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MBMRF: A Modified Bidirectional IPv6 Multicast Protocol with Mixed Upward and Downward Forwarding for TSCH-Enabled WSANs

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Simulation Modelling Practice and Theory 2025年

作者： Eden Teshome Hunde Shereen Ismail Department of Electronics and Informatics (ETRO) Vrije Universiteit Brussel Pleinlaan 2 1050 Brussels Belgium Department of Electrical and Computer Engineering (ECE) Jimma Institute of Technology Jimma University 378 Jimma Ethiopia Merit Network Inc. University of Michigan Ann Arbor MI 48108 USA

Wireless Sensor and Actuator Networks (WSANs) consist of numerous embedded devices that collaborate to perform complex tasks, surpassing the capabilities of traditional wired networks. This collaboration is efficiently enabled through multicast protocols. While multicast protocols offer significant advantages for WSANs, many fail to meet certain performance requirements. To address these challenges, we propose the Modified Bidirectional Multicast RPL Forwarding (MBMRF) protocol. This study tackles limitations in existing Internet Protocol version 6 (IPv6) multicast protocols, including the Routing Protocol for Low Power and Lossy Networks (RPL) and Bidirectional Multicast RPL Forwarding (BMRF). The MBMRF protocol introduces a novel mixed upward and downward multicast packet forwarding mechanism optimized for multi-channel Time Slotted Channel Hopping (TSCH) networks. Furthermore, to ensure sufficient timeslot allocation for scheduling mixed up-and-down packet transmissions, the protocol incorporates a modified version of the Orchestra scheduling algorithm. The MBMRF protocol was implemented and simulated on Zolertia (Z1) motes using the Contiki operating system and evaluated against existing IPv6 multicast protocols, including Stateless Multicast RPL Forwarding (SMRF), Enhanced Stateless Multicast RPL Forwarding (ESMRF), and BMRF. Results show that MBMRF significantly reduces buffer overflow and network-wide energy consumption compared to SMRF, ESMRF, and BMRF, with only a marginal increase in memory usage.

关键词： Wireless Sensor and Actuator Networks Bidirectional Multicast RPL Forwarding IPv6 Time Slotted Channel Hopping Zolertia

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Automatic document clustering and indexing of multiple documents using KNMF for feature extraction through Hadoop and lucene on big data

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Test engineering and Management 2019年第11-12期81卷 1107-1130页

作者： Laxmi Lydia, E. Sharmili, N. Nguyen, Phong Thanh Hashim, Wahidah Maseleno, Andino Computer Science and Engineering Vignan's Institute of Information Technology India CSE Department GayatriVidyaParishad College of Engineering for Women Viskahpatnam Andhra Pradesh India Department of Project Management Ho Chi Minh City Open University Viet Nam Institute of Informatics and Computing Energy UniversitiTenagaNasional Malaysia

The existence of unlabeledtext data in documents has become larger and excavating such datasets is a provocative task. The objective of Big Data is to store, retrieve and analyse multipletext documents. Problem Statement:The retrieval of the identical data over large databases is of major concern. Existing Solution:Existing problem is solved by Full-Text Search (FTS) which means pattern matching technique that allows searching of multiple keywords at specific *** Solution: In this paper, we consider multiple text documents as input and processed using text mining pre-processing algorithms like Key Phrase extraction, Porters stemming for tokenizing and TF_IDF toobtain all non-negative values. These values further processed to get matrix data throughNonnegative matrix factorization (NMF). On performing NMF, K-means algorithmis upgraded with NMF to obtain quality clusters of data *** of the algorithms are tested using Newsgroup20 data in Open Source Hadoop software environment which also analyses the performance of the MapReduce framework. The final outcome is to generate clusters and index them for the Newsgroup20dataset. Later on, Apache Lucene is presented for automatic document clustering with aGUI interface developed for indexing. Thus, this proposed algorithm resultsby improving the performance of document clustering through Map Reduce framework in Hadoop. © 2019 Mattingley Publishing. All rights reserved.

关键词： Big data

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Enhancing visual speech perception through deep automatic lipreading: A systematic review

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computers in Biology and Medicine 2025年 190卷 110019-110019页

作者： Rahmatullah, Griffani Megiyanto Ruan, Shanq-Jang Wisesa, I. Wayan Wiprayoga Li, Lieber Po-Hung Department of Electronic and Computer Engineering National Taiwan University of Science and Technology No. 43 Sec. 4 Keelung Rd Taipei10607 Taiwan Electrical Electronic Engineering Politeknik Negeri Bandung Jl. Gegerkalong Hilir Ciwaruga Kec. Parongpong West Java Bandung40012 Indonesia Department of Informatics Institut Teknologi Sumatera Jl. Terusan Ryacudu Way Huwi Kec. Jati Agung Lampung Lampung Selatan35365 Indonesia Faculty of Medicine and Institute of Brain Science National Yang-Ming Chiao-Tung University No. 155 Sec. 2 Linong St Beitou District Taipei112 Taiwan Department of Otolaryngology Cheng Hsin General Hospital No. 45 Zhenxing St Beitou District Taipei112 Taiwan Department of Medical Research China Medical University Hospital No. 2 Yude Rd. North District Taichung404327 Taiwan

Communication involves exchanging information between individuals or groups through various media sources. However, limitations such as hearing loss can make it difficult for some individuals to understand the information delivered during speech communication. Conventional methods, including sign language, written text, and manual lipreading, offer some solutions;however, emerging software-based tools using artificial intelligence (AI) are introducing more effective approaches. Many approaches rely on AI to improve communication quality, with the current trend of leveraging deep learning being a particularly effective tool. This paper presents a comprehensive Systematic Literature Review (SLR) of research trends in automatic lipreading technologies, a critical field in enhancing communication among individuals with hearing impairments. The SLR, which followed the Preferred Reporting Items for Systematic Literature Review and Meta-Analysis (PRISMA) protocol, identified 114 original research articles published between 2014 and mid-2024. The essential information from these articles was summarized, including the trends in automatic lipreading research, dataset availability, task categories, existing approaches, and architectures for automatic lipreading systems. The results showed that various techniques and advanced deep learning models achieved convincing performance to become state-of-the-art in automatic lipreading tasks. However, several challenges, such as insufficient data quantity, inadequate environmental conditions, and language diversity, must be resolved in the future. Furthermore, many improvements have been made to the deep learning models to overcome these challenges and become a massive solution, particularly for automatic lipreading tasks in the near future. © 2025

关键词： Contrastive Learning

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Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation

arXiv

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arXiv 2023年

作者： Zhao, Haoran Zhang, Yinghao Cheng, Debo Li, Chen Feng, Zaiwen College of Science Huazhong Agricultural University Wuhan430070 China College of Computer Science and Engineering Guangxi Normal University Guilin541000 China STEM University of South Australia Australia Department of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka820-8502 Japan College of Informatics Huazhong Agricultural University Wuhan430070 China Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan430070 China Hubei Hongshan Laboratory Huazhong Agricultural University Wuhan430070 China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University Wuhan430070 China State Key Laboratory of Hybrid Rice Wuhan University 299 Bayi Rd Wuhan430070 China Macro Agricultural Research Institute Huazhong Agricultural University Wuhan430070 China

Causal inference plays an important role in understanding the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational data with high-dimensional covariates due to the curse of dimension and the problem of data sufficiency. The existing matching methods can not effectively estimate individual causal effect or solve the problem of dimension curse in causal inference. To address this challenge, in this work, we prove that the reduced set by sufficient dimension reduction (SDR) is a balance score for confounding adjustment. Under the theorem, we propose to use an SDR method to obtain a reduced representation set of the original covariates and then the reduced set is used for the matching method. In detail, a non-parametric model is used to learn such a reduced set and to avoid model specification errors. The experimental results on real-world datasets show that the proposed method outperforms the compared matching methods. Moreover, we conduct an experiment analysis and the results demonstrate that the reduced representation is enough to balance the imbalance between the treatment group and control group individuals. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Security enhancement of big data in cloud application using block-chain

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AIP Conference Proceedings 2023年第1期2587卷

作者： Avein Jabar Al-Asadi J. V. S. Arundathi Omdev Dahiya Abdul Wahab Hashmi Ankur Gupta Malik Bader Alazzam Dept. of Information Technology Technical College of Informatics TCI Sulaimani Polytechnic University Sulaymaniyah Iraq Dept. of Computer Science SR & BGNR Government Arts and Science Degree & PG College (Autonomous) Khammam Telangana India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Advanced Manufacturing and Mechatronics Lab Mechanical Engineering Department Malaviya National Institute of Technology Jaipur India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

Organizations are inundated on a daily basis with vast, difficult-to-manage volumes of structured and unstructured data, which is known as “big data.” Analysis of large amounts of data may provide useful information that can be used to improve decision-making and boost employee morale. This kind of data storage makes it almost hard to change, hack, or otherwise deceive. Every computer in a block-chain network has a copy of the block-transaction chain’s log, which is called a “block-chain.” In previous researches it has been observed that security of big data is a major issue. Block chain has been found suitable mechanism to deal with such issues. Block-chain has also been considered a service in the context of cloud computing, termed Block-chain as a Service (BaaS). By using virtual storage nodes, block-chain technology may create whole new cloud storage services that are very resistant to data tampering. The proposed research has focused on the enhancement of security of big data from SQL injection attack that is transferred over cloud application. Research is making use of block chain technology in order to resolve the issue of security. The major objective of research is to propose block chain based security model for big data that should be more secure and efficient in case of SQL injection attack. Research work has proposed modified algorithm enhances the security of big data in cloud application against SQL injection, cross script.

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Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions

TechRxiv

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TechRxiv 2022年

作者： Gong, Tierui Gavriilidis, Panagiotis Ji, Ran Huang, Chongwen Alexandropoulos, George C. Wei, Li Zhang, Zhaoyang Debbah, Mérouane Poor, H. Vincent Yuen, Chau Pillar Singapore University of Technology and Design Singapore487372 Singapore The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates The CentraleSupelec University ParisSaclay Gif-sur-Yvette91192 France The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore

Future wireless systems are envisioned to create an endogenously holography-capable, intelligent, and programmable radio propagation environment, that will offer unprecedented capabilities for high spectral and energy efficiency, low latency, and massive connectivity. A potential and promising technology for supporting the expected extreme requirements of the sixth-generation (6G) communication systems is the concept of the holographic multiple-input multiple-output (HMIMO), which will actualize holographic radios with reasonable power consumption and fabrication cost. The HMIMO is facilitated by ultra-thin, extremely large, and nearly continuous surfaces that incorporate reconfigurable and sub-wavelength-spaced antennas and/or metamaterials. Such surfaces comprising dense electromagnetic (EM) excited elements are capable of recording and manipulating impinging fields with utmost flexibility and precision, as well as with reduced cost and power consumption, thereby shaping arbitrary-intended EM waves with high energy efficiency. The powerful EM processing capability of HMIMO opens up the possibility of wireless communications of holographic imaging level, paving the way for signal processing techniques realized in the EM-domain, possibly in conjunction with their digital-domain counterparts. However, in spite of the significant potential, the studies on HMIMO communications are still at an initial stage, its fundamental limits remain to be unveiled, and a certain number of critical technical challenges need to be addressed. In this survey, we present a comprehensive overview of the latest advances in the HMIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMO systems. We also compare the HMIMO with existing multi-antenna technologies, especially the massive MIMO, present various promising synergies of HMIMO with current and future candidate technologies, and provide

关键词： Beamforming

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Unraveling the Role of Boron Dimers in the Electrical Anisotropy and Superconductivity in Boron-Doped Diamond

SSRN

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SSRN 2024年

作者： Sobaszek, Michal Kwon, Soonho Winiarski, Michal Rutkowski, Bogdan Ryl, Jacek Klimczuk, Tomasz Wang, Dongying Li, Xinwei Bockrath, Marc Bogdanowicz, Robert Goddard, William A. Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Metrology and Optoelectronics 11/12 Narutowicza Str. 80-233 Poland Materials and Process Simulation Center California Institute of Technology 1200 East California Blvd. CA91125 United States Gdańsk University of Technology Faculty of Applied Physics and Mathematics and Advanced Materials Centre Department of Solid State Physics 11/12 Narutowicza Str. Gdańsk80-233 Poland Faculty of Metals Engineering and Industrial Computer Science AGH University of Krakow Al. A. Mickiewicza 30 Kraków30-059 Poland Advanced Materials Center Gdańsk University of Technology Narutowicza 11/12 Gdańsk80-233 Poland Department of Physics The Ohio State University ColumbusOH43210 United States

We use quantum mechanics (QM) to determine the states formed by B dopants in diamond. We find that isolated B sites prefer to form BB dimers and that the dimers pair up to form tetramers (BBCBB) that prefer to aggregate parallel to the (111) surface in the direction, one double layer below the H-terminated surface double layer. These tetramers lead to metallic character (Mott metal Insulator Transition) with holes in the valence band near the Γ point and electrons in the BBCBB tetramer promoted band along the X direction. Our experiments find very significant anisotropy in the superconductivity for boron-doped diamond thin films prepared with Microwave Plasma Assisted Chemical Vapor Deposition using deuterium-rich plasma. This leads to much higher conductivity in the X direction than the Y direction, as predicted by the QM. This phase transition to the anomalous phase is linked with the emergence of boson quantum entanglement states behaving as a bosonic insulating state. These anisotropic superconducting properties of the diamond film might enable applications such as single-photon detectors. We expect that this formation of a dirty superconductivity state is related to the BBCBB tetramers found in our QM calculations. © 2024, The Authors. All rights reserved.

关键词： Dimers

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L 2 -regularization based two-way weighted neutrosophic clustering with Manhattan and Euclidean distances

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Fuzzy Sets and Systems 2025年 518卷

作者： Haoye Qiu Zhe Liu Haojian Huang Sukumar Letchmunan Muhammet Deveci Tapan Senapati School of Computer Science and Engineering Southeast University Nanjing 210096 China College of Mathematics and Computer Xinyu University Xinyu 338004 China School of Computer Sciences University Sains Malaysia Penang 11800 Malaysia Department of Computer Science The University of Hong Kong Hong Kong 999077 China Department of Industrial Engineering Turkish Naval Academy National Defence University 34942 Tuzla Istanbul Turkey Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 Republic of Korea Department of Information Technologies Western Caspian University Baku 1001 Azerbaijan School of Mathematics and Statistics Southwest University Chongqing 400715 China Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

Neutrosophic c -means clustering (NCM) is a promising algorithm to solve uncertainty and imprecision in data analysis. Despite its intensive exploration, one major challenge still needs to be addressed, i.e. how to efficiently reveal the latent structure of the dataset and improve clustering performance by discriminating the contributions of different objects and features in the clustering process. In this paper, a general neutrosophic clustering framework is proposed to address the aforementioned issue. Specifically, we develop an L 2 -regularization based two-way weighted scheme to learn the discriminative capability of different features and gauge the importance of different objects. In the proposed framework, we employ Manhattan and Euclidean distances as two different dissimilarity metrics, respectively. Additionally, we design the iterative optimization procedures to obtain neutrosophic partition, cluster centers, object weights and feature weights. Extensive experimental studies corroborate that the proposed algorithms achieve superior performance over other comparative algorithms on five synthetic and twenty real-world datasets.

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Leveraging online shopping behaviors as a proxy for personal lifestyle choices: New insights into chronic disease prevention literacy

arXiv

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arXiv 2021年

作者： Wang, Yongzhen Liu, Xiaozhong Börner, Katy Lin, Jun Ju, Yingnan Sun, Changlong Si, Luo Institute of Science of Science and S&T Management Dalian University of Technology Dalian China Department of Computer Science Worcester Polytechnic Institute WorcesterMA United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Alibaba DAMO Academy Hangzhou China

Objective: Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles. This paper proposes leveraging online shopping behaviors as a proxy for personal lifestyle choices to improve chronic disease prevention literacy, targeted for times when e-commerce user experience has been assimilated into most people's everyday lives. Methods: Longitudinal query logs and purchase records from 15 million online shoppers were accessed, constructing a broad spectrum of lifestyle features covering various product categories and buyer personas. Using the lifestyle-related information preceding online shoppers' first purchases of specific prescription drugs, we could determine associations between their past lifestyle choices and whether they suffered from a particular chronic disease. Results: Novel lifestyle risk factors were discovered in two exemplars-depression and type 2 diabetes, most of which showed reasonable consistency with existing healthcare knowledge. Further, such empirical findings could be adopted to locate online shoppers at higher risk of these chronic diseases with decent accuracy [i.e., (area under the receiver operating characteristic curve) AUC=0.68 for depression and AUC=0.70 for type 2 diabetes], closely matching the performance of screening surveys benchmarked against medical diagnosis. Conclusions: Mining online shopping behaviors can point medical experts to a series of lifestyle issues associated with chronic diseases that are less explored to date. Hopefully, unobtrusive chronic disease surveillance via e-commerce sites can grant consenting individuals a privilege to be connected more readily with the medical profession and sophistication. Copyright © 2021, The Authors. All rights reserved.

关键词： Diseases

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Gashissdb: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer

arXiv

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arXiv 2021年

作者： Hu, Weiming Li, Chen Li, Xiaoyan Rahaman, Md Mamunur Ma, Jiquan Zhang, Yong Chen, Haoyuan Liu, Wanli Sun, Changhao Yao, Yudong Sun, Hongzan Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group MBIE College Northeastern University Shenyang110169 China Department of Pathology Cancer Hospital China Medical University Liaoning Cancer Hospital and Institute Shenyang110042 China Department of Computer Science and Technology Heilongjiang University Heilongjiang Harbin150080 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Department of Radiology Shengjing Hospital China Medical University Shenyang110122 China Institute of Medical Informatics University of Luebeck Luebeck Germany

Background and Objective: Gastric cancer has turned out to be the fifth most common cancer globally, and early detection of gastric cancer is essential to save lives. Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer. However, computer-aided diagnostic techniques are challenging to evaluate due to the scarcity of publicly available gastric histopathology image datasets. Methods: In this paper, a noble publicly available Gastric Histopathology Sub-size Image Database (GasHisSDB) is published to identify classifiers’ performance. Specifically, two types of data are included: normal and abnormal, with a total of 245,196 tissue case images. In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation. Seven classical machine learning classifiers, three Convolutional Neural Network classifiers, and a novel transformer-based classifier are selected for testing on image classification tasks. Results: This study performed extensive experiments using traditional machine learning and deep learning methods to prove that the methods of different periods have discrepancies on GasHisSDB. Traditional machine learning achieved the best accuracy rate of 86.08% and a minimum of just 41.12%. The best accuracy of deep learning reached 96.47% and the lowest was 86.21%. Accuracy rates vary significantly across classifiers. Conclusions: To the best of our knowledge, it is the first publicly available gastric cancer histopathology dataset containing a large number of images for weakly supervised learning. We believe that GasHisSDB can attract researchers to explore new algorithms for the automated diagnosis of gastric cancer, which can help physicians and patients in the clinical setting. Copyright © 2021, The Authors. All rights reserved.

关键词： Image classification

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