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LLM Integration in Workbook Design for Teaching Coding Subjects 21st

LLM Integration in Workbook Design for Teaching Coding Subje...

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21st international conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Westerlund, Magnus Shcherbakov, Andrey Arcada Univ Appl Sci Helsinki 00560 Finland

ISBN: (纸本)9783031618901;9783031618918

This work in progress paper explores the integration of Large language Models (LLMs) into educational support tools, particularly in studying basics of natural language processing. It investigates how generative LLMs can align with Bloom's Taxonomy to support various learning levels, and how Prompt engineering (PE) can improve communication with educational tools backed by LLMs. The approach involves planing integration LLMs into digital workbooks, employing techniques like Chain of Thoughts (CoT) and the Socratic method. These methods are utilized to provide tailored, interactive learning experiences that stimulate analytical thinking and deeper understanding of new concepts as well as providing instant response for coding exercises. Preliminary findings suggest that LLMs could significantly enhance the learning experience in coding education. The study suggests that PE combined with CoT and Socratic method could effectively addresses conversational problems with LLMs, enhancing learner engagement and understanding. This integration of LLMs facilitates a more dynamic form of learning, centered around dialogical exploration and practical experimentation, which aligns with the evolving objectives of lifelong learning. The ability to adapt and personalize knowledge acquisition to meet these ever-changing educational goals is a key advantage of this approach. The paper emphasizes the necessity for continuous research focused on the tailored integration of generative LLMs into educational tools. This involves employing PE techniques and leveraging a curated knowledge base to ensure that the LLMs' responses are both relevant and contextually appropriate for educational purposes.

关键词： LLM Educational Supportive Tool Teaching Coding Prompt engineering

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SUFFI-GPSC: Sufficient Geometry Problem Solution Checking with Symbolic Computation and Logical Reasoning 20

SUFFI-GPSC: Sufficient Geometry Problem Solution Checking wi...

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20th international Computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Yang, Yulong Fu, Hongguang Zhong, Xiuqin Zhang, Tianming School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350318982

Logical reasoning and computational solving skill play a vital role in geometry problem solving (GPS). The development of large-scale language models has facilitated the progress of artificial intelligence (AI) systems capable of solving math word problems and proving theorems. These models have achieved remarkable results across various geometry problem datasets, attracting significant attention in natural language processing community. However, it remains a challenge to effectively evaluate the logical correctness in the process of solution steps answered by the model due to the diversity of solution approaches. Therefore, we propose a powerful approach that can check the logical sufficiency of the solving process of given geometry problems step by step with symbolic computation and logical reasoning, called Sufficient Geometry problem solution checking (Suffi-GPSC). Suffi-GPSC divides the tasks into two categories, one focuses on equation expressions and checks whether the target equations are true with symbolic computation under the preconditions that contain a large number of equations. The other focuses on geometric relationships and checks whether the goal relation can be deduced within limited steps with logical reasoning by considering known facts as conditions and axiomatic knowledge as conditional rules. Inspired by the mainstream, a theorem predictor based on transfer text-to-text transformer(T5) has been designed to generate the theorem sequences for a more efficient and rational reasoning process. Extensive experiments on the Geometry3K dataset demonstrate that Suffi-GPSC achieves remarkable improvements. © 2023 IEEE.

关键词： natural language processing systems

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An Intent-based Network Empowered by knowledge Graph: Enhancement of Intent Translation and Management Function for Vertical Industry

An Intent-based Network Empowered by Knowledge Graph: Enhanc...

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2023 IEEE/CIC international conference on Communications in China, ICCC Workshops 2023

作者： Wang, Dong Zhang, Shenhu Su, Ruiran Li, Huan Xia, Xu China Telecom Research Institute Beijing100083 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China University of Oxford Engineering Science Department OxfordOX1 3BH United Kingdom

ISBN: (纸本)9798350345407

In recent years, Intent-based Networks (IBNs) have emerged as an effective approach for managing and optimizing complicated networks. However, contemporary IBN systems continue to struggle with understanding user intent and converting it into network configurations. To address this problem, this research presents an intent-based network empowered by knowledge graph (IBN-KG), which combines knowledge graph technology with IBN to enhance intent translation and management. We concentrate on the construction of a knowledge graph for grid scenarios and its application to improve IBN performance. The paper also introduces a tailored knowledge Graph construction scheme for vertical industry applications, focusing on smart grid scenarios. This involves a distinct data layer, knowledge extraction through natural language processing, knowledge fusion, knowledge updating, and knowledge application through user interfaces. In summary, the integration of knowledge Graph technology with IBN promises a more intelligent, flexible, and effective way of translating and managing user intent in network configurations, with particular emphasis on applications in smart grid scenarios. © 2023 IEEE.

关键词： knowledge graph

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Visual Answer Localization with Cross-Modal Mutual knowledge Transfer 48

Visual Answer Localization with Cross-Modal Mutual Knowledge...

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48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Weng, Yixuan Li, Bin Chinese Academy Sciences National Laboratory of Pattern Recognition Institute of Automation China Hunan University College of Electrical and Information Engineering China

ISBN: (纸本)9781728163277

The goal of visual answering localization (VAL) in the video is to obtain a relevant and concise time clip from a video as the answer to the given natural language question. Early methods are based on the interaction modelling between video and text to predict the visual answer by the visual predictor. Later, using the textual predictor with subtitles for the VAL proves to be more precise. However, these existing methods still have cross-modal knowledge deviations from visual frames or textual subtitles. In this paper, we propose a cross-modal mutual knowledge transfer span localization (MutualSL) method to reduce the knowledge deviation. MutualSL has both visual predictor and textual predictor, where we expect the prediction results of these both to be consistent, so as to promote semantic knowledge understanding between cross-modalities. On this basis, we design a one-way dynamic loss function to dynamically adjust the proportion of knowledge transfer. We have conducted extensive experiments on three public datasets for evaluation. The experimental results show that our method outperforms other competitive state-of-the-art (SOTA) methods, demonstrating its effectiveness1 © 2023 IEEE.

关键词： Cross-modal Mutual knowledge Transfer Visual Answer Localization

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Political Opinion Mining for Popularity Prediction using Machine Learning Techniques

Political Opinion Mining for Popularity Prediction using Mac...

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2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Dharani Devi, G. Hemalatha, R. Pradeep, R. Jagan, G. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798350305708

Twitter's utility among voters, politicians, and others keeps rising. This research offers the findings of an investigation into the viability of using Twitter data for purposes such as monitoring public sentiment and anticipating vote preferences. To gauge public sentiment, we amassed and categorised tweets regarding government programmes using natural language processing techniques. We were able to predict the electoral fortunes of various political parties using supervised learning techniques applied to a huge dataset of tweets. Unsupervised learning methods were used to analyse the cleaned data for trends and patterns. To identify whether a tweet was positive or negative towards a candidate, we used supervised learning techniques. Our findings suggest that Twitter data can be utilised to predict the electoral fortunes of political parties and the public's reaction to policy proposals. We ran across problems like the overuse of irony and sarcasm and the usage of difficult-to-decipher jargon and lingo. Because of these obstacles, thorough data pre-treatment and analysis is required before any results can be drawn. Our findings suggest political campaigns and parties might utilise Twitter data to gauge public opinion and attract new voters. Despite some reservations, this essay defends the use of social media data for this. © 2023 IEEE.

关键词： Cloud Computing Deep Learning Machine Learning Twitter Data

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natural language-Based Model-Checking Framework for Move Smart Contracts 10

Natural Language-Based Model-Checking Framework for Move Sma...

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10th international conference on Software Defined Systems, SDS 2023

作者： Nelaturu, Keerthi Keilty, Eric Veneris, Andreas University of Toronto Department of Electrical & Computer Engineering Toronto Canada

ISBN: (纸本)9798350382235

Significant efforts have been dedicated to employing model-checking as a formal verification approach in the context of smart contracts. The utilization of these tools necessitates an in-depth knowledge on the part of the developer regarding both the programming language and the implementation of model-checking techniques. To provide accessibility to developers with basic language proficiency, we present a technique for developing a conversational application framework that can be seamlessly linked with any model-checking tool for the purpose of creating a smart contract. This architecture offers a robust and effective approach to the development of safe and dependable smart contracts. The utilization of natural language processing techniques in conjunction with neural networks is employed for this objective. Using this methodology, a prototype implementation for Move smart contracts has been created and is used with the VeriMove model-checking tool. Using the offered graphical user interface, we were able to successfully build, compile and test Move smart contracts across four different classes of smart contracts. This strategy effectively decreases the amount of time and effort needed for manual coding and debugging. In addition, the use of the VeriMove model-checking tool guarantees that the smart contracts produced are devoid of any potential vulnerabilities and flaws. © 2023 IEEE.

关键词： Smart contract

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DeBERTa Beats Behemoths: A Comparative Analysis of Fine-Tuning, Prompting, and PEFT Approaches on LegalLensNER 6

DeBERTa Beats Behemoths: A Comparative Analysis of Fine-Tuni...

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6th natural Legal language processing Workshop 2024, NLLP 2024, co-located with the 2024 conference on Empirical Methods in natural language processing

作者： Tran, Hanh Thi Hong Chatterjee, Nishan Pollak, Senja Doucet, Antoine La Rochelle University France Jožef Stefan Institute Slovenia Jožef Stefan International Postgraduate School Slovenia

ISBN: (纸本)9798891761834

This paper summarizes the participation of our team (Flawless Lawgic) in the legal named entity recognition (L-NER) task at LegalLens 2024: Detecting Legal Violations. Given possible unstructured texts (e.g., online media texts), we aim to identify legal violations by extracting legal entities such as "violation", "violation by", "violation on", and "law". This systemdescription paper discusses our approaches to address the task, empirically highlighting the performances of fine-tuning models from the Transformers family (e.g., RoBERTa and DeBERTa) against open-sourced LLMs (e.g., Llama, Mistral) with different tuning settings (e.g., LoRA, Supervised Fine-Tuning (SFT) and prompting strategies). Our best results, with a weighted F1 of 0.705 on the test set, show a 30 percentage points increase in F1 compared to the baseline and rank 2 on the leaderboard, leaving a marginal gap of only 0.4 percentage points lower than the top solution. Our solutions are available at @honghanhh/lner. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Depth Wise Separable Convolutional Neural Network with Context Axial Reverse Attention Based Sentiment Analysis on Movie Reviews 4

Depth Wise Separable Convolutional Neural Network with Conte...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Kapruwan, Akanksha Pandey, Pooja Tripathi, Abhinav Priyadarshi Dhaundiyal, Pankaj Patil, Harshal Maranan, Ramya Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Akgec Ghaziabad Department of Mca Uttar Pradesh Ghaziabad201001 India Delhi NCR campus Uttar Pradesh Ghaziabad201003 India School of Computer Science & Engineering Iilm University Uttar Pradesh Greater Noida201306 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

Sentiment Analysis (SA) in movie reviews involves using natural language processing techniques to determine the sentiment expressed in reviews. This analysis helps in understanding the overall audience sentiment towards a movie, categorizing reviews as positive, negative, or neutral. It's useful for filmmakers, marketers, and audiences. The existing methods does not provide sufficient accuracy, error rate and complexity was increased. To overcome the aforementioned problem, Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN) is proposed for accurately classifying SA in movie reviews. In this input image is taken from two datasets such as IMDB dataset and Polarity dataset. The pre-processing is done using six steps namely, Cleaning, Tokenization, Case Folding, Normalization, Stop Word Elimination, and Stemming for the purpose of removing noises. Following that feature extraction are done using Bag-Of-Words and Term Frequency-Inverse Document Frequency (BOW-TF-IDF). After that classification are done using Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN)for classifying the AS in movie reviews. The efficiency of the proposed DWSCNN-CARAN-BOA is analyzed using a dataset and attains 99.94% accuracy, 98.76% recall and attains better results compared with the existing methods. In the future, this approach will use the adversarial instances it generated to conduct adversarial training and assess the potential improvement in classification performance. It also looks into the possibilities of creating adversarial examples at the word and sentence levels by combining structured knowledge from high-quality knowledge bases. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-cell MIMO Semantic Communication based Distributed Training

Multi-cell MIMO Semantic Communication based Distributed Tra...

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2023 IEEE/CIC international conference on Communications in China, ICCC 2023

作者： Ji, Hongyu Wang, Xiaoming Li, Dapeng Jiang, Rui Xu, Youyun Nanjing University of Posts and Telecommunications School of Communication and Information Engineering Nanjing210003 China

ISBN: (纸本)9798350345384

Driven by deep learning, natural language processing(NLP) has achieved great success in analyzing and understanding large volumes of text. As a result, a large number of communication transmission methods based on NLP have emerged, which can identify the semantic information behind data and compress the amount of transmitted data to achieve better transmission performance. However, applying natural language processing to practical scenarios still faces numerous challenges, such as user interference among multiple cells and limited computing power at base stations. To address these issues, we propose a multi-cell multiple input multiple output(MIMO) semantic communication transmission method. We also consider the limited computing power at base stations and introduce a distributed training method. Each base station trains independently and uploads its network weights to a central server for fitting. The fitted weights are then used as the initialization parameters for the next round of training. Simulation results show that our proposed transmission method is robust under different signal-to-noise ratios (SNR). Additionally, the distributed training at each base station can better understand the semantic information from other knowledge bases. © 2023 IEEE.

关键词： Base stations

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Building Customer Support Chatbots with Intent Recognition 2

Building Customer Support Chatbots with Intent Recognition

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2nd IEEE international conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rajesh, V. Perumal, B. Ganesh, Uppu Sai Rajkumar, Vemulakonda Kumar, Divi Mani Kumar, Manoranjan Kalasalingam Academy of Research and Education Electronics and Communication Engineering India Senior Design Engineer

ISBN: (纸本)9798350347982

In recent years, chatbots have emerged as a promising tool for delivering healthcare services. Chatbots can provide 24/7 access to healthcare information, improve patient engagement, and reduce the workload of healthcare professionals. However, building effective chatbots for healthcare requires expertise in natural language processing (NLP), machine learning, and healthcare domain knowledge. Chatbots have become increasingly popular in recent years, thanks to advancements in natural language processing (NLP) and machine learning (ML) technologies. One critical component of chatbots is intent recognition, which enables them to understand the user's purpose and respond appropriately. In this paper, we present a comparative study of different machine learning algorithms for intent classification in chatbots. We evaluate the performance of four ML algorithms: logistic regression, random forest, support vector machines (SVM), and deep learning (DL) neural networks, using a publicly available dataset. Our results show that SVM model outperform the other two algorithms in terms of accuracy and F1 score. © 2023 IEEE.

关键词： Logistic regression

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