Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Modern Frontend Frameworks for Building Large-Scale Web Applications

Some tools have endured in the constantly shifting world of technology, whereas many others have slipped into oblivion over time. One thing we can all agree on is that JavaScript-based libraries and frameworks are here to stay. This incredible technology is adored by countless clients and developers because of its adaptability, it can be installed on both the server and client sides of the web applications. However, due to its unique syntax and quirks, JavaScript may be a difficult language to master. We can admit that we've often caught ourselves staring at a JavaScript code, wondering what was transpiring behind the scenes. This paper will analyze the performance of such popular JavaScript-based frameworks and libraries, and explain why they remain among the most significant JavaScript frameworks of the previous decade. This paper will compare the web frameworks to find a possible winner based on web performance metrics.
Distributed Consensus and Fault Tolerance Mechanisms Using Distributed Machine Learning

In this paper, we develop a distributed consensus model to improve the process of fault tolerance in cloud. The distributed consensus mechanism uses distributed machine learning as its base estimator to predict the fault instances when a task is allocated for possible offloading of user contents in the cloud. The distributed machine learning model senses the number of tasks and available nodes to complete the possible offloading of task in the cloud. The python simulator is conducted to test the efficacy of the distributed consensus mechanism in allocating the offloaded task without faults in the cloud servers. The results of simulation shows an increased prediction accuracy, reduced latency in offloading the task and classifying the fault tolerant VMs or task in process the task update.
An Improvised Method on Detecting Fake Reviews Systems Through AI Analytics on Sales Data

Customers of internet enterprises are not the only ones negatively affected by fake online evaluations; the industry as a whole suffers as well. Researchers have spent a lot of time investigating bogus reviews, but no comprehensive survey has been conducted on the topic. In order to pave the way for future studies on false reviews, this one presents a conceptual framework based on causes and effects. The primary goal of this work is to treat the symptom by finding a solution to the problem at its source, which in this case is to create algorithms to detect false reviews. The strategies and reasons that contribute to the existence of false reviews are often overlooked in these kinds of actions. Even though there are algorithms that are very good at finding fake reviews, a lot of them still exist because they are always coming up with new ways to post misleading content that cannot be found.
Job and Internship Assistance Application

Today’s fast-paced world requires everyone to remain updated about new opportunities arising in their field of interest. It is especially crucial for engineering students who are eager to be placed in top IT companies. This research paper presents an android recruitment assistance application that directly targets the students of an engineering institution in search of a technical job or an internship. This job aggregator provides a user-friendly environment that assists students in all placement-related activities. The application is created using Android Studio and can run on version 6 and above. The design of the application has been implemented using Kotlin instead of Java.
A Peer Review on Natural Language Interface: Various Challenges and Scope

A natural language interface to query databases (NLIDB) permits users to access data stored in databases by typing queries stated in some natural language. It is typical for the elements in a SQL statement to refer to specific information in the database table rather than being expressly mentioned in the natural language query. As a result, it is vital to gauge the similarity of two texts from the standpoint of semantics rather than strings because it can be challenging to deduce the proper element values purely based on the original query in this circumstance. It can be challenging for a text-to-SQL model to decide which column to utilize if the query includes numerous columns with identical semantics. First, the column representations are improved using table contents, and then, the query representation is improved using the revised column representations. As a result of the efficiency of the column contents being less than ideal, a small-scale introduction is planned. A generic module for use in nlidb systems that enables such systems to conduct queries using aggregations is presented in great detail in this survey. These methods include query-based, pattern-based, general, keyword-based, and grammar-based systems.
Cloud Compliance Framework using Python

In today's Cloud environment there are number of issues related to cloud services such as data security, managing of resources. In this paper we are handling these issues by creating an eminent Framework using Python to audit all resources in the cloud platforms like AWS, Azure and GCP using AES and Diffie Hellman algorithm. There are works entitled on data security, which Handles, manages and mainly to store data using Triple DES, AES, RSA, BAT, HECC algorithms on specific cloud platforms. The proposed framework will imbibe AWS, Azure and GCP clouds and provide common platform to audit the resources before start using it and paying for it.
Role of Dialog and Explicit A.I. for Building Trust in Human-Robot Interaction

Robots and people can communicate similarly. Human-robot contact therefore has increasingly assimilated into society. Meanwhile, technology is continually being improved for greater communication with people. A.I. for dialogue and content detection is thus necessary. A.I. has become a tool for enhancing interactions between humans and robots. People’s increased online presence has resulted in a variety of content, and explicit A.I. assists in analysing and filtering that content by age group. Nonetheless, dialoguer AI aids in the process of responding in accordance with the input given, making it important for human-robot interaction. The following study has covered the working approach for analysing the function of Vocal Artificial Intelligence and Explicit Artificial Intelligence in Human Interaction. For a thorough understanding, difficulties are also listed in a table format. A thorough examination also covers the function of explicit and conversational Ai in interpersonal interactions.
Evolution of Machine Translation for Indian Regional Languages using Artificial Intelligence

Language translation has come a long way from the early invention of Morse Code to Natural Language Processing. This paper focuses on the evolution of Language translation of Indian Regional languages, which are diverse in nature. Initiatives of not only the private entities but also the government-based projects are also enshrined. Evolution of programming languages and algorithms for Machine Learning and Artificial Intelligence are enumerated. We have also discussed the development in compliance and regulation in this industry. Finally, the prospects in this field for research are highlighted.
Biomedical Image Segmentation Using Integrated FCM Clustering Modified with Regularized Level Set Method

Biomedical image segmentation is used widely for various diagnosis of various diseases and other medicinal purposes and help the radiologist and doctor fraternity to reduce their work and help them concentrate more on their research for new diseases. Researchers and medical practitioners use applications based on image segmentation for detecting abnormalities as well as analyzing the effect of certain deformations or deviations quantitatively. However, there are various issues faced while carrying out this task. The primary reason is the presence of inherent noise, the non-uniform intensity of the pixels, and other artifacts. The presence of artifacts not only limits the process of image segmentation but also increases the computational time for the segmentation process. In biomedical images, the problem is more complicated and recurrent. This is due to the different anatomical structures and multi-modal systems available. In this paper, a new algorithm is proposed where a modified fuzzy C-means (MFCM) clustering algorithm is integrated with Regularized Level set method to enhance the efficiency of the image segmentation process which improves the analysis exercise of the image processing system. The approach encompasses two crucial steps. Initially, the image is segmented using the Modified FCM. The MFCM approach has two basic updates with respect to the conventional FCM [1]. Firstly, we introduce a factor to the conventional FCM and secondly, Euclidean distance is replaced with the kernel-dependent distance measure. The factor increases the speed of computation of the FCM algorithm. Replacing the Euclidean distance with a kernel-dependent distance measure makes the algorithm more robust. After the initial segmentation, the Regularized Level Set method was used to refine the result and track the variation boundaries. The regularized level set method solves the reinitialization problem faced in the conventional level set method and enhances the capability and efficiency of the level set method. The combined approach not only enhances the computational speed but also helps to overcome the artifacts mentioned above.
IMAPACT OF EMERGING TECHNOLOGIES ON BUSINESS AND INDUSTRY ENVIRONEMENT: A CRITICAL REVIEW ON STRATEGIC COMMUNICATION

As a continuous process, digital world has been changing the way the business world in every aspect. In this process of transformation, industrial business has moved to the universal level for the development of the economic system and its sustainable growth being one of India's largest business sector, followed by agriculture. Various stakeholders and strategists of this business environment are currently confronted with enormous technological and industrial changes and challenges. The global industry is well positioned to benet from the introduction of new trends, tools, and technologies such as IoT, ICT, Big data, automation, standardization, disruption, inection, block chain, and so on. The developments in Cloud Computing, Articial Intelligence, and the Internet of Things hold greater promises in bringing a collaborative and integrated environment for the management. This work provides an in-depth review of the literature on the use of articial intelligence in managing the industry at large. Implementing these emerging technologies has a huge impact on improving communicative productivity across industries. The results of this study can open up innovative areas for future researchers and thinkers to make detailed inroads As a continuous process, digital world has been changing the way the business world in every aspect. In this process of transformation, industrial business has moved to the universal level for the development of the economic system and its sustainable growth being one of India's largest business sector, followed by agriculture. Various stakeholders and strategists of this business environment are currently confronted with enormous technological and industrial changes and challenges. The global industry is well positioned to benet from the introduction of new trends, tools, and technologies such as IoT, ICT, Big data, automation, standardization, disruption, inection, block chain, and so on. The developments in Cloud Computing, Articial Intelligence, and the Internet of Things hold greater promises in bringing a collaborative and integrated environment for the management. This work provides an in-depth review of the literature on the use of articial intelligence in managing the industry at large. Implementing these emerging technologies has a huge impact on improving communicative productivity across industries. The results of this study can open up innovative areas for future researchers and thinkers to make detailed inroads
BQBCC: Design of an augmented Bioinspired model for improving QoS of Blockchain IoT deployments via Context-based Consensus

Blockchain-deployments are highly secure, but lack in terms of scalability due to exponential increase in mining delay w.r.t. chain lengths. To overcome these issues, researchers have proposes used for low-complexity mining, sharding techniques, and other machine learning optimizations. But these models either depend on underlying blockchain, or showcase larger computational delays, which limits their scalability levels. Moreover, most of these models do not consider consensus optimizations, which further limits their deployment capabilities for large-scale networks. To overcome these issues, this text proposes design of an efficient bioinspired model for improving QoS of blockchain IoT (Internet of Things) deployments via context-based consensus. The proposed model initially collects temporal mining performance from existing miner nodes, and deploys a novel Proof-of-Temporal Trust (PoTT) based consensus for validating responses of these miners. The PoTT Model uses temporal mining delay, energy consumed while mining, and throughput levels for selection of high-performance miners for processing block-addition requests. Requests approved by these miners are stored on a set of Bacterial Foraging optimized (BFO) sidechains. These sidechains are automatically tuned based on spatial QoS performance of the network under real-time conditions. The BFO Model assists in segregating existing single-length blockchains into QoS-optimized sidechains. To perform this segregation, the BFO Model uses an exhaustive consistency metric that combines QoS & security levels that can be applied to specialized applications like Industrial IoTs. Thus, segregation into sidechains is done while maintaining high security under heterogenous attacks. Due to these optimizations, the model was able to reduce mining delay by 3.9%, reduce energy needed for mining by 2.5%, improve throughput by 4.5%, while maintaining high attack-detection efficiency under Sybil, Distributed Denial of Service (DDoS), and Masquerading attacks.
Improving Object Detection Robustness against Natural Perturbations through Synthetic Data Augmentation

Robustness against real-world distribution shifts is crucial for the successful deployment of object detection models in practical applications. In this paper, we address the problem of assessing and enhancing the robustness of object detection models against natural perturbations, such as varying lighting conditions, blur, and brightness. We analyze four state-of-the-art deep neural network models, Detr-ResNet-101, Detr-ResNet-50, YOLOv4, and YOLOv4-tiny, using the COCO 2017 dataset and ExDark dataset. By simulating synthetic perturbations with the AugLy package, we systematically explore the optimal level of synthetic perturbation required to improve the models’ robustness through data augmentation techniques. Our comprehensive ablation study meticulously evaluates the impact of synthetic perturbations on object detection models’ performance against real-world distribution shifts, establishing a tangible connection between synthetic augmentation and real-world robustness. Our findings not only substantiate the effectiveness of synthetic perturbations in improving model robustness, but also provide valuable insights for researchers and practitioners in developing more robust and reliable object detection models tailored for real-world applications.
Development of Recommendation System to Choose Best Courier Service

Now a day’s users finalizes any required service through searching on web but finding the best service has become more challenging task as the number of options accessible has increased. Additionally, people have no other option than to search for the service on several websites and look for it according to their needs. In this literature we mainly focused on different searching services available and came upon the decision like major features remains in background due to which users required things may got get fulfilled and hence shows wastage of time. In next stage, we will design a shipping service recommendation system which will solve users; problems by assisting them in selecting the best service that meets their needs in terms of both pricing and features. For the convenience of user decisions, this system displays outcomes graphically. Moreover, more reasonable and realistic results are possible through the proposed system.
Energy Efficient Implementation of Processing Elements for CNN Hardware Accelerator

Deep Learning has been a trending area for researchers in multi-domain applications. Extending its use to platforms with more limited resources, large volumes of data, and energy is a challenge in embedded devices or mobile technology. The objectives to decrease the model size and increase the hardware processing efficiency have led to significantly more compact structural designs for Deep Neural Networks and significant data sparsity. Since convolution in deep learning is the most computationally intensive and time-consuming operation, it is desirable to have specialized hardware with energy-efficient and high throughput features to enhance the throughput factor. This work will take advantage of the hardware parallelism and the sparsity of weights for performance enhancement. In this paper, the system design can provide better hardware acceleration and energy efficiency for fixed-size convolutional neural networks (CNN) processing. The RTL design implementation in System Verilog and the results are analyzed for power for both gate level synthesis in Cadence Genus for 45 nm node with FPGA synthesis using Xilinx Vivado.
Critical Analysis of Impact of Traffic Parameters in Traffic Count System

The Growth in use of vehicles on the road is a matter of worry for management authorities as it needs a faster and reliable method to manage traffic and its data. The detection of vehicles and count is very imperative in traffic studies. Ultrasonic sensors, Loop detectors these kinds of conventional sensors may damage the road surface. These sensors are required to be installed in urban areas which may increases the cost of work. Surveillance video cameras are being generally used for the purpose of traffic monitoring it provides the videos which can be used for the counting and detection of vehicles. The vehicle counting process delivers suitable data of traffic flow and traffic during peak hours on road. This project proposes the methodology for video processing of vehicles using YOLO.
An Approach Towards Decentralized E-Voting

Voting is an essential component of democratic regimes because it allows every member of a community to express their opinions. Voter turnout has decreased recently, while worries about the reliability, security, and accessibility of the current voting technologies have grown. Voting was introduced to address those concerns, however, it still requires full supervision by a central authority. The blockchain is an emerging, decentralized, and distributed technology. A shared, distributed record of all finished transactions or digital activities between all parties involved. Each vote is viewed as a separate transaction in this instance. We store voting transactions on a private blockchain that is built using a peer-to-peer network. Voting specifics are abstracted from the user by programming of this application. While system is running, users will have adequate time to cast their votes. This study is about (decentralized voting system) that protects voters privacy while maintaining transparency and security. The system has three implementation layers: a user interface for voters to register and vote a smart contract and MetaMask wallet for automatic vote transactions on the Ethereum blockchain, and an administrator function for managing the election. The smart contract is programmed to check for registration and validity, and once deployed on the blockchain, it cannot be tampered with. The administrator adds candidate information and manages the election.
Digital Restoration of Mural Images using TSCPMA with High Frequency Generation Scheme

State of art techniques based on patch matching have been developed to inpaint degraded regions of mural images. The application of these developed techniques are limited and are not versatile to cope with geographical noises. The performance of the pioneer algorithm developed by Criminisi suffers when order of the pixel chosen for patch matching is not appropriate and consumes long time. We proposed an efficient texture-structure conserving patch matching algorithm (TSCPMA) with state of art method developed by Purkait and Chanda, 2002 [1] for high frequency image enhancing. The image enhancement approach involves high frequency generation scheme using TSCPMA and anisotropic diffusion using Beltrami filtering. Though this technique achieves remarkable results in variety of applications, we have introduced the TSCPMA scheme to high textured image and the complete algorithm to mural images. The algorithm have ability to improve the visual quality of an image by strengthening the edges while preserving other smooth regions. While the TSCPMA reduces the time complexity in patch matching mechanism, the experimental analysis showed accurate recovery of high quality textures and structures.
The Multilingual Hive: Neural Machine Assistance As A Linguistic Expertise

The fundamental of translation of language is natural processing. This paper talks about the detection, translation and as well as the speech translation and detection of a language into the desired language. In the past years, there has been work done upon such translation projects. Multilingual Hive holds the advantage of translating a multilingual sentence into a monolingual sentence with accurate results, also it can the same in context of speech. In the past era, statistical methods were used to translate the monolingual text. Multilingual hive compares the textual content from the source language while translating it to the destination language. Nowadays, in this busy era sometimes all of us may enter a sentence without spaces and this sentence without spaces can also be translated into a readable sentence with accuracy. It grasps the textual content to replicate the text from source to target language. Comparing and then grasping the meanings of the textual content are the basic steps in the translation.
Skeleton-based Generative Adversarial Networks for Font Shape Style Transfer

This paper presents a new font shape style transfer technique that employs a generative adversarial network (GAN) and skeleton-based input feature maps to modify a target text to match a target font shape while retaining the original text content. Our GAN model is modified from a Shape-Matching GAN which utilizes a StyleNet generator and a PatchGAN discriminator. Rather than using a base-font character images as input to the generator like other existing font transfer models, we utilize the proposed skeleton-based features as input. The experimental results show that our model can produce the unseen characters in the desired font style better than an existing method.