Reviewerly/exHarmony huggingface dataset:
Fast Prediction of Electromagnetic Scattering Fields Based on Machine Learning and PSO Algorithm

In this paper, a method based on support vector regression (SVR) using radial basis function (RBF) and the particle swarm optimization (PSO) algorithm is proposed to accurately predict electromagnetic (EM) scattering fields versus any incident angle at the specific frequency we are interested in. Compared with the simulation results by FEKO using the method of moment (MoM), this method can accurately predict the monostatic RCS with the root-mean-square error (RMSE) less than −9 dBsm.
Efficient Inductive Logic Programming Based on Particle Swarm Optimization

Inductive Logic Programming (ILP) is an inductive reasoning method based on the first-order predicative logic. This technology is widely used for data mining using symbolic artificial intelligence. ILP searches for a suitable hypothesis that covers positive examples and uncovers negative examples. The searching process requires a lot of execution cost to interpret many given examples for practical problems. In this paper, we propose a new hypothesis search method using particle swarm optimization (PSO). PSO is a meta-heuristic algorithm based on behaviors of particles. In our approach, each particle repeatedly moves from a hypothesis to another hypothesis within a hypothesis space. At that time, some hypotheses are refined based on the value returned by a predefined evaluation function. Since PSO just searches a part of the hypothesis space, it contributes to the speed up of the execution of ILP. In order to demonstrate the effectiveness of our method, we have implemented it on Progol that is one of the ILP systems [6], and then we conducted numerical experiments. The results showed that our method reduced the hypothesis search time compared to another conventional Progol.
Creating Trust Within Population of Evolutionary Computation in an Uncertain Environment Using Blockchain

Various population-based optimization methods have been proposed following the development of evolutionary computation. Optimization is achieved through the interactions of many individuals in these systems. However, reliability becomes an issue when the system is implemented in a distributed environment. It may not be possible to trust others in such an environment. Many factors, such as malfunction of distributed parts or failure to synchronize, will break the trust. Therefore, there must be some mechanism that can build trust between distributed individuals. The record of past actions is usually a good source for trust building. This paper utilizes the blockchain mechanism for the population-based optimization system to make a trust management system. By using blockchain, we can implement trust without a central authority. In the system, all interactions are reviewed and get feedback, and the feedback is used to calculate the trust score.
An effective image retrieval watermarking technique for the copyright of digital images and digital image protection

Science and technology advancements have created a need for getting the data protected, authenticating the data, and integrating it, claiming ownership, labeling the content, and securing it. Because digital image watermarking is applied in grayscale, halftone, and color, all types of digital data are protected by watermarking. However, the Internet does not generally have secure links, so information in transit may also be susceptible to interruption. Watermarking can be applied to digital images of medical images and 3D models, for example. In the real world, reducing the possibility of knowledge being gathered during transmission is a big concern. The digital watermarking method makes it possible to distribute digital information quickly and inexpensively over the Internet. By using digital watermarking methods, copyright holders are able to protect their intellectual property during the dissemination process. This type of watermarking allows the insertion of various data into an image without affecting its value. In traditional approaches to supporting interactive searches within RF sessions, relevance feedbacks (RFs) have been incorporated into the process of expressing and satisfying users’ information needs. However, the user effort is considerable when searching large datasets through RF. Watermarking research encompasses a variety of fields, including multimedia security, forensics, data authenticity, and digital rights management. We have proposed a novelette semi-supervised methodology to secure RF-based searches at the present work by utilizing a novel semi-supervised methodology.
Influence of COVID-19 Confinement and Pandemic on the Academic Performance of Students

The outbreak of the pandemic caused by the SARS-CoV-2 virus in 2020 and the consequent confinement and other restrictions imposed by the authorities posed a great challenge for the entire society, and, particularly, it was a great challenge for the educational system. To analyse the effect that the pandemic has had on students’ performance, this paper analyses the academic results of three different courses at the Universitat Politècnica de València (UPV): before the pandemic (2018–19 academic year); at the beginning of the pandemic, where the strictest confinement measures were imposed (2019–20 academic year) and in later stages of the pandemic where the restrictions were eased (2020–21 academic year). We analysed three first-year physics courses from three different degrees. Despite the efforts made by teachers to try to achieve an evaluation as similar as possible to the situation before the pandemic, an apparent increase in the average grade was observed in the year of confinement. This increase can be explained because the evaluation was fundamentally online. Students were evaluated based on academic work and online exams, where they had at their disposal several means to help them to take those exams. After confinement, the academic results before the pandemic were recovered. This fact is very remarkable since it must be considered that these students are the ones who the previous year had completed a large part of the high school course in confinement conditions. In addition, they have followed the course in a semi-classroom modality recovering the face-to-face assessment. The fact that they had academic results that were similar to those before the pandemic indicates that the course's confinement and semi-attendance have not significantly affected their subsequent academic performance. The percentage of students who withdraw from the three courses has also been analysed.
Now You See Me. Now You Don’t.

Apart from skipping headings, ropes, and the fact that you can’t skip a stone 89 times, one of the most important elements that a designer can have an input on is contrast. Whether it is contrast between colors, contrast between sections within a website, or even contrast between shapes, it is an easy way to identify different elements on a canvas, and it is vital for accessibility and good usability. At least, in theory. And if you’re noticing a pattern in me arguing about things that have long been accepted as the norm and have been proven to be correct, you’re probably right. Call me argumentative all day long. You would be 100% correct, so there’s some data right there. Just because something has been accepted doesn’t mean we should not change our minds based on available information, as keeping an open mind to change is exactly what we’re talking about. Bottom line is that contrast matters. And it matters a lot.
A Comprehensive Review of CNN-Based Sign Language Translation System

One of the most crucial tools for connecting with others is communication. Effective communication skills can smooth our path and improve our interactions with people in our daily lives by allowing us to understand and be understood by others. Many deaf and mute people rely on sign languages as their primary mode of communication. Recent research in sign language translation systems (SLTS) has yielded impressive results. The aim of the paper is to study the existing translation mechanism of sign language. The review starts with the classification of sign language systems and contemplates country-wise sign languages, different data sets used for the development of the sign language translation system, the architecture of convolution neural network (CNN)-based models, and their performances. It is intended that this study will serve as a road map for future research and knowledge development in the field of sign language recognition as well as translation system in the field of CNN.
Handwritten Digit Recognition Using Machine Learning

Today, we are living in a world where we are totally surrounded by numbers, the digits, directly or indirectly. The digits are everywhere whether it be your smart phones, latest gadgets, vehicles, technology-oriented infrastructures, etc. Today, we are focusing more and more on the digitalization and automation of almost everything. So now, it becomes very much important to get into the technologies like machine learning, artificial intelligence, deep learning, etc. to classify and recognize those digits. So, in this project, we tried to execute a model which recognizes the digits and makes our tasks and our difficulties very easier. Our model identifies the given digits with an accuracy of 98.40% which is actually great. We are using the convolutional neural network (CNN) to train our model.
Anomaly Detection Techniques in Intelligent Surveillance Systems

Finding strange behavior in busy places is important and a hot topic in the computer vision and information retrieval communities. Unlike the hand-made features that are often used in traditional anomaly detection methods, the features found by deep neural networks are much more accurate representations of how things look and move in different situations. In this research, we came up with a new way to find things that are not normal by using convolution kernels to learn how to personify features. Our system is completely monitored. First, we feed our proposed CNN a set of raw frames by resizing images to 64 pixels and compressing them. We then prepare data for training and testing by splitting liable frames into training and testing sets. A multi-layer (CNN), which is a type of deep learning algorithm, was used to train the system. Lastly, this method is tested using a large-scale video surveillance dataset. Experimental results benchmark datasets: UCF-Crime. Compared to the most modern methods, it is also cost-effective and easy to use. We got to our version 99.99% precision.
Resource Provisioning Aspects of Reserved and On-Demand VMs in Cloud Computing Environments

On-demand resource availability and pay-as-you-go are the key benefits of cloud computing. It eases the cloud service users to adjust the computing demands and pay accordingly. However, cloud service providers offer different provisioning plans for virtual machines (VMs): reservation-based plans and on-demand access plans. The key premise of reserved VMs is the upfront fee paid by the users; i.e., the higher the upfront payments by users, the more significant the discounts. However, these benefits can only be availed by instance usage. In contrast, on-demand VMs can be leased with comparatively higher time quanta fees. Under such a scenario, leasing cloud VMs from an infrastructure service provider would be problematic. The variations in the arriving workloads can render either under-provisioning or over-provisioning for the Software as a Service (SaaS) provider. Under-provisioning would cause Service-Level Agreements (SLA) violations. In contrast, over-provisioning would inflict higher leasing costs at the SaaS provider. So, this paper chronologically investigates all such issues under high varying workloads as well as low varying workloads. Unlike other studies, this paper investigates VMs leasing plan for mobile device users by considering waiting time as a quality of service (QoS) metric. Simulation results show that purchasing a mixed blend of reserved and on-demand VMs yields better monetary cost-savings even in low varying workloads. We also outline the open challenges for the mixed resource leasing plans.
D-Test: Decentralized Application for Preventing Fake COVID-19 Test Certificate Scam by Blockchain

More than 6 million people have lost their lives due to COVID-19 across the world (Ghatkopar in Fake negative COVID-19 certificate scam unearthed, 2019, [2]; WHO (World Health Organization) in https://covid19.who.int/table , [3]). Recently, fake COVID-19 test certificate scams have spiked up drastically and become one of the reasons for the spread of COVID-19. In light of the current scenario, this paper proposes a decentralized approach called, “D-Test” for COVID-19 testing which allows the hospital and the general public to register themselves at a common platform which follows the concept of CIA triad (Confidentiality, Integrity, and Availability) and allows users to register without any fear of data breach. This platform registers users based on smart contract and enables the user to do the following once registered successfully: (a) Book Testing Slot, (b) Find nearby registered testing laboratories, and c) Generate the COVID-19 reports which could be imported and exported as and when required by the user. This has a higher value of trust because the source of the report can be traced back since usage of Blockchain prevents the likelihood of data tampering by an entity. This framework could help the government(s) keep track of distributing authentic COVID-19 testing certificates, prevent the fake COVID-19 testing certificate scams, and will speed up the process of verifying the users’ test reports, thereby saving lives of many citizens around the world.
Occlusion Reconstruction for Person Re-identification

Person re-identification is very important for monitoring and tracking crowd movement to provide security in public places. Although this domain has gotten enough attention in the last few years, with the introduction of deep learning to develop an automated re-identification model. There are several challenges in the task, one of them is performing re-identification in the presence of an unconstrained situation (i.e., occlusion) that has not been addressed effectively. In this work, we focus on re-identification from RGB frames captured by multiple cameras. Occlusion reconstruction in these captured non-sequential frames has been done by employing the generalization capability of generative adversarial networks (GANs). Our algorithm is based on a multi-model approach to jointly perform occlusion reconstruction along with re-identification. The performance of the proposed approach is evaluated on three publicly available datasets, and the results obtained are quite satisfactory and superior to all the existing approaches used in the study.
A Smart Movie Recommendation System Using Machine Learning Predictive Analysis

These days, recommendation engines are so pervasive that many of us are not even aware of them. A recommendation system is essential for a better user experience because no one could possibly read through all of a website’s offerings or information. Additionally, it makes more inventories visible that would otherwise be hidden. Amazon’s review sites, Netflix’s proposals for shows and movies in your newsfeed, YouTube’s suggested videos, Spotify’s suggested music, Instagram’s newsfeed, and Google AdWords are all examples of recommender systems in use. This study suggests a Python-based machine learning predictive analysis-based intelligent movie recommendation system (RS). In this study, RS uses the correlation between numbers of factors to obtain precise results. According to the simulation results, RS has improved in terms of content and data similarity from users. The purpose of this study is to acquire the skills necessary to carry out feature engineering, handle missing values, manipulate data in accordance with specifications on real-time data, and suggest appropriate things specific content and likeness.
Sentiment Analysis on Amazon Product Review: A Comparative Study

Social media has evolved into a highly strong means of communication between individuals, allowing them to express their opinions and ideas in each conversation or article, resulting in a massive volume of unstructured data. Organizations must process and research the data as well as gather business information in order to analyze it. In this article, machine learning models such as Multinomial Naive Bayes, Logit Regression, Linear Support Vector Classifier SVC, and Multinomial Random Forest are used to analyze Amazon's product reviews. We conducted a comparison examination of these models by implementing them and deciding which model detects the polarity of sentiments with the greatest accuracy, and we discovered that Logit Regression and Linear SVC both perform well, with 87.3% and 87.4% accuracy, respectively. To summarize, the purpose of this study is to do a comparison analysis in order for future researchers to choose the best algorithm for their research.
A Strategy to Accelerate the Inference of a Complex Deep Neural Network

Deep learning is an effective ML algorithm capable of learning and extracting deep representation of data with utmost accuracy. The outstanding performance of deep learning models comes with a series of network layers that demand high computational energy and add latency overhead to the system. Inference of a deep neural network (DNN) completes and delivers output after processing all the network layers irrespective of the input pattern. The complexity of the deep neural network prohibits its usage in energy-constrained low latency real-time applications. A possible solution is multi-exit neural networks that introduce multiple exit branches to the standard neural networks. These early exit neural networks deliver output from their intermediate layers through exit points based on specific confidence criteria. The majority of the input sample can be processed at the initial layers of the network, while more complex input samples can be forwarded further for processing to the remaining layers of the network. This paper analyzes the performance of early exit deep neural networks against their confidence criteria and the number of branches. This study also evaluates the classification accuracy among exit branches. For analysis, implements an object detection application using the early exit MobiletNetV2 neural network and Caltech-256 as datasets. The experiments prove that early exit DNN can speed up the inference process with acceptable accuracy, and the selection of confidence criteria has a significant impact on the system performance.
Journal of Scientific Temper

A student's academic performance is determined not only by the understanding of the subject in the school environment, but also by the student's ability to learn that subject at the school level. If the school environment is not friendly, this should provide students with another alternative teaching method, allowing them to study their subjects online. To meet their academic needs, teachers can choose any type of deal to share their lessons among students. Kalvi TV is the most important tool for the student community to fill gaps in subject and study skills. In the school curriculum, science subject is considered the most important factor in determining a student's success in future careers and other academic development.To strengthen students' scientific knowledge, we can teach science subject online and improve their study skills to become masters of science subject. To engage students and meet their needs without interrupting the school year, an alternative to Kalvi TV programming is the best TV broadcasts to improve their study skills and academic performance. Modern society provides students with various educational opportunities to study their subjects. However, some natural disasters can disrupt daily educational activities, thereby affecting students' learning. Note the need for some alternative arrangements to keep students' skills and knowledge up to date, etc. kalvi TV is one of the best media for students to study subjects within their own time constraints. Also, the level of students improving their expertise through the kalvi TV channel is at an average level
Alternating Projection Temporal Epistemic Logic

Model checking is an automatic technique used to verify the properties of software and hardware systems. In the verification process,the key problem is how to describe the properties of systems using logical formulas accurately. The existing model checking methods based on single logic can no longer meet the increasingly complex verification requirements of Multi-agent systems (MASs) in the current intelligent era. In the MASs, logics of knowledge (epistemic logics) have been advocated for expressing properties. Temporal logic is used for reasoning the correctness of systems and epistemic logic is used for reasoning the information of systems. In order to reason the correctness and information of systems simultaneously, the hybrid logic APTEL that integrating temporal logic APTL and epistemic logic is proposed in this paper.
An Approach of Transforming Non-Markovian Reward to Markovian Reward

In many decision-making problems, a rational reward function is required, which can correctly guide agents to make ideal operations. For example, an intelligent robot needs to check its power before sweeping. This kind of reward functions involves historical states, rather than a single current state. It is referred to as non-Markovian reward. However, state-of-the-art MDP (Markov Decision Process) planners only support Markovian reward. In this paper, we present an approach to transform non-Markovian reward expressed in $${LTL}_{f}$$ (Linear Temporal Logic over Finite Traces) into Markovian reward. $${LTL}_{f}$$ is converted into an automaton which is compiled to standard MDP model. Then the reward function of the model is further optimized through reward shaping in order to speed up planning. The reshaped reward function can be exploited by MDP planners to guide search and produce good training results. Finally, experiments with augmented International Probabilistic Planning Competition (IPPC) domain demonstrates the effectiveness and feasibility of our approach, especially the reshaped reward function can significantly improve the performance of planners.
Testing and Verifying the Security of COVID-19 CT Images Deep Learning System with Adversarial Attack

The Coronavirus disease 2019 (COVID-19) is a pandemic that occurred in December 2019 and spread globally. Most of the current research is on how to apply deep learning to detect COVID-19, but little research has been done on the security of COVID-19 deep learning systems. Therefore, we test and verify the security of COVID-19 CT images deep learning system with adversarial attack. Firstly, we build a deep learning system for recognizing COVID-19 CT images. Secondly, adding imperceptible disturbance to CT images will lead to neural network classification errors. Finally, we discuss the application of formal methods and formal verification to deep learning systems. We hope to draw more attention from researchers to the application of formal methods and formal verification to artificial intelligence.