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SSRN 2024年

作者： Huang, Zihang Fei, Teng Bian, Meng School of Resource and Environmental Sciences Wuhan University Wuhan430079 China Computer Science and Software Engineering department Auckland University of Technology Auckland92006 New Zealand School of Remote Sensing and Information Engineering Wuhan University Wuhan92006 China

Airborne pollen is a significant cause of allergies, leading to various discomforting symptoms. Many cities worldwide, including Beijing, face challenges related to airborne pollen. Accurate forecasting of pollen concentration can provide critical risk alerts for individuals with pollen allergies, enabling them to take preventive measures or mitigate the impact of their symptoms. However, existing studies have often overlooked the spatial variability of pollen concentrations, focusing predominantly on pollen from individual plant sources, which limits their applicability. To address these shortcomings, this study integrates the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model with the Random Forest algorithm to develop a novel method for predicting the spatial distribution of next-day pollen concentrations. Using Beijing as a case study, the proposed method demonstrates high prediction accuracy, with a coefficient of determination (R2) of 0.67. An analysis of pollen concentration distributions during Beijing's spring pollen season reveals that urban areas consistently exhibit higher pollen concentrations compared to the citywide average throughout the season. This trend suggests an elevated allergy risk in urban regions, likely due to pollen accumulation in semi-enclosed terrains. Furthermore, the study highlights that eliminating pollen sources in city centers alone is insufficient to achieve significant reductions in urban pollen concentrations. This research presents a new approach to predicting pollen concentrations with improved accuracy and enhanced spatial detail, offering valuable insights for allergy prevention and management strategies. © 2024, The Authors. All rights reserved.

关键词： Allergies

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A Federated and Explainable Approach for Insider Threat Detection in IoT

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SSRN 2022年

作者： Amiri-Zarandi, Mohammad Karimipour, Hadis Dara, Rozita A. School of Computer Science University of Guelph Guelph Canada Department of Electrical and Software Engineering University of Calgary Alberta Canada

An insider threat is a malicious action launched by authorized personnel inside the organization. Since insider actions may only leave a small digital footprint in the system, it is considered a major cybersecurity challenge in different applications. Along with the rapid growth of the Internet of Things (IoT) and the extensive attack surface in this technology, many concerns have been raised regarding the potential insider threats in IoT environments. Several studies have been conducted on Machine Learning (ML)-based insider threat detection solutions which are focused on the models’ performance while the trustability of these models is neglected. Trustworthy Learning refers to a new trend in ML that focuses on ways to ensure that the data collection and data analysis procedures in ML techniques follow ethical applications and are trustable to human users. This approach enforces the acceptance and successful adoption of ML-based solutions. This study aims to propose an improved trustworthy insider threat detection method that ensures two of the trustworthy learning requirements: Privacy and Explainability. The proposed solution protects the privacy of the utilized data and is capable of explaining why certain behaviors are detected as a threat. The proposed solution also leverages data collaboration between different data owners to increase the volume of data used in the training process and enhance the performance of the ML model. Experimental results show the proposed solution outperforms the learning models trained by individual data holders. © 2022, The Authors. All rights reserved.

关键词： Internet of things

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CompanyName2Vec: Company Entity Matching based on Job Ads

CompanyName2Vec: Company Entity Matching based on Job Ads

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Ran Ziv Ilan Gronau Michael Fire Efi Arazi School of Computer Science Reichman University Department of Software and Information Systems Engineering Ben-Gurion University

ISBN: (纸本)9781665473316

Entity Matching is an essential part of all real-world systems that take in structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data cleaning and integration processes require completion before any data analytics, or further processing can be performed. Although record linkage is frequently regarded as a somewhat tedious but necessary step, it reveals valuable insights, supports data visualization, and guides further analytic approaches to the data. Here, we focus on organization entity matching. We introduce CompanyName2Vec, a novel algorithm to solve company entity matching (CEM) using a neural network model to learn company name semantics from a job ad corpus, without relying on any information on the matched company besides its name. Based on a real-world data, we show that CompanyName2Vec outperforms other evaluated methods and solves the CEM challenge with an average success rate of 89.3%.

关键词： Couplings Semantics Neural networks Data visualization Companies Data science Cleaning

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A Proposal for a New E-Waste Image Dataset Based on the UNU-KEYS Classification

A Proposal for a New E-Waste Image Dataset Based on the UNU-...

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International Symposium on Electrical Apparatus and Technologies (SIELA)

作者： Dimitar I. Iliev Marin B. Marinov Frank Ortmeier Faculty of German Engineering and Industrial Management Technical University of Sofia Sofia Bulgaria Department of Electronics Faculty of Electronic Engineering and Technologies Technical University of Sofia Sofia Bulgaria Faculty of Computer Science Chair of Software Engineering Otto-von-Guericke- University Magdeburg Magdeburg Germany

ISBN: (数字)9798350382365

ISBN: (纸本)9798350382372

This paper introduces the largest and most diverse e-waste dataset to date, consisting of 19,613 pictures, 28,941 annotations, and 77 classes, where each class represents one visually distinctive type of electronic device. Furthermore, our dataset was structured using the United Nations University's UNU-KEYS classification, which ensures the compatibility of our dataset with the different e-waste classifications used worldwide and allowed us to choose more relevant dataset classes. The proposed methodology for image collection, annotation, and labeling is explained. The advantages and disadvantages of this dataset and how it can be improved in the future are presented. This dataset has been made open source with a permissive CC by 4.0 license so that it can be used to stimulate scientific progress in the field of automatic e-waste recognition, with the end goal of solving the global e-waste problem. The dataset is available online at https://***/electronic-waste-detection/e-waste-dataset-r0ojc.

关键词： Annotations Data acquisition Documentation Electronic waste Licenses Planning Proposals

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An Improved Marine Predators Algorithm Based on Group Learning

An Improved Marine Predators Algorithm Based on Group Learni...

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IEEE International Conference on Networking, Sensing and Control

作者： Liying Li Jian Zhao Jiacun Wang Xiwang Guo School of Science University of Science and Technogly Liaoning Anshan China Department of Computer Science and Software Engineering Monmouth University USA Computer and Communication Engineering College Liaoning Petrochemical University Monmouth University Fushun China

ISBN: (纸本)9781665472449

To address the shortcomings of the marine predators algorithm (MPA) in solving complex problems, such as low optimization accuracy and easily falling into local optimization, this paper proposes an improved marine predators algorithm based on group learning (GLMPA). An opposition-based learning method is adopted to enhance the quality of the initial solutions. Then, a group learning strategy is used to diversify the population. Two subgroups are produced by fitness evaluation and employing different updating mechanisms. In addition, a new position-updating rule is used to help the proposed algorithm escape from the local optima in the later stage of iteration. Finally, six test functions are utilized to test the GLMPA, and the simulation results verify the effectiveness of the proposed algorithm when compared with other famous algorithms.

关键词： Learning systems Simulation Sociology Sensors Statistics Optimization Convergence

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A survey of Intel SGX and its applications

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Frontiers of computer science 2021年第3期15卷 191-205页

作者： Wei ZHENG Ying WU Xiaoxue WU Chen FENG Yulei SUI Xiapu LUO Yajin ZHOU School of Software and Microelectronics Northwestern Polytechnical UniversityXi'an 710129China Faculty of Engineering and Information Technology University of Technology SydneySydney 2007Australia Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

This paper presents a comprehensive survey on the development of Intel SGX(software guard extensions)processors and its *** the advent of SGX in 2013 and its subsequent development,the corresponding research works are also increasing *** order to get a more comprehensive literature review related to SGX,we have made a systematic analysis of the related papers in this *** first search through five large-scale paper retrieval libraries by keywords(i.e.,ACM Digital Library,IEEE/IET Electronic Library,SpringerLink,Web of science,and Elsevier science Direct).We read and analyze a total of 128 SGX-related *** first round of extensive study is conducted to classify *** second round of intensive study is carried out to complete a comprehensive analysis of the paper from various *** start with the working environment of SGX and make a conclusive summary of trusted execution environment(TEE).We then focus on the applications of *** also review and study multifarious attack methods to SGX framework and some recent security improvements made on ***,we summarize the advantages and disadvantages of SGX with some future research *** hope this review could help the existing and future research works on SGX and its application for both developers and users.

关键词： Intel SGX cloud computing trusted execution environment TrustZone AMD SEV

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Collaborative Three-Stream Transformers for Video Captioning

arXiv

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arXiv 2023年

作者： Wang, Hao Zhang, Libo Fan, Heng Luo, Tiejian School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Institute of Software Chinese Academy of Sciences Beijing100190 China Department of Computer Science and Engineering University of North Texas DentonTX76203 United States

As the most critical components in a sentence, subject, predicate and object require special attention in the video captioning task. To implement this idea, we design a novel framework, named COllaborative three-Stream Transformers (COST), to model the three parts separately and complement each other for better representation. Specifically, COST is formed by three branches of transformers to exploit the visual-linguistic interactions of different granularities in spatial-temporal domain between videos and text, detected objects and text, and actions and text. Meanwhile, we propose a cross-granularity attention module to align the interactions modeled by the three branches of transformers, then the three branches of transformers can support each other to exploit the most discriminative semantic information of different granularities for accurate predictions of captions. The whole model is trained in an end-to-end fashion. Extensive experiments conducted on three large-scale challenging datasets, i.e., YouCookII, ActivityNet Captions and MSVD, demonstrate that the proposed method performs favorably against the state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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An Efficient Distributed Framework for Secured Multimedia Content Using Cloud Storage

An Efficient Distributed Framework for Secured Multimedia Co...

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computer Technologies (ICCTech), International Conference on

作者： Yunus Parvej Faniband Iskandar Ishak Fatimah Sidi Marzanah A. Jabar Deloria Shaniska Razin C. H. Yap Center for Communication & IT Research Research Institute King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor Malaysia

Applications that support multimedia data such as medical pictures and videos, video broadcasting, and secure video conferencing consume vast quantities of information that require large numbers of servers to provide services for the many client users who access them. Cloud-based sensitive data becomes increasingly important for the respective users to be kept confidential without infringing confidentiality, integrity, and availability. We propose a framework based on the OpenStack cloud-computing platform for multimedia data security. A prototype framework with a helper tool EncSwift is implemented, which allows stringent data access control and applying the data-at-rest Over-Encryption approach. The implementation is evaluated for computation of cryptography operations, cost of communications, and update policy consequences for the framework. With OpenStack Swift's open source and modular design, our multimedia data management and management framework is well suited to protecting and managing clouds' multimedia with low computational costs as shown in the experimental results.

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Kepler codebook 24

Kepler codebook

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Proceedings of the 41st International Conference on Machine Learning

作者： Junrong Lian Ziyue Dong Pengxu Wei Wei Ke Chang Liu Qixiang Ye Xiangyang Ji Liang Lin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Software Engineering Xi'an Jiaotong University Xi'an China Department of Automation Tsinghua University Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China and Peng Cheng Laboratory Shenzhen China

A codebook designed for learning discrete distributions in latent space has demonstrated state-of-the-art results on generation tasks. This inspires us to explore what distribution of codebook is better. Following the spirit of Kepler's Conjecture, we cast the codebook training as solving the sphere packing problem and derive a Kepler codebook with a compact and structured distribution to obtain a codebook for image representations. Furthermore, we implement the Kepler codebook training by simply employing this derived distribution as regularization and using the codebook partition method. We conduct extensive experiments to evaluate our trained codebook for image reconstruction and generation on natural and human face datasets, respectively, achieving significant performance improvement. Besides, our Kepler codebook has demonstrated superior performance when evaluated across datasets and even for reconstructing images with different resolutions. Codes and pre-trained weights are available at https://***/banianrong/KeplerCodebook

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Secure Audio Steganography with Reversible Data Hiding and Encryption

Secure Audio Steganography with Reversible Data Hiding and E...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： P. S. Arthy K. Sanjai Kumar S. Vinson Joshua G. Sahaana R. Kalpana D. David Neels Ponkumar S. Siamala Devi S. Jeya Lakshmi Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering (Cyber Security) Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This research study enhances secure data transmission by embedding sensitive information within audio files. Using Python for the frontend and PHP with MySQL as the backend, this approach employs reversible data hiding (RDH) to ensure that the original content of the audio file can be fully restored. Audio steganography is used to conceal encrypted messages within a WAV file, leveraging its varying compression rates for optimal steganographic performance. The Least Significant Bit (LSB) technique, a widely used method, embeds the hidden message into the audio stream without noticeable distortion. In this system, the secret message is scrambled, divided into segments, and embedded in even-value bytes of the audio file, with the first bit position chosen randomly for enhanced security. A permutation codebook is employed to rearrange and decrypt the message during recovery, ensuring both data confidentiality and complete reversibility of the audio file. This method provides a secure, efficient solution for transmitting sensitive information through audio channels.

关键词： Steganography Data privacy Distortion Encryption Data communication Informatics Mobile computing

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