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ECTSum: A New Benchmark dataset For Bullet Point Summarization of Long Earnings Call Transcripts

ECTSum: A New Benchmark Dataset For Bullet Point Summarizati...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Mukherjee, Rajdeep Bohra, Abhinav Banerjee, Akash Sharma, Soumya Hegde, Manjunath Shaikh, Afreen Shrivastava, Shivani Dasgupta, Koustuv Ganguly, Niloy Ghosh, Saptarshi Goyal, Pawan Department of Computer Science and Engineering IIT Kharagpur India Goldman Sachs Data Science and Machine Learning Group India Leibniz University of Hannover Germany

Despite tremendous progress in automatic summarization, state-of-the-art methods are predominantly trained to excel in summarizing short newswire articles, or documents with strong layout biases such as scientific articles or government reports. Efficient techniques to summarize financial documents, discussing facts and figures, have largely been unexplored, majorly due to the unavailability of suitable datasets. In this work, we present ECTSum, a new dataset with transcripts of earnings calls (ECTs), hosted by publicly traded companies, as documents, and experts-written short telegram-style bullet point summaries derived from corresponding Reuters articles. ECTs are long unstructured documents without any prescribed length limit or format. We benchmark our dataset with state-of-the-art summarization methods across various metrics evaluating the content quality and factual consistency of the generated summaries. Finally, we present a simple yet effective approach, ECT-BPS, to generate a set of bullet points that precisely capture the important facts discussed in the calls. © 2022 Association for Computational Linguistics.

关键词： Benchmarking

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Peli-Remo Optimization Algorithm for Reliable routing in MANET: K-means node clustering process and CNN-based Energy Prediction 2

Peli-Remo Optimization Algorithm for Reliable routing in MAN...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Biradar, Manjula A. Mallapure, Sujata Sharnbasava University Department of Computer Science and Engineering Sharan Nagar Karnataka Kalaburagi India Department of Artificial Intelligence and Machine Learning Sharan Nagar Karnataka Kalaburagi India

ISBN: (纸本)9798350317558

MANET, an ad hoc wireless network is made up of several nodes connected, governed by certain rules for packet movement from one node point to another. The structure of communication may be designed using a variety of protocols. For an ad hoc network, routing protocols are used to manage the connections. To improve communication, this article suggests an effective MANET cluster-based routing paradigm for multimedia communication. The clustering process is started using the K-means clustering method. The Deep Convolutional Neural Network (DCNN) model will first predict the node energy. The cluster head will be chosen using the Hybrid Remora with Pelicon Optimization technique (HRPOA), which integrates the Remora optimization technique and the Pelican optimization algorithm. The HRPOA model's channel quality for round 500 is around 1.56, whereas the conventional techniques obtained the lowest channel capacity, with BWO=~0.5, GWO=~0.78, BA=~0.98, POA=~0.57, and ROA=~0.78, in that order. © 2024 IEEE.

关键词： DCNN HRPOA Model K-Means Clustering Mobile Adhoc Wireless Network Reliable Retransmission

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Develop a Smart Android Application to Provide Laundry Service 15

Develop a Smart Android Application to Provide Laundry Servi...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jeyapradesh, Samuel Jebaseeli, T. Jemima Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798331300579

In the era of the Internet and smart applications, companies and institutions providing various services face significant challenges. The digital landscape, characterized by limitless connectivity and accessibility, poses a new paradigm where services transcend traditional boundaries of time and space. To remain at the forefront in their respective domains, businesses must embrace the electronic realm. Notably, web programming languages like PHP and ASP, alongside mobile application development languages such as Android have emerged as pivotal tools in this technological evolution. This design focuses on the development of an Android application integrated with a web control panel tailored for the service sector, with a specific emphasis on public laundry facilities. The study encompasses the system's architecture technological frameworks employed, and subsequently delves into the target audience, market requirements, and additional services to be offered. Throughout this exploration, the design aims to harness the potential inherent in these programming languages, drawing parallels to human language for enhanced user engagement and accessibility. © Grenze Scientific Society, 2024.

关键词： Laundries

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OncoNLP: Cancer Comprehend Annotation - A Pipeline for Cancer Phenotype and Clinical Extraction

OncoNLP: Cancer Comprehend Annotation - A Pipeline for Cance...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Duong, Thanh Thieu, Thanh University of South Florida Department of Computer Science and Engineering TampaFL United States Moffitt Cancer Center Department of Machine Learning TampaFL United States

ISBN: (纸本)9798350351552

Information extraction from clinical text is needed to comprehend patient conditions and determine anticancer treatment. Existing NLP systems, such as Amazon Comprehend Medical, Clamp, and DeepPhe, are proprietary and suboptimal due to the shift in the textual distribution and expression of cancer phenotypes. We introduce OncoNLP, a natural language processing toolkit that comprises deep neural network BERT models designed to extract cancer phenotype and related biomedical information. Currently, the toolkit contains two primary components: a Biomedical BERT(BiomedBERT) model to extract general medical information and a Cancer Bert(CaBERT) model to identify the primary tumor site and histology. We evaluate the performance of BiomedBERT on Informatics for Integrating Biology & the Bedside (i2b2) dataset against Amazon Comprehend Medical and Clamp. BiomedBERT outperforms all other methods with an exact matching F1-score of 88.5%. Next, we evaluate CaBERT on a Moffitt dataset with over 2000 clinical notes against DeepPhe, which shows a 50% improvement in both tumor site and histology tasks. Lastly, we introduce a knowledge prompt with OncoNLP and engineer it for large language models. We name it OncoNLP-Assist, a chatbot system powered by OncoNLP and Llama2 that could extract information from electrical health records and interact with physicians. © 2024 IEEE.

关键词： Diseases

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Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions 3

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Yashas, D. Kashyap, M. Shivani Charan, P.R. Hemanth Kumar, G.P. Abhishek, B.K. Deekshith, C. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Dept of Information Science and Engineering Bengaluru India Bnm Institute of Technology Dept of Artificial Intelligence and Machine Learning Bengaluru India Siddaganga Institute of Technology Dept of Artificial Intelligence and Data Science Tumkur India Vidyavardhaka College of Engineering Dept of Computer Science and Engineering Mysore India

ISBN: (纸本)9798331515911

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field. © 2025 IEEE.

关键词： Convolutional neural networks

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Predicting Soil Types and Crop Suitability through Comprehensive Soil Analysis

Predicting Soil Types and Crop Suitability through Comprehen...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Sivakumar, S. Dharani, U. Hephzibah, Dineep Srm Institute Of Science And Technology Department Of Computer Science And Engineering Tiruchirapalli India Srm Institute Of Science And Technology Dept. Of Comp. Sci. And Engineering Specialization In Artificial Intelligence And Machine Learning Tiruchirapalli India

ISBN: (纸本)9798350352689

The analysis of soil nutrients and water levels holds utmost significance in the realms of agriculture. Determining the soil type and identifying the crop suitable for that specific soil are critical factors in optimizing agricultural yield. These considerations play a pivotal role in precision agriculture, ensuring that the chosen crops align with the soil's characteristics. This strategic alignment enhances the efficiency of cultivation practices, promotes sustainable agricultural methods, and ultimately contributes to increased crop yields. In this research paper, we systematically identify key soil properties, including NPK concentrations, temperature, humidity, and soil pH. The collected dataset, encompassing these crucial soil attributes, is utilized to train machine learning models. The primary goal is to forecast the soil type utilizing the gathered data, and subsequently, undertake further analyses, to ascertain the relevant crop types that are most suitable for the identified soil characteristics. This comprehensive approach involves the integration of data-driven insights and machine learning techniques to enhance our understanding of soil composition and facilitate informed decision-making in agriculture. © 2024 IEEE.

关键词： Soil testing

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MULTI-TASK REINFORCEMENT learning WITH MIXTURE OF ORTHOGONAL EXPERTS 12

MULTI-TASK REINFORCEMENT LEARNING WITH MIXTURE OF ORTHOGONAL...

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12th International Conference on learning Representations, ICLR 2024

作者： Hendawy, Ahmed Peters, Jan D'Eramo, Carlo Department of Computer Science TU Darmstadt Germany Germany Center for Cognitive Science TU Darmstadt Germany Systems AI for Robot Learning Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany

Multi-Task Reinforcement learning (MTRL) tackles the long-standing problem of endowing agents with skills that generalize across a variety of problems. To this end, sharing representations plays a fundamental role in capturing both unique and common characteristics of the tasks. Tasks may exhibit similarities in terms of skills, objects, or physical properties while leveraging their representations eases the achievement of a universal policy. Nevertheless, the pursuit of learning a shared set of diverse representations is still an open challenge. In this paper, we introduce a novel approach for representation learning in MTRL that encapsulates common structures among the tasks using orthogonal representations to promote diversity. Our method, named Mixture Of Orthogonal Experts (MOORE), leverages a Gram-Schmidt process to shape a shared subspace of representations generated by a mixture of experts. When task-specific information is provided, MOORE generates relevant representations from this shared subspace. We assess the effectiveness of our approach on two MTRL benchmarks, namely MiniGrid and MetaWorld, showing that MOORE surpasses related baselines and establishes a new state-of-the-art result on MetaWorld. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Smart Security Management using IoT and HC-05 Bluetooth Module 8

Smart Security Management using IoT and HC-05 Bluetooth Modu...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Sam Michael, S. Ancy Jenifer, J. Benjamin, Ruth Moly Jacob, Cyril Shanthosh, S. Karunya Institute of Technology and Sciences Department of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350386578

Safety and security are essential for every individual worldwide, particularly in the context of home security. Modern advancements in science and technology have introduced various innovative solutions, including smart security locking mechanisms. This research study presents a novel smart lock system that leverages mobile phone applications integrated with built-in fingerprint authentication. Unlike other smart locks that rely on external biometric devices, this system utilizes the smartphone's internal fingerprint sensor, thereby reducing hardware costs and complexity. Additionally, the application supports multiple authentication methods, such as fingerprint and facial recognition, enhancing security and convenience. This solution eliminates the risk of key loss and ensures that only authenticated users can access the home. The lock mechanism operates automatically, allowing the door to open and close without human intervention, providing a seamless and secure user experience. © 2024 IEEE.

关键词： Keys (for locks)

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ROBUST ADVERSARIAL REINFORCEMENT learning VIA BOUNDED RATIONALITY CURRICULA 12

ROBUST ADVERSARIAL REINFORCEMENT LEARNING VIA BOUNDED RATION...

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12th International Conference on learning Representations, ICLR 2024

作者： Reddi, Aryaman Tölle, Maximilian Peters, Jan Chalvatzaki, Georgia D'Eramo, Carlo Department of Computer Science TU Darmstadt Germany Germany Systems AI for Robot Learning Germany Center for Cognitive Science TU Darmstadt Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany

Robustness against adversarial attacks and distribution shifts is a long-standing goal of Reinforcement learning (RL). To this end, Robust Adversarial Reinforcement learning (RARL) trains a protagonist against destabilizing forces exercised by an adversary in a competitive zero-sum Markov game, whose optimal solution, i.e., rational strategy, corresponds to a Nash equilibrium. However, finding Nash equilibria requires facing complex saddle point optimization problems, which can be prohibitive to solve, especially for high-dimensional control. In this paper, we propose a novel approach for adversarial RL based on entropy regularization to ease the complexity of the saddle point optimization problem. We show that the solution of this entropy-regularized problem corresponds to a Quantal Response Equilibrium (QRE), a generalization of Nash equilibria that accounts for bounded rationality, i.e., agents sometimes play random actions instead of optimal ones. Crucially, the connection between the entropy-regularized objective and QRE enables free modulation of the rationality of the agents by simply tuning the temperature coefficient. We leverage this insight to propose our novel algorithm, Quantal Adversarial RL (QARL), which gradually increases the rationality of the adversary in a curriculum fashion until it is fully rational, easing the complexity of the optimization problem while retaining robustness. We provide extensive evidence of QARL outperforming RARL and recent baselines across several MuJoCo locomotion and navigation problems in overall performance and robustness. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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A Type-1 Fuzzy Logic Framework for Early Risk Assessment of Gestational Diabetes Mellitus Using Clinical Risk Factors 2

A Type-1 Fuzzy Logic Framework for Early Risk Assessment of ...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Suman, Saurabh Pacharaney, Utkarsha Gote, Pradnyawant M. Faculty of Engineering and - Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Gestational Diabetes Mellitus (GDM) is a condition that can put at risk both mother and fetus but early and proper risk assessment need to be carried out for effective management. This paper discloses a Mamdani-kind Fuzzy Inference System to evaluate GDM risk dependent on clinical parameters like age, parity, glucose levels, body mass index (BMI), and insulin levels. The system can simulate all the numerous risk factors in a more gentle way by the utilization of the triangular membership function that converts accurate clinical data into a fuzzy format. A clear rule base, got from the knowledge maps in the clinical field, is put together from the input data to determining the GDM risk levels. This is neither then defuzzied via the centroid method to generate a determinable score. Results show that this system can be used along with the experts, thus yielding the same results as them, meanwhile, it is capable of providing an intelligible output. It lends further credence to the role of fuzzy logic in uncertainties in medical data and their potential by decision support systems in clinical context. While reasonably beneficial results from the system are reported, the static nature of the membership functions implies, for the future, adaptability in methodologies for relative accuracy. This study still emphasizes that fuzzy logic finds application in settings of clinical delineation, which are distinctly more complex for their intended usefulness for conditions such as GDM here. © 2025 IEEE.

关键词： Risk assessment

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