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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lei Zhang Yijing Yang Zhe Wang Dongdong Li Jingping Liu Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science University of Illinois Urbana-Champaign Champaign Illinois United States of America

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

With the development of next-generation sequencing technology, massive genomic data has been generated, primarily encouraging research on cancer driver genes. Many bioinformatics methods were proposed to identify driver genes. However, the results of driver gene identification a mong these methods show considerable differences. It is still challenging to obtain a comprehensive catalog of cancer drivers. Although current methods have greatly promoted the development of driver genes, few methods can integrate the identification results of existing methods. To solve such problems in cancer driver genes research, we proposed a cascade forest model to discover cancer driver genes(CFC) that can integrate multi-omics data and annotation scores from different cancer driver gene identification algorithms. The proposed method got precise results for 33 cancer types and Pan-cancer. The CFC framework identified 275 driver genes in Pan-cancer, of which 179 were included in the Gold standard. The identified genes were enriched i n t he principal cancer signaling pathways.

关键词： Sequential analysis Annotations Genomics Forestry Data models Bioinformatics Next generation networking

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How Do Teams of Novice Modelers Choose an Approach? An Iterated, Repeated Experiment in a First-Year Modeling Course 1

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21st International Conference on Computational science, ICCS 2021

作者： Giabbanelli, Philippe J. Jackson, Piper J. Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States Department of Computing Science Thompson Rivers University KamloopsBCV2C 0C8 Canada

ISBN: (数字)9783030779801

ISBN: (纸本)9783030779795

There are a variety of factors that can influence the decision of which modeling technique to select for a problem being investigated, such as a modeler’s familiarity with a technique, or the characteristics of the problem. We present a study which controls for modeler familiarity by studying novice modelers choosing between the only modeling techniques they have been introduced to: in this case, cellular automata and agent-based models. Undergraduates in introductory modeling courses in 2018 and 2019 were asked to consider a set of modeling problems, first on their own, and then collaboratively with a partner. They completed a questionnaire in which they characterized their modeling method, rated the factors that influenced their decision, and characterized the problem according to contrasting adjectives. Applying a decision tree algorithm to the responses, we discovered that one question (Is the problem complex or simple?) explained 72.72% of their choices. When asked to resolve a conflicting choice with their partners, we observed the repeated themes of mobility and decision-making in their explanation of which problem characteristics influence their resolution. This study provides both qualitative and quantitative insights into factors driving modeling choice among novice modelers. These insights are valuable for instructors teaching computational modeling, by identifying key factors shaping how students resolve conflict with different preferences and negotiate a mutually agreeable choice in the decision process in a team project environment. © 2021, Springer Nature Switzerland AG.

关键词： Cellular automata

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Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services

Seed Feature Maps-based CNN Models for LEO Satellite Remote ...

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IEEE International Conference on Web Services (ICWS)

作者： Zhichao Lu Chuntao Ding Shangguang Wang Ran Cheng Felix Juefei-Xu Vishnu Naresh Boddeti School of Software Engineering Sun Yat-sen University Zhuhai China School of Computer and Information Technology Beijing Jiaotong University Beijing China Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China New York University New York NY USA Department of Computer Science and Engineering Michigan State University East Lansing MI USA

Deploying high-performance convolutional neural network (CNN) models on low-earth orbit (LEO) satellites for rapid remote sensing image processing has attracted significant interest from industry and academia. However, the limited resources available on LEO satellites contrast with the demands of resource-intensive CNN models, necessitating the adoption of ground-station server assistance for training and updating these models. Existing approaches often require large floating-point operations (FLOPs) and substantial model parameter transmissions, presenting considerable challenges. To address these issues, this paper introduces a ground-station server-assisted framework. With the proposed framework, each layer of the CNN model contains only one learnable feature map (called the seed feature map) from which other feature maps are generated based on specific rules. The hyperparameters of these rules are randomly generated instead of being trained, thus enabling the generation of multiple feature maps from the seed feature map and significantly reducing FLOPs. Furthermore, since the random hyperparameters can be saved using a few random seeds, the ground station server assistance can be facilitated in updating the CNN model deployed on the LEO satellite. Experimental results on the ISPRS Vaihingen, ISPRS Potsdam, UAVid, and LoveDA datasets for semantic segmentation services demonstrate that the proposed framework outperforms existing state-of-the-art approaches. In particular, the SineFM-based model achieves a higher mIoU than the UNetFormer on the UAVid dataset, with 3.3 × fewer parameters and 2.2 × fewer FLOPs.

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PSIV-B-21 Genome-Wide Association Studies for Ketosis in Dairy Cattle

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Journal of Animal science 2022年第SUPPLEMENT_3期100卷 306–307页

作者： Gillette, Sydney Lunn, Morgan A Campos, Fernando Peterson, Daniel Sprayberry, Kim Sargolzaei, Mehdi McArthur, Gary Anderson, Paul La Frano, Michael Pokharel, Siroj Abo-Ismail, Mohammed Animal Science Department California Polytechnic State University Select Sires Swinging Udders Veterinary Service LLC Department of Computer Science and Software Engineering California Polytechnic State University Food Science and Nutrition Department California Polytechnic State University

Lactating dairy cows often experience a negative energy balance (NEB) and must mobilize adipose reserves to adapt to this metabolic stress. Dairy cows that are unable to adapt to this NEB often suffer from metabolic disorders such as ketosis causing significant financial losses due to a reduction in milk production, poor reproductive performance, and an increased risk of developing other disorders. Therefore, the aim of this study was to identify genes and their associated biological pathways to better understand the molecular genetic basis for the development of ketosis. Genome wide association studies analysis was performed using a mixed linear model on 436 Jersey and Holstein cows ranging in ketosis history and genotyped for high density single nucleotide polymorphisms (SNPs) panel. After quality control, 409 with 617,527 SNPs were included in further analyses. The association analysis was performed using a mixed linear model implemented in GCTA software and fitting SNP, breed, and pen as fixed effects with animal as random effect. The top 100 significant SNPs (P < 0.0003) were mapped to 29 genes. Gene enrichment analyses were conducted using DAVID software to determine biological pathways enriched for the identified list of genes. The genomic heritability estimated in this study was 0.17 indicating existence of genetic influence on ketosis in animals in this herd. The analyses revealed candidate genes to be involved in biological pathways involving coronary vascular development, platelet activation and aggregation and osteoclast differentiation. The results indicated a relationship between ketosis development and the immune system or development of other diseases. Further research is needed to validate the identified biological mechanisms underlying the variation between cows for ketosis incidence and progression in dairy cows.

关键词： ketosis dairy cattle GWAS

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Beyond MOT: Semantic Multi-Object Tracking

arXiv

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arXiv 2024年

作者： Li, Yunhao Li, Qin Wang, Hao Ma, Xue Yao, Jiali Dong, Shaohua Fan, Heng Zhang, Libo Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences China Department of Computer Science & Engineering University of North Texas United States

Current multi-object tracking (MOT) aims to predict trajectories of targets (i.e., "where") in videos. Yet, knowing merely "where" is insufficient in many crucial applications. In comparison, semantic understanding such as fine-grained behaviors, interactions, and overall summarized captions (i.e., "what") from videos, associated with "where", is highly-desired for comprehensive video analysis. Thus motivated, we introduce Semantic Multi-Object Tracking (SMOT), that aims to estimate object trajectories and meanwhile understand semantic details of associated trajectories including instance captions, instance interactions, and overall video captions, integrating "where" and "what" for tracking. In order to foster the exploration of SMOT, we propose BenSMOT, a large-scale Benchmark for Semantic MOT. Specifically, BenSMOT comprises 3,292 videos with 151K frames, covering various scenarios for semantic tracking of humans. BenSMOT provides annotations for the trajectories of targets, along with associated instance captions in natural language, instance interactions, and overall caption for each video sequence. To our best knowledge, BenSMOT is the first publicly available benchmark for SMOT. Besides, to encourage future research, we present a novel tracker named SMOTer, which is specially designed and end-to-end trained for SMOT, showing promising performance. By releasing BenSMOT, we expect to go beyond conventional MOT by predicting "where" and "what" for SMOT, opening up a new direction in tracking for video understanding. We will release BenSMOT and SMOTer at https://***/Nathan-Li123/SMOTer. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Machine Learning-based SDN Controller Framework for Drone Management

A Machine Learning-based SDN Controller Framework for Drone ...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Yazdinejad, Abbas Rabieinejad, Elnaz Dehghantanha, Ali Parizi, Reza M. Srivastava, Gautam University of Guelph Cyber Science Lab School of Computer Science ON Canada K.N.Toosi University of Technology Information Technology Department Tehran Iran Kennesaw State University College of Computing and Software Engineering GA United States Brandon University Department of Mathematics and Computer Science MB Canada

ISBN: (纸本)9781665423908

With the advancement of information and communication technology, Unmanned Aerial Vehicles (UAV), popularly known as drones, have also increased. The drones have been noted for their wide range of applications such as military, search and rescue operation, disaster detection and monitoring, agriculture, and delivery. Each type of drone has different characteristics and functionality based on its application, making them a security threat for some city zone. Therefore, there is an essential need for efficient drone management based on their type and application in different zones. To do this, we proposed a Machine learning (ML) based software Defined Network (SDN) drone management framework. In this framework, the SDN controller uses ML with the drone's radio frequency feature to detect its type and application and, according to its application, authenticate it and assign communication rules. SDN controller records authentication information in a DAG-based Distributed Ledger Technology (DLT) available for other SDN controllers. When a drone desires to migrate to another zone, the destination SDN controller can achieve authentication information by referring to DAG-based DLT, and there is no need for re-authentication. The experimental result shows authentication delay reduction in our proposed framework. Moreover, we adopted ML algorithms includes Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), to evaluate our proposed framework in drone's type classification. The result shows that the RF algorithm shows the best performance with 92.81% accuracy in the classification of the drone's type. © 2021 IEEE.

关键词： Drones

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MEA-Defender: A Robust Watermark against Model Extraction Attack

MEA-Defender: A Robust Watermark against Model Extraction At...

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IEEE Symposium on Security and Privacy

作者： Peizhuo Lv Hualong Ma Kai Chen Jiachen Zhou Shengzhi Zhang Ruigang Liang Shenchen Zhu Pan Li Yingjun Zhang Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Department of Computer Science Metropolitan College Boston University USA Institute of Software Chinese Academy of Sciences China

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Recently, numerous highly-valuable Deep Neural Networks (DNNs) have been trained using deep learning algorithms. To protect the Intellectual Property (IP) of the original owners over such DNN models, backdoor-based watermarks have been extensively studied. However, most of such watermarks fail upon model extraction attack, which utilizes input samples to query the target model and obtains the corresponding outputs, thus training a substitute model using such input-output pairs. In this paper, we propose a novel watermark to protect IP of DNN models against model extraction, named MEA-Defender. In particular, we obtain the watermark by combining two samples from two source classes in the input domain and design a watermark loss function that makes the output domain of the watermark within that of the main task samples. Since both the input domain and the output domain of our watermark are indispensable parts of those of the main task samples, the watermark will be extracted into the stolen model along with the main task during model extraction. We conduct extensive experiments on four model extraction attacks, using five datasets and six models trained based on supervised learning and self-supervised learning algorithms. The experimental results demonstrate that MEA-Defender is highly robust against different model extraction attacks, and various watermark removal/detection approaches.

关键词： Training Symbiosis Computational modeling Supervised learning Watermarking Artificial neural networks Speech recognition

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Bringing Efficient Synthetic Transmit Aperture Imaging to Consumer Technology: IoT-Enabled Portable Ultrasound Systems

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IEEE Transactions on Consumer Electronics 2025年

作者： Wang, Wenping He, Qiong Peng, Fan Feng, Ziliang Sichuan University National Key Laboratory of Fundamental Science on Synthetic Vision Chengdu610065 China Tsinghua University School of Medicine Department of Biomedical Engineering Beijing100084 China Chengdu University of Technology College of Computer Science and Cyber Security Pilot Software College Chengdu610059 China

This work advances consumer technology in holographic visualization and medical imaging by introducing an effective synthetic transmit aperture (STA) imaging technique for Internet of Things (IoT)-enabled portable high-frequency ultrasound (HFUS) equipment. The technique drastically lowers data size and computing requirements while preserving good imaging performance by using sign coherence factor (SCF) compounding and phase rotation beamforming using in-phase/quadrature (IQ) data. Real-time deployment in resource-constrained IoT-based systems is made possible by experimental results that demonstrate a 42% decrease in computing complexity when compared to traditional STA-IQ imaging. For effective processing, baseband IQ data is subjected to intricate demodulation, downsampling, and phase correction, which is in line with contemporary consumer technology-driven healthcare solutions. Even at low sampling rates, SCF improves contrast performance, maintaining a lateral resolution of 150 lm and providing a contrast-to-noise ratio (CNR) enhancement of 3.5 dB, which is equivalent to typical STA-IQ methods. Its enhanced image quality is further supported by simulations and in-vivo studies that show a peak signal-to-noise ratio (PSNR) enhancement of 2.8 dB. Furthermore, by combining this method with real-time holographic ultrasound imaging, high-fidelity 3D reconstructions are made possible, enhancing depth accuracy and spatial coherence while reducing phase distortion aberrations by 30%. These improvements enable real-time holographic imaging for telemedicine and preclinical applications, as well as IoT-based remote diagnostics. Thus, the suggested approach offers a low-power, high-accuracy imaging solution that promotes innovation in holographic display and medical imaging technologies while also advancing consumer healthcare diagnostics. © 1975-2011 IEEE.

关键词： Image enhancement

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Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification

arXiv

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arXiv 2023年

作者： Yang, Ziyuan Teoh, Andrew Beng Jin Zhang, Bob Leng, Lu Zhang, Yi College of Computer Science Sichuan University Chengdu China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Macau China School of Software Nanchang Hangkong University Nanchang China School of Cyber Science and Engineering Sichuan University Chengdu China

Palmprint as biometrics has gained increasing attention recently due to its discriminative ability and robustness. However, existing methods mainly improve palmprint verification within one spectrum, which is challenging to verify across different spectrums. Additionally, in distributed server-client-based deployment, palmprint verification systems predominantly necessitate clients to transmit private data for model training on the centralized server, thereby engendering privacy apprehensions. To alleviate the above issues, in this paper, we propose a physics-driven spectrum-consistent federated learning method for palmprint verification, dubbed as PSFed-Palm. PSFed-Palm draws upon the inherent physical properties of distinct wavelength spectrums, wherein images acquired under similar wavelengths display heightened resemblances. Our approach first partitions clients into short- and long-spectrum groups according to the wavelength range of their local spectrum images. Subsequently, we introduce anchor models for short- and long-spectrum, which constrain the optimization directions of local models associated with long- and short-spectrum images. Specifically, a spectrum-consistent loss that enforces the model parameters and feature representation to align with their corresponding anchor models is designed. Finally, we impose constraints on the local models to ensure their consistency with the global model, effectively preventing model drift. This measure guarantees spectrum consistency while protecting data privacy, as there is no need to share local data. Extensive experiments are conducted to validate the efficacy of our proposed PSFed-Palm approach. The proposed PSFed-Palm demonstrates compelling performance despite only a limited number of training data. The codes will be released at https://***/Zi-YuanYang/PSFed-Palm. Copyright © 2023, The Authors. All rights reserved.

关键词： Biometrics

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A computer Vision-Based System for Recognition and Classification of Urdu Sign Language Dataset for Differently Abled People Using Artificial Intelligence

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Mobile Information Systems 2023年第1期2023卷

作者： Zahid, Hira Syed, Sidra Abid Rashid, Munaf Hussain, Samreen Umer, Asif Waheed, Abdul Nasim, Shahzad Zareei, Mahdi Mansoor, Nafees Biomedical Engineering Department and Electrical Engineering Department Ziauddin University Karachi74600 Pakistan Biomedical Engineering Department Sir Syed University of Engineering and Technology Karachi75300 Pakistan Software Engineering Department Sir Syed University of Engineering and Technology Karachi74600 Pakistan Electronic Engineering Department Dawood University of Engineering & Technology Karachi74800 Pakistan Department of Computer Science & IT Hazara University Mansehra23100 Pakistan Department of Computer Science Women University Swabi Swabi23430 Pakistan School of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Begum Nusrat Bhutto Women University Faculty of Management Information Science and Technology Sukkur Pakistan Tecnologico de Monterrey School of Engineering and Sciences Monterrey64849 Mexico Dhaka Bangladesh

Communication between normal people and deaf people is the most difficult part of daily life worldwide. It is difficult for a normal person to understand a word from the deaf one in their daily routine. So, to communicate with deaf people, different countries developed different sign languages to make communication easy. In Pakistan, for deaf people, the government developed Urdu Sign Language to communicate with deaf people. Physical trainers and experts are difficult to provide everywhere in society, so we need such a computer/mobile-based system to convert the deaf sign symbol into voice and written alphabet that the normal person can easily get the intentions of the deaf one. In this paper, we provided an image processing and deep learning-based model for Urdu Sign Language. The proposed model is implemented in Python 3 and uses different image processing and machine techniques to capture the video and transform the symbols into voice and Urdu writing. First, we get a video from the deaf person, and then the model crops the frames into pictures. Then, the individual picture is recognized for the sign symbol such as if the deaf showed a symbol for one, then the model recognizes it and shows the letter which he/she wants to tell. Image processing techniques such as OpenCV are used for image recognition and classification while TensorFlow and linear regression are used for training the model to behave intelligently in the future. The results show that the proposed model increased accuracy from 80% to 97% and 100% accordingly. The accuracy of the previously available work was 80% when we implemented the algorithms, while with the proposed algorithm, when we used linear regression, we achieved the highest accuracy. Similarly, when we used the TensorFlow deep learning algorithm, we achieved 97% accuracy which was less than that of the linear regression model. © 2023 Hira Zahid et al.

关键词： Image recognition

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