Reviewerly/exHarmony huggingface dataset:
Defending Against Identity Threats using Adaptive Authentication

Defending against identity-based threats and attacks which have tremendously grown in number in the age of remote working and access, requires intelligent, strategic, nonconventional, and dynamic ways of authentication and authorization. This paper aims to make identity security risk-based and hence adaptive by devising risk-scoring algorithms for five real-time use cases in detail. Zero-trust security principles are incorporated by continually collecting sign-in logs and analyzing them to check for any suspicious activities or anomalies to make it a dynamic approach. Based on the risk scores calculated users are segregated as risky and non-risky. While many adaptive authentication approaches have been proposed, the identities are confined just to users. Moreover, they lack emphasis on practical risk evaluation techniques. This work considers devices as an identity too and categorizes them as registered and unregistered devices. Further, results are made available to security administrators by displaying them on a dashboard for them to analyze and make necessary decisions like mitigation, multi-level authentication, or any other access control policies as such.
Facilitating Leucocyte Count Using Deep Learning: A Paradigm Shift

White blood cell classification is a task that is given paramount importance in the field of pathology in order to accurately diagnose a plethora of ailments and diseases. Blood cell classification is essential for the calculation of the differential count of blood cells in a particular blood sample. Physicians derive a medical prognosis based on blood reports in which differential count plays a key role. To reduce the cost and difficulty of this task as well as to improve accuracy in cell classification, image processing and deep learning have proven to be viable alternatives to conventional pathological methods. To tackle this problem through image classification, we have successfully conducted a comparative study of accuracy metrics of various Convolution Neural Network models including the very popular Alexnet, VGG, ResNet, Inception, Densenet and EfficientNet. With multiple methods of preprocessing the dataset and deploying these well-known image classification models, the most accurate and computationally inexpensive deep learning model was identified for WBC classification.
Bitcoin Investopedia - Market and Price Predictor, Chatbot, and public survey on taxation

Cryptocurrency, a virtual asset, is an exemplary phenomenon floating about in the present day. Since Cryptocurrency is considered as highly volatile, knowing its future is imperative for the investors to make wise decisions in their investment. This project aims at deploying a web application with an integrated chatbot plugin which focuses primarily on assisting crypto enthusiasts looking forward to invest in cryptocurrencies and with additional tabloids like bitcoin market predictions, price predictions and a survey done based on the decision of the government to implement a tax of 30% on crypto gains and to provide an analysis of how this affects the long term investors invested during bear cycle using the best suitable Machine Learning Algorithms to gain the highest percent of accuracies.
Early Prediction of Dengue Cases Using Time Series Model

Dengue, is a seasonal infectious disease has been spreading widely throughout the world. Because of this many of the people are losing their lives. So public Health organizations and many other government organizations have been conducting surveys of dengue cases and they got to know that because of weather conditions like rain, precipitation, humidity are some of the main causes for dengue prediction. Also they use some ML algorithms and some Time Series models to predict dengue cases. Time series models they have used as (ARIMA) model and SARIMA model and seasonality. But out of this ARIMA model looks best to predict dengue cases. So we have taken ARIMA model to predicting the forecast of dengue cases. In ARIMA we have done two methods to find out the order of the model where it has values as autoregressive, moving average and non-seasonal in seasonality autocorrelation, partial autocorrelation, and AUTO-ARIMA for find out the best model for the order then we have checked for stationary also. If its stationary then we have proceeded to prediction of forecasting dengue cases, if it’s not then we have converted it into stationary and then we moved on to do ARIMA best fit which gives the forecasting cases.
Recognition of Human Activity using Signal Processing &amp; Deep Neural Networks

The task of tracking the activities being performed by humans is of immense importance as it finds application in healthcare, marketing, surveillance, etc. To keep track of this, they need to be recognized and classified into various activities. The data can be collected through smart devices such as smartwatches or smartphones which internally use various sensors to measure the physical movements of the person using it. We have used Human Activity Recognition Dataset from the UCI ML Repository, which consists of the data being collected from the smartphones of 30 volunteers. After choosing the appropriate features, we trained an Artificial Neural Network and a Long Short Term Memory Neural Network to classify the activities into various categories and performed a comparative analysis of these neural networks among themselves & also with respect to the previously leveraged techniques & algorithms on the dataset.
A Hybrid Model for Solar Radiation Forecasting towards Energy Efficient Buildings

Solar radiation forecasting is developed for electricity utilization in buildings to improve energy efficiency. In this paper, we first compare different machine learning methods for single-step solar radiation prediction, including multilayer perceptron (MLP), long short-term memory (LSTM), gradient boosting regression tree (GBRT), random forest (RF) and Informer. Then, we propose a hybrid model for short-term solar irradiation prediction, which is composed of a random forest model and an Informer model. Simulation results illustrate the effectiveness of our proposed hybrid model for balancing the accuracy-complexity tradeoff.
Effects of Particulate Matter Pollution in Predicting Solar Radiation: A Case Study of Eastern Cape, South Africa

Increasing air pollution reduces surface solar radiation, making it one of the most important factors when estimating monthly global solar radiation (MGSR). The artificial intelligence techniques (artificial neural networks and support vector regression) were compared in this study to ascertain which of the particulate matter has greater effect on the prediction of solar radiation in the Eastern Cape, South Africa. The artificial intelligence models were trained using monthly data from January 1990 to September 2011 and tested using data from October 2011 to December 2020. The data was analyzed using statistical methods such as mean absolute percentage error, coefficient of correlation, and coefficient of determination. It can be concluded that incorporating the sea salt mass concentration of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{PM}_{2.5}$</tex> yielded a better precision when estimating the MGSR in Eastern Cape of South Africa.
Real Time Human Assisted Path Planning for Autonomous Agent using VR

Path Planning is a term used in robotics that comprises motion planning approaches that can acclimate to real-time changes in the environment. Many problems in various fields are solved by proposing path planning which is widely applied in partially and unknown structured environments. Autonomous systems made up of mobile agents have established themselves as effective, durable, and adaptable instruments for military (such as search and rescue missions), commercial (such as Google self-driving cars), and exploration (such as space robots). When a target area needs to be rapidly searched (observed) by several military units (scouts, UAVs, UGVs), it is a very difficult issue for the defense to solve. This work is done considering problems faced by the defense in performing various operations in remote areas where path planning is essential. The main goal is to help defense to provide training in virtual reality and a better understanding of situations and to help making decisions in difficult times and increase performance. To overcome the risk and limitations this game is introduces actual (Real World) scenarios and provide the facility to present the whole operation virtually and perform various task remotely. It also includes situations like traffic light systems, AI car navigation algorithms, Swarm of vehicles and Rescue operations. Training in a virtual environment brings down the cost drastically because the replicas of simulate vehicles and tools that are simulated cost way less than actual inventory.
OptSpec: Optimization of Specifications Data for Engine Anatomy

Search engines are progressing with design and technology to meet the business models' continually changing and evolving needs. From crawling the links for indexing them to presenting the results based on the user query, search engine components have been researched in depth and breadth. This research investigation is focused on managing and processing the name-value pair data. 21K e-commerce data items were processed into JSON format in key-value pairs to explore the combinatorial explosion and its impact of the smaller subsets, items segregated by the domains. An item might not have all of its specifications contributing to the results in the query management. We propose a method to generate subsets of categories that can potentially contribute to the processing and results. One of the ways to manage huge data is using threads and parallel processing. We use the approach and compare the results with single threaded model. The processed and raw data storage methods have also been compared for different item sets. The model also presents the approaches of selecting the right set of specifications depending on the user query. Several methods are compared based on the limiting set threshold. The results appear to be an effective way of managing associative data. The known methods and parameters are positively better than the neural and deep learning networks that keep the methodology hidden and in the black box from the users. When the thresholds and sets are known, the algorithms can make better decisions.
Supervised Satellite Image Retrieval using Defined Patterned with ASMC

It is suggested to use remotely sensed image retrieval with query-by-example approach. This often entails using query techniques that permit descriptive semantics, queries that incorporate user feedback, machine learning-capable systems, and systems that can gauge user satisfaction. This study uses the Advanced Split and Merge Clustering approach to extract features from the textures of similar grayscale images and the spectral distribution using two-dimensional Gabor functions. The resulting pattern will be the concatenation of texture features and Spectral Distribution (SD). Modified Earth Mover's Distance (EMD) is used to limit how much of each vector can be predefined in pieces. EMD technique for establishing center correspondence between the patterns uses interactive search.
A Novel Fusion of Block Chain with IoT for Industrial IoT

Blockchain technology is propelled by distributed ledger technology (DLT). Due to its natural and unfalsified attributes (reliable, efficient, resilient and transparent) pushing it to a prime choice in several monetary related applications and financial services. With the amalgamation of artificial intelligence, various blockchain systems-based limitations can be resolved easily and rapidly in order to provide high-performing and useful results. This paper means to present a through survey on the topical developments and solutions in blockchain technology based on artificial intelligence for resolving its problems. The findings of this study will conclude the important knowledge and direction on the idea of proposing blockchain-based systems to support time-sensitive and real-time-specific applications.
An Analytical Review on Image Enhancement Techniques

This study presents an organized analysis and presentation of many existing image enhancing approaches. The fundamental idea behind image enhancement is to change images informational content so that it is more suited for certain purposes. Image Enhancement is the most significant part of Digital Image Processing (DIP). It is required to mitigate noise, blur, color distortion and artifacts. There are a lot of development in every aspect of society indeed, but still, there is lack of reliable, complete clear and availability and flow of visual, text and audio information, which is sometimes life-threatening in many sensitive areas, Image enhancement technology very much depends upon the type of picture and the domain for which the image is going to be used. There is a requirement of reliable visual data in most sensitive areas such as medical, geographical, and social security, seismology and weather forecasting. Image improvement of low-light images has grown in importance as computer vision research has become more complex due to the increased demands of the field. In this paper, first the fundamental techniques of image enhancement has been reviewed for understanding purpose and then findings together with the many benefits and drawbacks of the mentioned approaches as well as the potential for further study in this field has been presented. More Emphasize on model-based techniques has been given in this article, since they are interpretable and don’t require labeled training data.
Evidence based Employee Analytics using Taxonomy Enabled Knowledge Graph

Every organization produces a lot of data, analyzing this data, to gain insights and to make amendments is vital for running any sector. There are many documents that can be used to analyze the company’s staffing distribution. One such document is statement of work, this includes the staffing details, the client names, capabilities focused, assets and tools used in that specific project. In this paper we create a pipeline, by taking dataset comprised of all the sow documents generated by an organization and then generating insights from it. We created a utility to identify the expertise of every employee, this information can be used to further predict the employee similarity or to create an expertise-based search engine for employees. These tools can assist the management and staffing authorities at higher level. Hence, we can create a solution and save an enormous amount of human efforts and time.
Pramanik Shastra: Blockchain-based Natural User Interfaces and Interaction Approach for Storage and Translation of Sanskrit Scripts

The holy scriptures are the foundation of religion, civilization, and society. They strengthen a society's ethics and ideals. "Shastras" are Indian scriptures. Most of the important Shastras of ancient Indian civilization were destroyed by invaders, and the remaining Shastras are either too complex to understand or have been misinterpreted. Shastras must be preserved for society's ethics and wealth. Using blockchain to store vast volumes of data is efficient since the network grows stretched. Cloud storage uses attribute-based encryption to preserve security. In such systems, a private key generator can decrypt data, which can cause serious issues. The cloud is centralized; therefore, a failure can bring it down, whereas decentralized storage can avoid this. The Interplanetary File System (IPFS) overcomes the restrictions of cloud storage and blockchain when handling sensitive data. In the paper, a solution has been proposed for storing and sharing data in a decentralized way and combining IPFS with the Ethereum blockchain to create a platform where secured storage and translations of Shastras can be provided. The proposed framework is used for storing files to identify text using Web scraping, later translating the text using Natural Language Processing (NLP) and sharing the translated version of the file on the framework so that it can be easily accessed by the user as per the requirement. The proposal is evaluated with the help of IPFS, which provides a platform to store files, generate a hash value, and remove duplication as it uses content-addressing.
Automated Formative Assessment for Descriptive Solutions

Formative assessments are an essential part of any student's evaluation criteria. It assesses the student holistically and helps students monitor their learning progress. It allows students to identify the gaps between their current knowledge and the skills they wish to acquire. These assessments have the scope to be automated, and such advancement will inevitably improve the efficiency and accuracy of the evaluation process. Currently, some systems use a scoring guide and semantic meaning extractors to achieve this. There is a need for a solution that can analyze the answers written by students to help the instructor provide quality feedback. The proposed solution aims to help teachers and students by evaluating the students' responses and questions that the teacher will pre-set. This system will give relevant feedback to the student's explanatory answers. This feedback contains suitable keywords suggestions, the accuracy of the solution, peer comparison, and overall performance in the test. This test can be taken as often as required until the student achieves desirable performance, enabling relevant feedback. The test iterations can be as often as the teacher desires until the student reaches the desired performance. The experimental design was used to analyze the performance of the students. The results indicate significant improvements in the subsequent tests, including the feedback suggestion to the students.
Performance Analysis of Knowledge-Based Optimization Algorithm for Effective Resource Allocation

The word "adaptability" refers to the process of continuously adjusting the resources to meet the changing demands of the application or service. The cloud must be able to quickly scale up or down to meet the current demands of the application or service. This is important to maintain the performance and quality of service as the workload changes. Cloud computing also requires the use of automation to ensure that resources are allocated and utilized most efficiently. Automation also contributes to the adaptability of the cloud as it allows for the rapid deployment and configuration of applications and services. Knowledge-based optimization algorithms (KBO) are widely used for resource allocation in many areas such as transportation, scheduling, and energy management. KBO algorithms are a powerful tool for solving challenging optimization problems. They can quickly and accurately identify the best solution to complex problems. The effectiveness of the KBO algorithm in solving two resource allocation problems, the knapsack problem and the minimum cost flow problem, has been examined in this paper. The algorithm was evaluated based on its ability to find the optimal solution and its running time. The results indicate that the KBO algorithm can solve both problems quickly and accurately. In comparison to other algorithms such as heuristic algorithms, the running time of the KBO algorithm is significantly lower. Additionally, the KBO algorithm can find the optimal solution with a high degree of accuracy.
Engineering Education and the Use of Virtual Reality: Idea and Possibility

Prior research on using Virtual Reality and digital prototyping in the creation and evaluation of new items has shown that VR shortens development times and costs while boosting student engagement and creativity. Industries have made substantial use of VR and digital prototyping for product design and usability testing. Numerous studies have sought to investigate how VR affects cooperation, retention, engagement, and motivation in the setting of engineering education. Using VR in conjunction with a project-based learning strategy is hypothesized to increase learning outcomes for the course and foster effective communication while facilitating the accomplishment of desired objectives in the engineering design project.
VIhanceD: Efficient Video Super Resolution for Edge Devices

Video Super Resolution (VSR) aims to generate high-resolution (HR) frames from corresponding low-resolution (LR) frames. It draws a stark contrast from single image super-resolution (SISR) because of its high temporal dependency on misaligned supporting frames. The existing methods involve using RNNs to learn the temporal dependency by using other networks (CNNs, GANs) for predicting neighboring pixels. Due to the memory and processing constraints and the inference time required for up-scaling LR frames, a wide variety of VSR techniques cannot be applied to mid-range and budget mobile devices. This paper presents VIhanceD, a real-time sliding window-based network that can operate on budget smartphones and laptops while producing cutting-edge results on various video datasets. Our approaches include both spatial and temporal dependencies to make the up-scaled HR frames coherent and free of motion distortions. We focus on enhancing the user experience in areas with internet restrictions due to social, political, and geographical limitations. The mobile app (and PC client) provides a continuous stream of HR frames without buffering. We obtained 33.9 PSNR on REDS Dataset with a single frame inference time of 23.6 ms demonstrating state-of-the-art performance. Our subsequent experiments on VIMEO-90K dataset demonstrates that the suggested method is generalizable and works on natural video frames and textual data, making it suitable for infotainment and multimedia.
Performance Analysis of Rules Generated Hybrid Optimization Algorithm for Resource Allocation and Migration in the Cloud Environment

The cloud service model is generally a framework that provides three types of services to the client. These three commonly used models for delivering cloud computing services are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The IaaS model is sometimes referred to as the Hardware as a Service (HaaS) model. One of the most influential characteristics of IaaS is to have higher scalability in sub-service provisions. Dynamic and flexible, along with having automated administrative task operation ability. For any cloud computing system, Infrastructure as a Service (IaaS) is typically the foundational layer, providing access to physical servers, virtual machines (VMs), and other resources necessary to support running applications. In the present scenario, the global cloud data centers are facing a relatively higher number of issues like lacking proper capacity planning along with adequate capacity management, reducing the cost and reducing the energy stages or, on the other hand, increasing energy efficiency. This study aims to propose a resource allocation and migration technique for improving energy efficiency. The proposed study will be considering the Rule Generated Hybrid Optimization technique. The results indicated that the suggested approach outperform Spark Lion Whale Optimization when energy consumption and resource utilization was considered.