Reviewerly/exHarmony huggingface dataset:
Innovative Image Privacy Preservation Applied to Data Perturbation in Cloud

The proposed method is to protect the privacy of images in real-time by using encryption, salt key, and blurring techniques. The method is particularly useful in cloud environments where images may be processed or stored remotely. The method uses the Advanced Encryption Standard (AES) algorithm, which is a widely-used encryption method that is known for its security. The AES algorithm is applied to the image data, which is then protected with a unique salt key, making it even more difficult to decipher. In addition to encryption, the method also uses the Gaussian Blur algorithm to blur the image data, making it more difficult to recognize any details that might compromise privacy. This blurring technique can be adjusted to achieve varying degrees of blur, depending on the level of privacy required. Furthermore, the method applies data perturbation, which involves adding random noise to the image data. This helps to further obscure any sensitive information and makes it even more difficult for unauthorized users to extract useful data. Overall, the proposed system is a robust method for protecting image privacy in real-time, and it has many potential applications in various fields, such as healthcare, finance, and law enforcement.
Investigating the Impact of Visual Attention Models in Face Forgery Detection

With the recent rise of realistic face manipulation methods, building robust face tampering detection methods has become more important than ever before. Visual attention has played an important role in highlighting discriminative regions within input which is important for making accurate predictions. This manuscript presents a comparative study of several recently proposed visual attention models for the problem of face forgery detection. Specifically, five visual attention models namely, coordinate, selective kernel, triplet, CoT, and shuffle attention have been tested by integrating with a baseline deep learning model. The modified visually attentive architectures are trained and tested on popular public benchmark dataset FaceForensics++. The experimental results achieved by different attention approaches are compared. Additionally, the computational costs involved in each type of attention have also been discussed specifying the accuracy and computation tradeoff. Experimental results prove that Triplet Attention performs best by achieving accuracy scores of 0.9543 and 0.7190 on DF and NT categories of the FF++ dataset. Triplet attention is also extremely lightweight with only 1200 trainable parameters compared to the other attention modules under study.
Character Recognition Techniques using Machine Learning: A Comprehensive Study

Optical Character Recognition (OCR) technology is essential in converting printed or handwritten documents into machine-readable text. This paper provides an overview of machine learning based OCR technology, including its history, principles, and applications. The paper discusses the challenges and limitations of machine learning based OCR and presents various OCR models based on different feature extraction and classification methods. Specifically, it covers feature extraction methods such as Euclidean distance, HOG, Fourier descriptor, and directional chain code, which are used to improve the accuracy of classification models such as CNN, KNN, ANN, and SVM. The paper also discusses recent advancements in OCR for different languages and how OCR can be integrated with machine learning and AI to understand ancient scripts and convert them to modern languages. The paper concludes with a discussion on the future of multi-lingual OCR and integration with AI and machine learning models, which have the potential to revolutionize the field of OCR. In summary, this paper provides a comprehensive overview of OCR technology, its advancements in various languages and potential for future development.
An Intelligent Camera Based Eye Controlled Wheelchair System: Haar Cascade and Gaze Estimation Algorithms

This article proposes a system that aids people with disabilities. An Electric Eye Controlled Wheelchair System is built to help disabled people. With the designed system, disabled people can move effortlessly without support from others. The system uses image acquisition wherein the image of the eye is processed to find out the gaze direction of the eye using Haar cascade and gaze estimation algorithms and hence wheelchair moves according to the direction of eyeball movement. The gaze estimation algorithm is so precise and one single algorithm does the job of what two algorithms (Canny Edge detection, Hough Transform) are supposed to do and to execute the same task. With this technique, a disabled person can steer their wheelchair with their eye movement. The webcam is placed in Infront of the person which captures the live movements, and an image processing technique is used to track the position of the pupil in both eyes with the help of a raspberry pi processor. The image processing technique used here is Gaze tracking which uses Open CV. The gaze tracking tracks pupil movement and depicts its coordinates. According to pupil motion, the motor driver will be instructed to go forward, left, and right. A blink instruction is used to stop the wheelchair when the person blinks. Additionally, a front-mounted ultrasonic sensor that can detect obstructions and automatically halt wheelchair movement is mounted for safety reasons. The system is monitored by a Raspberry Pi device, which lowers the cost.
An Empirical Analysis of PoS Tagging for Kannada Machine Translation

Parts of Speech (POS) tagging process has emerged as one of the crucial and very basic preprocessing technique for any natural language processing tasks. In Kannada, a dominant language in southern India, morphologically rich and low resourced at the same time, PoS tagging process was difficult to achieve in the beginning. Later, studies show that Conditional Random Fields, Hidden Morkov Model and deep learning techniques have produced good accuracy. This paper investigates all the three above mentioned models and argues that deep learning model, which uses bidirectional Long Short Term Memory as a RNN unit, produces the highest accuracy of 93% in contrast to CRF and HMM model with a precision accuracy of 65% and 42% respectively. Also, the paper specifies how important a PoS tagging process is in the task of Machine Translation, which is booming in the world of computational linguistics.
Automatic Machine Translation System for Artificial Intelligence based on Parallel Corpus

Machine translation has evolved rapidly along with the development of statistics and linguistics. The acquisition of machine translation knowledge is based on the existing language, corpus and machine learning methods, from which knowledge is extracted to improve the translation effect. The aim of this article is to study artificial intelligence machine automatic translation systems based on parallel corpora. The article describes the process of building a multilingual-based parallel corpus and gives a CAT software package. The weighting of this feature is obtained by defining the evaluation features of multiple parallel corpora and combining each evaluation feature using a linear model using the Perceptron algorithm. By analyzing the coverage contribution factors and quality assessment features of the packets, a subset of data suitable for statistical machine translation is proposed, and the decoding cost of the system is reduced to some extent.
Research and Simulation of Improved Topic Web Crawler Algorithm based on Deep Learning

This article analyzes the limitations of traditional topic crawlers, and on this basis, compares depth first and breadth first crawling strategies to construct an improved topic URL crawling strategy. By using regular expressions and web page selectors to locate actionable positions in a webpage, the program simulates human operations on the webpage based on these positions, in order to obtain more topic related URLs and webpage content. Finally, by establishing the experimental process of themed crawler, designing and improving the themed web crawler algorithm, and finally comparing and analyzing the experimental results, it is shown that the improved URL crawler strategy in this paper can greatly reduce the number of total urls crawled by crawler, reduce the crawling time, and improve the efficiency of unit crawler crawling target themed web pages.
Medical Image Retrieval System for Endoscopy Images Using CNN

Today's technologically advanced period has led to an exponential growth in medical research. Nonetheless, there is still room to use AI to improve research. The process of retrieving an image can be difficult. This paper uses CNN to diagnose GI tract abnormalities from the given endoscopic images by extracting features of each image in the dataset. The proposed method, uses VGG-16 model to construct a feature database from each image in the dataset. By comparison of the features of the query image (which was computed similarly to the dataset images) and the features of the images in the feature database using Cosine Similarity metric to extract images matching to the image of the query. Performance measures, including precision and recall are taken into consideration while evaluating the suggested model's performance. The findings imply that the proposed CNN technique achieves a precision of 83.49, 79.23 and 77.12 for top-10, top-20 and top-30 image retrieval which outperform the other methods currently in use.
Cleanobot: Design of an Autonomous Bot for Cleaning Surfaces Leveraging Deep Learning and IoT Frameworks

The autonomous cleaning bot is a state-of-the-art robotic system that utilizes advanced cutting-edge technology to automate the cleaning process. This paper presents the design and implementation of an autonomous cleaning bot efficiently and effectively cleans a variety of indoor spaces, from homes to commercial buildings, using a combination of sensors, algorithms, and cleaning tools. The bot is capable of navigating through complex environments and detecting obstacles in its path, making it an ideal solution for areas that require regular cleaning. It also features a user-friendly interface that allows for easy customization of cleaning schedules and zones, as well as real-time monitoring of the bot's progress. The experimentation and results demonstrate the effectiveness of the system in autonomously cleaning and detecting objects. The bot successfully sends captured data to the cloud for analysis, and the results accurately indicate the type of object detected. With its rechargeable battery, modular design, and easy maintenance the autonomous cleaning bot is a cost-effective, efficient, and eco-friendly solution that enhances hygiene and safety standards while reducing the need for human labor. The system's design and implementation have been discussed in detail, including the integration of all hardware components. The proposed system has demonstrated its effectiveness in cleaning and detecting objects autonomously. The paper concludes with potential future improvements and research directions for the autonomous cleaning bot.
Certificate-Free Aggregate Encryption using Elliptic Curves

Having efficient and safe cryptographic techniques for online data exchange is now more important than ever with the rise of the use of cloud computing & the Internet of Things. In a perfect world, those storing data or files online would utilize encryption and provide selected individuals the keys to access such files. with the idea of aggregation, which enables users to decode several kinds of data with an individual key of constant size, is a modern and efficient proposal in this area. For online data sharing, we offer a dynamic key aggregating encryption technique that works on elliptic curve groups and permits dynamic withdrawal of user access rights. We extend this fundamental architecture to a generic two-tiered hierarchical structure that not only effectively permits expansion of data classes but also achieves optimum space and temporal complexity. Finally, we offer an addition to the generalized method that makes it possible to perform encryption and decryption using efficiently computable bilinear pairings. Formal proofs are provided that each scheme is semantically secure. All of the paper’s assertions have been verified by actual experiments.
MATLAB Simulation of Fresh Supply Chain based on Big Data and Internet +

Big data and cloud computing technology can greatly improve the accuracy of decision making by improving the integrity of information obtained by retailers and suppliers, realizing the precise docking of supply and demand and scene realization at low cost and high efficiency, and realizing the consistent storage of fresh supply chain data and the traceability reliability of products.The application of these new technologies provides strong intellectual support for the modern and intelligent management of the fresh supply chain industry, and gives new impetus to the rapid development of intelligent logistics.In this paper, the basic model of single retailer and supplier is constructed. Stackelberg game analysis method is used to solve the model equilibrium solution in the case of cooperative decision. The influence of fresh-keeping effort and freshness degree on the optimal selling price and wholesale price under different conditions is discussed.Secondly, the pricing decision model under decentralized decision is constructed, and the equilibrium strategy of the model is solved and analyzed by the same method. After discussing the influence of preservation effort and freshness, the role of consumer price sensitivity coefficient under competition channels is further analyzed, and numerical verification is carried out.
Sentiment Analysis of Tweets Using Machine Learning Algorithms

Social media is a popular platform for individuals to express their opinions on various topics. Sentiment analysis using Twitter datasets addresses important requirements such as conducting market research, performing political analysis, and gaining social insights. It empowers businesses to derive valuable insights from user-generated content on Twitter. The objective of this research is to create a sentiment analysis model that can be applied to social media data, specifically tweets from Twitter. The sentiment analysis model is built using machine learning (ML) techniques and natural language processing (NLP) to determine if each tweet is positive or negative. This allows organizations to gauge public opinion and make informed decisions. A vast collection of tweets is used to test the model’s performance, with a focus on accuracy, precision, and recall metrics. The results show that the model is successful in analyzing sentiment across various topics and domains. Out of the various machine learning algorithms examined, the Support Vector Machine (SVM) algorithm yielded the highest accuracy of 94.65%. The study as a whole emphasizes the importance of sentiment analysis in comprehending public sentiment on social media platforms.
Compare the Impacts of Data Correlation on Privacy Leakage in a Combined Privacy Preserving Approach

In the era of digitization, Data collection and sharing are the two essential phases in various real-time applications to provide better social benefits to users. Collection and sharing of users’ private data may lead to compromised users’ privacy. Differential Privacy (DP) and Local Differential Privacy (LDP) are the two variant privacy mechanisms, and these mechanisms offer a strict privacy guarantee against an adversary with unbounded background knowledge. The traditional DP and LDP mechanism fails to provide a strict privacy guarantee in a continuously correlated data release setting. Many works exist to address this correlated issue and proposed solutions under the protection of DP and LDP. However, any real-time applications require a privacy mechanism in either of three ways: either a privacy guarantee from the data collector (achieved using DP) or from the data provider itself (achieved using LDP) or from the both data collector and provider. To the best of our knowledge, none of the work has shown the impact of data correlation on privacy leakage in a combined (LDP+DP) approach. This paper analyzes and studies the impact of data correlation on privacy leakage in all possible combinations of the combined (LDP+DP) approach. We performed a series of experiments with the real and synthetic datasets and the results find that the combined LDP+DP of PBA mechanism is comparatively better than other state-of-the-art combined approaches.
Feature Extraction Strategy of Translated Text Based on Neural Network

In order to better solve the problems of difficult processing of information in text semantics, too much artificial prior information, and unstable performance in traditional natural language processing, a text feature extraction method based on neural network was proposed on the basis of relevant theories of neural network. This method constructs a translation model based on neural networks and an improved deep neural network feature extraction strategy. In order to enable the deep learning model to better handle the semantic content of translation, this paper first designs a semantic based translation segmentation model, which improves that the existing translation segmentation algorithm is not suitable for single semantic segmentation, and this model can also model language in training, and use the deep learning neural network to provide high-quality translation semantic word vectors for other natural language processing systems. In addition, we conducted a separate word segmentation test for sentences with semantic ambiguity, and then analyzed the existing hidden Markov model, maximum entropy Markov model, conditional random field model and other models based on traditional machine learning algorithms, as well as the advantages and disadvantages of deep learning algorithms such as LSTM sequence tagging model, LSTM-CRF sequence tagging model and Bi LSTM-CRF model. Through algorithm training, the proposed improved algorithm model iterated over 21 corpora. The result finding that the single stack bi LSTM CRF model constructed by a single-layer LSTM neural network was chosen as the final model in this paper. A total of 21 iterations of the corpus were trained, and the accuracy of all labels was 98.70%. On the development set, the recall rate is 89.31%, with a peak F score of 93.88. In addition, the accuracy of the test set obtained in the experiment was 97.81%, the recall rate was 83.94%, and the F-value was 90.35 points. This article improves the neural network structure in Bi-LSTM-CRF and obtains the performance of existing models on the named entity recognition test set.
Deep Learning Algorithm and Video Image Processing-based Basketball Training System

The training of basketball players is a complex and hard work. In the game, how to finish the ball quickly and accurately, and how to improve the goal rate and score through correct and effective methods. This paper first introduces the deep learning algorithm and analyzes its basic idea, then expounds the data mining based on the pixel method and the preprocessing of the original data from the two aspects of feature extraction and modeling, and finally uses the experimental results to prove that MATLAB can be applied to the video retrieval, motion recognition and other fields in the basketball training system to achieve efficient basketball game score statistics and teaching guidance.
Interior Design and Decoration Style Based on 3D Computer Software and Virtual Reality Technology

Since the advent of hand drawn sketches and solid models, interior design and decoration have made significant progress. With the emergence of 3D computer software and virtual reality technology, designers can now simulate it realistically before designing. This technology allows designers to experiment with different layouts, colors, materials, and lighting in a virtual environment. They can test different furniture layouts or wall colors without moving anything. This saves time and money, while also allowing for more creativity in the design process. Virtual reality technology goes further, allowing customers to experience it before the future space is built. They can walk through their new home or office and feel how it looks and feels in real life. This immersive experience helps customers make better decisions about their design choices and ensures that they are satisfied with the final result. Overall, 3D computer software and virtual reality technology have made the interior design industry more efficient, creative, and customer-centric, thus completely transforming the industry. This study is based on the design of indoor space and decorative style. This study will create a model using 3D computer software, virtual reality technology, and special cameras, which can be used as a prototype for creating internal spaces. The main purpose is to explore the possibility of creating three-dimensional models with interior design styles. In addition, we also want to explore people’s feelings in different spaces through virtual reality technology.
Design of Visual Information Model of Blockchain Intelligent Interactive Data

Visual information model design needs to integrate with big data sharing, intelligent interaction, authentication security, model building, optimization and energy saving, node privacy and other aspects to give play to the important role of blockchain intelligent interactive data. This paper builds a visual cognitive model around knowledge graph and intelligent interactive data, proposes multiple structures, thinking cognition, multiple modules and intelligent data, and aims at multiple visual cognitive paths of “driving task-effectiveness activity – map visualization – intelligent interaction – model processing” and “data rotation -information graph – multiple knowledge – cognitive model”. It draws the conclusion of the multi-distributed and information intelligentized non-center model structure, realizes the openness, transparency and traceability of all kinds of information data, and makes the blockchain intelligent interactive data more visualization, intelligence, humanization and technicalization. According to the intelligent type media of blockchain, large databases are stored and classified, and a partition block data model is established. A chain structure relationship is formed between block data models, and a layer-by-layer oriented structure is formed. The representation design and interaction design of blockchain intelligent interactive data visualization needs to meet the requirement that the cognitive subject and artificial intelligence can work cooperatively and integrate, so as to create a compound cognitive block model.