Reviewerly/exHarmony huggingface dataset:
Large Data Processing for Cloud Service Collaborative Authenticity Computing Model

The provision of a highly secure service is by far the most important responsibility of any cloud computing network. Users are able to entrust cloud data centers with their most sensitive data and computing operations since this phase in the cloud computing process is built on trust between users and cloud services providers. However, with the proliferation of collaborative cloud computing comes a significant obstacle in the form of the question of how to provide instant responses to a large number of client enquiries. In order to provide highly dependable services in a timely manner, tens of millions of customers' expectations must be met, and the underlying service platform must be able to efficiently and swiftly fulfil tens of thousands of service requirements automatically. The basic need for setting up a reliable and interactive cloud infrastructure is to use trust systems that are not only lightweight and speedy but also high-speed and low-cost. This paper proposes a novel and concurrent computing architecture for confidence that is centered on large data processing, and it is intended for usage in a world that relies on secure cloud infrastructure. Second, it is suggested that a distributed and scalable perceptive infrastructure for the operation of large virtual machines be built using remote monitoring agents. This infrastructure would be built using remote monitoring agents. After that, a technique for the calculation of confidence that is adaptable, lightweight, and parallel is provided for big, controlled data sets. According to what is currently known, this article is the first one to employ a disruptive and parallel computing method together with a significantly accelerated rate of confidence measurement. This enables the confidence calculation framework to be suitable for application in a large-scale cloud setting. The intended system's efficiency and effectiveness were evaluated based on the outcomes of the success review and experimental research.
Advanced Encryption Standard-128 bit Design Flow for ZYNQ-7 ZC702 Evaluation Board

The study of cryptography techniques on Field Programmable Gate Array needs extensive research attention. Evaluation Boards is one such area of hardware security measures. An encryption algorithm must be developed on a specific family board. The Advanced Encryption Standard 128-bit algorithm is proposed in this study for the ZYNQ family board. Particularly, the ZYNQ-7 ZC702 Evaluation Board. Then, its parameters are appropriate for top-down encryption algorithms. The results of the algorithm are generated as reports on Register Transfer Logic (RTL) and look-up table usage. This study provides recommendations for the future in terms of data security on hardware.
HybRDFSciRec: Hybridized Scientific Document Recommendation Framework

The Internet has a vast collection of information from different domains, including scientific knowledge in books, journals, and conference proceedings. When a user creates the query, these systems typically retrieve documents based on keywords that are irrelevant to the user in most cases. So, there is a need to retrieve the scientific knowledge more based on knowledge. This paper proposes a knowledge-centric scientific document recommendation framework for the recommendation of scientific documents. The recommendation is user query-centric and uses Lin Similarity for term enrichment. The preprocessing is done by Tokenization, Lemmatization, Stop Word Removal, and Named Entity Recognition (NER). Normalized Google Distance and Normalized Pointwise Mutual Information methods are used to compute semantic similarities to achieve ontology alignment. The final solution set is achieved using Flying Fox Algorithm, and the HybRDFSciRec achieves the best-in-class accuracy and high percentage of precision for a wide range of recommendations over the other baseline models, making it an efficient system for recommending scientific documents.
Study of Enhancing Usage of Data Visualization in Cyber Security- Quick, Efficient, and Complete

The main motive of our research is to interpret the enhancement of the way to show the data accurately. To get acquainted with the effort that is put in during the initial stages of implementing data science applications such as data -visualization tools in analyzing the cyber data logs as cyber threats and hacking are being improved by leaps and bounds. In this, to pinpoint the usages of data about data breaches around the globe is also one of our motives. To find the usage of forensic tools after utilizing data visualization tools to the fullest extent. This paper also focuses on the aspects where the enhancement is required in the usage of data visualization. Also, to think of the next steps that are required accordingly. In recent times, as a part of awareness to the present society, developers need to let the threats that a cyber-criminal or an attacker possess be known to every network user for a better understanding of the unreadable language of the cyber logs data. Visualization tools are used to process the analyzing tasks and interpret them.
G-SM3: High-Performance Implementation of GPU-based SM3 Hash Function

Hash is one of the most important algorithms of cryptography, it is widely used in cryptographic primitives, such as digital signature, key exchange and so on. Further, hash cryptography is also the core operation of blockchain technology. With the explosive growth of the number of IoT devices and the rapid development of blockchain technology, the computing performance of hash has received widespread attention. The GPU high-performance computing platforms with a number of arithmetic cores are widely used in cryptographic optimization and acceleration. In this paper, we propose an efficient parallel accelerated framework of SM3 cryptography hash function based on GPU parallel computing devices, short for GPU-based SM3 (G-SM3). Our G-SM3 optimizes the implementation of the hash cryptographic algorithm from three aspects: parallelism, memory access and instructions. On the desktop GPU NVIDIA Titan V, the peak performance of G-SM3 reaches 23 GB/s, which is more than 7.5 times the performance of OpenSSL on a top-level server CPU (E5-2699V3) with 16 cores. On the embedded GPU which consumes less than 40 W, the SM3 throughput reaches 3.8 GB/s, which is even better than the performance of the serverlevel CPU. Based on the same GTX 1080, our performance is 1.12 times that of the fastest known GPU implementation, and the latency is reduced by more than 95%. Compared to other platforms, our G-SM3 has a huge advantage.
Object Detection and Video Analyser for the Visually Impaired

The proposed paradigm in this research can aid visually impaired individuals in developing a sense of their environment. It might be difficult to both see and feel what is going on around you if you have vision impairment of any kind. It’s challenging to move around and carry out tasks on one’s own. Studies estimate that there were more than 285 million visually impaired people in the world in 2012. The majority of the things in and around us share the same shape and size, which makes it much harder for someone who is visually impaired to grasp his surroundings. More than twice as many visually impaired people as the general population suffer from clinical depression. Understanding user accessibility and the kind of output that would work best in this situation proved to be a challenge. The delay has to be taken into consideration because the output had to be in real-time. The user may not benefit much from straightforward object recognition. So, using object recognition, the features have been investigated. A number of changes to different YOLO versions were made in order to develop object recognition. To recognize objects, this paper suggests YOLO V5. Giving a description of the video being fed into the model is the second feature. For this encoder-decoder sequence has been put into use. The Model has been trained and examined using a variety of data sets, all of which have been further described. The next crucial element is distance estimation, which detects things in front of the user and alerts them if they are approaching too closely. In answer to all these demands, the proposed model with three attributes can benefit the blind and be supplied as a solution. The model processes the video or photo input to identify the things seen in it. LSTM (Long Short-Term Memory) and VGG (Visual Geometry Group)16 are used to characterize the video. Triangular similarity and frozen graphs are used to measure distance and inform users if a subject is approaching the camera too closely. The user is provided with an audio description of the video being used as input. Both tools deliver audio as their output.
Image Captioning with CNN and LSTM using Python

Our vision is our most vital sense. Software developers have utilized the capability of vision as they build more interactive, intelligent, and accessible software through images. However, there are scenarios where an image might not be sufficient alone. There might be extra context needed, or alternative text is displayed to circumvent bandwidth restrictions and provide a more accessible experience. In an era where there are simply a huge number of images to be described, the manual description fails at such a scale. Through the help of deep learning, image processing and natural language processing can combine to give way to empower the computer to describe images on its own. This can be presented as a consumable service through the use of a web frontend where users can simply provide the images they wish to be described. This allows anyone to leverage the power of this deep learning approach with relative ease and the use of adapti ve image descriptor functionality via an easy-to-use API while the computationally heavy tasks will be abstracted away. In this paper LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Networks) are discussed.
Stationary Wavelet Transform with Modified Grey Wolf Optimizer for Digital Image Watermarking

Robust digital image watermarking is a data security system which is extremely utilized for resolving many problems mainly concerned with copyright protection and ownership authentication. Robust watermarking conceals a lesser signal termed a "watermark" in a host image from a procedure imperceptible to human vision. The efficacy of conventional robust watermarking is dependent upon the frequency domain connected directly to the outcomes of performance for imperceptibility and robustness. This study designs a Stationary Wavelet Transform with Modified Grey Wolf Optimizer (SWT- MGWO) for Digital Image Watermarking. The presented SWT- MGWO technique aims to accomplish multimedia security via the watermarking process. To attain this, the presented SWT- MGWO technique initially employs the SWT technique for the image decomposition process. To determine the pixel positions for embedding, the MGWO technique was involved in this study. Besides, the SWT-MGWO technique performs a process of embedding and extraction. The performance validation of the SWT-MGWO technique is validated on a set of benchmark test images and the experimental outcomes represent its superiority in terms of different measures.
Adaptive t-distribution Drosophila Algorithm Based on Tent Mapping and Reverse Learning

The Fruit Fly Algorithm (FOA) is a global search algorithm that is easy to implement and has few parameters. Aiming at the problems of traditional fruit fly algorithms, an adaptive Drosophila T-distribution algorithm based on tent mapping and reverse learning (TTOBL-FOA) is proposed. The Drosophila population is initialized by tent chaotic map, which improves the diversity of the population. In the search phase, the Drosophila search step strategy adopts t distribution variation to reduce the algorithm faffing into local optimum In the iterative stage, the elite fruit flies in the reverse learning population generate reverse solutions, retain the individuals with better taste concentration in the population, and improve the search range of the algorithm, thereby enhancing the convergence accuracy. The experimental data of six standard test functions and high-dimensional multimodal test functions show that the TTOBL-FOA algorithm has been optimized in terms of the convergence of the optimal value, the stability of the algorithm and the performance of global optimization.
Visibility Analysis in Fog Weather Based on Convolution Neural Network and Transfer Learning

In this paper, a deep learning model based on convolutional neural network and transfer learning is adopted to realize the recognition and estimation of airport video images, and to evaluate the accuracy of the estimated visibility. In order to highlight the advantages of the transfer learning method, we have established a deep learning model and a machine learning model respectively. In the traditional image recognition method, 11-dimensional information features of the image are recognized, and BP neural network model and evaluation accuracy are established; On the deep learning model, we first match the airport video with the visibility data, and determine the parameters through the pretraining model. Then we randomly divide the image and data into training sets and testing sets at a ratio of 7:3. Fix the pre network parameter training and adjust the deep learning model. After 100 iterations, the accuracy of the training set and test set respectively reached 80.48% and 79.63%. The results show that the deep learning model based on convolutional neural network and transfer learning does have higher efficiency and recognition accuracy.
An Assessment of Object Detection in Thermal (Infrared) Image Processing

Computer vision is used to detect objects in clumps of space. This paper explores the potential of automatically identifying people and their actions in both moving and static images. Then describe a number of issues with human identification and activity recognition in terms of various processing, segmentation, and extraction procedures for human attributes. Additionally, examine the issues with each phase and present the most up-to-date techniques, gaps between them, technological obstacles, applications, and their limitations. Object motion activities are not accurately detected at night, for object detection and classification, shape-based detection, skin color extraction, edge detection, and an object detector are used. Tracking objects using thermal images has been used in a variety of settings, including surveillance systems, spotting pedestrians, spotting wildlife, and more. An evaluation of the most current developments in object detectors like YOLOV7 is planned. This would result in increased detection accuracy and quicker inference speed. To analyze the various approaches to object detection, a future research direction for detecting objects (humans) in thermal video or images was proposed.
An Efficient Text based Classification using Neural Networks and Long Short-Term Memory

For text-based classification tasks, recurrent neural networks (RNNs) with long short-term memory (LSTM) networks are particularly effective. They can process long sequences of data and retain information for long periods, making them effective at capturing the context and dependencies in language. This study presents a novel method for using LSTM networks to obtain efficient text classification. The proposed approach involves preprocessing the text data by tokenizing and encoding the words and then training the LSTM network on the resulting sequences. This study also introduces a novel technique for reducing the size of the network by only considering the most important words in the input sequences, which significantly reduces the number of parameters and the computation required for training. The proposed approach is evaluated based on several benchmark datasets and show that it outperforms traditional methods such as support vector machines and logistic regression. This article also shows how well the proposed approach works in real-time by delivering cutting-edge outcomes on performing sentiment analysis. Overall, this research study demonstrates the usefulness of LSTM networks for text classification and provides a simple and efficient method for applying them to real-world challenges.
ASocTweetPred: Mining and Prediction of Anti-social and Abusive Tweets for Anti-social Behavior Detection Using Selective Preferential Learning

In the present times, owing to a large number of social interactions via Online Social Networking platforms. Because the content is not moderated on the World Wide Web, especially on the Social Web, due to lack of moderation, there are many derogatory, Anti-Social, and racist comments. Specifically, Twitter, YouTube, Instagram, Facebook comments, and similar social network platforms. In this paper, ASocTweetPred for Mining and Prediction of Anti-Social and Abusive Tweets for Anti-Social Behavior Detection has been put forth by encompassing Ontologies of different phases, Knowledge Graph, and the classification of the dataset using the LSTM to generate and yield matching ontology snippets by computing the Semantic Similarity using the Bose Einstein’s Index and the APMI measure. Subsequently, the matched ontology snippets and the formulated Knowledge Graph are used in order to extract features by preferential selection using Shannon’s Entropy and Horn’s Index with differential step deviation measures. A triadic hybrid model of the LSTM, Bagging with Random Forest and SVC, and XGBoost classifiers are made use for Anti-Social Tweets prediction. An overall Precision of 97.93%, Recall of 98.09%, Accuracy of 98.01%, F-Measure of 98.00%, and with the lowest FNR of 0.02 has been achieved by the proposed ASocTweetPred framework.
Performance Analysis of Spatial Domain Image Steganography

Steganography is a technique used to hide data or message which is in any format including normal text or a media file like image, video, audio, inside another similar interactive media file which ensures that the hidden message can’t be read by anyone using physical means. Here, a steganographic technique in the spatial domain is employed to conceal simple text within an image by modifying its pixel values. In general, the most convenient and efficient method is to change the pixel of the image which is done to embed the message in binary format. This process is done by modifying the pixel of the image from the LSB of the pixel thus got its name LSB Substitution. For simpler medical application-based data hiding, the message is more important than the distortion which could be possibly created in the image. Thus, our proposed method modifies more than one bit of the pixel, allowing for a larger amount of data to be embedded within a single image. Additionally, by greatly reducing the number of pixels changed, the proposed method employs edge detection techniques to improve the embedding capacity with a higher peak signal-to-noise ratio (PSNR) and overall system efficiency and convenience.
Video Frame based Prompt Compression Model with Steganography for Secure Data Transmission

Steganography refers to the practise of hiding information in undetectable places. Space and motion are central to video steganography. In this case, information was discretely implanted within the cover image via spatial space calculation, with no discernible alteration to the image’s appearance. Although these kinds of computations are useful for Steganography, their lack of strength is a major drawback. Hidden information is computed by inserting it into the change space during the change space calculation. This method of computation is very steady, but it is also very slow. Stainless is completely useless for these kinds of calculations. This article proposes a cutting-edge steganographic plot for compressed video. Even and vertical parts of the motion vector conceal the information. Steganography in video is one of the most pressing problems with the safety of data transmission in the digital age because of the importance of secret communication. The main purpose of this paper is to ensure the secure transmission of sensitive information while keeping its contents secret. The protection of personal data has always been an important issue, from ancient times to the present. This article proposes a novel method for masking the presence of the message, making it increasingly unlikely that an aggressor will notice it. In order to conceal a video recording within another video, this paper discusses the mathematical principles behind video steganography and how they can be implemented using Patch-aware Code-Arrangement techniques. This method is reliable, produces much higher-quality videos, and can be used for authentication. Compression efficiency is measured with parameters like PSNR, MSE, CR, and BPP to assess how well the method is implemented.
Smart Vision Software Application using Machine Learning

The Smart Vision Application's premise is that there are numerous rising new technologies that are excelling in their fields. The following are a few of the technologies or models that are now in use: estimation of human pose, steering angle capture, lane detection, and object detection. These are all the various approaches and superb models created with Open Pose and other tools. Since each of these systems has unique characteristics, it is vital to separately construct each one before comprehending how it works. Because there is no trial version available for consumers to use to learn how the model works, these models must be constructed using codes creating a web application that will enable students to learn about and experience how each model functions by using the camera on their device. For business professionals who can use their own models to run, deploy, and test, not simply for users. Every module on the list has some connection to autonomous navigation. These systems have been combined into a single Web application so that students may easily experiment with them and see how they work in real-time. As a result, this platform presents excellent opportunities for students and enthusiastic learners to interact with the live demo and understand how each model functions. It is believed that the Web application will serve as an excellent tool for students to experiment with and gain a feel for the operation of the aforementioned computer vision models.
Crypto Analysis with Modified Diffie–Hellman Key Exchange based Sensor Node Security Improvement in Wireless Sensor Networks

Sensor nodes sense ecological situations, for example, strain, temperature, humidity, vibration, sound, and location. Because of the transportable information through the logically showing wireless channel, there is a considerable risk to the data security of wireless sensor networks (WSN). Since WSNs are decentralized across their whole structure, man-in-the-middle attacks, also known as MIM attacks, are more susceptible to becoming successful. This article presents a method for the protection of data that requires less time for both calculation and response. The Diffie–Hellman has been strengthened so that it can better withstand attacks by having it produce a hash of each value sent over the network. This approach proposes Cryptanalysis with Modified Diffie–Hellman key exchange (CMDH) based Sensor node Security improvement in Wireless Sensor Networks. This approach uses a modified version of the Diffie–Hellman algorithm to produce a key; the sender encrypts the data and sends the receiver the information in an encrypted form. Finally, the recipient sensor node decrypted the data. This approach is separated into two parts: generating key and data encryption. Every value is hashed, and the resulting hash is sent via the WSN. This approach is mainly used for military applications in the WSN. The simulation analysis shows that the CMDH method executes better performance.