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WSETO: wild stock exchange trading optimization algorithm enabled routing for NB-IoT tracking system

International Journal of Information technology (Singapore) 2024年第8期16卷 4895-4905页

作者： Sivadevuni, Sreeparnesh Sharma Naveen, J. Department of Computer Science and Engineering School of Engineering and Technology Christ University Karnataka Bangalore 560001 India

The Narrowband Internet of Things (NB-IoT) communication plays a significant role in the IoT due to the capability of generating broad exploration with the usage of limited power. Over the past few years, the Low Power Wide Area Networks (LPWAN) have been efficient in the data acquisition and remote monitoring area however they failed to generate high data rates, low latency, and the consumption of low power. To solve these problems, NB-IoT technology has developed in long-term asset tracking and it replaces the Global Positioning System (GPS) with its ubiquitous coverage. In this research, the Wild Stock Exchange Trading Optimization technique (WSETO) is proposed for a routing-based NB-IoT tracking system. The WSETO is the combination of the Wild Geese Algorithm (WGA) and SETO. By employing WSETO, the routing to the relevant target location is established effectively. The existing techniques like Low Power Asset Tracking of NB-IoT (LoPATraN), Monitoring system based on NB-IoT and BeiDou System/GPS (BDS/GPS), and Narrowband Physical Uplink Shared Channel (NPUSCH) are used to compare the WSETO approach. In rounds with a value of 2000, the WSETO demonstrates a superior location error of 0.001 in comparison to existing methods such as LoPATraN, a monitoring system utilizing NB-IoT and BDS/GPS, as well as NPUSCH. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Global positioning system Low power wide area networks Narrowband internet of things Stock exchange trading optimization Wild geese algorithm

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Exploration of New Dimensions for the Massive Internet of Things

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IEEE Access 2025年 13卷 89582-89599页

作者： Wang, Michael Wang, L. Zhang, J.J. You, X. Southeast University School of Information Science and Engineering School of Information Science and Engineering China Nanjing University of Science and Technology of Electrical and Computer Engineering School of Electronic and Optical Engineering China Nanjing University of Posts and Telecommunications School of Internet of Things China

Like all other types of wireless communications, massive Internet of Things (mIoT) communication is all about degrees of freedom. It holds the key to almost all mIoT challenging issues. However, it is also the most limited resource in mIoT due to its unique constraints. This paper derives a new class of waveforms, i.e., a canonical waveform, and explores the dimensions hidden in its underlying mathematical structure. The intrinsic characteristics and the degrees of freedom of the canonical waveform are studied and fully explored to reap the full potential of the waveform. Most significantly, a new dimension is uncovered and exploited as additional degrees of freedom to better address the mIoT challenges in energy efficiency, resource efficiency, and robustness under the mIoT constraint. The corresponding benefits are evaluated analytically and numerically in AWGN, frequency-flat, and frequency-selective channels. We demonstrate that no existing mIoT waveforms can fully address the mIoT challenges, whereas the new degrees of freedom from the canonical waveform offer a comprehensive solution. © 2013 IEEE.

关键词： Wireless communication Wireless sensor networks Temperature sensors Costs Monitoring Energy efficiency LoRa Complexity theory Temperature measurement Radio frequency

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Machine Learning-Based Chronic Kidney Cancer Prediction Application: A Predictive Analytics Approach

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Biomedical Materials and Devices 2024年第2期2卷 1028-1048页

作者： Uddin, Khandaker Mohammad Mohi Nahid, Md. Nuzmul Hossain Ullah, Md. Mehedi Hasan Mazumder, Badhan Khan, Md. Saikat Islam Dey, Samrat Kumar Department of Computer Science and Engineering Dhaka International University Faculty of Computer Science and Engineering Patuakhali Science and Technology University School of Science and Technology Bangladesh Open University

Chronic Kidney Cancer (CKC) is a disease that hindrances the blood-filtering mechanism of the kidney and is increasing at an alarming rate in the recent few years. As CKC does not show any earlier symptoms, the earlier prediction will be very effective to elevate its effect. In this paper, a machine learning-based method for diagnosing CKC at an early stage is proposed. SVM, Decision Tree, Random Forest, KNN, Native Bayes, Logistic Regression, LGBM, CatBoost, and AdaBoost are employed for classification purposes, and among these algorithms, Random Forest, AdaBoost, and LGBM give 99% accuracy. Data pre-processing and feature selection help improve the accuracy of the proposed model. The outcome of the comparative analysis of our proposed work with eight existing approaches proves the robustness and supremacy of the work. Furthermore, the best model is also used to develop a user-friendly web application. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC 2023.

关键词： AdaBoost Chronic kidney cancer Machine learning Random forest and LGBM Web application

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Leaving None Behind: Data-Free Domain Incremental Learning for Major Depressive Disorder Detection

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IEEE Transactions on Affective Computing 2024年第2期16卷 758-770页

作者： Chen, Tao Guo, Yanrong Hao, Shijie Hong, Richang Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China Hefei University of Technology Ministry of Education and School of Computer Science and Information Engineering Hefei230009 China

While deep learning techniques have shown promising performance in the Major Depressive Disorder (MDD) detection task, they still face limitations in real-world scenarios. Specifically, given the data scarcity, some efforts have resorted to aggregating data from different domains to expand the data volume. However, their effectiveness is currently limited by the domain gap and data privacy. Additionally, the class imbalance issue is particularly severe in our application, leading to biased classifying performance accordingly. To address these challenges, we propose Data-Free Domain Incremental Learning for the MDD detection (DIL-MDD) task, accommodating multiple feature distributions by only accessing well-trained models from previous domains and the data in the current domain. Specifically, DIL-MDD consists of two key modules: Adaptive Class-tailored Threshold Learning (ACTL) and Data-Free Domain Alignment (DFDA). The first module measures the discrepancy between the outputs of two sequential domains, based on which we learn a class-tailored threshold adaptively. Building on this, we differentiate between samples that either exhibit similarities or dissimilarities with the previous domain, where this similar sample set is identified to investigate the feature distribution of the historical data. The second module imposes an alignment constraint to narrow the gap between these two sample sets, thereby exploring the expertise of the previous domain. To validate the effectiveness of the proposed method, we conduct extensive experiments on the public MDD datasets, i.e., DAIC-WOZ, MODMA, and CMDC. We also apply our method to another mental health condition, Autism Spectrum Disorder (ASD), to further demonstrate its applicability. Finally, the ablation studies validate the superiority of the proposed modules. © 2010-2012 IEEE.

关键词： Adversarial machine learning

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FAME: Towards Factual Multi-Task Model Editing

FAME: Towards Factual Multi-Task Model Editing

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zeng, Li Shan, Yingyu Liu, Zeming Yao, Jiashu Guo, Yuhang School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798891761643

Large language models (LLMs) embed extensive knowledge and utilize it to perform exceptionally well across various tasks. Nevertheless, outdated knowledge or factual errors within LLMs can lead to misleading or incorrect responses, causing significant issues in practical applications. To rectify the fatal flaw without the necessity for costly model retraining, various model editing approaches have been proposed to correct inaccurate knowledge within LLMs in a cost-efficient way. To evaluate these model editing methods, previous work introduced a series of datasets. However, most of the previous datasets only contain fabricated data in a single format, which diverges from real-world model editing scenarios, raising doubts about their usability in practice. To facilitate the application of model editing in real-world scenarios, we propose the challenge of practicality. To resolve such challenges and effectively enhance the capabilities of LLMs, we present FAME, an factual, comprehensive, and multi-task dataset, which is designed to enhance the practicality of model editing. We then propose SKEME, a model editing method that uses a novel caching mechanism to ensure synchronization with the real world. The experiments demonstrate that SKEME performs excellently across various tasks and scenarios, confirming its practicality. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Novel Mobile Application to Empower Visually Impaired Individuals to Perform Independent Day-to-Day Activities 8

A Novel Mobile Application to Empower Visually Impaired Indi...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Paul, Dewanshi Patel, Ankit Kumar Dhanush, R. Amutha, S. Vellore Institute of Technology B.Tech Electronics and Computer Engineering School of Electronics Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

People with visual impairments struggle or require assistance from others to perform day-to-day activities. Hence, people with visual impairments suffer from a lack of independence and quality of life. For this purpose, the app proposed in the paper is a unique, creative, innovative mobile application that will aid people with visual impairments in becoming independent and improving their quality of life. This paper introduces the novel mobile application's three key features: QR (Quick Response) code scanning, OCR (Object Character Recognition), and an Indoor Object Navigation System. The QR code scanning feature allows users to make informed decisions quickly by providing comprehensive information about products or their environment. The OCR functionality reads out the printed text aloud and helps the users understand written content with the help of audio. The indoor object finder or navigation system is a noteworthy advancement in Assistive technology. The system allows users to select a target object and then move their camera around their surroundings;once the object is detected, the app provides voice instructions about the directions of the target object to guide users towards it. By integrating these complex technologies, the novel mobile application provides an innovative approach to some of the issues encountered by those with visual impairments. The application aims to increase user independence, improve functionality, and build confidence to improve their daily experiences and engagements with the world around them. © 2024 IEEE.

关键词： Navigation systems

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Stacked LSTM for Solar Irradiance Forecasting Using Minimum Redundancy Maximum Relevance (MRMR) Feature Selection 1

Stacked LSTM for Solar Irradiance Forecasting Using Minimum ...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Hari, Ninu G. Jisha, G. Rajagiri School of Engineering and Technology Apj Abdul Kalam Technological University Department of Computer Science and Engineering Kerala India Rajagiri School of Engineering and Technology Department of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350307795

To effectively combat atmospheric pollution caused by greenhouse gases, immediately switching to power plants that rely solely on renewable energy sources is imperative. With the vast availability of solar energy in Kerala throughout the year, it would be wise to make it the primary source for these plants. The spatiotemporal features that impact the Global Horizontal Irradiance (GHI) should also be analysed to choose a better location for solar power plants. The features should be identified and analysed yearly and seasonally to determine the relationship between GHI and those selected features. The Minimum Redundancy Maximum Relevance (MRMR) minimal optimal feature selection algorithm is integrated for seasonal and yearly GHI prediction for finding the feature relation. The stacked LSTM (Long Short-Term Memory) with a 3-layer architecture is used for solar irradiance forecasting and annual and seasonal analysis of the dataset since LSTM is a perfect model for time-series data and stacked LSTM performs better than single-layer LSTM. The study shows that feature identification and selection positively change the forecasting results. The comparison of results also shows that seasonal forecasting needs more focus than yearly forecasting since results fluctuate in different seasons. The tropical monsoon climate forecasting is more complex as it fluctuates most of the year and is considered for experimental analysis. © 2024 IEEE.

关键词： Forecasting

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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

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IEEE Transactions on Robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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SiamS3C: spatial-channel cross-correlation for visual tracking with centerness-guided regression

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Multimedia Systems 2024年第5期30卷 1-14页

作者： Zhang, Jianming Chen, Wentao He, Yufan Kuang, Li-Dan Sangaiah, Arun Kumar School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410076 China International Graduate School of AI National Yunlin University of Science and Technology Douliu64002 Taiwan

Visual object tracking can be divided into the object classification and bounding-box regression tasks, but only one sharing correlation map leads to inaccuracy. Siamese trackers compute correlation map by cross-correlation operation with high computational cost, and this operation performed either on channels or in spatial domain results in weak perception of the global information. In addition, some Siamese trackers with a centerness branch ignore the associations between the centerness branch and the bounding-box regression branch. To alleviate these problems, we propose a visual object tracker based on Spatial-Channel Cross-Correlation and Centerness-Guided Regression. Firstly, we propose a spatial-channel cross-correlation module (SC3M) that combines the search region feature and the template feature both on channels and in spatial domain, which suppresses the interference of distractors. As a lightweight module, SC3M can compute dual independent correlation maps inputted to different subnetworks. Secondly, we propose a centerness-guided regression subnetwork consisting of the centerness branch and the bounding-box regression branch. The centerness guides the whole regression subnetwork to enhance the association of two branches and further suppress the low-quality predicted bounding boxes. Thirdly, we have conducted extensive experiments on five challenging benchmarks, including GOT-10k, VOT2018, TrackingNet, OTB100 and UAV123. The results show the excellent performance of our tracker and our tracker achieves real-time requirement at 48.52 fps. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Object tracking

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TED-EL: A Corpus for Speech Entity Linking 30

TED-EL: A Corpus for Speech Entity Linking

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Li, Silin Song, Ruoyu Lan, Tianwei Liu, Zeming Guo, Yuhang School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9782493814104

Speech entity linking amis to recognize mentions from speech and link them to entities in knowledge bases. Previous work on entity linking mainly focuses on visual context and text context. In contrast, speech entity linking focuses on audio context. In this paper, we first propose the speech entity linking task. To facilitate the study of this task, we propose the first speech entity linking dataset, TED-EL. Our corpus is a high-quality, human-annotated, audio, text, and mention-entity pair parallel dataset derived from technology, Entertainment, Design (TED) talks and includes a wide range of entity types (24 types). Based on TED-EL, we designed two types of models: ranking-based and generative speech entity linking models. We conducted experiments on the TED-EL dataset for both types of models. The results show that our ranking-based models outperform the generative models, achieving an F1 score of 60.68%. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Entity Linking Speech Entity Linking TED-EL

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