Reviewerly/exHarmony huggingface dataset:
Category-aware Graph Neural Network for Session-based Recommendation

Session-based recommendation (SBR) aims to predict which item will be clicked next for the current session. Many studies have shown the advantages of utilizing graph neural networks (GNN) to learning complicated item relations in session-based recommendations. Most existing methods only utilize the item IDs to model the sequential characteristics for the current session while ignoring the inherent features of items, such as category. In some sessions, all items belong to the same category while in other sessions each item belongs to a different category. It is nontrivial to capture the inter-category and intra-category item relations effectively under this circumstance. In light of this insight, we propose Category-Aware Graph Neural Network (CAGNN) for session-based recommendation. We first construct two graphs to learn inter-category and intra-category item relations over all sessions. Then we fuse the item representations in the same category and get category representations and apply graph attention network to it to learn both item and category relations. Finally, we generate representations for each session and predict the next click. Extensive experiments on two real-world datasets show that our CAGNN model is better than most state-of-the-art baselines.
Real Time Object Distance and Dimension Measurement using Deep Learning and OpenCV

Deep learning is a subset of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher-level features from data. Deep learning is focused on improving the AI process of having machines learning new things on its own. In this paper, proposed real time object dimension detection and dimension analysis using python. For ML and AI-based papers, python programming language offers consistency, simplicity, and access to excellent libraries and frameworks. It also offers platform freedom, flexibility, and a large developer community. Proposed work, explorations were done on some methodologies, including You Only Look Once (YOLO), for object identification that is meant for speed and real-time application use, and Region-Based Convolutional Neural Networks (R-CNNs) built for model performance and analysis. To make it better, a canny edge detection algorithm is being used. Canny edge detector is a multistage algorithm-based on edge detection operator that can identify a variety of edges in image.
Traffic Prediction with Network Slicing in 5G: A Survey

In modern communication systems there are heterogeneous service request from the applications like mobile devices, virtual reality, automatic driving cars, IoT devices. These devices have different QoS requirements in which network slicing enabler plays a vital role in 5G. Network Slicing unfolds a new paradigm for the providers as well as for the users. In this context the resource management has gained importance in the field of networking. Since a huge data is been generated by these devices, it is very difficult to deliver high performance with resource utilization. In such situation these traditional monitoring techniques will not be able to handle such a huge data. Towards this, the researchers have started applying with Deep learning techniques with the network monitoring system. This paper focuses on the work done towards one of the key components of network analysis (i.e.) traffic prediction. This study has reviewed the articles, which have proposed the deep learning techniques for traffic prediction towards resource management in network slicing.*CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract. (Abstract)
Medical Image Encryption using Enhanced Rivest Shamir Adleman Algorithm

Medical photographs are considered sensitive and crucial data in medical informatics systems. To communicate medical photos via an insecure network, a trustworthy encryption solution must be created. Encryption is the most effective way for ensuring image secrecy since it eliminates the risk of data loss. The two types of encryptions are symmetric encryption, which uses a single key for both encryption and decryption and gives just confidentiality, making it less safe, and asymmetric encryption, which uses two keys for encryption and decryption and provides confidentiality, non-repudiation, and authentication, making it more secure. It is now simpler than ever for complete strangers to access the most sensitive information recorded on your computer due to hacking activities and privacy intrusions. As a result, asymmetric encryption outperforms symmetric encryption. Despite the fact that there are several security alternatives accessible, such as free anti-malware software for home users and cloud anti-virus for organizations, these attempts to secure data Some large corporations and government agencies already use encryption software to secure data, but it is also available and becoming more readily available to the general public. The RSA algorithm is an asymmetric encryption technique that safeguards the picture before transferring it. It is one of the most extensively used encryption tools.
Malayalam Handwritten Character Recognition Using Transfer Learning

A novel Deep Convolutional Neural Network (DCNN) model is proposed for handwritten Malayalam character recognition using Transfer Learning in this work. Popular Transfer Learning models such as Inception-V4, AlexNet, DenseNet, and VGG are used as a feature extractors. The implementation of popular models like AlexNet, DenseNet-121, DenseNet-201, VGG-11, VGG-16, VGG-19 and Inception-v4 was done with 15 epochs. 99% accuracy was achieved by Inception-V4 with an average epoch time of 16.3 minutes. At the same time, 98% accuracy was achieved by AlexNet with an average training time of 2.2 minutes per epoch, which shows that Inception-V4 performs well. Inception framework that has demonstrated excellent performance at a low computational cost. In this paper, we used residual connections within a traditional Inception architecture, which resulted in state-of-the-art learning performance with the highest accuracy of 99.69% and an average epoch time of 15.1 minutes.
Smart Payment Fraud Detection using QML – A Major Challenge

Digital fraud is a major problem that influences the financial sector and the Indian economy. There has been an increase in financial losses in recent years due to various kinds of smart frauds like unauthorized access, stolen cards, and phishing activities. Hence, finding fraudulent behavior is critical for both individuals and financial institutions effectively. The popularity of Quantum theory with Machine Learning (QML) applications has expanded to larger audience. In this paper, Quantum Machine Learning (QML) is proposed for fraud identification in digital transactional payments. This paper presents an in-depth survey of the difference between classical neural networks and quantum neural networks on various smart digital transaction fraud detection. This paper serves as a pathway for various researchers to understand the advancements in this domain.
Detection of Stages of Cervical Cancer using Deep Learning

Deep Learning approaches have gained importance recently specially in the field of medical image processing. They are widely used for image segmentation, analysis of an image and also used as image preprocessing tool. Amongst the other cancer, cervical cancer is one of the deadliest. Moreover, if they are not detected earlier can be fatal. Although, there are many algorithms that help to detect cancer, but their accuracy varies. The accuracy of the stages of cancer detection depends mostly on segmentation. More the accurate is the segmentation, the accuracy of the stages of cancer detection will increase. This article proposed an algorithm to segment a crowded nucleus using a neural network after preprocessing of the image. Then the cytoplasm of the nucleus is sent for feature extraction which predicts the stages of cancer. The proposed method compares the algorithm on terms of false positive, false negative, true positive and true negative. The results are tabulated in the result section of the paper.
Arduino based Dual Axis Smart Solar Tracking System

One of the most important concerns across the globe is energy crisis. The potential solution for this problem is renewable energy. In recent years, solar panels have been employed more frequently to transform solar energy into electrical energy. It is reasonably priced and almost completely safe for the environment. The electromagnetic radiation that is used to produce electricity is released by it. The major goal is to develop a workable autonomous solar tracking system that moves the solar panel so that it remains always perpendicular to the sun. In this system, the sensor will be a photoresistor. The horizontal and the vertical axes on the dual axis solar panel are rotated, so that the efficiency of the device can be increased. Hence, the dual axis provides precise control of planet elevation relative to the sun. This will provide better efficiency of the panel.
Analysis of the Effect of Adversarial Training in Defending EfficientNet-B0 Model from DeepFool Attack

Medical image diagnosis is a time-consuming process when done manually, where the predictions are subjected to human error. Various Deep Learning models have brought about an efficient and reliable automated system for medical image analysis. However, these models are highly vulnerable to attacks, upon exposure of which the models lose their reliability and misclassify the input images. Adversarial attack is one such technique which fools the deep learning models with deceptive data. DeepFool is an adversarial attack that efficiently computes perturbations that fool deep networks. With the help of two different datasets, we studied the impact of DeepFool attack on EfficientNet-B0 model in this research. There are several defense mechanisms to protect the model against various attacks. Adversarial training is one such defense method, which trains the model towards a particular attack. In this study, we have also analysed how effectively adversarial training would defend a model and make it robust.
Evaluation of Dilated CNN for Hand Gesture Classification

Convolutional neural networks (CNNs) have been widely used in hand gesture classification problems, and have made a major contribution to this area by overcoming the limitations of hard-code feature extraction techniques. CNN in hand gesture classification aims to improve performance through automatic feature engineering. Several researchers have used various CNN architectures to accurately classify hand gestures.In this paper, we investigate the performance of a popular CNN variant called dilated CNN to classify hand gestures into their corresponding classes. We compared the performance of the dilated CNN with that of the standard CNN on two benchmark ISL and ASL datasets. The experimental results demonstrate that the dilated CNN significantly enhances performance compared to the standard CNN. We obtained a significant increase in accuracy for both datasets using the dilated-CNN compared to the standard CNN.
Rapport of Counterfeit Profiles in Social Networking using Machine Learning Techniques

Day-By-Day the number is been increasing of individuals who clench accounts on the platform named social media but they conceal their identity for the motives of malevolent. Regrettably, we have done a small investigation to date to determine fake identifications that are been designed by the individuals mainly on social media. In the contradistinction, huge samples are subsisted in the form of cases as the fake accounts are been generated by automation or personal computers and these are dug out by utilizing the models of machine learning techniques. In the case of automation, models of machine learning are depending on the attributes of the engineered employing as the proportion of companion to followers. These attributes are brought out from the features like the count of the companion and the count of the follower that is straightly obtainable in the profiles of the social media. The investigation debated in this paper appeals to the similar attributes for the fake accounts so there will be some desire of proceeding the fake profiles detection which are been generated by individuals on the medial platform.
A Survey on Detection of Fraudulent Credit Card Transactions Using Machine Learning Algorithms

Fraudulent transaction in credit cards has frequently rise in couple of years. Credit card fraud is a major issue for financial organizations, and accurate fraud detection is often difficult. Over Fifty percent of Americans have encountered a fraudulent transaction on their debit or credit card, and more than 1/3 of those who use these cards have done so repeatedly, according to 2021 yearly research. This translates to one hundred and twenty-seven million Americans who have at least once experienced credit card theft. Detection of such fraud happening over huge database is very difficult and time consuming using conventional method. By taking help of AI technology and developing an automated fraud detection system to detect and classify such mishappening using machine learning is an efficient way to deal with this kind of problem. This paper reviews various researchers work on detection of credit card frauds on highly imbalance dataset and discusses some machine learning techniques as Random Forest, Logistic Regression, SVM, Naive Bayes, XGBoost and KNN which are generally used by various researchers to build a model. The findings obtained from various researchers work showed that ensemble machine learning technique such as XGBoost and Random Forest are more capable of providing all over good performance in classifying such fraudulent and non-fraudulent transactions in credit cards.
UI layers merger: merging UI layers via visual learning and boundary prior

With the fast-growing graphical user interface (GUI) development workload in the Internet industry, some work attempted to generate maintainable front-end code from GUI screenshots. It can be more suitable for using user interface (UI) design drafts that contain UI metadata. However, fragmented layers inevitably appear in the UI design drafts, which greatly reduces the quality of the generated code. None of the existing automated GUI techniques detects and merges the fragmented layers to improve the accessibility of generated code. In this paper, we propose UI layers merger (UILM), a vision-based method that can automatically detect and merge fragmented layers into UI components. Our UILM contains the merging area detector (MAD) and a layer merging algorithm. The MAD incorporates the boundary prior knowledge to accurately detect the boundaries of UI components. Then, the layer merging algorithm can search for the associated layers within the components' boundaries and merge them into a whole. We present a dynamic data augmentation approach to boost the performance of MAD. We also construct a large-scale UI dataset for training the MAD and testing the performance of UILM. Experimental results show that the proposed method outperforms the best baseline regarding merging area detection and achieves decent layer merging accuracy. A user study on a real application also confirms the effectiveness of our UILM.
Automatic feedback captions for eye-tracker based online assessment

In the era of Automation, there has to be a way to improvise online assessment. This paper has demonstrated a methodology to enhance the online assessment with the help of an Eye Tracker. That has the ability to categorize the behavior of the participant with the state of the art proposed within the paper. Compared with the traditional assessment, here with the help of this model, an automated feedback sentence is returned along with the score. This adds an advantage to the assessor for fair score awarding. The model has utilized an open-source eye tracker tool to capture eye movements during the assessment. Thereafter a customized classification model is used to provide the relevant keywords, out of which the sentence will be generated using a Deep Learning model.
HIREX: A Heterogeneous Interoperable Blockchain Solution For Hiring System

Traditional methods of document storage have cre-ated validity and safety issues for academic, professional, and other certifications. To alleviate these issues, many researchers use the blockchain to justify those documents' authenticity. Getting these certificates from different providers, putting them in order, and then applying for a job can take time and effort. This study developed heterogeneous interoperability by which all applicants are linked to this system and can list their certificates deployed on various blockchain networks. When a recruiter posts a job, the applicant can make a CV by choosing the needed certifications. After submitting a CV, the selected certificates will be transferred from the provider blockchain to our system for a limited time through interoperability and provide access to the recruiter. A recruiter can check the files by clicking on a candidate's CV to evaluate the authenticity of the certificates. We developed our system using Hyperledger Fabric and Ethereum to perform interoperability, but it is also generalizable to other programmable blockchain networks like IOTA, Quorum, Hyperledger Beau, etc.
Impersonated Human Speech Chatbot with Adaptive Frequency Spectrum

Recent trends in artificial intelligence and natural language processing models have led to the generation of highly efficient and versatile intelligent chatbot models., which have the potential to supplant human speech to the level of conversational proficiency. The proposed method creates a chatbot model that trains itself on open conversation datasets and aims to impersonate without compromising the emotional sentiments in the voice. These datasets extract from the applications such as WhatsApp., Telegram., Messenger., or any other chatting platform. Datasets convert to a machine-readable format., which is dynamically updated in real-time during the conversation., and then using speech conversion algorithms convert the reply into the desired individual's voice. The proposed model's conversational ability depends on the amount of conversation data., which gives the output in the person's voice frequency. By using an NLP-based chatbot trained on personalized data using KNN., and handling misses by pipelining the chatbot inputs to the GPT-2 model., the model can generate human-like replies even if there is data insufficiency. The natural replies are complemented with matching human voice and tone characteristics by using the vocoder model., which matches the spectral characteristics of the target voice onto the required voice. This opens a plethora of commercial and therapeutic applications that provide excellent insights into implementing natural communication models for humanoid and robotics innovations.
Improving Energy Efficiency of Permissioned Blockchains Using FPGAs

Permissioned blockchains like Hyperledger Fabric have become quite popular for implementation of enterprise applications. Recent research has mainly focused on improving performance of permissioned blockchains without any consideration of their power/energy consumption. In this paper, we conduct a comprehensive empirical study to understand energy efficiency (throughput/energy) of validator peer in Hyperledger Fabric (a major bottleneck node). We pick a number of optimizations for validator peer from literature (allocated CPUs, software block cache and FPGA based accelerator). First, we propose a methodology to measure power/energy consumption of the two resulting compute platforms (CPU-only and CPU+FPGA). Then, we use our methodology to evaluate energy efficiency of a diverse set of validator peer configurations, and present many useful insights. With careful selection of software optimizations and FPGA accelerator configuration, we improved energy efficiency of validator peer by 10$\times$ compared to vanilla validator peer (i.e., energy-aware provisioning of validator peer can deliver 10$\times$ more throughput while consuming the same amount of energy). In absolute terms, this means 23,000 tx/s with power consumption of 118W from a validator peer using software block cache running on a 4-core server with AMD/Xilinx Alveo U250 FPGA card. Index Terms--Energy-efficient blockchains, Hyperledger Fabric, FPGA accelerators
A Transformable NVMeoF Queue Design for Better Differentiating Read and Write Request Processing

NVMeoF is the latest extension of NVMe for remote storage access which allows remote access to NVMe controllers through high-speed RDMA, FC, and TCP networks. NVMe over TCP (NoT) can build on the basis of large-scale common network infrastructure in datacenters and standard TCP/IP software protocol stack, enabling a wide availability compared with RDMA-enabled specific network infrastructure for NVMe-overRDMA. However, the processing of read/write I/O at the host and target prominently shows significantly different characteristics and requirements, where one side sends the NVMeoF instruction of the request, while the other side sends the requested data. The existing NoT implementation can not meet the different characteristics of requests in the datacenter, which eventually results in the I/O performance being limited by the common processing pipeline and sending strategy. In this paper, we propose RNoT, a transformable queue that can meet the differentiated processing scheme of read or write request characteristics respectively in NoT implementation. Specifically, RNoT defines a switchable working attribute and separates resources for read and write I/O to achieve intra-queue long-term exclusivity, delivers read and write requests into other RNoT queue pairs to achieve inter-queue I/O scheduling, and transfers request command and data with targeted approaches to achieve short and long flow optimization. We implemented RNoT in Linux Kernel and evaluated it using realistic benchmarks and applications. Our experimental results demonstrate that RNoT can achieve 30.39% and 29.27% lower latency than i10 and NoT respectively, increase IOPS by up to 41.34% than NoT on average, thus RNoT can effectively optimize the read and write I/O performance in NoT with dedicated processing scheme.
Content based Video Recommendation System

We launched a new method of recommending youTube videos. Our algorithm suggests videos based on their semantic similarity to the query using the transcripts of the videos and ranking them using natural language ranking algorithms. In terms of quality and information, our method has the potential to provide better-related videos than tag-based recommendation. This can be useful for recommending educational videos and can be used on ed-tech platforms and MOOC course platforms.