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

作者： Wenlong Dong Qing Zhu Qirong Mao School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Video Character Social Relationship Recognition (VCSRR) requires a comprehensive consideration about spatio-temporal and multi-modal clues in videos. Most existing methods mainly focus on integrating multi-modal clues and modeling interactions among characters. However, they fail to discover key clues in the complex video data or fully understand the clues related to social relationships. In this article, we propose a novel Large Language Model Enhanced Key Clues Selection (LE-KCS) framework to address the aforementioned issues. The core of LE-KCS is to mine multi-scale key clues from the perspectives of time, space and multi-modality, then transfer the knowledge about social relationships of the Large Language Model to VCSRR for understanding the selected clues. We evaluated LE-KCS on the MovieGraphs dataset and the experimental results indicate that our proposed LE-KCS achieves state-of-the-art performance.

关键词： Accuracy Large language models Speech recognition Signal processing Acoustics Character recognition Speech processing

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Global Enhanced Frame Prompt Tuning for Sound Event Detection

Global Enhanced Frame Prompt Tuning for Sound Event Detectio...

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

作者： Shiyu Yu Lijian Gao Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Sound Event Detection (SED) often employs pre-trained models to address data scarcity issues. However, existing systems usually treat the pretrained models as frozen feature extractors, resulting in suboptimal efficiency, or fully fine-tune the pretrained models, which requires substantial computational resources. To fully leverage the knowledge from pretrained models, we propose a novel Global Enhanced Frame Prompt Tuning (GE-FPT) framework, providing global and local insights tailored for SED tasks. Additionally, Frame Prompt Tuning (FPT) is proposed in our GE-FPT to effectively explore local temporal information, i.e., temporal details and context, which is essential for SED tasks, and in particular, for precise event boundary detection. Extensive experiments claim that our approach significantly outperforms full fine-tuning methods while substantially reducing computational costs. Our system achieves new state-of-the-art results, with PSDS1/PSDS2 scores of 0.628/0.845 on the DCASE2023 Challenge Task4 dataset. The source code is publicly available 1 .

关键词： Event detection computational modeling Source coding Signal processing Feature extraction data models Acoustics computational efficiency Speech processing Tuning

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Social network analysis of manga: similarities to real-world social networks and trends over decades

arXiv

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arXiv 2023年

作者： Sugishita, Kashin Masuda, Naoki Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Center for Computational Social Science Kobe University Kobe657-8501 Japan

Manga, Japanese comics, has been popular on a global scale. Social networks among characters, which are often called character networks, may be a significant contributor to their popularity. We collected data from 162 popular manga that span over 70 years and analyzed their character networks. First, we found that many of static and temporal properties of the character networks are similar to those of real human social networks. Second, the character networks of most manga are protagonist-centered such that a single protagonist interacts with the majority of other characters. Third, the character networks for manga mainly targeting boys have shifted to denser and less protagonist-centered networks and with fewer characters over decades. Manga mainly targeting girls showed the opposite trend except for the downward trend in the number of characters. The present study, which relies on manga data sampled on an unprecedented scale, paves the way for further population studies of character networks and other aspects of comics. Copyright © 2023, The Authors. All rights reserved.

关键词： Population statistics

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Optimizing Educational Spaces: Achieving Optimal Thermal Comfort, Occupancy Precision, and Energy Efficiency through the Integration of IoT Sensor Technology and Fuzzy Logic Control

Optimizing Educational Spaces: Achieving Optimal Thermal Com...

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IEEE Southeastcon

作者： Hiba Darwish Koundinya Challa Anisha Sharma Courey Graves AKM Kamrul Islam Raymond Tesiero Issa W. AlHmoud Balakrishna Gokaraju Computational Data Science and Engineering North Carolina Agricultural and Technical State University Greensboro USA Electrical and Computer Engineering North Carolina Agricultural and Technical State University Greensboro USA Civil Architectural and Environmental Engineering North Carolina Agricultural and Technical State University Greensboro USA

Individuals allocate a substantial portion of their time within indoor environments, wherein the thermal conditions wield considerable influence over their well-being and efficacy across diverse domains. This study employs a suite of sensors, notably encompassing an MS1100 CO2 concentration level, a DHT 11 humidity sensor, and a Logitech web camera for visual data acquisition, to monitor the occupancy of enclosed spaces. Principal aims encompass discerning the optimal operating temperature, establishing a state of thermal comfort, and managing energy consumption, particularly pertaining to illumination. The determination of the optimal operational temperature relies on a specific model, while the delineation of the thermal comfort envelope adheres to the PMV methodology as delineated by the ASHRAE 55 standard, facilitated through the CBE tool. The secondary objective entails a dual consideration of room occupancy and C02 concentration, with fuzzy logic control (FLC) being employed to regulate lighting in response to fluctuations in occupancy and C02 levels. The investigation underscores that prudent management of thermal comfort yields enhancements in energy efficiency. Nonetheless, shortcomings in parameter configuration within the PMV methodology are acknowledged.

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Experimenting on Markov Decision Processes with Local Treatments

arXiv

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arXiv 2024年

作者： Chen, Shuze Simchi-Levi, David Wang, Chonghuan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200030 China Institute for Data Systems and Society Department of Civil and Environmental Engineering Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States Center for Computational Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Utilizing randomized experiments to evaluate the effect of short-term treatments on the short-term outcomes has been well understood and become the golden standard in industrial practice. However, as service systems become increasingly dynamical and personalized, much focus is shifting toward maximizing long-term cumulative outcomes, such as customer lifetime value, through lifetime exposure to interventions. To bridge this gap, we investigate the randomized experiments within dynamical systems modeled as Markov Decision Processes (MDPs). Our goal is to assess the impact of treatment and control policies on long-term cumulative rewards from relatively short-term observations. We first develop optimal inference techniques for assessing the effects of general treatment patterns. Furthermore, recognizing that many real-world treatments tend to be fine-grained and localized for practical efficiency and operational convenience, we then propose methods to harness this localized structure by sharing information on the non-targeted states. Our new estimator effectively overcomes the variance lower bound for general treatments while matching the more stringent lower bound incorporating the local treatment structure. Furthermore, our estimator can optimally achieve a linear reduction with the number of test arms for a major part of the variance. Finally, we explore scenarios with perfect knowledge of the control arm and design estimators that further improve inference efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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WikiIns: A High-Quality dataset for Controlled Text Editing by Natural Language Instruction

arXiv

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arXiv 2023年

作者： Chen, Xiang Li, Zheng Wan, Xiaojun Wangxuan Institute of Computer Technology Peking University China Center for Data Science Peking University China The MOE Key Laboratory of Computational Linguistics Peking University China Schools of Electronic Engineering and Computer Science Peking University China

Text editing, i.e., the process of modifying or manipulating text, is a crucial step in human writing process. In this paper, we study the problem of controlled text editing by natural language instruction. According to a given instruction that conveys the edit intention and necessary information, an original draft text is required to be revised into a target text. Existing automatically constructed datasets for this task are limited because they do not have informative natural language instruction. The informativeness requires the information contained in the instruction to be enough to produce the revised text. To address this limitation, we build and release WikiIns, a high-quality controlled text editing dataset with improved informativeness. We first preprocess the Wikipedia edit history database to extract the raw data (WikiIns-Raw). Then we crowdsource high-quality validation and test sets, as well as a small-scale training set (WikiIns-Gold). With the high-quality annotated dataset, we further propose automatic approaches to generate a large-scale "silver" training set (WikiIns-Silver). Finally, we provide some insightful analysis on our WikiIns dataset, including the evaluation results and the edit intention analysis. Our analysis and the experiment results on WikiIns may assist the ongoing research on text editing. The dataset, source code and annotation guideline are available at https: //***/CasparSwift/WikiIns. Copyright © 2023, The Authors. All rights reserved.

关键词： Silver

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An Effective Implementation of Detection and Retrieval Property of Episodic Memory 22

An Effective Implementation of Detection and Retrieval Prope...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Aniket Sharma Pramod Kumar Singh Jay Prakash Computational Intelligence and Data Mining Research Laboratory ABV-Indian Institute of Information Technology and Management India Department of Computer Science and Engineering National Institute of Technology India

ISBN: (纸本)9781450396943

A deep understanding of the brain can lead to significant breakthroughs in Artificial Intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With the intention of better understanding the episodic memory aspect of the human mind, we propose a deep learning model to implement the detection and retrieval properties of human episodic memory, a part of long-term memory. A model based on LSTM and CNN is proposed, which follows the architectural methodology of Rosenblatt’s experiential memory model. A comparison of detection efficiency and accuracy and the proposed model’s retrieval property with a recently suggested method demonstrate its effectiveness and superiority.

关键词： Long Short-Term Memory Rosenblatt’s Experiential Memory Model Episodic Memory Recurrent Convolutional Networks

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Accuracy of a one-dimensional reduction of dynamical systems on networks

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Physical Review E 2022年第2期105卷 024305-024305页

作者： Prosenjit Kundu Hiroshi Kori Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Department of Complexity Science and Engineering The University of Tokyo Chiba 277-8561 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Resilience is an ability of a system with which the system can adjust its activity to maintain its functionality when it is perturbed. To study resilience of dynamics on networks, Gao et al. [Nature (London) 530, 307 (2016)] proposed a theoretical framework to reduce dynamical systems on networks, which are high dimensional in general, to one-dimensional dynamical systems. The accuracy of this one-dimensional reduction relies on three approximations in addition to the assumption that the network has a negligible degree correlation. In the present study, we analyze the accuracy of the one-dimensional reduction assuming networks without degree correlation. We do so mainly through examining the validity of the individual assumptions underlying the method. Across five dynamical system models, we find that the accuracy of the one-dimensional reduction hinges on the spread of the equilibrium value of the state variable across the nodes in most cases. Specifically, the one-dimensional reduction tends to be accurate when the dispersion of the node's state is small. We also find that the correlation between the node's state and the node's degree, which is common for various dynamical systems on networks, is unrelated to the accuracy of the one-dimensional reduction.

关键词： Complex systems Dynamics of networks Network resilience

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Optimizing HVAC Efficiency via Deep Neural Networks for Real-Time Classroom Occupancy

Optimizing HVAC Efficiency via Deep Neural Networks for Real...

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IEEE Southeastcon

作者： Koundinya Challa Anisha Sharma Hiba Darwish Issa W. AlHmoud AKM Kamrul Islam Corey Graves Raymond Tesiero Balakrishna Gokaraju Computational Data Science and Engineering North Carolina Agricultural and Technical State university Greensboro USA Electrical and Computer Engineering North Carolina Agricultural and Technical State university Greensboro USA Civil Architectural and Environmental Engineering North Carolina Agricultural and Technical State university Greensboro USA

Accurately determining the number of occupants in a room is crucial for optimizing smart environments and energy efficiency in HVAC systems. This paper presents a deep learning approach for precise, real-time classroom occupancy estimation to facilitate smart HVAC control. Utilizing a YOLOv4 object detection model, trained on an extensive dataset of labeled human faces, we developed a robust computer vison model with OpenCV libraries This model performs facial recognition and occupant counting through live video feeds from a Logitech c20 camera, achieving over 98% accuracy in typical classroom settings. We investigate the different techniques to address challenges such as occlusion and variability. The integration of our occupancy estimation model with HVAC control systems underscores a significant stride towards achieving energy conservation and sustainability goals in educational institutions, aligning with the emerging paradigms of smart building management systems.

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Topological Correction of Subject-Level Intrinsic Connectivity Networks

Topological Correction of Subject-Level Intrinsic Connectivi...

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IEEE International Symposium on Biomedical Imaging

作者： Noah Lewis Armin Iraji Robyn Miller Vince D. Calhoun School of Computational Science and Engineering Georgia Institute of Technology Tri-Institutional Center for Translational Research in Neuroimaging Data Science (TReNDS) Georgia Institute of Technology Georgia State University Emory University School of Electrical and Computer Engineering Georgia Institute of Technology

Over the last several decades, researchers have sought to capture the underlying functional activity of the human brain from functional magnetic resonance imaging (fMRI). One well-studied and promising avenue of research is independent component analysis (ICA) to capture the maximally independent set of elements known as functional networks. These functional networks are represented as spatial maps or 3D images in which each voxel has a value associated with the given network. The current state-of-the-art methods use group-level spatial maps as a reference to provide estimates of subject-level maps, which are vulnerable to the low contrast-to-noise (CNR) ratio of fMRI signals. However, such approaches do not account for all group-level spatial information. As such, their subject-level estimate is still quite noisy. This work presents a novel method that leverages the topological properties of the group maps to improve subject-level estimations. We show that adding topological similarity constraints also improves subject-specific information.

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