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关键词： Cloud computing   Secure storage   Privacy   Computer science   Task analysis   Intserv networks   Games   cloud storage auditing   time-based auditing   privacy preserving   integrity  

摘要： Cloud storage auditing mechanism is used to check whether the data of users stored in the cloud is intact. Most existing auditing schemes for secure cloud storage are designed to check the integrity for specified files based on file identities. In some scenarios, the user would like to check the integrity of the files generated and uploaded to the cloud in a certain time period. Existing cloud storage auditing schemes cannot work well for supporting this practical requirement because the private information will be exposed. To satisfy this requirement, we propose a brand-new paradigm termed as privacy-preserving time-based auditing for secure cloud storage. The proposed paradigm allows the user to check whether the files generated and uploaded in a certain time period are intactly stored in the cloud. When intending to check the integrity of the files uploaded in this time period, the user only provides the challenged time period t to the Third Party Auditor (TPA). The TPA can verify the integrity of all the files based on this time period, but cannot know how many files and which files the user has generated and uploaded to the cloud in this time period. To decrease the complex overhead associated with certificate management, we introduce an identity-based auditing mechanism. We provide a specific security analysis to show the correctness, auditing soundness and privacy preserving of this scheme. The experiments demonstrate the efficiency of the proposed scheme.

关键词： Motion pictures   Feature extraction   Visualization   Deep learning   Spatiotemporal phenomena   Explosions   Accuracy   Media   Convolutional neural networks   Computer science   Videos   Classification algorithms   Violence classification   deep learning   animated video classification   sequence learning   shot segmentation  

摘要： Cartoon movies are a primary source of entertainment for children. However, concerns arise when these movies inadvertently expose children to violent scenes. This paper addresses the challenge of detecting subtle instances of violence within cartoon movies. The main difficulty in this task is the sequential nature of movies, where a sequence of frames must be considered in their given order. Existing methods have not effectively addressed the issue. In this study, we tackled this challenge by employing a sequential model. The research comprises three key steps. Initially, a histogram technique was implemented to select key frames from the video sequences. Subsequently, a Convolutional Neural Network (CNN) was utilized to extract prominent features from these selected key frames. In the third phase, the acquired features were utilized to train a sequential model using sequence-based learning. The model was then refined through transfer learning, using a dataset containing scenes devoid of violence, as well as scenes depicting varying forms of violence, including bloodshed, fights, gunshots, and explosions. A significant contribution of this study is the meticulous categorization of violent scenes into four distinct types, allowing for further investigation into the diverse effects of different violence categories. Furthermore, the study introduces an innovative approach by integrating a dense layer into the sequential model to enhance final classification. The trained model's performance was comprehensively evaluated using metrics such as F1 score, precision, accuracy, and recall. To validate the effectiveness of the proposed model, it was benchmarked against state-of-the-art methods. This study presents an innovative deep-learning methodology for the identification of violent scenes in cartoon movies. Its potential applications encompass a wide range, including safeguarding children from inappropriate content.

关键词： Data visualization   Minority groups   Visualization tools   Educational strategies  

摘要： The computer science field has become an essential part of our daily lives, and the demand for positions in this field continues to increase. Although numerous attempts and initiatives have aimed to introduce computer science at a young age, minority groups remain underrepresented and less involved in the computer science sector. Thus, to work towards creating a more diverse computer science community, it is crucial to understand the factors leading to the lack of participation by these groups. This project proposes an interactive visualization dashboard to help gain insights into methods to recruit minoritized girls in computer science by utilizing data visualization tools and techniques. The dataset employed in this research contains simulated responses from high school students related to their experiences with computer science and the teaching environment. To analyze and find patterns in the students' data, I developed an interactive visualization dashboard with multiple charts using the ObservableHQ data visualization tool. By implementing the features of the Vega-Lite library and JavaScript, I was able to add interactivity and display the perspectives of each gender. Consequently, I gained a clear understanding of the underlying dataset and explored different trends related to multiple *** dashboard is beneficial for decision-makers to indicate the factors impacting minority groups’ interest and motivation in Computer Science, assisting the creation of educational strategies aimed at recruiting minority girls into the computer science field.

ISBN: (纸本)9798350381764

关键词： Encryption   Network security   Ciphers   Computer science   Heuristic algorithms   Three-dimensional displays   chaos   random numbers   S-Box   Rook   chess  

摘要： In an era characterized by ever-increasing data exchange across different networks, the importance of robust network security measures cannot be overemphasized. Cryptographic algorithms, on the other hand, perform a vital role in imparting security to the sensitive information during transmission, with S-Boxes (Substitution Boxes) serving as a fundamental component of symmetric-key ciphers. This study has endeavored to engineer a novel S-Box algorithm to bolster the network security. Moreover, the core focus of the suggested algorithm lies in enhancing the confidentiality, defiance to nefarious designs of cryptanalysis savvy, and overall cryptographic strength of data encryption in network communication. Random numbers spawned by the 5D multi-wing hyperchaotic system have been employed to design the proposed box. Apart from that, Rook -a chess piece has been used in this regard. Particularly, the random walk of Rook on the 16 x 16 hypothetical chessboard has been capitalized to sufficiently permute the data of the potential S-Box. Through a comprehensive performance evaluation and cryptographic analyses, the proposed S-Box algorithm exhibits superior resilience against diverse threats thus fortifying network security effectively. In particular, the state of the art security parameters like bijectivity, non-linearity (NL), strict-avalanche criterion (SAC), bit-independence criterion (BIC), linear probability (LP) and differential probability (DP) have been used. This work, no doubt, contributes significantly to the ever-evolving field of network security.

关键词： Remote sensing   Image segmentation   Feature extraction   Accuracy   Semantics   Computer science   Complexity theory   Object detection   Belief propagation   Multi object classification   remote sensing   feature fusion   object detection   deep belief network  

摘要： Object Classification in Remote Sensing Imagery holds paramount importance for extracting meaningful insights from complex aerial scenes. Conventional methods encounter challenges in achieving precision amidst diverse landscape features. This paper introduces an innovative hybrid model to enhance the accuracy of remote sensing multi-object classification. Incorporating a feature-level fusion approach inspired by successful methods, we leverage Adoptive Fuzzy C-means segmentation for precise object classification and Conditional Random Field labeling. Our model excels in capturing diverse features within remote sensing images using multiple feature extraction methods. The distinctive feature of our methodology lies in the thoughtful incorporation of a Deep Belief Network. Through rigorous experimental evaluations on two standard datasets, our proposed system demonstrates exceptional performance, emphasizing its significant potential for advancing methodologies in remote sensing multi-object classification. This tactful integration results in substantial improvements, yielding high accuracies of 97.24% (UCM) and 96.84% (RESISC45). The proposed model is methodologically novel and effective solution for advancing remote sensing image classification.

关键词： Sentiment analysis   Deep learning   Feature extraction   Neural networks   Analytical models   Linguistics   Computer science   Social networking (online)   Object recognition   Cleaning   Machine learning   deep learning   word embedding   text encoding   sentiment analysis   text classification  

摘要： Automating sentiment analysis in texts has become an important task in recent years due to the exponential growth of user-generated content, including comments and opinions on products and services. This represents a valuable opportunity for businesses to glean insights into customer sentiment and, in turn, to refine their offerings. Motivated by this, the machine learning field has witnessed a surge of innovation, with an introduction of models and tools being introduced to streamline sentiment analysis. This paper offers a thorough review of the recent advancements in machine learning and deep learning approaches for text sentiment analysis. We propose a novel framework for studying these models, distinguishing them by their structural intricacies. Additionally, we delve into the challenges, prospects, and emerging directions in research, as illuminated by our framework. Consequently, this paper equips researchers with a detailed panorama of the cutting-edge machine learning methodologies for dissecting text sentiment, easing the way for future explorations in this vibrant field.

关键词： Deep learning   Accuracy   Feature extraction   Crops   Computer science   Training   Instance segmentation   Data models   Data augmentation   Plant diseases   Agricultural products   Semantic segmentation   Agricultural applications   coffee leaf disease   deep learning   semantic segmentation   SUNet   SegNet   U-Net  

摘要： Leaf mining, rust, bacterial blight, and berry pathology are major diseases in coffee plants. These diseases not only reduce yield but also affect quality. Early detection and targeted treatment are crucial to mitigate their effects. This paper introduces an efficient hybrid deep learning model, SUNet, for prediction and classification of healthy and diseased coffee leaves. SUNet integrates U-Net with Segnet's encoding system, using VGG16 for robust feature extraction. A decoder with skip connections is used to preserve spatial details. Mask R-CNN is also employed for instance segmentation, accurately localizing disease spots. A pyramid pooling module captures multi-scale contextual information. The model is tested using two benchmark datasets, JMuBEN and JMuBEN2. These datasets contain a wide range of coffee leaves affected by phoma, cercospora, or rust, along with healthy samples. SUNet achieved significant performance improvement over other models in terms of accuracy, Intersection over Union (IoU), F1-score, precision, and recall by 1.22%, 1.21%, 1.17%, 1.19%, and 1.24%, respectively. These improvements demonstrate that SUNet can be used for the early detection and classification of coffee leaf diseases. Therefore, with precise and timely interventions, SUNet can help farmers minimize crop losses, enhance coffee production quality, and reduce reliance on harmful chemical treatments.

关键词： Computer Science   Engineering & Technology   MathML   US - Fairmont Press  

ISBN: (数字)9788770228510 ISBN: (纸本)9788770228657

摘要： CNTs have a variety of applications because of their distinctive molecular structure and show unique electronic and mechanical properties because of their curvature. Nanotubes and micro-beams can be cited as one of the very applicable micro- and nano-structures in various systems, namely, sensing devices, communications and the quantum mechanics. The application of the tiny structures, specifically, carbon nanotubes in the sensors and actuators enforce the engineers to study vibrational properties of those structures experimentally and theoretically. In addition, they are utilized in different fields such as bioengineering, tissue engineering, computer engineering, optics, energy and environmental systems. This book presents vibration modeling and analysis of carbon nanotubes which be helpful in applications such as oscillators and in non-destructive testing, and also vibrations characteristics of single-walled CNT by means of continuum models. These models are established by inferring the dynamical equations in to different theories, which is our particular motivation. The suggested methods to investigate the solution of fundamental relations are Rayleigh Ritz, Galerkin and wave propagation, which are well-known and efficient techniques to develop the fundamental frequency equations. The frequencies of three different types of SWCNTs are calculated. Also, the vibrations of the chiral single-walled carbon nanotube (SWCNTs) with theories using various approaches are investigated. It has been investigated that by increasing the aspect ratios decreases the frequencies and on increasing the height-to-diameter ratios increases the frequencies throughout the computation frequencies of clamped-free lower than that of clamped-clamped.

关键词： Computer Science   Data Structures and Algorithms  

摘要： We consider the matching problem on the line with advice complexity. We give a 1-competitive online algorithm with advice complexity n - 1 , and show that there is no 1-competitive online algorithm reading less than n - 1 bits of advice. Moreover, for each 0 < k < n we present a c(n/k)-competitive online algorithm with advice complexity O(k(log N + log n)) where n is the number of servers, N is the distance of the minimal and maximal servers, and c(n) is the complexity of the best online algorithm without advice.