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arXiv 2023年

作者： Baskar, A. Srinivasan, Ashwin Bain, Michael Coiera, Enrico Dept. of Computer Science Information Systems BITS Pilani Goa Campus India Dept. of Computer Science Information Systems and APPCAIR BITS Pilani Goa Campus India School of Computer Science Engineering University of New South Wales Australia Centre for Health Informatics Macquarie University Sydney Australia

Machine Learning (ML) has emerged as a powerful form of data modelling with widespread applicability beyond its roots in the design of autonomous agents. However, relatively little attention has been paid to the interaction between people and ML systems. In this paper we view interaction between humans and ML systems within the broader context of communication between agents capable of prediction and explanation. We formalise the interaction model by taking agents to be automata with some special characteristics and define a protocol for communication between such agents. We define One- and Two-Way Intelligibility as properties that emerge at run-time by execution of the protocol. The formalisation allows us to identify conditions under which run-time sequences are bounded, and identify conditions under which the protocol can correctly implement an axiomatic specification of intelligible interaction between a human and an ML system. We also demonstrate using the formal model to: (a) identify instances of One- and Two-Way Intelligibility in literature reports on humans interacting with ML systems providing logic-based explanations, as is done in Inductive Logic Programming (ILP);and (b) map interactions between humans and machines in an elaborate natural-language based dialogue-model to One- or Two-Way Intelligible interactions in the formal model. © 2023, CC BY.

关键词： Inductive logic programming (ILP)

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Heterogeneous Treatment Effect Estimation with Subpopulation Identification for Personalized Medicine in Opioid Use Disorder

Heterogeneous Treatment Effect Estimation with Subpopulation...

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IEEE International Conference on Data Mining (ICDM)

作者： Seungyeon Lee Ruoqi Liu Wenyu Song Ping Zhang Department of Computer Science and Engineering The Ohio State University USA Department of Biomedical Informatics The Ohio State University USA Department of Medicine Brigham and Women’s Hospital Harvard Medical School USA

Deep learning models have demonstrated promising results in estimating treatment effects (TEE). However, most of them overlook the variations in treatment outcomes among subgroups with distinct characteristics. This limitation hinders their ability to provide accurate estimations and treatment recommendations for specific subgroups. In this study, we introduce a novel neural network-based framework, named SubgroupTE, which incorporates subgroup identification and treatment effect estimation. SubgroupTE identifies diverse subgroups and simultaneously estimates treatment effects for each subgroup, improving the treatment effect estimation by considering the heterogeneity of treatment responses. Comparative experiments on synthetic data show that SubgroupTE outperforms existing models in treatment effect estimation. Furthermore, experiments on a real-world dataset related to opioid use disorder (OUD) demonstrate the potential of our approach to enhance personalized treatment recommendations for OUD patients.

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Millimeter Wave Full-Duplex Networks: MAC Design and Throughput Optimization

arXiv

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arXiv 2023年

作者： Liu, Shengbo Wu, Wen Fu, Liqun Qu, Kaige Ye, Qiang Zhuang, Weihua Shen, Sherman Frontier Research Center Peng Cheng Laboratory Shenzhen China School of Informatics Xiamen University Xiamen China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Department of Computer Science Memorial University of Newfoundland St. John’s Canada

Full-duplex (FD) technique can remarkably boost the network capacity in the millimeter wave (mmWave) bands by enabling simultaneous transmission and reception. However, due to directional transmission and large bandwidth, the throughput and fairness performance of a mmWave FD network are affected by deafness and directional hidden-node (HN) problems and severe residual self-interference (RSI). To address these challenges, this paper proposes a directional FD medium access control protocol, named DFDMAC to support typical directional FD transmission modes by exploiting FD to transmit control frames to reduce signaling overhead. Furthermore, a novel busy-tone mechanism is designed to avoid deafness and directional HN problems and improve fairness of channel access. To reduce the impact of RSI on link throughput, we formulate a throughput maximization problem for different FD transmission modes and propose a power control algorithm to obtain the optimal transmit power. Simulation results show that the proposed DFDMAC can improve the network throughput and fairness by over 60% and 32%, respectively, compared with the existing MAC protocol in IEEE 802.11ay. Moreover, the proposed power control algorithm can effectively enhance the network throughput. Copyright © 2023, The Authors. All rights reserved.

关键词： Millimeter waves

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Autoencoder for 4-Dimensional Fiber Orientation Distributions from Diffusion MRI

Autoencoder for 4-Dimensional Fiber Orientation Distribution...

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IEEE International Symposium on Biomedical Imaging

作者： Shuo Huang Lujia Zhong Yonggang Shi Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California (USC) Los Angeles CA USA Alfred E. Mann Department of Biomedical Engineering Viterbi School of Engineering University of Southern California (USC) Los Angeles CA USA Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California (USC) Los Angeles CA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Fiber orientation distributions (FODs) are widely used in connectome analysis based on diffusion MRI. Spherical har-monics (SPHARMs) are often used for the efficient repre-sentation of FODs; however, SPHARMs over the 3-D image volume are in essence four-dimensional. This makes it highly memory-consuming for applying advanced deep learning methods, such as the transformer and diffusion model, to FODs represented by high order SPHARMs. In this work, we present an order-balanced order-level (OBOL) autoencoder to compress the FODs with high accuracy after decoding. Our OBOL method uses separate encoders for FODs in each SPHARM order to balance the feature map size of FODs in different orders. This helps the encoder to better preserve information from the low-order coefficients that have more information but a smaller number of volumes. In our ex-periments, we demonstrated that the decoded FODs of our OBOL autoencoder have better accuracy than the spatial-level or order-level autoencoder without order balance. We also tested the encoded latent space of the OBOL autoencoder in FOD super-resolution. Results show high accuracy with feasible memory usage in commonly available GPUs.

关键词： Training Accuracy Three-dimensional displays Magnetic resonance imaging Autoencoders Superresolution Memory management Transformers Reliability Optical fiber testing

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Healthcare Industry 5.0: Pareto Optimal IoT-Based Health Monitoring With Federated Learning

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IEEE Internet of Things Journal 2025年

作者： Diamantoulaki, Ioanna Tegos, Sotiris A. Bouzinis, Pavlos S. Sarigiannidis, Panagiotis Chatzakis, Christos Petousis, Stamatios Maglaveras, Nicos Karagiannidis, George K. Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Kila Kozani MetaMind Innovation P.C. Kozani50100 Greece University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani50100 Greece Aristotle University of Thessaloniki AUTH Campus Medical School 2nd Dept. of Obstetrics and Gynaecology Thessaloniki54636 Greece

With the recent expansion of patient data availability and storage capabilities, healthcare entities tend to store an increasing amount of medical data locally. In addition, ongoing advances in the era of healthcare industry 5.0 and Artificial Intelligence of Things can lead to more efficient use of medical data, resulting in better health monitoring at lower costs. However, due to strict privacy restrictions related to the sensitive nature of medical data, it is often only used locally and ultimately underutilized. To this end, federated learning (FL) offers a promising solution for the efficient use of medical data, facilitating the development of reliable and robust healthcare tools. This can be achieved thanks to its decentralized nature, which allows participating entities to collaborate and thus develop and train a centralized shared model without requiring data sharing. Taking this into account, we present a Pareto-front optimization framework for FL-based health monitoring that is able to mitigate false negative predictions for the required level of false positives. By applying the proposed framework to four different medical applications, it is shown that the risk of misdiagnosis is significantly reduced, providing an additional tool for medical professionals. © 2014 IEEE.

关键词： Federated learning

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Reconfigurable intelligent surfaces for wireless communications:Overview of hardware designs,channel models,and estimation techniques

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Intelligent and Converged Networks 2022年第1期3卷 1-32页

作者： Mengnan Jian George C.Alexandropoulos Ertugrul Basar Chongwen Huang Ruiqi Liu Yuanwei Liu Chau Yuen the Wireless Research Institute ZTE CorporationBeijing 100029China the State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China Department of Informatics and Telecommunications National and Kapodistrian University of AthensAthens 15784Greece Communications Research and Innovation Laboratory(CoreLab) Department of Electrical and Electronics EngineeringKoçUniversityIstanbul 34450Turkey College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK. the Engineering Product Development(EPD)Pillar Singapore University of Technology and DesignSingapore 487372Singapore

The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing *** the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of *** RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field *** by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal *** also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless ***,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

关键词： reconfigurable intelligent surfaces beyond 5G 6G channel modeling hardware architecture channel estimation smart wireless environment standardization

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Analysis of the Effect of Capacitors on the Voltage Generated by a 3-Phase Induction Generator

Analysis of the Effect of Capacitors on the Voltage Generate...

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2022 International Conference on Electrical engineering and informatics, ICELTICs 2022

作者： Syukri, Mahdi Syuhada, Ahmad Tarmizi Suriadi Ramadhani, Satika Marwan Affan, Muzailin Yanis, Muhammad Universitas Syiah Kuala Doctoral Program School of Engineering Post Graduate Program Banda Aceh23111 Indonesia Universitas Syiah Kuala Dept. of Mechanical and Industrial Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Geophisic Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Informatics Fmipa Banda Aceh Indonesia

ISBN: (数字)9781665487665

ISBN: (纸本)9781665487665

Induction motors can be operated as induction generators when additional capacitors are added to the stator terminals. Capacitors connected to induction generators can generate voltage and can provide reactive power. The advantage of induction generators can be used in small-scale plants such as micro-hydro power plants. However, the weakness of induction generators are weak voltage regulation and dependence on capacitors to generate voltage. This study conducted a test to see how the effect of capacitor capacity on the voltage generated by a induction generator when serving multiple incandescent lamp loads. The capacitor used is connected with a wye connection because the Wye connection can generate a higher voltage than the Delta connection circuit. This research has been successfully carried out with variations in capacitor values, namely 45 F, 60 F, and 75 F and variations in lamp load values, namely 5W, 15W, 25W, 40W and 60W. Based on the results of the study, the induction generators uses a 45 F capacitor with a load of 5 W, the terminal voltage for line to line is 374 V. However, when using a 75 F capacitor with a 60W load, the terminal voltage for line to line is 239 V. The terminals voltage of the induction generators can also be kept at a constant 380 V by providing varying capacitor values even though it serves varying loads. © 2022 IEEE.

关键词： Asynchronous generators

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Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

arXiv

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arXiv 2023年

作者： Li, Mengke Cheung, Yiu-Ming Lu, Yang Hu, Zhikai Lan, Weichao Huang, Hui Shenzhen China The Department of Computer Science Hong Kong Baptist University Hong Kong The Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This paper therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. Source code is available at https://***/Keke921/GCLLoss. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest

Borneo Smart Forest Information System for Management of Dip...

引用

Information Technology International Seminar (ITIS)

作者： Masna Wati Novianti Puspitasari Ummul Hairah Joan Angelina Widians Anindita Septiarini Ade Fiqri Tjikoa Dept. of Informatics Faculty of Engineering Universitas Mulawarman Samarinda Indonesia Dept. of Informatics Universitas Mulawarman Doctoral Program Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9798350398205

Kalimantan’s tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan’s rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation.

关键词： Seminars Memory Forestry QR codes Vegetation Wood industry Information technology

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Internet of Things Based System for Health Monitoring for Distribution Transformer

Internet of Things Based System for Health Monitoring for Di...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： B. Arun Kumar Angel Swastik Duggal Arshi Naim Mohammad Rashid Hussain Mohammad Nadeem Ahmed Muhammad Shahid Dildar School of Electronics and Electrical Engineering Lovely Professional University Phagwara Punjab India Department of Business Management AlSamer University campus King Khalid University Abha KSA Department of Business Informatics College of Business King Khalid University KSA Department of Computer Science College of Computer Science King Khalid University KSA

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Electricity distribution from substations to residences and businesses depends on distributed transformers. However, a number of things, including overload, overheating, and outside elements like moisture and dust, can harm them. For distributed transformers to remain operational and prevent damage, health monitoring is crucial. Using sensors to assess vital variables like voltage, current, temperature, and humidity is one approach to keep an eye on the condition of distributed transformers. To find any possible issues, the data from these sensors can be gathered and analysed. If an issue is found, it can be fixed before the transformer is harmed. A system employing Node MCU and Arduino was suggested in a study for the health monitoring of distributed transformers. An open-source platform called Node MCU makes use of the ESP8266 microcontroller. Interactive projects are developed on the open-source Arduino electronics platform. The suggested system gathers data from the sensors and transmits it to an Arduino using a Node MCU. The data is subsequently transmitted from the Arduino via the internet to a cloud server. The data can be stored on the cloud server, which can also analyse it to find any potential issues. An alert can be provided to the user if a problem is found. This technology offers a simple and affordable solution to keep track of the condition of distributed transformers. It can aid in avoiding damage and ensuring the ongoing functionality of these crucial devices.

关键词： Temperature sensors Cloud computing Voltage Transformers Real-time systems Sensors Maintenance Servers Monitoring Business

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