Reviewerly/exHarmony huggingface dataset:
3. Ant colony optimization and reinforcement learning

Ant Colony Optimization (ACO) used to be one of the most frequently used algorithms in metaheuristic optimization. It can be interpreted as a Bayesian accumulation of information about an optimization problem, and bears some similarity to reinforcement learning. With this parentage, given a better theoretical understanding, ACO could be advanced into a practically competitive optimization algorithm. We are trying to support this development by studying the complex effects of parameters in ACO. For a toy example, which is, however, highly instructive in regard to the ability of the algorithm to overcome deception, we investigate how the parameter such as the number of ants and the rate of pheromone evaporation influences the performance. In addition, we investigate the suggestive link between ACO and reinforcement learning (RL) which does not only have the potential to facilitate the analysis of ACO, but which provides also options for the generation of new algorithms by the transfer of innovations across categories of algorithms. Based on these ideas, we propose an embedding of both ACO and RL into an joint evolutionary framework.
8. Machine learning approach for exploring computational intelligence

The new concept provoked with genetic and natural characteristics like neuron structure in brain [neural network (NN)] is known as computational intelligence (CI). CI is depending upon three main stakes, namely, NN, fuzzy systems, and evolutionary computing (EC). Fuzzy system utilizes natural language or more specifically language used by human being to represent linguistic ambiguity as well as solves the difficulties in approximate reasoning computation. NN can be defined as computational model designed in line with structure and working of human brain which can be trained and perform job accordingly. This chapter focuses on EC and various facets of EC for machine learning (ML) applications. EC provides a way out to problem of optimization by generating, calculating, and transforming different likely solutions like use of evolutionary programming, genetic algorithms, multi-objective optimization, and evolvable hardware (EH), etc. In the recent years of ML and BigData, computer systems/applications are creating, gathering, and consuming a huge amount of data. Since such enormous data are utilized for ML functions along with applications which are data demanding, rate at which data transmission occurs from storage unit to host unit will be the reason for a bottleneck. Evolvable programming and EH jointly could be a key to solve abovementioned problem of optimization. Solution could be provided by using near-data processing (NDP) or in-situ processing. NDP allows execution of compute-centered applications in situ, which means within or very close to the storage unit (memory/ storage). Outcome of this may be that NDP seeks to decrease costly data movements and thus performance improvement. NDP is made up of a grouping of software system plus hardware system derived from the collection of central processing unit (host unit) plus smart storage system. Smart storage system has various elements namely various disks for storage, cache, frontend, and backend. Now cache memory consists of large capacity which is used for storing hot data (frequently used data) temporarily. The cold data (data used less frequently) are moved forward to underlying storage layer. Frontend is intended for connection between host system and storage system. Backend is intended to support communication among cache and multiple storage disks. One part of backend is backend controller that pedals these disks having capability of Redundant Array of Independent Disks (RAID) and having error finding as well as error-correction methods. This chapter talks about different architectures of NDP which is compatible for ML methods and in which way mentioned NDP architectures boost execution of ML methods. The chapter focuses on developments in recent research in NDP designed for various methods of ML.
Hand Calculator system using Convolutional Neural Networks

Computer vision has great attention in recent years as it identifies and similarly processes images that human vision does and they provide suitable output. In computer vision, hand gesture recognition is one of the important and fundamental problems. The hand gesture recognition system has gained significant importance in the recent few years because of its manifoldness applications. This paper aims to give a new approach for vision-based, fast and real time hand gesture recognition, a new light that can be used in many HCI applications. The proposed algorithm first detects and segments the hand region and by using our innovative approach, it finds the fingers and classifies the gesture. The proposed algorithm is invariant to orientation, hand position or distance from the webcam. Based on this proposed algorithm we have progressively developed a gesture-based mathematical tool (calculator) as a practical application.
SSEL: An Extensible Specification Language for SoC Security

A critical component of System-on-Chip (SoC) design entails specification of security requirements for trustworthy system operation. In this paper, we address this problem through the design of a new Security Specification Language (SSEL) to standardize the definition and implementation of SoC security requirements. We demonstrate that a viable path to the development of such a language is to build it on top of an existing programming language, extending a subset of the underlying language constructs and introducing new security-specific constructs as APIs. SSEL realizes this idea by building on top of a subset of C. We show how SSEL constructs can be used to develop executable specifications of a diverse set of usecases in modern SoC platforms. We show the application of SSEL in designing specifications for unlocking Logic Locked (LL)IPs, Authentication, and Secure Boot.
Cy Sician-Revolutionizing Patient Health Monitoring System with auto-diagnosis

Cardiovascular diseases (CVD) has emerged as one of the major causes for death in all over the world. This paper displays a framework to remotely screen, health of Cardiovascular disease affected patients utilizing Machine to Machine (M2M) innovation which is a part of the project called CySician . Real time patient health monitoring system is advantageous to the patients and society as it will significantly reduce medical charges, waiting time for patient and improve patient handling capability of any hospital. In this patient health monitoring system pulse rate, ECG, body temperature, Body Mass Index(BMI) and general clinical interrogation is finished by a chatbot named “LifeBot”. The primary components associated with this project are pulse sensor, Raspberry Pi 3B+ (processing unit), temperature sensor module sensor, utilizing Machine Learning (ML) calculation it automatically analyzes the accumulated information to propose prescription to the patient. After the patient is diagnosed and the disease is detected, the patient will be notified with the kind of medication he needs. If the problem is nominal, the patient will be suggested with a basic treatment and will be monitored regularly. If the problem is of major scale, the patient will be directed to the payment gateway where he will be asked to pay a nominal fee for appointment from doctors to continue his check-up .Ultimately, the final well-being report is displayed to the doctor on the User interface that is visible on PC/Laptop.
Social Media and Networks for Sharing Scholarly Information Among Social Science Research Scholars in the State Universities of Tamil Nadu

The study examines the impact and utilization of social networks and media for disseminating scholarly information among social science research scholars in south universities of Tamil Nadu, India. The study analyzed the respondent coverage from the universities: University of Madras, Bharathiar University, Bharathidasan University, Madurai Kamaraj University, Alagappa University, Manonmaniam Sundaranar University, and Periyar University. An aggregate of 520 polls appropriated to the respondents out of eight universities in Tamil Nadu and 501 (96.34%) of the filled survey got from the respondents in the social science departments of the universities for the examination. The task of research has taken effort to collect the data to find out usefulness and impact of the social media and networks among the respondents in the selected state universities of Tamil Nadu.
Retraction: Intelligent defaulter Prediction using Data Science Process (J. Phys.: Conf. Ser. 1916 012001)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Research on Block Matching Three-Dimensional Cooperative Filtering Optical Image Denoising Algorithm Based on Noise Estimation

Optical image denoising is one of the important means to improve the overall clarity of the image or highlight the texture details of the target. However, studies have found that the existing optical image denoising algorithms have a good effect on the images with known noise levels, and have average denoising effects on random noising. Therefore, a novel BM3D optical image denoising algorithm based on noise estimation is proposed. Firstly, the wavelet packet transform is used for the noise estimation of the optical image. Then, according to the noise estimation result, BM3D is used to realize the denoising analysis of the optical image. Finally, PSNR is used to evaluate the effectiveness of the algorithm. Experiments show that the proposed algorithm not only achieves effective denoising of optical image, but also has strong robustness.
Research on Ultra-efficient Motor Design and Manufacturing Technology

The use of ultra-efficient motors is an important way to improve energy efficiency and reduce energy consumption. With the introduction of the national YE4 series motor technical standards, the implementation of YE4 series motor manufacturing technology has become the key to popularizing YE4 series motors. Due to the limitation of the size of the frame, following the traditional design ideas, improving the energy efficiency of the motor by increasing the effective material consumption has reached the limit. This article combines the practice of designing and producing YE4 series motors and introduces several key technologies for the production of YE4 series motors. On the basis of YE3 series motors, the consumption of effective materials is not increased, that is, under the condition of keeping the original motor frame and the dimensions of the three circles unchanged, To produce qualified YE4 series motors and promote the popularization and application of YE4 series motors.
THE USE OF RADON TRANSFORMATION AS A SARONG MOTIF

In this paper I will present the use of Radon transformation to design sarong motifs. The sarong is a large piece of cloth sewn at both ends to form a tube. Sarongs are made of a variety of materials: cotton, polyester, or silk. There are various kinds of sarong motifs, namely geometric, non geometric and a combination of geometric and non geometric. The concept of Radon transformation has spread across all fields of science, medicine and geophysics. This paper aims to provide an algorithm for making non-geometric patterns that can be used as sarong motifs. Algorithm consists of base, transformation and coloring. To help readers better understand the algorithm, I will present a computer program using Matlab. I describe an algorithm based on the Radon transformation that can fill a spatial region based on the Radon transformation of a base image with a random sampling area. With this algorithm, we can generate thousands of beautiful variations of patterns, of which I will show some examples.
Second-order physics constrained learning

Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions.
Target Tracking Method based on Reinforcement Learning

In recent years, target tracking has become a hot topic in the field of computer vision. Many tracking algorithms based on deep learning have been proposed, but some of them have a large amount of computation or do not perform well in the face of target deformation and loss. The combination of reinforcement learning and deep learning is used and the actor critical framework is introduced, the target tracking task has been solved to a certain extent in the above problems. By improving the existing target tracking algorithm based on reinforcement learning, we can make it better adapt to complex scenes and target deformation, we test on OTB-100 dataset and have some improvement over the improved algorithm, Our method has better effect on OTB-100 dataset than the improved algorithm.
Rural Bus Connectivity and Quality of Services under Assam State Transport Corporation (ASTC)

Road Transport Sector (RTS) is crucial for the people living in rural areas because other modes of transportation are hardly available in these regions. People are basically dependent on road transport services to run their day to day works in rural areas. Assam is a state of India where majority of the population is living in rural areas. The state comprises different geographical locations such as remotest of the remote locations, flood affected areas etc. and on the other hand, other modes of transportation such as trains, flights etc. are not adequately available in all places of the state. Thus, road transport plays a crucial role in moving goods and services in the state. So far as the welfare states are concerned, Public Road Transport Corporations (PRTCs) play a major role in delivering bus services to the people so all sections of the people can travel comfort journey at reasonable prices. Assam State Transport Corporation (ASTC) is only the Public Sector Road Transport Corporation (PSRTC) in Assam which provides bus services to the people of the state and in its connected regions. Here, the present study has been made to understand the status of services provided to the people living in rural areas of Assam under ASTC. Total 100 respondents from the group of passengers travelled by ASTC’s buses (Lakhimpur Division) have been purposively contacted for collecting primary data and the data collected from passengers would help to understand the availability/unavailability and the quality of services provided by ASTC to the passengers. Apart from the passengers, 8 ASTC’s officials (four from each station) and 2 higher officials from ASTC’s headquarters have been interviewed to understand the challenges faced by them in operating buses. Based on the collected data, the study finds that the corporation has failed to provide quality services to the passengers in many regards. The findings of the study highlight unavailability of push back system in seats in many buses, absence of a/c connections in buses allotted to rural areas, unhygienic bus stations etc. The study also reveals that service delivery is affected by the problems which are related to operation of buses such as rigid official procedures, financial deficiency, dissatisfaction of employees etc. Based on the findings, the study suggests concerned authority to take necessary steps to improve the quality of services regarding different aspects and to introducing such rules and regulations wherein employees feel comfort to discharge their duties. The study also suggests concerned authority to keeping proper surveillance over the properties of ASTC, focusing on proper maintenance of buses and bus stations etc.
Symmetry and Physics

Special relativity and quantum mechanics form the foundation of twenty-first century physics. At the risk of abusing a metaphor, symmetry is the rebar, or supporting steel, that threads through this foundation, providing strength and allowing a simplicity of form that would be otherwise unachievable. In this chapter we introduce the concept of symmetry and detail the many important roles it plays in physics. It is important to distinguish between two types of symmetry: the symmetry of things, such as paintings, faces and the Universe, and the symmetry of the equations that describe them. Despite its vital role, symmetry is only useful if it is not perfect. Too much symmetry would lead to a Universe that was not only boring, but also impenetrable to our scrutiny. In fact, sometimes a symmetry of the system of equations under consideration is not respected by the physically relevant solution. This phenomenon, called spontaneous symmetry breaking, isSpontaneous symmetry breaking how the famous Higgs particle endows elementary particles with mass. Spontaneous symmetry breaking is demonstrated by minimizing a suitable “cost function” (a.k.a. principle of least action), aPrinciple of least action method that plays an integral part of most, if not all, physical theories.
Compute and Resonate

With the development of free accessible artificial intelligence (AI) musical compositional tools such as Google’s Magenta Studio, it is now possible for musicians with little or no computer programming experience to utilize AI to assist in their compositions of musical works. This chapter discusses and evaluates the techniques used in creating electronic works of the acid genre for performance utilizing these tools and the performance of this created work. This research suggests that musicians can utilize AI as part of their compositional process without the need for in-depth programming knowledge alongside their traditional compositional methods. The tools reported on do not replace compositional techniques, but rather, they can augment the compositional process.
Using Cinquain as a Form of Representation of Natural Science Knowledge

Cinquain is a poetic form, or stanza, of five lines that do not rhyme, which follows some strict rules.Cinquain is also considered to be a training means, a method, a technique, an instrument used for reflection, for synthesis of all knowledge on a particular topic (a being, an object, an ensemble of objects), towards developing one's creativity.In this research, the quintets were created by 47 university students in the last phase of a learning activity structured on the evocation-realisation of meaningreflection model.The process of preparation and creation of quintets and the quintets created have been analysed, the conclusion being that quintets are an efficient technique in highlighting representations on a specific topic.
Language Identification based on Support Vector Machine using GMM Super vectors

This paper proposes a novel approach that combines the power of generative Gaussian mixture models (GMM) and discriminative support vector machines (SVM). The main objective this paper is to incorporating the GMM super vectors based on SVM classifier for language identification (LID) task. The GMM based LID system to capture all the variations present in phonotactic constraints imposed by the language requires large amount of training data. The Gaussian mixture model (GMM)- universal background model (UBM) modeling require less amount of training data. In GMM-UBM LID system, a language model is created by maximum a posterior (MAP) adaptation of the means of the universal background model (UBM). Here the GMM super vectors are created by concatenating the means of the adapted mixture components from UBM. Then these super vectors are applied to a SVM for classification purpose. In this paper, the performance of GMM-UBM LID system based on SVM is compared with the conventional GMM LID system. Form the performance analysis it is found that GMM-UBM LID system based on SVM is performed well when compared to GMM based LID system.
Analysis on the Language Independent and Dependent Aspects of Deep Learning based Question Answering Systems

Natural languages are ambiguous and computers are not capable of understanding the natural languages in the way people really understand them. Natural Language Processing (NLP) is concerned with the development of computational models based on the aspects of human language processing. Question Answering (QA) system is a field of Natural Language Processing that provides precise answer for the user question which is given in natural language. In this work, a MemN2N model based question answering system is implemented and its performance is evaluated with a complex question answering tasks using bAbI dataset of three different language text corpuses. The scope of this work is to understand the language independent and dependant aspects of a deep learning network. For this, we will study the performance of the deep learning network by training and testing it with different kinds of question answering tasks with different languages and also try to understand the difference in performance with respect to the languages
Elevator Door Automatic Stopper for Covid-19 Physical Distancing

Fast spreading covid-19 demand people to quickly cut the transmission over humans. Physical distancing is one of the efforts by keeping safe distance with at least 1 meter between people. This work is to find ways and implement computer vision methods to monitor, detect, and provide warnings of violation of physical distancing in public areas such as in elevators (lifts) which are equipped with monitoring cameras. Yolov3 still has problem of detecting half or full body proportion of human. Finally in this work, human proportional face over body detection has been checked whether it is full body or half body bounding boxes of human object detection to increase accuracy by 12.5 percent of distance measurement. Uncertainty of changing state of microcontroller has also been solved by collecting consecutive states which contain a number of warning/no warning condition of physical distancing violation.
Face Recognition Using Deep Neural Network

There is rapid development in the field of image processing. Now, there are a lot of models for face recognition in the whole world. In the field of image recognition, the arrival of deep learning theory has evolved drastically. In today’s world, biometric is used everywhere and the expectations from the system are that it provides positive results in any kind of situation as the quality, alignment, facial expression, and reflection of the picture make it difficult for the machine to validate it. Thus, there is an alternative model to the traditional neural network model which is Convolutional Neural Network (CNN) models which are deep learning models. This model includes the process in which at first the machine is trained with the data set and then the validation is done.