In computerscience, cultural assumptions are embedded in programming languages and problem prompts. This paper investigates the impact of cultural assumptions on internationalcomputerscience students in the US. By ...
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ISBN:
(纸本)9798400706264
In computerscience, cultural assumptions are embedded in programming languages and problem prompts. This paper investigates the impact of cultural assumptions on internationalcomputerscience students in the US. By performing thematic analysis on semistructured interviews with 12 international graduate students at North Carolina State University, the authors found six main themes. Analyzing these themes provided insight into what barriers international students face and how they can be alleviated. By shedding light on this topic, the authors hope to inform computerscience educators and researchers on the importance of creating inclusive and culturally relevant learning environments that accommodate the needs of diverse students.
Emotion recognition plays a crucial role in computerscience, particularly in enhancing human-computer interactions. The process of emotion labeling remains time-consuming and costly, thereby impeding efficient datase...
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ISBN:
(纸本)9783031612800;9783031612817
Emotion recognition plays a crucial role in computerscience, particularly in enhancing human-computer interactions. The process of emotion labeling remains time-consuming and costly, thereby impeding efficient dataset creation. Recently, large language models (LLMs) have demonstrated adaptability across a variety of tasks without requiring task-specific training. This indicates the potential of LLMs to recognize emotions even with fewer emotion labels. Therefore, we assessed the performance of an LLM in emotion recognition using two established datasets: MELD and IEMOCAP. Our findings reveal that for emotion labels with few training samples, the performance of the LLM approaches or even exceeds that of SPCL, a leading model specializing in text-based emotion recognition. In addition, inspired by the Chain of Thought, we incorporated a multi-step prompting technique into the LLM to further enhance its discriminative capacity between emotion labels. The results underscore the potential of LLMs to reduce the time and costs of emotion data labeling.
Reports show that the number of phishing web sites is exponentially increasing and it is estimated that between 80% to 93 % of the data breaches are involving phishing attacks. With both probability of occurrence as w...
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In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vis...
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To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO)...
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The purpose of this study is to design and implement a method to assess the investment effect of Guangdong investment funds based on improved genetic algorithm and computer simulation. First, the key factors affecting...
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ISBN:
(纸本)9798350386783;9798350386776
The purpose of this study is to design and implement a method to assess the investment effect of Guangdong investment funds based on improved genetic algorithm and computer simulation. First, the key factors affecting the investment effect of Guangdong investment funds are identified through literature review and market data analysis. Second, an improved genetic algorithm is proposed to optimize the portfolio allocation of GD investment funds, taking into account multiple indicators such as risk, return and liquidity. Then, computer simulation technology is utilized to conduct simulation experiments on different investment strategies of GD investment funds and evaluate their performance in different market environments. Finally, the validity and superiority of the proposed methodology of GDIF is verified through a comparative analysis with the traditional methodology. The experimental results show that the method has significant advantages in improving the investment efficiency and reducing the risk of GDIF, which can provide investors with more scientific decision-making support.
Effective and efficient cyber incident handling is crucial for maintaining the security of information systems and organizational data. This research aims to develop a priority-based cyber incident handling method by ...
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Based on the current demand for real-time detection and reading of the size and spatial distribution of the extracted beam by negative ion sources, this paper proposes a multi-channel weak current quasi synchronous me...
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Point cloud completion is crucial in point cloud processing, as it can repair and refine incomplete 3D data, ensuring more accurate models. However, current point cloud completion methods commonly face a challenge: th...
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In present days, multi Label Text Classification (MLTC) has become an important research area in Natural Language Processing (NLP). Existing models of MLTC have been facing domain specificity and fine-tuning challenge...
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