Reviewerly/exHarmony huggingface dataset:
Implementing an Artificial Intelligence based Ideal form of Virtual Personal Assistant Design for Various Communication Medium

Currently various sectors are growing due to the continuous rise of technological development. Thus the use of machinery has increased and the use of humans has decreased. Machines work and humans oversee it. In this case, artificial intelligence technologies such as Amazon Echo and Google’s Voice Assistant are seen as the most advanced technologies based on human voice comment. As a result most of the time is saved and advanced technologies that kill this kind of artificial intelligence have the power to easily complete even some complex tasks that cannot be done by man. In this paper, a virtual assistant algorithm with such advanced artificial intelligence is proposed. It is designed with the ability to get the required data as input through voice and text, and get the job done. And its potential possibilities have been compared with some of the algorithms currently in practice. Thus the optimal performance of the proposed algorithm is determined based on the results.
ROS based Obstacle Detection Robot using Ultrasonic Sensor and FMCW RADAR

In the field of robotics, integration has been one of the major challenges due to the complex architecture of hardware and software. The extended scale and scope of the robots make the robot software development complex. Robotic Operating System (ROS) solves the problem by serving as a flexible framework for robot software development. With easy access to code, robots with ROS became reachable to more around the world. This paper proposes the implementation of an autonomous mobile robot based on ROS. The system utilizes an ultrasonic sensor and also exploits the possibility of using BGT60TR13C, a Frequency Modulated Continuous-wave (FMCW) radar sensor for obstacle detection. All the tasks have been performed on a four-wheeled cart mobile robot with Raspberry pi pre-installed with ROS, ultrasonic sensor, radar, and motor driver. The ultrasonic sensor and radar can detect the obstacles and calculate the range of the obstacle. This range information is used to decide the movement direction and signals are sent to the motor driver through the Raspberry pi. The robot can move around the indoor environment using the sensors and an onboard single-board computer.
Blockchain based Qinsurancechain: Role of Smart Contracts in Fast and Secure Healthcare Insurance Claim

The insurance sector in the healthcare industry rely on the insurer, insured, physicians, hospitals, and third-party service providers etc. Although this market is overgrowing, it lacks openness and trust. Because of this, there only appears to be controversy in a number of claim settlement instances. The increase in competition is pushing the insurance companies to use advanced technologies for addressing multiple challenges.In order to do this, blockchain technology can be applied to the insurance industry as an egressing system that provides transparent and secure data storage and transit. . The hybridization of IoT and blockchain technology (BT) can offer diverse benefits to the functioning to the health insurance and mediclaim sector. This article applies blockchain technology to the healthcare insurance industry. Transactions will be handled by contracting out the middlemen engaged in the transaction process and by facilitating quicker payments between stakeholders. We develop a queueing model in detail that evaluates an Insure Chain system's characteristics. We also measures the performance of the model, including the transactions that may be waiting in the unpooled buffer before entering in to the block, transaction waiting time and a transaction’s confirmation time.
Advanced Collision Detection Warning System for Enhanced Safety in Highway Driving

An Advanced Collision Detection and Warning (ACDW) system for enhancing automotive driver safety during highway driving is presented in this paper. The proposed system uses camera vision for capturing on-road views and the captured images are subsequently processed using an electronics hardware. The overall objective of this work is to conceive a low-cost and portable ACDW system that can continuously monitor on-road views at rapid frame rates using edge computing device hardware and provide advanced warning to the driver for enhanced safety. The main contributions of this work include the ACDW system hardware architecture and the method/image processing algorithm used for detecting critical highway situations. The proposed system has been verified in real-time using a Raspberry PI3 edge computing device and the reported results are compared with other related algorithms.
CLQLMRS: improving cache locality in MapReduce job scheduling using Q-learning

Abstract Scheduling of MapReduce jobs is an integral part of Hadoop and effective job scheduling has a direct impact on Hadoop performance. Data locality is one of the most important factors to be considered in order to improve efficiency, as it affects data transmission through the system. A number of researchers have suggested approaches for improving data locality, but few have considered cache locality. In this paper, we present a state-of-the-art job scheduler, CLQLMRS (Cache Locality with Q-Learning in MapReduce Scheduler) for improving both data locality and cache locality using reinforcement learning. The proposed algorithm is evaluated by various experiments in a heterogeneous environment. Experimental results show significantly decreased execution time compared with FIFO, Delay, and the Adaptive Cache Local scheduler.
“<scp>Security Gap</scp>” as a metric for enterprise business processes

Abstract Security is becoming an indispensable factor for the well‐being of an enterprise. Enterprises are making huge investments to fulfill the demand for security. A big challenge faced by an enterprise while securing itself is to find the gap between the demand for security and the actual security status. Finding out a consistent metric for measuring this gap can enable security administrators to utilize the allocated funds more appropriately. Popular control gap analysis methods practiced in enterprises are mostly subjective in nature and results in imprecise measurements. To address this issue, a novel security metric “Security Gap” is introduced in this paper. This metric finds out the business process‐level insecurity from the security requirements and the estimated security. The methodology uses business process modeling, attack graph modeling, and relevant base metrics to compute Security Gap.
On Establishing Robust Consistency in Answer Set Programs

Abstract Answer set programs used in real-world applications often require that the program is usable with different input data. This, however, can often lead to contradictory statements and consequently to an inconsistent program. Causes for potential contradictions in a program are conflicting rules. In this paper, we show how to ensure that a program $\mathcal{P}$ remains non-contradictory given any allowed set of such input data. For that, we introduce the notion of conflict-resolving ${\lambda}$ -extensions. A conflict-resolving ${\lambda}$ -extension for a conflicting rule r is a set ${\lambda}$ of (default) literals such that extending the body of r by ${\lambda}$ resolves all conflicts of r at once. We investigate the properties that suitable ${\lambda}$ -extensions should possess and building on that, we develop a strategy to compute all such conflict-resolving ${\lambda}$ -extensions for each conflicting rule in $\mathcal{P}$ . We show that by implementing a conflict resolution process that successively resolves conflicts using ${\lambda}$ -extensions eventually yields a program that remains non-contradictory given any allowed set of input data.
Hand-eye calibration for an unfocused light-field camera

A calibration framework is established for an unfocused light-field camera and a robotic arm. With Gaussian optics and light-field imaging principles, the mapping relationship between a point light source and its corresponding plenoptic disc feature is established, and the intrinsic and extrinsic parameters of the unfocused light-field camera are calculated through nonlinear optimization. Transformation matrices for eye-to-hand and eye-in-hand configurations are subsequently solved and are validated by applying them to an industrial light-field camera-robotic arm system. With the proposed calibration method, 3D reconstruction for calibration board in different poses is demonstrated and calibration uncertainty is discussed in detail.
Research on English Vocabulary and Speech Corpus Recognition Based on Deep Learning

In order to investigate how to recognize English words and speech corpus, an English vocabulary and English speech recognition model based on deep learning algorithm was proposed. Through recommending key technical problems and solutions based on deep learning algorithm, how to realize the recognition of English vocabulary and speech corpus was investigated. In the research, the accuracy of the method on the English vocabulary and speech corpus recognition based on the deep learning algorithm increased 79% over the previous methods. Combined with the principle of the deep automatic encoder and deep learning algorithm, the research emphasis was on the effects of speech recognition framework for speech corpus. The speech recognition research based on the theory of deep learning not only had a theoretical guidance meaning but also had the use value in the practical application.
Daubechies Wavelet Scaling Function Approach to Solve Volterra’s Population Model

In this paper, we focus on a collocation approach based on Daubechies wavelet scaling functions for approximating the solution of Volterra’s model of population growth of a species with a closed system. We present that the integral and derivative terms, which appear in Volterra’s model of the population, will be computed exactly in dyadic points. Utilizing this collocation technique, Volterra’s population model reduces into a system of nonlinear algebraic equations. In addition, an error bound for our method will be explored. The numerical results demonstrate the applicability and accuracy of our method.
The Components of Smart Sports in Universities with the Assistance of Smart Devices

With the continuous maturity of information technology, the cause of intelligent sports continues to innovate and develop. By comparing and analyzing the relevant contents of intelligent sports and traditional sports in colleges and universities, this paper fully demonstrates the unique advantages brought by the development of concepts, basic technologies, educational resources, and intelligent applications in colleges and universities under the construction of intelligent sports and provides a feasible basis for the extensive development of intelligent sports. In order to solve the problems of multiparty data collection, equipment availability, and accuracy of knowledge formation caused by the interdisciplinary nature of sports science, it is necessary to establish an integrated system that can effectively support sports information analysis. In this paper, we propose an intelligent sports information analysis system architecture supported by SmartDev technology, design and implement the SmartDev intelligent sports information analysis system and apply it to university sports training. The practical results show that the use of this system can support multisource data collection, high accuracy of analysis results, and availability of equipment, which is beneficial to the development of sports.
EA-UNet: A Macrophages Image Segmentation Model Based on U-Net with External Attention

When people are exposed to air pollution for a long time, inhaling pollution particles can cause systemic diseases. Studies have shown that carbon content in Airway Macrophages (CCAM) can be used to evaluate particulate matter exposure. The automatic detection of macrophages from electron microscopy is an essential prerequisite for researchers to study the exposure of fine particles in macrophages. However, at present, the mainstream medical image segmentation algorithms are more aimed at the CT images of the lungs or brains with relatively fixed shapes, and the segmentation performance of macrophages with uncertain shapes and sizes is inaccurate. Segmentation of macrophages faces several challenges, including the high similarity of cell images leading to false detection and missed detection, and blurred boundaries in the segmentation of macrophages. Further, no public macrophage segmentation datasets are available for deep model training. To address these challenges, we propose a Macrophage Segmentation Network that combines the U-Net convolutional network as the structure of the main body network with the attention mechanism to automatically identify macrophages from induced sputum microscopy images. In our network, a parallel partial decoder is used to aggregate diverse scale features from U-Net encoder to incorporate fine-grained details with coarse-grained semantics from diverse scale features. Then, the external-attention module tokenizes image patches from the multi-scale feature map as the input sequence for extracting global contexts which focus on regions of interest to obtain more accurate segmentation results and solve the disadvantage of insufficient data. Experiments on our MASEG dataset demonstrate that the EA-UNet proposed in this paper has higher segmentation accuracy than other state-of-art segmentation methods.
Machine Comprehension System in Tamil and English based on BERT

Question Answering is a part of information retrieval. It is a task of machine-reading comprehension and presents a medium to assess the ability of machines to understand human language. A question-and-answer system is preferable to search engines that return a collection of documents. Any question answering system's high level design consists of three primary components: Question analysis, retrieval of context, answer is being extracted. With an availability of training resources and Transformer-based models trained on huge English corpora, the precision and accuracy of English Question Answering systems has increased dramatically over the years. Such big datasets, however, are not available for low-resource languages like Tamil. For low-resource languages, multilingual BERT (mBERT) models are utilized. The translations from the same language family is used to supplement the available data and fine-tune the mBERT-based model. Cross-lingual learning uses zero, and few-shot techniques applied to transfer the knowledge of a QA model trained on many source examples to a given target language with fewer training data. Pre-training and fine-tuning of the model improves performance, according to model tests.
Image Generation Method of Bird Text Based on Improved StackGAN

Text to image is a difficult task, which needs to focus on the semantic relationship between text and image. On account of this, an improved StackGAN network is applied to generate bird images according to text in study. Based on StackGAN twostage network, the network is optimized by deep separable convolution to reduce the amount of model parameters; SE attention mechanism is embedded in the model to enhance the key features of the image and suppress the invalid features; The residual structure is used to deepen the network and improve the generation effect. The experimental results show that the average IS of the algorithm on the CUB dataset is 3.94 and the FID is 36.47. Compared with StackGAN, the IS is increased by about 5%, the FID decreases by about 30%, and the model size is only about 1/2 of StackGAN, which improves the reasoning speed and generation effect.
OTP Camouflaging using LSB Steganography and Public Key Cryptography

One Time Password (OTP) provides an additional layer of authentication to a web server, protecting against fraudulent attempts so that the information is protected between the user and the recipient. In the field of Steganography, various techniques are employed to conceal data in the media in such a manner that human perception cannot identify it. As the proposed algorithm is a combination of both Cryptography and Steganography it meets all the challenges very efficiently. Initially, the data is ciphered with a novel algorithm of asymmetric key cryptography and then the ciphered message is camouflaged in the cover media using the Least Significant Bit (LSB) steganography technique. Even if the intruder cracks the LSB algorithm used here, he will get the encrypted data since the source data is not present on the cover media. To obtain the original data, the ciphered data must be deciphered with the deciphering algorithm. With the additional security, it is impossible to crack the original data from the cover media
BSMFS: Blockchain assisted Secure Multi-keyword Fuzzy Search over Encrypted Data

Cloud computing has enabled outsourced storage of large amounts of data and reliable access to it. Privacy of remotely stored data has been a concern for every data owner, which would be resolved by applying suitable encryption mechanism. On the other hand, searching is difficult in the case of encrypted documents. In this work, we propose a Blockchain assisted Fuzzy search over encrypted data named BSMFS. BSMFS is resistant against leakage of information by well-known attacks like search, access and volume pattern leakage attacks. A smart contract is developed for Ethereum blockchain to assist the interaction between data user and cloud server, along with a bloom filter for efficient search. Experimental analysis is performed on Ethereum test networks to check the feasibility of BSMFS.
Endoscope Image Enhancement Algorithm Based on Pair of Complementary Gamma Functions

In order to solve the problem of uneven illumination and low contrast in most endoscope images, a simple and effective endoscope enhancement algorithm is proposed. It is mainly divided into two parts. The first part performs a complementary gamma function pair on the V component of HSV space to get a very dark and very bright image. The input image is divided into bright image, medium brightness image and dark image, and then different fusion functions are used to fuse the image to get the brightness adjusted image. The second part is the discrete wavelet transform of L component in Lab space to obtain the high frequency component and low frequency component. Adaptive space-weighted histogram equalization (CLASWHE) with contrast limitation is used to adjust the local contrast of the image at low frequency, and semi-soft threshold function is used to denoise the image at high frequency. Experimental results show that the proposed method achieves good results in brightness adjustment and contrast enhancement, and retains the sense of reality while suppressing noise.
An Efficient Retrieval System Framework for Fabrics Based on Fine-Grained Similarity

In the context of "double carbon", as a traditional high energy consumption industry, the textile industry is facing the severe challenges of energy saving and emission reduction. To improve production efficiency in the textile industry, we propose the use of content-based image retrieval technology to shorten the fabric production cycle. However, fabric retrieval has high requirements for results, which makes it difficult for common retrieval methods to be directly applied to fabric retrieval. This paper presents a novel method for fabric image retrieval. Firstly, we define a fine-grained similarity to measure the similarity between two fabric images. Then, a convolutional neural network with a compact structure and cross-domain connections is designed to narrow the gap between fabric images and similarities. To overcome the problems of probabilistic missing and difficult training in classical hashing, we introduce a variational network module and structural module into the hashing model, which is called DVSH. We employ list-wise learning to perform similarity embedding. The experimental results demonstrate the superiority and efficiency of the proposed hashing model, DVSH.
Literacy Culture in Elementary Schools: The Impact of the Literacy Movement Program and Library Facilities

This study aims to analyze the impact of literacy movement program and library facilities on literacy culture in elementary schools in Binjai City, North Sumatra Province. The type of research used is quantitative with a survey scoring method, the instrument in survey research used a questionnaire to collect large amounts of data. The population in this study amounted to 2,477 Binjai elementary school teachers and the research sample was 247 people. The research data analysis technique used statistical tests, namely normality test, linear regression test and hypothesis testing. The results showed that the literacy program had a positive effect on literacy culture in Elementary schools and library facilities have a positive effect on literacy culture in elementary schools and simultaneously literacy programs and library facilities have a positive effect on literacy culture in elementary schools. The implications of this research are 1) literacy movement and library facilities foster students' reading habits and interest, 2) students are accustomed to using library facilities to support learning, and 3) increase students' insight because of reading and utilizing library facilities, this will have an impact on learning outcomes.
Development of the real-time winding angle measurement device for the laboratory-scale 3-axis winding machine

It is well-known that an automatic system can reduce errors to a great extent, which increases productivity and reduces labor costs. The existing filament winding machines or equipment lack the winding angle detection or measurement system, which cannot provide high precision and stable winding angle. This paper proposes a real-time winding angle device that can detect and measure the fiber orientation of filament wound composite products. The real-time winding angle measurement system consists of an Arducam 5MP OV5647 camera and OpenCV software. It is developed as the programming function for processing the real-time image of the winding angle. Furthermore, the traveled movement slider and the winding angle measurement image can be acquired during the dry/wet process. The laboratory-scale 3-axis winding machine integrated with the real-time winding angle measurement device is successfully developed, assembled, and tested.
Adversarial Detection Based on Inner-Class Adjusted Cosine Similarity

Deep neural networks (DNNs) have attracted extensive attention because of their excellent performance in many areas; however, DNNs are vulnerable to adversarial examples. In this paper, we propose a similarity metric called inner-class adjusted cosine similarity (IACS) and apply it to detect adversarial examples. Motivated by the fast gradient sign method (FGSM), we propose to utilize an adjusted cosine similarity which takes both the feature angle and scale information into consideration and therefore is able to effectively discriminate subtle differences. Given the predicted label, the proposed IACS is measured between the features of the test sample and those of the normal samples with the same label. Unlike other detection methods, we can extend our method to extract disentangled features with different deep network models but are not limited to the target model (the adversarial attack model). Furthermore, the proposed method is able to detect adversarial examples crossing attacks, that is, a detector learned with one type of attack can effectively detect other types. Extensive experimental results show that the proposed IACS features can well distinguish adversarial examples and normal examples and achieve state-of-the-art performance.