Reviewerly/exHarmony huggingface dataset:
Raspberry Pi-based Vehicle Starter on Face Detection

This paper gives a real-time approach to starting a vehicle using face detection and image processing using Rasp-berry Pi. In earlier, For various safety purposes, current systems make use of many forms of identification, including iris scanning, passwords, and fingerprints both transmitters and receivers are seldom used in systems to detect and initiate vehicles. The disadvantage of the current solutions is that not everyone will find them useful because few of them need the user to remember the password and even fewer make RF Id cards mandatory. The major flaw in the current system is that technology may even replace and change data at will. Our existing system can recognize the registered vehicle’s owner’s face and helps to start the vehicle. The car will remain off while the buzzer alert will go off if an illegal individual is detected. The authorized individual can add the car’s information to the device via the mobile app. The person’s photos recorded by the web camera attached to the central module are processed to carry out the detecting procedure. This approach is quite effective when implemented with a Raspberry Pi and image processing.
Deep Learning Algorithms for Secure and Efficient Compression of Genomic Sequence Data

The idea that DNA encodes the human body is uncontested. Everything about a person, including behaviour and physical characteristics, is influenced by genetic information. Genomic data is a rich source of information when it is analysed, genomic data has a significant potential for use in disease diagnosis. Despite the fact that One size does not fit all, the medical advice is usually generic. The intelligent machine may provide us with additional human body knowledge on the contrary to build intelligent robots. The ongoing development of genomic data is either beneficial for engineering as well as medical research. Our search for personalised medicine may have a solution in the overlapping fields of engineering and medicine. The introduction of new emergent multimedia applications with an increase in demand and consumption of genetic data, as well as the invasion of digital media into every aspect of diagnosis and prognosis, are compelling new challenges for next-generation sequencing. The processing of genomic data is intended to meet hitheito unrecognised requirements, including compression, robustness, security, unambiguity, and the prediction of future anomalies, in addition to delivering individualised treatment. Deep neural network based deep learning has been intensively researched in recent years as one of the possible methods for overcoming these difficulties. Because digital data must be maintained effectively, with very high quality, and shouldn’t be easily modified by computer standards. The goal is to develop a safe genome compression method that significantly outperforms existing sequencing methods in terms of all performance parameters.
LIED: A Lightweight and Ensemble learning approach for fake face Detection

For many years, machine learning problems have primarily been driven by the availability and quality of data. Being the key, data has been equally vulnerable and got a savior in the form of generative adversarial networks (GANs) which opened the floodgates for generating almost any type of real, yet synthetic data. Human face became one of the initial victims to this superior technology, where in highly realistic and convincing fake content is generated using deep learning technologies or ‘‘DeepFakes’’. Taking a giant leap forward from manipulating facial attributes, to be now able to swap expressions seamlessly and even generate new (non-existent) synthetic faces poses a grave threat not only to chosen few, but for the entire society. This upshoot has been reciprocated with significant efforts and investments for its detection, but the techniques are often marred with either lower accuracies, or, higher computation costs. This is where convolutional reservoir networks (CoRN) come to rescue owing to their lightweight nature, able to do ensemble feature extraction and its generalization ability. This paper investigates, implements and demonstrates the application of CoRN based architectures to the task of human fake face detection. The steep performance improvements as evident from our results further ratify the effectiveness of this approach, which is also shown to perform exceedingly well against smaller datasets.
Smart Self-Checkout System for Supermarkets

This paper deals with the implementation of selfcheckout software application, which is a 100% digital approach to offline shopping and aims to reduce the overall time required for shopping. This approach is implemented via bar code technology, real-time database and payment gateway. This software application aims to reduce time spent in shopping along with providing better security, self-pickup option and new wish list concept. This application skims through the barcodes of the products already present on each and every product and the user can exclusively select multiple products, finally bill will be generated which will be displayed to the user along with various payment options. The study concludes that the overall time for the basic work of super markets is minimized.
FPGA Based on Voting System along with Cross Voters Detection

This article presents the FPGA Based on Voting System along with Cross Voters Detection using Python. The proposed system consists of three modules, namely the Vote Monitoring Module (VMM), Image Acquisition Module (IAM) and Image Detection Module, for casting and knowing the result of the election, image capturing purpose and Detecting the cross voters using facial recognition respectively. The Voting Unit (VMM) unit is implemented on FPGA(Zed board- ZYNQ 7000) using Verilog in such a way that consists three modules namely Button control module, Vote logger module and mode control. Button control module is used for casting the vote and knowing the result of each candidate. Mode control is used to choose the mode whether it is in voting mode or result mode and vote logger module is used to store the votes of each candidate separately. The Image acquisition module is used to detect the motion of the voter capture his face and vote casting image and that it sends to dedicated server for backup purpose and cross checking purpose. Simultaneously Image Detection Module (IDM) unit is used to check the cross voter images if it finds any cross voter face those will be removed and respective votes will treat as invalid votes. Those invalid votes will remove from genuine votes.
Enhancing the Security of Online Voting System Using Defined Biometrics

Voting is everyone’s right and one must vote according to his or her choice. Manual voting is not so secure and sometimes malpractices take place. So, there is a requirement for an online voting system. The manual voting system needs to shift to the online voting system. This project mainly aims to provide a secure and user-friendly online voting system. Aadhaar cards, fingerprints, and two-step verification are used in this web-based online voting system to provide high performance and high security to the voting system. Enough security is provided by using a biometric fingerprint, which lowers the number of fake votes. Python, HTML, self-made datasets and JavaScript are being used.
Efficient NTRU Lattice-Based Certificateless Signature Scheme for Secure Mobile Payment Systems

The digitization of payments has greatly contributed to the development of a convenient, fast, and se-cure payment method. Since mobile payments are becoming increasingly vulnerable to security threats, completing the payment process effectively and securely has emerged as an urgent issue that must be addressed. For a resourceconstrained mobile devices, the type of cryptography methods used in the protocol is critical. A number of digital signature schemes have been proposed to address these issues in recent years. However, the security of existing signatures is primarily based on traditional difficult mathematical problems, which are vulnerable to quantum computing attacks; and the availability of practical quantum computers is closer than previously thought. The proposed payment protocol makes the transaction process transparent, trustwoithy, and maintains user anonymity, resulting in secure payments. Through the security analysis, the proposed protocol proved to be secure under Small lnteger Solution(SlS) problem. Based on performance evaluation, the proposed scheme proves to be feasible and efficient for resource-limited smart devices.
Comparing the Use of Short Video Sharing Applications for Optimizing User Engagement

Since the COVID-19 Pandemic outbreak and the accompanying lockdown, the short film business has seen an unparalleled transformation. Young people have a platform thanks to the software to express themselves through lip-syncing, dancing, humour, and singing. Users can also create movies and upload them to their social networks. Get access to a brand-new, sizzling stage where you may wow the world with your fiery ability while watching never-ending fascinating videos. Every day, people face problems while discovering new and fresh skills. On the app, anyone from anywhere in the world can meet intriguing individuals and enjoy amusing short movies. One of the technologies that has fundamentally altered the social networking industry is artificial intelligence (AI) [1]. Artificial intelligence is the ability of computers and other machines to learn, comprehend, and make judgments similarly to humans (AI) [2]. The programme essentially relies on AI and ML technology in two ways. Both from the producer’s and consumer’s perspectives. This study examines how SpotLight continuously enhances user engagement through the usage of AI and ML.
iAlert: An Alert System based on Eye Gaze for Human Assistance

People in this fast-growing world knowingly or unknowingly will suffer from various kinds of accidents. It might be severe or moderate. In severe cases, some patients may not be able to move their limbs. This work provides an assistive system for patients who cannot move from one place to another and who have injuries to their limbs but are not limited to cases of accidents. In this scenario, the only means for the patient to communicate is through eye movement or blinking. Eye tracking uses numerous technological procedures to measure eye movements, locations, and points of gaze. The primary goal of this research is to establish a human-computer interface for patients by tracking eye movement with an open-source webcam. In case of any need for the patient, an alarm system is designed to notify or alert the caretaker. When the patient wants to communicate, patient can make eye contact with the provided screen and then the caretaker will be notified. This low-cost eye tracking and eye gaze technology can be integrated into mobile phones and laptop, computers. The system shows good accuracy in finding out the need by the patient and alerting the caretaker through a mail.
Secure Splitting of Bills and Managing Expenditure Using Blockchain

After its introduction in Web 3.0, blockchain has become the dominant technology for securely transferring data through decentralized peer-to-peer systems. Blockchain is an unchangeable ledger that enables safe, decentralized transaction execution. This paper proposes a system for dividing group expenses and managing expenses that employs blockchain technology for data security. In this project, we will integrate Blockchain technology with a web client so that users can easily access the application. This application will save all of the user’s transaction data in a secure manner, ensuring that the data is not tampered by anyone. Also, options for dividing the bill equally and unequally are also available. This application will also keep a track of user’s expenses, this feature will help them to manage their expenses accordingly.
A Profound Method for Three-Tier Toxic Word Classification using LSTM-RNN

Text classification is the activity of labeling natural language texts with relevant categories from a predefined set. In the research work, The words are categorized from a sentence according to their toxicity and according to modification will be done. As Online platforms such as social media provide opportunities to users to express their thoughts and their opinions on various topics and incidents. Due to the different opinion of every individual, an explicit language in some cases which hurt the readers. So, to solve a novel method is proposed by building a model wherein toxic words are categorized into three classes in keeping with their toxicity. In the primary class, words that are least toxic are indicated through crimson traces to the person, withinside the secondary class words are extra poisonous than the primary one, and again and again. The use of those phrases through individual, that words will be eliminated automatically and withinside the remaining or third class, words are the maximum toxic in assessment than that of the first and second class, through the use of those phrases’ person account could be deleted/blocked. As a punishment, the account will be blocked for a specific time period and the length could be improved as in increasing of the occurrences of those words.
Binary Optimization Using Hybrid Owl Optimization For Biomarker Selection From Cancer Datasets

The enhancement of medical technology generates a massive amount of disease data. For accurate disease detection, feature subset selection plays an important role in solving different classification problems. The selection of a good feature subset enhances the accuracy of the machine learning model and reduces the training time. However, it becomes tricky and challenging to identify the optimal feature subset from the high-dimensional datasets. Different hybrid models are proposed by various researchers to deal with these types of issues. The Owl optimization algorithm proves its efficiency in selecting the optimal features. Keeping an eye on the performance of this stochastic optimization, a binary version of the hybrid Owl optimization algorithm with simulating annealing is proposed to detect the biomarker features from the cancer datasets. K nearest neighbors (KNN) are used as a wrapper to find the best biomarkers. Eight benchmark datasets were employed for the proposed model’s performance evaluation. The results show that the proposed model is significantly better than binary Owl optimization, and other counterparts using various performance matrices such as accuracy, selection of best biomarkers, and computational time. The proposed model Owl-SA performs with an accuracy of 100% in the case of CNS and lymphoma datasets. And it retains the accuracy of 99.14%, 97.16%, 98.41%, 97.94%, 98.93%,98.32% for breast, colon, ALL-AML, ovarian, SRBCT, and MLL respectively.
Banking and financial-services contribute Country Cities combat COVID-19 pandemic Block Chain technology and its application

Block chain is a technology that is used to save the record of all information related to crypto currency transactions in a public ledger. Digital currency transactions can be done without any third party. When a coin is transferred by a computer, its information reaches all the computers connected to the network, and the transaction is verified. After this, the transaction data is secured by encoding it in blocks using cryptography technology. Similarly, many types of blocks together form the Public Ledger, which is called Block chain. During this hackathon, the winners will also be awarded up to three lakh rupees. Block chain has been the topic of discussion all over the world in recent times. Ever since the crypto currency era began, block chain has come into the limelight more and more, because crypto currency works on block chain technology itself.
FARMSUPPLY: Food Supply Chain Management using Blockchain Technology

The Distributed Ledger Technology (DLT) that underpins various crypto currencies may have a profound impact on the global economy. The Supply Chain Management (SCM) is one of the areas where the DLT is currently being used. By using the Distributed Ledger Technology, blockchain has the potential to vastly enhance different supply chains by facilitating faster and more cost-effective delivery of products. The main benefits of using blockchain in SCM is that it provides an enhancement technique for products traceability, improves coordination among the producer, manufacturer, distributor, retailer and consumer. Also, facilities easier access for funding. In the recent years, the Food Supply Chain (FSC) has been using the blockchain to utilize the benefits of SCM and provide better results for the Food Supply Chain. In this paper, a model is proposed, FARMSUPPLY, which uses blockchain in a Food Supply Chain to provide a better view of farmers, products and retailers. In this model, a Ethereum blockchain method and smart contract for the verification and validation of various attributes at each stage of the food supply chain is presented.
Multi-Featured Cluster based Keyframe Extraction for Efficient Video Processing and Management

The amount of video content currently available in daily life has expanded enormously due to recent advancements in video capturing, storage, and distribution technologies. For efficient video processing, keyframe extraction is one of the main tasks to manage this voluminous video data. Various keyframe extraction techniques are used for video processing and management. However, clustering-based keyframe extraction techniques are the most popular for video content analysis due to their effectiveness, high robustness, and good feasibility. Many video summarization methods have utilized clustering-based keyframe extraction techniques; however, most of the existing techniques use a single feature to build the cluster such as histogram, color, shape, and many more. But clustering using more than one feature performs better than the one feature because a single feature may not distinguish between data points which can be resolved by incorporating multiple features. Thus, this paper proposes a Multi-Featured Cluster based Keyframe Extraction Method (MFCKEM) which uses color and shape features to build clusters. The K-means clustering algorithm is used to make clusters. However, the algorithm’s performance depends upon the k value and fixing the k value is challenging. For deciding the k value, a cluster validation approach named silhouette coefficient (SC) is used to decide the optimal number of clusters or k value. The performance of the proposed MFCKEM is evaluated using precision, recall, and F-measure. Moreover, the extracted keyframes are compared with the ground truth values provided in the dataset used for this experiment. The proposed model achieved precision, recall, and F-measure of 54.74%, 71.2%, and 59.09%, respectively, which are comparatively better than the existing keyframe extraction method that uses only color features.
Investigating Vision Transformers for Bridging Domain Gap in Satellite Pose Estimation

Autonomous onboard estimation of the pose from a two dimensional image is a key technology for many space missions requiring an active chaser to service an uncooperative target. While neural networks have shown superior performances with respect to classical image processing algorithms, their adoption is still facing two main limitations: poor robustness on real pictures when trained on synthetic images and scarce accuracy-latency trade off. Recently, vision Transformers have emerged as a promising approach to domain shift in computer vision owing to their ability to model long range dependencies. In this work we therefore provide a study on vision Transformers as a solution to bridging domain gap in the framework of satellite pose estimation. We first present an algorithm leveraging Swin Transformers and adversarial domain adaptation which achieved the fourth and fifth places at the 2021 edition of the ESA’s Satellite Pose Estimation Competition, challenging researchers to develop solutions capable of bridging domain gap. We provide a summary of the main steps we followed showing how larger models and data augmentations contributed to the final accuracy. We then illustrate the results of a subsequent development which tackles the limitations of our first solution, proposing a lightweight variant of our algorithm, not requiring access to test images. Our results show that vision Transformers can be a suitable tool for bridging domain gap in satellite pose estimation, although with limited scaling capabilities.
Explainable AI with the Information Bottleneck Principle

Deep Learning (DL) has proven to be a very powerful technique. Meanwhile, DL achievements have not been match by theoretical understanding. Without firm basis, the design of deep neural networks (DNNs) overlooks profound learning dynamics and remains an empirical process. Developing tools for in-depth understanding could instead speed up design, boost final performance and assure reliability. The information bottleneck principle (IB (Shwartz-Ziv and Tishby in Opening the black box of deep neural networks via information (2017) [1])) is the first framework that offers an explanation of the information flow inside a network. Being a new and unfamiliar framework its results are still actively debated (Geiger in On information plane analyses of neural network classifiers—a review (2020) [2]), but within the IB framework new dynamics have been discovered and performance upper bounds have been set. With a pragmatic eye, inside the IB theory may be the tool we are searching for: the Information Plane, that quantifies how much each part layer knows about the input as a whole and about the desired output. The IB limit is the computational complexity of its main component: mutual information (MI). In this work we adapted the Mutual Information Neural Estimator (MINE (Belghazi in Mutual information neural estimation, pp. 531–540 (2018) [3]) to reproduce the original results on a 12bit dataset and on the smallest prototype of a real-world application (MNIST). The considerable jump in input size has not affected the IB dynamics: the same IB patterns have been observed in the scaled up networks. Hints of new dynamics with train/validation distinction have been observed. This work is a first step towards testing the IB theory on the largest scales and making information plane computation feasible on real-world training applications.
Computational Analysis of a Real-World Scenario of Organisational Learning for a Project Management Organisation

This chapter describes how the recently developed self-modeling networkSelf-modeling network modeling approach for multilevel organisational learningMultilevel organisational learning has been tested on applicabilityApplicability for a real-world case of a project-based organisationProject-based organisation. The modeling approach was able to successfully address this complex case by designing a third-order adaptive network modelAdaptive network models. Doing this, as a form of further innovation three new features have been added to the modeling approach: recombination of selected high-quality mental modelMental model parts, refinement of mental modelMental model parts, and distinction between context-sensitiveContext-sensitive detailed controlControl and global controlControl.