Reviewerly/exHarmony huggingface dataset:
A Secure Platform for Storing, Generating and Verifying Degree Certificates using Blockchain

Humans deal with many document generation and verification processes in day–to–day life, such as academic certificates, land registries, vehicle registration, medical records, etc. Academic certificates play an essential role in graduates' lives, as it is the proof of completing a required educational qualification for applying jobs or higher education. The current era of technology is rapidly evolving every day, and as a result, the generation of fake certificates becomes easier. So the utmost priority is given to preserving these certificates and making them tamper-proof. There are various methods to secure these certificates. One such method involves a decentralized storage system which uses blockchain technology to generate and store the certificates. The Universities will add the student details on to the blockchain which generates the unique certification ID and transaction hash which cannot be easily tampered. Later the organization can verify the candidate who is seeking the job using these details. Hence blockchain technology can be used to to secure and standardize a digital certificate format, in which institutions and organizations can benefit by making the verification process faster and easier by eliminating the fake certificates.
Course Recommendation System using Content-based Filtering

The E-learning infrastructure is growing rapidly, choosing the right skills set to built a career in an area of interest sometimes can be mystifying and hence a recommendation system is helpful to narrow down the information or choices based on user's data or preferences. A recommender system automates the process of filtering and make it feasible for a user to search through vast information available online and thus provide a personalized experience for the user. This study has attempted to implement a recommender system based on content based filtering and Machine Learning algorithm to filter skills and courses available digitally based on user's input information.
A Deep Learning Based Model for Defect Prediction in Intra-Project Software

Testing of software ensures the supply of meaningful software and hence prediction of defects in producing high quality software has become an inevitable one. Software defect prediction's main aim is to find out various bugs present in software and focus on testing efforts. Many of existing software defect prediction frameworks are much simple, making it difficult for developers to get detailed reference information. Nowadays, many deep learning models, like the Radial Base Functional Neural Network(RBF) and the Convolutional Neural Network (CNN), are applied to features which are created automatically from deep learning models and abstract syntax trees (AS Ts) to aid in the improved performance of predicting defects. But the results generated using RBF and CNN algorithms are not able to provide much accuracy due to its restricted size of dataset and improper baseline selections. To resolve these state-of-the-art problems, we have constructed a dataset taken from various defect datasets namely the Kamei Dataset, NASA Dataset and the PROMISE Source Code (PSC) dataset. In this research, the dataset is named as Combination Defect Analysis Dataset (CDA). Then, an Enhanced Convolutional Neural Network (ECNN) model is proposed for predicting defects in Intra-Project software (IPDP) and associated results to different models. Experimental results implied that Enhanced CNN(ECNN) model is efficient compared to the other associated models, along with it outclassing the other machine learning models suggested for IPDP.
Pedestrian and Object Detection using Image Processing by YOLOv3 and YOLOv2

Detecting instances of semantic objects of a specific class, such humans and other things, in digital photos and videos is the goal of object detection, a branch of computer vision and image processing. It is frequently used for activities including picture annotation, vehicle counting, and object tracking, such as tracing a ball during a football game, a cricket bat's movement, or primarily a person in a movie. In this study OpenCV is used with YOLOv3 neural network to detect pedestrians and objects from an input video or a real time webcam. In order to determine how accurately the pedestrians and objects are recognized using the You Only Look Once (YOLO) algorithm, a box is produced along its boundaries with its name and Intersection Over Union (IOU) value, which is determined using the formula area of Intersection/Area of Union. The pre-trained model and the weights of the COCO dataset of YOLOv3 tiny algorithm are used for the detection and compared with YOLOv2 algorithm.
Autonomous Object Detection and Counting using Edge Detection and Image Processing Algorithms

Machine vision applications are commonly utilised in manufacturing lines as low cost, high precision measuring devices. Output facilities can accomplish high production numbers without mistakes thanks to these solutions that offer contactless control and measurement. A camera may be used to carry out machine vision tasks including product counting., error checking., and dimension measuring. This study makes a recommendation for a vision system application that can do inanimate object item enumeration. The recommended solution uses Otsu thresholding., Hough transformations., edge detection methods., and other image processing algorithms to accomplish automatic counting without taking into account the kind or colour of the product. The system primarily uses one camera. The general idea is to get image with balanced contrast., brightness and appropriate HSV values in it. A picture of the items being captured using camera using android device., and different image processing techniques are then applied to the picture. Further., a real-time machine vision programme was deployed and took photos taken from an actual experimental setup. The practical experiments conducted have shown that the suggested technique yields quick., precise., and trustworthy results based on the comparative study of various detection techniques.
Task Scheduling in cloud using Heuristic Technique

One of the most prevalent technologies today is cloud computing. It provides a lot of opportunities for studying in a number of fields, including allocating resources, job scheduling, protection and secrecy among others, etc. Under this era, cloud technology also became significant because of virtual machines and IT services. Scheduling is one of the most meaningful and reasonable factor to enhance the effectiveness in every cloud related service. Task scheduling in cloud computation is utilized for assigning tasks to the available resources best suited to perform the tasks. Multiple work scheduler strategies are available for scheduling the various tasks. During job scheduling, several factors including processing time, processing costs, waiting time and various complexities are being taken into consideration. The Cloud framework is normally made from a few server farms and shoppers' approach just a cut of the computational control over an adaptable organization. Different asset difficulties, designation are needs to fulfilling client's or application necessities This study has proposed a modified cooperative asset assignment calculation is proposed to fulfil client requests by decreasing the holding up time.
Attacks on Continuous Chaos Communication and Remedies for Resource Limited Devices

The Global Wearable market is anticipated to rise at a considerable rate in the next coming years and communication is a fundamental block in any wearable device. In communication, encryption methods are being used with the aid of microcontrollers or software implementations, which are power-consuming and incorporate complex hardware implementation. Internet of Things (IoT) devices are considered as resource-constrained devices that are expected to operate with low computational power and resource utilization criteria. At the same time, recent research has shown that IoT devices are highly vulnerable to emerging security threats, which elevates the need for low-power and small-size hardware-based security countermeasures. Chaotic encryption is a method of data encryption that utilizes chaotic systems and non-linear dynamics to generate secure encryption keys. It aims to provide high-level security by creating encryption keys that are sensitive to initial conditions and difficult to predict, making it challenging for unauthorized parties to intercept and decode encrypted data. Since the discovery of chaotic equations, there have been various encryption applications associated with them. In this paper, we comprehensively analyze the physical and encryption attacks on continuous chaotic systems in resource-constrained devices and their potential remedies. To this aim, we introduce different categories of attacks of chaotic encryption. Our experiments focus on chaotic equations implemented using Chua’s equation and leverages circuit architectures and provide simulations proof of remedies for different attacks. These remedies are provided to block the attackers from stealing users’ information (e.g., a pulse message) with negligible cost to the power and area of the design.
Image Quantization Tradeoffs in a YOLO-based FPGA Accelerator Framework

Until recently, FPGA-based acceleration of convolutional neural networks (CNNs) has remained an open-ended research problem. Herein, we evaluate one new method for rapidly implementing CNNs using industry-standard frameworks within Xilinx UltraScale+ FPGA devices. Within this workflow, referred to as Framework for Accelerating YOLO-Based ML on Edge-devices (FAYME), a TensorFlow model of the You Only Look Once version 4 (YOLOv4) object detection algorithm is realized using Xilinx’s Vitis AI toolchain. We test various levels of model bit-quantization and evaluate performance while simultaneously analyzing the utilization of available memory and processing elements. We also implement a ResNet-50 model to provide additional comparisons. In this paper, we present our YOLO model, which achieves a mAP of 0.581, and our ResNet model, which achieves a Top-5 accuracy of 0.950. Furthermore, we demonstrate that these results are possible while utilizing less than 25% of the throughput offered by a single hardware accelerator in an UltraScale+ FPGA.
An Optical XNOR-Bitcount Based Accelerator for Efficient Inference of Binary Neural Networks

Binary Neural Networks (BNNs) are increasingly preferred over full-precision Convolutional Neural Networks (CNNs) to reduce the memory and computational requirements of inference processing with minimal accuracy drop. BNNs convert CNN model parameters to 1-bit precision, allowing inference of BNNs to be processed with simple XNOR and bitcount operations. This makes BNNs amenable to hardware acceleration. Several photonic integrated circuits (PICs) based BNN accelerators have been proposed. Although these accelerators provide remarkably higher throughput and energy efficiency than their electronic counterparts, the utilized XNOR and bitcount circuits in these accelerators need to be further enhanced to improve their area, energy efficiency, and throughput. This paper aims to fulfill this need. For that, we invent a single-MRR-based optical XNOR gate (OXG). Moreover, we present a novel design of bitcount circuit which we refer to as Photo-Charge Accumulator (PCA). We employ multiple OXGs in a cascaded manner using dense wavelength division multiplexing (DWDM) and connect them to the PCA, to forge a novel Optical XNOR-Bitcount based Binary Neural Network Accelerator (OXBNN). Our evaluation for the inference of four modern BNNs indicates that OXBNN provides improvements of up to 62× and 7.6× in frames-persecond (FPS) and FPS/W (energy efficiency), respectively, on geometric mean over two PIC-based BNN accelerators from prior work. We developed a transaction-level, event-driven pythonbased simulator for evaluation of accelerators (https://github.com/uky-UCAT/B_ONN_SIM).
Design Space Exploration of Modular Multipliers for ASIC FHE accelerators

Fully homomorphic encryption (FHE) promises data protection by computation on encrypted data, but demands resource-intensive computation. The most fundamental resource of FHE is modular multiplier, which needs to be evaluated for efficient implementation. In this work, we develop and evaluate ASIC implementations of the modular multiplier at the block-level and at the system-level. We study the efficiency of the multipliers in terms of performance-for-area and performance-for-power. Since these ASICs are used in FHE, we explore these multipliers within this system-level context with on-chip memory and interconnect limits. We explore ASIC implementations of modular multiplications using a state-of-the-art 22nm technology node with constant operand throughput to ensure a fair comparison. The study yields key insights about the performance-for-area efficiency and power efficiency of bit-serial and bit-parallel designs: Bit-parallel designs are more efficient than their bitserial counterparts. Montgomery multipliers with constrained modulus are the most power-efficient and area-efficient design. Iterative Montgomery multipliers incur minimum peak power for a polynomial multiplication, making them suitable for low-power voltage sources.
A Classifiers Experimentation with Quantum Machine Learning

Quantum Computing is a new way to create a different type of computer system from Quantum Mechanics. Quantum machine learning is one of the applications took the benefit from qauntum computers. In this experiment, we use the IBM Qiskit library to study how well a quantum machine learning classifier works. We made two different colored dots to represent the two-class dataset. The quantum circuit can be collected from the Qiskit library. The 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> order feature map schemes operated by oracle quantum oracle functions are applied before making a classification. Finally, we can call the quantum support vector machine or QSVM function to classify data. Kernel matrix from the studied qauntum model shows that quantum computing can be used to clearly classify classes of the dataset with 100 percent accuracy.
Machine Learning based Intelligent Career Counselling Chatbot (ICCC)

The Intelligent Career Counselling Chatbot (ICCC) is proposed in this paper that provides career guidance to 10th and 12th standard students for clearing their doubts and for learning purpose. It is also helpful for choosing a career for higher studies. A special module has been developed for CSE and IT domain of BTech students to provide help in their studies and guidance for choosing career for their higher studies. The user is prompted by the chatbot with a variety of questions, to which the user must respond with 1 for true and 0 for false. Through these questions, the chatbot defines a potential future event or result based on the user’s input and displays it on the GUI. Apart from career counselling, it also communicates with users. The result developed for this chatbot consists of four major branches that are training chatbot, adding functionalities like adaptors and other modules, machine learning algorithms for predictions, and a server for GUI-based chatbot. The chatbot can be used for counselling different students to help them finding their interests and to choose their careers accordingly.
Classification of Tweets Using Natural Language Processing from Twitter API Data

With coarse, diverse, and likely unfiltered general knowledge and the unrealistic state of manually tagging a significant number of tweets using coaching classifiers, job sentiment analysis classification Obtaining information for coaching children becomes a major challenge. In this article, we present a solution to axiomatically retrieve labeled, filtered, large-scale coaching job information from Twitter that can be used as input to a support vector machine classifier. There are various implements and methods for performing sentiment detection and analysis on the side of supervised machine learning algorithms. This algorithm performs categorization on a prey collection instructed on coaching job information. Here, we utilized support vector machines (SVMs) for sentiment analysis on Weka. SVM is a well-known supervised machine-learning technique for detecting ground polarity.
Methodical Tamil Character Recognition Using Fabricated CNN Model

Recognizing text is one of the most challenging problems in computer vision. To put it simply, handwritten character recognition is the process of recognising text in photographs, papers, and other media and converting it into a form that a computer can read. You can solve this issue by employing the image classification method. We will be able to recognise the user’s typed Tamil characters in this paper. We designed a brand new model, MVGG16, to handle character recognition and classification. Its basic structure is analogous to that of VGG16, but we added a few more layers to improve accuracy. So far, 156 Tamil characters have been learnt, with 350 images each class. In experimental settings, this model achieves an accuracy of 91.8%. As an input method, this work makes use of a whiteboard where the user can scribble the Tamil character to be recognised. The character will be sent for verification after the user has written it and the image has been exported. Once the model identifies the character, it will show it to the user.
Enhancing the Security for Healthcare Data using Blockchain Technology

Cyber security is the safest way to protect the data from hackers and unauthorized users. Healthcare technologies nowadays face lots of cyber security issues related with the security of the health information and privacy of the data. Cyber security is one of the popularized ways to protect the data from hackers and spammers. HealthCare is the field where the data is highly sensitive and the security for the systems is low. Protecting the sensitive data is achieved by the blockchain method, which is similar to a database but the difference is the data stored in the blockchain in blocks. The new blocks included are connected to previous blocks from a chain like structure, it is very secure, each block stores data and also the hash of previous blocks. So, data cannot be easily accessed or manipulated. The mechanism used in blockchain for the security of the data consensus mechanism which contains the different methodologies includes Proof of Work (PoW), Proof of Stake (PoS), Proof of Space and Proof of Authority. Enhanced proof of stake is a combination of the PoS and DPoS used to increase the security of the system by eliminating the 51% attack in blockchain and reduces the data theft threats and protects the medical records of patients from hackers.
Traffic Sign Detection Using Hybrid Network Of Yolo And Resnet

Deep learning (DL) methods have outperformed machine learning (ML) and statistical techniques in predicting road traffic. Neural networks serve as the foundation for deep learning algorithms. In smart transport systems, the recognition and identification of traffic signs is a critical issue. Automated traffic sign detection and recognition Driving is essential for achieving autonomous transportation.. There are numerous traffic signs, and it takes time to train a good model. A TSR system can raise a driver’s awareness of the road situation and condition, potentially lowering traffic accidents. The YOLOv3 (You Only Look Once, Version 3) algorithm, a real-time object identification technique with cutting-edge features that detects individual items approach for detecting traffic flow, is what we suggest as a solution to this problem. The YOLO algorithm uses features that have been learned to find objects. For more accuracy we are combining two algorithms to make the prediction more efficient and accurate. ResNet is used to extract features from the training data set and the classification is done by YOLO V3 algorithm. Versions 1-3 of the YOLO machine learning algorithm were produced by the third version of the methodology, which is an improved version of the original DL algorithm. Before classification and the output of the optimised result, the image will undergo pre-processing to extract features. The proposed method makes it easier for drivers to detect traffic signs and reduces the number of accidents on the road. Experiment results show that the YOLO-v3 approach outperforms other previously existing algorithms in terms of average accuracy
Cross Site Scripting (XSS) vulnerability detection using Machine Learning and Statistical Analysis

In our current technological development, usage of social networking, e-commerce, media, and management, web application plays a very indispensable role on the Internet. organizations use web applications to reach information to the public, e-commerce sites like Amazon and Flipkart use web applications to sell their products, and social networking sites like Facebook and Instagram use web applications. Many other services are provided on the web. Every mobile application will have its equivalent web application. Web Application Security plays a very vital role around the world. Cross Site Scripting (XSS) attacks are by far the most common and widely used method for stealing data from web applications. This paper discusses the XSS vulnerability detection using different deep learning and machine learning models. XSS attacks are a common type of web-based attack in which malicious code is injected into a website or web application, allowing attackers to steal sensitive information or perform other malicious actions. To ensure web-based systems’ security, XSS attack detection and prevention are essential. If the attacker successfully executes the XSS script, then the website will be compromised, and the attacker can steal sensitive data. The Open Web Application Security Project (OWASP) has listed XSS attacks as a top three risk to web applications. This research paper proposes a novel approach for detecting XSS attacks using different models. Deep learning algorithms such as Long Short Term Memory (LSTM), Convolution Neural Networks (CNN) and boosting algorithms such as AdaBoost and Gradient Boosting algorithms, and classification algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), Naive Bayes (NB), and Decision Tree (DT) algorithm for the detection of XSS attacks. To evaluate the effectiveness of our approach, we conducted experiments on a dataset of real-world XSS attacks and non-attack web requests. Our experiments showed that our machine-learning model was able to accurately identify XSS attacks with a high degree of accuracy, outperforming several baseline approaches. Overall, our research demonstrates the potential for using machine learning to detect XSS attacks effectively.