Reviewerly/exHarmony huggingface dataset:
Effective Surveillance using Computer Vision

Human society significantly relies on CCTV cameras to maintain a high standard of security. CCTV footage is usually only utilized a few hours or days following an incident. Although it frequently serves as important evidence in court, it is rarely used for crime prevention or real-time intervention. The fact that only few security officers are primarily in charge of watching the ample amounts of CCTV footage is what causes inefficiency. Human supervision cannot be completely relied due to the weariness and boredom among the workforce, and lag in observation. Developing an effective surveillance system is a significant challenge in computer vision, data processing, and decision-making process. Smart Surveillance Systems use image analysis and machine learning techniques to extract and analyze the data from CCTV footage far more quickly and effectively than a human observer. This would encourage the relevant authorities to create a platform using image processing and machine learning techniques so that the violence in public places might be prevented. The system must also involve cops when the situation worsens and make sure that it captures every human involved in the respected frame. The system must be trained thoroughly to ensure that it can recognize whether there is violence.
A Survey on Artificial Intelligence (AI) based Job Recommendation Systems

Job recommendation systems are AI-powered platforms that aim to provide job seekers with personalized job recommendations based on their skills, experience, and preferences. However, these systems face several challenges such as dealing with sparse data, maintaining data privacy and security, overcoming bias and discrimination, and ensuring transparency and interpretability. Recent techniques used in job recommendation systems include deep learning, reinforcement learning, and knowledge graphs. These techniques help to address the challenges such as dealing with sparse data, improving the accuracy of recommendations, and ensuring transparency as well as interpretability. The results of this study also helps to improve the job search experience for job seekers, reduces the time taken to find suitable job opportunities, and increase job satisfaction. Consequently, the job recommender architecture serves as a mediator. Accordingly, this work presents a detailed comprehensive survey of several filtering, machine learning and deep learning techniques that has revolutionised job recommendation system. Multiple applications and the associated challenges with existing job recommendation system are also discussed briefly.
E-Voting Through Two Step Verification using Machine Learning

Most people are incorrect in their assumption that using paper ballots is indeed a relic of the past. Voter confidentiality was severely compromised by these early paper ballots, making them easy targets for a variety of electoral fraud schemes. Thus, it is crucial to guarantee that the ruling body is chosen through free and fair elections. The primary objective of this study is to design a trustworthy and simple-to-use platform for conducting elections online. In terms of security, the voting problem has not been resolved. A web-based deciding to vote system that makes use of face recognition software and Gmail verification is the focus of this system. There are no barriers that can be avoided when using the “E-Voting System” outlined above. At their core, online voting systems ensure that your vote counts by capping the amount of times an individual can vote. As a web-based service, there is no need for rallies, ballots, or any other analogue voting procedures (e.g. email, insecure survey software). Voting machines, online voting, and online election systems represent the same system: a voting system that takes place entirely online. All of these have to do with the same thing: having a reliable voting system that lets you survey everyone in your organization and analyze the results thoroughly in real time.
Role of Blockchain Technology in Healthcare

One of the high significant findings and inventive advances which play a major part of today’s workplace is blockchain automation. Blockchain technology is moving towards a continuous revolution and change. Blockchain is often considered as the most necessary and optimal solution in healthcare technology for solving security and interoperability challenges. The main purpose of this study is to secure the data. The enormous growth of blockchain automation has wide applications in the field of supply chain, business, IoT including healthcare. This study intends to provide an analysis on the application of blockchain in healthcare.
Exode: Humanoid Healthcare Robot

A Humanoid healthcare robot is a type of robot that is designed to assist with healthcare tasks, such as administering medication or monitoring vital signs. These robots may be used in hospitals, nursing homes, or other healthcare facilities to help healthcare workers perform their duties more efficiently and effectively. They may also be used in patients’ homes to provide ongoing care and support. Humanoid healthcare robots can be programmed to perform a wide range of tasks, including dispense medication, take vital signs, provide companionship to patients, and assist with physical therapy exercises. Some Humanoid healthcare robots are designed to be used by healthcare professionals, while others are intended for use by patients or caregivers. The system could work by having the companion robot, which could be a small, mobile robot designed for use in the home, interact with the older adult through natural conversation. The robot could be programmed to ask the older adult about their medications and when they last took them, and to provide reminders and alerts as needed. The system could also be designed to allow healthcare professionals, such as pharmacists or primary care providers, to remotely monitor the older adult’s medication adherence and to provide support and guidance as needed. This could be done through the cloud, allowing healthcare professionals to remotely access the system and interact with the older adult through the companion robot. Overall, a Humanoid healthcare robot and cloud technology could potentially be a useful tool for helping older adults manage their medications more effectively, improving their health outcomes and quality of life.
Automated Object Detection and Classification using Metaheuristics with Deep Learning on Surveillance Videos

Object detection and classification from surveillance videos is a procedure of exploiting computer vision (CV) and machine learning (ML) techniques for categorizing and identifying objects from realtime video streams. This assists in understanding and analyzing the scene, tracking object movement, and identifying potential security threats. This technology has been extremely utilized in security and surveillance schemes, autonomous vehicles, and retail analytics. This study develops an Automated Object Detection and Classification using Metaheuristics with Deep Learning on Surveillance Videos (AODC-MDLSV) technique. The presented AODC-MDLSV technique proficiently detects and classifies the objects into multiple classes. To do so, the AODC-MDLSV technique initially performs object detection using YOLO-v5 model. In the next stage, the AODC-MDLSV technique employs random vector functional link network (RVFL) method for object classification purposes. Finally, artificial bee colony (ABC) optimization methodology is used as a parameter optimization approach to improve the detection efficiency. The simulation values of the AODC-MDLSV approach are tested on benchmark video and the results showcased the better performance of the AODC-MDLSV method with maximum accuracy of 99.93%.
Forensic Speaker Recognition System using Machine Learning

Forensic speaker identification is the process of determining the identity of an unknown speaker based on their voice. It is often used in legal proceedings to determine who made certain recorded statements or phone calls. Several different methods can be used for forensic speaker identification, including acoustic analysis, which involves examining the physical characteristics of the voice, and linguistic analysis, which involves analyzing the language and speech patterns used by the speaker. In order to be admissible in a court of law, forensic speaker identification must be conducted by the experts, who follow established scientific protocols and can provide a high level of accuracy.
Solar Radiation Prediction using Machine Learning Model

The two primary categories of methods for predicting the world’s solar radiation were scientific estimates & machine learning models. The purpose of this work is to provide a summary of solar radiation prediction in this context using machine learning algorithms. It will be shown that, despite the fact that several studies describe methodology such neural network models or support vector regression, different methods (such as regression model, random forest, XGBoost, etc.) tend to be used in this prediction. Ranking the efficiency of such methods is difficult because of the diversity of the data gathering, time step, predicting range, setup, and performance metrics. The prediction inaccuracy is quite comparable overall. To improve the effectiveness of forecasts, some authors advocated the adoption of hybrid models. The primary goals of this study are to assess the level of prediction by using meteorological information and to determine accuracy following training and testing. Results from Gradient Boosting (XGBoost) were compared to those obtained from LSTM model simulations. According to the results, LSTM network surpassed XGBoost on the same dataset by a large margin with a normalized the Root Mean Square Error (RMSE) value of 0.02%. Because of dataset is a time - series data one, LSTM performs better.
Role of Cloud Computing in Goods and Services Tax(GST) and Future Application

Due to the availability of IT infrastructure and a shift in government advisors’ perspectives, cloud-based e-governance is currently becoming a reality. To effectively monitor and manage governmental policies, this article offers a practical strategy that combines the capabilities of cloud computing and social media analytics. The foundation of every economic system is taxation. Tax evasion, tax calculation, compliance process, and return filling are some major regulatory challenges. This is a problem that technology can handle perfectly. India’s implementation of the goods and services tax is a significant shift in indirect taxation that would not have been achieved without technology. The manual processes used in the pre-GST era resulted in cost compliance issues and input tax credit ambiguities. Important technologies utilized in GST include big data, AI, cloud computing, etc. This study emphasize about role of cloud computing in GST.
Use of Big Data Technologies for Credit Card Fraud Prediction

Globalization has led to an increased usage of credit cards as the mode of payment for various kinds of transactions, both online as well as offline. Just as a coin has two sides, Globalization also has another side to it – increased affinity of the Banking industry to fall prey to fraud. The main objective of this paper is to study the best methodology to predict credit card fraud accurately. By utilizing big data technologies and Machine learning algorithms (Logistic Regression, Decision Tree, Random Forest, etc.), fraudulent transactions in credit card operations can be predicted in advance to mitigate further risk. This paper outlines the best pre-processing practices for accurate results of credit card fraud prediction. The secondary mode of Qualitative research is chosen for this research paper. This study would benefit the banking industry, credit card companies, and academicians by helping them understand how algorithms can be refined to detect and mitigate fraudulent credit card practices.
Yoga Pose Recognition with Real time Correction using Deep Learning

In day-to-day life, it can be difficult for a person to devote his time to attend Yoga classes. In Yoga sessions, there might be a lack of individual attention for each person. While performing poses, incorrect muscle usage might lead to long-term muscle pain, back pain or many other deformities. To solve the aforementioned problems, a web application is built where a person can correct yoga pose. The Proposed methodology is working with TensorFlow lite Pose detection python module for recognizing human action based on Yoga Pose Classification using Image Processing and Deep Learning. The Objective of pose estimation is for monitoring the movement of human pose for distinct exercises. From this, the recognition of yoga poses can be done using backend part and wrongly recognized yoga poses can be corrected using frontend part. A real-time test is also carried out within a group of 5 people (three men and two women), and the accuracy attained is around 90%. Using deep learning, the proposed model accuracy is evaluated by fitting the training data and predicting it over the testing data which is estimated to be around 98%.
An Investigative Analysis of Blockchain in the Supply Chain Management

At the outset, supply chain technology is evolving rapidly with new technologies and provide promising opportunities. Implementation of blockchain technology in the supply chain has the capacity to increase efficiency and transparency of the supply chain while decreasing administrative costs. By using blockchain technology for supply chain, vendors can track various details of orders like location, date, price, quality, certification and some other inventory details like quantity of stock further needed etc. more effectively. This data through blockchain will improve visibility and compliance over any contract that is outsourced for manufacturing because now every vendor involved will have access to the block, it improves traceability during acquiring raw material for supply chain, which will lead to reduction in losses from the gray market by reducing the selling of counterfeit products, and will strengthen an organization’s position as a pioneer to manufacture ethically.
Keypoint-enhanced adaptive weighting model with effective frequency channel attention for driver action recognition

Traffic accidents caused by distracted driving have become a threat to people's lives and properties, so it is necessary to recognize driver actions for early warning effectively. Since it is sometimes impossible to distinguish similar activities only by global driving image features, we explicitly extract the action-related keypoint features and propose a keypoint-enhanced model for classification. Specifically, we construct an effective frequency channel attention module to generate discriminative global representations. Considering the diversity of keypoint information, we design an adaptive weighted residual bottleneck to make the model weights of the input keypoint features dynamic. Furthermore, we propose a keypoint-guided conditional computation module. Under the guidance of keypoint features, the expert weights generated by conditional computation enable the model to adapt to different categories of driving images. Essentially, the model generates keypoint-enhanced attention to scale the classification feature channels. We also propose a model backbone training strategy combining self-supervised and supervised contrastive learning so that the model can achieve better results without large-scale labeled driver behavior data.
Symmetric polynomials over finite fields

It is shown that two vectors with coordinates in the finite q-element field of characteristic p belong to the same orbit under the natural action of the symmetric group if each of the elementary symmetric polynomials of degree pk,2pk,…,(q−1)pk, k=0,1,2,… has the same value on them. This separating set of polynomial invariants for the natural permutation representation of the symmetric group is not far from being minimal when q=p and the dimension is large compared to p. A relatively small separating set of multisymmetric polynomials over the field of q elements is derived.
Credibility logic for artificial intelligence

The limitations of current logical systems in addressing vagueness and randomness restrict their application in the field of artificial intelligence (AI). To address this issue, we introduce an axiomatic mathematical system called statemental credibility logic (SCL). First, we jointly define logical operations and equivalence. Then, we introduce the concept of a truth measure. By extending the law of excluded middle and the law of non-contradiction, and using them in conjunction with other self-evident rules in the axiomization of SCL, we can incorporate classical propositional calculus and probability theory. Furthermore, SCL can handle statements that exhibit varying degrees of vagueness and randomness. We also extend SCL to deal with predicates that involve uncertainty. We believe that SCL and its extension have the potential to improve the reasoning capabilities of intelligent systems.
Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm

This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation. Firstly, a Depth Sorting Fast Search (DSFS) algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees∗ (Q-RRT∗) algorithm. A cost inequality relationship between an ancestor and its descendants was derived, and the ancestors were filtered accordingly. Secondly, the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm, taking into account the fitness, safety, and asymptotic optimality of the routes, according to the gravity suitability distribution of the navigation space. Finally, experimental comparisons of the computing performance of the ChooseParent procedure, the Rewire procedure, and the combination of the two procedures for Q-RRT∗ and DSFS were conducted under the same planning environment and parameter conditions, respectively. The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT∗ algorithm while ensuring correct computational results.
Offline text-independent writer identification using a codebook with structural features

Understanding handwritten documents is a vital and challenging problem that attracts many researchers in the fields of forensic and authentication science. This paper presents an offline system for text-independent writer identification of handwritten documents. The system extracts a handwritten connected component contour, which in turn is divided into segments of specific length. The system utilizes the concept of a bag of features in the writer recognition domain and considers handwritten contour segments to extract two conceptually simple and effective structural features. These features are the contour point curve angle and the CONtour point CONcavity/CONvexity. The system uses the proposed features to train a k-means clustering algorithm to construct a codebook of size K. The method then uses occurrence histograms of the extracted features in the codebook to create a final feature vector for each handwritten document. The effectiveness of the proposed features is evaluated in the writer identification domain using two widely used classification methods: the nearest neighbor and the support vector machine techniques. The proposed writer identification is evaluated on two large and public datasets from different language domains, the Arabic KHATT and English IAM datasets. The experimental results show that the proposed system outperforms state-of-the-art methods on the IAM dataset and provides competitive results on the KHATT dataset with respect to the identification rate.
Tampering localization and self-recovery using block labeling and adaptive significance

This paper proposes a scheme for localization and restoration of image tampered regions using block labelling and adaptive significance. To generate the watermark information which includes authentication data and recovery data, we propose a block coordinate labelling method, which extracts the exact coordinate position information of each block, while the recovery data is composed of Block Adaptive Significances (BAS) and bitmaps, which are composed of high and low adaptive significance. To detect the tampered area more effectively, we propose a dual detection approach that combines the block-based labeling (BBL) and pixel-based labeling (PBL). We embed the authentication data into each pixel in the block sequentially and embed the position coordinate information of the block into the whole image in ascending order. The PBL approach can be used to rapidly complete tamper detection when the requirements for PBL are satisfied, whereas the BBL is used to increase the possibility of successfully detecting tampering if the conditions are not satisfied. Furthermore, we propose a block-level partially symmetric mapping and apply it to self-recovery bits in block units, thereby reducing the possibility of recovery bits being lost. The experimental results show that in our scheme, the average precision reaches 86.70%, which is 4% higher than the existing results, and the average F1score reaches 92.02%, which is 2% higher than the existing results.
Textual entailment classification using syntactic structures and semantic relations

In this paper, we propose a method exploiting syntactic structure, semantic relations and word embeddings for recognizing textual entailment. The sentence pairs are analyzed using their syntactic structure and categorization of sentences in active voice, sentences in passive voice and sentences holding copular relations. The main syntactic relations such as subject, verb and object are extracted and lemmatized using a lemmatization algorithm based on parts-of-speech. The subject-to-subject, verb-to-verb and object-to-object similarity is identified using enhanced Wordnet semantic relations. Further similarity is analyzed using modifier relation, number relation, nominal modifier relation, compound relation, conjunction relation and negative relation. The experimental evaluation of the method on Stanford Natural Language Inference dataset shows that the accuracy of the method is 1.4% more when compared to the state-of-the-art zero shot domain adaptation methods.
A new attack on some RSA variants

Some variants of the RSA cryptosystem use a modulus of the form N=pq, a public exponent e, and a private exponent d satisfying a key equation of the form ed−k(p2−1)(q2−1)=1. In this paper, we use Coppersmith's method to solve the key equation when the prime factors p and q share an amount of their least significant bits. Our attack breaks the systems and improves all the former attacks on such variants when d is suitably small and the amount of the shared bits is suitably large.