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7th International Conference on Informatics and Applied Mathematics, IAM 2024

作者： Hadji, Atmane Boumaza, Farid Bali, Dina sirine LISI Laboratory Computer Science Department University Center A. Boussouf Mila Mila43000 Algeria Computer Science Department University of Mohamed El Bachir El Ibrahimi Bordj Bou Arreridj34030 Algeria LAPECI Laboratory University of Oran1 Oran31000 Algeria Department of Computer Science University Center A . Boussouf Mila Mila43000 Algeria

A Sentiment analysis on social networks has become an increasingly important research field in recent years, driven by the rapid growth of social media and the vast amount of user-generated data. Understanding online opinions and sentiments is crucial for gaining insights into public attitudes and trends. In this study, we compare two approaches for sentiment detection: the first relies on ontologies, and the second utilizes machine learning techniques. Ontologies provide a structured framework to represent domain-specific knowledge, thus enhancing the accuracy of sentiment analysis. In the machine learning approach, we employed four algorithms: Support Vector Machines (SVM), K-Nearest Neighbors (K-NN), Decision Tree, and Random Forest. SVM demonstrated superior performance compared to other algorithms such as K-NN. Our approach was applied to sentiment analysis of Facebook discussions about Bitcoin, demonstrating the practical application of both ontology-based and machine learning techniques in the financial domain. The results highlight the effectiveness of both approaches in economic sentiment analysis, offering valuable insights into trends and sentiments that could be extended to other fields such as finance and commerce. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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An Automated and Real-time Approach of Depression Detection from Facial Micro-expressions

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computers, Materials & Continua 2022年第11期73卷 2513-2528页

作者： Ghulam Gilanie Mahmood ul Hassan Mutyyba Asghar Ali Mustafa Qamar Hafeez Ullah Rehan Ullah Khan Nida Aslam Irfan Ullah Khan Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurPakistan Department of Computer Skills Deanship of Preparatory YearNajran UniversityNajranSaudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Biophotonics Imaging Techniques Laboratory Institute of PhysicsThe Islamia University of BahawalpurPakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

Depression is a mental psychological disorder that may cause a physical disorder or lead to *** is highly impactful on the socialeconomical life of a person;therefore,its effective and timely detection is *** speech and gait,facial expressions have valuable clues to *** study proposes a depression detection system based on facial expression *** features have been used for depression detection using Support Vector Machine(SVM)and Convolutional Neural Network(CNN).We extracted micro-expressions using Facial Action Coding System(FACS)as Action Units(AUs)correlated with the sad,disgust,and contempt features for depression detection.A CNN-based model is also proposed in this study to auto classify depressed subjects from images or videos in *** have been performed on the dataset obtained from Bahawal Victoria Hospital,Bahawalpur,Pakistan,as per the patient health questionnaire depression scale(PHQ-8);for inferring the mental condition of a *** experiments revealed 99.9%validation accuracy on the proposed CNN model,while extracted features obtained 100%accuracy on ***,the results proved the superiority of the reported approach over state-of-the-art methods.

关键词： Depression detection facial micro-expressions facial landmarked images

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Enhanced Fake News Detection through the Fusion of Deep Learning and Repeat Vector Representations 2

Enhanced Fake News Detection through the Fusion of Deep Lear...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Mahmud, Tanjim Hasan, Imran Aziz, Mohammad Tarek Rahman, Taohidur Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350327533

The rapid dissemination of information across social networks, online news portals, and digital platforms has given rise to a pervasive issue - fake news. This misinformation, deliberately propagated by groups with ulterior motives, has the potential to divert societies in alarming directions. In a digital landscape inundated with millions of daily stories, distinguishing fact from fabrication has become a formidable challenge, casting a dark cloud of uncertainty over the information age. In response to this critical issue, this paper presents an innovative approach for enhanced fake news detection through the fusion of deep learning and Repeat Vector representations. In an era where individuals of all ages increasingly rely on online news sources, the dissemination of misinformation has become an alarming norm. It is a challenge to discern deceptive narratives that aim to sow discord and create undue *** confront this challenge head-on, we propose a deep neural network model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architectures using the Repeat Vector method. This hybrid model excels in deciphering complex patterns within 1D data, effectively tackling the menace of fake news. Our rigorous evaluation demonstrates that our proposed model outperforms contemporary methods, achieving an impressive validation accuracy of up to 98.94% on our *** a world increasingly plagued by misinformation, our approach represents a significant stride toward safeguarding the integrity of information in the digital age. © 2024 IEEE.

关键词： Autoencoder Technique Convolutional Neural Network Deep Learning Long Short-Term Memory

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ASLiquid: Non-Intrusive Liquid Counterfeit Identification with Your Earphones 24

ASLiquid: Non-Intrusive Liquid Counterfeit Identification wi...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Li, Zhaohui Luo, Wei Zhang, Yongmin Chen, Jianxi Shu, Yuanchao Zhang, Yaoxue School of Computer Science and Engineering Central South University China College of Control Science and Engineering Zhejiang University China Department of Computer Science and Technology BNRist Tsinghua University China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798400706974

As society progresses, liquid identification plays an increasingly important role in human life. But for now, minority of existing liquid identification solutions on the market can meet daily requirements of being ubiquitous, cost-effective and non-intrusive enough. In this work, we propose ASLiquid, the first liquid counterfeit identification system with commercial off-the-shelf earphones. Our core insight is that earphones can effectively induce acoustic resonance in container, and this phenomenon is observed highly associated with the changes in liquid density and solute compositions. Deploying ASLiquid introduces three main challenges: hardware heterogeneity among different earphones, diversity of user operations, and data complexity due to variations in liquid volume and device placement. To address these issues, we first propose to eliminate the existence of hardware noise and frequency response diversity for an earphone-irrelevant solution. Afterwards, we design a user operation adaptation algorithm to extract valuable feature data during each measurement period. To alleviate problems in data complexity, we propose a spectrum projection algorithm that can effectively generate CFR data of unknown liquid volumes and a VAE based anomaly detection model for counterfeit identification. We evaluate our system with six different earphones and under various conditions. Experimental results reveal that ASLiquid can achieve F1 scores of 95%-99.25% for seven frequently occurring liquid counterfeit tasks, even in specialized attacks on liquids with 1% difference in mass fraction and different types of solutions but with the same density. © 2024 Copyright is held by the owner/author(s).

关键词： Earphones

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Iterative Methods via Locally Evolving Set Process 38

Iterative Methods via Locally Evolving Set Process

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Baojian Sun, Yifan Harikandeh, Reza Babanezhad Guo, Xingzhi Yang, Deqing Xiao, Yanghua The School of Data Science Fudan University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Department of Computer Science Stony Brook University United States Samsung SAIT AI Lab

Given the damping factor α and precision tolerance ϵ, Andersen et al. [2] introduced Approximate Personalized PageRank (APPR), the de facto local method for approximating the PPR vector, with runtime bounded by Θ(1/(αϵ)) independent of the graph size. Recently, Fountoulakis & Yang [12] asked whether faster local algorithms could be developed using Õ(1/(√αϵ)) operations. By noticing that APPR is a local variant of Gauss-Seidel, this paper explores the question of whether standard iterative solvers can be effectively localized. We propose to use the locally evolving set process, a novel framework to characterize the algorithm locality, and demonstrate that many standard solvers can be effectively localized. Let vol(St) and γt be the running average of volume and the residual ratio of active nodes St during the process. We show vol(St)/γt ≤ 1/ϵ and prove APPR admits a new runtime bound Õ(vol(St)/(αγt)) mirroring the actual performance. Furthermore, when the geometric mean of residual reduction is Θ(√α), then there exists c ∈ (0, 2) such that the local Chebyshev method has runtime Õ(vol(St)/(√α(2 − c))) without the monotonicity assumption. Numerical results confirm the efficiency of this novel framework and show up to a hundredfold speedup over corresponding standard solvers on real-world graphs. © 2024 Neural information processing systems foundation. All rights reserved.

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Hierarchical Encoder-Decoder with Addressable Memory Network for Diagnosis Prediction 28th

Hierarchical Encoder-Decoder with Addressable Memory Networ...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Mingxia Xiong, Yun Zhang, Yao Yu, Philip S. Zhu, Yangyong Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Department of Computer Science University of Illinois Chicago Chicago United States

ISBN: (纸本)9783031306778

Deep learning methods have demonstrated success in diagnosis prediction on Electronic Health Records (EHRs). Early attempts utilize sequential models to encode patient historical records, but they lack the ability to identify critical diseases for patient health conditions. Besides, some works focus on the hierarchical structure of EHR data during learning patient representations, but neglect it in diagnosis prediction process, which leads to insufficient utilization of hierarchical information. To tackle these challenges, we propose a new Hierarchical Encoder-Decoder with Addressable Memory Network for Diagnosis Prediction named HAMNet. Specifically, we employ a hierarchical encoder-decoder framework to utilize the hierarchical structure of historical visits both during representation learning and diagnosis prediction. Furthermore, we propose the addressable memory network to distinguish core diseases towards patient health status at next visit through the well-designed addressing mechanism. It employs feed-forward layers to build the memory network, which can automatically update the memory module through gradient backpropagation without explicit read/write operations. Finally, we evaluate the performance of HAMNet on a large real-world EHR dataset MIMIC-III, and it achieves the state-of-the-art performance compared with baselines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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FESR - Framework to evaluate Software Reliability based on software metrics 24

FESR - Framework to evaluate Software Reliability based on s...

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8th International Conference on High Performance Compilation, Computing and Communications, HP3C 2024

作者： Fahad, Hurriya Ahmed, Irfan Mahmood, Jabar Saman Batool, Khadija Ali, Qasim Department of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310007 China

ISBN: (纸本)9798400716904

Evaluating reliability of software is hot concern for decision makers and software engineers seeing as if we assess, it cannot be mastered. It is common that reliability of system stratum could be employed for evaluating growth of testing of system with help of comparison of failure of currently implemented software with required system. It is also generic that reliability of system echelon assures improved utilization by the user consequently enhanced contentment of customers. In this paper, our goal is to put forward a meticulously analysis of reliability measurement process. As the existing approaches still lack software reliability, quality and its growth, successful generation of test cases, scalability with large software systems, cost and customer satisfaction. we have projected a new framework of software reliability measurement process based on software metrics. Our model is evaluated by comparing it with an existing model and a published case study. The control experiment is used to evaluate FESR Framework. © 2024 ACM.

关键词： Software quality

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A Blockchain-Based Framework for Secure Storage and Sharing of Resumes

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computers, Materials & Continua 2022年第9期72卷 5395-5413页

作者： Huanrong Tang Changlin Hu Tianming Liu Jianquan Ouyang Key Laboratory of Intelligent Computing and Information Processing Ministry of EducationComputer Science College of Xiangtan UniversityXiangtanHunan ProvinceChina Department of Computer Science The University of GeorgiaAthensGeorgiaUSA

In response to problems in the centralized storage of personal resumes on third-party recruitment platforms,such as inadequate privacy protection,inability of individuals to accurately authorize downloads,and inability to determine who downloaded the resume and when,this study proposes a blockchain-based framework for secure storage and sharing of *** can employ an authorized access mechanism to protect their privacy *** proposed framework uses smart contracts,interplanetary file system,symmetric encryption,and digital signatures to protect,verify,and share *** keys are split and stored in multiple depositories through secret-sharing technology to improve the security of these *** key escrow incentives are implemented using smart contracts to automatically verify the correctness of keys and encourage the active participation of honest key escrow *** framework combines blockchain and searchable symmetric encryption technology to realize multikeyword search using inverted indexing and Bloom filters and verify search results on the *** search service fees are charged through *** after the search results are verified can the search service provider obtain the search fee,thus ensuring fair and efficient search for encrypted *** framework is decentralized,secure,and tamper-evident,and achieves controlled sharing while protecting personal privacy and information security.

关键词： Resume blockchain interplanetary file system secret sharing searchable encryption

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Revisiting Stochastic Realization Theory using Functional Itô Calculus 26

Revisiting Stochastic Realization Theory using Functional It...

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26th International Symposium on Mathematical Theory of Networks and Systems, MTNS 2024

作者： Veeravalli, Tanya Raginsky, Maxim Department of Electrical and Computer Engineering The Coordinated Science Laboratory University of Illinois UrbanaIL61801 United States

This paper considers the problem of constructing finite-dimensional state space realizations for stochastic processes that can be represented as the outputs of a certain type of a causal system driven by a continuous semimartingale input process. The main assumption is that the output process is infinitely differentiable, where the notion of differentiability comes from the functional Itô calculus introduced by Dupire as a causal (nonanticipative) counterpart to Malliavin's stochastic calculus of variations. The proposed approach builds on the ideas of Hijab, who had considered the case of processes driven by a Brownian motion, and makes contact with the realization theory of deterministic systems based on formal power series and Chen-Fliess functional expansions. Copyright © 2024 The Authors.

关键词： Stochastic systems

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Intrusion Detection with deep learning: A literature review 6

Intrusion Detection with deep learning: A literature review

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Sedraoui, Brahim Khalil Benmachiche, Abdelmadjid Makhlouf, Amina Chemam, Chaouki Lima Laboratory Faculty of Sciences & Technology University of Chadli Bendjedid Department of Computer Science El Tarf Algeria

ISBN: (纸本)9798350350265

As technology continues to grow and becomes increasingly integral to education, institutions and universities worldwide are making substantial investments in e-learning platforms to help and enhance their students' learning and performance experiences. Recently, Internet users have witnessed an increase in vulnerability to cyber-attacks due to the diversity and prevalence of scams across the web. This threat is phishing attacks and malware through stealing users' personal and financial information, unauthorized access to their online accounts, and tarnishing their reputation across the network. This is due to the exploitation of vulnerabilities in systems and lack of security awareness among users. In recent years, have seen the emergence of IDS systems leveraging machine learning (ML) and deep learning (DL) technologies as promising solutions for efficiently detecting network intrusions. In this study, we present a literature review of deep learning algorithms for intrusion detection. A brief demonstration of some DL techniques is provided. Then we discuss how each of the DL methods is used for intrusion detection. We cover a broad array of attack types including malware. We also present a comparison with the efficiency of methods, field implementation to demonstrate network safeguard enhancement technological advantages. Overall, the objective of this paper is to give an orientation and recommendations for researchers and also experts in the field of cyber-security, especially in the context of deep learning. © 2024 IEEE.

关键词： Cybersecurity

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