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9th International Conference on computer and Communications, ICCC 2023

作者： Yan, Degao Wang, Lusheng Kai, Caihong Peng, Min Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

ISBN: (纸本)9798350317251

Vehicular networks with service robots and vehicles have been widely studied in the past few years, and a large number of these vehicles and robots may appear in people's lives, such as in science and technology park, university town, restaurant, and public transportation hub. Energy-saving in vehicular networks becomes a key issue in these scenarios for ensuring the endurance time of vehicles and robots, so it is useful to dynamically adjust their working modes based on the pedestrian flow density around them. In this paper, we predict the peaks and troughs of the pedestrian flow based on four technical indexes, and adjust between active and ordinary modes for the vehicles and robots to save their energy while still guaranteeing their services. Simulation results show that the used technical indexes can predict the peaks and troughs of a pedestrian flow, hence the proposal effectively reduces the energy consumption of the vehicular networks. © 2023 IEEE.

关键词： Vehicles

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Artist-Specific Analysis of Spotify using BI Tools 14

Artist-Specific Analysis of Spotify using BI Tools

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharma, Varsha Atwal, Hetal Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350335095

Spotify is one of the world's largest music audio streaming and media service provider with over 489 million monthly active users, including 205 million paying subscribers as of December 2022. The music from various artists around the globe can be found on Spotify where songs from these artists compete daily in order to top the charts. In this paper, we present an analysis of the music charts using many features that describe a song such as acousticness, danceability, etc., and present artist-specific suggestions that can help the artists know the genre of the songs that is most popular among the listeners and therefore, produce songs accordingly and hence help the artists gain popularity among the audience. © 2023 IEEE.

关键词： Audio acoustics

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Early and Automated Diagnosis of Dysgraphia Using Machine Learning Approach

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SN computer science 2023年第5期4卷 523页

作者： Agarwal, Basant Jain, Sonal Beladiya, Kishan Gupta, Yash Yadav, Avijit Singh Ahuja, Neelu Jyothi Department of Computer Science and Engineering Central University of Rajasthan Ajmer India PG Department of Computer Science and Technology Sardar Patel University Vallabh Vidhyanagar India Department of Computer Science and Engineering Indian Institute of Information Technology Kota Kota India Department of Computer Science School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India

Dysgraphia is a handwriting problem that impairs a person’s ability to write. Even the diagnosis of this condition is challenging, and there is currently no cure. Researchers from all over the world have studied this issue and offered several solutions. Motivation to work on this problem did arise after meeting with a few students struggling in achieving performance despite putting in sincere efforts. This paper also discusses the various forms of dysgraphia and its associated symptoms and proposes machine-learning models to detect dysgraphia. Unsupervised machine learning techniques are used to detect dysgraphia-related handwriting impairment. To accomplish the goal, a fresh handwriting dataset is created by conducting handwriting exercises and a wide variety of features are extracted to represent various handwriting characteristics. Results indicate that Random forest returns the best accuracy but scores less while detecting dysgraphic samples correctly. One class SVM has been tried to deal with the issue of the availability of dysgraphic samples required to train machines. Results indicate good hope in identification with a scope of improvement with increase in sample size for machine training. This paper also seeks to raise awareness of the dysgraphia issue and its effects on society. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Dysgraphia Learning difficulties Machine Motor ability OCC One class SVM Random forest

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Smart Trap Detecting the Whitefly Pest using Deep Neural Networks 2

Smart Trap Detecting the Whitefly Pest using Deep Neural Net...

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

作者： Deepika, Manenalli Kavitha, P. Zareen, Afshan Parikh, Swapnil Patil, Harshal Natravan, L. Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Vijayawada520012 India CMR Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560037 India BGS Institute of Technology Department of Computer Science and Engineering Karnataka 571418 India Parul Institute of Engineering and Technology Department of Computer Science and Engineering Parul University Gujarat 391760 India Maharashtra Pune412115 India Saveetha School of Engineering Department of Mechanical Engrineering SIMATS Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350327533

This study analyzes the suitability of using convolutional neural network designs for detecting the whitefly pest in different agricultural contexts. The goal of this research work is to develop a smart trap suitable for field use. As an initial step, the existing state-of-The-Art whitefly detection and counting techniques are reviewed. Then, the datasets related to whitefly infestations in diverse environments, encompassing both laboratory and field conditions are collected. The proposed approach involves training with various algorithms tailored for object detection with the collected datasets. Subsequently, the results are evaluated and compared to identify the most effective architectures. © 2024 IEEE.

关键词： Agricultural Context Counting Deep Neural Networks Whitefly Detection

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SuperGlue-Based Deep Learning Method for Image Matching from Multiple Viewpoints 23

SuperGlue-Based Deep Learning Method for Image Matching from...

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8th International Conference on Mathematics and Artificial Intelligence, ICMAI 2023

作者： Li, Wotong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781450399982

With the ability to identify the same physical content from two or more images, image matching has always been a crucial and fundamental task in the fields of computer vision. Effective methods dealing with image matching are of great significance for 3D vision problems like Structure from Motion (SfM). In this work, a deep learning method is proposed based on SuperGlue to match images from multiple viewpoints with excellent performance. We utilize the SuperPoint network as the CNN backbone to extract keypoints and feature descriptors of an image pair. The descriptors are subsequently processed by SuperGlue using graph neural networks to identify then match the feature points. The matching results are finally optimized by the USAC_MAGSAC algorithm, which is a combination of MAGSAC++ implementation and degeneracy tests. The performance of our method is evaluated by the Kaggle competition Image Matching Challenge 2022, which is quantified by the mean Average Accuracy 0.84824/0.83940 on the public/private test set. This result places our team in the 15th position overall out of 643 teams and qualifies us for the silver medal in this competition. This work could have the potential to facilitate the development of generalized techniques of 3D vision tasks. © 2023 Copyright held by the owner/author(s)

关键词： Image matching

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Enhanced Play FAIR Cipher

Enhanced Play FAIR Cipher

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Syed Siraj Ahmed, N. Fathimath Nihala, T. Presidency University School of Computer Science and Engineering & Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350317060

Security plays a crucial role in the field of communication between any two devices. Design of complex unpredictable keys are essential to make the communication more secure in various dimensions of data such as small, medium, and large. However, modern portable and IoT peripheral devices have limited computational power that hinders the process of key generation. Further, their communication system does not have strong protection. To overcome this, we propose a model to encrypt and decrypt the messages which requires less computational power. This can be used in modern portable and IoT peripheral devices of various data lengths. The proposed method also provides additional security through octal and hexadecimal play fair cipher. The proposed model is relatively harder to crack when compared to play fair cipher. The principle of the proposed design is similar to that of play fair cipher, however it uses a completely different cipher matrix of dimensions 8x8, 16x16 and a different replacement rule as well. This model supports the design of a multi-character encryption cipher. Here, a plaintext is taken as input and is transformed into corresponding cipher text. The proposed design is applied to various communication systems with additional security compared to play fair cipher between devices of portable and IoT peripheral devices of different data lengths. Since it is implemented with octal number, hexadecimal number and hence named enhanced play fair cipher. © 2023 IEEE.

关键词： Cryptography

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PriFace: a privacy-preserving face recognition framework under untrusted server

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2967-2979页

作者： Zhao, Shan Zhang, Lefeng Xiong, Ping School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China Center for Cyber Security and Privacy School of Computer Science University of Technology Sydney Sydney Australia

Alongside the rapid development of artificial intelligence and mobile Internet, face recognition has gradually become a mainstream intelligent terminal authentication technology that is widely applied in security, finance, and social networking, among other fields. However, face images usually contain sensitive personal information, which can have serious consequences for users if this information is leaked or misused. Most existing face privacy protection methods are based on data encryption techniques or differential privacy mechanisms, which make it difficult to strike a good balance between privacy and usability. In this paper, we propose PriFace, a new privacy-preserving method for face recognition based on eigenfaces and locality-sensitive hashing (LSH). PriFace aims to achieve accurate and efficent face recognition with privacy preserved. It applies eigenface technique to reduce the dimension of face images for improving retrieval performance. With the assumption that the server is untrustworthy, PriFace uses the locality-sensitive hashing technique to introduce randomness into the face information for privacy preservation so that the server, or other adversaries, can not reconstruct any user’s face image. Experiments on real-world datasets demonstrate that the proposed PriFace can effectively preserve the privacy of face images and further outperforms the traditional methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Face recognition

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Exploration of C++ Teaching Reform Method Oriented by Ability Output 17th

Exploration of C++ Teaching Reform Method Oriented by Abilit...

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17th International Conference on computer science and Education, ICCSE 2022

作者： Zheng, Yunping School of Computer Science and Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)9789819924455

Aiming at the main problems existing in traditional C+ + curriculum teaching, relying on the background of the "new engineering" era, a "five in one" C++ teaching innovation mode is proposed. The "five in one" means: a national quality resource sharing course, a system combining competition and teaching, a C++ intelligent examination system, an ideological and political classroom, a laboratory combining competition and teaching. The "five-in-one" teaching model highlights the "student-centered" teaching concept. According to the teaching objectives of each class, it is cleverly integrated into the curriculum ideological and political content with "problem solving" and "live-code" as the main teaching line. Information-based teaching is used as a way to promote the integration of students’ theory and practice with ACM/ICPC, organically integrate process assessment and summative assessment, and implement diversified assessment forms. The results of more than ten years of practical teaching show that the "five-in-one" teaching innovation mode effectively cultivates students’ computational thinking, and significantly improves students’ programming ability and innovation ability. In the previous ACM/ICPC, our students have repeatedly achieved good results. At present, the "five-in-one" teaching innovation mode has been effectively promoted and applied in the three campuses of our university, which has a certain leading and exemplary role in cultivating the strong professional programming skills, computational thinking, the family and country feelings, the global vision and the all-round development of the top-notch innovative talents in the computer field. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning 25

A Case Study on Context-Aware Neural Machine Translation wit...

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25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna India Wipro AI Lab45 London United Kingdom School of AI and Data Science Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781068690709

In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies (Li et al., 2020) have shown that the context encoder generates noise and makes the model robust to the choice of context. This paper further investigates this observation by explicitly modelling context encoding through multi-task learning (MTL) to make the model sensitive to the choice of context. We conduct experiments on cascade MTL architecture, which consists of one encoder and two decoders. Generation of the source from the context is considered an auxiliary task, and generation of the target from the source is the main task. We experimented with German-English language pairs on News, TED, and Europarl corpora. Evaluation results show that the proposed MTL approach performs better than concatenation-based and multi-encoder DocNMT models in low-resource settings and is sensitive to the choice of context. However, we observe that the MTL models are failing to generate the source from the context. These observations align with the previous studies, and this might suggest that the available document-level parallel corpora are not context-aware, and a robust sentence-level model can outperform the context-aware models. © 2024 The authors, © 2024 European Association for Machine Translation.

关键词： Neural machine translation

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment 14

Image based and Point Cloud based Methods for 3D View Recons...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vasikarla, Shantaram Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati India Indian Institute of Technology Dept. of Electrical Engineering Kanpur India California State University Dept. of Computer Science NorthridgeCA United States

ISBN: (纸本)9798350360134

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more. © 2024 IEEE.

关键词： Network coding

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