Reviewerly/exHarmony huggingface dataset:
A Novel Design and Development model for people counting in a Closed Environment with Machine Learning Approach

Image processing can be used to solve some daily applications. Computer vision and Artificial Intelligence in the computer system provide the capability to work like the human mind. In the last decade, computer programming is moving towards processing images and finding useful results and analysis. This experiment is an effort to count the number of people in a closed environment. there are many situations when it is required to count the persons in the room. An application can do this work with accuracy and in less time. this can be enhanced to another area where it can count some other objects also. In this experiment efforts have ben done for the identification of people and count them correctly. It has not been tested on large numbers yet. You only look once (YOLO) real time object detection technique is used with some more microenvironments. this can be used for real-life situations and for the safety of any objects. This paper is based on YOLO. The experiments have many benefits and applications in all kinds of industries. in the future, it may be extended to videos.
Analysis of Machine Learning algorithms used in Face Recognition Attendance System

Biometric is learning of human features and behavior. The Face Recognition system is used for security. Its purpose is to identify someone’s face with his 2D image, which involves the extraction of his facial features and recognize it regardless of aging, beard, expression or pose which is very difficult. Image Analysis and Computer Vision are the challenges of face recognition for which many techniques have been built which we are going to study in this paper. So how different algorithm work and their comparison is discussed in this paper.
Machine Learning based Workload Prediction for Auto-scaling Cloud Applications

Cloud computing is a ubiquitous computing paradigm that offers its users access to software, platforms, and infrastructure as services, on-demand, over the Internet. User requests for these services (also known as workload) are placed over Virtual Machines (VMs) for execution. These VMs are hosted over Physical Machines (PMs) to abstract processing capabilities of these PMs. Over a period, Cloud services experience fluctuations in the workload pattern. To match the resources required for serving the varying workload, VMs must be added or removed autonomically as performing the same task manually is inefficient. Moreover, VMs take some fraction of time to be setup before they can be used. The goal behind automatic scale-up and scale-down operations is to ensure that the Service Level Agreement (SLA) between the cloud service provider and cloud client is upheld and cloud users experience acceptable levels of Quality of Service (QoS). The essential task in achieving this aim of automated scaling is to forecast future workload, using the data on past workload history, so that enough VMs may be setup in advance to match the anticipated change in workload trend. Auto-scalers which provision resources by predicting the workload are categorized as proactive auto-scalers. This work aims at proposing a Machine Learning (ML) based workload prediction model for proactive auto-scaling of cloud resources. To this end, we demonstrate the use of Support Vector Machine (SVM) for Multi-class classification to predict the future workload by using a real cloud dataset. The results found are plausible as they indicate efficient and accurate workload prediction.
Gesture Controlled Bot

Gesture Controlled Bot is a robot that is simply controlled with hand movements. The major application of this bot is, it can be used for physically disabled people to navigate their wheelchairs or vehicles with hand gestures. The differential mechanism controls the movement of wheelchairs. The system causes the wheelchair to revolve around its own axis without any forward or backward motion by causing each of the fourth and rear wheels on the left or right side to move in an anticlockwise direction and the other pair to move in a clockwise direction.
Modernising E-commerce Warranties using Non-Fungible Tokens on the Blockchain

With the rise of internet accessibility and situations like the pandemic, the world has seen an ever-increasing number of people turning to online shopping. Over the years, these E-commerce websites have been able to win the trust of their users. With this trust in mind, people have started buying high-value goods like electronics, smart gadgets and even furniture. While these goods are delivered with ease, another important concern in regard to these items is product warranties. Present-day warranties exist in two forms a) Physical Warranties and b) Digital Warranties. Physical warranties involve paperwork, can be easily tampered with and manipulated and are almost non-transferable. Digital warranties, although they do not need paperwork, do not ensure complete ownership transfers. To overcome the abovementioned issues, we propose a new blockchain-based solution. Our proposed solution uses Non-Fungible Tokens to issue and verify warranties. This research proposes a new way of issuing, managing and validating product warranties by making use of Non-Fungible Token(NFT). The NFT Warranty stays on the blockchain as long the warranty is valid after which it ‘burns’.
Review on Artificial Intelligence and Human Computer Interaction

The Artificial Intelligence (AI) is one of the most popular fields. AI has been used in a lot of different fields for a few decades now, including virtual help, medical care, and security. Human Computer Interaction (HCI) is a field that has been combining AI and HCI over the last few years to make a smart framework that is easy for people to use. Different algorithms related to HCI are being used in a wide range of fields to make AI-based intelligence, which is linked to HCI, and more transparent for the humans, so that they can have more faith in the machine. We have discussed at both AI and HCI, as well as their subfields, in this paper.
Genetic Operator based Binary Chaotic Grey Wolf Optimizer for Feature Selection

In today’s world, enormous data is generated through various IT devices. It is tedious and complex to process these data for extracting useful information. Feature selection (FS) plays a meaningful role in selection of highly relevant features from high dimensional dataset. The presented paper introduces a wrapper-based FS technique that integrates chaotic map (CM), genetic crossover operator (CR) and mutation(M) with binary Grey Wolf optimizer (BGWO) called CBGWO, to tackle FS problem. To increase the efficiency of the proposed approach for the FS domain, three crossover operators have been used: one-point, two-point, and uniform. To validate the effectiveness of proposed method eleven well-known UCI datasets is utilized for experiment and comparison is carried out with other well known literature.The results obtained from the proposed methods has optimal performance than the other baseline appraoches, especially when using binary chaotic GWO with uniform crossover.
Value-Based Pricing for Specialty Drugs: Solving the implementation puzzle using Blockchain

Specialty drugs are used to treat complex chronic conditions, but their high costs have attracted the attention of both payers and policy makers. Value-based Pricing (VBP) can solve the shortcomings of traditional fixed pricing by using outcome indicators of the medication to provide financial benefits. Although such pricing agreements have been widely touted, there are significant implementation barriers which include outcome measurement challenges, absence of suitable data, lack of trust among stakeholders and data privacy concerns. Blockchain technology has various intrinsic features such as decentralized storage, smart contracts, and interoperability which can be leveraged to solve these challenges. In this paper, our primary objective and contribution is to present ‘‘RxValueChain’’, a dual consortium blockchain application, that allows, in a decentralized and secure fashion, the implementation of value-based agreements for specialty drugs. The paper concludes with areas of future research for VBP implementation using blockchain to improve patient access and affordability.
Different Algorithms for Lung Cancer Detection and Prediction

In many cases we see the lung cancer play main role for human death. The premature detection of cancer is hampered by the overlapping of malignant cells. To create more effective methods for its prevention, identifying genetic and environmental causes is essential and time is a crucial aspect in identifying anomalies in target photos. The decision tree algorithm is utilized in this study to predict lung cancer, and the key patterns are examined along with their related weights and scores. The Histogram Equalization is make use of for tasks like feature extraction and image preprocessing and a neural network use different classifier that used to determine if the patient is normal or abnormal. Early detection of lung cancer will limit many treatment options, improve survival rates and lower the likelihood of invasive surgery. Therefore, a simple, affordable, and time-saving technology will yield encouraging results for lung cancer screening and prediction.
A Novel Feature Based Review score to Classify True Reviewer

With the enormous growth of online business, customers are enjoying a prioritized role, as a vital source for product information, since the review given by a customer is the major influencer to the business. Not all reviews are reliable as causality may also be a part of review writer. Many of the ecommerce websites are facing a tough challenge of identifying customers equinity in giving a reliable review for the products. Reviews do mislead at a times. Genuine customer review really affects the true opinions about the product and can even have a great impact on user recommendations which truly enhances the business. Review rewards are true biases to classify the opinions in the customer reviews. Many of customers give focused reviews to bag these rewards. NLP is todays technology handy tool in analyzing the reviews to hold any true context. In this paper, we present a feature-based review score, generated by mining the opinions of customer, to penalize customers for whom giving review is a child’s play. Our model emphasis on true review writers are real deservers of user benefits and serving them can raise the business. Our approach used contextual sense of the words used in text reviews to generate a feature based for that reviewer. The proposed method introduced three types of feature scores: the contextual score, rating score and the upvotes score for a single user, which are generated based on the context and consistency of short and long reviews given by him for various products. This score for the reviewer is used to classify the reviewer to be genuine. Our approach used a stack, to pile various scores generated from features that have high influence on text reviews. Experimental results showed the proposed feature based reviewer classification is more accurate and appropriate, in classifying true reviewers who really matter for the business.
Extreme Contextual Multi-Anomaly Identification And Tracking From Aerial Down Shot View

The total performance of tracking has improved because of the presence of several MOT (multi-object detection) systems. However, it has problems with motion fluctuations, puzzling visual changes, object detection, and tracking in crowded areas. Due to the imbalance between normal and abnormal data points, the video anomaly detection problem has long been regarded as the most difficult in visual anomaly tracking and identification. The objects of interest in the experimental study briefly appear throughout the entire tape, making it an uncommon occurrence. In addition to spatial and temporal data, video anomaly detection also considers contextual anomalies, which are anomalies resulting from the local context. The method of the Authors for detecting anomalies entails tracking many anomalies until they disappear. The down shot angle and abrupt change in video color in the Experiments set them apart from all previous video anomaly detection problems. The authors create several models that are listed below making this a novel Video anomaly detection and tracking paper.
Rabin and ElGamal Cryptosystem for a secured communication: A Detailed Cryptanalysis

Rabin and ElGamal are famous asymmetric key cryptography aside from that of RSA. Rabin and ElGamal are asymmetric cryptography algorithms that make use of two exceptional keys for encryption and decryption. This paper is novel in finding cryptanalysis of both of these algorithms. The statistical analysis we did are entropy evaluation, histogram, Ngram, floating frequency, autocorrelation and periodicity analysis with CrypTool.
A Novel Approach to Pointer Analysis

Pointer analysis refers to finding out memory locations that are accessed in many ways. Related techniques are points-to and alias analysis. More specifically, it is a compile time technique that resolves the issue of mapping of pointer variables to memory locations. This analysis has variety of applications from bug detection, memory leak identification to performing other static analysis. Our work in this paper is towards a parallel version of pointer analysis. The basis of our approach is live variable analysis performed on individual basic blocks and then sharing information about live variables at the beginning and end of each block to reassess the set of memory locations pointed to by each pointer variable. We intend to implement this as a pass in gcc. Index Terms— Pointer Analysis, gcc, Points-to Analysis, Alias Analysis
Mine Machinery Detection (MMD) from Remote sensing images: A comparative study

In the age of this industry 4.0, application of different automation techniques and different state of technology for seamless management of different unit operations in mining is very important. If we undergo a drone survey in a mine, even if in a very big mine, it does not require more than a few hours. So, within those few hours, we are going to get a sufficient number of images with that we can automatically fetch out cars/machines and thereby we can introduce an audit system in the mine frequently and this can be a very accurate system to rely on. Here in this particular investigation, we have tried to start a work that basically aims to detect ‘‘manmade object particularly like car/machine automatically. To detect the object with high precision, deep learning approach has been used which includes Yolo, Faster R-CNN, Mask R-CNN, Cascade R-CNN. The comparative study has shown that quality and quantity of data has greatly influences the performance of the model.
Product Constituents and FDA regulations for Consumer Safety Using OCR

Numerous brands employ marketing strategies to persuade customers to buy their products while concealing the potentially dangerous side effects of some of the product’s constituents, leaving the customer open to unanticipated repercussions. Therefore, a system that can use OCR, or optical character recognition, to determine whether the ingredients list in a particular product is safe for customers to use, can aid in resolving this issue. A document image is dissected into its component characters using optical character recognition (OCR). OCR can assist in turning an image into text that may be utilized to extract helpful information about a product. Barcode scanners are another option for gathering more details about the company and the item. By making the user fully aware of the common things they consume or may intend to consume, this system can empower the user.
Multi-Stage Edge-Guided Stereo Feature Interaction Network for Stereoscopic Image Super-Resolution

Stereo image super-resolution (SR) aims to simultaneously increase the resolution of stereo image pairs, which benefits many downstream three-dimensional (3D) multimedia broadcasting and stereo vision-related tasks, such as 3D television broadcasting and stereo matching. A key insight in convolutional neural networks-based stereo image SR is to enforce stereo feature interactions between the two stereo views to explore complementary cross-view features that can facilitate the SR in both views. To fully exploit the cross-view stereo features, in this paper we propose a new multi-stage network, cascaded by several stereo feature interactions, progressively improving the SR quality from coarse to fine. In particular, an edge-guided stereo attention mechanism is proposed to be embedded into each stereo feature interaction to better capture consistent structure details of the cross-views. Followed by stereo feature fusion and reconstruction modules, we finally put together a multi-stage edge-guided stereo feature interaction network (MESFINet) for stereo image SR. Comprehensive experiments on KITTI2012, KITTI2015, Middlebury, and Flickr1024 benchmark datasets show that the proposed MESFINet achieves superior performance against the state-of-the-art stereo image SR methods and can be used to improve the accuracy of stereo matching.
Efficient Feature Reconstruction via <i>l</i> <sub>2,1</sub>-Norm Regularization for Few-Shot Classification

Few-shot classification, which aims to recognize novel classes with the aid of learned knowledge and a few labeled samples, remains challenging and draws emerging concerns in computer vision. Metric-based methods incorporate the idea of metric learning into tackling the few-shot classification problem, i.e., the class membership of query samples can be determined in the latent space. Several recent methods propose to infer such memberships by feature reconstruction, which, however either exploit attention mechanisms or closed-form solutions. In this paper, we revisit the essence of feature reconstruction applied to few-shot classification and introduce a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm regularization to constraint it. As with the intention of attention mechanisms, we attempt to guide feature reconstruction towards focusing more on semantically rich target regions and diminishing the contribution of profitless features. We thereby obtain more discriminative query reconstructions for each class and perform classification based on these reconstruction errors. We formulate the proposal as a constrained optimization problem and design a simple and efficient iterative method as a solution. Furthermore, we provide the analysis on the convergence of proposed iterative method in theory. We conduct few-shot classification on two fine-grained and two general datasets. Extensive experimental results reveal that our method achieves new state-of-the-art performance.
A Novel AD-PDA-BACF Algorithm for Real-Time Moving Target Shadow Tracking Using ViSAR Imagery

Detection and tracking using video synthetic aperture radar (ViSAR) have attracted a great deal of attention in recent years due to its ability to produce high-resolution videos for regions of interest. In this work, we have chosen the background-aware correlation filter (BACF) as a key algorithm due to its superior performance in real-time tracking scenarios. Dim and small shadows with weak visual features, time-varying shadows, and complex environments pose serious challenges to the target tracking problem using ViSAR videos. These factors lead to a multipeak problem in the BACF and many other correlation filter-based algorithms. As a result, incorrect target detection, template model contamination, and tracker drift or failure during long-term tracking can occur. In this work, we propose a novel appearance-distance information-assisted probabilistic data association (AD-PDA) algorithm to tackle the multipeak problem. Based on the correlation outputs of the BACF, the AD-PDA algorithm selects multiple peak locations as validated measurements. By exploiting the appearance-distance probability distribution functions, the AD-PDA algorithm calculates the mixed appearance-distance association weights and estimates target states accurately. Furthermore, we propose an efficient AD-PDA-BACF algorithm that can track targets accurately by combining the AD-PDA and BACF algorithms. This study conducts experiments using two public ViSAR video datasets released by the Sandia National Laboratory. Our results demonstrate that the proposed algorithm outperforms several state-of-the-art algorithms in tracking dim and small targets in terms of tracking accuracy, success rate, and tracking speed.
Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization

LASSO regularization is a popular regression tool to enhance the prediction accuracy of statistical models by performing variable selection through the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> penalty, initially formulated for the linear model and its variants. In this paper, the territory of LASSO is extended to two-layer ReLU neural networks, a fashionable and powerful nonlinear regression model. Specifically, given a neural network whose output <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> depends only on a small subset of input <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {x}$ </tex-math></inline-formula> , denoted by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {S}^{\star }$ </tex-math></inline-formula> , we prove that the LASSO estimator can stably reconstruct the neural network and identify <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {S}^{\star }$ </tex-math></inline-formula> when the number of samples scales logarithmically with the input dimension. This challenging regime has been well understood for linear models while barely studied for neural networks. Our theory lies in an extended Restricted Isometry Property (RIP)-based analysis framework for two-layer ReLU neural networks, which may be of independent interest to other LASSO or neural network settings. Based on the result, we advocate a neural network-based variable selection method. Experiments on simulated and real-world datasets show promising performance of the variable selection approach compared with existing techniques.