Reviewerly/exHarmony huggingface dataset:
Automated Library Management System using Face Recognition and OCR

Traditional methods of managing a library have their disadvantages like long waiting queues and expensive scanner machines. This paper aims to overcome these drawbacks by introducing a new application that makes use of face and text recognition to issue books from the library. The main idea is that a picture of the book with the issuer will be taken from which the required data will be extracted. The account of the user will be created by the library admin by uploading a picture of the face of the user. The application consists of two types of logins - user and admin, where administrators can keep track of user transactions and history. The face recognition algorithm and book recognition algorithm gave an overall accuracy of 99.43% and 82.5% respectively. The user can also see the information related to books and this information is extracted from a third-party API. Further, easy payment and reissue options for the books on the application have also been implemented.
OCR using CRNN: A Deep Learning Approach for Text Recognition

Optical Character Recognition (OCR) is a widely used technology that converts image text or handwritten text into digital form. However, recognizing handwritten text, printed text, and image text poses a significant challenge due to variations in writing styles and the complexity of characters. This paper proposes a novel approach for OCR using Convolutional Recurrent Neural Network (CRNN) that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The proposed CRNN architecture can automatically learn and extract features from raw image pixels and recognize sequential patterns of characters. This research paper presents a robust OCR system using CRNN architecture with 7 convolutional layers and 2 LSTM layers for recognizing text in images with complex backgrounds and varying fonts. The proposed system achieved state-of-the-art performance on several benchmark datasets, demonstrating the effectiveness of the proposed approach. Our experimental results demonstrate that the proposed CRNN approach is better than other methods and achieves higher accuracy with less latency in recognizing text from an image. We also analyze the impact of different parameters, such as the number of layers, filter sizes, and hidden units, on the performance of the CRNN model. This paper provides a comprehensive study on OCR using CRNN and its potential to improve the accuracy and efficiency of recognizing text.
Encrypted Dedup-Upload, Download and Update Securely With Proof of Ownership

Over the past decade, the Internet of Things (IoT) has experienced rapid expansion, and the storage of necessary files at the network edge has become increasingly prevalent through the use of data deduplication. Data deduplication increases network and storage efficiency by only retaining a single copy of duplicated data on the server. However, storing files without any encryption leads to data leakage. Hence existing models utilise Message-Locked Encryption (MLE). Nonetheless they are vulnerable to brute-force attacks. Meantime, these methods also use Proof of Ownership (PoW) approaches which mitigate duplicate faking attacks, but are susceptible to replay attacks or result in high computational costs. This work proposes an encrypted deduplication model that utilises enhanced PoW. There are 4 modules in the model: upload, download, subsequent upload and update. The model employs an inter edge server aided Randomised Convergent Encryption (RCE) method to encrypt files. Then, blocks are encrypted using an intra-edge edge-aided MLE technique. The PoW algorithm is implemented to prevent duplicate-faking and also one time token is used to prevent replay attacks. The model also incorporates verification of a cuckoo filter to increase security for PoW.
End-to-End Person Re-Identification for Security and Analysis

There are millions of CCTVs deployed in public and private places which gather tremendous amounts of video data. It requires a huge amount of manpower to monitor this video data and get the required information extracted in a timely manner. To handle this problem the paper proposes a novel architecture to automate the Video surveillance system, capable of tracking each individual across a multi camera network in real time. Numerous important technologies are incorporated into the Video surveillance system, such as person detection, person re-identification and person tracking.The effectiveness of person re-identification has been greatly improved by researchers, but majority of these methodologies use manually annotated bounding boxes for person detection and only concentrate on matching between query image and cropped gallery images with no regard to person tracking. The paper proposes the following pipeline of processes to build an end-to-end video surveillance system.•Collecting CCTV footage from a network of cameras•Person Detection using YOLOv4•Person Re-Identification using Locally Aware Transformer model for feature extraction•Person Tracking using Deep - SORT algorithm•Logs generation for further analysis and insightsThe Person Re-ID model trained using the aforementioned algorithms achieved an average Rank 1 accuracy of 81% when tested using the custom generated datasets. The resulting product is an End-to-End Person Re-ID Framework deployed as a Web portal which tracks persons across cameras and generates human readable logs for analysis.
Voice Assistant Technology: The Case of Jarvis AI

This paper proposes a system for Jarvis AI voice assistant that aims to enhance its capabilities and functionality. The system utilizes various techniques and technologies such as speech recognition, natural language processing, and machine learning algorithms. The proposed system will enable Jarvis to perform a wide range of tasks through voice commands, such as sending emails, scheduling appointments, and making phone calls. The system will also incorporate personalized user profiles to provide a more personalized and efficient experience. Furthermore, the system will include features such as sentiment analysis and emotion recognition to enable Jarvis to better understand the user’s needs and provide appropriate responses. The proposed system will be implemented using Python programming language and various open-source libraries and APIs.
Blockchain Trust based Authentication Protocol with Malicious Data Analysis Using Deep Learning Architectures: Electronic Medical Record Application

New opportunities for effective patient data management have emerged as a result of introduction of electronic health records (EHRs). By utilizing ML to mine digital patient record datasets, for instance, preventative rather than reactive medical practice is feasible. EHR is vulnerable to both insider and external threats due to sensitive nature of data, but security applications typically face the network's outer perimeter. Using deep learning methods, this study aims to enhance cloud data storage and malicious data detection. Blockchain trust based authentication is used to improve security-based cloud data storage in this study. After that, fuzzy rule Bayesian discriminant analysis is used to find malicious data. Utilizing results of malware analysis as well as detection and ML methods to evaluate difference in correlation symmetry, it was demonstrated that it was possible to detect harmful traffic on computer systems, thereby increasing network security. Data transmission rate, random accuracy, computation cost, communication overhead, mean average precision, and specificity are all examined in the experimental analysis for various electronic medical record datasets.
Inter-Platform Consistency Inspection Method

Inter-platform consistency is a central inter-usability attribute in cross-platform service design. However, limited studies have investigated the importance of the different characteristics of inter-platform consistency for user experience (UX) of cross-platform services. A discounted, easy, fast inter-platform inspection method for cross-platform service design is still unavailable. In this paper, the authors present the results of a study on inter-platform service consistency using a new inspection method. Three UX experts evaluated three cross-platform services using predefined inter-platform consistency heuristics (perceptual, lexical, operational, and compositional). The evaluation yielded 287 inter-platform consistency findings (194 negative and 93 positive). The results indicated that all predefined consistency heuristics that represent inter-platform consistency characteristics are important and should be considered when designing the UX of cross-platform services. The evaluators assessed our inspection method and found it appropriate and effective.
New Age Chess Engine

A chess engine, used in computer chess, is a programme that analyses chess or chess-variant positions and produces a move or series of moves that it deems to be the strongest. The back end of a chess engine often has a command-line interface without any graphics or windowing. Engines are typically used in conjunction with a front end, a windowed Graphical User Interface (GUI) that allows for keyboard, mouse, or touchscreen interaction, such as a chessboard or winboard. It also enables the user to play against various engines without having to become familiar with each one's unique user interface. The availability of numerous chess engines for smartphones and tablets has increased their accessibility. This work aims to develop an artificial intelligence capable of amateur chess play.
Movie Recommendation System using RNN and Cognitive thinking

Systems such as Netflix, Amazon, Flipkart, etc. have a huge user base spanning the entire globe. The products and services that these giants provide are also commensurately overwhelming in amount. A particular user can’t be expected to browse through the entire repertoire on his/her own. This is where a recommendation system comes in handy. Such a system can suggest a user similar as well as completely different products and services without any explicit search being carried out by the user. A recommendation system helps increase user engagement and may ensure a loyal consumer base if deployed correctly. Incase of movies, demographic factors such as age and region play an important role in determining user preferences. Age of a person is a latent factor which influences his or her genre preferences. Hence, we have decided to explore the trends that come into play in determining a person’s genre preferences. The main aim is to build a recommendation system for an app in order to increase convenience as well as user interaction using an ensemble Recommendation System model which will utilize 4 different individual learners and combine their results accordingly to provide near perfect recommendations to any user.The movie recommendation system will incorporate cognitive thinking by considering the age of the user and recommending genres based on age group psychology.
Design and Development of Time and Memory-Efficient Video Translation Tool

There are very few open-source tools available for translating videos into different languages. Translating videos into multiple languages is a boon for everyone as it can make communication between people very easy. This paper presents a proposed video translation tool that recognizes the speech and translates the audio into ten new languages. The proposed tool ensures its immense implementation in benign educational platforms and for platforms like YouTube as it eliminates the manual dubbing part. The proposed video translational tool ensures 75% computational time efficiency and 89% CPU memory utilization efficiency.
Anatomy of Quantum Computer Framework using Qiskit

We use computers, mobile phones, and numerous other devices for computation and storage purposes. All these devices use some specific hardware to process data and give us a meaningful outcome in our everyday activities. Classical computer which we use in our daily lives are probabilistic, deterministic and logical. These devices help us tackle many tasks such as education, medical issues, banking and many others. As the data increases in our world the shortage of storage capacity and computation of such a massive data is increasing day by day. We therefore need a new device that can deal with the problem and deliver outcomes that are satisfactory. Quantum Computers are a new, forthcoming technology that can help with these challenges. These computers can store and process information that a classical computer cannot, by using entangled quantum bits (qubits). The power of computation and storage grows exponentially as qubit entanglement rises. This could introduce us to a new realm of powerful computation and alter how we handle huge datasets.
2Q-Learning Scheme for Resume Screening

Resume screening is a tedious process as it involves a lot of challenges in terms of handling a voluminous number of resumes, performing screening operations, extracting precisely suitable competent profiles out of all available profiles, and so on. In literature several techniques are available for resume screening which include manual inspection, resume parsing, and many more. These techniques produce inconsistent results and consume a lot of time of recruiters as all the techniques lack automation and learning capabilities. This paper uses a 2Q-Learning-based approach enriched with natural language processing capability to accurately evaluate resumes. Additionally, by offering advice on potential resume improvements, it helps to enrich the candidate’s resume. It also eliminates the unconscious prejudice and human errors that occur throughout the traditional recruitment procedure. Overall, it is advantageous for both job seekers and recruiters in terms of learning rate, scalability, and accuracy. Overall, it is advantageous for both job seekers and recruiters.
Predicting The General Election 2024 Using ML And Data Analytics

The world today is full of emerging technologies and one of the most rising fields is Artificial Intelligence. As a result of increased computation power, numerous new machine learning algorithms have emerged. We are working on experimental research which aims to predict the election results of the 2024 Indian General Elections/Lok Sabha Election which will we contested for 545 Lok Sabha Seats. To parametrize computations such as coalitions and swings on all seats, Data Analysis would be employed. The swing parameters are then calculated using machine learning techniques like Linear Regression, Naive Bayes, Random Forest, Time Series, etc. using prior election data specific to relevant seats. To reckon emotions important to the elections, a huge corpus of recent publications and/or twitter tweets can be used. We may be able to apply swings to the vote shares of each party in each constituency and produce viable projections with the necessary biases based on subjective data. Various parameters will be taken in account to thoroughly analyze the data to get the right swings and the mindset of the respondent to know what their needs are, reasons to switch and are they satisfied with the current candidate and whether their conditions are met or not et cetera. Our vision is to detect the popular trends to get the insights into existing potential voters. Determine voter’s response to promote cross opportunities. Attract, Retain & Grow improving decision making & better drive decisions.
The Application of Blockchain Technology in The Promotion of Cultural and Creative Products

The rapid development of modern information and communication technology has greatly enriched and improved the means of cultural and creative communication. As the underlying technical framework of the Internet, blockchain has been applied in many fields, and "Internet plus+cultural and creative industry" is also increasingly valued. Nowadays, the media technology potential of blockchain has been applied to various media platforms, and has optimized the original communication process with its own advantages of decentralization, asymmetric encryption and autonomy. Based on this new modern underlying technology framework, the paper uses the relevant theories of cultural and creative communication, and adopts interdisciplinary and comparative analysis methods to explore the possible changes that blockchain technology may cause in the field of information communication, aiming at bringing new perspectives and reference values to the traditional media that need to be innovated on how to actively use new technology communication. Cultural and creative products have the functions of spreading culture, protecting culture and promoting cultural heritage. However, the full play of cultural and creative products depends on the successful promotion of the products. Therefore, this paper proposes a cultural and creative product promotion system based on blockchain technology. Through research, blockchain technology can better promote cultural and creative products, indicating that blockchain technology has good application value in promotion.
Analysis of Human Action Recognition Using Machine Learning techniques

The hand gesture is one of the nonverbal communication strategies used in sign language. Deaf individuals & dumb and have hearing or speech use it most frequently. Communication issues with either one another or with regular individuals. Many sign language systems have been created by different makers, but they're not very useful or affordable for deaf or hard of hearing persons. So, the software has a prototype that can automatically detect signs to assist deaf and dumb persons in communicating with each other and with other people more easily. The majority of individuals find it challenging to comprehend and communicate with others who are deaf or mute, so they're usually denied regular social interaction. These individuals are dependent on an using an interpreter or a visual medium of communication. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> An There won't always be an interpreter present, and visual communication is mainly challenging to comprehend The main language is sign language a method of communication for the population of the deaf and dumb. As a the grammar and meaning of numerous words are unknown to the average person. the hand gestures that make up a sign language, which are mostly their relatives and/or the community of the deaf and dumb. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup>
Anonymous Decentralized Chat Engine Over Overlay Networks

Chatting privately without having trust in a third party is a challenge and has not been addressed completely in the literature. In this context, we explore an existing concept of having an overlay network on an overlay network. This helps one overlay network inherit strengths and maybe also weaknesses from the host overlay network. In this paper, we explore a decentralized anonymous file system that can help someone talk privately with anyone he/she wishes. We propose a technological solution AnonFS that runs over an anonymous overlay network such as Tor and I2P. With the use of Tor or I2P, AnonFS inherits the property of Anonymity of connection. AnonFS is zero-trust cloud storage for synchronization. One can share content or even synchronize with multiple devices using AnonFS. AnonChat works by initiating a direct connection from one Tor/I2P peer to another Tor/I2P peer. AnonChat uses AnonFS to store and retrieve data anonymously. The identity of the user is never tied to any physical entity such as email ID, phone number, IP address, or any other personally identifiable information. This means that if the user is really careful about whom he shares the information with, AnonChat can be completely untraceable. We qualitatively compare AnonFS with several popular services.
Enhanced Zero shot Learning using Deep Neural Network ResNet50

Computer vision has advanced with lot of development in visual recognition systems, which poses restrictions to expand for huge numbers of image classes. This restriction is due to huge image classes with unlabeled images which cannot be used in training the machine learning algorithm. As Traditional machine learning method of classification are based on the classification of categories which are available at the time of training. Technique of Zero-shot learning (ZSL) recognizes categories of test sets which is not appearing while training the model. The enhanced ZSL technique proposed is based on deep visual semantic embedding method. In this method Visual and semantic features are used for the classification of unknown categories. The extraction of visual features is accomplished with convolutional neural network (CNN) and ResNet 50. FastText is used to convert labels of classes into word embedding vectors. Visual and word embedding features are mapped. The model is predicting the Top 5 labels for an unknown image category (zero-shot class). The experiments are performed on standard datasets SUN and AWA2. The proposed technique of enhanced ZSL with ResNet 50 gives better accuracy and reduced model loss then CNN model.
Image Tampering Detection Using Error Level Analysis and Metadata Analysis

As the volume of digital content continues to increase, the problem of information pollution has become increasingly pressing. Information pollution refers to the dissemination of false, misleading, or biased information through various channels such as social media, news outlets, and websites. This can include fake news, propaganda, as well as tampered or edited images and videos. With the proliferation of digital image editing software and artificial intelligence (AI) techniques, it is increasingly challenging to determine the originality of a photograph. Field of digital image forensics, which aims to identify and prevent image forgery, has gained significant attention in last few years. In this study, we propose a combined approach of Error Level Analysis (ELA) and Metadata Analysis for the passive authentication of images. Utilizing deep neural networks, our proposed approach seeks to evaluate the originality of an image. System demonstrated a test accuracy of 87.24% for the ELA approach and a test accuracy of 73% for the metadata analysis approach.
Complex Scene Loading Optimization Based On Virtual Reality Algorithm

In recent years, virtual reality has gradually become a hot topic, which involves many model scenes. However, large-scale scenes cannot be directly imported into the game engine due to their large size, and complex scene models need to be optimized for rendering before they can be used. This paper focuses on the optimization of complex scene loading based on virtual reality algorithm. Virtual reality algorithm is a method to optimize the loading of complex scenes. It can be used to optimize the loading of scenes containing many objects, such as large cities or battlefields. The algorithm works by dividing the whole scene into several sub scenes and calculating the number of objects in each sub scene. In this way, it can find out which sub scenes have not been loaded and should be loaded first, so that after all other parts are loaded, they will be displayed on the screen. In this case, the user will see a smooth transition between different parts of the scene, instead of seeing a sudden change in his /her viewpoint when one part is loaded.