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IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback 25

IdeaSynth: Iterative Research Idea Development Through Evolv...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Pu, Kevin Feng, K. J. Kevin Grossman, Tovi Hope, Tom Dalvi Mishra, Bhavana Latzke, Matt Bragg, Jonathan Chang, Joseph Chee Siangliulue, Pao Department of Computer Science University of Toronto TorontoON Canada Human Centered Design & Engineering University of Washington SeattleWA United States Allen Institute for Artificial Intelligence SeattleWA United States AI2 Allen Institute for AI SeattleWA United States

ISBN: (纸本)9798400713941

Research ideation involves broad exploring and deep refining ideas. Both require deep engagement with literature. Existing tools focus primarily on broad idea generation, yet offer little support for iterative specification, refinement, and evaluation needed to further develop initial ideas. To bridge this gap, we introduce IdeaSynth, a research idea development system that uses LLMs to provide literature-grounded feedback for articulating research problems, solutions, evaluations, and contributions. IdeaSynth represents these idea facets as nodes on a canvas, and allow researchers to iteratively refine them by creating and exploring variations and combinations. Our lab study (N = 20) showed that participants, while using IdeaSynth, explored more alternative ideas and expanded initial ideas with more details compared to a strong LLM-based baseline. Our deployment study (N = 7) demonstrated that participants effectively used IdeaSynth for real-world research projects at various ideation stages from developing initial ideas to revising framings of mature manuscripts, highlighting the possibilities to adopt IdeaSynth in researcher's workflows. © 2025 Copyright held by the owner/author(s).

关键词： Research Ideation Scientific Literature Human-ai Collaboration

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Skill-Based Edge-Brain Smart Manufacturing: A Case of Grasp Pose Selection Skill 6

Skill-Based Edge-Brain Smart Manufacturing: A Case of Grasp ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Lee, Sukhan Jang, Byungwoo Hyeon, Seokjong Lee, Soojin Lee, Jaesun Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Artificial Intelligence Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of AI System Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Regional Industry Innovation Korea Institute of Industrial Technology Ulsan44413 Korea Republic of

The edge-brain framework (EBF) is a S/W framework that allows manufacturers to easily integrate ai-empowered robot work skills into a legacy production system in an edge computing environment with minimal time and effort. For EBF-based smart manufacturing, is a skill-based task-level robot programming environment that plays a key role. In this paper, we present the grasp pose selection skill as a case study to demonstrate the proposed skill-based edge-brain smart manufacturing. The proposed grasp-pose selection skill determines the optimal 6D grasp pose associated with the target object in a cluttered workspace by taking into consideration grasp stability, object manipulability, and collision-free path efficacy. To achieve a real-time operation of optimal grasp pose selection skill, deep learning networks are designed to compute, in particular, the manipulability and collision-free path efficacy indices. Experiments demonstrate the effectiveness of the proposed real-time grasp pose selection skill developed for EBF-based smart manufacturing. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart manufacturing

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ECAPA++: Fine-grained Deep Embedding Learning for TDNN Based Speaker Verification 24

ECAPA++: Fine-grained Deep Embedding Learning for TDNN Based...

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24rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2023

作者： Liu, Bei Qian, Yanmin MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In this paper, we aim to bridge the performance gap between TDNN and 2D CNN based speaker verification systems. Specifically, three types of architectural enhancements to ECAPA-TDNN are proposed: 1) follow depth-first design to significantly increase network depth while maintaining its complexity. 2) introduce recursive convolution to better capture fine-grained speaker information. 3) propose pyramid-based multi-path feature enhancement module to yield more discriminative speaker representation. Experiments on Voxceleb show that our final model, named ECAPA++, achieves 25%, 23% and 24% relative improvements on Vox1-O, E and H respectively, while with 2.4x fewer parameters and 2.3x fewer FLOPs over the previous best TDNN-based system. Meanwhile, it is comparable to the state-of-the-art ResNet-based systems with higher computational efficiency. In addition, further performance gains can be achieved by fusing ECAPA++ and ResNet-based systems. © 2023 International Speech Communication Association. All rights reserved.

关键词： Computational efficiency

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Phishing Websites Classification using Extreme Learning Machine

Phishing Websites Classification using Extreme Learning Mach...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Swathi Gowroju Shilpa Choudhary G. Jyothi B. Sabitha B. Bikram Kumar R. Srilakshmi Department of Computer Science &Engineering (AI& ML) Sreyas Institute of Engineering and Technology Hyderabad India Department of Computer Science &Engineering(AIML) Neil Gogte Institute of Technology Hyderabad India Department of CSE(DS) Sreyas Institute of Engineering and Technology Hyderabad India Department of Computer Science & Engineering CVR College of Engineering Hyderabad India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of ET(CSE-DS) department CVR College of Engineering Hyderabad India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Phishing presents a widespread and perilous menace within the domain of cybercrime, functioning as a rapid and uncomplicated means for cybercriminals to get confidential personal data. Phishing attacks specifically target unsuspecting persons with the intention of obtaining sensitive information, such as usernames, passwords, and financial credentials. Phishing websites frequently incorporate indicators inside their textual content and browser cookies. In order to address this issue, the suggested method makes use of the UCI Phishing Websites Data and implements categorization based on Extreme Learning Machine (ELM). In contrast to gradient-based algorithms, the ELM approach employs a solitary hidden layer of neurons that include input weights produced randomly. This design facilitates effective feature learning from data with a high number of dimensions, eliminating the need for iterative changes. The evaluation yielded a remarkable accuracy of 95.34% when compared to other machine learning techniques such as Random Forest Classification and Logistic Regression.

关键词： Representation learning Logistic regression Machine learning algorithms Extreme learning machines Phishing Neurons Passwords

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Optimizing Resume Parsing Processes by Leveraging Large Language Models

Optimizing Resume Parsing Processes by Leveraging Large Lang...

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IEEE Region 10 Symposium

作者： Vazzula Manish Yugandhar Manchala Y. Vijayalata Sudeep Banerjee Chopra Kothi Yashwanth Reddy Dept. of Information Technology Vardhaman College of Engineering Hyderabad India Dept. of Computer Science & Engineering Vardhaman College of Engineering Hyderabad India Dept. of Computer Science (AI & ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

In the present era of internet revolution, organizations have to go through numerous resumes in order to identify the most suitable candidates for their Job Description (JD), while ensuring that no acquisition of talent is overlooked through human mistakes. Thus, tools like Applicant Tracking Systems (ATS) have taken over the human process of resume screening, enabling assessment of thousands of resumes in a matter of seconds. Although these technologies are incredibly effective, they are not flawless. Consequently, highly qualified individuals may miss out on said opportunities if their resumes are not formatted correctly. Therefore, individuals must ensure that their resume is appropriately structured prior to submitting it to any organization. The study centers on the present literature and suggests an enhanced approach for parsing resumes by leveraging Large Language Models (LLMs).

关键词： Large language models Resumes Organizations Cleaning Data mining IEEE Regions

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HuBERT-AGG: Aggregated Representation Distillation of Hidden-Unit Bert for Robust Speech Recognition 48

HuBERT-AGG: Aggregated Representation Distillation of Hidden...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Wei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

Self-supervised learning (SSL) has attracted widespread research interest since many successful SSL approaches such as wav2vec 2.0 and Hidden-unit BERT (HuBERT) have achieved promising results on speech-related tasks such as automatic speech recognition (ASR). However, few works have been conducted to improve the noise robustness of SSL models. In this paper, we propose HuBERT-AGG, a novel method that learns noise-invariant SSL representations for robust speech recognition by distilling aggregated layer-wise representations. Specifically, we learn an aggregator that computes the weighted sum of all hidden states of a pretrained vanilla Hu-BERT by fine-tuning it on a small portion of labeled data. Then a noise-robust HuBERT is trained on the simulated noisy speech by distilling from the aggregated representations and layer-wise hidden states produced by a pretrained vanilla HuBERT with parallel original speech as input. Experiments on libriSpeech simulated noisy test sets show 13.1%-17.0% relative word error rate (WER) reduction with very slight degradation on the original test sets. On CHiME-4 1-channel real speech test sets, we have surpassed the best results achieved by all published fully supervised ASR models as well as other SSL approaches adopting the same data usage as ours. © 2023 IEEE.

关键词： Distillation

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The Influence of Mobile Commerce on Consumer Behavior: A FCM-RF-DNN Analysis

The Influence of Mobile Commerce on Consumer Behavior: A FCM...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Akram, Nawab Dahiya, Suman Chittapragada, Hemanand Periyasamy, V.M. Vidhya, P. Chauhan, Amit Magadh Professional Institute Patna India Universal Ai University Department of Management Karjat India Madanapalle Institute of Technology & Science Computer Science & Engineering Madanapalle India Sona College of Technology Department of Eee Salem India M.Kumarasamy College of Engineering Department of Artificial Intelligence and Data Science Karur India Department of Life Sciences Bengaluru India

ISBN: (纸本)9798350360660

For m-commerce vendors, the difficulty is to decipher what factors impact customer actions in the ubiquitous mobile setting. In addition, companies are attempting to incorporate social media into their mobile approach in some way. This proposed approach to the findings of a qualitative exploratory study regarding the use of social media and smartphones within the framework of mobile commerce. Keep in mind the order of importance while doing data preprocessing, feature selection, and training the model. The usual steps in getting data ready for processing, such as cleaning it, identifying users and sessions, and finding episodes. The IS-DT suggested method's implementation technique is utilized in feature selection. Unified FCM-RF-DNN models need to be trained after features have been retrieved. Two state-of-the-art approaches, RF and DNN, are outperformed by the suggested approach. Following the implementation of the method, accuracy improved by 96.13%. © 2024 IEEE.

关键词： Mobile commerce

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Mitigating Catastrophic Forgetting in Deep Learning Models for Sentiment Analysis

Mitigating Catastrophic Forgetting in Deep Learning Models f...

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International Conference on Advances in Information Technology (ICaiT)

作者： J. Ranjith Santhi Baskaran B. Adithya Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Inofrmation Technology Puducherry Technological University Puducherry India Department of CSE(AI&ML) Geethanjali College of Engineering and Technology Hyderabad India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Deep learning models have been employed to evaluate sentiment analysis. However, the issue of catastrophic forgetting is frequently seen in these models. Cognitive decline occurs when the model's performance on previously learned tasks deteriorates as it acquires new ones. A study is conducted to investigate the impact of catastrophic forgetting on sentiment analysis algorithms and propose potential solutions to address this issue. This study evaluates the adaptability of Elastic Weight Consolidation (EWC) and Learning without Forgetting (LwF) in effectively handling novel sentiment analysis tasks while preserving the model's performance on previously acquired tasks. Experimental results on the Amazon Product Reviews dataset demonstrate that both EWC and LwF significantly reduce the impact of catastrophic forgetting, with LwF outperforming EWC in terms of sentiment classification accuracy. Our findings highlight the importance of addressing catastrophic forgetting in sentiment analysis models to ensure their robustness and reliability in real-world applications.

关键词： Deep learning Training Sentiment analysis Analytical models Adaptation models Accuracy Reviews Robustness Data models Real-time systems

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Prediction of Weather Forecasting with Long Short-Term Memory using Deep Learning 4

Prediction of Weather Forecasting with Long Short-Term Memor...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Karthika, S. Priyanka, T. Indirapriyadharshini, J. Sadesh, S. Rajeshkumar, G. Rajesh Kanna, P. Intelligence and Machine Learning Bannari Amman Institue of Technology Department of Artificial Sathyamangalam India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India Sri Krishna College of Engineering and Technology Department of Mechatronics Engineering Coimbatore India Velalar College of Engineering and Technology Department of Ai and Ds Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350300888

Weather forecasting has a greater effect on human life, recently this filed has gained a significant research attention. The existing numerical weather prediction models are very complex to solve. Therefore, the primary goal of this research work is to create a novel and lightweight weather forecasting model. Here, the time series data have been used from Kaggle website. The obtained dataset is then processed with the help of deep learning techniques. The Long Short-Term Memory (LSTM) algorithm is used to produce better result with more accuracy when compared with other deep learning methodologies. Therefore, from the results it is evident that the proposed model can be used to predict the weather conditions with greater accuracy and assist farmers and others who depend on weather. © 2023 IEEE.

关键词： Long short-term memory

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Enhancing Patient Care: A Data-Driven Approach Using Decision Tree-ELM Models for Improved Safety and Quality

Enhancing Patient Care: A Data-Driven Approach Using Decisio...

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Advancement in Technology (ICONAT), International Conference for

作者： Supriya Shrikant Telsang Akhya Sanjay Singh Simarpreet Kaur Bramah Hazela Shikha Singh Hemanand Chittapragada Vishwakarma Institute of Technology Bibwewadi Pune Maharashtra India Computer Engineering (AI & ML) Shah & Anchor Kutchhi Engineering College Mumbai India Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

In recent years, educational initiatives that seek to combine teaching medical students about patient safety and quality improvement (QI) have received funding from both the World Health Organization and the American Association of Medical Colleges. When developing curriculum, it is important to keep in mind the interests and values of the target audience. The proposed used a poll to find out how students felt about lessons on patient safety and quality improvement. It would be more efficient to incorporate patient safety and quality improvement into clinical education rather than teach it in separate computer modules or during pre-clinical lectures. This proposed approach consists of three stages, which are feature selection, model training, and preprocessing. Before doing thorough text processing, stop words are typically filtered out in conventional methods of natural language processing and information retrieval. Unigram and bigram selection are part of feature selection. During the entire model training process, a Decision Tree-ELM Ensemble was utilized. This innovative approach outperforms ELM and Decision Tree with an average accuracy of ${9 4 . 3 6 \%}$.

关键词： Training Accuracy Medical services Feature extraction Information filters Natural language processing Safety Decision trees Systems thinking Text processing

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