Reviewerly/exHarmony huggingface dataset:
On Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments

Automated bug report assignment is critical for large-scale software projects where reported bugs are frequent and expert developers are required to fix them on time. Finding an appropriate developer with the necessary skill sets and prior experience in fixing similar bugs is difficult and can be an expensive process, depending on the severity of the reported bug. To address this issue, researchers have proposed several machine learning and deep learning-based automated bug report assignment techniques that make use of historical data on reported bugs as well as fixer information. However, there is still room for improvement in the performance of these techniques. In this paper, we propose a novel deep learning-based approach that utilizes two sets of features from the reported bugs’ textual data, namely contextual information and the occurrence of repeating keywords. We develop convolutional neural network and artificial neural network modules to mine these features. We then fuse these two sets of extracted features to assign a bug to an appropriate developer. We conduct extensive experiments on eight benchmark datasets of open-source, real-world software projects to assess the effectiveness of our approach. The experimental results demonstrate that our information fusion-based approach outperforms previous models and improves automated bug report assignment. Furthermore, we debug the errors of our proposed model and publish all source code so that future researchers can contribute to this problem.
Deformable Linear Objects 3D Shape Estimation and Tracking From Multiple 2D Views

This letter presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DLO3DS</i> , an approach for the 3D shapes estimation and tracking of Deformable Linear Objects (DLOs) such as cables, wires or plastic hoses, using a cheap and compact 2D vision sensor mounted on the robot end-effector. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DLO3DS</i> can be applied in all those scenarios in which the perception and manipulation of DLO-like structures are needed, such as in the case of switchgear cabling, wiring harness manufacturing and assembly in the automotive and aerospace industries, or production of hoses for medical applications. The developed procedure is based on a pipeline that first processes the images coming from the 2D camera extracting key topological points along the DLOs. These points are then used to model each DLO with a B-spline curve. Finally, the set of splines obtained from all the images is matched by exploiting a multi-view stereo-based algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DLO3DS</i> is validated both on a real scenario and on simulated data obtained by exploiting a rendering engine for photo-realistic images. In this way, reliable ground-truth data are retrieved and utilized for assessing the estimation error achievable by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DLO3DS</i> , which on the employed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">test set</i> is characterized by a mean reconstruction error of 0.82 mm.
Logic-Based Inference With Phrase Abduction Using Vision-and-Language Models

Recognizing Textual Entailment (RTE) is among the most fundamental tasks in natural language processing applications, such as question answering and machine translation. One of the main challenges in logic-based approaches to this task is the lack of background knowledge. This study proposes a logical inference system with phrasal knowledge by comparing their visual representations based on the intuition that visual representations enable people to judge entailment relations. First, we obtain candidate phrase pairs for phrasal knowledge from logical inference. Second, using a vision-and-language model, we acquire the visual representations of these phrases in the form of images or embedding vectors. Finally, we compare these obtained visual representations to determine whether to inject the knowledge corresponding to the candidate. In addition to simple similarity between phrases, we also consider asymmetric relations when comparing visual representations. Our logical inference system improved accuracy on the SICK dataset compared with a previous logical inference system, SPSA (Selector of Predicates with Shared Arguments). Moreover, our asymmetric evaluation functions using vision-and-language models are effective at capturing the entailment relations of word pairs in HyperLex.
An Evolutionary Navigation Algorithm for Multi-Robot With Priority Order

This study proposes a novel multi-robot navigation algorithm with priority order called, in short, PONA2.0. This algorithm is based on the generalized Voronoi diagram and contains an adjustable multipath switching mechanism and a collision prevention strategy, such that the arrival order of robots is in line with the priority order as much as possible, and the average trajectories length is as short as possible. The given average trajectories length of all robots ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ATLA$ </tex-math></inline-formula> ) and arrival order ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AO$ </tex-math></inline-formula> ) are used to be the two performance indices for the comparison between the proposed algorithm and recent existing algorithms NSPP (Huang et al., 2021), PONA (Huang et al., 2022), ROA, and SDA (Ali et al., 2016). The comparison shows that the PONA2.0 can reduce the average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AO$ </tex-math></inline-formula> by more than 56% compared with NSPP and PONA and reduce the average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ATLA$ </tex-math></inline-formula> between 5% and 17% compared with ROA and SDA.
Video Implementation in Learning English in Class VIII MTs Al-Hasanah Islamic Boarding School Bengkulu Tengah

The Implementation of video in Teaching and Learning Process at Class VIII of MTS Ponpes Al-Hasanah Bengkulu Tengah. This research aims to know the use of video in teaching and learning process in MTS Ponpes Al-Hasanah Bengkulu Tengah. The research applied quantitative approach by using questionnaires. The questionnaires shared by 41 students of class VIII containing 5 questions. Based on the data, it was obtained that 59% answered that the video was never used during teaching and learning. 63% of the were interested if video would be used in future learning. They also agreed that the video would make them easily understand English.
Investigation of Superpixel Image Segmentation

Much effort is required to improve the quality of the captured images and to reduce the noise without losing any image features. Thus, image processing is a way to do it, and segmentation is one of the techniques. So far, many researchers have offered numerous segmentation methods which locate the spots of objects and help in the detection of noise. We can also apply a denoising approach to remove the noise. Every approach of segmentation and denoising methods has its own pros and cons. In this work, we perform investigated segmentation and denoising techniques to preprocess the images. We summarize key research findings in the field of image segmentation and denoising to distinguish between the original and processed image, focusing on superpixels or oversegmentation images. The selection of the technique depends on the user, and their target varies from application to application. We have worked on Felzenszwalbs, SLIC, Quick shift, and Compact watershed segmentation techniques and applied non-local means (NLM) denoising techniques to study their performance. A direction for future work is to apply the medical images in identifying the small parts which may help doctors in detecting the presence of any abnormalities.
Recommender Systems and Artificial Intelligence in Digital Marketing

Technology is advancing rapidly is today’s day and age. Machine learning and artificial intelligence algotrithms in particular have seen huge leaps forward in the past few years. AI is being used everywhere, from education to medicine, from tourism to marketing. Recommender systems belong to a class of machine learning algorithms which are used to provide personalized recommendations to a user of a service, helping them make faster and better purchase decisions. Because of this recommender systems have reshaped the online marketplace drastically and have become essential to any online seller. Because of the same, it has become a popular data science domain and many researchers have developed various different types of algorithms and techniques to suit each type of implementation of the systems. In this paper, we discuss the various types of recommender systems, their importance and applications, and develop a small model for understanding.
Study and Implementation of Location-based Access control Mechanism in Cloud services

Access to cloud-based services requires identity and access control management. This control management mechanism is required to provide security, to prevent data loss and protection from malware attacks. For security reasons, the tendency of using multi-factor authentication is widely used over single-factor authentication as single-factor authentication are more prone to security infringements. Here the use of location-based authentication is suggested as another level of authentication over primary level. By making the user’s location grounded at a particular point, attackers supposedly have more difficulty in compromising such a system. Authentication is the most important security feature during exchange of data between client and server. The location based authentication helps to add another layer of security on top of traditional access patterns. Most commonly, authentication schemes depend upon three factors: (1) known by the user e.g. personal identity number(PIN), password; (2) owned by the user e.g. token,etc.(3) individual identity of the user e.g. bio-metrics, such as voice recognition, fingerprints, retinal scans,etc. In this paper, we propose a location-based authentication scheme for cloud connectivity. The proposed mechanism consists of three parts: location registration, authentication and location verification. We aim to minimize the risk of identity theft by employing an extra layer to user access verification.
PDE based Diffusion Filters for Image Denoising

The main purpose of reducing noise is to enhance the quality of an image and to provide efficient information that are too minute. So, the main focus is on methods utilizing partial differential equations for removing the noise from the image. This paper focuses on utilizing diffusion equations, both isotropic and anisotropic diffusion, to reduce the noise level of the image. Specifically, we study the effectiveness of linear and nonlinear diffusion filters. Here we mainly discuss about following three models: Linear diffusion model and its limitations; Perona Malik model and its limitations; Edge Enhancing diffusion model (EED) and its limitations.
Bengali language-based Disease diagnosis system for rural people using Bidirectional Encoder Representations from Transformers

Machine intelligence is an essential branch of computer science that imitates human behavior. A human recognizes every real-world object and incident by their distinguishable features. If a computerized system incorporates human cognitive abilities, the system will be able to classify many real-world phenomena accurately. Therefore, an automated system that detects the disease based on symptoms has been addressed. Bidirectional Encoder Representations from Transformers (BERT) has been used to design this system. The BERT model has been developed based on the Transformers concept, forecasting a token by looking at every token in the series. The proposed system recognizes each and every individual’s illness in the Bengali language. Punctuation marks are stripped from the user-supplied query which is in form of disease symptoms. Tokens are obtained from the individual’s query. Tokens that have been filtered are sent to the proposed system. The proposed method employs filtered tokens to diagnose the disorder. The BERT model is trained using a dataset of diseases and their symptoms obtained from Kaggle. Precision, recall, fl-score, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) have been used to describe the proposed system’s performance. The accuracy of the proposed system is 98.12% whereas RMSE, MSE, and MAE exhibit 1.8618, 1.0852, and 1.0826 respectively.
Self-supervised learning based low light image enhancement using convolutional neural networks

In general images captured under inevitable low light situations undergo low contrast and low visibility which affects in a bad manner on image analysis tasks. Improving the visibility of low-light images is a critical but difficult problem because it is typically significant but still complex with spatially varying characteristics. Pre-trained networks struggle with poor generalization capacity due to sample bias. Retinex theory based techniques are commonly used in image enhancement. Because many retinex-based algorithms ignore reflectance as enhancements and eliminate illumination, over-enhancement and unusualness are unavoidable. A self-supervised learning neural network for improving images with poor illumination is proposed to address these issues. The proposed convolutional neural network architecture have decomposition and enhancement modules. The decomposition module computes the illumination, followed by the enhancement module which produces retinex. Experimental results shows output images with enhanced contrast and visibility better than existing methods.
Security analysis of Three-Factor Authentication Protocol Based on Extended Chaotic-Maps

In data security network communication, the authentication protocol is crucial. Therefore, the authentication protocol must offer protection against all recognised threats, including password guessing, insider, and identity theft assaults. However, a lot of protocols in the literature fall short of these security standards. In this paper, we evaluate the very lately put out Shuming et al. protocl. The three-factor authentication protocol demonstrates that it is susceptible to assaults from privileged insiders, offline password guessing attacks, user identity attacks, and parallel session attacks; as a result, their system is unable to thwart known session-based transient data attacks. Additionally, it lacks robust user anonymity and is susceptible to DoS and smart card loss attacks.
Autonomic Management of Cloud Resources to meet Service Level Agreement

Several studies have used either Autonomic Resource Management (ARM) or Service Level Agreements (SLA) to manage cloud resources. To the best of our knowledge, no study has been conducted on SLA with ARM to maintain Quality-of-Services (QoS) features. Therefore, this study highlights the applications of ARM, which focused on providing cloud services automatically for cloud consumers. Prior research has shown that QoS-aware methods for enhancing cloud performance. Additionally, QoS maintains security at the same time, which depends on ARM and SLA. Thus, this study contributes to investigating dynamic autonomic Service-based applications (SBAs) in the cloud, which has not been addressed in previous studies. As a result, QoS in cloud security management did not cover self-protection. Consequently, this research suggests using ARM with SLA to maintain QoS techniques for providing selfprotection requirements in a cloud environment.
A Hybrid Method for Anomaly Detection Using Distance Deviation and Firefly Algorithm

This paper proposes an outlier detection algorithm that combines distance based outlier detection method with the optimization by swarm intelligence for detection of anomalies in the large sensor data. To detect outliers, the algorithm calculates the Multi-granularity DEviation Factor (MDEF) of each data point from all its neighbors which lie within a circle of radius r. The basic idea of the proposed method is to use a self-optimizing algorithm to find the best value of distance (radius) over which MDEF calculation is done for all data point to correctly identify outliers from data. The proposed technique does not only identify the outliers in the data but also gives a score to indicate the degree to which a point deviates from normal data. The proposed method has been simulated using well known real world data set. The results show that the proposed method is more accurate when compared to other outlier detection algorithm.
Smart Blind Stick Using Node MCU with Voice Alert

In this research, Internet of Things (IoT) technology was used to create a low-cost smart stick for those who are blind or visually impaired. This Smart Walking Stick makes navigating and works easier and more comfortable for blind individuals by determining the distance between an object and a person. Previously, users used a regular stick to detect impediments, as a result, blind individuals are incapable of distinguishing objects in front of them which made them ineffective and made moving tough for a blind person. Hence, the concept of a “Smart Blind Stick” is offered as a solution to this severe problem. The ultrasonic sensor on our stick uses ultrasonic waves to find obstacles in front of us. The sensor transmits this information to the microcontroller when it detects impediments and it also estimates the distance between items. When the object is in the specified range then the buzzer rings. An Audio playback module is an additional advantage to the Smart Stick which enables the blind person to get alerted by hearing the sound as it is capable of recording different voices. This strategy is quite helpful for visually challenged people who frequently need assistance. A blind person will be helped to walk more comfortably and avoid danger.
Sensitive Data Protection at Blockchain Endpoints and Transactions

The adoption of blockchain technology in prevalent applications has substantially increased the use of blockchain wallets. Even though consensus, cryptography, and decentralization make blockchain data structures pretty safe, it’s still hard to protect sensitive data at endpoints because each new block of data connects to all the previous blocks in a nearly unbreakable way. Thus, we seek to improve upon data security and transaction ease. Blockchain wallet store most of the users’ sensitive data in the form - namely initial-seed, private, public keys and other sensitive asset information. In order, we propose to attain sensitive data protection using an air-gapped hardware wallet and transactional privacy by hashing the transaction with the blake3 algorithm. More precisely, the mechanism consists of a credit card-sized air- gapped hardware wallet owned only by each party. Along with the wallet, a mobile app ‘‘Crypto-Trans’’ for certificate verification is introduced which acts as an interface between the hardware wallet and the blockchain. Last but not least, the proposed wallet is compared to existing wallets on key parameters to establish its relative superiority in terms of effectiveness and efficiency.
A smart contracts based approach for the E-commerce Platforms

The E-commerce System in India is a highly widespread industry with a very complicated procedure including many intermediaries, which is why the number of fraudulent instances is rising. These problems can be solved by using blockchain for transactions in E-commerce. This paper provides a blockchain based smart contracts solution to such problems. The paper helps in understanding the concept and working of smart contracts and then proposes a smart contract based payment transaction system. It emphasizes the importance of smart contracts for payments in E-commerce systems.
Management of IT Operations and IT Infrastructure with Virtual Reality

Virtual reality (VR) technology has been successfully implemented in multiple industries and VR has brought us a vast number of new displays and input devices. The purpose of this paper is to conduct an in-depth study of the various applications of virtual reality (VR) in industrial operations in order to collect evidence of VR’s benefits, limitations, and potential future directions. Recent advances in VR software and hardware have made it possible to apply the technology in a wide range of manufacturing settings, including design, engineering, production, O&M, and more. The human-computer interface of the digital VR platform is both user-friendly and immersive, which can be beneficial for IT and computing instruction as well as maintenance services. This will allow VR to be directed to better serve IT industry operations. This is a fact that cannot be denied, both humans and VR technology can be trained to work together effectively through training programs designed for both.
Deep learning model for speech emotion classification based on GCI and GOI detection

The temporal regions of considerable stimulation to the vocal tract during the production of spoken speech are referred to as glottal closure instants (GCIs) and glottal opening instants (GOIs). These instants from speech signals are important for various speech and biomedical applications. The performance of the GCI and GOI detection algorithm is not studied for emotion speech using deep learning technique. Hence, the motivation behind this work is to use the current edge technology. To the best of our knowledge ours is the first deep learning CNN model to classify different emotions of speech based on GCI and GOI. In this work, an attempt is made to study the performance for different emotions using GCI and GOI detection algorithm and make the comparison with the state of art methods. The performance of the developed model is evaluated using traditional evaluation metrics. The results show that GCIs and GOIs are helpful in classification of speech emotion and that performance of the proposed model decreases for anger and sad emotions.
The effect of the Radon field for efficient medical image compression

The medical image accumulated over time for the purpose of research confronts us with the problems of transmission and storage. So, an efficient compression scheme is necessary to eliminate this serious problem. The integration process is one of the powerful characteristics of the Radon transform leading to the attenuation of the quantization noise. We introduce in this paper a medical image compression method based on the Radon transform and the Discrete Cosine Transform (DCT) with high quantification. The primary goal of this method is to demonstrate the possibility of exploiting a high scale of quantization in the Radon field for effective compression of the medical image databases. A comparative study with the basic DCT compression scheme is presented. The results of this comparison showed that despite the high quantization scale, the presented method ensures an excellent rate-distortion compromise with a high compression with an excellent quality. A comparative study is performed to show the power of the Radon transform against the quantization noise.