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43rd Annual Meeting of the cognitive science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Schnell, Eric Xu, Yang Cognitive Science and Mathematics Programs University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

A fundamental goal in cognitive and historical linguistic research on semantic change is to characterize the regularity in how word meanings change over time. We examine a common belief that has not yet been evaluated comprehensively, which asserts that gender of a word influences its direction of semantic change. By this account, female terms like mistress should undergo pejorative change in meaning systematically more so than male terms like master. We evaluate this claim in gender-marked word pairs in English and French respectively as languages without and with grammatical gender. Our results provide supporting evidence for gender asymmetry in semantic change of English words but not French words. Our study raises questions about the generality of the claim about gender asymmetry in semantic change and provides a scalable computational framework for understanding the social roots of word meaning change. © cognitive science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Sentiment analysis

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Utterance as A Bridge: Few-shot Joint Learning of Empathy Detection and Empathy Intent Classification

Utterance as A Bridge: Few-shot Joint Learning of Empathy De...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Liting Jiang Di Wu Zhe Li Shuangyong Song Yanbing Li Hao Huang School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Multi-Lingual Information Technology Urumqi China Institute of Artificial Intelligence (TeleAI) China Telecom Corp Ltd Beijing China Department of Electrical and Electronic Engineering Hong Kong Polytechnic University Hong Kong China Department of Electrical Engineering Stanford University USA Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Empathy detection (ED) and empathy intent classification (EIC) aim to identify the empathy direction expressed in user utterances and the underlying empathy intent behind them. Previous studies show that facilitating information transfer between tasks can enhance model performance. However, the interaction between ED and EIC in few-shot learning remains underexplored. To this end, we identify the challenges in jointly training ED and EIC in a few-shot setting: establishing effective information transfer between them and improving the model’s generalization capability. We propose a novel model called USB. For information transfer, the interactive module maps empathy and empathy intent labels through utterances to model task correlations. For generalization capability, after capturing empathy and empathy intent representations with an adaptive fusion module, we introduce a multi-level contrastive learning strategy to optimize representations at task and label levels, enhancing generalization. Experimental results on two public datasets show that our model outperforms all baselines.

关键词： Training Bridges Adaptation models Intent recognition Focusing Contrastive learning Signal processing Universal Serial Bus Few shot learning Speech processing

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Neural Rendering and Flow-assisted Unsupervised Multi-view Stereo for Real-time Monocular Tracking and Scene Perception

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IEEE Transactions on Automation science and engineering 2025年

作者： Tong, Kevin W. Cai, Yandong Jie, Yu-Wen Duan, Ya Hou, Yuhong Wu, Edmond Q. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence Nanjing210023 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian116024 China Shanxi University School of Automation and Software Engineering Shanxi 237016 China Flight Test Technology and Engineering Center of the Chinese Flight Test Establishment Xian710089 China China Civil Aviation Shanghai Aircraft Airworthiness Certification Center Shanghai200335 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

The existing camera tracking and perception methods mainly rely on sparse SLAM, which limits the dense perception ability of the scene and affects the reliability of auxiliary decision-making. Different from this, this work proposes a real-time tracking and unsupervised dense sensing framework. Firstly, the dense depth value of the scene is predicted by unsupervised multi-view stereo to remove the dependence on labeled data. Then, the quality of synthetic pseudo-reference image is quantified according to the predicted depth map and used as a weighted guidance to train the unsupervised model, thus reducing the ambiguity of feature matching in areas such as specular reflection. Moreover, the sparse optical flow of the keyframes is solved by real-time and robust ORB feature matching operator, which assists the high-precision training of unsupervised depth inference model. To increase the prediction accuracy of occluded area, a novel rendering consistency loss via neural radiance fields is designed to constrain the geometric characteristics of object surface. Finally, dense direct image alignment is performed from a global model to improve the tracking robustness, which is incrementally constructed from dense depth prediction. Extensive experiments on synthetic datasets and real datasets validate the effectiveness and practicability of the proposed work, which is an effective supplement to the existing SLAM work. © 2025 IEEE.

关键词： Stereo image processing

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Fuzzy logic evaluation for student performance analysis in higher education

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AIP Conference Proceedings 2025年第1期3280卷

作者： Anand Swaroop Ravindra Bharadwaj Suhani Yadav Abhishek Singh Shiv Prakash Department of Computer Science & Engineering Institute of Engineering & Technology Dr. A.P.J Abdul Kalam Technology University Lucknow Uttar Pradesh INDIA Department of Physics and Computer Science Dayalbagh Educational Institute Agra Uttar Pradesh INDIA Department of Cognitive Science Dayalbagh Educational Institute Agra Uttar Pradesh INDIA Department of Computer Science & Engineering University of Allahabad Prayagraj Uttar Pradesh INDIA

Higher education establishments are presently confronted with a competitive landscape that includes growing employer demand and Industry 4.0 obstacles. Because of this, graduates are expected by industry and society to possess a broad knowledge base, strong critical thinking abilities, and a positive disposition. Higher education establishments must thus make sure that students succeed in this cutthroat climate. To ensure that this, a comprehensive analysis of the pupils’ performance must be conducted, highlighting both their strengths and weaknesses. This essay concentrated on the annual study of student performance utilizing fuzzy logic assessment techniques. Fuzzy logic was created using a subset of student traits performance evaluation using MATLAB software. In this approach, the evaluation qualities of students (knowledge, communication, etc.) served as the weighing criteria for assessing both the academic performance and the development of the students’ personalities. Next, an expert system built based on the Mamdani technique and employing fuzzy logic was evaluated on a sample of actual student marks. The outcomes of the traditional and fuzzy expert systems were subsequently contrasted. Based on the findings, the causes of the pupils’ shortcomings were determined, including whether they lacked academic or in the formation of personality. This finding might help universities choose the right lectures, exercises, and evaluation methods for both to better meet the industry’s need for human resources and to students.

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CIWARS: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

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Journal of Molecular Biology 2025年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech Blacksburg 24060 VA United States Fralin Life Science Institute Virginia Tech Blacksburg 24060 VA United States Department of Civil and Environmental Engineering Virginia Tech Blacksburg 24060 VA United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24060 VA United States

The rise of antibiotic resistance (AR) poses a substantial threat to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate analysis of longitudinal metagenomic data are lacking. To address this, we introduce CyberInfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive profiling of ARGs and taxonomic profiling of pathogen-associated bacterial taxonomic groups, identifies potential associations of ARGs with mobile genetic elements (MGEs) and pathogen-containing taxa, and assesses resistome risk based on the co-occurrence of ARGs, MGEs, and pathogen-like sequences. Additionally, it detects anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and can serve as a tool to inform effective and timely interventions to mitigate the spread and transmission of AR. Here, CIWARS is demonstrated using longitudinal metagenomic data from a wastewater treatment plant (WWTP) influent and effluent, but it can be extended to any environment. CIWARS provides a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use. The web server is freely available at https://***/. © 2025 Elsevier Ltd

关键词： anomaly detection antibiotic resistance (AR) AR indicator profiling longitudinal metagenomics wastewater-based surveillance

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Avoiding Non-Manhattan Obstacles Based on Projection of Spatial Corners in Indoor Environment

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1190-1200页

作者： Luping Wang Hui Wei the School of Mechanical Engineering University of Shanghai for Science and TechnologyShanghai 200093China the Laboratory of Algorithms for Cognitive Models Shanghai Key Laboratory of Data ScienceSchool of Computer ScienceFudan UniversityShanghai 201203China

Monocular vision-based navigation is a considerable ability for a home mobile robot. However, due to diverse disturbances, helping robots avoid obstacles, especially nonManhattan obstacles, remains a big challenge. In indoor environments, there are many spatial right-corners that are projected into two dimensional projections with special geometric configurations. These projections, which consist of three lines,might enable us to estimate their position and orientation in 3 D scenes. In this paper, we present a method for home robots to avoid non-Manhattan obstacles in indoor environments from a monocular camera. The approach first detects non-Manhattan obstacles. Through analyzing geometric features and constraints,it is possible to estimate posture differences between orientation of the robot and non-Manhattan obstacles. Finally according to the convergence of posture differences, the robot can adjust its orientation to keep pace with the pose of detected non-Manhattan obstacles, making it possible avoid these obstacles by itself. Based on geometric inferences, the proposed approach requires no prior training or any knowledge of the camera’s internal parameters,making it practical for robots navigation. Furthermore, the method is robust to errors in calibration and image noise. We compared the errors from corners of estimated non-Manhattan obstacles against the ground truth. Furthermore, we evaluate the validity of convergence of differences between the robot orientation and the posture of non-Manhattan obstacles. The experimental results showed that our method is capable of avoiding non-Manhattan obstacles, meeting the requirements for indoor robot navigation.

关键词： Avoiding obstacle monocular vision navigation non-Manhattan obstacle spatial corner

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EXTENDED PERSON-MACHINE INTERFACE

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ARTIFICIAL INTELLIGENCE 1984年第2期22卷 157-218页

作者： REICHMANADAR, R Department of Electrical Engineering and Computer Science Institute for Cognitive Science University of California La Jolla CA 92093 U.S.A

Current computer natural language systems lack discourse capabilities. They mainly treat utterances in isolation and are not able to track the coherence of extended dialogue. In general, however, human communication and task performance are not accomplished in this manner. Useful interface systems should incorporate extended conversational power. In this paper, we present an abstract computational system for extended person-machine interface. The system is theoretically motivated by an investigation into the human conversational system. The computer system incorporates many of the rules used by individuals in everyday interactions. Use of these rules enable implicit communication between conversants about their underlying models of an interaction. They function in place of explicit meta-communication about the structure of an ongoing exchange. Such communication is necessary since building compatible models of an interaction is necessary for effective interaction. Providing a comparable system of rules for the computer is then mandatory if we expect it to be able to integrate subsequent requests and exchanges to earlier ones.

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AgriField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel

arXiv

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arXiv 2025年

作者： Kimara, Elvis Hadadi, Mozhgan Godbersen, Jackson Balu, Aditya Jubery, Talukder Li, Yawei Krishnamurthy, Adarsh Schnable, Patrick S. Ganapathysubramanian, Baskar Department of Computer Science Iowa State University Ames United States Translational AI Research and Education Center Ames United States Department of Mechanical Engineering Iowa State University Ames United States Plant Science Institute Iowa State University Ames United States Interdepartmental Genetics and Genomics Graduate Program Iowa State University Ames United States Department of Agronomy Iowa State University Ames United States

The application of artificial intelligence (AI) in three-dimensional (3D) agricultural research, particularly for maize, has been limited by the scarcity of large-scale, diverse datasets. While 2D image datasets are abundant, they fail to capture essential structural details such as leaf architecture, plant volume, and spatial arrangements that 3D data provide. To address this limitation, we present AgriField3D (website), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research. Our dataset comprises over 1,000 high-quality point clouds collected using a Terrestrial Laser Scanner, complemented by procedural models that provide structured, parametric representations of maize plants. These procedural models, generated using Non-Uniform Rational B-Splines (NURBS) and optimized via a two-step process combining Particle Swarm Optimization (PSO) and differentiable programming, enable precise, scalable reconstructions of leaf surfaces and plant architectures. To enhance usability, we performed graph-based segmentation to isolate individual leaves and stalks, ensuring consistent labeling across all samples. We also conducted rigorous manual quality control on all datasets, correcting errors in segmentation, ensuring accurate leaf ordering, and validating metadata annotations. The dataset further includes metadata detailing plant morphology and quality, alongside multi-resolution subsampled versions (100k, 50k, 10k points) optimized for various computational needs. By integrating point cloud data of field grown plants with high-fidelity procedural models and ensuring meticulous manual validation, AgriField3D provides a comprehensive foundation for AI-driven phenotyping, plant structural analysis, and 3D applications in agricultural research. © 2025, CC BY-NC-SA.

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The development of a summer study abroad program for engineering and computer science students at Baylor University

The development of a summer study abroad program for enginee...

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34th Annual Frontiers in Education: Expanding Educational Opportunities Through Partnerships and Distance Learning - Conference Proceedings, FIE

作者： Fry, Cynthia C. Maastricht Summer Study Abroad Program School of Engineering and Computer Science Baylor University One Bear Place #97356 Waco TX 76798

The Baylor In Maastricht (BIM) Summer Study Abroad program is a 41-day study abroad program in the Netherlands. Baylor In Maastricht is a summer program that is sponsored by the School of engineering and science of Baylor University in Waco, Texas, in conjunction with the Universiteit Maastricht, in the Netherlands. The program gives engineering and computer science students an opportunity to take two Baylor courses (six hours), while also providing an abroad experience in the heart of Europe. It also answers the charge given four years ago by the Dean of the School of engineering and computer science to get our students out of the building! This paper will discuss the goals and objectives of such a program, and will chronicle the design, development, and execution of the resulting study abroad program. We will explore, in depth, the number and type of participants, the details of trip planning and organization, the decision on which courses to offer, planned weekend group trips, the facilities and support of the Universiteit Maastricht, and accommodations. This paper will also discuss the execution of the first two study abroad programs to Maastricht, the Netherlands - what worked, what didn't work, what was good, where we have improved. A summary of the student evaluations from the program is included. © 2004 IEEE.

关键词： engineering education

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Chaining and the formation of spatial semantic categories in childhood 43

Chaining and the formation of spatial semantic categories in...

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43rd Annual Meeting of the cognitive science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Xu, Aotao Xu, Yang Department of Computer Science University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

Children face the problem of extending a limited spatial lexicon to potentially infinite spatial situations. Previous work has examined how spatial semantic categories may be formed in child development, but it is unclear how children extend these categories to novel situations over the developmental time course. Drawing on cognitive linguistic theories of category extension, we present a framework that models the incremental extension of spatial relational words to novel situations through time. We describe a longitudinal dataset and computational analyses for investigating the extension of spatial word meanings in a developmental setting. Our preliminary results suggest that the formation of spatial categories takes place through an exemplar-based process of chaining, similar to the process underlying the growth of linguistic categories in history. Our work offers opportunities to explore the connection between ontogeny and phylogeny in the process of word meaning extension. © cognitive science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Semantics

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