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关键词： biotechnology   computer science   robotics  

摘要： Deciphering how different behaviors and ultrasonic vocalizations (USVs) of rats interact can yield insights into the neural basis of social interaction. However, the behavior -vocalization interplay of rats remains elusive because of the challenges of relating the two communication media in complex social contexts. Here, we propose a machine learning -based analysis system (ARBUR) that can cluster without bias both non -step (continuous) and step USVs, hierarchically detect eight types of behavior of two freely behaving rats with high accuracy, and locate the vocal rat in 3-D space. ARBUR reveals that rats communicate via distinct USVs during different behaviors. Moreover, we show that ARBUR can indicate findings that are long neglected by former manual analysis, especially regarding the non -continuous USVs during easyto -confuse social behaviors. This work could help mechanistically understand the behavior -vocalization interplay of rats and highlights the potential of machine learning algorithms in automatic animal behavioral and acoustic analysis.

关键词： Biochemistry   Computer science   Computational mathematics  

摘要： The prediction of drug -target interactions (DTIs) is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. Computational approaches to predicting DTIs can provide important insights into drug mechanisms of action. However, current methods for predicting DTIs based on the structural information of the knowledge graph may suffer from the sparseness and incompleteness of the knowledge graph and neglect the latent type information of the knowledge graph. In this paper, we propose TTModel, a knowledge graph embedding model for DTI prediction. By exploiting biomedical text and type information, TTModel can learn latent text semantics and type information to improve the performance of representation learning. Comprehensive experiments on two public datasets demonstrate that our model outperforms the state-of-the-art methods significantly on the task of DTI prediction.

关键词： Computer science   artificial intelligence  

ISBN: (数字)9781394234271 ISBN: (纸本)9781394234240

摘要： This book provides state-of-the-art approaches to deep learning in areas of detection and prediction, as well as future framework development, building service systems and analytical aspects in which artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. Deep learning algorithms and techniques are found to be useful in various areas, such as

关键词： Geographical information science   Computer science   Machine learning  

摘要： In this study, we addressed two primary challenges: firstly, the issue of domain shift, which pertains changes in data characteristics or context that can impact model performance, and secondly, the discrepancy between semantic similarity and geographical distance. We employed topic modeling in conjunction with the BERT architecture. Our model was crafted to enhance similarity computations applied to geospatial text, aiming to integrate both semantic similarity and geographical proximity. We tested the model on two datasets, Persian Wikipedia articles and rental property advertisements. The findings demonstrate that the model effectively improved the correlation between semantic similarity and geographical distance. Furthermore, evaluation by real -world users within a recommender system context revealed notable increase in user satisfaction by approximately 22% for Wikipedia articles and 56% for advertisements.

关键词： Computer science   Machine learning  

摘要： The number of errors produced by an LLM can be reduced by grouping its outputs into semantically similar clusters. Remarkably, this task can be performed by a second LLM, and the method’s efficacy can be evaluated by a third.

关键词： Visual languages  

摘要： The recent integration of visual capabilities into Large Language Models (LLMs) has the potential to play a pivotal role in science and technology education, where visual elements such as diagrams, charts, and tables are commonly used to improve the learning experience. This study investigates the performance of ChatGPT-4 Vision, OpenAI’s most advanced visual model at the time the study was conducted, on the Bachelor in Computer Science section of Brazil’s 2021 National Undergraduate Exam (ENADE). By presenting the model with the exam’s open and multiple-choice questions in their original image format and allowing for reassessment in response to differing answer keys, we were able to evaluate the model’s reasoning and self-reflecting capabilities in a large-scale academic assessment involving textual and visual content. ChatGPT-4 Vision significantly outperformed the average exam participant, positioning itself within the top 10 best score percentile. While it excelled in questions that incorporated visual elements, it also encountered challenges with question interpretation, logical reasoning, and visual acuity. A positive correlation between the model’s performance in multiple-choice questions and the performance distribution of the human participants suggests multimodal LLMs can provide a useful tool for question testing and refinement. However, the involvement of an independent expert panel to review cases of disagreement between the model and the answer key revealed some poorly constructed questions containing vague or ambiguous statements, calling attention to the critical need for improved question design in future exams. Our findings suggest that while ChatGPT-4 Vision shows promise in multimodal academic evaluations, human oversight remains crucial for verifying the model’s accuracy and ensuring the fairness of high-stakes educational exams. The paper’s research materials are publicly available at https://***/nabormendonca/gpt-4v-enade-cs-2021. © 202

关键词： Codes (symbols)  

摘要： Large language models (LLMs) have demonstrated significant potential in advancing various fields of research and society. However, the current community of LLMs overly focuses on benchmarks for analyzing specific foundational skills (e.g. mathematics and code generation), neglecting an all-round evaluation of the computer science field. To bridge this gap, we introduce CS-Bench, the first multilingual (English, Chinese, French, German) benchmark dedicated to evaluating the performance of LLMs in computer science. CS-Bench comprises approximately 10K meticulously curated test samples, covering 26 subfields across 4 key areas of computer science, encompassing various task forms and divisions of knowledge and reasoning. Utilizing CS-Bench, we conduct a comprehensive evaluation of over 30 mainstream LLMs, revealing the relationship between CS performance and model scales. We also quantitatively analyze the reasons for failures in existing LLMs and highlight directions for improvements, including knowledge supplementation and CS-specific reasoning. Further cross-capability experiments show a high correlation between LLMs’ capabilities in computer science and their abilities in mathematics and coding. Moreover, expert LLMs specialized in mathematics and coding also demonstrate strong performances in several CS subfields. Looking ahead, we envision CS-Bench serving as a cornerstone for LLM applications in the CS field and paving new avenues in assessing LLMs’ diverse reasoning capabilities. Our project homepage is available at https://***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Media discourse   Online journalism   Artificial intelligence (AI)   Media observatories  

摘要： PurposeThis article explores the synergy between journalism studies and computer science in the context of observing online news. By establishing web applications of online media observatories as research tools, researchers can employ various analytical approaches to gain valuable insights into online news discourse and ***/methodology/approachDrawing eight months of data (01.08.2022-30.04.2023) from the Labservatory's web application, i.e. over 250,000 news items, the article demonstrates how some of this web application's main functionalities may be useful in implementing (1) news flow analysis, (2) news topic distribution analysis and (3) media discourse *** capabilities provided by this web application, (1) to simultaneously analyse the daily news production of ten media outlets with varying features, (2) to rapidly collect a large volume of news items, (3) to identify the news categories as classified by the media themselves, (4) to present the results of the search in relevance order and (5) to automatically generate a search report, highlight the significance of this interdisciplinary collaboration for implementing comprehensive analyses of online ***/valueThe article concludes by emphasising the importance of continuing this joint effort, as it opens new avenues for further research and provides a deeper grasp of the intricate relationship between journalism, technology and society in the digital era. The Labservatory also contributes to society since it may be used by the broader public for immediate access to more pluralistic information and thus for promoting both news media literacy and news media accountability.

关键词： Machine Learning   Big Data   Computer Science   Internet Of Things   Digital Signal Processing   Postdoctoral Fellow   Forward Error Correction   Science Degree   Decoder Architecture   Department Of Computer Science   IEEE Transactions  

摘要： Jia Wu received the PhD degree in computer science from the University of Technology Sydney, Ultimo, NSW, Australia. He is currently an ARC DECRA fellow with the Department of Computing, Macquarie University, Sydney, Australia. Prior to that, he was with the center for Artificial Intelligence, University of Technology Sydney. His current research interests include data mining and machine learning.