Reviewerly/exHarmony huggingface dataset:
A Software Quality Characteristics Optimization Model to Reduce Evaluation Overhead

Software quality evaluation is one of the critical processes for delivering good quality software products. The software quality evaluation has a significant effect on the entire project. However, for software quality evaluation a number of techniques are available. Most of the models are based on the characteristics of software and relevant scores. But consideration and evaluation of software on all the quality characteristics are time is taken and complex, which is not suitable for all kinds of projects. Therefore, in order to reduce the software quality assessment effort, this paper proposes a categorization of essential quality characteristics based on software project needs. Additionally, a Particle Swarm Optimization Process is proposed for recommending the suitable software quality evaluation attributes. The simulation of the model has been carried out and their performance analysis has been done. Based on the experimental analysis the results are presented and the future scope of the proposed work is explained.
Decentralized Digital Identity: A New Form of Secured Identity Using Blockchain Technology

Decentralized Digital Identity is the recent research focus for all identity management policy making authorities. The demand for the development robust digital identity solution is constantly rising. Conjugation of AI and biometric authentication at one side makes the identity process more streamlined to the users but at other side opens up ample of opportunities for cybercrime and identity theft also. Hence, the centralized process for maintaining the identity has become vulnerable both for the identity holder and the identity maintaining organization as well. This paper proposed a secure blockchain based generic provable claim model to be adopted for the benefit of identification of general public. The proposed model depicts secure decentralized ledger for issuer and verifier in identity model to mitigate the risk of exploitation over digital identity by the hackers to protect from duplication with fake identification. MD5 and SHA-512 driven secure hashing function is applied in a blockchain for identity verification in two application domains in Biomedical and Transport blockchain as per recent state-of-art.
Ant Algorithm for GPS Surveying Problem with Fuzzy Pheromone Updating

GPS system has important part of our life. We use it everyday for navigation, but it has other important use too. The GPS navigation is used by vessels, aircrafts and vehicles for safety passage. Other use is geodetic survey. GPS gives more precise coordinates comparing with traditional methods. Scientific use of GPS includes measurement of crystal deformation, monitoring of earthquake processes, volcano eruption process, ruffing, mountain building. These applications needs very precise measurement. We propose Ant Colony Optimization (ACO) algorithm with two variants intuitionistic fuzzy pheromone updating. The proposed algorithm is tested on 8 test problems with dimension from 38 to 443 sessions. The variants of our algorithm is compared with traditional ACO algorithm.
Indian Sign Language Digit Translation Using CNN with Swish Activation Function

With the change in technology at such a fast pace, every piece of information is available on the Internet. But certain sections of society like deaf-mute persons are still struggling for their communication rights. They use countenance, kinesics, and gestures to communicate with each other. Further, due to the paucity of linguistically annotated and documented material on Sign Language, research on grammatical and phonetic aspects of the language is limited. Despite the well-established purposes of Sign Language, the use of technology to facilitate deaf persons is under-explored in research studies. Consequently, the design of an efficient and automatic Indian Sign Language Translation System (ISLTS) is essential. This paper proposes a deep learning-based seven-layered two-dimensional Convolution Neural Network (2D-CNN) for efficient translation of Indian sign language digits using the swish activation function. The proposed framework uses max pooling, batch normalization, dropout regularization, and Adam optimizer. An open-access numeric customized dataset of India Sign language of approximately 12 K images has been utilized. Our model achieves the highest validation accuracy of 99.55% and average validation accuracy of 99.22%. The results show that the swish activation function outperforms traditional ReLU and Leaky ReLU activation functions.
A Multi Objective Hybrid Collision-Free Near-Optimal Dynamic Path Planner for Autonomous Robots in Dynamic Environments

Autonomous Mobile robots are finding increasing applications in industry and everyday life, so the path-planning of robot in dynamic environments has become an emerging research problem. Dynamic path planning has, therefore, received more attention. In this paper, a innovative path planning method, a Multi-Objective-Hybrid Collision-free-Near-Optimal Dynamic-Path Planner (MOHC-NODPP) is proposed and is an advancement of former research work “A Multi-Objective Hybrid Collision-free Optimal Path Finder for Autonomous Robots in known static environments. It is utilized for autonomous mobile robots and has the ability to avoid both stationary and moving objects in dynamic environments. Challenge is to get a nearly optimal result by expanding a lesser number of nodes compared to existing approaches, while taking into consideration the non-holonomic constraints of the robot, with the end goal of generating near-optimal drivable paths for the robot. The proposed method has two operating modes. In the first mode, referred to as Offline-path-plan mode, the global search technique, A Multi-Objective-Hybrid Collision-free Optimal-Path-Finder, MO-HC-O-PF is used to discover an initial optimal route in the environment containing stationary impediments. The second mode, Onlinepath-Plan mode, uses help from sensors to find stationary obstacles which are dynamically occurred in environment. In order to bypass collisions with these obstacles alternate path segment is found and final path is updated. In order to minimize collision with these obstacles and to preserve the sub path segment’s optimality, a new method, MOHC-NODPP is utilized. To simulate and evaluate the effectiveness of the suggested strategy, the standard maps were utilized. The results of the simulation illustrate the capability of the proposed method to recognize stationary and dynamically appeared barriers, as well as its capacity to discover a collision-free and nearly optimal path to a goal location in settings that are subject to dynamic change. The length of the path, the absence of collisions on the way, the amount of time it takes to execute, and the smoothness of the path are the performance metrics that are utilized to select the best path. Studies conducted using the new method reveal that, in comparison to the conventional procedures, there is an average reduction of 15% in the length of the path, a reduction of 20% in the essential time obligatory for its accomplishment, and a success rate of 95%.
Demanded Scale for Modern Numerical Optimisation

Modern online services facilitate many aspects of the human life such as goods and food shopping and delivery. The number of requested services is growing fast and at the same time can be seen growth of unattended constraints, unforeseen limitations and accidental events which affect the quality and feasibility of the service. Efficient management of online delivery requires optimisation at appropriate scale. This article discusses examples of online delivery services, which require optimization, points out advantages, and limitations, highlights their scale and the role of numerical methods for reducing the cost and time for delivery. Initial investigation illustrates algorithms capabilities to resolve such tasks and discusses current issues. Experimental results on high dimensional tests and consideration of further work conclude the article.
Object Detection with YOLO Version 3 for Big Data

Object detection is the most vital task for the application of computer vision. The You only Look Once (YOLO) version3 is the most promising technique used for deep learning-based object detection. It is the k-means cluster method, which estimates initial weight and height of the predicting bounding boxes that are sensitive to the initial cluster centers. This paper focuses on study of approaches for efficient object detection techniques in the field of computer vision. The improvement in the accuracy had been studied based on the deep learning techniques. It aims to develop an online tool for a better study about the object detection techniques as a platform to review the object detection in both online and offline images. A popular state of art object detection algorithm called YOLOv3 which is trained on COCO and ImageNet. The result obtained in the developed online tool has shown a positive result with a possibility in detecting the most common objects available in this real world.
Fault Estimation and Tolerant Control for Multiple Time Delayed Fuzzy Systems with Sensor and Actuator Faults

The second chapter studies the fault estimation and tolerant control for time delayed T-S fuzzy systems. In general, many factors, such as disturbances, unknown nonlinear dynamics, actuator faults, and sensor faults, may need to be considered in fault estimation and tolerant control for T-S fuzzy systems.
Multivariate and Univariate Anomaly Detection in Machine Learning: A Bibliometric Analysis

Anomaly detection enables the identification of the nature of the outliers to determine if they are errors or new trends that need to be understood and learnt by the model for improved generalisation capability. Multivariate and univariate anomaly detection is used to find an unusual point or pattern in a given set. A substantial number of articles have focused on this research domain; however, they primarily reflect on the impacts and trends of multivariate and univariate time-series data research. However, there is still a lack of bibliometric reports exhibiting the exploration of an in-depth research pattern in multivariate and univariate anomaly detection approaches. This study addresses that gap by analysing the widespread multivariate and univariate anomaly detection activities conducted thus far. This study analysed the Scopus database by using bibliometric analysis in a pool of more than 1385 articles that were published between 2010 and 2022, of which 679 are journal articles, 609 are conference papers, 72 are conference reviews, 13 are book chapters, 8 are reviews, 2 are erratum, and 1 is a book and a short survey. The multivariate and univariate anomaly detection bibliometric analysis was developed using R, an open-source statistical tool, and bibliophily was used to analyse the results. The findings reveal the following: (1) multivariate and univariate anomaly detection in collaboration with machine learning can enhance intrusion detection systems; (2) Researchers are interested in combining multivariate and univariate techniques with machine learning and deep learning classification problems to distinguish normality from abnormality; (3) the most active country in this research domain are the United States, China, France, India, Italy, and Germany; (4) Norway, Sweden, and Taiwan published few articles, however, receive many citations; (5) the United States, China, France, and Italy are the countries that collaborate the most in publishing articles on multivariate and univariate anomaly detection approaches; (6) keyword analysis reveals that researchers are adopting multivariate and univariate approaches to detect anomalous patterns in big data and data mining application domain. Supervised, unsupervised and semi-supervised machine learning algorithms, in collaboration with multivariate and univariate, play a significant role in classifying abnormality from normality.
Design for Robust and Efficient Neuromorphic Photonic Accelerator

In this work, we propose a novel architecture for building a robust integrated photonic neuromorphic accelerator based on a crossbar array design. Our architecture is based on an asymmetric multimode Y-coupler. A Y-coupler has the inherent benefit of high fabrication tolerance and broad optical bandwidth. From simulations, we show that our proposed Y-coupler has high coupling efficiency. Using modal decomposition analysis of our coupler, we numerically estimate the energy efficiency performance of a large-scale photonic network and show a 10% improvement in energy efficiency for large-scale photonic networks with high fabrication tolerance and broadband application and a small footprint.
Transfer Learning of Mammogram Images Using Morphological Bilateral Subtraction and Enhancement Filter

Deep Learning in healthcare immediately affects a person’s health and well-being. Deep Learning in medicine helps in early diagnosis. Medical data retrieval is time-consuming. According to a study, breast cancer is more common among women than men. Mammography is a frequent computer-assisted diagnosis of breast cancer. Mammography isn’t always accurate despite being widely used. Only 30% of breast cancers are detected early. In this post, we’ll look at the second step of preprocessing, which involves adding a filter to the image once it’s been split up. To enhance the appearance, breast-border-erasing filtering techniques are used for the data. The proposed segmentation approach subtracts two pictures after utilizing morphological procedures to restore the breast image’s boundaries. We found that employing homomorphic filters and a modified bi-level histogram to identify noise and boost contrast improved picture quality.
Automated Spoken Language Identification Using Convolutional Neural Networks &amp; Spectrograms

The automated identification of spoken languages from the voice signals is attributed to automatic Language Identification (LID). Automated LID has many applications, including global customer support systems and voice-based user interfaces for different machines. The hundreds of different languages are popularly spoken around the world and learning of all languages is practically impossible for anyone. The machine learning methods have been used effectively for automation and translation of LID. However, machine learning-based automation of the LID process is heavily reliant on handcrafted feature engineering. The manual feature extraction process is subjective to individual expertise and prone to many deficiencies. The conventional feature extraction not only leads to significant delays in the development of automated LID systems but also leads to inaccurate and non-scalable systems. In this paper, a deep learning-based approach using spectrograms is proposed. The Convolutional Neural Networks (CNN) model is designed for the task of automatic language identification. The proposed model is trained on a dataset from VoxForge on the speech from five different languages, viz. Deutsche, Dutch, English, French, and Portuguese. For this study, evaluation measures like accuracy, precision, recall, and F1-score are used. The new proposed approach has been compared against traditional approaches as well as other existing deep learning approaches for LID. The proposed model outperforms its competitors with an average F1-score of above 0.9 and an accuracy of 91.5%.
From temporal data to dynamic causal models

We present a brief review of dynamic causal model inference from data. A vector autoregressive models is of our prime interest. The architecture, representation and schemes of measurement of temporal data and time series data are outlined. We argue that require- ment to data characteristics should come from the nature of dynamic process at hand and goals of model inference. To describe and evaluate temporal data one may use terms of longitude, measurement frequency etc. Data measurement frequency is crucial factor in order to an inferred model be adequate. Data longitude and observation session duration may be expressed via several temporal horizons, such as closest horizon, 2-step horizon, influence attainability horizon, oscillatory horizon, and evolutionary horizon. To justify a dynamic causal model inference from data, analyst needs to assume the dynamic process is stationary or at least obeys structural regularity. The main specificity of task of dynamic causal model inference is known temporal order of variables and certain structural regularity. If maximal lag of influence is unknown, inference of dynamic causal model faces additional problems. We examine the Granger’s causality concept and outline its deficiency in real circumstances. It is argued that Granger causality is incorrect as practical tool of causal discovery. In contrast, certain rules of edge orientation (included in known constraint-based algorithms of model inference) can reveal unconfounded causal relationship.
Traffic Sign Detection and Recognition Using YOLOv5 and Its Versions

A Traffic Sign Detection and Recognition (TSDR) System, which helps navigate vehicles through computer vision and human-machine communication, has to perform quickly as vehicles using them travel at high speeds. During this study, a speedy one-stage detector such as YOLOv5, a deep learning model, was chosen to dive into. This study explores creating a TSDR model by comparing four different versions of YOLOv5, namely YOLOv5 Nano, Small, Medium, and Large. This study was accomplished by first creating a new traffic sign dataset. The four versions of the YOLOv5 algorithm were then trained with a 75–25 train validation split, and 24 models were created. Afterwards, the models were tested on a test set, and their metrics were tallied. Results showed that YOLOv5 Medium and Large offer a 10% increase in accuracy performance when compared to YOLOv5 Small, but due to the slower detection speed of YOLOv5 Large, the YOLOv5 Medium models are a better fit when it comes to the detection of traffic signs when prepared by a relatively small dataset. This study provides an overview of the performance of the different YOLOv5 versions in traffic sign detection and recognition that aims to contribute to the improvement of traffic sign detectors.
Holistic Features and Deep-Guided Depth-Induced Mutual-Attention-Based Complex Salient Object Detection

The multi-stream-based convolution neural network is the recent trend in saliency computation and receiving tremendous research interest. The existing models preferred the color- or depth-modality-based independent stream to extract saliency features, which are insufficient in complex and challenging scenarios. The proposed model produces an enormous set of saliency features (holistic feature space) by three-stream networks to target the above challenges. Two streams are independent, and the third one depends on the other to acquire all essential features. These holistic-feature-based learning include non-complementary, cross-complementary, and intra-complementary features. These features are utilized in middle-level fusion strategies to produce complete and correct salient objects. A novel, deeply guided depth-induced mutual attention map is proposed to start the cross- and intra-complementary fusion. This deep learning model learns the essential features to predict exact and robust salient objects. The result analysis is performed extensively and evaluated across the four benchmark RGBD datasets. The experimental results show better performance than twelve closely related state-of-the-art methods.
A scalable recommendation system approach for a companies - seniors matching

Recommendation systems are becoming more and more present in our daily lives, whether it is for suggesting items to buy, movies to watch or music to listen. They can be used in a large number of contexts. In this paper, we propose the use of a recommendation system in the context of a recruitment platform. The use of the recommendation system allows to obtain precise profile recommendations based on the competences of a candidate to meet the stated requirements and to avoid companies to have to perform a very time-consuming manual sorting of the candidates. Thus, this paper presents the context in which we propose this recommendation system, the data preprocessing, the general approach based on a hybrid Content Based Filtering (CBF) and Similarity Index (SI) system, as well as the means implemented to reduce the computational cost of such a system with the increasing evolution of the platform.
Real Time Analysis on Bus Passenger for Unmanned Door Operation using Overhead Fisheye Cameras

In this study, we propose a robust method for ensuring safe unmanned bus door operation using commonly available surveillance cameras. Bus door operation is a critical function of bus services because passengers can be injured unless they work appropriately. Our method leverages only image processing technologies and is composed of an object detector, an optical flow analyzer, and a pose estimator. We carefully combined the modules so that they judged the readiness of the door operation accurately and in real time. We confirmed that our proposed method can ensure safe bus door operation using only two industry fisheye surveillance cameras installed on a modified Toyota Coaster microbus. Our system runs at 10 fps using a commonly available GPU desktop.
A Survey on Semantic Segmentation Models for Underwater Images

Semantic segmentation remains a key research field in modern day computer vision and has been used in a myriad of applications across various fields. It can be extremely beneficial in the study of underwater scenes. Various underwater applications, such as unmanned explorations and autonomous underwater vehicles, require accurate object classification and detection to allow the probes to avoid malicious objects. However, the models that work well for terrestrial images rarely work just as well for underwater images. This is because underwater images suffer from high blue light intensity as well as other ill effects such as poor lighting and contrast. This can be fixed using preprocessing techniques to manually improve the image characteristics. Trying to improve the model to account for bad image quality is not a great method as the model may misidentify noise as an image characteristic. In this chapter, 6 different deep learning semantic segmentation models—SegNet, Pyramid Scene Parsing Network (PSP-Net), U-Net, DNN-VGG (Deep Neural Network-VGG), DeepLabv3+, and SUIM-Net—are explored. Their architectures, technical aspects with respect to underwater images, advantages, and disadvantages are all investigated.
Language Model for Statistics Domain

Since transformer has appeared, there were many studies that proposed variants of some representative language models (e.g., Bidirectional Encoder Representations from Transformers (BERT) [1] and Generative Pre-Training (GPT) series [2]). Huge language models are appearing recently (e.g., Chinchilla [3], Megatron LM), whereas there are studies of domain-specific (or language-specific) language models. For example, BioBERT for bio-informatics [4], SwahBERT for Swahili language [5], and FinBERT for financial domain [6]. Without doubt, statistics must be one of the domains with many collected data (e.g., reports of statistics). Pre-trained language model for the statistic domain will probably deliver much performance improvement in down-stream tasks such as industry code classification and job code classification, and more accurate system for the code classification tasks will contribute to better national statistics and taxation. Indeed, many countries are trying to develop such system, and this paper summarizes some relevant findings and provides suggestions to develop language models for statistics domain.