Reviewerly/exHarmony huggingface dataset:
Vision based Interpreter for Sign Languages and Static Gesture Control using Convolutional Neural Network

Sign languages are the primary mode of communication for deaf people, and this interpreter aims to provide an effective means of communication between deaf and non-deaf individuals. The proposed system uses computer vision techniques to capture and interpret hand gestures and movements, and OpenCV is used as the primary tool for image processing and analysis. The system's accuracy and efficiency are evaluated using various metrics, and the results demonstrate its effectiveness in real-world scenarios The system aims to assist people with hearing and speech impairments by providing a means of communication through hand gestures and sign languages. The proposed system comprises of a camera module to capture the hand gestures and sign languages, a CNN-based image processing module for gesture recognition, and an OpenCV-based gesture control module for controlling external devices. The CNN-based image processing module is trained on a dataset of hand gestures and sign languages, to recognize and interpret the gestures accurately. The proposed system is evaluated on a set of benchmark datasets and achieved high accuracy in gesture recognition and control. The results of the study suggest that the proposed vision-based interpreter for sign languages and static gesture control can be an effective means of communication for people with hearing and speech impairments. The system recognized static hand postures and hand movements with a 94% accuracy rate. The system achieved an accuracy of 92% in recognizing the sign language gestures. The system's accuracy and robustness make it suitable for real-time applications, and it has the potential to improve the quality of life for people with disabilities.
Sentiment Classification of Low Resource Language Tweets Using Machine Learning Algorithms

Sentiment analysis is a way of figuring out if a piece of writing or a sentence is positive or negative. Over the past few years, it has garnered a significant amount of interest in the English language. As a result of the rapid expansion of social media in the modern day, an increasing amount of content can be found in languages apart from English. Telugu represents one of the many native languages for which there is comparatively little information on social media. Finding labeled data of sentences in Telugu for sentiment analysis is becoming increasingly challenging. This study provides Sentikanna, a standard Telugu speech corpus built to assist Telugu Sentiment Analysis across multiple domains, including Recipe, Tourism, and Movie reviews. Sentikanna is a corpus including preprocessed and manually annotated Telugu sentences from a variety of sources, annotated according to annotation rules. All the scripts written in the Telugu language have been used. Apart from the creation of the corpora, polarities have been assigned to the sentences. In total, 3113 Reviews are annotated that have been extracted from various domains. Proposed dataset is used to evaluate different sentiment analysis methods and its performances. Later, sentiment evaluation algorithms using support vector machines (SVM) and Naive Bayes (NB) were applied to the proposed dataset in addition to Sentirama data samples. Performance evaluation has been done and it has been observed that SVM and NB showed promising results on proposed dataset comparatively.
Identification of Malaria Parasites in Thin Blood Smear Using SVM Classifier

The manual viewing of stained blood films using a microscope, or ultimately the operation needing skilled technicians, is the best method for diagnosing malaria. To do a calculation of the malaria parasite, our study proposes a computation that detects and counts red blood cells (RBCs) as well as stained parasites. This suggested structure aids in the development of a fully automated framework for the characterization of erythrocytes from pre-processed images that have been affected by the malaria parasite. The double SVM classifier has provided measurable and varied highlights in the blood spreads with malaria-infected erythrocytes. The findings show 97.91% accuracy and 97.95% sensitivity for recognizing contaminated red platelets.
Enhancing Security in SMS by Combining NLP Models Using Ensemble Learning for Spam Detection with Image Steganography Integration

Spam SMS messages are a prevalent problem in today's world and have become a source of annoyance for users. This research study proposes a novel approach to detect SMS spam using Natural Language Processing (NLP) and creating multiple models and apply ensemble learning on them. The proposed method includes pre-processing the data with NLP techniques such as stopwords removal, stemming, and lemmatizing, extracting relevant features from the text data, and transforming it into numerical representations. The performance of the proposed method was evaluated on a real-world dataset and compared to traditional machine learning algorithms such as Naïve Bayes, SVM. The multiple classifiers are trained on the transformed data, and an Ensemble Learning algorithm is applied to combine their predictions to obtain a more accurate result. The resulting model KNR gave higher accuracy than traditional models. The custom model was integrated into a custom Image Steganography tool which can hide textual data into an image using 1 bit Lease Significant Bit (LSB) technique and while decrypting a hidden message, the custom model would detect if the textual data is spam or not. The purpose of the Image Steganography tool is to provide users with a secure method of communication. For example, sender can hide sensitive data they wish to send to the receiver by hiding it inside an image and the receiver decrypting it to get the original message. If a malicious actor finds out about this communication method and tried to send spam links or messages by hiding it in an image and sending it to its victim, the custom spam detection model will detect it.
Developing an OpenStack Environment: An Exploration of Experimentation and the Diagnostic Research

OpenStack technology allows the building of a cloud services like Infrastructure, Platforms & Applications as a Service (IaaS, PaaS & SaaS) for various customers. The experimentation presents a mapping of general cloud architecture with OpenStack running on a single node for Saas development, unfolding how OpenStack works, its dashboard, creating an OpenStack working environment, and how web server, application server and database server is deployed on OpenStack. For experimentation purpose openstack is used to create a cloud environment and deploy software applications that could be accessed within a given private and public network. It is also illustrated how it is beneficial to run any website or web application on OpenStack. OpenStack is compared with one of the top competitor namely Amazon Web Services (AWS propriety source code environment) and it is found that OpenStack is a cost-effective, elastic, flexible, open-source and can be used for testing and research purposes.
A Multi-Layered Hybrid Security Algorithm Based on Integrity for the Cloud Computing Environment

Cloud computing has emerged as significant and paradigm shift in outsourcing storage and computations. Data stored in public cloud without encryption is not safe due to many reasons such as insider attacks, flawed hardware, security vulnerabilities and service provider stealing or deleting data for monetary gains. Therefore, it is essential for data owner to outsource data to cloud after encryption. However, the existing encryption schemes such as RSA, DES and AES, though they provide strong security, may become victims to post-quantum securityscenarios. In this paper, a hybrid security algorithm is proposed for stronger encryption and decryption procedures. The algorithm is named Multi-Layer Hybrid Security Algorithm (ML-HSA) which has multiple data transformations using AES for encryption of given data, Information Dispersal Algorithm (IDA) for ensuring data integrity and hashing for data integrity verification. The proposed scheme is evaluated using Amazon EC2 (Elastic Compute Cloud) cloud platform and Amazon S3 (Simple Storage Service). The performance of ML-HSA is compared against state of the art security schemes such as RSA, DES andAES. Experimental results revealed that the proposed post-quantum security scheme provides stronger security with relatively less overhead due to multiple transformations involved.
Face Mask Detection and Social Distancing using Deep Learning

Social distancing and wearing a face mask correctly is known to be one of the most effective measures to fight against a pandemic like Covid 19. Thereupon no such precise system has been made and in this domain, research is still going on. In this study, mainly two deep learning models namely CNN, and YoloV5 are employed for object detection of face masks and social distancing and Vgg-19 for feature extraction. For the evaluation of the models, various parameters like precision, recall, mAP-mean average precision, accuracy, validation and training loss have been calculated. This has been observed that among all deployed deep learning models on the collected data, CNN (Convolutional Neural Network) outperformed with an accuracy of 99.3% and a precision of 98%.
Security of Smart Premises to Prevent Unauthorized Entrance by using IoT Enabled Face Recognition Technique

Security is one of the key issues in the era of technology. So, Facial recognition plays an important role in the biometric authentication that is used in different applications, especially in security. Security of smart premises should be simple, easy to use, and quite accurate. The authentication of a person by recognition of their face is one of the major security paradigms used these days. In this research work, facial recognition by using an Internet of Things (IOT) based solution is proposed. The information of the subjects is stored in the cloud databases. An image is captured by the camera in front of the door, then the real-time face recognition is done using Local-binary pattern (LBP). If the image matches with the known subject's information from the database, entry is granted otherwise denied. An email or a notification is sent to the owner who may or may not allow the entry of that person. Various facial recognition algorithms have been explored to evaluate their working and efficiency.
Text Extraction and Mining Methods Used in Data Science

Online Customer Reviews (OCRs) make it difficult for firms to examine them due to their number, diversity, pace, and validity. The big data analytics study predicts OCR reading and its usefulness. Titles with positive emotion and sentimental reviews with neutral polarity attract more readers. Online merchants may use this work to build scale automated processes for sorting and categorizing huge OCR data, benefiting vendors and consumers. Current OCR sorting approaches may prejudice readership and usefulness. Python crawled, processed, and displayed data using Natural Language Processing (NLP). The crawling dataset collected literature using a Pubmed Application Programming Interface (API) module. Natural Language Toolkit (NLTK) processed text data. Tokens were processed into bigrams and trigrams using n-grams. According to study abstracts, West Java has the most stunting research. Text mining and NLP may enhance oral history and historical archaeology. Text mining algorithms were intended for enormous data and public texts, making them inappropriate for historical and archaeological interpretation. Text analysis can effectively handle and evaluate vast amounts of data, which may substantially enrich historical archaeology study, especially when dealing with digital data banks or extensive texts.
Research of Application on Deblurring model for Guqin Performance

In order to improve the quality of fingering fuzzy image enhancement in guqin performance and further promote the digital research work on guqin art, a two-stage method of hand analysis network and SNAD generator is proposed to solve the fingering fuzzy problem in guqin performance. First of all, aiming at the feature that the camera does not move and the finger moves, resulting in finger motion blur, a hand analysis mesh is proposed to be constructed. Second, an adaptive denormalization network is constructed to achieve accurate reconstruction of the hand structure. Finally, an adaptive denormalization network is constructed to achieve accurate hand structure reconstruction. Experimental results show that the performance of our non-uniform blur model on real data can be further improved, similar to traditional iterative optimization-based BID methods, but slower than emerging instance-driven methods.
QR Code-Based Vending Machine Using FSM

Vending machines are now widely used and recognized worldwide. Compared to traditional purchasing, vending machines are more convenient and accessible. Coinoperated vending machines are becoming obsolete as digital trade grows quickly. With the rise of smartphones & IoTs, QR codes are becoming more and more common these days. As a result, QR codes are being accepted for payments everywhere. To create a non-cash payment method utilizing a contactless mobile payment system, this vending machine leverages QR codes as a way for authorizing payments. The Finite State Machine is utilized to carry out this task. Behavioral modelling is used to create the Verilog code for the FSM-based machine, and the XILINX Vivado Design Suite tool is used to simulate the test bench.
ClassLib: An Library for Solving Linear Classification Problems on Apache Spark

Nowadays, Apache Spark has achieved tremendous success in the field of big data, and it can be used to complete various data processing tasks. Moreover, Spark's feature of in-memory computing makes it suitable for iterative computing tasks, so linear classification problems can be solved using Spark. However, there are few optimization algorithms provided in Spark for solving linear classification problems. The Spark MLlib only implements SGD and L-BFGS optimization algorithms, which limits users' choices. Therefore, this paper proposes an optimization algorithm component called ClassLib, which implements the ADMM optimization algorithm in the component and solves Logistic Regression and SVM problems based on this algorithm.
Fast Algorithm Of Saturation Enhancement Based on HSI

By analyzing the conversion algorithms between RGB and HSI, this paper proposed a fast algorithm of saturation enhancement based on HSI. The traditional algorithm converts RGB to HSI, then enhances the saturation S and finally converts the enhanced HSI to RGB. In this paper, the saturation can be enhanced without conversion between RGB and HSI. Rather than using the floating point multiplication, division and triangle functions, it used integer operations and shift operator methods in this algorithm, so that improved the processing speed. Finally, the experiments show that the fast algorithm can save 70% of the computing time than the traditional algorithm. It can process the 720p video images real time.
A Cryptographic Protocol Vulnerability Analysis Framework based on Fuzz Testing and Model Learning

Aiming at the increasingly prominent security issues in cryptographic protocols, based on the model learning technology in automata theory, the black-box test of cryptographic protocols is carried out by using the protocol state fuzzy testing technology, and a cryptographic protocol analysis system is designed and implemented to realize inferencing the target protocol's state machine automatically. The proposed system is used to test the OpenVPN protocol, clearly showing the specific behavior characteristics of the protocol and multiple special behavior paths outside the expected behavior path and possible security analysis and comparison, which has important reference significance on the vulnerability analysis of other cryptographic protocols.
Improved lightweight YOLOv5s Algorithm for Traffic Sign Recognition

A lightweight traffic sign recognition method based on the YOLOv5s algorithm is proposed to address the drawbacks of the current road traffic sign model, such as sluggish detection speed, huge model, and many parameters. To increase the speed of detection, the lightweight GhostNet backbone network is first deployed, which further reduces the parameters and size of the model based on YOLOv5s. Second, the Anchor that is appropriate for the CCTSDB 2021 dataset is recreated using the K-means clustering technique. The NMS algorithm of the original network is then replaced with DIoU-NMS to enhance the recognition of veiled indicators and lower the missed detection rate. To increase the model's detection precision, the CIoU loss function of the original network is swapped out for the EIoU loss function. Research on the CCTSDB 2021 dataset reveals that while the parameters are lowered by 16.5%, the model size is reduced by 16%, and the FPS is increased by 7, the detection accuracy is only dropped by 2.1% when compared to the original YOLOv5s model. The improved algorithm can fulfill the mobile end of many scenarios with a balance of speed and accuracy requirements, as opposed to YOLOv3-tiny and other algorithms.
Entity and Relation Joint Extraction Based on Semantic Enhancement Network

In order to solve the problems of semantic information loss, insufficient utilization of external resources and low utilization rate of sample information in Think tanks information entity and relation extraction, we proposed a method of joint entity and relation extraction based on semantic enhancement. First of all this method extracts the natural language representation of the label by using entity and relation Chinese label encoding on the basis of extracting direct semantics, in the second place extracts the internal semantic information in all aspects through dependency semantic analysis and semantic role labeling, finally, a double-layer attention mechanism is used to fuse and enhance the semantic information extracted from multiple dimensions to improve the precision of the joint extraction of entities and relations. Experimental results show that the proposed method performs better than other methods in entity and relation joint extraction on Think tanks information datasets.
Imbalanced radar micro-motion target classification based on k-means SMOTE and deep residual network

In the practical application of radar target classification based on deep learning, there are problems such as incomplete and imbalanced radar datasets making it difficult for deep learning to leverage its advantages. Based on these problems, an imbalanced radar micro-motion target classification method based on k-means SMOTE and deep residual network is proposed. Firstly, based on the imbalance of various target samples collected in practice, in order to make full use of the micro-motion features of targets, the K-means SMOTE algorithm is proposed to optimize and balance training datasets. Then, accurate classification of micro-motion radar targets is achieved based on the residual network, which uses the ResNeXt101 network as the core structure. Finally, based on measured radar target data, experimental verification is conducted. The experimental results show that compared to traditional deep learning direct object classification methods, the algorithm proposed in this paper can more effectively address the problem of data imbalance and achieve higher classification accuracy.
Design of ZUC-256 Stream Cipher Coprocessor for 5G Communication Security

With the rapid development of 5G communication technology, network security has become a major concern. In the context of 5G, the security of encryption and authentication algorithms is of utmost importance. This research focuses on 5G communication security and presents the design of a cryptographic coprocessor for the ZUC-256 cipher algorithm. ZUC-256 is a byte-oriented stream cipher algorithm that provides 256-bit security and is widely used for encryption and authentication in 5G communication. However, there is limited research on hardware implementations of the ZUC-256 algorithm, especially for resource-constrained devices. This paper leverages the RISC-V architecture processor, known for its simplicity and scalability, to design the ZUC-256 stream cipher coprocessor. To enhance the efficiency of keystream generation, targeted design and optimization techniques are applied to the hardware implementation of the ZUC-256 algorithm. Through the synergy of software and hardware for encryption and decryption, efficient and secure 5G communication applications are achieved. Experimental results demonstrate that the designed ZUC-256 cryptographic coprocessor exhibits excellent performance and reliability in 5G communication security. Its high throughput and low power consumption make it suitable for resource-constrained devices.
Point Cloud Compression for Augmented Reality-Based Campus Navigation Application

Due to the high demand of Augmented Reality for civilian and military applications, it has been taking a lot of attention recently. In our country the university's campus is such urgent demanding of such applications where new coming students, guests, and temporary teachers might have trouble finding buildings, facilities, and administrations on a vast university campus with many buildings, making it difficult to move among them. This paper presents the implementation of a complete Campus Navigation System using Augmented Reality technique that is user-friendly and applicable for almost all hand-held smart devices. The proposed applications are built with Unity using ARcore, NavMesh and point cloud localization techniques, and carried out at the campus of Al-Nahrain University College of Information Engineering in Baghdad Iraq. Due to the large size of point cloud data and the current limitation in wireless channels, a method of point cloud compression is also presented. MATLAB is used for the purpose of system efficiency analysis. According to test results, the proposed application could improve pathfinding efficiency and usefulness on campus.
A Pooling Algorithm of Convolutional Neural Network Based on Frequency Domain Transformation

Pooling is an important component in neural networks. The main methods of pooling are mean pooling, maximum pooling, and random pooling. At the same time, there are also related frequency domain pooling methods. However, these methods all have corresponding drawbacks. In this paper, a pooling operation method of convolutional neural network based on frequency domain change is proposed. This algorithm has the characteristic of retaining the most original information in the image and better transmitting the main information in the neural network. The neural network will therefore have better accuracy performance and complete pooling operations while convolution is in progress. Therefore, the computation time of the entire neural network will also be reduced.