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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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Sparse Graph Tensor Learning for Multi-View Spectral Clustering

SSRN

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SSRN 2023年

作者： Chen, Man-Sheng Li, Zhi-Yuan Lin, Jia-Qi Wang, Chang-Dong Huang, Dong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Sun Yat-sen University Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China

Multi-view spectral clustering has achieved impressive performance by learning multiple robust and meaningful similarity graphs for clustering. Generally, the existing literatures often construct multiple similarity graphs by certain similarity measure (e.g. the Euclidean distance), which lack the desired ability to learn sparse and reliable connections that carry critical information in graph learning while preserving the low-rank structure. Regarding the challenges, a novel Sparse Graph Tensor Learning for Multi-view Spectral Clustering (SGTL) method is designed in this paper, where multiple similarity graphs are seamlessly coupled with the cluster indicators and constrained with a low-rank graph tensor. Specifically, a novel graph learning paradigm is designed by establishing an explicit theoretical connection between the similarity matrices and the cluster indicator matrices, in order that the constructed similarity graphs enjoy the desired block diagonal and sparse property for learning a small portion of reliable links. Then, we stack multiple similarity matrices into a low-rank graph tensor to better preserve the low-rank structure of the reliable links in graph learning, where the key knowledge conveyed by singular values from different views is explicitly considered. Extensive experiments on several benchmark datasets demonstrate the superiority of SGTL. The source code of SGTL would be publicly available soon. © 2023, The Authors. All rights reserved.

关键词： Tensors

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Classifying Emotions in Roman Urdu Posts using Machine Learning 1

Classifying Emotions in Roman Urdu Posts using Machine Learn...

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Nabeel, Zahra Mehmood, Maseeha Baqir, Anees Amjad, Anam Dept. of Software Engineering Sialkot Pakistan Ca'Foscari University of Venice Dept. of Environmental Sciences Informatics and Statistics Venice Italy National University of Sciences and Technology Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665424134

Emotion, a state of human mind has a significant impact on human behavior, social interactions and decision making. Few emotions from Carroll E. Izard model such as long-term fear, anger and sadness cause mental disorders which can lead to deterioration of mental and physical health. For the well-being of mental health, different psychological parameters such as voice, facial expressions and body movements were used to extract these emotions in earlier times. This paper focuses on verbal language for emotion classification. With the huge popularity of Web 3.0, people feel more comfortable in sharing their emotions over social media due to the anonymity of the web. Recently, researchers around the globe are interested to classify the emotions from social media posts (i.e., tweets, comments or micro-blogs etc.) using resource-rich languages e.g., English. However, less attention is paid towards the resource-poor language such as Urdu in Roman script. According to the Gallup survey in Pakistan, Urdu in Roman script is widely used to transmit text. To detect emotions in Roman Urdu text is a big challenge due to its scripting and morphology. In this paper, the following contributions are made;1) A corpus of Roman Urdu is developed for emotion classification. 2) We aim to identify user's mental states using textual posts on social media plat-forms;Instagram, Facebook, Twitter and YouTube. Particularly, Natural Language Processing methods incorporated with feature extraction methods (TF-IDF) and four supervised classifiers such as Support Vector Machine (SVM),Naïve Bayes, Random Forest and K-nearest Neighbor are used. By utilizing 80% training and 20% testing data, Naive Bayes secures 70.57% accuracy. 3) Moreover, suitability analysis is performed to identify the most suitable classifier in terms of accuracy, training and testing time. Results show that Naive Bayes is the most suitable classifier for emotion classification. © 2021 IEEE.

关键词： Deterioration

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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...

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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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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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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.

关键词：

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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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Newsgroup Topic Extraction using Probabilistic Inverse Cluster Frequency Term-Cluster Weighting and Growing Neural Gas Clustering

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2021年第1期48卷

作者： Adinugroho, Sigit Rahman, Muh Arif Syauqy, Dahnial Department of Informatics Faculty of Computer Science Brawijaya University Malang Indonesia Department of Computer Engineering Faculty of Computer Science Brawijaya University Malang Indonesia

Effectiveness of a topic extraction method is depends on the capability to extract information from a large amount of data. This paper proposes a new solution for selecting a set of topics from text documents as well as a new approach to set the weight of a term in clusters of documents. The process of revealing topics from multiple documents starts with a preprocessing step, which aims to omit the unnecessary portion of the text. After that, the weights of terms in documents are calculated based on the term frequency-inverse document frequency method. Then, feature transformation based on singular value decomposition is employed to build weight for clustering. The clustering process is conducted using the Growing Neural Gas method. Finally, to determine the weights of terms in clusters as a way of selecting topics from clusters, the proposed probabilistic inverse cluster frequency term-cluster method is applied. Experiments show that the framework attains satisfactory results indicated by the average accuracy of 0.8606, 0.7406, 0.4039, and 0.6647 for topics obtained from Binary2, Multi5, Multi7, and Multi10 categories of 20Newsgroup dataset. © 2021, IAENG International Journal of computer Science. All Rights Reserved.

关键词： Singular value decomposition

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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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Industrial Speed Control of im Based Model Predictive Controller Using Zeta Converter 1

Industrial Speed Control of im Based Model Predictive Contro...

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1st International Conference on Advances in Computational Science and engineering, ICACSE 2020

作者： Pattanaik, Balachandra Barani Sundaram, B. Mishra, Manish Kumar Thirumoorthy, Dhanabal Rastogi, Umang Department of Electronics and Communications Bule Hora University Bule Hora Ethiopia Department of Computer Sciences Bule Hora University Bule Hora Ethiopia Department of Computer Sciences University of Gondar Ethiopia College of Informatics Bule Hora University Bule Hora Ethiopia Department of Computer Science Engineering Meerut Institute of Engineering and Technology Uttar Pradesh Meerut India

For induction, machine-based configuration, the model predictive control scheme, power converter based renewable system is presented in this paper. The PV system is proposed to provide an input source for the model predictive control (MPC) based system. The control method predictive is used based on vector control. According to this, the drive system behaviour of the fault-tolerant capacity is enhanced. The ZETA converter is proposed to improve the system input source, which fed on the PV system. The proposed ZETA converter is used to increase the DC link voltage of the system. The motor's speed and torque can be controlled by the model predictive control method based on vector control. The results of the proposed system are obtained and verified using the MATLAB/Simulink. © Published under licence by IOP Publishing Ltd.

关键词： Model predictive control

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