical consultation and health knowledge-sharing services. For patients, OHC can recommend high-quality doctors according to their recommendation popularity (...
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Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models have shown the ability of in-context ...
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ISBN:
(纸本)9798350329964
Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models have shown the ability of in-context learning (ICL). ICL employs task instructions and a few examples as demonstrations, and then inputs the demonstrations to the language models for making predictions. This new learning paradigm is training-free and has shown impressive performance in various natural language processing and code intelligence tasks. However, the performance of ICL heavily relies on the quality of demonstrations, e.g., the selected examples. It is important to systematically investigate how to construct a good demonstration for code-related tasks. In this paper, we empirically explore the impact of three key factors on the performance of ICL in code intelligence tasks: the selection, order, and number of demonstration examples. We conduct extensive experiments on three code intelligence tasks including code summarization, bug fixing, and program synthesis. Our experimental results demonstrate that all the above three factors dramatically impact the performance of ICL in code intelligence tasks. Additionally, we summarize our findings and provide takeaway suggestions on how to construct effective demonstrations, taking into account these three perspectives. We also show that a carefully-designed demonstration based on our findings can lead to substantial improvements over widely-used demonstration construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%, 175.96%, and 50.81% on code summarization, bug fixing, and program synthesis, respectively.
A large number of bug reports are created during the evolution of a software system. Locating the source code files that need to be changed in order to fix these bugs is a challenging task. Information retrieval-based...
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ISBN:
(纸本)9781665457019
A large number of bug reports are created during the evolution of a software system. Locating the source code files that need to be changed in order to fix these bugs is a challenging task. Information retrieval-based bug localization techniques do so by correlating bug reports with historical information about the source code (e.g., previously resolved bug reports, commit logs). These techniques have shown to be efficient and easy to use. However, one flaw that is nearly omnipresent in all these techniques is that they ignore code refactorings. Code refactorings are common during software system evolution, but from the perspective of typical version control systems, they break the code history. For example, a class when renamed then appears as two separate classes with separate histories. Obviously, this is a problem that affects any technique that leverages code history. This paper proposes a refactoring-aware traceability model to keep track of the code evolution history. With this model, we reconstruct the code history by analyzing the impact of code refactorings to correctly stitch together what would otherwise be a fragmented history. To demonstrate that a refactoring aware history is indeed beneficial, we investigated three widely adopted bug localization techniques that make use of code history, which are important components in existing approaches. Our evaluation on 11 open source projects shows that taking code refactorings into account significantly improves the results of these bug localization techniques without significant changes to the techniques themselves. The more refactorings are used in a project, the stronger the benefit we observed. Based on our findings, we believe that much of the state of the art leveraging code history should benefit from our work.
News recommendations heavily rely on Natural Language Processing (NLP) methods to analyze, understand, and categorize content, enabling personalized suggestions based on user interests and reading behaviors. Large Lan...
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ISBN:
(纸本)9798400704369
News recommendations heavily rely on Natural Language Processing (NLP) methods to analyze, understand, and categorize content, enabling personalized suggestions based on user interests and reading behaviors. Large Language Models (LLMs) like GPT-4 have shown promising performance in understanding natural language. However, the extent of their applicability to news recommendation systems remains to be validated. This paper introduces RecPrompt(1), the first self-tuning prompting framework for news recommendation, leveraging the capabilities of LLMs to perform complex news recommendation tasks. This framework incorporates a news recommender and a prompt optimizer that applies an iterative bootstrapping process to enhance recommendations through automatic prompt engineering. Extensive experimental results with 400 users show that RecPrompt can achieve an improvement of 3.36% in AUC, 10.49% in MRR, 9.64% in nDCG@5, and 6.20% in nDCG@10 compared to deep neural models. Additionally, we introduce Topic-Score, a novel metric to assess explainability by evaluating LLM's ability to summarize topics of interest for users. The results show LLM's effectiveness in accurately identifying topics of interest and delivering comprehensive topic-based explanations.
knowledge distillation requires pre-trained teachers, while self-knowledge distillation can perform knowledge distillation without pre-trained teachers. Therefore, this method is widely used in medical image classific...
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This paper describes a study on the design and implementation of a smart crop harvesting system that applies artificial intelligence (AI) technology to address some of the major challenges facing modern agriculture, p...
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