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SUMMER: Bias-aware Prediction of Graduate Employment Based on Educational Big Data

ACM/IMS Transactions on Data science 2021年第4期2卷 1–24页

作者： Xia, Feng Guo, Teng Bai, Xiaomei Shatte, Adrian Liu, Zitao Tang, Jiliang School of Engineering It and Physical Sciences Federation University Australia Ballarat3353 Australia School of Software Dalian University of Technology Dalian116620 China Computing Center Anshan Normal University Anshan114007 China Tal Education Group Beijing100080 China Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

The failure of obtaining employment could lead to serious psychosocial outcomes such as depression and substance abuse, especially for college students who may be less cognitively and emotionally mature. In addition to academic performance, employers' unconscious biases are a potential obstacle to graduating students in becoming employed. Thus, it is necessary to understand the nature of such unconscious biases to assist students at an early stage with personalized intervention. In this paper, we analyze the existing bias in college graduate employment through a large-scale education dataset and develop a framework called SUMMER (biaS-aware gradUate eMployMEnt pRediction) to predict students' employment status and employment preference while considering biases. The framework consists of four major components. Firstly, we resolve the heterogeneity of student courses by embedding academic performance into a unified space. Next, we apply a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to overcome the label imbalance problem of employment data. Thirdly, we adopt a temporal convolutional network to comprehensively capture sequential information of academic performance across semesters. Finally, we design a bias-based regularization to smooth the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework. © 2022 Association for Computing Machinery.

关键词： Forecasting

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UGV-CBRN: An Unmanned Ground Vehicle for Chemical, Biological, Radiological, and Nuclear Disaster Response

arXiv

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arXiv 2024年

作者： Schwaiger, Simon Muster, Lucas Novotny, Georg Schebek, Michael Wöber, Wilfried Thalhammer, Stefan Böhm, Christoph University of Applied Sciences Technikum Wien Faculty of Industrial Engineering Vienna1200 Austria Graz University of Technology Faculty of Computer Science and Biomedical Engineering Institute of Software Technology Inffeldgasse 16b/II Graz8010 Austria University of Natural Resources and Life Sciences Department of Biotechnology Institute for Computational Biology Muthgasse 18 Vienna1190 Austria

Robotic search and rescue (SAR) supports response teams by accelerating disaster assessment and by keeping operators away from hazardous environments. In the event of a chemical, biological, radiological, and nuclear (CBRN) disaster, robots are deployed to identify and locate radiation sources. Human responders then assess the situation and neutralize the danger. The presented system takes a step toward enhanced integration of robots into SAR teams. Integrating autonomous radiation mapping with semi-autonomous substance sampling and online analysis of the CBRN threat lets the human operator localize and assess the threat from a safe distance. Two LiDARs, an IMU, and a Geiger counter are used for mapping the surrounding area and localizing potential radiation sources. A mobile manipulator with six Degrees of Freedom manipulates valves and samples substances that are analyzed by an onboard Raman spectrometer. The human operator monitors the mission’s progression from a remote location defining target locations and directing the semi-autonomous manipulation processes. Diverse recovery behaviours aid robot deployment, system state monitoring, as well as recovery of hard- and software. Field tests showcase the capabilities of the presented system during trials at the CBRN disaster response challenge European Robotics Hackathon (EnRicH). We provide recorded sensor data and implemented software through a GitHub repository: https://***/TW-Robotics/ search-and-rescue-robot-2024. © 2024, CC BY.

关键词： Disasters

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Detection of brain tumor and identification of tumor region using deep neural network on FMRI images 19

Detection of brain tumor and identification of tumor region ...

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19th International Conference on Machine Learning and Cybernetics, ICMLC 2020

作者： Mashiat, Afsara Akhlaque, Reza Rifat Fariha, Fahmeda Hasan Reza, Tanzim Rahman, Md Anisur Parvez, Mohammad Zavid Brac University Software Engineering and HCI Research Group Department of Computer Science and Engineering Bangladesh Charles Sturt University School of Computing and Mathematics Australia

ISBN: (纸本)9780738124261

As brain is the most vital organ of the human body, the affects of brain related diseases can be severe. One of the most harmful diseases is brain tumor, which results in a very short life expectancy of the affected patient. Detection of brain tumor is a challenging task in the early stages. Still, with the help of modern technology and machine learning algorithms, it has become a matter of great interest for research. While detecting the brain tumor of an affected person, we are considering the fMRI data of the patient. Our aim is to identify whether the tumor is present in the patient's brain or not. We use a Convolutional Neural Network(CNN) that is good enough to generate high accuracy. We have used some deeper architecture design VGG16, VGG19, and Inception v3 for better accuracy. Three classification techniques are used namely binary classification, lobe based classification, and position based classification. The main contribution of our proposed work is that we have identified the specific region of the brain where the tumor is located. The region-based classification distinguishes our work from others that are applied on the same dataset. For binary classification, we found approximately 95% accuracy from all the three architectures. Furthermore, we found approximately 78% accuracy for lobe based classification and approximately 97% accuracy for position based classification. The experimental results indicate the superiority of our proposed method in terms of identifying the brain tumor. © 2020 IEEE.

关键词： Brain

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A Hybrid Adaptive Object Identification Algorithm for Neglected Tropical Disease Diagnosis

A Hybrid Adaptive Object Identification Algorithm for Neglec...

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E-Health and Bioengineering Conference (EHB)

作者： Serhan Turgul Saumya Reni Izzet Kale Jaya Shrivastava Peter L Chiodini Applied DSP and VLSI Research Group (ADVRG) School of Computer Science and Engineering University of Westminster London United Kingdom Department of Health and Social Care UK Health Security Agency London United Kingdom University College London Hospitals (UCLH) NHS Foundation Trust London United Kingdom

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

This paper provides an insight into the cross-disciplinary work that combines image processing principles and pathological analysis to diagnose Neglected Tropical Diseases (NTDs). The work described is a novel image processing algorithm that automatically identifies parasitic eggs from Kato-Katz images, in particular, Ascaris and Taenia species. The research outcomes have the potential to be further used for identifying other species that are present in the Kato- Katz images such as Schistosoma Mansoni. The algorithm described in this paper is robust and is able to identify the parasitic eggs at varying levels of magnification and orientation. This algorithm was tested on 4 image sets containing almost 1000 images of Ascaris and Taenia, at lOx and 40x magnification levels.

关键词： Image processing Microscopy Mobile handsets Object recognition Medical diagnosis Kernel Diseases Biomedical engineering

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Delay-Induced Watermarking for Detection of Replay Attacks in Linear Systems

Delay-Induced Watermarking for Detection of Replay Attacks i...

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American Control Conference (ACC)

作者： Christoforos Somarakis Raman Goyal Erfaun Noorani Shantanu Rane Data Science and Applied Mathematics Group Merck & Co West Point PA USA Palo Alto Research Center - An SRI Company Palo Alto CA USA Department of Electrical and Computer Engineering the Institute for System Research (ISR) at the University of Maryland College Park College Park MD USA A. James Clark School of Engineering

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A state-feedback watermarking signal design for the detection of replay attacks in linear systems is proposed. The control input is augmented with a random time-delayed term of the system state estimate, in order to secure the system against attacks of replay type. We outline the basic analysis of the closed-loop response of the state-feedback watermarking in a LQG controlled system. Our theoretical results are applied on a temperature process control example. While the proposed secure control scheme requires very involved analysis, it, nevertheless, holds promise of being superior to conventional, feed-forward, watermarking schemes, in both its ability to detect attacks as well as the secured system performance.

关键词： Linear systems System performance Process control Watermarking Control systems Temperature control Signal design

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A Neuroimaging ML-Based Framework for Anosmia Grading in Covid-19

A Neuroimaging ML-Based Framework for Anosmia Grading in Cov...

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IEEE International Symposium on Biomedical Imaging

作者： Hossam Magdy Balaha Mayada Elgendy Ahmed Alksas Mohamed Shehata Norah Saleh Alghamdi Fatma Taher Mohammed Ghazal Mahitab Ghoneim Eslam Hamed Fatma Sherif Ahmed Elgarayhi Mohammed Sallah Mohamed Abdelbadie Salem Elsharawy Kamal Harpal Sandhu Ayman El-Baz Department of Bioengineering J.B. Speed School of Engineering University of Louisville Louisville USA Applied Theoretical Physics Research Group Physics Department Faculty of Science Mansoura University Egypt Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia The College of Technological Innovation Zayed University Dubai UAE Electrical Computer and Biomedical Engineering Depatrment Abu Dhabi University UAE Department of Radiology Faculty of Medicine Mansoura University Egypt Otolaryngology Department Faculty of Mediine Mansoura University Egypt Higher Institute of Engineering and Technology Egypt

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

SARS-CoV-2 is the cause of the acute respiratory illness COVID-19. In order to identify whether a COVID-19 patient has normal, moderate, or severe anosmia, this study suggests a computer-assisted diagnostic system. It utilizes fluid-attenuated inversion recovery Magnetic Resonance Imaging and Diffusion Tensor Imaging to extract the olfactory nerve’s appearance, morphology, and diffusivity markers. The suggested CAD system consists of the following stages: (1) extract appearance markers, morphology markers, and diffusivity markers, (2) apply markers fusion, and (3) select the most promising classifier as the basis for the decision and the relevant performance indicators. The current work is unique in that it uses an ensemble of the trained, tuned, and improved ML classifiers to identify OB anosmia using majority voting. From the fusion tests using different folds, stacking boosted the weight accuracy, weighted precision, weighted specificity, weighted sensitivity, and weighted F1-score to 85.30%, 80%, 89.50%, 78.90%, and 78.30% respectively. Regarding the various performance criteria, the LGBM outperforms other ML classifiers.

关键词： COVID-19 Neuroimaging Sensitivity Shape Stacking Olfactory Morphology

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A Tree-Guided CNN for Image Super-Resolution

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Song, Mingjian Fan, Xiaopeng Zheng, Xiangtao Zhang, Bob Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Northwestern Polytechnical University School of Software Xi’an710129 China Fuzhou University College of Physics and Information Engineering Fuzhou350108 China University of Macau Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science Macau999078 China School of Science and Engineering Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks can extract more accurate structural information via deep architectures to obtain good performance in image super-resolution. However, it is not easy to find effect of important layers in a single network architecture to decrease performance of super-resolution. In this paper, we design a tree-guided CNN for image super-resolution (TSRNet). It uses a tree architecture to guide a deep network to enhance effect of key nodes to amplify the relation of hierarchical information for improving the ability of recovering images. To prevent insufficiency of the obtained structural information, cosine transform techniques in the TSRNet are used to extract cross-domain information to improve the performance of image super-resolution. Adaptive Nesterov momentum optimizer (Adan) is applied to optimize parameters to boost effectiveness of training a super-resolution model. Extended experiments can verify superiority of the proposed TSRNet for restoring high-quality images. © 1975-2011 IEEE.

关键词： Cosine transforms

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Detection of stress for visually impaired people using EEG signals based on time-frequency domain analysis 19

Detection of stress for visually impaired people using EEG s...

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19th International Conference on Machine Learning and Cybernetics, ICMLC 2020

作者： Sultana, Samia Rahman, Md Anisur Zavid Parvez, Mohammad BRAC University Software Engineering and HCI Research Group Department of Computer Science and Engineering Bangladesh Charles Sturt University School of Computing and Mathematics Australia

ISBN: (纸本)9780738124261

Stress refers to body's physical, emotional and psychological reaction to any environmental change needing adjustment with major impact on human psychology. Stress is specially difficult to manage for visually impaired people (VIP) as they can become easily stressed in unknown situations. Electroencephalogram (EEG) signals can be used to detect stress as it basically represents the ongoing electrical signal changes in human brain. Literature shows that the stress detection techniques are mostly based on either time or frequency domain analysis. However, using either time or frequency domain analysis may not be sufficient to provide appropriate outcome for stress detection. Hence, in this paper a method is proposed using empirical mode decomposition (EMD) and short-term Fourier transform (STFT) are used to extract features considering spatio-temporal information from EEG signals. In the EMD, the signal is first decomposed into intrinsic mode functions (IMFs) representing a finite number of signals while maintaining the time domain and STFT is used to convert time domain to time-frequency domain. Support vector machine (SVM) is applied to classify the stress of VIP in unfamiliar indoor environments. The performance of the proposed method is compared with a state-of-the-art technique for stress detection. The experimental results demonstrate the superiority of the proposed technique over the existing technique. © 2020 IEEE.

关键词： Empirical mode decomposition

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imc-precip-iso: open monthly stable isotope data of precipitation over the Indonesian Maritime Continent

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Journal of Data, Information and Management 2024年第2期6卷 99-110页

作者： Suwarman, Rusmawan Herho, Sandy Belgaman, Halda Ichiyanagi, Kimpei Uesugi, Takayuki Irawan, Dasapta Yosa, Imam Utami, Arika Prayogo, Sonny Aldrian, Edvin Atmospheric Science Research Group Bandung Institute of Technology (ITB) Jalan Ganesha 10 West Java Bandung 40132 Indonesia Department of Earth and Planetary Sciences Department of Earth and Planetary Sciences University of California 900 University Ave. Riverside 92521 CA United States Research Center for Climate and Atmosphere (PRIMA) National Research and Innovation Agency (BRIN) Jalan Dr. Djundjunan 133 West Java Bandung 40173 Indonesia Faculty of Advanced Science and Technology Kumamoto University 2-39-1 Kurokami Kumamoto Perfecture Kumamoto 860-8555 Japan Applied Geology Research Group Bandung Institute of Technology (ITB) Jalan Ganesha 10 West Java Bandung 40132 Indonesia Indonesian Meteorology Climatology and Geophysical Agency (BMKG) Jalan Angkasa I/2 Central Jakarta DKI Jakarta 10610 Indonesia Software Engineering Division Manvis Teknologi Enjinering Jalan Cikutra 27 West Java Bandung 40124 Indonesia

Stable isotopes, δ2H, δ18O, and d-excess, are valuable tools as natural tracers of diffusion processes and phase changes in the global hydroclimatological cycle. The Indonesian Maritime Continent (IMC) is an archipe... 详细信息

Stable isotopes, δ²H, δ¹⁸O, and d-excess, are valuable tools as natural tracers of diffusion processes and phase changes in the global hydroclimatological cycle. The Indonesian Maritime Continent (IMC) is an archipelago area surrounded by very warm waters which induce convective activities as the primary heat source driving global atmospheric circulation. Given the central role of IMC in this hydroclimatological cycle, comprehensive study, and data collection on the stable isotopes of precipitation in this region is crucial. In this study, we collected monthly stable isotope data from 62 stations spread throughout the Indonesian archipelago from September 2010 to September 2017. We cleaned the data and conducted quality control activities by comparing the Local Meteoric Water Line (LMWL) to previous studies in a similar climatic region. We shared these data openly on our GitHub repository, making them easier to update and interact with users in the future. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Data sharing Indonesian Maritime Continent (IMC) Precipitation Stable isotopes

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'Permanent' algorithm for the meeting shedule problem 16

'Permanent' algorithm for the meeting shedule problem

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16th IEEE International Conference on computer science and Information Technologies, CSIT 2021

作者： Turbal, Yuriy Babych, Sergii Kunanets, Nataliia Melnyk, Leonid Pasichnyk, Volodymyr National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine College of National University of Life and Environmental Sciences of Ukraine Software Engineering Rivne Ukraine National University 'Lviv Polytechnic' Department of Information Systems and Networks Lviv Ukraine National University of Water and Environmental Engineering Department of Finance and Economic Security Rivne Ukraine

ISBN: (纸本)9781665442572

An algorithm for solving the problem of scheduling meetings in the process of working on a project is proposed in the paper. The problem of project managers meetings with working groups is considered under certain restrictions., the possibility of a simultaneous meeting of the project manager with one or several project groups is considered. The proposed procedure for finding the permanent allows to build all possible combinations of the meeting schedule at a given time. The specificity of the proposed approach is the ability to take into account any number of additional conditions. © 2021 IEEE.

关键词： Managers

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