Reviewerly/exHarmony huggingface dataset:
Object Detection and Tracking using Faster R-CNN

This paper uses a deep learning model called Faster R-CNN to detect and track objects in images. Two backbone networks such as ResNet-101 and VGG-16 are tested on a self-created dataset and PASCAL VOC dataset. Intersection over union (IoU) technique is used for the purpose of object tracking. The impacts of batch size, number of iterations and learning rate are analysed. The paper finds that ResNet-101 outperforms VGG-16 significantly by 13% on test data. This finding reinforces that deeper network is better in feature extractions and generalizations. IoU is able to track multiple objects and can identify the loss of track. The processing of frames per second is found to be 5 fps. The study has implications for many computer vision applications. For example, the deep learning based object detection and tracking can either augment the capability of LiDARs and Sensors or become an alternative to them in self-driving vehicles.
BFS Parallel Algorithm Based on Sunway TaihuLight

In recent years, more and more attention has been paid to the research and application of graph structure. As the most typical representative of graph structure algorithm, breadth first search algorithm is widely used in many fields. However, the performance of traditional serial breadth first search (BFS) algorithm is often very low in specific areas, especially in large-scale graph structure traversal. However, it is very common to deal with large-scale graph structure in scientific research. At the same time, the computing performance of supercomputer has also made great progress. China’s self-developed supercomputer system Sunway TaihuLight (SW) has won the top 500 list for three consecutive times. The huge computing performance of supercomputer is the key to solve this problem. It can be seen that if we use the computing power of supercomputing to solve the problem of large-scale graph structure traversal, the efficiency of graph structure traversal will be greatly improved. This paper expounds how to realize the breadth first search algorithm of graph structure on the Sunway TaihuLight, and achieved some results. In this way, MPI and thread library called athread of SW platform are used, and the traversal performance is improved dozens of times through the above related technologies and some partition methods of graph structure.
Exploration of Attribute Based Encryption Schemes on Cloud Computing Storage

The storage of Electronic Health records on cloud storage has lead to the need for security and privacy concern. Different methods were proposed to prevent both internal and external threat in the healthcare framework. To guarantee security on health care records access control approach is required. In this paper different access control mechanisms used to provide security on Personal Health Records.
Facial Expression Recognition using Ensemble Learning Technique

The most natural, influential and powerful way to communicate or convey a message is face expressions. In the field of computer engineering, facial expression recognition system, is helpful in areas like healthcare system, computer graphics, biometric devices, mobile phones, etc. Technologies such as virtual reality (VR) and augmented reality (AR) make use of facial expression recognition to implement a natural, friendly communication with humans. In this paper an approach for Facial Expression Recognition using Ensemble Learning Technique has been proposed. Ensemble methods use various learning algorithms to obtain good predictive performance that could be obtained from any of the basic learning algorithms alone. In the proposed method, initially the features are extracted from static images using color histograms. This process is done for all images gathered in the training dataset. The ensemble technique is then applied on the featured dataset in order to categorize a given image into one of the six emotions, happy, sad, fear, angry, disgust, and surprise. A satisfactory result has been obtained using static image dataset taken from kaggle and uci machine learning repository.
Analysis of sentimental images using deep learning approach

Deep learning also known as universal learning approach is a kind of machine learning used to carry out classification tasks straightforwardly from Medias like images, text or sound. The paper centers on execution examination of three pre-trained deep learning network with an end goal of classification of images which are related to sentimental analysis. The pre-defined convolutional neural networks (CNN) handled are AlexNet, ResNet50 and VGG16 with different Epoch. These networks are pre-trained on Twitter dataset. We focus on the structure of feelings deduced by our model and contrast it with what has been proposed in the psychology literature, and confirm our model on a bunch of pictures that have been utilized in psychology studies. At long last, our work likewise gives a helpful instrument to the developing scholarly investigation of pictures consists of both photographs and memes on social networks. The network architectures are analyzed dependent on different means including, accuracy, precision, recall and F1-score. As per the experiment, out of three networks AlexNet gives better outcome as far as precision when compared to other networks.
A new software development paradigm for intelligent information systems

The continuous growth in software management cost requires the development of self-managed software systems. Using self-managed property, a system will take intelligent decisions to make a system work properly. Autonomic computing is the technique, which is used to develop such systems. Autonomic computing systems are highly reliable software systems. To enhance the quality of software systems, implementation of autonomic computing-based software development life cycle process may be a novel idea. It involves autonomous decision making by the autonomic component during the development of software. This approach reduces the complexity of the software development process. In addition, it resolves the purpose of autonomic computing to reduce software complexity and do real-time exception handling. In this paper, the implementation of the autonomic advisor-based software development process is proposed using the cloud computing technique. Cloud computing helps the developers to develop software, applications using deliverable services such as platform, infrastructure, and software. During the implementation and usage of autonomic advisor, the database becomes heavier. Therefore, to resolve such issues, cloud computing will be a beneficiary step. Other benefits of such an autonomous software development life cycle process are discussed further in this paper.
The emerging threats from disruptive technologies

New technologies have always influenced the delicate balance of power between nations and the people within them. Last month, New Scientist gathered a group of defence experts to discuss how disruptive technologies are creating a new set of threats for society
Design Education in the Post‐Digital Age

All the new materials and technologies of the digital age cannot and should not eclipse the role of creativity and the beauty of “making.”
Building a Competency Model Student Training

Decision-making in the field of education is a complex, multi-faceted process, in which a large circle of stakeholders is involved. Of no small importance for making a correct decision is the analysis of information coming from participants in the educational process at its various stages. The article proposes a methodology for constructing and applying a competency-based training direction model. The technique is based on the use of developed algorithms for building competency models. Due to the use of the Bayesian network, an assessment of the formation of the level of competence is possible even with missing data, i.e. with unknown results of competency-based tasks. The technique bridges the gap between strictly subject structuring of assessment tools, which does not fully correspond to the competency-building model of constructing the main educational program, and activity-based structuring. The article describes the conduct of two experiments conducted to verify the algorithms proposed in the theoretical part. Experimental testing showed that the developed algorithms, method and methodology are suitable for constructing a competency-based model of discipline and the direction of training. The models built according to the methodology, make it possible to make informed judgments regarding the level of students' competency levelling, as well as to predict student performance. Thus, using the methods of intellectual analysis of educational data, the tasks of decision support are solved.
Applications Research of Multi-Modal in English Vocabulary Teaching Based on Experimental Analysis

Multimodal vocabulary teaching means that teachers use pictures, colors, audio, video, actions and other means and resources to make the teaching ecology diversified. Even at present, multimodal vocabulary teaching has become an upsurge. Therefore, the application of multimodal theory in senior high school English vocabulary teaching has great theoretical and practical value. The author selects two classes of senior one in a senior middle school in Hangzhou as the research object, a total of 84 people. Among them, class 1 is the experimental class and class 2 is the control class. The experimental class adopts the vocabulary teaching method based on multimodal theory to carry out teaching activities, while the control class still adopts the traditional English vocabulary teaching method. Through the analysis of the experimental data, it can be concluded that the multimodal vocabulary teaching mode and language symbols interact to participate in the construction of meaning. Their interaction can not only effectively mobilize students’ vocabulary learning initiative, but also gradually improve students’ comprehensive ability of listening, speaking, reading, writing and translation.
Knee-Jerk Cardiopulmonary Resuscitation (CPR) Machine

Cardiopulmonary resuscitation method is used to save more number of peoples from the neurological problem. In this case, the neurological problem denotes brain death. This brain death is mainly caused due to cardiac arrest and happen within 4 to 5 minutes. To avoid this problem we go for cardiopulmonary resuscitation method. It will be helpful to relieve the patient from cardiac arrest. The manual CPR is not that much efficient when compared to automatic CPR because, the experts who are giving CPR to the patient are cannot able to give the continuous CPR to the patient but, the automatic CPR machine is able to give the continuous CPR to the patient. The article reviews such kind of automatic devices. The automatic CPR machine already exists. But, the cost of that machine is high. So we intended to design the low cost CPR machine. This is achieved by replacing the component like Arduino microcontroller and solenoid lock. The replaced component also do the same work like in the high cost CPR machine.
Handwriting Recognition using Deep Learning based Convolutional Neural Network

Handwriting is a learned skill that had been an excellent means of communication and documentation for thousands of years. The simple way to communicate with the computers is through either speech or handwriting. Speech has some limitation; hence input through handwriting is recommended. It is difficult to input data for computers for Indian language scripts because of their com-plex character set. This paper focuses on exploring convolutional neural networks (CNN) which is deep learning based for the recognition of handwritten script. The proposed method has shown 99% for handwritten English numerals and promising recognition accuracy for Kannada numerals.
Exploring Demographic Effects on Speaker Verification

Voice biometrics (e.g., Speaker Verification) is a critical type of biometrics based on human voice characteristics and is known for security and user-friendliness. It has been widely applied in worldwide applications, such as voice assistants and online banking. However, a concern is raised rapidly about the demographic fairness that different subgroups may have different speaker verification performance due to the inherent voice characteristics. And little work done investigates this concern. A diverse group of 300 speakers by race and gender is recruited for exploration. After running some speaker verification evaluations, three conclusions were reached. Firstly, the Latinx are performed the worst among the four major races in the US (White, Black, Latinx, and Asian) in speaker verification. Secondly, that gender shows little difference in performance between men and women. Thirdly, that high entropy voices performed better than low entropy voices in speaker verification performance.
Energy-efficient Scheduling of Cloud Data Center Servers

Data center is a cost-effective infrastructure to store large data volumes and host large-scale service applications. Providers of cloud computing services are deploying data centers worldwide quickly. With lots of servers and switches. These data centers consume substantial quantities of energy, which contributes to high operating costs. Optimizing server and network energy consumption in information centers can therefore decrease operating costs. In a data center, power utilization is chiefly because of servers, network devices, and cooling systems, an effective energy-saving strategy is to consolidate computing and communication into fewer servers and network devices and then power off as many unneeded servers and network devices as possible. A new method of reducing the energy utilization of computer systems and networks in data centers while meeting the requirements of the cloud tenants for quality of service (QoS) is proposed here in this paper.
Artificial Intelligence based Human Facial Action Recognition by Deep Learning Neural Network

The modern era of digital devices which will be equipped with the dedicated machine will have the built in features to perform the special needs of the human. The mobile devices comes with the special cameras which will produces the high quality images. Human face and the facial expression system has been in the interesting field due to the great level of application it possesses. The face unlock, face detection in security areas and also the facial expression recognition has been in demand of development. The human exhibits the multiple type of expressions anger, sad, surprise, happy and many more. These expressions have special notation based on the human feelings and scenario. The Digital image processing technology has provided the tools to work with the images and will use the edge features, skin mapping and supervised knowledge based classifier to recognize the expressions posed by the human to assist the need.
Floating pontoon keeps water flow

Low rainfalls mean low water levels. In Ireland, Campion Pumps had to find a solution to maintain the water flow at the Inniscarra Dam. A floating pump arrangement discharging directly into an opening in the centre of the dam gave the answer.
Using self-constructing recurrent fuzzy neural networks for identification of nonlinear dynamic systems

In this paper, the self-constructing recurrent fuzzy neural network (SCRFNN) is applied for nonlinear dynamical system identification (NDSI). The SCRFNN is a novel fuzzy neural network (FNN) by adding a recurrent path in each node of the hidden layer of self-constructing FNN, which contains two learning phases. Specifically, the structure learning is based on partition of the input space and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. The SCRFNN can decrease the minimum firing strength in each learning cycle and the number of hidden neurons which is an FNN with high accuracy and compact structure compared with several other neural networks. The performance of SCRFNN in NDSI is further verified in simulation.
Degree of a Vertex in Max-Product of Intuitionistic Fuzzy Graph

Graph theory has applications in many areas of computer science, including data mining, image segmentation, clustering and networking. Product on graphs has a wide range of application in networking system, automata theory, game theory and structural mechanics. In many cases, some aspects of a graph-theoretic problem may be uncertain. Intuitionistic fuzzy models provide more compatible to the system compared to the fuzzy models. An intuitionistic fuzzy graph can be derived from two given intuitionistic fuzzy graphs using max-product. In this paper, we studied the degree of vertex in intuitionistic fuzzy graph by the max-product of two given intuitionistic fuzzy graph. Also find the necessary and sufficient condition for max-product of two intuitionistic fuzzy graphs to be regular.
Mitigating the Threat due to Data Deduplication Attacks in Cloud Migration using User Layer Authentication with Light Weight Cryptography

The widespread adoption of multi-cloud in enterprises is one of the root causes of cost-effectiveness. Cloud service providers reduce storage costs through advanced data de-duplication, which also provides vulnerabilities for attackers. Traditional approaches to authentication and data security for a single cloud need to be upgraded to be best suitable for cloud-to-cloud data migration security in order to mitigate the impact of dictionary and template attacks on authentication and data integrity, respectively. This paper proposes a scheme of user layer authentication along with lightweight cryptography. The proposed simulates its mathematical model to analyze the behavioral pattern of time-complexity of data security along with user auth protection. The performance pattern validates the model for scalability and reliability against both authentication and data integrity.
The PIAAC LogDataAnalyzer

Log files for 18 countries that participated in the first round of the Survey of Adult Skills, a product of the Programme for the International Assessment of Adult Competencies (PIAAC) (hereafter referred to as “PIAAC”) in 2011/12 have recently been released and can be downloaded from the German Social Science Infrastructure Services (GESIS) Data Catalogue (OECD, 2017[1]). More information and documentation is available at www.oecd.org/skills/piaac/log-file/. The files have been fully anonymised to prevent identification of individual respondents. The records can nevertheless be matched with information already available in the PIAAC Public Use File, which contains the individual answers to the background questionnaire, as well as the performance of test-takers in the PIAAC assessment.
Internet Role in Remote Sensing and Geo Informatics System

Remote sensing and geo informatics system (GIS) are difficult to find the source and accessibility of various applications by end users such as students, scholars, scientists and professionals. The overall objective of the learning is to compare as well as analyze the various applications of remote sensing by end users particularly in Tamilnadu. In this technical survey, total 114 people have participated, among them males and females are about 61.2% and 38.8% respectively, most of the people are at age group of 17-27. It is observed that students are the highest of about 36.9%, professional are 31.1%, research scholars are 26.2% and finally professors by 5.8% took the survey. Remote sensing and GIS applications are mostly used in the fields of survey/planning (51.5%), disaster management (49.5%), climate change (43.7%) and followed by agriculture/forestry (45.6 %.). The most generally used software’s are identified as ArcGIS, ENVI, ERDAS and QGIS in remote sensing and GIS users. It is revealed that, 37% of people have moderate usage of cloud mapping and 21% of people have never used of cloud mapping. From ANOVA results, there is no significant difference in remote sensing and GIS role among location, educational qualification, type of sector, type of data and web GIS. There is a significant difference in remote sensing and GIS role among the age, gender, occupation, experience and software groups. The survey leads to the conclusion that the above groups have an effect on internet role in remote sensing and GIS. It is concluded that Internet plays a major role in geographic information systems and remote sensing by 95.1% of people and only 4.9
Shared Experiences

In this chapter, we'll learn the fundamentals of building a multiuser experience using Photon Unity Networking (PUN). PUN is one of several networking options available to Mixed Reality developers to create shared experiences.
Guided Analytics Platform for Southern region of Tamilnadu Farmer Fraternity

Agriculture has its role to play in our economy like any other sector but the unique role and importance is comparatively high than the other sectors. Most of the families who are farmers in our country live in rural areas and find themselves aloof from technological world. Essential agricultural support services needed to be carried out in farming activities are left unaware for them. Moreover, farmer’s income and productivity can be improved through extension and advisory services which would be of greater support. With the advent of technological resources that is suitable, cost effective, user friendly and scalable for the farmers certain farming activities can be improved. This paper intends to propose a data analytic platform using modern digital technology enabling effective collaboration among farmer fraternity and peripheral partners. The platform ensures of delivering enhanced insights to the agricultural community with essential guidance and tips to improve with on high crop yield; forecast and report right time for sowing and irrigation; corrective measures to be performed during pest attack so that the farmers enrich their agricultural activities with the support of efficient decision making system.
Evaluation of a context-sensitive speech recognition system in the air traffic control simulation

The use of situational knowledge for automatic speech recognition in the ATC simulation domain is discussed. A cognitive model of the ATC controller is proposed that supports the recognition process with an estimation about the probability of clearances in the actual situation. The most probable clearances are written to a syntax file which is then handed to the speech recogniser. Experiments designed to analyse the power of this approach show that the use of dynamic syntax reduces the recognition error rate by about 50 percent, compared to the use of a static, non context-sensitive syntax.
Leader-Follower UAV Formation Model Based on R5DOS-Intersection Model

This paper proposes a formation of multiple unmanned aerial vehicles (UAVs) based on the R5DOS (RCC-5 and orientation direction) intersection model.After improving the R5DOS-intersection model, we evenly arranged 16 UAVs in 16 spatial regions.Compared with those of the rectangular formation model and the grid formation model, the communication costs, time costs, and energy costs of the R5DOS model formation were effectively reduced.At the same time, the operation time of UAV formation was significantly enhanced.The leader-follower method can enhance the robustness of the UAV formation and ensure the integrity of communication during UAV formation operation.Finally, we conducted a simulation experiment on the model and found that the R5DOS model formation was stable and could maintain the desired formation.The randomly generated UAVs could quickly fly to the formation path in a short time, establish formation, and carry out operations.When the leader fails, the follower could travel to the original trajectory of the failed leader in a short time, replace the leader, and continue to communicate and improve the robustness of the formation.To sum up, the UAV formation based on the R5DOS model has the advantages of long operation time, strong endurance, low communication cost, and stable formation, which is of great significance for research on UAV formation.