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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Mao, Jun-Xiang Rui, Yong Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China Information Technology and Data Management Department China Mobile Communications Group Zhejiang Co. Ltd. China Lenovo Research Lenovo Group Ltd. Beijing China

ISBN: (纸本)157735897X

Multi-label metric learning, as an extension of metric learning to multi-label scenarios, aims to learn better similarity metrics for objects with rich semantics. Existing multi-label metric learning approaches employ the common assumption of equal labeling-importance, i.e., all associated labels are considered relevant to the training instance, while there is no differentiation in the relative importance of their semantics. However, this common assumption does not reflect the fact that the importance of each relevant label is generally different, even though such importance information is not directly accessible from the training examples. In this paper, we claim that it is beneficial to leverage the implicit Relative Labeling-Importance (RLI) information to facilitate multi-label metric learning. Specifically, the manifold structure within the feature space is exploited by local linear reconstruction, and then the RLIs are recovered by transferring such structure to the label space. Subsequently, a discrimiative multi-label metric learning framework is introduced to align the predictive modeling outputs with the recovered RLIs, under which instances with similar RLI are implicitly pulled closer to each other, while those with dissimilar RLI are pushed further apart. Comprehensive experiments on benchmark multi-label datasets validate the superiority of our proposed approach in learning effective similarity metrics between multi-label examples. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Nameplates

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Sustainability Analysis of Residential Photovoltaic Systems: A Life Cycle Assessment Approach

Sustainability Analysis of Residential Photovoltaic Systems:...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Lorenzo Gabriel C. Caballa King Harold A. Recto Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

Renewable energy has emerged as a prominent topic in dialogues about energy production sustainability in the Philippines. This is mostly attributed to the excessive dependence of the country on imported petroleum and fossil fuels, which are well recognized for their detrimental effects on the environment. Solar energy is often acknowledged as a very accessible kind of renewable energy, especially in tropical nations like the Philippines, owing to the country's abundant sun irradiation levels throughout the year. Despite the significant progress made in solar technology and its effective implementation in residential environments, some limits continue because of constraints associated with photovoltaic technology, economic feasibility, and environmental implications. The use of the life-cycle assessment (LCA) methodology offers a structured framework for the examination and evaluation of the sustainability aspects pertaining to residential PV systems. This research does this by investigating PV systems throughout their lifecycle, starting with the selection of materials, manufacturing process, operation, and maintenance, and concluding with the environmental impacts at the end-of-life phase. Subsequently, this paper will also discuss the policy implications arising from the study results. The research aims to provide valuable insights into PV residential systems in the Philippines and aspires to contribute to policy-relevant literature in this field.

关键词： Life cycle assessment Photovoltaic systems Renewable energy sources Solar energy Production Recycling Sustainable development

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Design and Development of a Semi-Automated Kitchen Waste Composter

Design and Development of a Semi-Automated Kitchen Waste Com...

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International Conference on computer and Communication engineering, ICCCE

作者： Moshe Coleen N. Adique Aaron Jimson L. Mandap Maria Leonora C. Guico Jan Kevin A. Galicia Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines

Waste generation is a significant challenge exacerbated by factors such as population growth, industrialization, and urbanization, particularly in densely populated areas like Metro Manila. Consequently, effective waste management becomes increasingly crucial. This study focused on the development of a waste management device designed to address waste generation at its source, specifically targeting the residential sector. The researchers have created a semi-automated kitchen waste composter to facilitate the conversion of kitchen waste into valuable fertilizers, even for individuals with limited composting knowledge. The system incorporates a sensor network that monitors key parameters of the composting process, including temperature and moisture levels. An actuator network ensures that these parameters remain within optimal ranges. Additionally, an image processing algorithm has been implemented to detect compost maturity. By implementing this waste management device, households in urban areas can actively contribute to waste reduction efforts. It empowers individuals to participate in composting without requiring extensive expertise. This technology represents a promising solution to mitigate waste generation and promote sustainable practices in residential settings, ultimately contributing to a cleaner and more environmentally friendly urban environment.

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Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression

Wet and Dry Cough Classification System Using Support Vector...

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International Conference on computer and Communication engineering, ICCCE

作者： P.Co Sean Andrei Claudine Anne J. Madamba Maria Leonora C. Guico Jan Kevin A. Galicia Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines

Dry cough has been recognized as a common symptom of coronavirus respiratory diseases, emphasizing the importance of accurately identifying and classifying cough types to mitigate the spread of the disease. The study employs various acoustic features and a Python-based data processing algorithm to extract and analyze the Energy Envelope Peaks, Crest Factors, Zero-Crossings, and Formant Frequencies 1-4 from a dataset of 870 cough samples. The analysis of 347 wet cough sound samples and 523 dry cough samples reveals distinctive characteristics. Wet coughs exhibit a higher number of peaks and zero-crossings, while dry coughs display a slightly higher crest factor on average. Moreover, the F1 and F2 formant frequencies are higher in wet coughs, whereas the F3 and F4 formant frequencies are higher in dry coughs. To classify the cough types, both Support Vector Machine (SVM) and Logistic Regression Method (LRM) classifiers are trained using the identified features. The SVM classifier achieves an average accuracy of 71.26%, sensitivity of 72.73%, specificity of 70.87%, and F1-score of 67.94% during testing. Similarly, the LRM classifier achieves an accuracy of 71.26%, sensitivity of 70.59%, specificity of 71.55%, and F1-score of 68.45%. Such automated classification systems have the potential to aid in the early detection and monitoring of respiratory diseases in enclosed spaces.

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IoT Intelligence Empowered by End-Edge-Cloud Orchestration

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China communications 2022年第7期19卷 152-156页

作者： Yaoxue Zhang Feng Lyu Peng Yang Wen Wu Jie Gao Department of Computer Science and Technology at Tsinghua University China School of Computer Science and Engineering Central South UniversityChangshaChina School of Electronic Information and Communications HUST Frontier Research Center Peng Cheng LaboratoryShenzhenChina 渥太华卡尔顿大学信息技术学院

To support intelligent Internet of Things(IoT)applications,such as autonomous driving,smart city surveillance,and virtual reality(VR)/augmented reality(AR),cloud services are expected to be pushed to the proximity of IoT devices for quality *** instance,to facilitate safe autonomous driving,the service delay of most vehicular applications is required to be within milliseconds,and any information delay may result in dangerous on-road conditions.

关键词： IoT delay driving

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Conversation-Oriented ASR with Multi-Look-Ahead CBS Architecture

Conversation-Oriented ASR with Multi-Look-Ahead CBS Architec...

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

作者： Huaibo Zhao Shinya Fujie Tetsuji Ogawa Jin Sakuma Yusuke Kida Tetsunori Kobayashi Department of Communications and Computer Engineering Waseda University Tokyo Japan Line Corporation Tokyo Japan

During conversations, humans are capable of inferring the intention of the speaker at any point of the speech to prepare the following action promptly. Such ability is also the key for conversational systems to achieve rhythmic and natural conversation. To perform this, the automatic speech recognition (ASR) used for transcribing the speech in real-time must achieve high accuracy without delay. In streaming ASR, high accuracy is assured by attending to look-ahead frames, which leads to delay increments. To tackle this trade-off issue, we propose a multiple latency streaming ASR to achieve high accuracy with zero look-ahead. The proposed system contains two encoders that operate in parallel, where a primary encoder generates accurate outputs utilizing look-ahead frames, and the auxiliary encoder recognizes the look-ahead portion of the primary encoder without look-ahead. The proposed system is constructed based on contextual block streaming (CBS) architecture, which leverages block processing and has a high affinity for the multiple latency architecture. Various methods are also studied for architecting the system, including shifting the network to perform as different encoders; as well as generating both encoders’ outputs in one encoding pass.

关键词： Computational modeling Oral communication computer architecture Signal processing Real-time systems Encoding Delays

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Automated People Counter for Restricted Capacity Monitoring on Philippine Public Utility Bus

Automated People Counter for Restricted Capacity Monitoring ...

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IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： John Neil M Chan Maria Leonora C Guico Carlos Gabriel P De Guzman Jan Kevin A Galicia Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines

The ability to inform transportation businesses and regulatory bodies about the demand for transportation services and how resources may be best deployed to meet this need is strategic to resource allocation and planning of national transportation system. Case in point, the pandemic has forced the government to reevaluate the number of utility buses and its ridership and implement various restrictions and regulations within society. One of the restrictions that is often affected is the seating capacity for Public Utility Vehicles. This study provides a proof of concept to an easier and more efficient way to be able to monitor capacity in a Public Utility Bus through the use of a people counter. Photoelectric sensors will be used to monitor the number of people entering and exiting the bus with information being uploaded to ThingSpeak while also being displayed onboard using an LCD. The system is capable of bi-directional counting through the use of two pairs of sensors and will work most accurately when the sensor pairs are at a distance of at least 12 cm from each other and passengers will board and depart in intervals of one second or greater.

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Scene Plane Recognition via Depth-Infrared Intensity Fusion Using ToF Camera

Scene Plane Recognition via Depth-Infrared Intensity Fusion ...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Yang, Dongzhao An, Dong Wang, Qiang Pan, Zhongqi Yue, Yang School of Information and Communications Engineering Xi'an Jiaotong University Xi'an710049 China Institute of Modern Optics Nankai University Tianjin300350 China Technology Co. Ltd. Tianjin300450 China Department of Electrical & Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States

A scene plane information recognition method is demonstrated based on data fusion using a single ToF camera. This approach effectively tackles general LiDAR's deficiencies in identifying planar content, achieving ... 详细信息

关键词： Data fusion

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Modular Extremely Large-Scale Array Communication:Near-Field Modelling and Performance Analysis

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China communications 2023年第4期20卷 132-152页

作者： Xinrui Li Haiquan Lu Yong Zeng Shi Jin Rui Zhang National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211111China The Chinese University of Hong Kong Shenzhen Research Institute of Big DataShenzhen 518172China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced *** module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical *** accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array *** on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)*** addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are ***,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of *** simulation results are presented to validate our findings.

关键词： modular extremely large-scale array practical deployment projected apertures non-uniform spherical wave near-field modelling

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Implementation of an IoT-Based Car Park Availability Monitoring System

Implementation of an IoT-Based Car Park Availability Monitor...

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IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)

作者： Jerome Daniel P. Navarro Radyn Matthew H. Punla Luisito L. Agustin Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines

To assist drivers, the researchers propose a car park occupancy monitoring system that uses a Raspberry Pi to obtain photos needed by YOLOv7 to determine the presence of vehicles in a parking area. OpenCV is used to count the number of available parking spaces from the analysis of YOLOv7. The information is uploaded to the internet in real-time for access by users of the parking area. The data is uploaded and processed in Google Sheets and is accessed by a chatbot, using FlowXO. The chatbot is then deployed in Facebook Messenger, available to the targeted end users.

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