Large Language Models (LLMs) demonstrate general knowledge, but they suffer when specifically needed knowledge is not present in their training set. Two approaches to ameliorating this, without retraining, are 1) prom...
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The energy difference between the 1s and 2p exciton states of bilayer GaN/AlN is measured and computed by comparing excitonic resonances in photoluminescence excitation spectra following single- versus two-photon abso...
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Software security analysts typically only have access to the executable program and cannot directly access the source code of the *** poses significant challenges to security *** it is crucial to identify vulnerabilit...
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Software security analysts typically only have access to the executable program and cannot directly access the source code of the *** poses significant challenges to security *** it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining ***,these tools suffer from some *** terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search ***,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information *** this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation *** leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion *** combination allows for the unified handling of binary programs across various architectures,compilers,and compilation ***,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)***,the graph embedding network is utilized to evaluate the similarity of program *** on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target *** solved content serves as the initial seed for targeted *** binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity *** approach facilitates
Automatic Modulation Recognition (AMR) is crucial for optimizing communication systems, facilitating effective spectrum management and robust signal processing. Traditional AMR techniques, leveraging Machine Learning ...
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This paper explores a novel server observability approach using eBPF (extended Berkeley Packet Filter) for detailed request-level performance metrics of data center latency-sensitive applications. Utilizing eBPF syste...
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Black-box optimization (BBO) can be used to optimize functions whose analytic form is unknown. A common approach to realising BBO is to learn a surrogate model which approximates the target black-box function which ca...
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Human action recognition plays a crucial role in intelligent monitoring systems, which are based on analyzing the possibility of anomalous events related to human behavior, such as theft, fights, and other incidents. ...
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This innovative practice full paper describes an indepth analysis of the pedagogical implications of incorporating generative artificial intelligence (genAI) tools, specifically Chat-GPT, into a data science course fo...
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ISBN:
(纸本)9798350351507
This innovative practice full paper describes an indepth analysis of the pedagogical implications of incorporating generative artificial intelligence (genAI) tools, specifically Chat-GPT, into a data science course for postgraduate masters computing students. This research is grounded in the implementation of ChatGPT in a data analysis course, aiming to evaluate its effectiveness in fostering students' analytical and decision-making capabilities. The study employs a qualitative methodology to assess the educational outcomes of integrating ChatGPT, focusing on its impact on student engagement, learning efficiency, and the development of critical thinking skills in the context of data science. Through a combination of interviews, and analysis of students' project outcomes, we gather insights into the challenges and opportunities presented using genAI in the data science course. A notable innovation of our approach is the introduction of a dual-report assessment method, which not only evaluates the students' project results but also their proficiency in prompt engineering - a crucial skill for effective interaction with genAI tools. Our findings suggest that while students demonstrate enhanced data analysis skills, they also face difficulties in accurately framing queries to yield useful results from genAI, highlighting an essential area for further curriculum development. Further-more, the work delves into the pedagogical strategies that can optimize the benefits of genAI tools in education. It emphasizes the importance of a structured framework that guides students in the ethical use of genAI, encourages critical reflection on AI-generated content, and fosters a deeper understanding of the underlying algorithms and their implications for data science. The implications of this research extend beyond the classroom, offering valuable insights for instructors, curriculum developers, and policymakers on integrating AI technologies into educational practices. By providing
We study the problem of learning feature repre-sentations from a pair of random variables, where we focus on the representations that are induced by their dependence. We provide sufficient and necessary conditions for...
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A multiresolution chain coding scheme for contours based on Freeman chain codes in four direction is proposed. It progressively refines the grid size and encodes by the proposed R11 chain codes. Encoding and decoding ...
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