Reviewerly/exHarmony huggingface dataset:
Variational autoencoder for prosody‐based speaker recognition

Abstract This paper describes a novel end‐to‐end deep generative model‐based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker‐specific prosody representations for the speaker recognition task is examined herein for the first time. The speech signal is first automatically segmented into syllable‐like units using vowel onset points (VOP) and energy valleys. Prosodic features, such as the dynamics of duration, energy, and fundamental frequency ( ), are then extracted at the syllable level and used to train/adapt a speaker‐dependent VAE from a universal VAE. The initial comparative studies on VAEs and traditional autoencoders (AE) suggest that the former can efficiently learn speaker representations. Investigations on the impact of gender information in speaker recognition also point out that gender‐dependent impostor banks lead to higher accuracies. Finally, the evaluation on the NIST SRE 2010 dataset demonstrates the usefulness of the proposed approach for speaker recognition.
Privacy protected product differentiation through smart contracts based on bilateral negotiations in peer-to-peer transactive energy markets

Local peer-to-peer (P2P) energy markets have decentralized nature with bilateral negotiations and possibility of product differentiation to respect market participants' preferences for attributes such as energy source. An extension is presented for a Transactive Energy based decentralized P2P market framework to support product differentiation along with a Hyperledger Fabric (HPLF) open-source blockchain network integration to enable secure P2P energy transactions while maintaining distribution line power flow limits. HPLF is chosen as its permissioned nature enables recording of higher number of transactions per second compared to famous blockchain ledgers such as Bitcoin. There is a need to protect the transaction details while recording on common blockchain ledger as the same buyer may be offering different energy rates to different sellers. Market clearing logic is encoded into smart contracts and a novel smart contract approach is proposed that serves dual purpose. First, it automatically verifies the negotiated energy prices and quantities among buyer–seller pair to validate transactions. Second, it records verified market transactions on common immutable blockchain using private data stores without exposing the transaction details while maintaining verifiability. Numerical co-simulation studies with standard HPLF integration show that participants can inherently align their bidding strategies to suit their product differentiation preferences such as higher prices for renewable energy producers (REPs). Detailed results describing the recording of energy transaction on HPLF ledger as transfer of virtual asset are included to demonstrate operation of proposed smart contract. Further, multi-step market clearing results show that the revenue for such REPs can be increased significantly.
Sentiment Analysis: Amazon Electronics Reviews Using BERT and Textblob

The market needs a deeper and more comprehensive grasp of its insight, where the analytics world and methodologies such as “Sentiment Analysis” come in. These methods can assist people especially “business owners” in gaining live insights into their businesses and determining wheatear customers are satisfied or not. This paper plans to provide indicators by gathering real world Amazon reviews from Egyptian customers. By applying both Bidirectional Encoder Representations from Transformers “Bert” and “Text Blob” sentiment analysis methods. The processes shall determine the overall satisfaction of Egyptian customers in the electronics department - in order to focus on a specific domain. The two methods will be compared for both the Arabic and English languages. The results show that people in Amazon.eg are mostly satisfied with the percentage of 47%. For the performance, BERT outperformed Textblob indicating that word embedding model BERT is more superior than rule-based model Textblob with a difference of 15% - 25%.
Utilization of Three Software Size Measures for Effort Estimation in Agile World: A Case Study

Functional size measurement (FSM) methods, by being systematic and repeatable, are beneficial in the early phases of the software life cycle for core project management activities such as effort, cost, and schedule estimation. However, in agile projects, requirements are kept minimal in the early phases and are detailed over time as the project progresses. This situation makes it challenging to identify measurement components of FSM methods from requirements in the early phases, hence complicates applying FSM in agile projects. In addition, the existing FSM methods are not fully compatible with today’s architectural styles, which are evolving into event-driven decentralized structures. In this study, we present the results of a case study to compare the effectiveness of different size measures: functional -COSMIC Function Points (CFP)-, event-based - Event Points-, and code length-based - Line of Code (LOC)-on projects that were developed with agile methods and utilized a microservice-based architecture. For this purpose, we measured the size of the project and created effort estimation models based on three methods. It is found that the event-based method estimated effort with better accuracy than the CFP and LOC-based methods.
Arabic Documents Layout Analysis (ADLA) using Fine-tuned Faster RCN

At present, there is a massive interest in document digitization, image searching, and natural language processing models, using different types of models. The first step in applying any type of processing like the image to text converting, is layout analysis, which is the paper's interest field. The problem in layout analysis comes in Arabic language, where there is a well-noticed gap for research in this field. The main limitations of the existed research are common, the dataset size, where in its return, gives a not very accurate result. In this paper, we are using two distinct types of Arabic language datasets. We propose a tuned model for layout analysis for Arabic printed and early printed documents using Faster RCNN (ADLA). The proposed model is based on tuning Faster Region-based Convolutional Neural Network (RCNN) model to match our two datasets, with different regions of interest (RoI). For evaluation, we compared the proposed model with two distinct existing models (LABA & FFRA). The F1 score result for our proposed model exceeds the LABA model with 99.4%, whereas the LABA model has 90.5%. Our model exceeds the FFRA model with 99.59% accuracy, whereas the FFRA model got 99.83% accuracy result.
On the Role of Personality Traits in Implementation Tasks: A Preliminary Investigation with Students

The Software Engineering (SE) research community has been showing an increasing interest in peopleware, which refers to anything that has to do with the role of human factors in software development. Individuals’ personality is one of the human factors that can affect software development. In this paper, we present the results of a preliminary empirical study to understand whether there is a relationship between the personality traits (i.e., openness, conscientiousness, extraversion, agreeableness, and neuroticism) and productivity of undergraduate students in Computer Science (CS), and internal quality of the programs they developed in an implementation task. In our study, we involved 30 (last-year) undergraduate students in CS, who had to implement a series of features. Our results suggest that there are correlation relationships between some personality traits (i.e., conscientiousness, extraversion, and neuroticism) and software quality. As for productivity, we could not find any correlation relationship.
The Proposed Pre-Configured Deployment Model for Amazon EC2 Cloud Services

Small, medium, and big organizations get several advantages from cloud computing, but it also presents obstacles. Whether a firm is in the financial, technology, or engineering sector, a cloud component might be beneficial. Though there are numerous obstacles associated with cloud computing, experts think that the benefits outweigh the drawbacks. The issues will be addressed when more research in the field of cloud computing is conducted. Cloud services are provided by a number of significant companies, including Amazon Web Services, Microsoft Azure, and Google Cloud Platform, among others. Among them, AWS (Amazon Web Services) is one of the fine cloud service providers that comprises several features, including the AWS EC2 (Elastic Compute Cloud) which is one of the widely used by many organizations. Amazon's Elastic Compute Cloud Web service delivers highly adjustable processing capacity throughout the cloud, allowing developers to construct applications with incredible scalability. Using EC2 (Elastic Compute Cloud) by using the proposed deployment method can be more effort saver for any IT development and deployment team for any organization. There should be an easy deployment method that auto-configures EC2 Instance. The aim of this research paper is to showcase the current deployment and service models provided by Amazon Web Services EC2 and present the proposed solution in order to the existing scenario. Furthermore, its advantages are also present so that it becomes easier to select the most appropriate one for deployment and research development.
A LSTM based Deep Learning Model for Text Summarization

As different users provide different reviews for a product/service, it has become increasingly difficult for common people to understand the customer reviews found on various apps or websites. People are sometimes too lazy to read reviews on various subjects all the way through before making a judgement, despite the fact that they can take time. Even if they wanted to, people cannot read every line of a review. As a result, a text summary model would greatly simplify this process. The purpose of a text summary is to draw out the most significant data from a long document and leave out any that are superfluous or uninteresting. This text summarizer will automatically produce a useful summary from reviews using LSTM. Sentences from the input text will be separated and converted into vectors. A material summary is a process of reducing a large body of text while preserving its original context. The summary should read easily. In this project, our goal is to create a model that accepts reviews of foods as input and outputs a summary of the review. This helps the people who are ordering the food if they want to know about the food that they are looking for.
Modelling and Simulation of Quadrotor-based Cable-Driven Parallel Manipulator

Cable-Driven Parallel Manipulators (CDPMs) are widely used in many applications, including heavy load transportation, Spidercams, 3D printers, etc. Still, they are restricted from operating in a specific workspace and cannot be used for applications requiring mobility. The current work proposes a novel architecture of Cable-Driven Parallel Manipulator, which has enhanced mobility compared to the existing CDPMs, through a collection of ground vehicles and aerial drones. The proposed system addresses the major drawbacks of existing over-constrained mobile CDPRs that don't have the freedom to vary the cable exit points and simultaneously addresses the inherent lower durability of aerial drones for executing such tasks. The suggested system features cable exit points attached to each of its eight robots, allowing it to freely locate itselfin a better or more appropriate location independent of other robot locations. Aerial drones often have a shorter flying time due to their low battery capacity. The proposed system gives the drones a separate prolonged power source carried by mobile ground robots, which can increase their durability and the system's overall endurance. This paper demonstrates the modelling, simulation and demonstration of navigation for the proposed Quadrotor-based Cable-Driven Parallel Manipulator (QCDPM) system using ROS and CoppeliaSim. The simulated results depict that it is possible to have a full mobile CDPM system using quadrotors.
BreastNet: Design and Evaluation of a Deep Learning model for recognizing Breast Cancer from Images

Breast cancer is one of the most common cancers in the world, and is a very dangerous cancer among all cancers which mainly affect the woman, and hence leads many researchers in medical field to work towards its early diagnosis. There are many ways to detect breast cancer like, Biopsy which is the commonly used diagnosis method. Computer-Aided Diagnosis (CAD) is a capable tool for the pathologists for diagnosing cancer, which points out the cancerous cells. The CAD system includes the pre-processing of feature extraction, and feature selection. In this research paper, deep learning neural network technique is used to recognize breast cancer from the BreakHis dataset. This method firstly applies the reshaping of data and normalizing the data. After completion of these methods, different algorithms are used. These methods contain data augmentation layers, dropout layers, and dense layers. For this purpose, the dataset is split up into two groups, one containing 70% for training phase and the other containing 30% for testing phase for the images present in the BreakHis dataset of different magnification factors (40x, 100x, 200x, 400x). The performance is given in the accuracy value of different methods. The result obtained for the method having one data augmentation and more dense layers, the accuracy value is found to be high after the completion of all classifications of benign and malignant The proposed method achieves a higher accuracy for the image extraction and feature selection, and significantly improves the overall accuracy of classification.
MRI Super-Resolution using Generative Adversarial Network and Discrete Wavelet Transform

Deep Learning Approaches have brought in major advances in super-resolution. Deep Learning architectures like convolutional neural networks or auto-encoders are trained to beget high-resolution (HR) images from the low-resolution (LR) images. Discrete wavelet transformations (DWT) is used to bring out the high frequency (HF) sub-bands of the image, which is further used to reduce the error while synthesizing super-resolution images. The proposed architecture combines the features generated by the DWT and Super-Resolution Generative Adversarial Networks (SRGAN) to produce images of high resolution. The implemented topology initially applies DWT to the image and separates out the image into two fundamental bands (HF and LF sub-bands). The features are then generated by separate generators and then Combined by inverse 2d discrete wavelet transformation (IDWT) which is then passed on to the discriminator to evaluate the generated image. A key feature of this design is that each feature is given importance and is generated by separate generators which makes the reconstruction and improvising the quality of the image more feasible. From the experiments conducted on medical images such as magnetic resonance imaging (MRI), the proposed design is computationally simpler and yet produces competitive and often improved results than state-of-the-art alternatives in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
Deep Learning Based Night Time Vehicle Detection for Autonomous Cars using Generative Adversarial Network

Vehicular detection is an important research area in the field of target detection. The implementation of accurate vehicle recognition models for both day and night conditions will make future automatic vehicle technologies and driver assistance systems more reliable. Our research proposes a novel approach for night vehicle detection. The system is made up of two modules: one for image translation and one for vehicle detection. When a nighttime image is given, the picture translation module uses the Generative Adversarial as a basic network to produce daytime images as an output. Faster R-CNN and YOLOv5 algorithms are used during the vehicle detection phase to extract daytime features and identify vehicles. The YOLOv5 technology achieves a detection accuracy of 96.75%.
AI-based Attendance Tracking System using Real-Time Facial Recognition

A fundamental system necessary for monitoring student performance is attendance tracking. It is always a harder task to monitor the student consistently all through the learning period. A conventional based attendance system is not efficient enough, as it tracks the student's presence at the start or the end of the hour. This research work has proposed a facial recognition and artificial intelligence (AI)-based attendance tracking system. The proposed system includes a face recognition model to recognize real time images and upload the data to cloud server by using a deep learning TensorFlow framework. As a part of the proposed attendance tracking system, the face recognition system can also be utilized to maintain “punch card” records. It can be used to enhance recognition, authentication and security.
Machine Learning Algorithm for Fall Classification Using Wearable Device

Significant injuries that sometimes cause fatality due to fall, is even severe for elderly who lived alone. The goal of this paper is to investigate the use of supervised machine learning (ML) algorithms in classifying fall and normal states. Based on our previous 2245 dataset from ten subjects, we employed three types of ML namely, k-Nearest Neighbour (k-NN), support vector machine (SVM) and also decision tree (DT). Initially, the optimal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> value of k-NN, 6, is determined by comparing a range of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{k}$</tex> values in terms of mean error. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{k}$</tex> value equal to 6 is then used in the k-NN and a comparative study is conducted. Results show that the DT outperforms in sensitivity, 98% and also the accuracy, 98.52%. However, the k-NN outperforms in terms of specificity, 99.31 % when it detects the normal status. On the other hand, SVM demonstrated the lowest performance probably due to the data scaling issue.
Device Free Human Body Fall Detection to Aid Senior Citizen

Recently, the risk of fatal falls among the elderly has grown. According to the Centers for Disease Control and Prevention (CDC), falls are a substantial contributor to both fatal and non-fatal injuries. Slipping and falling may appear innocent, but they can cause serious injuries or even death in elderly people. To make fall prevention commonplace, the CDC launched the Stopping Elderly Accidents, Deaths, and Injuries campaign. The systems underlying bodily action and idleness differ, and they are not effectively maintained. Additionally, exercise largely avoids accidents, so take precautions. We are building a system where the patient's whole activity is tracked using a sophisticated accelerometer sensor, and where the patient's predetermined alert can be identified and alerts can be sent to the patients. A body-mounted sensor is used in this instance. Devices for tracking motion that meet the high performance, low cost, and low power needs of smart wearable sensors. In this, the data processing unit is provided the decided data. People have been subjected to the sample test, in which different gestures are present and used to explore the actions of the body while using samples of data from the
SMS: SIGNS MAY SAVE – Traffic Sign Recognition and Detection using CNN

Traffic sign classification automatically detects roadside traffic signs, such as speed limit signs, yield signs, etc. Automatically recognizing traffic signs enables the development of “smarter automobiles.” Self-driving automobiles require traffic sign recognition to interpret and comprehend the roadway accurately. Similarly, “driver alert” systems within cars must understand the surrounding roadway to assist and protect drivers. Our automation would assist drivers in detecting and identifying traffic signs without distracting them from the road. With convolution neural networks, the signboards can be accurately classified. The precision can be improved by adding more layers. The GTSRB dataset is utilized here for training and testing; by fine-tuning the parameters, the 43 types of traffic signs are categorized accurately, and the detection speed also increases.
Designing Web Crawler Based on Multi-threaded Approach For Authentication of Web Links on Internet

In the generation with the growth of communication technology and improving social requirements, this web crawler has been used extensively in the majority of search engines and research fields. In single-threaded crawlers, concurrency cannot be achieved because it is the single-threaded approach. Due to the use of Single threaded crawlers parsing takes higher time consumption. To overcome this a multi-threaded web crawler is proposed which can reduce the parsing time. An online crawler is a web bot that consistently browses the World Wide Web, generally for the aim of Web indexing. Any computer program uses these indexes, web graphs, and associated acceptable algorithmic rules to rank the pages or keep archival records of websites. Mostly the focus of the project would be to implement a multi-threaded transferer website crawler that may download multiple websites and their paths through the initial hostnames at a certain number of threads with their ranking and at a reduced time.
Real Time Object Detection using YOLO Algorithm

This research work aims to perform object detection by using the You Look Only Once (YOLO) method. This method is much efficient to the existing models in terms of speed and performance. Some of the algorithms do not scan all the regions in single forward propagation but in YOLO, the algorithm analyzes the entire image by predicting binding boxes using convolutional neural network and class opportunities. YOLO performs faster when compared to other algorithms.
Designing Applications for Collaborative Learning in Virtual Reality

For the past decade the virtual reality technology had a noticeable impact on many aspects of life, including education. Virtual reality has the potential to significantly support students in developing their abilities and knowledge, according to several studies cited in this paper. In fact, combining virtual reality and education can enhance learning and teaching in an interactive way. In this paper we aimed to study the impact that virtual reality adds to the education process and how important is the collaborative aspect in a virtual reality application. Consequently, we describe the design, development, and evaluation of an application for collaborative learning in virtual reality that demonstrates these approaches.