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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Yan, Zipei Xu, Linchuan Suzuki, Atsushi Wang, Jing Cao, Jiannong Huang, Jun The Hong Kong Polytechnic University Department of Computing Kowloon Hung Hom Hong Kong King's College London Department of Informatics London United Kingdom University of Greenwich Department of Computing and Mathematical Sciences London United Kingdom Anhui University of Technology School of Computer Science and Technology Ma'anshan China

ISBN: (纸本)9781665480451

Computational color naming (CCN) aims to learn a mapping from pixels into semantic color names, e.g., red, green and blue. CCN has wide applications including color vision deficiency assistance and color image retrieval. Existing research on CCN mainly studies pixels collected under laboratory settings or studies images collected from the web. However, laboratory pixels are very limited such that the learned mapping may not generalize well on unseen pixels, and the mapping discovered from images is usually data-specific. In this paper, we aim to learn a universal mapping by studying pixels collected from the web. To this end, we formulate a novel classification problem that incorporates both the pixels and the RGB color model. The RGB color model is beneficial for learning the mapping because it characterizes the production of colors, e.g., the addition of red and green produces yellow. However, the characterization is rather qualitative. To solve this problem, we propose ColorMLP, which is a multilayer perceptron (MLP) embedded with graph attention networks (GATs). Here, the GATs are designed to capture color relations that we construct by referring to the RGB color model. In this way, the parameters of the MLP can be regularized to comply with the RGB model. We conduct comprehensive experiments to demonstrate the superiority of ColorMLP to alternative *** expand the application of CCN, we design a novel data augmentation method named partial color jitter (PCJ), which performs color jitter (CJ) on a subset of pixels belonging to the same color of an image. In this way, PCJ partially changes the color properties of images, thereby significantly increasing images' diversity. We conduct extensive experiments on CIFAR10/100 and ImageNet datasets, showing that PCJ can consistently improve the classification performance. Our data and software can be found at https://https://***/yanzipei/CCN_and_ItsApp. © 2022 IEEE.

关键词： Mapping

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INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

arXiv

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arXiv 2023年

作者： Zheng, Chuanpan Qi, Jianzhong Fan, Xiaoliang Chen, Chaochao Wang, Cheng Chen, Longbiao Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University China School of Computing and Information Systems University of Melbourne Australia College of Computer Science and Technology Zhejiang University China

Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires inductive learning. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to perform accurate kriging results because of the heterogeneous spatial relations and diverse temporal patterns. In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. We first encode heterogeneous spatial relations between the unobserved and observed locations by their spatial proximity, functional similarity, and transition probability. Based on each relation, we accurately aggregate the information of most correlated observed locations to produce inductive representations for the unobserved locations, by jointly modeling their similarities and differences. Then, we design relation-aware gated recurrent unit (GRU) networks to adaptively capture the temporal correlations in the generated sequence representations for each relation. Finally, we propose a multi-relation attention mechanism to dynamically fuse the complex spatio-temporal information at different time steps from multiple relations to compute the kriging output. Experimental results on three real-world datasets show that our proposed model outperforms state-of-the-art methods consistently, and the advantage is more significant when there are fewer observed locations. Our code is available at https://***/zhengchuanpan/INCREASE. Copyright © 2023, The Authors. All rights reserved.

关键词： Kriging

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Editorial: A glimpse of EVC2023

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Transportation Research Procedia 2023年 70卷 1-4页

作者： Muhammad-Sukki, Firdaus Bani, Nurul Aini Tingas, Efstathios-Al School of Computing Engineering and the Built Environment Edinburgh Napier University Merchiston Campus 10 Colinton Road Edinburgh EH10 5DT United Kingdom Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia

The Editorial describes the main aim and results of the 8th International Electric Vehicle Conference (EVC2023) held in Edinburgh Napier University, Edinburgh, United Kingdom from 21 – 23 June 2023.

关键词： electric vehicle policy technology

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LibreHealth Cost-of-Care Explorer: Mobile Application for Patient-friendly Access to Hospital Chargemasters

LibreHealth Cost-of-Care Explorer: Mobile Application for Pa...

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2022 IEEE-EMB Special Topic Conference on Healthcare Innovations and Point of Care Technologies, HI-POCT 2022

作者： Merine, Regina Pinnamraju, Jahnavi Singh, Darshpreet Gichoya, Judy W. Purkayastha, Saptarshi Indiana University Purdue University Indianapolis School of Informatics and Computing IndianapolisIN46202 United States Institute of Technology Management New Delhi10053 India Emory University School of Medicine Emory University AtlantaGA30322 United States

ISBN: (纸本)9781665496155

The Centers for Medicare Medicaid Services (CMS) mandated that Charge Description Master (CDM) be available online in a machine-readable format by January 1, 2019. These changes were made to increase availability of hospital price information in order to provide patients with more access to their health information and allow physicians to spend more time with their patients. However, the mandated machine-readable formats make it impossible for a patient to interpret the pricing and hence limit price transparency. To bridge this gap, we developed a mobile application that can be used by patients to reveal the cost of a medical procedure or service before receiving it. Patients are able to compare the costs of medical procedures available in surrounding hospitals by viewing the hospital's CDM and determine which hospital delivers the best care/medical procedures at the best price. Clinical relevance - The LibreHealth cost-of-care explorer app presents the cost of medical procedures in a consumer-friendly format. It provides patient-friendly cost estimates for medical procedures performed in US hospitals and promotes price transparency to the end users. © 2022 IEEE.

关键词： Hospitals

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A Knowledge-Based System to Predict Crime from Criminal Records in the Case of Hossana Police Commission

A Knowledge-Based System to Predict Crime from Criminal Reco...

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Information and Communication technology for Development for Africa (ICT4DA), International Conference on

作者： Betelhem Zewdu Wubineh Biniyam Yirsaw Metekiya Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland Department of Information Technology School of Computing & Informatics Wachemo University Hossana Ethiopia Department of Information System College of Informatics University of Gondar Gondar Ethiopia

Crime is a complex social phenomenon, and its cost continues to rise over time. Police have collected a large amount of data on criminals every day for different crimes. However, extracting valuable information from these data takes time and effort for the law enforcement organization at hand. Therefore, effective crime prevention strategies and policies are necessary to prevent crime. Criminology is one of the most important areas for data mining. The purpose of this study is to extract useful crime patterns from past data and develop a knowledge-based system to prevent crime. Different criminals commit different crimes, but we do not know which criminal commits which crime. For this purpose, the 910 criminal records are used to train and test the model. To predict a crime, the classification technique and the J48 decision tree, random forest (RF), PART, and ensemble algorithms are used. As a result, the algorithm scored an accuracy of 75.38%, 80.4%,73.7%, and 81.3% in the J48, RF, PART, and ensemble methods, respectively, in a 10- fold cross-validation. Additionally, the offender's job is the determining factor that causes and predicts crimes. Finally, we integrate the rules extracted from a data mining task into a knowledge base system using PROLOG, which is used to make decisions.

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Multivariate Aggregated NOMA for Resource Aware Wireless Network Communication Security

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Computers, Materials & Continua 2023年第1期74卷 1693-1708页

作者： V.Sridhar K.V.Ranga Rao Saddam Hussain Syed Sajid Ullah Roobaea Alroobaea Maha Abdelhaq Raed Alsaqour ECE Department Vidya Jyothi Institute of TechnologyIndia CSE Department Neil Gogte Institute of TechnologyIndia School of Digital Science Universiti Brunei DarussalamJalanTungku LinkGadong BE1410Brunei Darussalam Department of Information and Communication Technology University of Agder(UiA)N-4898 GrimstadNorway Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourahbint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network *** the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are *** by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression *** on the classification,resource and trust-aware devices are selected for *** of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling *** simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.

关键词： Mobile network multivariate renkonen regression weighted preference bootstrap aggregation resource-aware secure data communication NOMA

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A Feasible Compression Scheme for Bangla Natural Text

A Feasible Compression Scheme for Bangla Natural Text

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Md. Ashiq Mahmood Md Ashiqul Amin Pallab Kumar Nandi Bishwajit Kumar Mondal Institute of Information and Communication Technology Khulna University of Engineering & Technology Khulna Bangladesh School of Informatics Computing and Cyber System Northern Arizona University AZ USA

The most significant zone of study for Natural Language Processing (NLP) is text compression. Since the number of characters in Unicode are many, 16 bits are needed to process the entire code. This study suggests a useful method for compressing Bangla natural text called the 5 Bit Compression Scheme for Bangla Text(5BCS), which uses a 5-bit encoding method to change the 16-bit Bangla letters into a 5-bit format. This technique provides an encoding algorithm which representing the 16-bit Bangla characters to 5 bits by utilizing a query table. The query table contains 4 sets which comprises all the Bangla characters as well as transliterations and special symbols. An m-series technique is used for logically determining index of a given characters combination while translating characters into 5 bits. By utilizing the 5BCS, a natural text can be compressed by a maximum of 84%. The algorithm for decompress the compressed data to translate the original data is also detailed in this paper. Additionally, a comparison section is developed to compare our 5BCS technique with the widely used Huffman and LZW technique. The experimental result of the comparison looks very much optimistic.

关键词： Dictionaries Codes Databases Symbols Programming Load management Encoding

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The Impact of Hard and Easy Negative Training Data on Vulnerability Prediction Performance

SSRN

引用

SSRN 2023年

作者： Al Debeyan, Fahad Madeyski, Lech Hall, Tracy Bowes, David School of Computing and Communications Lancaster University Lancaster United Kingdom Department of Applied Informatics Wroclaw University of Science and Technology Wroclaw Poland

ContextVulnerability prediction models have recently been shown to perform poorly in the real world. Better selection of negative training data has improved predictive performance in other domains. In computer vision, the classification of images has been improved by differentiating between negative training data that is easily distinguished from positive data (‘easy’ negatives) and negative training data that is hard to distinguish from the positive data (‘hard’ negatives).ObjectiveWe investigate how different strategies to collect the negative sample in a vulnerability prediction dataset influence the performance of vulnerability prediction *** measure the performance of vulnerability prediction models on datasets that only differ in the strategy to collect the negative sample. We used AutoML to find the best model for each dataset to see if the best choice of model changes depending on the negative sampling strategy. Results: Models that are evaluated only on easy negatives show better performance than models evaluated only on hard negatives. AutoML’s choice of the best machine learning algorithm changes depending on the negative sampling strategy. Models trained on a particular negative sampling strategy do not perform as well when evaluated on a dataset that follows a different negative sampling *** on how the negative sample is collected, models may produce overly optimistic performance results that may not reflect how the model will perform in the real world. When two or more models are compared, a fair comparison should include both models being evaluated on datasets that use the same negative sampling strategy. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Investigation of the Impact of Routing Protocol in Green IoT with Netsim 1

Investigation of the Impact of Routing Protocol in Green IoT...

引用

1st International Conference on Information System and Information technology, ICISIT 2022

作者： Lakarani, Muhammad Fadhil Bin Sajak, Aznida Binti Abu Bakar Ismail, Mohd Nazri Bin Allias, Noormadinah Binti Computer Engineering Technology Kuala Lumpur Malaysia Universiti Pertahanan Nasional Malaysia Department of Computer Science Kuala Lumpur Malaysia Albukhary International University School of Computing and Informatics Kedah Alor Setar Malaysia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

technology evolution nowadays moves vigorously to improve daily human life and directly affect the earth's resource consumption. Reducing energy consumption leads to reducing carbon dioxide (CO2) emission to the surrounding, which can cause environmental and health issues. The balance of fast data transmission with efficient energy consumption is vital for achieving green IoT. One part of the IoT system, the wireless sensor, performs a duty to operate continuously without data delay. That is why most wireless sensors only collect raw data and do packet forwarding, where later it will be analyzed by another physical device. Although the sensors still need to recognize the routing topology by themselves, the energy consumed to forward the packet to its destination can be optimized. Researchers have used various ways to control energy consumption using different hardware and power management, so this research wants to focus on how using different routing protocols can impact energy consumption. © 2022 IEEE.

关键词： Routing protocols

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HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection

arXiv

引用

arXiv 2024年

作者： Cao, Liujuan Lin, Jianghang Hong, Zebo Shen, Yunhang Lin, Shaohui Chen, Chao Ji, Rongrong Media Analytics and Computing Lab School of Informatics Xiamen University 361102 China Youtu Lab Tencent200233 China School of Computer Science and Technology East China Normal University 200062 China

As an emerging problem in computer vision, weakly supervised object detection (WSOD) aims to use only image-level annotations to train object detectors. Most WSOD methods rely on traditional object proposals to generate candidate regions and are confronted with unstable training, which easily gets stuck in a poor local optimum. In this paper, we propose a unified and high-capacity WSOD network with holistic self-training framework, termed HUWSOD, which is self-contained, and requires no external modules or additional supervision. To this end, the proposed framework innovates in two perspectives, i.e., unified network structure and self-training scheme, both of which are rarely touched in WSOD before. First, we design a self-supervised proposal generator and an autoencoder proposal generator with multi-rate resampling pyramid to hypothesize object locations, which replace the traditional object proposals and enable the strictly end-to-end training and inference for WSOD. Second, we introduce holistic self-training scheme that consists of step-wise entropy minimization and consistency-constraint regularization, which refine both detection scores and coordinates progressively, as well as enforcing consistency to match predictions produced from stochastic augmentations of the same image. Extensive experiments on PASCAL VOC and MS COCO show that the proposed HUWSOD performs competitively to the state-of-the-art WSOD methods, while getting rid of offline proposals and additional data. Moreover, the upper-bound performance of HUWSOD with class-agnostic ground-truth bounding boxes approaches Faster R-CNN, which demonstrates that HUWSOD is capable to achieve fully-supervised accuracy. Our work also brings one important finding that random initialized boxes, although drastically different from the offline well-designed object proposals, are also effective object candidates for WSOD training. The code is available at: https://***/shenyunhang/HUWSOD. Copyright © 2024, Th

关键词： Object detection

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