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Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models 31

Leveraging Language-based Representations for Better Solving...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Yile Cheng, Sijie Sun, Zixin Li, Peng Liu, Yang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Tsinghua University Beijing China Jiangsu Collaborative Innovation Center for Language Competence Jiangsu China

ISBN: (纸本)9798891761964

Symbols such as numerical sequences, chemical formulas, and table delimiters exist widely, playing important roles in symbol-related tasks such as abstract reasoning, chemical property prediction, and tabular question-answering. Compared to tasks based on natural language expressions, large language models (LLMs) have limitations in understanding and reasoning on symbol-based representations, making it difficult for them to handle symbol-related problems. In this paper, we propose symbol-to-language (S2L), a method that converts symbol-based representations to language-based representations, providing valuable information for language models during reasoning. We found that, for both closed-source and open-source LLMs, the capability to solve symbol-related problems can be largely enhanced by incorporating such language-based representations. For example, by employing S2L for GPT-4, there can be substantial improvements of +21.9% and +9.5% accuracy for 1D-ARC and Dyck language tasks, respectively. There are also consistent improvements in other six general symbol-related tasks such as table understanding and Tweet analysis. We release the GPT logs in https://***/THUNLP-MT/symbol2language. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing DNA Sequence Classification Through Machine Learning: A Comparative Study on Human and Chimpanzee Genomic Data

Enhancing DNA Sequence Classification Through Machine Learni...

引用

Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Kushagra Pandya Heli Hathi Ravikumar R N Sivakumar Nadarajan Manoj Praveen Vinod Kumar Pal Associate Software Engineer - AI TSS Consultancy Pvt. Ltd. Rajkot India Dept. of Computer Engineering Marwadi University Rajkot India Dept. of Computer Science and Engineering Alliance University Bangalore India Dept. of AI & DS Velalar College of Engineering and Technology Erode India Dept. of Computer Science Marwadi University Rajkot India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

DNA sequence classification is a major challenge in biological processing of data. The classification of DNA sequences is an important study field in bioinformatics since it allows researchers to perform genomic analysis. Through this research, we tried to analyze the performance of various machine learning (ML) algorithms in identifying DNA genes in humans and chimpanzees to find the most effective algorithm for classification of DNA sequences. The results of this research outperformed earlier studies, which had an accuracy around 98%. The dataset was preprocessed using k-mer counting, which assisted in finding key characteristics for the model's training. Naïve Bayes stood out, with an outstanding accuracy of 98.4% for human DNA and 91.4% for chimpanzee DNA. After improving the parameters, Naïve Bayes continued to improve, with human DNA accuracy increasing to 99% and chimpanzee DNA accuracy to 92% in all categories followed by Logistic Regression at 93.9% for human DNA sequences and Ridge Classifier at 90.2% for chimpanzee DNA sequences. K-Nearest Neighbors, Ridge Classifier, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Extra Trees, XGBoost, and Naïve Bayes were among the algorithms tested, with accuracy, F1 score, precision, and recall used in assessing their performance. These findings highlight the necessity of selecting the appropriate models and tweaking their parameters to improve performance.

关键词： Logistic regression Accuracy Machine learning algorithms Computational modeling Genomics Classification algorithms Bayes methods Bioinformatics Random forests Tuning

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Fairness in Healthcare: Assessing Data Bias and Algorithmic Fairness

Fairness in Healthcare: Assessing Data Bias and Algorithmic ...

引用

Medical Information Processing and Analysis (SIPAIM), International Symposium on

作者： Farzaneh Dehghani Nikita Malik Joanna Lin Sayeh Bayat Mariana Bento dept. Biomedical Engineering University of Calgary Calgary Canada dept. Information Technology Manipal Institute Of Technology MAHE Manipal India dept. Computer Science University of Calgary Calgary Canada dept. Biomedical Engineering dept. Geomatics Engineering University of Calgary Calgary Canada dept. Biomedical Engineering dept. Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (数字)9798331528652

ISBN: (纸本)9798331528669

With the wide employment of Artificial Intelligence-based systems in healthcare, there is an increasing need to further quantify and analyze potential biases in data and algorithms, mitigating health disparities. We aim to investigate publicly available healthcare datasets to identify potential biases, and investigate the impact of these biases on the predictive performance of the models. The results indicate that bias in these datasets exerts a negative impact on the fairness of the learned models. In the context of a highly imbalanced dataset, we further investigate the impact of various imbalance handling techniques on both the performance and fairness of the models. This study underscores the importance of early detection of bias to mitigate the risk of introducing such biases in real-world applications, particularly within a sensitive domain, such as healthcare.

关键词： Measurement Prevention and mitigation Decision making Pipelines Medical services Predictive models Stroke (medical condition) Prediction algorithms Reliability Artificial intelligence

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A data-driven approach for context definition in situation-aware wearable computing systems

A data-driven approach for context definition in situation-a...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Giuseppe D’Aniello Giancarlo Fortino Matteo Gaeta Zia Ur Rehman Dept. Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy Dept. Computer Engineering Modeling Electronics and Systems University of Calabria Rende Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

A situation-aware wearable computing system is defined as a wearable device with the capability to perceive, comprehend, and project the situations occurring in the environment in order to adapt its behavior accordingly. For this kind of devices, the identification of complex situations related to the activities performed by users in various contexts is of great interest. This requires the capability to identify context states. A well-known technique for this task is the Context Space Theory (CST), which provides a multidimensional space representing contexts. Although powerful and lightweight, this technique has the drawback of requiring manual definition of such a space, a time-consuming process that involves domain experts. To address this issue, this work proposes a data-driven approach for defining context spaces in CST using kernel density estimation. This approach is compared with a state-of-theart expert-based CST technique and a fuzzy inference system for context representation, demonstrating superior performance on the Extrasensory dataset.

关键词： Performance evaluation Fuzzy logic computer aided software engineering Estimation Manuals computer architecture Feature extraction Object recognition Wearable devices Kernel

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Sensing Together: Cooperative Task Adaptation and Scheduling for IoT-Nets Using Renewable Energy

Sensing Together: Cooperative Task Adaptation and Scheduling...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Elizabeth Liri K. K. Ramakrishnan Koushik Kar Flavio Esposito Dept. of Computer Science and Engineering University of California Riverside Riverside CA USA Computer Science Dept. Saint Louis University St. Louis MO USA Electrical Computer and Systems Engineering Dept. Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

IoT devices used in various applications, such as monitoring agricultural soil moisture, or urban air quality assessment, are typically battery-operated and energy-constrained. We develop a lightweight and distributed cooperative sensing scheme that provides energy-efficient sensing of an area by reducing spatio-temporal overlaps in the coverage using a multi-sensor IoT network. Our “Sensing Together” solution includes two algorithms: Distributed Task Adaptation (DTA) and Distributed Block Scheduler (DBS), which coordinate the sensing operations of the IoT network through information shared using a distributed “token passing” protocol. DTA adapts the sensing rates from their “raw” values (optimized for each IoT device independently) to minimize spatial redundancy in coverage, while ensuring that a desired coverage threshold is met at all points in the covered area. DBS then schedules task execution times across all IoT devices in a distributed manner to minimize temporal overlap. On-device evaluation shows a small token size and execution times of less than 0.6s on average while simulations show average energy savings of 5% per IoT device under various weather conditions. Moreover, when devices had more significant coverage overlaps, energy savings exceeded 30% thanks to cooperative sensing. In simulations of larger networks, energy savings range on average between 3.34% and 38.53%, depending on weather conditions. Our solutions consistently demonstrate near-optimal performance under various scenarios, showcasing their capability to efficiently reduce temporal overlap during sensing task scheduling.

关键词： Performance evaluation Schedules Renewable energy sources Simulation Redundancy Soil moisture Smart systems Sensors Internet of Things Meteorology

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Driver Assistance System, Based on the Convolutional Neural Network

Driver Assistance System, Based on the Convolutional Neural ...

引用

Electrical engineering Faculty Conference (BulEF)

作者： Simona Filipova-Petrakieva Petar Yanev dept. Fundamentals of Electrical Engineering Technical University of Sofia Sofia Bulgaria dept. Computer Systems Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350391671

ISBN: (纸本)9798350391688

The continuously rising number of deaths due to car accidents on the roads is a critical problem nowadays. To reduce the number of risky situations on the road, it is necessary to increase the attention of drivers when driving motor vehicles. In this context, an advisory system to assist driving is proposed in this paper. The mathematical model uses a convolutional neural network to recognize traffic signs and detect real road situations. It is implemented using Python in the Jupyter Notebook environment. The following libraries were used: Tensorflow (Tensors and Keras libraries, for machine learning and deep learning, respectively), Tkinter (GUI library), Pillow (image processing library), matplotlib (data visualization library), NumPy (library for performing complex computations), and Pandas (data analysis and processing library). The developed application is installed on the onboard vehicle computer. The detected sign is displayed in an appropriate form on the automobile dashboard. Simulations of real traffic situations are implemented in the system. All this will facilitate driving and help drivers with less experience acquire the necessary driving reactions.

关键词： Tensors Roads Image processing Neural networks Libraries Mathematical models Convolutional neural networks Automobiles Vehicles Graphical user interfaces

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Re-engineering higher education by models suitable for COVID-19 and students with disabilities 16

Re-engineering higher education by models suitable for COVID...

引用

16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Saniei, Namdar Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9781665414685

Thanks to computers and the internet, since the rise of COVID-19, different methods of online teaching have replaced the face-to-face teaching;however there has not been a viable method of assessment that is fully capable of replacing in class assessments. It is hard, if not impossible, to maintain the integrity of the exams, when conducted remotely. Even prior to the COVID attack, arranging and conducting exams for students, who depend on Students Accessibility Services (SAS), were becoming cumbersome and a burden for universities and instructors. If there were no exams required, as an assessment tool, both the abovementioned issues, would not be a problem. In this paper a few theoretical common solutions to both the issues mentioned above is theorized, proposing elimination or reduction of the exams, as major assessment tool for undergraduate students. The model in which all the exams are eliminated is referred to as Stress-free, and the one with reduced exams is called Stress-less model. Implementing the models discussed in this paper, demand an overhaul of the entire higher education system. © 2021 IEEE.

关键词： Students

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IoT Security: A Deep Learning-Based Approach for Intrusion Detection and Prevention

IoT Security: A Deep Learning-Based Approach for Intrusion D...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Bhanu Kauhsik Himanshu Nandanwar Rahul Katarya Dept. of Software Engineering Delhi Technological University Delhi India Dept. of Computer Science and Engineering Delhi Technological University Delhi India

The size and market worth of the Internet of Things (IoT) have expanded, but unfortunately, the likelihood of user data being compromised has also risen. This presents a notable danger that has the potential to create disorder. Consequently, the main aim of this paper is to recognize any weaknesses within existing security solutions and suggest an innovative approach to address them. In order to accomplish this, the paper puts forth four research inquiries concerning IoT security that leverage Machine Learning and Deep Learning techniques. Additionally, it conducts an extensive review of the latest publications and concludes that a combination of specific techniques can provide a potential solution to the security problems in IoT. Overall, the findings of this study suggest that this systematic literature review could help in significantly enhancing the security of IoT devices and applications, safeguarding user data from unauthorized access and potential misuse.

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Deep Learning for Drug Response Prediction with Gene Expression Data

Deep Learning for Drug Response Prediction with Gene Express...

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International Conference on Information Networking

作者： Sardar Jaffar Ali Muhammad Omer Duc Tai Le Syed M. Raza Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea Dept. of Computer Science and Engineering Sungkyunkwan University Suwon South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea James Watt School of Engineering University of Glasgow Glasgow United Kingdom

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Accurately predicting drug responses based on individual patient profiles is a critical challenge in personalized medicine, primarily due to the complex biological variability involved. This paper presents a deep learning framework for predicting changes in gene expression, providing insights into how drugs impact cells at the molecular level. Using data from the Kaggle competition, several models have been evaluated, including LSTM, GRU, Transformer, and Autoencoder architectures. Among these, the 3 -stacked GRU with Attention demonstrated superior performance, achieving the highest sign accuracy of 79% and the lowest mean absolute error across diverse biological conditions. The robust performance of the model highlights the effectiveness of attention mechanisms in capturing critical patterns in gene expression data.

关键词： Drugs Deep learning Attention mechanisms Precision medicine Biological system modeling Autoencoders Transformers Data models Gene expression Long short term memory

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Composite metamorphic relations for integration testing 22

Composite metamorphic relations for integration testing

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8th International Conference on computer Technology Applications, ICCTA 2022

作者： Iakusheva, Sofia Khritankov, Anton Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Russia Faculty of Computer Science Dept. of Software Engineering Hse University Russia

ISBN: (纸本)9781450396226

Metamorphic testing is a testing method for problems without test oracles. Integration testing allows for detecting errors in complex systems that may not be found during the testing of their components. In this paper, we propose a novel approach that applies metamorphic testing in integration testing. The main idea is to develop a composite metamorphic relation for the system represented as an acyclic graph. This relation is a logical function of metamorphic relations for the parts of the system (vertices of the graph). It takes into account the features of the parts. Also, it can simplify the search for failure by identifying the subsystem with error. In this paper's theoretical part, we describe an algorithm of relation design. Then, we apply our method to a bioinformatics system for comparative genetic analysis of tissues using production tools. This experiment proves our method can be applied to real-life pipelines and find errors in them. © 2022 ACM.

关键词： Integration testing

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