Reviewerly/exHarmony huggingface dataset:
Research on E-voting Based on Blockchain

Network technology is changing with each passing day, and informatization has penetrated in life, which has promoted the development of electronic voting and brought new security tests to the voting system. The continuous development of blockchain technology has gradually developed from Bitcoin as the core support to other fields. The characteristics of decentralization make it a new development direction of electronic voting technology. Based on this, through theoretical analysis, this paper expects to design a relatively safe electronic voting process based on blockchain, and further solve the security problems in the system through auxiliary technology. The system uses the PFBT consensus algorithm to complete the synchronization and consistency problem, which solves the problem of large resource consumption and low data processing efficiency. The POW consensus algorithm enables the system to better meet the voting data processing needs. The design also uses SHA-256 to calculate the hash value, ECDSA algorithm for encryption and decryption, and uses the public key to encrypt the identity information of each voter. At the same time, blind signatures are used in electronic voting to prevent signers or other malicious parties from manipulating or leaking voters' voting results through signature information, which improves the security, privacy protection and fairness of the system.
Performance Analysis of Power Data Compression Based on Beidou Short Messages

There are situations in the power grid information collection system in remote areas, such as weak and unstable signals. By using the Beidou short message service based on the satellite communication technology, the communication blind area and the transmission bit error rate can be reduced effectively. However, Beidou short message communication has the disadvantages of small transmission bandwidth and low communication frequency. In this paper, we focus on the IEC101 protocol message and CDT protocol message commonly used in the electricity information collection system. To address the communication challenges associated with these protocols, we propose a compression transmission mechanism based on Beidou short message, leveraging a lossless compression method. In order to maximize the utilization of BDS short message communication channel bandwidth, we design a mechanism for unpacking, grouping and reliable transmission long message within the BDS short message communication, and Huffman, LZ77, LZSS, LZW, LZMA are used for transmission segments of different lengths. Compression algorithm is used for compression test, and the average compression ratio and overall transmission time are analyzed. On the basis of balancing the compression ratio and real-time transmission, the appropriate length composition of the transmission message and the compression algorithm conforming to the message characteristics are discussed. The experimental results demonstrate that the LZMA compression algorithm improves the transmission efficiency of Beidou short messages in long-distance communication seenarios. Specifically, we split IEC101 protocol packets into 900 bytes and CDT protocol packets into 800 bytes, while the transmission is real-time and the transmission efficiency of the IEC101 protocol and CDT protocol messages in the communication of the electricity information acquisition system, is increased to 2.88 times and 2.56 times respectively.
Aikyam: A Video Conferencing Utility for Deaf and Dumb

With the advent of the pandemic, the use of video conferencing platforms as a means of communication has greatly increased and with it, so have the remote opportunities. The deaf and dumb have traditionally faced several issues in communication, but now the effect is felt more severely. This paper proposes an all-encompassing video conferencing utility that can be used with existing video conferencing platforms to address these issues. Appropriate semantically correct sentences are generated from the signer’s gestures which would be interpreted by the system. Along with an audio to emit this sentence, the user’s feed is also used to annotate the sentence. This can be viewed by all participants, thus aiding smooth communication with all parties involved. This utility utilizes a simple LSTM model for classification of gestures. The sentences are constructed by a t5 based model. In order to achieve the required data flow, a virtual camera is used.
OralMedNER: A Named Entity Recognition System for Oral Medicine and Radiology

Recent years witnessed an unprecedented growth of unstructured text data in the healthcare domain. Manual analysis and extraction of useful patterns from these large data collections are extremely difficult. Biomedical named entity recognition (BioNER) identifies entities of clinical importance, such as symptoms, treatment, names of drugs, and organisms, from unstructured text documents such as electronic health records and radiology reports. Even though this is the first step for building artificially intelligent applications such as conversational agents and information retrieval, this is difficult and challenging. Recently pre-trained language models gained high popularity in NLP, considering their capacity to manage contexts satisfactorily. This paper proposes an approach that fine-tunes BioBERT, a state-of-the-art pre-trained biomedical language model for extracting named entities for the Oral Medicine and Radiology domain. This work compares various machine learning algorithms on different feature encoding techniques to establish the superior performance of the proposed approach. We assess the proposed approach by employing metrics like precision, recall, and f-measure and found it to be outperforming our chosen baselines.
Intruder Detection System for Video Surveillance Using Machine Learning

An intruder detection system for video surveillance is a security system that uses cameras and image analysis software to detect and alert authorities of any unauthorized access to a protected area. The system continuously analyzes video footage from the cameras, and when it detects a potential intrusion, it sends an alert to the authorities or the property owner. The goal of this type of intruder detection system is to provide early warning of any potential security breach and to help prevent unauthorized access to the protected area. Presently there exist several video surveillance systems and a few of them support face recognition. The proposed video surveillance system combines multiple face detection with incremental learning to dynamically add new faces to the database based on user confirmation.
Handwritten Documents Conversion To Digital Documents

Handwritten documents are a critical viewpoint of chronicled records and individual reports. With the approach of computerized innovation, it has ended up essential to change over handwritten documents into advanced frame for conservation and openness purposes. Manually written to advanced archive transformation includes the utilize of Optical Character Acknowledgment (OCR) program to recognize and change over written by hand content into advanced shape. This paper gives insights and work on the procedures and challenges included in transcribed to digital document conversion. In recent years, the requirement for transcribed to digital archive conversion has expanded essentially. With the expanding digitization of authentic records and individual archives, it has ended up fundamental to change over handwritten documents into digital form for conservation and availability purposes. This has helped researchers in the advancement of modern OCR computer program that can recognize and change over written by hand content precisely. In this paper we work on the challenges faced in manual written to computerized document change, counting the exactness of OCR program, the conservation of the first record, and the dealing with the delicate data.
A Glance On Various Image Denoising Techniques

Since more and more digital images are being shot every day, there is an increase in demand for images that are more precise and aesthetically beautiful. But noise continually reduces the clarity of the visual images taken by the cameras that are now available. Therefore, efforts must be made to handle edges, corners and other sharp elements independently in order to extract useful information without degrading visual quality. Until now academics have presented a wide range of noise reduction strategies in an effort to eliminate noise while maintaining the image’s characteristics. The advantages and disadvantages of each technique vary acccordingly. This paper summarizes some of the important image denoising techniques and their feature research. Furthermore, the paper presents the various evaluation metrics used for performance evaluation of different techniques.
A Novel Approach For English-Hindi Cross lingual Summarization

Cross-lingual summarization (CLS) is the process of producing a gist in a specific language for a source text in another language. There is a scarcity of CLS datasets for English to Hindi language summarization. This work aims to introduce a hybrid approach for CLS using abstractive summarization and machine translation. The source text along with the target summary is fed into deep learning model, which produces an abstractive summary, which consists of newly synthesized sentences. Two models one for generating brief summaries and other for detailed summaries were trained. The generated summary will be translated into Hindi language using a pre-trained transformer. These models were evaluated for their summarization and machine translation capabilities and was found to perform well, compared to the baseline models.
GCRS – Green Computing Resource Scheduler: An Optimized Energy Efficient Cloud Data Centers to Scale Down Carbon Emission

Cloud computing is a powerful technology that is rapidly growing in popularity with energy consumption being a major concern. Cloud data centers consume a lot of energy, which can lead to high operating costs and carbon emissions. This is a significant bottleneck restricting the development of cloud computing. If people continue to rely on traditional data centers, the environmental impact of cloud computing will only get worse. Green cloud computing solutions are needed to address this challenge. Green cloud computing solutions are designed to utilize cloud resources while minimizing energy consumption efficiently. Cloud computing providers offer different levels of service, which are defined in service-level agreements (SLAs). SLAs typically include things like the availability of the service, the response time for requests, and the resources that are assigned to the cloud clients. Cloud computing providers try to best exploit their resources by allocating them to users which enhances utilization and lowers the idle time. This can be done by using scheduling algorithms. In the proposed work“ data centers or servers maintain data for clients to read and write. Servers are classified as read-and-write servers separately based on their capacity, and read-and-write tasks are assigned accordingly. The model uses every server's full potential, resulting in an environment that consumes less energy. The objective of the proposed Green Computing Resource Scheduling (GCRS) is to reduce the energy consumed and the carbon emitted by the data center through scheduling resources.
Metaverse Creation And Decentralized Land NFTs Over Blockchain

A metaverse is a rapidly evolving concept in the realm of virtual reality that refers to a network of three-dimensional virtual environments centered on social interaction. The word is widely used in futurism and science fiction to depict a potential iteration of the internet as a single, universal virtual environment assisted by the usage of virtual and augmented reality headgear. There are several Metaverse currently available, such as Sandbox and Decentraland, each offering unique and immersive experiences for users. Non-Fungible Tokens (NFTs) are a novel form of digital assets that are stored on a blockchain, a decentralized digital ledger that keeps track of transactions. NFTs can prove ownership and validity for these digital assets via a blockchain, increasing their value and collectability. NFTs can be linked to digital assets that can be reproduced, such as images, movies, and audio. To give a public certificate of authenticity or proof of ownership, NFTs use a digital ledger. As part of our ongoing project, the creation of the Metarealm, a metaverse for Kochi city, promises to provide an engaging and cutting-edge platform for users to buy, sell, and trade virtual land and assets. This Metaverse will enable users to explore the city and its landmarks in a fully immersive 3D environment, fostering a sense of community and engagement among its users. As the Metaverse and NFTs continue to evolve, the possibilities for virtual experiences and digital ownership are limitless.
Advanced UAV Construction Technique and Real-Time Image Processing for Enhanced Aerial Intelligence

Keeping a record of catastrophes is critical in order to mitigate their consequences, as well as the harm they do to the living world and inhabitants; unmanned aerial vehicles (UAVs) may mitigate this by outfitting the cameras that capture images of the regions of interest. The rapid expansion of unmanned aerial vehicles in several industries has driven researchers to investigate the potential advantages of employing them in disaster management. UAVs contribute in the construction of the link between those affected, those who survived, and the assigned rescue crew. This work tackles the communication technologies that brought the development of UAV disaster management systems, such as humanitarian interpersonal interaction, and live picture processing, as well as the initial study that was conducted aimed at showing the positive aspects of UAV systems.
Enhanced Communication Through Isolated Sign Language

Isolated sign language recognition is a vital area of research that aims to bridge the communication gap between the hearing-impaired community and the general population. Sign language is the main medium of communication for the deaf and dumb persons. Isolated sign language is a useful and important communication method, breaking barriers, and promoting inclusion in various aspects of daily life for the deaf and hearing-impaired communities. The GISL (Google Isolated Sign Language) comprises a wide range of isolated sign language gestures performed by individuals from different cultural backgrounds. The dataset is annotated with corresponding labels to facilitate supervised learning algorithms. The dataset consists of recorded video samples of human gestures which resembles the communication through human actions. To evaluate the performance of the model, accuracy is computed to assess the model's performance in recognizing isolated sign language gestures. The model is also tested for real-time processing capabilities, ensuring its usability in practical scenarios.
Bubble-Sheet Assessment Checker with Test Grader Using Computer Vision Through Raspberry Pi

Manual checking of test papers is still one of the most tedious tasks of being an educator. The immediate feedback of these exam results from the teachers enhances the student's learning experience since it also enables the teachers to identify the knowledge gap in the class efficiently. Thus, this study is mainly designed to develop an intelligent bubble-sheet test checker with a scoring system employing computer vision for automated checking using Python. This system requires the examiner to input the student's name and ID number into the system using the touchscreen monitor, then place the bubble sheet properly inside the chamber. The camera button must be pressed to capture the bubble sheet's image and undergo image processing; after a few seconds, the student's score is displayed. Canny edge detection, image segmentation, and inverse warp perspective are some of the image processing techniques involved in this study. To examine the system's accuracy, confusion matrix analysis and paired t-test are used. The confusion matrix analysis assesses the device's correctness in checking each letter on the shaded bubble sheet, which revealed a high accuracy rate of 96.80%. On the other hand, the paired t-test evaluates the accuracy of automated checking versus manual checking, which resulted in a p-value of 0.01.
Figurative Health-mention Classification from Social Media using Graph Convolutional Networks

The recent advancements in information and communication technologies paved the way for the widespread availability and use of social media networks. People use social media networks like Twitter and Facebook to share their views, opinions, and criticisms on any topic, from gaming to space science. Recently, the amount of health-related content shared by social media users witnessed an exponential increase. Users share information about many health contents, such as treatments, medications, and adverse drug reactions. Identification of such health mentions is crucial for devising health surveillance measures, but there are challenges associated with the same. We must differentiate personal health references from other metaphoric uses of those terms, such as figurative mentions where words are used in other contextual situations. This work proposes a figurative health mention classification approach using a graph convolutional neural network to classify health mentions better. When compared with the chosen baselines, our proposed method showcased better results in terms of precision, recall, and f-measure.
English to Indian Sign Language Gloss Structure Translation using Sequence to Sequence Model

A significant portion of the deaf and hard of hearing population in India uses Indian Sign Language (ISL) for communication. However, they frequently encounter significant communication and educational challenges as a result of the lack of ISL-related resources and technology. In this work, we aim to create a system for translating English text to ISL gloss structure. It can be viewed as a significant step towards ISL generation from normal text. The proposed method uses a seq2seq BiLSTM encoder-LSTM decoder based approach for performing the grammatical transformation of sentences to convert them from English to ISL gloss structure. The evaluation of the system, using BLEU score and ROUGE-L F1 score yielded good results for different batch sizes.
Prediction of Student Achievement Based on the Use of Internet Networks Using the Random Forest Method

Students are the most frequent users of the internet network for educational purposes, such as looking up references for college assignments. In reality, in order to determine the effect of using the internet network on student achievement or the cumulative index value, it is necessary to predict student achievement results based on internet network use. A survey was distributed to students with the following indicators: number of social media, internet time usage (a week), internet usage time (a day), amount of internet usage for entertainment (a day), amount of internet usage for social media (one day), time using the internet for studying (one day), and GPA. Meanwhile, classification refers to the process of classifying an object using the Random Forest algorithm. The outcomes of these predictions can be used as a foundation for universities or departments to develop regulations or policies governing the use of Internet networks, particularly on campus. The Random Forest algorithm was used in this study to make predictions with an accuracy level of 95.08%.
A Fuzzy Logic-Based Transaction Traffic Control Scheme with Enhanced Kalman Filter to Improve EHR Blockchain Performance

Using a blockchain instead of traditional databases or protocols is a desired choice that compromises impractical requirements, including performance, cost, and security. The limited transaction processing capability of the blockchain system is the biggest obstacle to replacing well-known alternatives like distributed database systems. In this paper, a fuzzy logic-based transaction traffic control scheme with an enhanced Kalman filter is proposed to enhance the performance of blockchain systems. The Kalman filter predicts the transaction throughput with an artificial neural network (ANN) learning module to improve the prediction accuracy. A fuzzy inference system is designed to govern the blockchain network's real-time transaction traffic flow in accordance with the estimated transaction throughput value. A Hyperledger Fabric-based EHR testbed is used to assess the efficiency of the proposed scheme. The results indicate that the proposed scheme can increase the transaction throughput and reduce the transaction latency of the blockchain system.
A GAN-Based Power Quality Anomaly Detection Method for Imbalanced Multivariate Time Series

Time series anomaly detection is an important and well-researched topic with extensive applications in finance, healthcare, biological data, and industrial production. However, in the field of power quality, detecting anomalies in complex and imbalanced multivariate long time series data remains a challenge. This paper proposes a GAN-based method for multivariate time series anomaly detection, which directly trains on raw data captured by power monitoring stations. It addresses the issues of the lack of original examples, difficulty in acquisition, and simulation of abnormal data in power quality. Additionally, the proposed method introduces an attention mechanism and a multi-scale feature fusion mechanism, reducing the accumulation of errors during model training and effectively addressing the problem of overfitting due to the scarcity of abnormal samples and high noise in power quality data. The comprehensive experiments conducted on four original power quality datasets demonstrate that the proposed method significantly improves the performance of anomaly detection in the field of power quality compared to baseline methods.
Path Planning of Unmanned Ships Using Modified APF Algorithm

Artificial potential field algorithm is a classical path planning algorithm which has been developed continuously in the past decades. Although it is suitable in virous kinds of situations, there are also some shortcomings which prevent it from further applications. This paper improves the algorithm for some special obstacle avoidance scenarios, such as the problem that the traditional APF method cannot be applied to when the threat area is an irregular polygon and so on. The improved APF algorithm is based on the traditional APF algorithm with a combination of new modelling ideas. To further verify the modified algorithm, MATLAB software is used for simulation under different situations. The new program can be applied in more complex obstacle avoidance scenarios.
Identification and Classification of Leukemia Using Machine Learning Approaches

Leukemia is a form of cancer that affects people of any age but is particularly frequently seen in kids aged between the ages of five and eight. One of two methods is typically used for diagnosing it. The first approach is the traditional method of manually examining samples of blood extracted from microscope pictures; however, this method has a drawback in that when leukemia cells and healthy ones are the same, the diagnosis procedure gets more complicated. The second method employs image categorization algorithms. Algorithms employed in data mining techniques are extremely important in the field of health care, particularly in the event of discovering previously unknown patterns or models in databases. The classification is becoming more popular in Machine Learning and Deep Learning methods. The purpose of this research is to examine ML and DL approaches for recognizing and forecasting leukemia. The present research attempts to uncover several ML and DL-based classification methods that exploit the greatest prediction accuracy for leukemia cancer cells in added to the existing methods.