Reviewerly/exHarmony huggingface dataset:
Secure User Management Gateway for Microservices Architecture APIs Using Keycloak on XYZ

XYZ agency has an application that supports administrative business processes such as scheduling activities, accessing mail, etc. This application uses a microservices architecture that consists of various XYZ services. There is a user authentication process for every access to application services. Therefore, Keycloak centralizes all user authentication. This study will implement the REST API as a User Management Gateway application using Keycloak on the XYZ Internal Web Application Microservices to facilitate user management. This application implements JSON Web Token authentication with the RSA-256 algorithm on every use of the XYZ Keycloak REST API. The features of the REST API Keycloak developed are tailored to the needs and business processes of the XYZ application. We develop the User Management Gateway using the SCRUM development method, divided into three sprint stages: design, manufacturing, and testing. Results of this study show that the development of the User Management Gateway has been made following the needs of the XYZ application business process and has met the required security factors.
Human Activity Recognition based on Stacked Autoencoder with Complex Background Conditions

Human activity recognition is one of the prime focus areas of computer vision having a range of current and evolving applications in the real-world environment such as abnormal activity recognition, pedestrian traffic with action detection, video indexing, gesture recognition, etc. The goal of this paper is to propose a human action recognition framework that can efficiently work in complex background by exploiting the stacked autoencoder principle. Due to the rapid development of artificial intelligence (AI) aided approaches of decision making, deep learning (DL) is a preferred area of research. Among several known DL approaches, the stacked autoencoder has received extensive research interest and is considered to be among the current state-of-the-art approaches. In particular as part of this work, a stacked autoencoder with three hidden layers is trained in the first stage for representation learning. In the second stage, a SoftMax layer is integrated as a final output layer for the classification of various human actions. We applied the proposed method to a publicly available human action database to evaluate its performance. The feasibility and the effectiveness of the proposed stacked autoencoder-based human action recognition framework have been demonstrated by experimental simulation in this paper.
A Campaign Mining in Social Media using Improved K-Means: The Perspective of Candidate President's Mission

The presidential candidate socializes the vision and mission to persuade supporters and voters as much as possible during the campaign. Since 2010, presidential campaigns in Indonesia have extensively used social media networks as a standard form of presidential communication. However, the presidential candidate's campaign duration has not been maximized to convey the vision and mission to prospective voters. This study aims to identify each candidate's focus based on tweet analysis using Improved K-Means and a second-level normalization algorithm. The data consisted of 2,786 tweets from January to April 2019, obtained from two sources: 1,485 tweets from candidate#1 and 1,301 tweets from candidate#2. The results show that there are 16 clusters created from candidate#1 and 14 from others. Most candidate#1 aligns with its mission. Consequently, our study could clarify the presidential candidate's mission focus. The more frequently the mission is discussed on social media, the more notable it is as the President's primary work program. The first candidate's tweets mainly mentioned the fourth mission, and the second one was about the first mission. Furthermore, there is a link between the frequency of tweets from the presidential candidate's mission and the theme of the presidential debate.
Optimizing Defect Removal Efficiency by Defect Prediction using Machine Learning

In current world, complexity and volume of software applications are increasing exponentially. Applications are expected to perform without defects as critical real world transactions are being handled through software design and development. Quality of a software can be impacted by software defects and thus leading to unavoidable high cost and customer dissatisfaction. Preventing defects at early stages of development ensures high quality software. Different defect prevention and detection techniques are used to identify the defects before delivery. In the last decade, machine learning models as defect detection techniques have taken a lot of attention from researchers as this concept narrows down the volume of code under inspection. In this research work, six machine learning algorithms are implemented. The prediction results are based on PROMISE public datasets containing more than ten thousand records. Performances of these algorithms have been compared through Confusion Matrix and Area Under the Curve (AUC) of Receiver Characteristic Operator (ROC) which are the most informative indicators of predictive accuracy in software defect prediction. The result analysis shows MLP is the best fit model in both CM and AUC-ROC showing maximum accuracy.
Malayalam Character Recognition from Palm Leaves Using Deep-Learning

Ancient manuscripts like palm leaves, available in museum libraries, are a rich source of knowledge. Digitization helps store this knowledge protected for the future & enables its global access. Varying writing styles, presence of currently discarded & rare characters, quality of imaging, and palm leaves are some of the challenges to be handled while building an offline handwritten recognition system for these manuscripts. This paper focuses on recognizing Malayalam characters available in palm leaves using deep learning techniques. With the help of the histogram and contour method, lines are segmented from palm leaves first. Subsequently, individual characters are extracted from the lines. A customized Convolution Neural Network (CNN) is employed to recognize these segmented characters. This trained CNN recognizes forty-eight classes of segmented characters with 86% accuracy. Additionally, this paper compares the results with other standard CNN models.
Detection of Lung Cancer Using CT-Scan Image - Deep Learning Approach

Cancer is a disease that comes in many forms and is the largest cause of death worldwide for men and women alike. Early detection of cancer has the highest chance of saving a person's life. Some of the procedures used to diagnose cancer include CT scans, bone scans, MRIs, PET (Positron Emission Tomography), ultrasound, and X-rays. Cancers such as lung cancer are among the deadliest worldwide, killing approximately five million people every year. This chapter focuses on lung cancer detection. The diagnosis of Cancer is usually a very difficult task in the biomedical and the bioinformatics field. Now, computed tomography (CT) scans can provide useful information for lung cancer diagnosis. In recent advances, deep learning approaches have improved to outperform humans in some tasks like classifying objects in images and also predicting better accuracy. Therefore, these techniques have been utilized in this model for the treatment of cancerous conditions. We detect lung cancer nodules from a given input and classify cancer as Adenocarcinoma, Large Cell Carcinoma, or Squamous Cell Carcinoma in our research. To detect the location of lung nodules, researchers used revolutionary deep learning approaches. In this paper basically, we used three deep learning case studies to diagnose lung cancer such as VGG16, INCEPTIONV3 and RESNET50 and also, we are discussing various measures for evaluating the performance of our model to get better accuracy. SS
Facial expression recognition using DenseNet

FER is a field of study that focuses on categorizing human emotions based on their facial expressions. It may be utilized in video games, identifying suspicious people, patient's painful situation at the hospital, online meeting, the E-Iearning system, music player playing songs based on person's mood, driver's tiredness from expressions while driving, robotics, behavioral science, and so on could all benefit from a system that could automatically recognize human emotions from their facial expressions. There are a number of obstacles to overcome while implementing the FER system. The majority of datasets contains photographs of posed humans wearing a specific expression. This is the first hurdle, as real-time applications necessitate a model with no posed or guided emotions. The second issue is that the labels in the datasets are broadly categorized, which implies that in real time, the system may be able to accurately identify some phrases. Here the DenseNet based algorithm may be used to identify real-time facial expressions. Our suggested framework has an AUC of 91.2 percent on the FER-2013 dataset and obtains new state-of-the-art performance.
Autoencoders Learning Sparse Representation

Many regularized autoencoders learn a sparse rep-resentation of data. This type of representation enhances robust-ness against noise and computational efficiencies. Our objective in this paper is to provide the conditions under which sparsity is encouraged by AE under a little less restrictive view of data. We have shown a relaxed observed representation of input data and given the conditions on AE to promote sparsity.
Data Mining Using C4.5 Algorithm in Predicting Student Graduation

Education is guaranteed by the government of the Republic of Indonesia through the 1945 Constitution. Universities must maintain the ratio of incoming students equal to the number of graduating students, which affects accreditation. The problem faced by the ISB Atma Luhur Campus is that students who graduate on time still have to be improved due to many dropout students. As a result, there is a considerable accumulation of students in each graduation period. The solution to this problem is that it is necessary to apply data mining in predicting graduation using the C4.5 algorithm. The results of this study are the implementation of the C4.5 algorithm to predict student graduation on time or not on time. The training data used is student data from 2012 to 2015. The acceptance rate generated by the confusion matrix gets 78% accuracy, 82.75% precision, 80% recall, and 75% specificity with 100 training data and 50 test data. The F1 prediction score for graduation is 0.813, which shows good application performance. It is hoped that the prediction data obtained can be found a solution so that all students can graduate on time.
Formal Analysis and Improvement of Zero-Knowledge Password Authentication Protocol

The Internet as a communication medium has become a common thing in society. Communicating via the Internet requires a communication service where users are obliged to have an account. The login process to an account usually uses a password authentication protocol that is not provably secure. Datta offers Zero-Knowledge Password Authentication Protocol, an alternative protocol claimed to be safer. We conducted a formal analysis of the protocol proposed by Datta using Scyther tool and later found an attack compromising the security of the protocol i.e., man-in-the-middle attack. Furthermore, we modified Datta's protocol to cope with this attack.
Comparative Analysis of ControlGAN and ControlGAN-GP Models based Text-to-Image Synthesis

This manuscript discuss the concept of Text-to-Image synthesis using machine learning methods. For machine learning purpose gradient adversarial network is used. Two different gradient adversarial network namely ControlGAN and ControlGAN-Gradient penalty method are used for the above mentioned task. The inclusion of Gradient-penalty in ControlGAN improves the convergence of the model which is evident from the performance matrices of the system. Microsoft COCO dataset is used for simulation and result validation purposes.
Artificial Intelligence based Indian Sign Language Recognition with Accelerated Performance under HPC Environment

Communicating with a person having a hearing or speech disability is always a major challenge. Sign Language (SL) is a medium to remove the barrier of such type of communication. It is a very tough task for a common man to understand SL and interprets its meaning. So, an automated system is necessary which can recognize the SL characters and display its meaning and semantics. In this context, this article has presented a systematic investigation of Artificial Intelligence (AI) based approaches towards examining the difficulties in the classification of characters in Indian Sign Language (ISL). In this work, we adapted ISL recognition using Computer Vision, Machine Learning and Deep Learning methodologies. To achieve this requirement, the captured image undergoes a series of pre-processing steps which include various Computer Vision techniques such as conversion to gray-scale and thresholding using OTSU algorithm. Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and pre-trained models, VGG-19 and Inception-V3using Transfer Learning mechanism are used to train the system. Further, due to large image dataset, the training time of the models are also accelerated using PARAM SHAVAK HPC system which shows a reasonable improvement in the performance of the models.
Symptoms Prediction of Tuberculosis using Soft Computing Technique

It has been challenging to conduct research on the likelihood and consequences of TB survival. Significant advancements have been accomplished in a few linked domains from the initial phases of the associated study. For instance, improvements in biomedicine have raised estimations and records for prognostic aspects. Cheap computer hardware and software can also deliver better information, and data has been evaluated using a number of analytics techniques. One of the most common diseases and the leading cause of death in developed countries like India is tuberculosis. In recent years, the high prevalence of tuberculosis among all people has increased. In this work, we have discussed different types of Tuberculosis (TB) and, using Rough Set Theory (RST), find that Pulmonary TB is the most alarming in our state Odisha. We then active dataset of active cases of the Pulmonary dataset to generate a set of rules using Rough Set Theory (RST). We validate our claim by using a statistical method using, attest.
Blockchain base Solution for Trust Management Challenges Internet of Things application

Both industry and academics have recently paid close attention to the Internet of Things (IoT). The overall functionality of the IoT network depends on a dependable and secure IoT connection and communication. Enabling and establishing reliable communication between the items is one method of achieving strong security in an IoT network. In a network of diverse things like the Internet of Things, trust management is crucial. An current answer to this control issue is standardisation via an IoT gateway. Unfortunately, this calls for additional infrastructure. In this paper, initially trust challenges in IoT applications are identified and explained in details. Then impact of trust issues in IoT applications are explained. Blockchain based approach is taken as to address some of the existing trust challenges in IoT application.
Vision-Based Traffic Hand Sign Recognition for Driver Assistance

Authorized traffic control has the highest priority in the case of high-traffic situations. In such situations, it is very important to have a smooth flow of traffic which is the job of ATC. ATCs make use of hand gestures for controlling traffic. It is very important for drivers to understand these gestures in order to follow their instructions otherwise it might lead to accidents, traffic jams, etc. However, most drivers nowadays are unaware of these signals and in the age of autonomous vehicles, it has become of utmost importance that the vehicles have a high level of understanding of these gestures to better assist the drivers. In this study, various approaches to this problem are tested. A detailed comparison of the proposed methods with previous works on this topic is done and the room for further improvement of the performance is discussed.
Compound sentence and pronoun generation in Sanyog: An Iconic Communication System for People with Speech and Motor Impairments

Serious Speech and Motor Impairment (SSMI) affects a sizeable portion of the Indian population. They are unable to communicate normally because of their physical impairment. For persons who are physically disabled, such as those who have cerebral palsy, speech disorders, or other motor neuron difficulties, the Sanyog is a tool that helps them speak and communicate. The user communicates with icons in this Iconic Communication System (ICS). The collection of chosen icons is transformed into an instantiated representation, which resembles a frame. A natural language simple sentence generator accepts this intermediate representation. The proposed work aim to creates compound sentence from the subject's input in Sanyog in Bengali language. Two simple sentences are aggregated to form compound sentence. After that we applies the rules of pronolninalization to generate pronouns which makes the compound sentence more fluent. In this paper among the different categories of pronouns we have only concentrate on anaphoric pronoun generation. Lastly the correctness of the generated sentences are checked.
Comparative Performance Analysis of Particle Swarm Optimization and Artificial Bee Colony Algorithm for Optimization of Missile Gliding Trajectory

Swarm intelligence algorithms were widely employed for trajectory optimization problem. The current study presents a comparative performance analysis of two well known swarm intelligence algorithms such as particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm for optimization of missile gliding trajectory. The gliding range was maximized through trajectory optimization of missile by descretizing the angle of attack (AOA) as control parameter and solving control problem. Performance characteristics of PSO and ABC were evaluated based on computational efficiency, accuracy of solution and convergence ability. The obtained results reveal the superior performance of PSO with regard to accuracy of solution, computational efficacy and convergence ability in comparison to ABC.
Somali Extractive Text Summarization

Somali is an Afro-asiatic language of the Cushitic family. Somali is one the most spoken languages in the Horn of Africa. It is the national language of Somalia, Official language in Ethiopia and Northern Kenya. It is also the most widely spoken language in Djibouti. Somali is also spoken by the Somalis in the diaspora. Somali is considered to be a morphologically complicated language with limited corpus and datasets. In this paper, we have scrapped paragraphs from various Somali sources and summarized the text using Extractive Text Summarization Techniques to create an extractive text summarization for Somali language.
A YCbCr Model Based Shadow Detection and Removal Approach On Camouflaged Images

A shadow in an image can disturb the actual outcome in computer vision and pattern recognition applications. The reason is that the shadow will act as an individual object resulting in the false interpretation and performance degradation of subsequent computer vision tasks. Here we propose a process to detect and remove shadows from an image using the YCbCr colour model. A small portion of the image is identified as a shadow area. The features at the pixel level and along the boundaries in the shadow area are learned. A method based on the locations of the border of the shadow is applied to remove the shadow. Experiments have been conducted on the benchmark camouflaged image dataset and the non-camouflaged image dataset to evaluate the approach. The methodology achieves promising performance in detecting and removing shadows from an image.
IncentiveChain: Blockchain Crypto-Incentive for Effective Usage of Power and Water in Smart Farming

This paper discusses how agriculture has become one of the prime reasons for the wastage of energy and water during food production. In order to control the use of resources in farming, we introduce a novel concept called IncentiveChain. The application idea is to distribute crypto ether as a reward to the farmers because they play key roles in keeping a check on resource usage and can benefit through these schemes economically. We provide a state-of-the-art architecture and design, which includes participation from national agricultural departments and local regional utility companies to embed various technologies and data together to make the IncentiveChain application practical. We have successfully implemented IncentiveChain to show the transfer of ether from utility company accounts to farmer accounts and the currency being collected by the farmer in a more secure way using the blockchain, removing third-party vulnerabilities.
Deadline-Sensitive Workflow Scheduling With On-Demand and Spot Instances in the Cloud

Due to the scalable and pay-per-use nature of Cloud computing, more and more users are deploying their workflow applications in the Cloud. Proper scheduling of the tasks of a workflow in the Cloud is a challenging area of research, especially if the workflow is deadline-sensitive. Moreover, the users want the Cloud services at a low monetary cost. Hence, the scheduling approach must be cost-effective. Cloud service providers like Amazon offer virtual machine (VM) instances through different pricing models- on-demand, reserved, and spot. The spot VMs are generally available at a much lower price than the on-demand VMs. However, spot VMs are unreliable. We propose a scheduling algorithm for deadline-sensitive workflows in a heterogeneous Cloud environment using the on-demand and spot instances. Our algorithm reduces the resource renting cost while ensuring that the workflow gets completed within its deadline. It also provides tolerance against premature termination of the spot instances. Rigorous simulation using real-world workflows and comparison with some existing approaches validate the efficacy of our proposed algorithm. Experimental results also show that our algorithm generates around 66% of cost savings compared to using only on-demand instances.
Novel Framework for Identifying Anomalies in High Volume of Data using Robust Machine Learning Algorithm

Anomaly detection in unlabelled data is very tedious task using unsupervised learning models and also to study the model validation process. Furthermore, it is also getting difficulty to get large-scale labelled data and evaluate the model performance. In real-world problem, in general, for unlabelled data set, evaluation process not only takes time but also increase the project cost. To minimize this issue, here we proposed a novel framework merging with both the unsupervised and supervised learning process. In this framework, the process will generate global label followed by a majority voting ensemble approach. With the availability of global label, the framework latter established a classifier to train and finally predict the appropriate normal or anomaly label of any unlabelled data. This framework has seven stage or seven process and takes very low computing cost. To design this frame-work, we used five unsupervised learning models including Isolation forest (IF), Local Outlier Factor (LOF), Gaussian Mixture, One Class SVM and Auto encoders, in addition to this, we used six supervised learning models like Random Forest, Logistic Regression, CART, Gaussian NB, K-nearest neighbour (KNN) and XG-Boost.All the applied model performance discussed extensively in the report. It is found the proposed framework has able to enhance the accuracy of anomaly detection rate.