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Analysis of Software Trustworthiness Based on FAHP-CRITIC Method

Journal of Shanghai Jiaotong university(science) 2024年第3期29卷 588-600页

作者：高晓彤马艳芳周伟 School of Computer Science and Technology Huaibei Normal UniversityHuaibei 235000AnhuiChina School of Computer Science and Information Engineering Changzhou Institute of TechnologyChangzhou 213000JiangsuChina

Software trustworthiness includes many *** weight allocation of trustworthy at-tributes plays a key role in the software trustworthiness *** practical application,attribute weight usually comes from experts'evaluation to attributes and hidden information derived from ***,when the weight of attributes is researched,it is necessary to consider weight from subjective and objective ***,a novel weight allocation method is proposed by combining the fuzzy analytical hierarchy process(FAHP)method and the criteria importance though intercrieria correlation(CRITIC)***,based on the weight allocation method,the trustworthiness measurement models of component-based software are estab-lished according to the seven combination structures of ***,the model reasonability is verified via proving some metric ***,a case is carried *** to the comparison with other models,the result shows that the model has the advantage of utilizing hidden information fully and analyzing the com-bination of components *** is an important guide for measuring the trustworthiness measurement of component-based software.

关键词： trustworthiness weight component-based software measurement

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MalFSM: Feature Subset Selection Method for Malware Family Classification

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Chinese Journal of Electronics 2023年第1期32卷 26-38页

作者： KONG Zixiao XUE Jingfeng WANG Yong ZHANG Qian HAN Weijie ZHU Yufen School of Computer Science and Technology Beijing Institute of Technology School of Space Information Space Engineering University

Malware detection has been a hot spot in cyberspace security and academic research. We investigate the correlation between the opcode features of malicious samples and perform feature extraction, selection and fusion by filtering redundant features, thus alleviating the dimensional disaster problem and achieving efficient identification of malware families for proper classification. Malware authors use obfuscation technology to generate a large number of malware variants, which imposes a heavy analysis burden on security researchers and consumes a lot of resources in both time and space. To this end, we propose the MalFSM framework. Through the feature selection method, we reduce the 735 opcode features contained in the Kaggle dataset to 16, and then fuse on metadata features(count of file lines and file size)for a total of 18 features, and find that the machine learning classification is efficient and high accuracy. We analyzed the correlation between the opcode features of malicious samples and interpreted the selected features. Our comprehensive experiments show that the highest classification accuracy of MalFSM can reach up to 98.6% and the classification time is only 7.76 s on the Kaggle malware dataset of Microsoft.

关键词： Correlation Fuses Filtering Cyberspace Machine learning Metadata Feature extraction

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A Multi-path Droplet Routing Protocol for Digital Microfluidic Biochip

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Informatica (Slovenia) 2024年第7期48卷 95-102页

作者： Swain, Jyotiranjan Pyne, Sumanta School of Computing and Information Technology REVA University Bangalore India Department of Computer Science and Engineering National Institute of Technology Rourkela India

Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It involves challenges like moving droplets without violating the fluid constraints. This article proposed a multi-path droplet routing protocol. Multiple routes are explored and validated using special packets. These routes are then classified based on user-defined heuristics. A single route is sequentially selected for each droplet. The routes are then compacted to generate a parallel moving sequence. The simulation result shows 2.087% and 4.952% improvement in the latest arrival time for free and virtual topology, respectively. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Biochips

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Fusion of Wavelet Decomposition and N-BEATS for Improved Stock Market Forecasting

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SN computer science 2024年第7期5卷 869页

作者： Pramanick, Neha Singhal, Vatsal Neeraj Mathew, Jimson Agarwal, Mayank Department of Computer Science and Engineering Indian Institute of Technology Patna Patna 801103 India

Stock market forecasting is one of the most exciting areas of time series forecasting both for the industry and academia. Stock market is a complex, non-linear and non-stationary system with many governing factors and noise. Some of these factors can be quantified and modeled, whereas some factors possess a random walk behavior making the process of forecasting challenging. Various statistical methods, machine learning, and deep learning techniques are prevalent in stock market forecasting. Recently, there has been a paradigm shift towards hybrid models, showing some promising results. In this paper, we propose a technique that combines a recently proposed deep learning architecture N-BEATS with wavelet transformation for improved forecasting of future prices of stock market indices. This work uses daily time series data from five stock market indices, namely NIFTY 50, Dow Jones Industrial Average (DJIA), Nikkei 225, BSE SENSEX, and Hang Seng Index (HSI), for the experimental studies to compare the proposed technique with some traditional deep learning techniques. The empirical findings suggest that the proposed architecture has high accuracy as compared to some traditional time series forecasting methods and can improve the forecasting of non-linear and non-stationary stock market time series. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 00-01 99-00 Deep learning Forecasting N-BEATS Stock market Time series

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Enhancing STEM curriculum with virtual reality: electricity and magnetism simulations

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Multimedia Tools and Applications 2024年 1-20页

作者： Shinde, Nishant Sarma, Himangshu Human Computer Interaction Lab Computer Science & ampEngineering Indian Institute of Information Technology Sri City 630 Gnan Marg Andhra Pradesh Sri City517646 India

This study explores the use of virtual reality (VR) as an educational tool to enhance the understanding of electricity and magnetism among 10th-grade students in India. A VR application was developed using Unity and Meta Quest 2 (https://***/quest/products/quest-2/;Access Date: 22nd January 2024), featuring thirteen interactive activities-six on electricity and seven on the magnetic effects of electric current. Thirty participants engaged with the VR platform, which offers a hands-on learning experience not provided in the current curriculum. The methodology involved pre- and post-intervention assessments to evaluate the effectiveness of the VR application. Results indicate a significant improvement in students’ comprehension of electricity and magnetism, suggesting that VR can be a cost-effective and engaging supplement to traditional laboratory setups. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetoplasma

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Deep Learning-Based Classification of Lung Cancer Lesions in CT Scans: Comparative Analysis of CNN, VGG-16, and MobileNet Models 4th

Deep Learning-Based Classification of Lung Cancer Lesions in...

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4th International Conference on Image Processing and Capsule Networks, ICIPCN 2023

作者： Hareesh, P.M. Bellamkonda, Sivaiah Computer Science and Engineering Department Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9789819970926

Lung cancer exhibits one of the lowest survival rates among various cancer types over a five-year period, necessitating the utmost importance of early detection. Lung cancer can be detected in its early stage widely through the Computed Tomography (CT) scans. In recent years, deep learning models, particularly Convolutional Neural Networks (CNNs), have been integrated into end-to-end methodologies for the accurate perception of lung nodules. This research project presents a novel system constructed using CNN, VGG-16, and MobileNet models to automatically classify between nonmalignant and malignant tumor lesions within the lungs. The proposed CNN model, trained with hyper parameters including batch size of 8, learning rate of 0.01 with 50 epochs, achieved a peak accuracy of 95.15%. Likewise, VGG-16 achieved a high accuracy of 95.88% with same hyper-parameters except the learning rate is changed to 0.1. Furthermore, MobileNet demonstrated exceptional performance with an accuracy of 98.39% for learning rate of 0.001 with same values for other hyper-parameters batch size and epochs. The classification of lung cancer lesions into benign and malignant categories was effectively accomplished using the LIDC (Lung Image Database Consortium), IDRI (Image Database Resource Initiative) data sets. This study underscores the efficiency of deep learning-based approaches in automated lung cancer detection and emphasizes the significance of early diagnosis in improving patient outcomes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Sentiment and hashtag-aware attentive deep neural network for multimodal post popularity prediction

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Neural Computing and Applications 2024年第4期37卷 2799-2824页

作者： Bansal, Shubhi Kumar, Mohit Raghaw, Chandravardhan Singh Kumar, Nagendra Department of Computer Science and Engineering Indian Institute of Technology Indore Khandwa Road Simrol Madhya Pradesh Indore453552 India

Social media users articulate their opinions on a broad spectrum of subjects and share their experiences through posts comprising multiple modes of expression, leading to a notable surge in such multimodal content on social media platforms. Nonetheless, accurately forecasting the popularity of these posts presents a considerable challenge. Prevailing methodologies primarily center on the content itself, thereby overlooking the wealth of information encapsulated within alternative modalities such as visual demographics, sentiments conveyed through hashtags and adequately modeling the intricate relationships among hashtags, texts, and accompanying images. This oversight limits the ability to capture emotional connection and audience relevance, significantly influencing post popularity. To address these limitations, we propose a seNtiment and hAshtag-aware attentive deep neuRal netwoRk for multimodAl posT pOpularity pRediction, herein referred to as NARRATOR that extracts visual demographics from faces appearing in images and discerns sentiment from hashtag usage, providing a more comprehensive understanding of factors influencing post popularity. Moreover, we introduce a hashtag-guided attention mechanism that leverages hashtags as navigational cues, guiding the model’s focus toward the most pertinent features of textual and visual modalities, thus aligning with target audience interests and broader social media context. Experimental results demonstrate that NARRATOR outperforms existing methods by a significant margin on two real-world datasets. Furthermore, ablation studies underscore the efficacy of integrating visual demographics, sentiment analysis of hashtags, and hashtag-guided attention mechanisms in enhancing the performance of post popularity prediction, thereby facilitating increased audience relevance, emotional engagement, and aesthetic appeal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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Learning Salient Features for Speech Emotion Recognition using Attention-based Residual Bidirectional LSTM with Federated Learning 4

Learning Salient Features for Speech Emotion Recognition usi...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kaur, Dilsheen Misra, Anuradha Vyas, O.P. Amity University Lucknow Campus Amity School of Engineering and Technology Department of Computer Science and Engineering India Indian Institute of Information Technology Allahabad Department of Information Technology India

ISBN: (纸本)9798350343670

In human communication, expressing human emotion plays an essential role in transferring information to the other individual. The expression forms of human communication are very rich, in different patterns like facial expressions, voice tone, eye contact, body language and laughter. Moreover, the languages used by individuals in the entire world are different, yet without understanding the communication language, the individuals can realize the message that other individuals require to convey as emotional expressions. Among all the human emotional expressions, the conveyed expressions through human voice are experimented with in different studies. However, identifying the emotional situation of the presenter is complicated for the machine learning techniques, which gained more attraction in Speech Emotion Recognition (SER). Here, the SER framework plays an efficient role in different real-world applications in observing human actions, virtual reality, emergency centers and robot communication to observe the emotional phase of the speakers. Thus, an innovative federated learning-based SER is presented in this paper. The essential speech signals are initially collected from the standard database. After that, the collected signal is fed to the feature extraction stage. Here, the deep features in the input signal are obtained by the Variational Autoencoder (VAE). Further, the deep features obtained from the VAE are passed to the "Attention-based Residual Bidirectional Long Short-Term Memory (A-RBiLSTM)"to identify the emotions in the speech signal. At last, different experimental analyses are executed to observe the effectualness of the implemented framework. © 2024 IEEE.

关键词： Attention-based Residual Bidirectional Long Short-Term Memory Deep Features Feature Extraction Federated Learning Speech Emotion Recognition Variational Autoencoder

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A novel approach for image retrieval in remote sensing using vision-language-based image caption generation

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Multimedia Tools and Applications 2025年第6期84卷 2985-3014页

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

Recent advancements in satellite technologies have resulted in the emergence of Remote Sensing (RS) images. Hence, the primary imperative research domain is designing a precise retrieval model for retrieving the most pertinent images based on the query. Present Remote Sensing Image Retrieval (RSIR) systems use visual descriptors to characterize the primitives (such as various land-cover types) that are visible in the images. However, the visual descriptors are inadequate for defining the complicated content of RS images. To solve this problem, a new model is devised for image retrieval based on image captions. The goal is to generate textual illustrations with captions to define relations amongst objects precisely. Here, image captioning is attained based on the vision-language pre-training model. The image captions are utilized for generating features like term frequency-inverse document frequency (TF-IDF), length of text, and Bag of Words. Meanwhile, query text is utilized wherein features like TF-IDF, text length, and Bag of Words are obtained. The similarity between query text features and the image captions features has been computed on the basis of a hybrid similarity measure wherein weights are tuned with the proposed Honey Badger Political Optimizer (HBPO) to retrieve the image. The proposed HBPO provided enhanced efficiency with elevated precision of 93.3%, recall of 93.7%, F1-score of 93.5%, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) of 0.441. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Visual languages

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On the Maximum Distance Sublattice Problem and Closest Vector Problem 26

On the Maximum Distance Sublattice Problem and Closest Vecto...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Kumar, Rajendra Mehta, Shashank K Rajasree, Mahesh Sreekumar Indian Institute of Technology Delhi Department of Computer Science & Engineering New Delhi India Indian Institute of Technology Kanpur Department of Computer Science & Engineering Uttar Pradesh India

ISBN: (纸本)9798331532833

In this paper, we introduce the Maximum Distance Sublattice Problem (MDSP). We observed that the problem of solving an instance of the Closest Vector Problem (CVP) in a lattice L is the same as solving an instance of MDSP in the dual lattice of L. We give an alternate reduction between the CVP and MDSP. This alternate reduction does not use the concept of dual lattice. © 2024 IEEE.

关键词： CVP geometry GSO Karp reduction lattice

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