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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zeshi Zhang Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Ministry of Education Key Laboratory of Computer Vision and System China Faculty of Engineering University of Bristol Bristol U.K.

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Due to cloud security concerns, there is an increasing interest in information retrieval systems that can support private queries over public documents. It is desirable for oblivious document ranking and retrieval in public cloud at lower cost and faster speed without revealing query-related information. Currently, the term frequency-inverse document frequency (TF-IDF) and private information retrieval (PIR) techniques are used to solve this problem, but the encryption operation time is over dominant. Motivated by the observation of the sparsity of the TF-IDF matrix, we propose an efficient approach for oblivious document ranking and retrieval, called E-Coeus. It takes advantage of the high sparsity of the TF-IDF matrix to rearrange the matrix. Our method accelerates the speed of PIR inadvertently retrieving documents and reduces the user retrieval delay time. In a stand-alone experiment for a TF-IDF matrix of 1.2M rows and 64K columns with the sparsity of 10%, E-Coeus improves the document ranking and retrieval performance by 23% over the state-of-the-art approach, Coeus. With cluster of 64 machines, E-Coeus improves the performance by 34% over Coeus when the TF-IDF matrix sparsity is 30%.

关键词： Time-frequency analysis Privacy Costs Cloud computing security Information retrieval Encryption Delays

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Towards Survivable In-Memory Stores with Parity Coded NVRAM

Towards Survivable In-Memory Stores with Parity Coded NVRAM

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhixuan Wang Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Engineering University of Bristol Bristol U.K.

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Erasure codes have been widely applied to in-memory key-value storage systems for high reliability and low redundancy. In distributed in-memory key-value storage systems, update operations are relatively frequent, especially the partial-stripe update, which makes data update more challenging. Recently, existing research has been based on appending logs to accelerate parity data write. However, its logs are stored on disks, which decreases the system performance significantly. Therefore, we propose a novel in-memory key-value storage architecture, DNVPL, which utilizes NVRAM to log parity data. Our main idea is to design an appending-only update scheme to tradeoff the memory cost and the update overhead. We implement DNVPL with an in-memory key-value storage prototype, called LogKV. We evaluate it with different workloads. The experiments show that our scheme achieves high update performance from different metrics. Our scheme can reduce update latency by up to 49% and save storage space by 48% compared to the state-of-the-art schemes.

关键词： Privacy Nonvolatile memory System performance Redundancy Random access memory Prototypes computer architecture

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Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Muhammad Huzaifa Shah Muhammad Abu Bakar Raja Hashim Ali Zain Ul Abideen Usama Arshad Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data).

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A-REA: Relation-Aware Graph Convolutional Network Entity Alignment Via a Differential Adjacency Matrix

A-REA: Relation-Aware Graph Convolutional Network Entity Ali...

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Image Processing, Electronics and computers (IPEC), Asia-Pacific Conference on

作者： Yuemei Dang Chen Qiu Feng Gao Jinguang Gu Lihua Zhang Ghazaleh Arman School of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Software College East China Jiaotong University Nanchang Jiangxi China Faculty of Information Technology and Computer Engineering Urmia University of Technology Urmia Iran

Entity Alignment is the task of seeking entities with the same semantics from distinct knowledge graphs (KGs). Researchers have recently proposed Graph Convolutional Network (GCN) based approaches to achieve this task. Most existing GCN-based methods embed all the relations into an identical value, making the underlying loss on the diversity of the relationship. Relation semantics of entities and relation structure have benefitted the entity alignment task in the monolingual scenarios. This paper proposes a Relation-Aware Graph Convolutional Network with a Differential Adjacency Matrix (A-REA), which exploits the relation-specific semantics and structural information of entities in the KGs to promote the entity representations. The experiments on DBP-15K and DWY-100K datasets, including three cross-lingual subsets and two cross-knowledge graph sub-datasets, reveal that our approach leads to advance in entity alignment task in both the cross-lingual and cross-knowledge KGs. Discriminating the relation shows the effective signal for entity alignment, especially for the schema heterogeneity of different KGs.

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Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

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Artificial Intelligence in Medicine 2024年 149卷 102779-102779页

作者： Aminizadeh, Sarina Heidari, Arash Dehghan, Mahshid Toumaj, Shiva Rezaei, Mahsa Jafari Navimipour, Nima Stroppa, Fabio Unal, Mehmet Medical Faculty Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Haliç University Istanbul34060 Turkey Tabriz University of Medical Sciences Faculty of Medicine Tabriz Iran Urmia University of Medical Sciences Urmia Iran Tabriz University of Medical Sciences Faculty of Surgery Tabriz Iran Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Department of Mathematics School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.

关键词： Quality of service

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An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

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computers, Materials & Continua 2021年第3期66卷 2409-2423页

作者： Muhammad Yahya Saeed Muhammad Awais Muhammad Younas Muhammad Arif Shah Atif Khan M.Irfan Uddin Marwan Mahmoud Department of Software Engineering Government College UniversityFaisalabadFaisalabad38000Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Computer Science Islamia College PeshawarPeshawarPakistan Institute of Computing Kohat University of Science and TechnologyKohatPakistan Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia

Text Summarization is an essential area in text mining,which has procedures for text *** natural language processing,text summarization maps the documents to a representative set of descriptive ***,the objective of text extraction is to attain reduced expressive contents from the text *** summarization has two main areas such as abstractive,and extractive *** text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding *** such text extractions have limitations in providing the basic theme of the underlying *** this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word *** study compared these forms of the text summarizations with the actual text,by calculating cosine ***,TF-IDF based PageRank keywords are extracted from the other two extractive *** intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is *** technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each *** form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized *** also solves the issue of deciding the number of representative keywords for a specific text *** evaluate the technique,the study used a sample of more than eighteen hundred text *** abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text *** proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.

关键词： Metadata page rank sentence score word2vec cosine similarity This

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Dheya Mustafa Safaa M. Khabour Ahmed S. Shatnawi Eyad Taqieddin Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan Dept. of Software Engineering Jordan University of Science & Technology Irbid Jordan Dept. of Cyber Security School of Computing and Informatics Al-Hussein Technical University Amman Jordan

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier.

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Optimizing Results in Aerial Images through Post-Processing Techniques on YOLOv7 23

Optimizing Results in Aerial Images through Post-Processing ...

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12th International Symposium on Information and Communication Technology, SOICT 2023

作者： Vo, Nguyen D. Nguyen, Thu M. Nguyen, Triet H.M. Nguyen, Khang Multimedia Communications Laboratory University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh city Viet Nam Faculty of Computer Science University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh city Viet Nam Faculty of Software Engineering University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh city Viet Nam

ISBN: (纸本)9798400708916

Object detection in aerial images has garnered significant attention from the research community in recent years. The challenges posed by small objects, diverse orientations, and complex backgrounds have spurred extensive research efforts. In this paper, we focus on object detection in a YOLOv7-based framework, to address the issue of overlapping predicted regions, we introduce and apply the Soft-NMS (Non-Maximum Suppression) technique, a post-processing method known for its effectiveness in improving detection accuracy in such scenarios. Soft-NMS adjusts bounding box scores based on the extent of overlap, allowing for more accurate localization of objects in densely populated regions. Furthermore, we present comprehensive experimental results to validate the efficacy of our proposed approach. The analyses encompass a thorough evaluation on the UCAS Aerial Object Detection (UCAS AOD) dataset, comprising over 1500 aerial images captured from diverse perspectives. Our method has demonstrated an improvement in object detection performance, particularly in scenarios with closely positioned objects. The proposed framework showcases its ability to handle complex aerial scenes with higher precision and recall rates compared to conventional methods. © 2023 ACM.

关键词： Object detection

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Improving WiFi Fingerprint Database With Irregular Distribution of Reference Points Using Interpolation Methods

Improving WiFi Fingerprint Database With Irregular Distribut...

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International Conference on Advanced Technologies for Communications, ATC

作者： Trung Vu-Thanh Jing He Ninh Duong-Bao Seon-Woo Lee Luong Nguyen Thi Khanh Nguyen-Huu College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Mathematics and Informatics Dalat University Dalat Vietnam Division of Software Hallym University Chuncheon Korea Faculty of Information Technology Dalat University Dalat Vietnam Department of Electronics and Telecommunications Dalat University Dalat Vietnam

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Using WiFi Fingerprinting for indoor positioning is becoming increasingly popular due to its cost-effectiveness. However, to ensure the system can operate with high accuracy and stability, this technique requires a burden of data collection tasks from a dense distribution of reference points (RPs). To decrease the cost of data collection, as well as to improve the WiFi fingerprint database with irregular distribution of RPs, this study applies and compares different interpolation methods. Evaluating these methods in a public dataset including multiple sub-datasets with irregular distributions of RPs, the results highlight the Linear Barycentric interpolation (LB) as the most favorable method due to its low complexity and stable performance. The LB-based fingerprint database achieves an average interpolation error of less than 3 dBm compared to the original one. When applying the interpolated database to positioning tasks, it can help reduce acquisition costs by up to 50% while improving positioning accuracy by up to 11%. The study reveals the feasibility of interpolation in optimizing WiFi fingerprint data collection, especially in large and complex environments where regular distribution of RPs cannot be deployed.

关键词： Interpolation Costs Accuracy Databases Fingerprint recognition Data collection Reliability Computational complexity Wireless fidelity Floors

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SCALABLE LINK PREDICTION IN TWITTER USING SELF-CONFIGURED FRAMEWORK

arXiv

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arXiv 2022年

作者： Daud, Nur Nasuha Hamid, Siti Hafizah Ab Seri, Chempaka Saadoon, Muntadher Anuar, Nor Badrul Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Link prediction analysis becomes vital to acquire a deeper understanding of events underlying social networks interactions and connections especially in current evolving and large-scale social networks. Traditional link prediction approaches underperformed for most large-scale social networks in terms of its scalability and efficiency. Spark is a distributed open-source framework that facilitate scalable link prediction efficiency in large-scale social networks. The framework provides numerous tunable properties for users to manually configure the parameters for the applications. However, manual configurations open to performance issue when the applications start scaling tremendously, which is hard to set up and expose to human errors. This paper introduced a novel Self-Configured Framework (SCF) to provide an autonomous feature in Spark that predicts and sets the best configuration instantly before the application execution using XGBoost classifier. SCF is evaluated on the Twitter social network using three link prediction applications: Graph Clustering (GC), Overlapping Community Detection (OCD), and Redundant Graph Clustering (RGD) to assess the impact of shifting data sizes on different applications in Twitter. The result demonstrates a 40% reduction in prediction time as well as a balanced resource consumption that makes full use of resources, especially for limited number and size of clusters. © 2022, CC BY.

关键词： Efficiency

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