Reviewerly/exHarmony huggingface dataset:
Ledger-based Electronic Voting System using a multi-tier security approach

The right to vote is one of the fundamental tenets upon which democracies are built. The voting process has several difficulties in the modern day due to the prevalence of digital technology. By developing a prototype application that makes use of the idea of a ledger-based data store on a blockchain and a graph database, the purpose of this paper is to search for a workable answer to the problems of security, accessibility, and transparency in the electoral process, in an effort to identify an answer that is best suited and adaptable on a large scale. A distributed ledger uses linked nodes to store data in such a way that once a block is written, it cannot be modified on the chain without compromising the chain's integrity and the trust that users have placed in it. This property of blockchain is utilised by us in developing a secure data store for the application.
Reflection on the Machine Translation and Human Translation in the Age of Artificial Intelligence

Machine translation has gone through decades of tortuous development and made tremendous progress. In the age of artificial intelligence, machine translation has a rapid development. Whereas, the bottlenecks that plague the quality of machine translation still exist. This paper analyzes the problems of machine translation in terms of language and culture. What's more, the difficulty, complexity and chronicity of improving the quality of machine translation are also discussed. In addition, further research and development ideas are explored. Due to the problems of machine translation in the age of the artificial intelligence, a conclusion is drawn that machine translation and human translation should be complementary to each other and coexist together.
An Approach of Augmented Reality in Field of Furniture Shopping

The world as we know it today is driven by technology. If servers of tech behemoths like Google, Facebook, and others stop working for a short period of time, the world suffers significant losses, usually measured in the millions. To operate, every business relies on a number of technologies in addition to its hardware and software. Although we live in a technologically advanced world, commercial applications for augmented reality are still relatively rare. Just two examples of businesses that have employed augmented reality to make money from the entertainment sector are Snapchat and Pokémon Go. However, augmented reality hasn't been widely applied in e-commerce or other economic areas. The premise of the provided application is that augmented reality technology is vastly underutilized in the e-commerce industry. As a result, we conduct a thorough literature review of the academic corpus that is centered on AR retail in this study. We discuss the use of augmented reality (AR) in retail, its technological characteristics that are frequently examined, as well as the potential psychological and behavioural effects that augmented reality may have. This essay will discuss how to develop applications, conduct research, and conduct tests.
Research on the Construction of “Cloud-network-edge-device” Integrated Campus Intelligent System Based on 5G+AI

The new generation of information technology represented by 5g + AI has changed China's education and teaching methods and brought new technological changes to education and teaching. The “cloud-network-side-end” integrated campus intelligent system based on artificial intelligence has the advantages of providing personalized services, realizing the interaction and integration of information, and enhancing the humanization of services. However, at present, many colleges and universities have problems such as backward technology, low degree of integration of information technology and education, and low network security in the process of smart campus construction. Therefore, the smart campus platform system based on artificial intelligence technology should include six layers: perception layer, network layer, data layer, application layer, service layer and technical specification layer, covering education and teaching, educational administration, life and entertainment, etc. application services. In order to give full play to the advantages of 5G+AI in the “cloud-network-edge-device” integrated campus intelligent system, colleges and universities should strengthen the construction of campus infrastructure, speed up the reform of classroom teaching, and attach importance to the construction of information security systems, so that smart campuses can be Provide better service experience for teachers and students.
Intelligent Building Control System Based on Artificial Intelligence and CAN Algorithm

With the development of network communication, computer technology and artificial intelligence, it has become an inevitable development trend to promote “intelligent buildings” characterized by intelligence, comfort and safety. In order to solve the shortcomings of the existing research on intelligent building control system, this paper discusses the functional equation of CAN algorithm, artificial intelligence fuzzy controller and intelligent building, and aims at the intelligent building control system based on artificial intelligence and CAN algorithm. Hardware settings and parameter settings are briefly introduced. And the work flow design of the intelligent building control system structure based on artificial intelligence and CAN algorithm is discussed, and finally the application of the intelligent building control system based on artificial intelligence and CAN algorithm is experimentally tested. The correct number of switch control by artificial intelligence and CAN algorithm in the intelligent building control system is relatively high. The correct number of switch control in the interference environment is less than that in the humid environment, and the number of switch operations for the control system is the least in the humid environment. The error rate of 500 and up to 2500 is less than 5%, thus verifying the superiority of the application of intelligent building control system based on artificial intelligence and CAN algorithm.
Feature Selection Based Naïve Bayes Algorithm for Twitter Sentiment Analysis

A intriguing areas of study now is Twitter sentiment investigation. It fuses the data mining methods used to create such systems with natural language processing techniques. The majority of currently available methods for analyzing Twitter sentiment do not do well when presented with messages that are both brief and ambiguous, since this is the only kind of information they take into account. Better classification outcomes are guaranteed if the right collection of features is used to identify emotion in online textual material. The computational difficulty of doing optimal feature selection drives the need for the development of creative approaches to enhancing classifier performance. In this work, an effective method for analyzing Twitter user sentiment was presented. A machine learning model was developed by the suggested method to identify good and negative tweets. Characteristics are extracted after pre-processing, and it's possible that include irrelevant features can lower classification accuracy. For this reason, the model implemented the Vortex Search Algorithm (VSA) to choose the best characteristics and discard the rest. The final tweets categorization is done using the Naive Bayes (NB) algorithm. A total of four Twitter datasets are used for the studies, each measuring a unique set of parameters and made accessible to the public. Marketing, detecting political polarization, and product reviews are just a few of the many areas that may benefit from the suggested system's ability to gauge user sentiment based on tweets.
RTGBO based Recurrent Neural Network for Sarcasm Recognition using Twitter Data

Increases in popularity of social media stages have been meteoric in fresh years. Through social media, a massive quantity of information is being released to the public. This massive amount of freely accessible data has several potential uses. This work aims to discourse issues with the social media dataset, such as its short text nature (with just 140 to 160 characters of text data), its continuous cyclosis nature, its use of abbreviations and slang, and the growing prevalence of sarcasm in online communications. Sentiment analysis and sarcasm recognition are two of the most important jobs in NLP. To communicate sarcasm, one must provide an outside element that reverses the positive and negative emotions involved. There is a risk that data mining efforts may be misclassified if they are fed sarcastic tweets. In this study, we provide a Recurrent Neural Network-based technique for analysing the tone of tweets (RNNs). The proposed approach combines a memory cell-level and network-level localization system for identifying sarcastic and non-sarcastic tweets with an attention mechanism designed to improve the network's performance. It uses an algorithm called Ring-Toss-Game-Based Optimization (RTGBO) to fine-tune the hyper-parameters. The primary focus of RTGBO is to provide a simulation of player behaviour and rules for the ring toss game. One distinguishing feature of the RTGBO procedure is that it has no tuning knobs. Since newspaper headlines often include sarcasm in an effort to draw in readers, the newspaper sarcasm dataset is used for this research. Existing methods are used to verify the suggested classifier in terms of a number of metrics. The model works quite well and accurately identifies which sentences are sarcastic and which are not.
Performance Analysis of RNS Arithmetic Operations using Reversible Logic

The need for portable communication devices with high performance and low power has grown significantly in recent years. The proliferation of portable communication devices has raised the bar for system performance. The essential building blocks of every DSP system are the fundamental arithmetic units like adders and multipliers. The speed, power, and area performance of these blocks has an effect on the system as a whole. Arithmetic unit computation time can be lowered to improve system performance. Due to the concurrent computations performed by the Residue Number System (RNS), the circuits are faster and more suited for high-speed digital signal processing. Reversible logic (RL) is regarded as one of the power optimization strategies in low-power CMOS VLSI architecture. In this paper, Reversible logic is employed to implement basic operations such as addition multiplication, forward and reverse converter on RNS using three moduli set (5,3,2). All the implementations are done on Xillinx 14.7 platform. Results shows that forward converter uses 165 LUTs and operates at a lower frequency of 68MHz because of the accessibility to ROM. The reverse converter consumes more power 10.5mW compared to other circuits as it involves more of multiplication operations. The methodology applied can be used in power efficient DSP applications to achieve low power and high speed.
Restoration of Images Corrupted by Multipath Fading Channel with Weighted Encoding

Multipath fading affects the radio communication links in one form or another. Rayleigh and Rician are the two types of multipath fading channels. During the transmission of data over these channels, the images are affected by many types of noise similar to Additive White Gaussian Noise (AWGN), Impulse Noise (IN) or the combination of both called as “mixed noise”. Removal of such noise is a critical and challenging work. The noise spreading in this case does not have any predefined model and due to this, the quality of the image further reduces. In the proposed method, the mixed noise is removed using Weighted Encoding with Sparse Nonlocal Regularization (WESNR). The Weighted Encoding technique performs better when compared to the existing image denoising methods. The parameters, PSNR and SSIM are considered to compare the performance of Adaptive Median Filter (AMF) and WESNR.
Software Effort Estimation Using Hard Limiting Techniques with Special Reference to Small Size Technical &amp;Analytical Projects

Effort estimation is the process of predicting a software's likely cost and development time. Estimating software development effort is still a complex problem that receives a lot of research attention. The accuracy with which the effort necessary for software development is estimated, and it is a significant success element in software project management. The estimated effort should be roughly equal to the actual effort in a good software estimation model. Accurate estimation enables managers to allocate resources for the planning and coordination of all activities. In this paper the effort estimation for small size projects involving technical and analytical aspects of engineering intent. The estimation of time resources is done in three steps firstly Identification of seven novel traits of software (LOC, N&C, CGPA, R, CC, AC, FP). Secondly PCA was implemented to decide the significant parameters among the software features. Lastly linear regression and polynomial regression models were developed using the significant features predicted by the PCA. The result of evaluation of the two models are encouraging with the minimum RMSE of 32-204 minutes and maximum regression coefficients(r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of 0.95-0.98
An Optimization of Conversational System Based Conceptual Natural Language Understanding

A method for dialogue comprehension based on rules-based in-depth parsing and semantic analysis is presented in the abstract of this paper. A semantic evaluation carried out as part of the EVALDA/MEDIA initiative yielded positive findings. We were able to achieve a score comparable to that of statistical systems without the use of a manually annotated corpus, despite the fact that the conditions of the evaluation made it difficult to use a symbolic processing-based method. Our methodology, which depends on a LTAG parser and two ontologies, gives very strong discourse understanding and offices for communicating with an exchange supervisor and the actual application, and we accept it tends to be utilized in genuine discourse frameworks, despite the fact that the engineering has been intended for this mission, which is solely committed to spoken discourse understanding.
An Analysis on Techniques of Image Compression Lossy And Lossless

Image compression is a kind of information compression that encodes genuine images with few pieces. Image compression is utilized to lessen repetitive and unessential image information so it tends to be recorded or sent in a proficient way. Thus, image compression abbreviates network transmission times and velocities up information move. No information is lost during the compression interaction while involving the lossless procedure for packing images. Different picture compression techniques are applied to these sorts of issues. Questions like “how to achieve image compression” and “what sorts of innovation are utilized” may now come up. Therefore, two unique sorts of approaches — lossless and lossy image compression draws near — are habitually portrayed. These strategies require next to no memory and are easy to apply. The Huffman encoding techniques have likewise been utilized in a calculation that packs images and afterward de-pressurizes them.
Design of Personalized Platform for Sports Fitness Alliance Based on Internet Technology

The fitness alliance platform aims to build a system which combines the gymnasium and the Internet and it will collect the gymnasia with different styles in different regions together for management. The exercise plan will be tailored to suit individual requirements for all customers, and then the most suitable and high-quality fitness resources should be recommended. The system better connects the gymnasium with users, and taps prospective customers in the market, and achieves a win-win situation between gymnasia and customers.
Big Data Intelligent Analysis Based Web Front-end Performance Optimization and Its Application Research

With the development of information technology, Web application systems are being used by more and more users. As the user base increases, the number of concurrent accesses continues to increase, leading to a decline in the quality of service of web systems. This paper will analyse the importance of web front-end performance optimisation of big data intelligent analysis to the whole system, and introduce the optimisation model of web front-end performance based on big data intelligent analysis. The specific application strategies after web front-end performance optimisation are explained in terms of request-related optimisation, HTML structure optimisation and page JavaScript optimisation, which can further improve the user experience.
Modeling of Feature Based MCA Classifier of LBP-HOG-Statistical-Wavelet Transform

Face recognition innovation has become one of the most well-known research regions in PC vision since it can possibly be utilized for an assortment of business and government applications. PC calculations are utilized by face recognition frameworks to pick specific, perceived perspectives on an individual's face. There should be a mutual component that shows the innate characteristics of the face that are free of the image modalities since a similar individual's face pictures from different picture modalities are connected to a similar face object. In this paper, a Mutual Component Analysis (MCA) is proposed and different element extraction strategies are likewise tried to contrast execution all together with construe the mutual components for reliable heterogeneous face recognition. A face ID strategy in view of thick framework histograms of oriented gradients (HOG) is proposed to catch facial highlights in complex circumstances sufficiently. The HOG highlights are first taken from the face picture after it has been isolated into a few thick lattices. Then, at that point, the closest neighbor classifier is utilized for recognition in the wake of making completely out of the framework HOG highlight vectors to understand the component appearance of the whole face. As per the trial discoveries, the thick matrix HOG approach is more fit to represent changes in time and climate. The thick matrix HOG and LBP both take about a similar measure of time to remove highlights. The thick lattice HOG approach, instead of the LBP, accomplishes a higher recognition rate while utilizing less aspects.
An Exploration on Verification of Educational Assets Through Blockchain Technology

Emergence and development of the blockchain technology, which is able to transform into “a most powerful disruptive innovation”, shall definitely concern universities. Moreover, nowadays the blockchain technology meets the challenges that both the system of higher education and the entire society are currently facing. Advantages of the blockchain technology are decentralized open data, absence of forgeries, safe storage of information, and reduction of transaction expenses related to data checkup, control, and verification. As of right moment, blockchain is considered to be a widely applicable technology with huge potential. In addition, information stored on a Blockchain record can be accessed from anywhere at any time. The potential for blockchain to manage and maintain student records is enormous. Blockchain is the key piece of technology used to create digital currencies like bit coins. Blockchain technology is a component of the fourth modern revolution after the development of the steam engine, the electric motor, and the computer. It has been used in many fields, including finance, law, and trade. The current article focused on its potential educational applications and looked into how blockchain technology might be used to address various challenges with education.
A Detection of Violence From CCTV Cameras in Real-Time Using Machine Learning

In this we look at and break down brutality location techniques to take apart the ongoing scene and anticipate future advancements around here of study. After the periods of distinguishing proof, screening, and qualification, 90 examination papers were picked for this review. Five internet based libraries and three profoundly respected PC vision meetings distributed somewhere in the range of 2015 and 2021 filled in as the exploration sources. We initially give a concise outline of the central ideas and issues encompassing video-based brutality discovery, prior to grouping existing strategies into one of three expansive camps as per their separate methodologies: customary, start to finish profound learning, and AI. At long last, we present openly accessible datasets for assessing the adequacy of video-based viciousness identification techniques and drawing examinations between them. We likewise give a concise outline of the ongoing difficulties in video savagery identification and give an evaluation of its possible future turns of events.
Reducing Image Compression Time using Improvised Discrete Cosine Transform Algorithm

The motivation of this project was to prepare a tool that can compress a given image to save storage space, and reduce compression time with reduction in the needed bandwidth for transferring a file over a network. This is useful for majority of the real time applications where the demand is to be supplied on the run. The compression time taken for sample images was compared for three developed program executions with standard JPEG compression 1) using system call, 2) using in memory execution and 3) using improvised discrete cosine transform (DCT). A feature known as quiescent factor (QF) was prepared so that tradeoff can be maintained between the size and the quality of a given image. Three levels of compression and quality were considered by means of three quantization matrices referred as Q10, Q50 and Q90 for analysis. The compression time using in memory execution was up to 57% lower while using improvised DCT was up to 95% lower as compared with the compression time using system call. Negligible difference in compression time was observed for compression using improvised DCT with three levels of compression and quality.
An Investigation on Sentiment Analysis merged with CNN and Deep BI-GRU Model

Convolutional neural networks (CNN) and recurrent neural networks (RNN) have been utilised in message sentiment analysis, and both have produced promising results. CNN overlooks the coherent semantic information between words despite its ability to separate nearby information between successive articulations of a sentence. Many of the current sentiment analysis methods are seeking to extract the complicated semantic also, linguistic data from the message, however they are not especially applicable to the examination of understudy opinions. For the study of student message sentiment, the CNN-BiGRU-AT model—often referred to as a novel convolutional neural network with the bidirectional gated recurrent unit and an attention mechanism—is developed. The removal of n-gram information from each sentence is subsequently accomplished using a convolutional neural network (CNN). The sentences are then consecutively condensed through the bidirectional gated intermittent unit to dispose of the message's significant semantic data parts (BiGRU). The CNN-BiGRU model is then given a thinking mechanism, and by including the attention score, different learning loads are introduced to the model. The disclosures display that its structure's accuracy rate and audit rate dependably outperform 0.9, and the F1 regard isn't lower than 0.8. These figures are superior to those of various models.