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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Santhuja, P. Choudri, Subramani Roy Balaji, S. Reddy, Elangovan Guruva Muthulekshmi, M. Department of Computer Science Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Information Technology Sri Shanmugha College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Nowadays, dietary issues are increasing around the world. Numerous problems, such as weight gain, obesity, diabetes, etc., can arise from an unbalanced diet. By integrating image processing, the system can assess food images in new ways to provide personalized solutions for better eating habits. The system uses machine learning, IoT, and image processing techniques to evaluate food image data and extract useful information. IoT devices like smartphones or specialized cameras take images of food and send to the cloud for analysis. The Support Vector Machine (SVM) is a machine learning technique that is integrated with Internet of Things (IoT) technology in this study to propose a novel method for creating a system that uses personalized nutritional suggestions. The SVM algorithm is then used to analyze and analyze this data to find patterns, correlations, and individual dietary requirements. A cloud database is used to process and store all the data related to food consumption. The system analyzes food images and determines the nutrition and caloric content of the food using image processing and segmentation. The system takes up new abilities using an extensive collection of dietary records that have been labeled and include details about the sorts of foods, portion sizes, and nutritional value. The system can predict and identify users' nutritional needs, deficits, and personalized dietary objectives by using this trained SVM model to their nutritional data. © 2023 IEEE.

关键词： Dietetics IoT Machine Learning Nutrition Guidance SVM Algorithm

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LEARNING SPARSE GROUP MODELS THROUGH BOOLEAN RELAXATION 11

LEARNING SPARSE GROUP MODELS THROUGH BOOLEAN RELAXATION

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Yijie Zhou, Yuan Huang, Xiaoqing Huang, Kun Zhang, Jie Ma, Jianzhu Computer Science Department Indiana University Bloomington United States Yau Mathematical Sciences Center Department of Mathematical Sciences Tsinghua University China Department of Biostatistics & Health Data Science Indiana University United States Department of Medical and Molecular Genetics Indiana University United States Institute for AI Industry Research Tsinghua University China

We introduce an efficient algorithmic framework for learning sparse group models formulated as the natural convex relaxation of a cardinality-constrained program with Boolean variables. We provide theoretical techniques to characterize the equivalent condition when the relaxation achieves the exact integral optimal solution, as well as a rounding algorithm to produce a feasible integral solution once the optimal relaxation solution is fractional. We demonstrate the power of our equivalent condition by applying it to two ensembles of random problem instances that are challenging and popularly used in literature and prove that our method achieves the exactness with overwhelming probability and the nearly optimal sample complexity. Empirically, we use synthetic datasets to demonstrate that our proposed method significantly outperforms the state-of-the-art group sparse learning models in terms of individual and group support recovery when the number of samples is small. Furthermore, we show the out-performance of our method in cancer drug response prediction. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Relaxation processes

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***: A Benchmark for Evaluating Questioning Capability in Education for Large Language Models

arXiv

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arXiv 2024年

作者： Chen, Yuyan Wu, Chenwei Yan, Songzhou Liu, Panjun Zhou, Haoyu Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Electrical Engineering and Computer Science Department University of Michigan United States School of Computer Science Beijing Institute of Technology China

Teachers are important to imparting knowledge and guiding learners, and the role of large language models (LLMs) as potential educators is emerging as an important area of study. Recognizing LLMs’ capability to generate educational content can lead to advances in automated and personalized learning. While LLMs have been tested for their comprehension and problem-solving skills, their capability in teaching remains largely unexplored. In teaching, questioning is a key skill that guides students to analyze, evaluate, and synthesize core concepts and principles. Therefore, our research introduces a benchmark to evaluate the questioning capability in education as a teacher of LLMs through evaluating their generated educational questions, utilizing Anderson and Krathwohl’s taxonomy across general, monodisciplinary, and interdisciplinary domains. We shift the focus from LLMs as learners to LLMs as educators, assessing their teaching capability through guiding them to generate questions. We apply four metrics, including relevance, coverage, representativeness, and consistency, to evaluate the educational quality of LLMs’ outputs. Our results indicate that GPT-4 demonstrates significant potential in teaching general, humanities, and science courses;Claude2 appears more apt as an interdisciplinary teacher. Furthermore, the automatic scores align with human perspectives. © 2024, CC BY.

关键词： Problem solving

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Medical Image Analysis Using Deep Learning Technnique 23

Medical Image Analysis Using Deep Learning Technnique

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Surabhi Saxena Nikhat Parveen Devendra Agarwal Department of Artificial Intelligence and Data Science Poornima Institute of Engineering & Technology India Department of Computer Science and Engineering Konneru Lakshmaiah Education Foundation India Computer Science Goel Instiute of Technology and Management India

ISBN: (纸本)9798400709418

Clinical picture classification, pattern recognition, and quantification have seen significant advancements with the help of artificial intelligence, particularly through deep learning techniques. Deep learning has rapidly emerged as the most rapidly evolving field within AI, and its applications have been successfully demonstrated across various domains, including medicine. This review briefly examines recent applied research in several medical fields, such as neurology, brain imaging, retinal analysis, pneumonias, computerized pathology, breast imaging, cardiovascular studies, musculoskeletal imaging, and gastrointestinal imaging. Deep learning networks prove to be highly effective when dealing with large scale medical datasets, enabling information discovery, knowledge dissemination, and knowledge-based prediction. This research aims to present both foundational knowledge and state-of-the-art deep learning techniques to facilitate the interpretation and analysis of medical images. The primary objectives of this work are to explore advancements in medical image processing research and implement the identified and addressed key criteria in practical applications.

关键词： KEYWORDS

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A Remark on the Expressivity of Asynchronous TeamLTL and HyperLTL

arXiv

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arXiv 2024年

作者： Kontinen, Juha Sandström, Max Virtema, Jonni Department of Mathematics and Statistics University of Helsinki Helsinki Finland Department of Computer Science University of Sheffield Sheffield United Kingdom

Linear temporal logic (LTL) is used in system verification to write formal specifications for reactive systems. However, some relevant properties, e.g. non-inference in information flow security, cannot be expressed in LTL. A class of such properties that has recently received ample attention is known as hyperproperties. There are two major streams in the research regarding capturing hyperproperties, namely hyperlogics, which extend LTL with trace quantifiers (HyperLTL), and logics that employ team semantics, extending truth to sets of traces. In this article we explore the relation between asynchronous LTL under set-based team semantics (TeamLTL) and HyperLTL. In particular we consider the extensions of TeamLTL with the Boolean disjunction and a fragment of the extension of TeamLTL with the Boolean negation, where the negation cannot occur in the left-hand side of the Until-operator or within the Global-operator. We show that TeamLTL extended with the Boolean disjunction is equi-expressive with the positive Boolean closure of HyperLTL restricted to one universal quantifier, while the left-downward closed fragment of TeamLTL extended with the Boolean negation is expressively equivalent with the Boolean closure of HyperLTL restricted to one universal quantifier. © 2024, CC BY.

关键词： Temporal logic

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Improved Deep Matrix Factorization for Signal Recovery in Random Frequency Diversity Array Radar 2

Improved Deep Matrix Factorization for Signal Recovery in Ra...

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2nd IEEE International Conference on Signal, Information and data Processing, ICSIDP 2024

作者： Guo, Dayan Lin, Zijian Huang, Tianyao Qi, Yue He, Jie Zhang, Xiao-Ping Wang, Xueqian Li, Gang University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China Tsinghua Shenzhen International Graduate School Institute of Data and Information Shenzhen China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798331515669

Motivated by the success of recent deep learning researches on radar, we consider a deep learning based matrix factorization method for suppressing the high range-angle side-lobes in random frequency diversity array (FDA) radars. While the existing deep matrix factorization approach is designed for real-valued situations and signals of FDA radars are complex-valued cases, we propose two methods to extend the existing approach into complex-valued framework: 1) adjusting the neural network structures and activation functions for complex-valued operations, and 2) constructing a real-valued representation of the original complex-valued input. Simulation results demonstrate that the proposed methods outperform traditional ones in multi-target scenarios. © 2024 IEEE.

关键词： Matrix factorization

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Crop Type Classification using Multi-temporal Sentinel-2 Satellite Imagery: A Deep Semantic Segmentation Approach

Crop Type Classification using Multi-temporal Sentinel-2 Sat...

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2023 International Conference on Robotics and Automation in Industry, ICRAI 2023

作者： Khan, Asim Hameed Zafar, Zuhair Shahzad, Muhammad Berns, Karsten Fraz, Muhammad Moazam Islamabad Pakistan Data Science in Earth Observation Munich Germany University of Reading Department of Computer Science BerkshireRG6 6AH United Kingdom Technical University of Kaiserslautern Robotics Research Lab Germany

ISBN: (纸本)9781665464727

Crop Type classification using Semantic Segmentation and remote sensing data is an important tool for decision-making related to precision agriculture. Such classification remains an unsolved challenge due to the choice of landscape, processing methodology and selected satellite imagery and its optical features, and most importantly the availability and usage of such datasets in a developing country like Pakistan. State-of-the-art semantic segmentation models lack in processing the temporal dimension of time series imagery and evident solution to process multi-spectral bands available in the satellite imagery. We propose a methodology to overcome these shortcomings by selecting appropriate band combinations for crop type classification and treating time series visual data as a single image. The proposed methodology is evaluated on the data set of six different crops collected from National Agriculture Research Center (NARC) Islamabad. The experimental results yield 85% accuracy for classifying various crop types based on the evaluation of five different semantic segmentation models. The code and the trained models are available at https://***/asimniazi63/crop-type-narc for other researchers working in the same domain. © 2023 IEEE.

关键词： Satellite imagery

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Large Language Model-based Human-Agent Collaboration for Complex Task Solving

Large Language Model-based Human-Agent Collaboration for Com...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Feng, Xueyang Chen, Zhi-Yuan Qin, Yujia Lin, Yankai Chen, Xu Liu, Zhiyuan Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798891761681

In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite this, LLM-based agents frequently demonstrate notable shortcomings in adjusting to dynamic environments and fully grasping human needs. In this work, we introduce the problem of LLM-based human-agent collaboration for complex task-solving, exploring their synergistic potential. To tackle the problem, we propose a Reinforcement Learning-based HumanAgent Collaboration method, ReHAC, which trains a policy model designed to determine the most opportune stages for human intervention within the task-solving process. We conduct experiments under real and simulated human-agent collaboration scenarios. Experimental results demonstrate that the synergistic efforts of humans and LLM-based agents significantly improve performance in complex tasks, primarily through well-planned, limited human intervention. datasets and code are available at: https://***/XueyangFeng/ReHAC/. © 2024 Association for Computational Linguistics.

关键词： Autonomous agents

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Diabetes Prediction using Hybrid Machine Learning Techniques

Diabetes Prediction using Hybrid Machine Learning Techniques

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Bhukya Devendar Naik Anumula Spoorthy Sakshi Baherji Saroja Kumar Rout Bijaya Kumar Sethi Department of Information Technology Vardhaman College of Engineering Hyderabad India Department of Computer Science & Engineering Data Science Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The rise in blood glucose levels is the primary factor contributing to the development of diabetes. Given the significance of preventing diabetes or delaying its onset, despite numerous efforts utilizing machine learning for medical diagnostics, there remains a notable gap in research concerning long-term disease prediction, especially for type 2 diabetes. However, the traditional method of diagnosing diabetes involves patients undergoing blood glucose tests administered by doctors, which can be limited by clinical resources. Many patients consequently encounter delays in getting a diagnosis. To create a predictive model for diabetes diagnosis, this study used six traditional machine learning models: boosting, neural networks, decision trees, random forests, logistic regression, and support vector machines. The study employed machine learning (ML) algorithms to predict diabetes using an authentic dataset from Safety Pressure Primary Health Care. With a validation accuracy of 84%, the study offers important new information on who is most likely to develop type 2 diabetes. By precisely predicting the type of diabetes and examining the importance of each indication in the prediction process, the goal of this study is to improve the accuracy of diabetes prediction.

关键词： Support vector machines Logistic regression Accuracy Computational modeling Predictive models Prediction algorithms Diabetes

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ANPMI: Assessing the True Comprehension Capabilities of LLMs for Multiple Choice Questions

arXiv

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arXiv 2025年

作者： Cho, Gyeongje So, Yeonkyoung Lee, Jaejin Graduate school of Data Science Seoul National University Korea Republic of Department of Computer Science Seoul National University Korea Republic of

Multiple-choice benchmarks, consisting of various prompts and choices, are among the most widely used methods to assess a language model’s natural language understanding capability. Given a specific prompt, we typically compute P(Choice|Prompt) to evaluate how likely a language model is to generate the correct choice compared to incorrect ones. However, we observe that performance measured using this approach reflects not only the model’s comprehension of the prompt but also its inherent biases for certain choices regardless of the prompt. This issue makes it challenging to accurately measure a model’s natural language understanding, as models may select the answer without fully understanding the prompt. To address this limitation, we propose a novel metric called ANPMI, which normalizes Pointwise Mutual Information (PMI) by − log P(Choice). ANPMI provides a more accurate assessment of the model’s natural language understanding by ensuring that it is challenging to answer a question without properly understanding the prompt. © 2025, CC BY-NC-SA.

关键词： Benchmarking

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