Reviewerly/exHarmony huggingface dataset:
Visual Design of Human-Computer Interaction Software Interface Information Based on Human Factors Engineering

Visual design of human-computer interaction software interface information based on human factor engineering is a kind of visual design, which is the basis of designing visual appearance, color and font. It can be used to make the interface more beautiful, easy to understand and convenient. Good user interfaces are developed from the experience of banks, telecommunications, insurance companies and other industries. These interfaces are usually simple and easy to use, and the operation instructions are clear. It is the process of designing and creating a visual representation of a user interface that meets user goals, needs and expectations. In other words, it makes the interface look good and easy to use. The visual design should also be consistent with the overall theme or style of a specific product or service.
Performance Improvement of Model View Controller based Applications through Linda’s-Key

The Model-View-Controller (MVC) design pattern is widely used in software engineering for developing user interfaces. While MVC offers many benefits, handling data in a way that is efficient and effective can be a challenge. One approach to optimising the performance of MVC applications is converting lists to dictionaries. This paper discusses the benefits and drawbacks of this approach and presents the findings of recent research on this topic. The main advantage of converting lists to dictionaries is that it can improve the performance of MVC applications by offering faster access times and making code easier to read and maintain. However, there are drawbacks to this approach, such as increased memory usage and slower performance for certain operations. Several studies have been conducted on the performance of MVC applications when using lists versus dictionaries, with varying results. This paper overviews this research and highlights the implications for MVC development.
AlumNet: A Donation Platform for Alumni

Blockchain is a revolutionary technique that is becoming increasingly popular for addressing a wide range of security-related issues that are controlled by both the public and private sectors. Charitable organizations are showing an increasing interest in blockchain technology. People have lost faith in charities as a matter of fact of the transactions’ lack of clarity involving donations, which prevents donors from knowing whether their contributions are being used effectively. This paper suggests a decentralized blockchain-based Alumni Network Donation App: AlumNet that will deliver to the target group total transparency, integrity, and access directly. The alumni network is willing to support any purpose, including projects, conferences, etc., through this platform. It is focused upon the blockchain of Ethereum. The smart contract consists of token creation, issue and transfer functions and initially some tokens are provided by the smart contract. This is a totally decentralized system and the donations made are stored onto the blockchain.
Proof of Opinion (PoO): A New Consensus Algorithm for Decentralized Blockchain Networks

This paper introduces the Proof of Opinion (PoO) consensus algorithm as an alternative to the traditional Proof of Work (PoW) and Proof of Stake (PoS) algorithms. PoO is designed to address the issues of energy efficiency, decentralization, privacy protection, scalability, and incentivization that are associated with PoW and PoS. PoO relies on the collective opinion of network nodes to reach consensus, which reduces the computational and energy requirements compared to PoW and PoS. PoO can be customized to provide a greater degree of privacy protection, such as by enabling private transactions and hiding transaction details from other nodes. PoO is designed to be more scalable than PoW and PoS, as it relies on the collective opinion of nodes, which reduces the computational requirements and enables greater scalability. PoO’s reputation-based approach incentivizes nodes to act honestly and perform their duties diligently, which promotes network security, stability, and better consensus decision-making. Our simulation results show that PoO can achieve comparable performance to PoW and PoS while consuming significantly less energy. PoO also provides greater decentralization, privacy protection, and scalability compared to PoW and PoS.
Video Captioning using Pre-Trained CNN and LSTM

Digital video is more prevalent nowadays because of more usage of video data among users. The short and catchy videos among social media attract the attention of people. On the same time, the lengthy videos are found to be left without being fully watched. So, video captioning overcomes this issue by automatically generating captions for a video. The process of generating meaningful natural language sentences for the corresponding scenes in the video is called video captioning. Video captioning involves two steps, namely, feature extraction and caption generation. Here, the pre-trained CNN such as InceptionV3 and VGG16 were used for extracting the features from the video. The caption generation is done through LSTM with the help of extracted features. The relevant captions are achieved using LSTM with the help of word embeddings.
Intelligent Vehicle Road Detection and Early Warning Algorithm Based on Vision

The research of vision based intelligent vehicle road detection and early warning algorithm is a very important research field in the field of computer science. It is carried out by different researchers from different countries, who have contributed to this field with their valuable research. The main purpose of this study is to develop an intelligent system that can use cameras installed at different locations along the road to detect the presence of other vehicles, pedestrians and cyclists. These cameras are used to detect these objects to avoid accidents or other events that may lead to death or injury. This research includes the development of algorithms for target detection, tracking and recognition, which will help to identify targets.
Scheduling Strategy for Batch Scientific Workflows based on Improved Discrete Binary Particle Swarm Optimization in Cloud Platform

The batch scientific workflow scheduling strategy based on discrete binary group optimization (DB-S) is a mechanism that uses the power of group intelligence to solve multi-objective optimization problems. DB-S algorithm was originally proposed by Dorigo et al. in 2003, and has recently been extended to support different types of discrete objects, such as particles and molecules, also known as discrete binary particle swarm optimization (DBPSO). This is a new method that combines the advantages of classical evolutionary algorithms with the advantages of traditional optimization methods. Aiming at the security threats faced by the cloud workflow scheduling problem, firstly, the cloud model is used to quantify the security of tasks and virtual machine resources, and the security cloud similarity is used to measure users' satisfaction with the security of virtual machine resources allocated by tasks; Then, a cloud workflow scheduling model considering security, completion time and usage cost is established, and a cloud workflow scheduling algorithm based on discrete particle swarm optimization is proposed; Finally, the proposed algorithm is simulated. The experimental results show that compared with similar algorithms, this algorithm has better performance in terms of security utility value, completion time, usage cost and load balancing deviation.
A Comparative Study of Traditional and Deep Learning Methods in Image Depth Measuring

This study compares traditional and deep learning methods in image depth measuring. Traditional methods often rely on manually created features and mathematical models to estimate the depth of an image, while machine learning methods use convolutional neural networks to learn considerable dataset directly from the image data. The present study evaluates the performance of both methods on a dataset of images with real depth values. The results reveal that deep learning methods prevail over traditional methods in accuracy, especially when dealing with complex and uncalibrated images. In addition, we looked into the factors that affect the performance of each method and provide an overview of their strengths and weaknesses. Overall, this study contributes with a comprehensive analogy of traditional and deep learning methods in image depth measuring and emphasizes the prospects of deep learning in supporting the field.
Named Entity Recognition using Multiple Smaller LSTM in Parallel Recurrent Neural Networks

In recent years, End-to-End NER with Bidirectional Long-Short-Term Memory (BiLSTM) has attracted increasing attention. However, it remains a great challenge for BiLSTM to have parallel computing, extensive dependencies, and single-function spatial mapping. We propose a deep neural network model based on a parallel computing self-attention mechanism and parallel recurrent neural networks to address these problems. We only use a small amount of BiLSTM to capture the time series of texts and then we use the self-care mechanism, which allows parallel calculations to capture wide-range dependencies.
Decentralized Data Storage Solutions using Hyperledger Fabric

As geospatial data grows exponentially with the various advanced photographic capture technologies and satellite imagery being readily available and accessible, the need for secure and scalable storage systems grows larger. Blockchain-based distributed data storage and access systems can help provide an apt solution to this ever-looming issue. This paper talks about how Private Blockchain using IPFS (Interplanetary file System) and Linux Hyperledger technologies can be utilized for the Storage & Retrieval of Geospatial data. This paper explains the flow of the data from the point of generation to the point of retrieval from the Blockchain. The image is stored into IPFS Decentralized servers where we are provided with a CID (Content Identifier) for every unique entity. The CID is stored into the Hyperledger for the additional layer of blockchain-based security. For data retrieval, we simply enter the specific CID into the Hyperledger server through the authorized nodes with Keys to the private blockchain. This study aims to make the technology for the safe storage and reading of such data available.
Classification of Leukemia using Fine Tuned VGG16

Leukemia is a hematological disorder which affects the ability of the body to resist against diseases and infection. Early detection of the disease can play a vital role in the treatment of a patient. Computer aided detection system based on machine learning and deep learning algorithms can reduce the burden of doctors and the mortality rate due to leukemia. Transfer learning technique is frequently used in biomedical field due to unavailability of huge and well annotated dataset. The proposed work applies transfer learning to classify leukemia using 1358 microscopic images of blood smears. Pre-trained VGG16 is fine tuned on the leukemic dataset to classify an image as acute leukemia instance, chronic leukemia instance or a healthy instance with an accuracy of 93.01%.
BlockChain Based Chat Application

Peer-to-peer networks are used by decentralized applications to ensure that no network failure can be caused by a failing central node. Today's environment, where holding data on a centralized server might be unsafe and expensive, requires a decentralized application for communication and resource sharing. We developed a programme that uses blockchain technology and decentralized networks to guarantee user privacy and make it simple for blockchain administrators to track down the source of false rumors that circulate throughout the network. The workload for file synchronization and sharing is distributed among a service, a client and a provider. This paper focuses on how decentralization could assist in successfully overcoming such problems. Decentralization would result in an increase in workers who will transfer data from a client node to a server node. When sharing data, blockchain would guarantee security. The suggested model can be utilized to move large data in a comparatively faster time frame. Additionally, the study elaborates on the design implications and factors, a user must comprehend in order to use this model properly and to obtain better outcomes when deployed onto the systems.
Abstractive Text Summarization Using BERT and GPT-2 Models

This paper aims to study the research articles related to Covid-19 and provide abstractive summarization on the same, demystifying the myths related to covid-19 as well as finding the possible root cause of hesitation in taking the vaccine. As per the government of India’s official site, as on 20th Jan 2023, 1 billion people have been fully vaccinated out of India’s total population of 1.4 billion. To fully eradicate Covid - 19, which emerged 3 years ago, the entire population needs to be vaccinated, but that’s not the case as people hesitate to get vaccinated due to various articles published in newspapers, social media, etc., the authenticity of which are unknown. In this paper we will try to summarize all the articles, as available in the CORD-19 dataset, using BERT and GPT-2 models. For extractive summarization, BERT models performed well, but there is a scope for improvement in abstractive summarization. Our approach involves utilizing regularization to suppress local similarity while simultaneously promoting global similarity, using the distill-gpt2 version with higher computing resources. We used V100 GPU with 100 computing engines from google collab-pro for faster computation and higher accuracy. The result will elaborate on the Rouge and Bleu score and its relevant significance for summarization.
MedSay-Tamil: A Pharmacological-Translator Mobile Application for the aid of Native Tamil Speakers

Medication error is one of the major causes of death, as victims intake the wrong medicine or dosage which can cause chronic effects. One such cause of medication error comes from the misinterpretation of information due to language barrier. There is a need for translation of information in medicinal strips to create awareness to the user. For this purpose, we propose an Innovative Mobile Application Using Flutter and Dart, empowering all native-Tamil speakers who find difficulty in acquiring information from tablet strips. The app is designed to revolutionize the way people obtain information from tablet strips such as general information, dosage, side effects, etc. with the help of web scraping. With its advanced Optical Character Recognition (OCR) engine, the app can accurately recognize text from images and convert it into editable text. The app’s user-friendly interface with its state-of-art innovation mainly focusing native-Tamil speakers, built using the Flutter framework, allows users to quickly and easily scan and process strings. The application then translates and displays the information present in the Tamil language along with a text-to-speech feature. Upon testing, the application displayed a 90% accuracy in retrieving relevant information about the tablet present in images.
An approach to Generation of sentences using Sign Language Detection

Deaf and mute people use sign language naturally. This article provides an application that addresses the problem of sign language detection by using computer vision and machine learning. The proposed system is a sign language interpreter that recognizes and understands the sign language words. These detected words and phrases are placed together as a sentence, enabling the user to get a complete translation. The system will collect video of a signer using a camera, and computer vision algorithms to recognize hand motions and movements. The user’s dominant hand (left or right) will conduct most of this activity. Single Shot MultiBox Detector (SSD) MobileNet V2 Deep learning technique is used to recognize the hand motions and movements and convert the identified signs into text output. The system will be trained on a dataset of sign language phrases, and its accuracy will be assessed using a range of performance indicators. The suggested technique is 96% accurate in identifying the type of sign language and 100% accurate in translating it to interpretation.
Fingerprint-Based Vote Marking System for Elector Identification

India being the largest democracy in the world, it is crucial to conduct a free, fair and secure election. This paper aims to develop a fingerprint-based vote marking system. The Fingerprint based vote marking system is a mobile application that helps the government to conduct polls smoothly. The objective is to develop a system that decreases fraud and safeguard voting through fingerprint verification. Voter information is saved in a database along with their fingerprints. The system retrieves the electoral information of the respective voter if the fingerprint matches. The officer verifies the elector ID and identifies the person as a valid voter. Thus, our system provides a reliable, secure, fast and cost-effective solution in the election process.
Analyzing the likeness of a person based on DNS logs using machine learning

In a technology filled world with a lot of online data it is very hard to find a person’s attitude or behavior or his/her likeness. This project, which can predict the likeness of the person using their online logs can be used for this. The present study of a person is based on their online activities, but to identify which category they like the most comes from their personal behavior on the domains they visit. This particular project finds the likeness of a person based on their most liked webpages. This uses the records of DNS logs and tries to identify the most seen webpages and figures out which category they like the most. The program used in this project is a multiclass classification model that would classify and predict the type of webpage the user has visited the most. This will help in effectively predicting the particular person’s likeness. With this method tests were conducted with three main algorithms, Support Vector Machine, Convolutional Neural Network and Naive Bayes out of which we were able to get an accuracy of 95% using the Naive Bayes algorithm, which helped in predicting the user’s likeness. This can further be enhanced with much higher real time log activity finder and real time log analyser which helps in finding or keeping a track of the person’s behavior. This program can widely be used to study humans.
An Innovative Approach Towards Enhancing The Indian Higher Education Systems Using Blockchain.

The purpose of this study is to illustrate the technologies used as well as the repercussions and problems brought about by the rising competition and expansion of the Indian educational system. We aim to create a uniform framework for universities to use in developing nations like India that will help students by using technologies like blockchain. Our study is being laid out with the goal of appreciating the use of unconventional methods where students are most responsible for increasing student engagement in learning and creating a more welcoming and competitive atmosphere.
Trust Model to Identify Reputed Miners in Blockchain Pool Mining

Blockchain mining has received a growing amount of interest as the modern technologies started using Blockchain network as it provides immutability and secure access through consensus process. In Blockchain networks that rely on proof-of-work (POW) for consensus process, where miners compete to solve crypto-puzzles and publish new blocks in order to gain rewards. Because solo mining is challenging, most miners decide to join a mining pool. Given the variety of mining pools and the potential adoption of different reward systems, many questions remain open regarding the miner’s selection in pool mining. However, ensuring trust of a node during consensus and mining process has few challenges to explore. Nodes in a mining pool contribute their processing power toward the effort of adding a block as part of consensus. If the pool is successful in these efforts, they receive a reward. Therefore, to ensure the execution of the consensus protocol a trust model is proposed in our work. Our proposed trust paradigm focused on firstly, how to evaluate the unstable behavior of miners based on their performance on the network. Secondly, how to identify highly ranked-trusted miner in mining pool to maximize pool profit. These issues necessitate ranking methods in mining pool, to rank reputed miners. Therefore, this paper proposes a trust model to identify highly ranked-trusted miners in pool mining. Further, proposed Trust Model is suitably analyzed for transaction volume, latency, and block propagation time with the Hyper ledger framework to ensure robustness.
Indoor Violence Detection using Lightweight Transformer Model

Human activity recognition from surveillance videos is an active research area in Computer Vision and Machine Learning. The detection of indoor violent activities is a major challenge in the surveillance sector due to the lack of sufficient indoor footage, and the presence of obstacles inducing occlusions in a household environment. Several studies on the topic of violence detection have been done, with the best performing models using Convolutional Neural Networks for spatial feature learning, followed by a Recurrent Neural Network variant for temporal feature learning. Recent studies suggest that Vision Transformers have a better run-time and are more robust to partial occlusions than their deep convolutional network counterparts. In this paper, we present a light-weight transformer model, drawing upon the recent success of video vision transformers in action recognition. The model extracts spatial features from the input frames, and adds temporal relation to the selected frames with the help of tubelet embedding, which are then encoded using various transformer layers. Generally, although transformer models are shown to need extensive training on large datasets, it has been proven that using efficient preprocessing, we can train the model on comparatively small sized datasets. The light-weight model has achieved an accuracy of 88.24% on our self-curated indoor violence dataset and a 98% accuracy on a subset containing videos in which the subjects involved in the activity are occluded from view.
Performance Comparison of Real Time Object Detection Techniques with YOLOv4

Computer vision is a recent technological advancement to digitally perceive the real world at an advanced level, through digital images and videos. Object detection is a subset of computer vision which is one of the prominent techniques used for object tracking, automatic driving, anomaly detection, etc. Object detection can be based on either machine learning or deep learning algorithms, it can be used for the localization of the image and classification of elements into diverse classes. This work provides a comparison of the object detection approaches such as Region with Convolutional Neural Network (R-CNN), Fast R-CNN, and You Only Look Once(YOLO) and Single Shot multibox Detector (SSD). The implementation of an object detection technique YOLOv4 and a custom model are done, which recognizes the objects from an input image, webcam image and live stream webcam video.