Reviewerly/exHarmony huggingface dataset:
Analysis on Automobile Reviews using Machine Learning Techniques

Abstract: Automobile reviews provide a valuable opportunity for sentiment analysis in order to gain a better understanding of consumer opinions and improve product quality. As a result of the complexity of natural language and differences in user opinions, accurate and robust sentiment analysis by computer models remains challenging. Traditional machine learning algorithms such as Support Vector Machines (SVM) and Naive Bayes (NB) have been used for sentiment analysis on textual data. They can, however, be limited by the heterogeneity of user opinions and language variations. In contrast, deep learning models and natural language processing have shown great promise in sentiment analysis. In particular, the VADER (Valence Aware Dictionary and sentiment Reasoner) algorithm has been proposed as a powerful tool for sentiment analysis on social media texts, and has shown promising results in other domains as well. In this work, The aim is to achieve accurate sentiment analysis on automobile reviews by comparing the accuracy performance of SVM, NB, and the VADER algorithm.
MPHM: Model poisoning attacks on federal learning using historical information momentum

Federated learning(FL) development has grown increasingly strong with the increased emphasis on data for individuals and industry. Federated learning allows individual participants to jointly train a global model without sharing local data, which significantly enhances data privacy. However, federated learning is vulnerable to poisoning attacks by malicious participants. Since federated learning does not have access to the participants’ training process, i.e. , attackers can compromise the global model by uploading elaborate malicious local updates to the server under the guise of normal participants. Current model poisoning attacks usually add small perturbations to the local model after it is trained to craft harmful local updates and the attacker finds the appropriate perturbation size to bypass robust detection methods and corrupt the global model as much as possible. In contrast, we propose a novel model poisoning attack based on the momentum of history information (MPHM), that is, the attacker makes new malicious updates by dynamically crafting perturbations using the historical information in the local training, which will make the new malicious updates more effective and stealthy. Our attack aims to indiscriminately reduce the testing accuracy of the global model with minimal information. Experiments show that in the classical defense case, our attack can significantly corrupt the accuracy of the global model compared to other advanced poisoning attacks.
Multikernel: Operating system solution to generalized functional safety

With the trend of digitalization, intelligence, and networking sweeping the world, functional safety and cyber security are increasingly intertwined and overlapped, evolving into the issue of generalized functional safety. Traditional system reliability technology and network defense technology cannot provide quantifiable design implementation theories and methods. As the cornerstone of software systems, operating systems in particular are in need of efficient safety assurance. The DHR architecture is a mature and comprehensive solution, and it is necessary to implement an OS-level DHR architecture, for which the multi-kernel operating system is a good carrier. The multi-kernel operating system takes the kernel as the processing scenario element and constructs redundancy, heterogeneity, and dynamism on the kernel, so it has the generalized robustness of the DHR architecture. This article analyzes the significance and requirements of OS-level DHR architecture, and systematically explains how the multi-kernel operating system responds to the requirements of OS-level DHR architecture by analyzing the technical routes of multi-kernel operating systems and develops an operating system solution idea for the generalized functionally safety.
Characterizing the superposition of arbitrary random quantum states and a known quantum state

The superposition of states is one of the most fundamental issues in the quantum world. Generally there do not exist physical operations to superpose two unknown random states with nonzero probability. We investigate the superposition problem of unknown qubit states with respect to a known qubit state. It is shown that under trace-nonincreasing completely positive operations the superposable state sets are located in some circles on the Bloch sphere. Meanwhile, we prove that the quantum states in a circle on the Bloch sphere are superposable with respect to a known state. Finally, for the high-dimensional case, we illustrate that any superposition transformation protocols will violate the no-cloning principle for almost all the states. Our results also promote the understanding and applications of the superposition principle in view of quantum no-go theorems.
Deep Rotation Correction Without Angle Prior

Not everybody can be equipped with professional photography skills and sufficient shooting time, and there can be some tilts in the captured images occasionally. In this paper, we propose a new and practical task, named Rotation Correction, to automatically correct the tilt with high content fidelity in the condition that the rotated angle is unknown. This task can be easily integrated into image editing applications, allowing users to correct the rotated images without any manual operations. To this end, we leverage a neural network to predict the optical flows that can warp the tilted images to be perceptually horizontal. Nevertheless, the pixel-wise optical flow estimation from a single image is severely unstable, especially in large-angle tilted images. To enhance its robustness, we propose a simple but effective prediction strategy to form a robust elastic warp. Particularly, we first regress the mesh deformation that can be transformed into robust initial optical flows. Then we estimate residual optical flows to facilitate our network the flexibility of pixel-wise deformation, further correcting the details of the tilted images. To establish an evaluation benchmark and train the learning framework, a comprehensive rotation correction dataset is presented with a large diversity in scenes and rotated angles. Extensive experiments demonstrate that even in the absence of the angle prior, our algorithm can outperform other state-of-the-art solutions requiring this prior. The code and dataset are available at https://github.com/nie-lang/RotationCorrection.
Temporal Feature Matching and Propagation for Semantic Segmentation on 3D Point Cloud Sequences

In real-world LiDAR-based applications, data is generated in the form of 3D point cloud sequences or 4D point clouds. However, the topic of semantic segmentation on 4D point clouds is under-investigated and existing methods are still not able to achieve satisfactory performance to meet the requirement for real-world applications. The temporal information across different point clouds plays an important role in dynamic scene understanding, which is not well explored in existing work. In this paper, we focus on exploring effective temporal information across two consecutive point clouds for semantic segmentation on point cloud sequences. To this end, we design three novel modules to enhance the features of target frames by extracting different temporal information in the local regions and global regions. Experimental results on SemanticKITTI and SemanticPOSS demonstrate that our method achieves superior performance in 4D semantic segmentation by utilizing temporal information.
Children Specifically Language Impairment Severity Level Prediction using Improved Conditional Random Fields and Comparison with Traditional Models

Disorders in children are naturally higher in terms of lacking food habitats, birth state itself shows the impairment like visual blindness, speech loss, hearing loss etc. Research in the medical field in the United States focuses on speech incompetency, hearing deficiency, and memory loss. Natural Language processing has many involvements to recognize the impairment stages and predictions at its earlier stages. The objective is to predict the affected Children Specifically Language Impairment (CSLI) one of the disability using the new proposed approach highlighted as Improved Conditional Random Fields (ICRF). Using the sound signal from open databases, features are converted to a corpus which is generated from collective locations. In the existing system, the feature extraction in child impairment was challenging which failed in text summarization with mathematical modelling. Automatic extraction of speech disability from speech id, audio signal conversion and complete matching of possible corrected factorization is lacking. These work with training positive feature correction by recognizing the features from supervised learning. Accurate features are evolved by applying ICRF segments that can be segregated into partitions which remove negative factorized and its matrix are identified. Results are with 89.14% accuracy to show the positive matrix combination that can calculate the speech signals in a binary formation.
Secure Blockchain Banking Cloud with Error Recovery Processes

Cloud computing technology is an excellent source for increasing data management capacity and providing a variety of services. Clients can access a variety of services via the internet using this technology. This Cloud technology provides several advantages to banks, including lower costs, more data processing capability, and better competence in financial services. Financial firms would use cloud services with greater efficiency and flexibility as needed. However, many firms are hesitating to employ this technology because of security concerns. Keeping data safe in the cloud is becoming more and more important. The paper aims to propose a solution to the aforementioned problem using blockchain and biometrics. Biometrics is a digitalized method for recognizing people based on physiological characteristics. Biometrics and cryptography might be used to make the cloud server extra secure. The cloud-based protected data is secured using bio-cryptography concealment. The method secures the protection and deciphering of data in the cloud by using biometrics as the primary component. We discuss how to make the biometric system safer by utilizing cloud computing, as well as how to improve its performance so that criminals cannot access your data. Biometric authentication is a viable solution for cloud services. We will explore how to construct a cryptographic technique within a blockchain to overcome these concerns. Blockchain technology will improve data security by utilizing cryptographic keys. The system also manages error recovery, which leads to the failing cloud instance being immediately identified and corrected.
Effect of Artificial Intelligent on Empathy Quotient (EmQ) and Responsiveness of Customer Care Executive- A Study from Customer's Lenses

The workplace is going digital with the inclusion of artificial intelligence tools. These tools are deeply reshaping the service industry and influencing customer relationship management. There have been various studies that have shown the correlation between technology and its effect on the organization. Through the study, the effect of AI tools on the EmQ of Customer Care Executives has been analyzed as perceived by the customers. The study highlights the effect of AI tools on the affective and cognitive empathy of the CCE and thus on responsiveness on the job.
Automation of Grievance Registration using Transfer Learning

Grievance redressal is an indispensable service but involves a lot of issues, which can be resolved if a proper automated application is introduced which involves grievance classification and location fetching mechanism. To arrive at the solution, machine learning techniques can be used, but another major facet of this application is that it should be compatible and transportable. Hence the solution needs to be in the form of a mobile application. The machine learning model must be incorporated into the mobile application. Since mobile phones have minimal computational power to run a model, an architecture which uses minimal resources must be used. MobileNet V2 is an architecture which is specially designed to incorporate Deep learning (DL) algorithm especially Image classification. MobileNet uses minimal computational resources, and interoperability is achieved through Google's Teachable machine learning, which provides a tft lite (TensorFlow Lite) model for our trained dataset and the model can be imported in to the project's asset. Location manager of android's architecture can be used to fetch the user's current latitude and longitude, which can be used by grievance redressal organization to navigate. On achieving this solution, a lot of tedious processes in our existing grievance management system can be automated. Both the public and the government can be benefited and as a result, a lot of data will be in hand which is of prominent importance now a days.
Perceptual Video Summarization Using Keyframes Extraction Technique

The growth of video content in recent years is a challenging problem due to increased memory storage and time consuming for analyzing content of the video. Therefore, there is a need to reduce the content for human usage. This paper presents a keyframes technique that uses co-occurrence matrix and permutation computation for perceptual video summarization (PVS). The Human Visual System (HVS) needs to incorporate the PVS. It helps to allow for the importance of perceptually significant contents. The proposed method uses different kinds of videos to evaluate the effectiveness of the work. The subjective evaluation scores have evaluated the proposed work.
An Assessment on Deviations of the Teaching-Learning Based Optimization Algorithm and its Applications

Swarm intelligence algorithms and evolutionary algorithms have been two well-known optimization approaches since the beginning of optimization. These population-based meta-heuristic algorithms are applied to a wide range of challenging multi-national computing challenges in the real world. Recently conducted studies on various multi-goal optimization techniques, on the other hand, shows that those inherently evolved meta-heuristics are incapable of dealing with multi-dimensional problems due to flaws. R.V. Rao proposed the Teaching-Learning Based Optimization (TLBO) method as a revolutionary population-based completely meta-heuristic for evaluating this type of situation in 2011. TLBO's applicability has surpassed many milestones since its inception, compared to today's advanced meta-heuristics for use in a number of engineering tasks.
Hybrid Optimization Algorithm to Mitigate Phishing URL Attacks In Smart Cities

To improve the detection of phishing sites, this proposal introduces feature weights for intelligent phishing site detection based on hybrid bio-inspired algorithms. The proposed approach uses Gray Wolf Optimization (GWO) and Firefly Algorithm (FF), which examines a wide range of website attributes, to more precisely identify phishing sites. Then employs Artificial Neural Network (ANN) to classify different website elements according to the importance of each component in differentiating between legitimate and phishing websites using bioinspired-based recommended site feature weights. According to experimental findings, the suggested hybrid bioinspired-based feature weighting greatly improved classification accuracy, true positive (TPR), and negative rates (TNR), as well as precision and F1 score. Phishing is an online crime that entails the gathering of private information like passwords, account numbers, and credit card numbers. Attackers use alluring URLs to entice phony website visitors. Recently, Artificial Intelligence-based phishing detection has seen some success, and in this study, ANN was used to detect phishing. This ANN classifier may make phishing websites simpler to spot. However, it has been shown that the effectiveness of detection can be increased by applying a genetic algorithm to improve feature selection.
A Comprehensive Analysis on Co-Saliency Detection on Learning Approaches

Co-saliency recognition is a fast-growing field in computer vision. Co-saliency detection, a unique field of optical sensitivity, relates to the identification of shared and salient backgrounds from two or even more pertinent images, and it is broadly applicable to a variety of computer vision tasks. Various co-saliency detection techniques are composed of three modules: extracting image characteristics, examining informative signals or elements, and creating effective computer foundations to construct co-saliency. Even though several strategies have been created, there hasn't yet been a thorough analysis and assessment of co-saliency prediction methods in the literature. This work intends to provide a thorough analysis of the foundations, difficulties, and implications of co-saliency detecting. An overview is offered based on the connected computer vision works, investigates the detection history, outline and classify the important algorithms and address certain unresolved difficulties, explain the possible co-saliency identification applications as point out certain unsolved obstacles and interesting future appears to work.
Smart Watch to Avoid Face Touching During Pandemic Condition

The most dangerous Coronavirus, COVID-19, is the source of this pandemic illness. This illness was initially identified in Wuhan, China, in December 2019, and currently sweeping the globe. The virus spreads quickly because it is so simple to transmit from one person to another. Fever is one of the obvious signs of COVID-19 and is one of its prevalent symptoms. The mucosal areas, such as the nose, eyes, and mouth, are among the most significant ways to catch this virus. In order to prevent and track the corona virus infection, this research suggests a face-touching detection and self-health report monitoring system. Their hygiene will immediately improve thanks to this system. In this pandemic circumstance, people use their hands in dirty environments like buses, trains, and other surfaces, where the virus can remain active for a very long time. With an accelerometer and a pulse oximeter sensor, this system alerts the user when they are carrying their hands close to their faces.
Visual Object Detection in Extreme Dark Condition

Independent monitoring as well as video surveillance have a lengthy history. In both controlled indoor and outdoor environments, many currently available devices can accurately monitor human mobility. As a constant part of our everyday lives, low-light conditions have a significant impact. Nevertheless, one of the biggest challenges in visual surveillance is still object detection at night. There has been a rise in poor light image studies, especially in the area of image improvement, but no relevant database serves as a standard. One use of object detection is the remote or centralized management of a large number of security and video surveillance devices. It is suggested that night vision monitoring could benefit from the use of an object detection technique. The method relies on detecting motion. PIR sensors might pick up on unnoticed motion to kick off the search. Due to motion prediction, this method works well in practice for night-time detection. Furthermore, we discuss our interesting and insightful findings concerning the impacts of low light on the object detection job on developing a Deep Learning (DL) method. If an object is spotted, an alert message is sent to the user's registered mobile phone through GSM technology, and send an email with a half-minute video clip of the surroundings. Our investigation on dark images is meant to pave the way for more studies in the low-light domain.01
Dynamic Traffic Management System

Traffic and Congestion has become inevitable nowadays. The metropolitan cities end up being gridlocked due to the increasing traffic congestion. The conventional traffic management system is unable to control this increasing traffic faced by people in day-to-day life. So, we have proposed a Dynamic Traffic Management System that takes lively captured image frames as input and analyse the traffic at real time. The captured images are collected from the cameras that are placed at the traffic junctions and is processed through image processing. The output is going to be efficient time slots for signals. This methodology involves a real time object detection algorithm called, You Only Look Once (YOLO). This reduces the waiting time and help reduce fuel consumption. The proposed system records the traffic volume on different roads and allocates time using cameras and image processing modules.
Major Software Security Risks in Banking Industry: Design Phase Perspective

As the globe transitions to the internet age, software has emerged as the factor primarily essential to the success of the digital realm. Software now permeates every aspect of daily existence in the age of computers. It is relatively simple to administer software in any business for employees, employers, and other components of the organization. Our daily lives are made more convenient and hassle-free by this software. Shopping, news, processing sales data, architectural, medical, banking, and other aspects of our lives are all handled digitally. One of the most significant fields today is online banking, which also relies on software. Nevertheless, software security is also a significant concern. Any program, especially banking software, that has security flaws can cause us problems. The development of secure software becomes extremely difficult if there is a compromise at the design stage. Therefore, it must be avoided throughout the design phase. Software security risks are software flaws that allow harmful activities to occur accidentally. Consequently, it is a matter that must be properly managed during the design phase. The majority of Developers rely more on their knowledge and experience in risk management than on risk management frameworks. From the Common Weakness Enumeration (CWE), the authors have compiled a list of potential vulnerabilities that could be discovered during software design.
Handwritten Text Extraction from Application Forms using Image Processing and Deep Learning

Around 400 million people worldwide use English as their first language, resulting it as the most extensively spoken language across the globe. Many government offices and other businesses use offline forms, the majority of which must be filled out in English. Manually digitalizing those forms is an impossible, time-consuming, and error-prone task, so extracting text from them may solve the problem. In many organizations, they have forms that have text boxes to fill out the information. So extracting text from these forms is a crucial solution. The proposed solution comprises image recognition, so the neural network is a far more convincing approach. A deep learning model, i.e., convolutional neural networks, is used to classify the characters. The handwritten alphabets and numbers are collected from Kaggle and Mnist datasets. Using those datasets, two CNN models were trained. Image processing techniques were used, which helped in the preprocessing of the image. Finding the image's coordinates and performing a perspective transform results in the removal of undesirable areas of the input image. Horizontal and vertical lines were detected, which resulted in the finding of the rectangular boxes in the form where the data is contained. According to the type of the detected box, each character in the box is sent to the respective model, which results in identifying the character and helping to find the content in that particular field. All detected content is automatically saved in an Excel sheet. The proposed system achieves an accuracy of 85%.