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Adaptive Malware Detection in Android: An Active Learning and XGBoost Approach

Adaptive Malware Detection in Android: An Active Learning an...

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Global Conference for Advancement in Technology (GCAT)

作者： Anshitha Prattipati S. Saravanan S. Veluchamy Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The growing dependence on mobile devices like smartphones has caught the consideration of cybercriminals. It creates numerous chances for intruders to infiltrate and compromise them. This current continuing challenge demands the need for more efficient and effective methods to shield Android users by recognizing various malwares. Though researchers and companies strive for a steady and consistent approach to increase the classification algorithms accuracy to categorize malicious Android applications, the search for the ultimate answer continues. Hence, we introduce an active learning-based classifier to advance the accuracy of classification in Android mal-ware. In our proposed system, we find the prediction probability of each test instance. If this probability falls below a certain threshold, we generate a similar test instance, incorporate it into the original dataset and retrain the model to get better accuracy of the classification. We evaluated the proposed system on CIC-Maldroid2020 dataset which comprises of 17,341 instances with five various android application categories consisting of Adware, Banking, SMS, Riskware, and Benign. This experiment results imply that the XGBoost (eXtreme Gradient Boosting) algorithm outperforms the various other five machine learning algorithms. Further, the active learning component of our proposed system improves accuracy from 95.34% to 98.5%.

关键词： Training Machine learning algorithms Accuracy Operating systems Active learning Companies Feature extraction Malware Classification algorithms Smart phones

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Integrative Day Trading Stock Trend Prediction using Web Scraping and Sentiment Analysis

Integrative Day Trading Stock Trend Prediction using Web Scr...

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International Conference for Convergence of Technology (I2CT)

作者： Anjali Chennupati Bhamidipati Prahas Bharadwaj Aaditya Ghali Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In the digital era, data is pivotal across disciplines like business, marketing, engineering, and social sciences. This research proposes a new method of acquiring intraday stock data from the National Stock Exchangeand sentiment-laden news articles, via the web-scrapping technology. Spark’s distributed computing framework that provides powerful processing capabilities has been employed to perform sentiment analysis and quantify sentiment scores for the chosen stock over the past month. Concurrently, the XGBoost algorithm processes the historical stock data to predict future values along with the cross-validation process. With the integration between the XGBoostpredictions and the sentiment score, this model offers stock recommendations to the user to either buy or sell a certain stock. The proposed model performance is evaluated for future value predictionsbased on the average RMSE score reported as *** extremely systematic take reaches across financial prediction, natural language processing, and machine learning to supply recommendations for investors.

关键词： Sentiment analysis Systematics Machine learning algorithms Social sciences Machine learning Predictive models Prediction algorithms

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Double Cancelable Hashing for Protecting Biometrics of Users in Crowd 18

Double Cancelable Hashing for Protecting Biometrics of Users...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Almutairi, Mishaal M. Apostolopoulou, Dimitra Stupples, David Sen, Adnan Ahmed Abi Yamin, Mohammad Halikias, George City University of London School of Science and Technology Dept. of Electrical and Electronic Engg London United Kingdom Islamic University Deanship of Information Technology Madinah Saudi Arabia The Australian National University School of Computing College of Engineering and Computer Science Canberra Australia University of Athens Department of Mathematics Greece

ISBN: (纸本)9789380544519

Smart gates are considered one of the most important methods for managing crowds. Often, methods of authentication and verification of users depend on the user's biometric fingerprints, such as hand or eye prints. Sending a picture of the fingerprint or eye to the server for verification is deemed unacceptable, especially in crowds, as it will significantly impact service performance in addition to imposing a heavy load on the network. Another, more serious challenge is related to the security and privacy of this data. If a user's fingerprint is stolen, it implies that all systems and services related to it, whether financial, medical, or otherwise, will be vulnerable to hacking. Unfortunately, the use of the traditional hash function is not sufficiently strong against a Replay Attack, and integration with blockchain requires substantial resources and suffers from delays that may impact the quality of sensitive services. In this paper, a Hash Function is proposed to enhance the level of privacy and security of biometric information. The hashing process will be doubled on one hand, and fog computing will be employed as an intermediary and additional protection on the other hand. Finally, the proposed technology will provide a solution to the problem of Relay Attack. The effectiveness and applicability of the proposed approach will be implemented and assessed. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Crowd management data privacy data security encryption hackers

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Spiking Transformer with Experts Mixture 38

Spiking Transformer with Experts Mixture

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Zhaokun Lu, Yijie Jia, Yanhao Che, Kaiwei Niu, Jun Huang, Liwei Shi, Xinyu Zhu, Yuesheng Li, Guoqi Yu, Zhaofei Yuan, Li School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China Peng Cheng Laboratory China College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science Peking University China Institute for Artificial Intelligence Peking University China Institute of Automation Chinese Academy of Sciences China Deep NeuroCognition Lab I2R and CFAR Agency for Science Technology and Research Singapore

Spiking Neural Networks (SNNs) provide a sparse spike-driven mechanism which is believed to be critical for energy-efficient deep learning. Mixture-of-Experts (MoE), on the other side, aligns with the brain mechanism of distributed and sparse processing, resulting in an efficient way of enhancing model capacity and conditional computation. In this work, we consider how to incorporate SNNs' spike-driven and MoE's conditional computation into a unified framework. However, MoE uses softmax to get the dense conditional weights for each expert and TopK to hard-sparsify the network, which does not fit the properties of SNNs. To address this issue, we reformulate MoE in SNNs and introduce the Spiking Experts Mixture Mechanism (SEMM) from the perspective of sparse spiking activation. Both the experts and the router output spiking sequences, and their element-wise operation makes SEMM computation spike-driven and dynamic sparse-conditional. By developing SEMM into Spiking Transformer, the Experts Mixture Spiking Attention (EMSA) and the Experts Mixture Spiking Perceptron (EMSP) are proposed, which performs routing allocation for head-wise and channel-wise spiking experts, respectively. Experiments show that SEMM realizes sparse conditional computation and obtains a stable improvement on neuromorphic and static datasets with approximate computational overhead based on the Spiking Transformer baselines. © 2024 Neural information processing systems foundation. All rights reserved.

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Emotion Recognition Using Meta-Learning based on Facial Expressions

Emotion Recognition Using Meta-Learning based on Facial Expr...

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Global Conference for Advancement in Technology (GCAT)

作者： Prashanth V. J. Sri Naga Jathin P. Suraj Gopinath Udith S. Kavitha C. R Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Emotion recognition is essential in many real-life problems and applications such as computer-human interaction, health monitoring, etc. In this project, we propose a meta-learning approach for developing a computer visionbased emotion recognition system. The aim of the work is to find human emotions from visual inputs and sort them accordingly. Meta learning techniques are used to allow the model to quickly adapt itself to emotion patterns using little training data that is short and compact. Therefore, meta learning technique is important since it allows the algorithm to perform well among different datasets with diverse data sets including unseen emotions (unknown emotions). Emotion recognition should address generalization problems while having ability to handle adaptation issues. It therefore seeks transparency in emotion recognition in addition to overcoming challenges of adaptability. Its objective is to offer answers to practical cases where emotion detection can be applied within dynamic environments. Thus at the end of this project we expect a reliable, faster, more dynamic way of classifying and recognizing human emotions based on visual inputs which highly valuable in understanding human emotional state for multiple other applications.

关键词： Metalearning Training Emotion recognition Visualization Accuracy Training data Prototypes Data models Reliability Monitoring

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A Survey of Low-Resource Named Entity Recognition 7th

A Survey of Low-Resource Named Entity Recognition

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7th International Conference on Information science, Communication and computing, ISCC 2023

作者： Tang, Xiangyan Xia, Dongwan Li, Yajing Xu, Taixing Xiong, Neal N. School of Computer Science and Technology Hainan University Haikou570228 China College of Intelligence and Computing Tianjin University Tianjin300072 China Hainan Blockchain Technology Engineering Research Center Haikou570228 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States

ISBN: (纸本)9789819971602

Named Entity Recognition, as one of the typical tasks of information extraction, has a wide range of applications. However, the difficulty of data collection and the lack of data annotation are common problems in real scenarios. It is difficult for classical named entity recognition methods to fully obtain hidden information when the dataset and data labels are insufficient. In this case, the recognition accuracy will drop significantly. In resource-poor scenarios, the use of multi-task learning, data augmentation, and transfer learning methods can effectively utilize limited data resources or introduce data from other fields to optimize model performance. In this survey, firstly, we introduce the reader to the state of research on named entity recognition, and outline the reasons for the survey and the contribution of this paper. Then, we comprehensively describe the datasets and evaluation methods included in named entity recognition, and propose a Classification of named entity recognition in the case of lack of resources. Finally, we analyze the existing problems on the basis of the above, and further look forward to the future development direction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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AutoGrading Classification of Tamil Descriptive Answers FastText and Roberta-Base Sentence Embedding

AutoGrading Classification of Tamil Descriptive Answers Fast...

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Global Conference for Advancement in Technology (GCAT)

作者： Vaan Amuthu Elango Peeta Basa Pati Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Considering the challenges associated with manually grading student answer scripts and the significance of the language, an automated approach is developed for assessing Tamil descriptive answers. Three-way assessment system of correct, incorrect, and partially correct is used to improve the autograding categorization accuracy. A total of eight datasets derived from four different dataset's sentence embedding produced by sophisticated natural language processing techniques RoBERTa-Base and FastText was created. To determine the optimal combination of data input, ten models inclusive of deep learning and machine learning models were utilized to perform a grade-based classification. This classification study proved that combination of the QuestionAnswer dataset with FastText embedding yielded the most promising result of F1 Score as 92.1%. The automatic grading methods tailored to the nuances of Tamil language are improved by this research.

关键词： Support vector machines Deep learning Accuracy Data models Natural language processing Classification tree analysis

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Sentiment Analysis of Airline Tweets Using Word Embeddings and Deep Learning Techniques

Sentiment Analysis of Airline Tweets Using Word Embeddings a...

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International Conference on computing and Networking Technology (ICCNT)

作者： Surapaneni Pavan Nikhith Popuri Varun Kumar Aira Udaybhasker Tangaturu Devendranath Reddy Parlapalli Bhargav Reddy Tripty Singh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This project deals with the testing of different word embedding, deep learning techniques, and XAI tools for text classification tasks. It talks about the effect of Word2Vec embeddings in CNN models on text context and shows their capability of grasping it. Furthermore, this research sees the application of BERT and RoBERTa embedding and shows their influence on CNN and BiLSTM models and their comparable performance. Besides, this project also proves the significance of transformer-based embeddings in the information extraction from texts with various contextual nuances and points to their possible to increase the text classification accuracy on different deep learning architectures. We lastly, look into the function of eXplainable AI tools in the interpretation of model decisions, which ultimately helps to understand and support the model prediction outcomes in the field of text classification.

关键词： Accuracy Atmospheric modeling Text categorization Bidirectional control Predictive models Transformers Encoding Convolutional neural networks Long short term memory Context modeling

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Streamlining Software Development: A Comprehensive Study on CI/CD Automation

Streamlining Software Development: A Comprehensive Study on ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Sanjay Baitha Vijayakumar Soorya Osho Kothari Shinu M. Rajagopal Niharika Panda Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidypeetham India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Continuous Integration and Continuous Deployment (CI/CD) automation have emerge as critical for modern-day software development. Despite its blessings, demanding situations including integration complexity, inconsistent trying out environments, and deployment troubles persist. Recent strategies like superior containerization, cloud-based scaling, and improved testing frameworks intention to address those issues. This take a look at implements an self sustaining CI/CD pipeline using gear such as Jenkins, GitHub, and SonarQube to automate processes which include coding, building, checking out, and deployment. By addressing those demanding situations, our proposed objective is to enhance efficiency and reliability in the software program development lifestyles cycle, allowing improvement teams to seamlessly add features and solve problems.

关键词： Automation Codes Machine learning algorithms Gears Pipelines Manuals Continuous integration Software Software reliability Testing

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ShadowNets: Efficient and Accurate Face Recognition for Resource-Constrained Devices 25

ShadowNets: Efficient and Accurate Face Recognition for Reso...

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25th International Conference on Digital Image computing: Techniques and Applications, DICTA 2024

作者： Mirza, Inzela Iqbal, Shahzaib Alhaqbani, Bandar Alshammari, Bandar Khan, Tariq Razzak, Imran Department of Computing Islamabad Pakistan Tcc Research and Development Labs Technology Control Company Riyadh Saudi Arabia Computer and Information Sciences College Jouf University Saudi Arabia School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798350379037

Over recent years, deep convolutional neural networks have advanced the field of face recognition in both verification and identification applications. In this paper, we present ShadowNets, a highly efficient and compact face recognition network. We employed multiple shadow blocks in a cascading manner where the output of each shadow block is given as the input to the next shadow block. By integrating the advantages of Focal Modulation Blocks, Shadow Block and Ghost Modules, our proposed ShadowNets achieves superior face recognition performance, optimized specifically for edge devices. This network architecture not only minimizes computational costs and storage needs but also delivers high accuracy, making it suitable for deployment in edge environments. Our extensive experiments on challenging benchmark face datasets showcase the outstanding performance by achieving accuracy 98.85% on LFW, 84.37% on CFP-FP, and 91.23% on AgeDB-30 which validates the effectiveness of ShadowNets over state-of-the-art lightweight and deep face recognition models. © 2024 IEEE.

关键词： Convolutional neural networks

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