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SSRN 2023年

作者： Zhang, Jiehua Yang, Zhiwen Li, Liang Chen, Chuqiao Wang, Hongkui Wang, Tingyu Yan, Chenggang Gong, Yihong School of Software Engineering Xi’an Jiaotong University Xi’an710000 China Hangzhou Dianzi University Hangzhou310018 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Lishui Institute of Hangzhou Dianzi University Hangzhou310018 China Department of Automation

Depth estimation with sparse reference has emerged recently, and predicts depth map from a monocular image and a set of depth reference samples. Previous works randomly select reference samples by sensors, leading to severe depth bias as this sampling is independent to image semantic and neglects the unbalance of depth distribution in regions. This paper proposes a Coplane-Constrained sparse Depth (CCD) sampling to explore representative reference samples, and design a Local Depth Propagation (LDP) network for complete the sparse point cloud map. This can capture diverse depth information and diffuse the valid points to neighbors with geometry prior. Specifically, we first construct the surface normal map and detect coplane pixels by superpixel segmenting for sampling references, whose depth can be represented by that of superpixel centroid. Then, we introduce local depth propagation to obtain coarse-level depth map with geometric information, which dynamically diffuses the depth from the reference to neighbors based on local planar assumption. Further, we generate the fine-level depth map by devising a pixel-wise focal loss, which imposes the semantic and geometry calibration on pixels with low confidence in coarse-level prediction. Extensive experiments on public datasets demonstrate that our model outperforms SOTA depth estimation and completion methods. © 2023, The Authors. All rights reserved.

关键词： Semantics

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SJA: Server-driven Joint Adaptation of Loss and Bitrate for Multi-Party Realtime Video Streaming

SJA: Server-driven Joint Adaptation of Loss and Bitrate for ...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Kai Shen Dayou Zhang Zi Zhu Lei Zhang Fangxin Wang Dan Wang The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen School of Science and Engineering The Chinese University of Hong Kong Shenzhen College of Computer Science and Software Engineering Shenzhen University Peng Cheng Laboratory The Guangdong Provincial Key Laboratory of Future Networks of Intelligence Department of Computing The Hong Kong Polytechnic University

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing significant challenges to service providing. First, unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as bitrate and *** this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based relaxation and the SJA algorithm are further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ∼ 46.5%.

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Automatic segmentation for meniscus magnetic resonance images of knee joint based on Mask region-based convolution neural network 5

Automatic segmentation for meniscus magnetic resonance image...

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5th International Conference on Computer Science and Artificial Intelligence, CSAI 2021

作者： Zhang, Liyan Zhou, Hao Wang, Juan Wang, Lei Xia, Chengyi Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin300384 China Department of Knee Surgery Tianjin Hospital Tianjin300211 China

ISBN: (纸本)9781450384155

Over the past two decades, magnetic resonance imaging (MRI) has been widely applied into the diagnosis of knee joint diseases. Due to the complexity and diversity of MRI data, traditional feature extraction requires manual searching for features to segment meniscus, and the final segmentation results still need to be further filtered. Therefore, it is necessary to design a novel method to automatically extract features directly from images. In this study, we develop a framework to implement this goal by using a mask region-based convolution neural network (Mask R-CNN) without manual intervention. In order to highlight the proportion of meniscus, we first preprocess the original image data so that it is reduced to about 1/8 of the original size, and then input the preprocessed image data into the trained Mask R-CNN. Afterwards, transfer learning is used to generate the weight of our network. By testing 1000 images, the mean intersection over union (IOU) and dice similarity coefficient (DSC) are up to 83.68% and 91.13%, respectively. The current results demonstrate that our approach is feasible and has a potential significance in clinical practice. © 2021 ACM.

关键词： Magnetic resonance imaging

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Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Comparative Analysis of Machine Learning Models for Students’ Performance Prediction

Comparative Analysis of Machine Learning Models for Students...

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International Conference on Advances in Digital Science, ICADS 2021

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates Al Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9783030717810

Machine learning for education is an emerging discipline where a model is developed based on training data to make predictions on students’ performance. The main aim is to identify students who would have difficulty in their learning and to take precautionary measures to help them. In this paper, we conduct a comparative analysis of the most used machine learning classification models in the literature. We evaluate the performance of the models in terms of accuracy, F-measure, and execution time using two real-life education datasets. The performance of the models is data-driven. We give insights into the models’ performance and advise on the best model to use accordingly. We believe the results of this paper will be widely used by education professionals for accurate predictions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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A Low-Cost Smart Office Design Framework Using Arduino

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Advances in Internet of Things 2023年第3期13卷 83-108页

作者： Ssemwogerere Rajab Nabagesera Veronica Kawalya Melese Alemsra Tsehayu Logose Masitula Wamwoyo Faruk Rahman Shiddiqur Ekwaro Dominic Mbalyowere Pasis Maritah Nambobi Mutwalibi Sayo Nakeleh Turay Mugerwa Derrick Kasagga Usama Seguya Asad Department of Computer Science & Information Technology Islamic University in Uganda Kampala Uganda College of Computing & Informatics Technology Makerere University Kampala Uganda School of Information and Software Engineering University of Electronic Science & Technology of China Chengdu China School of Computer Science Neijiang Normal University Neijiang China School of Computer Science and Engineering University of Electronic Science & Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science & Technology of China Chengdu China

The main motive of our research work is security enhancement and light energy conservation. This paper describes a study investigating the potential of a controlled office solution by integrating the Internet of Things (IoT) with wireless sensor networks (WSNs). A prototype of a smart office is developed using a global system of mobile Bluetooth and Radio Frequency Identification (RFID) technology. The user can turn on and off the fan remotely at any time. This prototype focuses on security and provides human-friendly assistance when in or out of the Office by integrating a mobile application platform. The innovative automated smart Office is designed with intelligent Security doors, lights, alarms, temperature humidifiers, and bright Liquid Crystal Display (LCD) screens for viewing. Our study has opened up virtual possibilities for producing cheap innovative frameworks in this Generation of IoT and the fifth Generation (5G) technology. Therefore, when implemented, this innovation will ease and improve human quality of life. So, this paper aims to provide a low-cost, effective Internet of the things-based automated smart Office.

关键词： Intelligent Office Automation System Wireless Sensor Networks Internet of Things Ultrasonic Radio Frequency Identification Arduino

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ran Su Jin Xiao Hui Cui Ping Xuan Xikang Feng Leyi Wei Qiangguo Jin School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University China School of Software Northwestern Polytechnical University China AIDD Faculty of Applied Science Macao Polytechnic University Macao SAR China School of Information Xiamen University Xiamen China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGA mm , which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer.

关键词： Knowledge engineering Representation learning Pathology Genomics Prediction methods Data models Gene expression Bioinformatics Prognostics and health management Cancer

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Detecting of vertically-oriented texts in images containing natural scenes 17

Detecting of vertically-oriented texts in images containing ...

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17th EAI International Conference on Mobile and Ubiquitous Systems: computing, Networking and Services, MobiQuitous 2020

作者： Ling Ong, Yi Theng Lau, Bee Chai, Almon Mccarthy, Chris Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia Department of Computer Science and Software Engineering Swinburne University of Technology MelbourneVIC Australia

ISBN: (纸本)9781450388405

Universally, scene text in natural images is an expressive means of communication. Texts available in an image could provide important information which can enhance the interpretation of the image, for instance texts on product packages and road signs. Recently, scene texts detection has been recognized as an important research field in computer vision. However, texts detection in natural scene images has been challenging due to the complicated scene background involving varying fonts, sizes, image resolutions etc. As to date, different methods of detecting texts in natural scene images have been proposed for horizontal texts, arbitrarily-oriented texts and curved texts, but there is a lack of detection of vertically-oriented texts in the natural scene images. Hence, this research proposed a framework for detecting the top-to-bottom, bottom-to-top and horizontally-stacked vertical texts in natural scene images. In this paper, Multi-directional Text Detector (MTD) is modelled and developed to locate the vertically-oriented texts in natural scene images, and the Vertical Scene Texts Dataset-700 (VSTD-700) is developed. The preliminary testing shows that the success rate of detection of MTD is 87% on ICDAR 2013, 73% on ICDAR 2015, 71% on MSRA-TD500 and 87% on VSTD-700 datasets. Hence, the results showed that MTD is able to detect vertically-oriented texts on top of the horizontally-oriented and arbitrarily-oriented texts in natural scene images simultaneously. © 2020 ACM.

关键词： Image resolution

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Enhancing Dental Caries Identification with Deep Learning: A Study of Convolutional Neural Networks and Transfer Learning Approaches

SSRN

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SSRN 2024年

作者： Mannan, Fayqa Adeel, Muhammad Arif, Erssa Li, Jianqiang Ullah, Amin Nawazish, Samra Mahmood, Tariq Rehman, Amjad Shoukat, Ijaz Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Government College University Faisalabad38000 Pakistan Department of Computer Science School of Science National Textile University Faisalabad37610 Pakistan Department of Computer Science Faculty of Computing Riphah International University Faisalabad38000 Pakistan Beijing Engineering Research Center for IoT Software and Systems 100124 China Department of Computer Science Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan School of Computer Science and Engineering Southeast University Nanjing China Software Engineering Department University of Engineering and Technology Taxila Pakistan Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia Faculty of information Sciences University of Education Vehari Campus Vehari61100 Pakistan

Various deep learning techniques have been employed to diagnose dental caries using X-ray images. In this study, we utilized deep learning models, including Convolutional Neural Networks (CNNs) and transfer learning models such as Visual Geometry Group (VGG16), VGG19, ResNet50, and Inception V3, to identify dental caries in periapical radiographs. The traditional CNN model comprises three sets of 2D convolutional layers, followed by an activation layer, a max-pooling layer, a flatten layer, one dense layer, another activation layer, dropout, another dense layer, and a final activation layer. For the VGG models, ResNet50, and Inception V3, the top convolutional layers were frozen to prevent retraining of the model, with only the last layers trained to produce the required output. The accuracy achieved by CNN, VGG16, VGG19, ResNet50, and Inception V3 was 90%, 96%, 73%, 70%, and 73%, respectively. VGG16 exhibited the highest accuracy among the models. Hyperparameters were carefully selected using a grid search approach, and the results were validated using the Shuffle-Split-Cross (SSC) validation method. These findings underscore the effectiveness of transfer learning in analyzing medical images, particularly in the diagnosis of dental caries. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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BeSafe: Air Quality Monitoring and Control with IoT

BeSafe: Air Quality Monitoring and Control with IoT

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Evolutionary Algorithms and Soft computing Techniques (EASCT), International Conference on

作者： K.C Arun Sadia Azam Ayesha Siddique Mubashir Ali Aamir Hussain School of Computing Asia Pacific University Kuala Lumpur Malaysia Dipartimento di Informatica Universita di Verona San Francesco Italy Department of Computer Science University of Agriculture Faisalabad Pakistan Department of Software Engineering Lahore Garrison University Lahore Pakistan Department of Computer Science Muhammad Nawaz Shareef University of Agriculture Multan Pakistan

Air pollution is a common matter nowadays. It’s defined as contamination of physical, biological, or chemical alteration in the air which results in harmful effects on living objects and makes it difficult for them to survive. The prominent cause of air pollution is the discharge of injurious gases from industrial waste and the use of fossil fuels. The effects of air pollution are very severe climate change, soil erosion, different diseases, and many more thus it’s very important to take proper note of air superiority as long-term effects of air pollution can result in heart diseases, lung cancer, etc. We have devised a system to measure the contamination level of air through sensors base hardware which sends the data directly to the associated server making it easier to read even on mobile "through a software application. The sensors would be plotted in different routes and locations i.e., industrial region, traffic zone, etc." to investigate the pollution stages of the air making it easier for the user to take a safer route. One of the important usages of the system is to give information about the low contaminated areas near roads and industrial areas so that they can be safeguarded through certain measures.

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