Reviewerly/exHarmony huggingface dataset:
A Boosted Random Forest Algorithm for Automated Bug Classification

During software development and maintenance, predicting software bugs becomes essential. An essential activity of the quality assurance process, defect prediction at the beginning of the software development life cycle has received extensive research over the past two decades. Early detection of defective modules in software development can assist the development team in making efficient and effective use of the resources at hand to produce high-quality software in a short amount of time. Using a machine learning approach, which finds hidden patterns in software attributes, it is possible to recognize the problematic modules. In the NASA data set JM1, the suggested work is contrasted with various machine learning classification procedures. The limit of random forest speculation is higher than that by other multi-class classifiers because of the effect of bagging and feature selection. Since ensemble learning of random forest requires a ton of decision trees to acquire elite execution, the situation is not appropriate for carrying out the design on the limited-scale equipment like an embedded system. In this paper, we propose a boosted random forest, experimental outcomes show that the proposed technique, which comprises of less decision trees, has higher speculation capacity contrasting with the traditional technique. The experimental findings demonstrated that our proposed boosted random forest model results in greater levels of defect prediction accuracy, improving software quality.
Analysis of Search Based Algorithms for Prediction of Aging Related Bugs

Genetic algorithms (GAs) and Search based algorithms (SBAs) are very powerful optimization methods which are inspired by the success of evolutionary processes in the natural world. These optimization methods have been used to develop effective classifiers and have been successfully applied to various domains. This paper aims to evaluate GA based methods for the task of ARB prediction and analyze their effectiveness for the same. ARBs are software defects that manifest in a software module after prolonged usage. Such bugs are extremely hard to detect through traditional software testing methods and can have a very high impact when encountered during the operation of a software. Predictive models that can analyze a software code and flag the possibility of an ARB can be useful in mitigating the impact of ARBs. In this paper, we present an empirical study, that analyses statistically, the performance of GA/SBA based classifiers for ARB prediction on five datasets. We account for the data imbalance by creating synthetic minority samples using SMOTE. Results of this study show that SBA algorithms are effective in developing predictive models for ARB detection and their performance is comparable to machine learning algorithms (ML) for the same.
Perceptors: A Real Time Object Detection System with Voice Feedback and Distance Approximation for Blind

Engineering solutions have impacted everyone’s lives, but those designed to aid people with disabilities have been especially significant. However, the current pricing of modern assistive gadgets does not match the market’s needs. In its latest study, WHO puts the number of visually impaired people at 285 million. Of these, 246 million are blind and an estimated 39 million are visually impaired. Blind people face many problems and difficulties in business and social activities. It also has the potential to contribute to society if the opportunity arises. In this paper we propose to you “Perceptors”—An A.I. powered smart glasses that can detect objects in real time and alert users when objects are nearby. This research is an attempt to give visually impaired people a chance to experience a moderately normal life by warning users in advance of approaching objects and helps users identify objects in real time.
Traffic Surveillance and Vehicle Detection YOLO and MobileNet-Based ML Pipeline Transfer Learning

In today’s complex and interconnected transportation ecosystem, real-time vehicle sensing is critical for a complex and interconnected transportation ecosystem built on advanced technology networks of intelligent systems spanning a wide range of applications such as autonomous vehicles, traffic monitoring, and advanced driver assistance systems. This study utilizes machine learning approaches to create a pipeline for vehicle identification and classification. Count the number of cars in a frame and divide them into two categories: SUVs and sedans. This article requires knowledge of machine learning fundamentals, deep learning, convolutional networks, and transfer learning. In this paper the pipeline is broken down in order to build and implement a computer vision pipeline.
Distributed Training of Large-Scale Deep Learning Models in Commodity Hardware

Running deep learning models on a computer is often resource intensive and time-consuming. Deep learning models require high-performance GPUs to train on big data. It might take days and months to train models with large datasets, even with the help of high-performance GPUs. This paper provides an affordable solution for executing models within a reasonable time interval. We propose a system which is perfect to distribute large-scale deep learning models in commodity hardware. Our model consists of creating distributed computing clusters using only open-source software which can provide comparable performance to High-Performance Computing clusters even with the absence of GPUs. Hadoop clusters are created by connecting servers with a SSH network to interconnect computers and enable continuous data transfer between them. We then set up Apache Spark on our Hadoop cluster. Then, we run BigDL on top of Spark. It is a high-performance Spark library that helps us scale to massive datasets. BigDL helps us run large deep learning models locally in Jupyter Notebook and simplifies cluster computing and resource management. This environment provides computation performance up to 70% faster than a single machine execution with the option of scaling in case of model training, data throughput, hyperparameter search, and resource utilization.
Iterative Refinement Versus Generative Adversarial Networks for Super-Resolution Towards Licence Plate Detection

Licence plate detection in unconstrained scenarios can be difficult because of the medium used to capture the data. Such data is not captured at very high resolution for practical reasons. Super-resolution can be used to improve the resolution of an image with fidelity beyond that of non-machine learning-based image upscaling algorithms such as bilinear or bicubic upscaling. Technological advances have introduced more than one way to perform super-resolution, with the best results coming from generative adversarial networks and iterative refinement with diffusion-based models. This paper puts the two best-performing super-resolution models against each other to see which is best for licence plate super-resolution. Quantitative results favour the generative adversarial network, while qualitative results lean towards the iterative refinement model.
An Effective Multi-exposure Fusion Approach Using Exposure Correction and Recursive Filter

The visual quality of a natural scene is constantly degraded when using conventional imaging sensors. The dynamic range is the factor that limits the sensors to capture more information as it is limited to a specific range during capturing. Multi-exposure image fusion (MEF) is a technique for creating a well-exposed, high dynamic range (HDR)-like image from a collection of inconsistently exposed, low dynamic range (LDR) images. The fused image may still have image artifacts and lose information, nevertheless. To overcome this, we propose an MEF approach using an effective exposure correction mechanism. The technical strategy for the proposed method is to initially improve the source images using an exposure correction strategy, followed by merging the images using pyramidal decomposition to produce HDR-like images. Experimental comparison with existing methods demonstrates that the proposed procedure produces positive statistical and visual outcomes.
Academic Dishonesty Detection in Exams Using Pose Extraction

Academic dishonesty has been a significant setback to the proper functioning of educational institutions worldwide, as cheating in examinations is a significant and persistent issue contributing to academic dishonesty. With cases of cheating in examinations increasing year after year, it can be asserted that traditional invigilation and monitoring methods are ineffective. This work proposes a method to analyze cheating behavior in examination halls through pose estimation. First, we analyze the frame for any movement using a motion detection algorithm. The marked frame is sent to the pose extraction phase, where the pose of the person/persons present in the frame is extracted and given to the machine learning model, which identifies whether a student has been cheating. Furthermore, the frame marked with face recognition is stored for later use, along with the time and name of the student, in a separate file for reference.
Arabic Sentiment Analysis of YouTube Comments Using Deep Learning Model

With the present prevalence of social media websites, acquiring user priorities has become an essential chore for assessing their online habits and behaviors. Because Arabic is among the most prevalent languages throughout the globe as well as the quickest expanding language on the Internet, we are motivated to create dependable automated technologies. We developed a sentiment analysis on YouTube comments premised on manually collected Arabic comments in this research. This work was divided into four stages: preprocessing, feature extraction, optimum feature selection, as well as classification. Tokenization, stemming, and stop word removal are all part of the preprocessing process. We designed two unique features, Proposed Term Frequency Inverse Document Frequency (Proposed TD-IDF) and improved word2vect-based features, to improve feature extraction with the conventional bag-of-words feature extraction. In addition, for optimal feature selection, we introduced a new optimization technique called the Self-Improved Honey Badger Algorithm (SIHBA).
Sentiment Component Extraction from Dependency Parse for Hindi

With the advent of Web 2.0, Natural Language Processing (NLP) gained attention and a new dimension in the NLP application arena. Sentiment analysis is one such popular modern NLP application that tries to extract the feeling, emotions, opinions, etc., expressed in digital text using NLP techniques. Sentiment analysis has become a subtle application over the last decade and found a firm foundation in AI applications. This is especially applicable to product services, reviews, and recommendations domains. It tries to quantify the sentiment better and helps understand the views expressed in a text leading to better decision-making. There is a variety of approaches available for carrying out sentiment analysis ranging from naive to sophisticated machine learning approaches. However, less attention is being paid to linguistics aspects. We undertook a study to project sentiment analysis concerning linguistic dimensions of natural language text which is the least-explored approach to sentiment analysis. Sentiment components of a text are deeply rooted in its syntactic constituents. The only way to figure out the syntactic constituents is parsing. We have tried to portray the use of dependency parsing for extracting the sentiment components from Hindi input text. We have explored the applications of various dependency relations to derive the possible sentiment compositionality from Hindi sentences. Our study and findings related to sentiment components extraction from dependency parse are presented in this paper with a linguistic insight.
Learning Movement Patterns for Improving the Skills of Beginner Level Players in Competitive MOBAs

League of Legends is a massively multiplayer online battle arena (MOBA)—a form of online competitive game in which teams of five players battle to demolish the opponent’s base. Expert players are aware of when to target, how to maximise their gold, and how to make choices. These are some of the talents that distinguish them from novices. The Riot API enables the retrieval of current League of Legends game data. This data is used to construct machine learning models that can benefit amateur players. Kills and goals can assist seasoned players understand how to take advantage of micro- and macro-teams. By understanding how professional players differ from novices, we may build tools to assist novices’ decision-making. 19 of 20 games for training a random forest (RF) and decision tree (DT) regressor produced encouraging results. An unseen game was utilised to evaluate the impartiality of the findings. RF and DT correctly predicted the locations of all game events in Experiment 1 with MSEs of 9.5 and 10.6. The purpose of the previous experiment was to fine-tune when novice players deviate from professional player behaviour and establish a solid commencement for battles. Based on this discrepancy, the system provided the player with reliable recommendations on which quadrant they should be in and which event/objective they should complete. This has shown to be a beneficial method for modelling player behaviour in future research.
Real-Time Sign Language Detection Using OpenCV

Sign language is a communication commonly used by people, who are deaf and dumb. They communicate with hand gestures within their community and with the remaining people in the world. Real-time sign language detection is the solution that enables communication between normal people and those who are deaf and dumb. It deals with image acquisition and continues until text generation. Previously provided solutions are sensor based, and they do not result in good accuracy. The existing models are customized with limited gestures. In order to overcome this problem, this study has developed a model to detect the 40 different classes of signs, which include alphabets, numbers and some special symbols. This model can effectively recognize gestures. The dataset is created in a well-planned manner to include ten numbers, 24 alphabets and six special symbols. The sign language recognition steps include data collection, image preprocessing and segmentation, training model and at last classification and result recognition. The captured images are preprocessed to remove the extra background. A Convolutional Neural Network (CNN) algorithm is used to train the model and perform multi-class classification on image data. The results are then displayed in the monitor along with text recognition. Some future improvements are also suggested.
CISUM: Novel Research on Cloud Computing Simulators and Future Scope for Computational Research

For the primary purpose of this research, key emphasis is on comprehensively reviewing existing literature on the usage of cloud computing simulators. For concluding this research, several types of cloud simulators are identified, reviewed, and compared with one another based on specific criteria of technical features. As concluded in the research, the most crucial aspects related to cloud simulation are the scope of accessing it from any location, and hence, presenting enhanced flexibility for the users, delocalising staff training, and ensuring learning on basis of collaborative and dynamic learning. Hence, the future scope of simulation lies in the system of cloud computing with a possibility that all engineering simulations will be conducted using cloud computing.
Sentiment Analysis on Feedback Data of E-commerce Products Based on NLP

In today’s competitive business world, companies seek strategies to keep their existing customer base satisfied while also attracting new customers by launching new offers or products with a mix of chosen marketing mix. It has become necessary to keep track of whether customers like or dislike a product and how they react to a particular offering in order to improve the service and stay ahead of competitors. The focus of this paper will be on using NLP technique-sentiment analysis, which analyzes the customer sentiments based on customer reviews/feedback data for a product, which benefits businesses by keeping track of consolidated review on a product. In this paper, we used Amazon product review datasets form Kaggle, data world and performed sentiment analysis to understand the overall customer sentiments based on customer reviews in a better and refined manner. The current study’s dataset includes 30,653 customer reviews for various products. Python NLTK Library has been used for pre-processing and to generate a model using the Regression Algorithm. The model performs well with F1-Score 0.87.
Resume Analysis Using NLP

Job recruitment is one of the main activities for individuals, and it might be difficult to discover a fruitful talent. Our suggested model’s main elements include the extraction of statistics and information from the resume and the evaluation of the resume in line with the preferences of the associated firm and its criteria for natural language processing (NLP). The hiring process is simplified and made more effective by parsing and ranking the resume. Any reputable parser must be able to extract the different tiny pieces of information contained in a resume, including information on education, experience, projects, addresses, and other things. So, in essence, we will create a job platform where employees and candidates may upload their resumes for every available position. The required information will be parsed with NLP. Additionally, the corporate skill requirements and the talents of the employees in the provided CV will be taken into consideration when ranking the employee resumes.
Text-to-Speech and Speech-to-Text Converter—Voice Assistant

According to the International Agency for Prevention of Blindness’s Vision, there are around 32 million people who live with avoidable blindness and a further 259 million with preventable visual impairment that is decent to acute. Though there may be a few existing solutions to this problem, none of the solutions provide an all-in-one experience like this voice assistant does. Our proposed model uses automatic speech recognition (ASR) to recognize to user’s speech and convert it into text format. For converting text-to-speech, we are using google text-to-speech (gTTS) engine and Microsoft’s SAPI5 which provides voice to our model. This voice assistant can not only be used to read a Word document or a PDF file but also search content on Google or Wikipedia.
A Detailed Analysis on Spam Emails and Detection Using Machine Learning Algorithms

Spam email is the unwanted junk and solicited email sent in bulk to the receivers, using botnets, spambots, or a network of infected computers. These spam emails can be phishing emails that trick users to get their sensitive information, download malware into the user devices or scam the users stealing confidential data. This paper shows a systematic analysis of spam and its types. It also details the procedure of how the spammers get the email addresses of the receivers. It analyses the problems with spamming. A detailed state of the art on spam filters and the factors that put an email into the spam or ham category is also explained. The paper also discusses spam filtering methods of Gmail, Yahoo and Outlook. Finally, it brings out several solutions to detect spam using principles of Machine Learning and Data Mining.
Technical and legal framework of Initial coin offerings

Today, blockchain-based technologies, such as the Initial Coin Offering (ICO), are capable of providing solutions for financing SMEs. ICOs are the issuing of digital tokens that will be sold to investors in return for a cryptocurrency, allowing a project to be funded. This research examines the process of this novel and alternative method of business financing. At first glance, the ICO protocol appears to be very similar to the Initial Public Offering (IPO) procedure raising the question of how ICOs ought to be regulated. To answer this question, descriptive and analytical research will be conducted. On a legal level, the study found that three approaches to the regulation of ICOs exist, with the American option being the most fascinating because it represents a middle ground between banning and developing a new independent law for token issuance, allowing ICOs to be included within an existing legal framework. The study recommends that the Qatar Financial Markets Authority adopt this American approach, so ICOs will be introduced in Qatar smoothly and without the need for new legislation. This will result in a desired goal of promoting Qatari SMEs.
A NetHack Learning Environment Language Wrapper for Autonomous Agents

This paper describes a language wrapper for the NetHack Learning Environment (NLE) [1]. The wrapper replaces the non-language observations and actions with comparable language versions. The NLE offers a grand challenge for AI research while MiniHack [2] extends this potential to more specific and configurable tasks. By providing a language interface, we can enable further research on language agents and directly connect language models to a versatile environment.
Dow Jones Index Time Series Forecasting Using Feedforward Neural Network Model

A neural network-based method for modeling and forecasting the time series with regular periodic components is considered. As an example, the DJIA index time series has been selected that is distinguished by its volatility. The process of neural network modeling (restoring) of the DJIA index time series is carried out using the Neural Networks Toolbox within the MATLAB software package. The corresponding tools of this software package allowed users to focus on the application of neural network models in the process of empirical research and free themselves from the need, in fact, to design multilayer neural networks, and strain about their correctness, stability and performance under all sorts of conditions for forecasting time series. Therefore, in solving the problems of forecasting time series, the choice and justification of the declared type of neural network, adjustment (training) of its parameters and correct interpretation of the results have already become the main ones.
Risk Management in Bank: Fuzzy Inference Based Process Model

Any commercial bank (CB) is trying to determine the degree of acceptable risks of material and/or reputational losses, believing that its potential losses are inversely proportional to the volume of its capital. Therefore, the basis of successful banking is the presence of a Risk Management System (RMS) in the CB structure, the main task of which is to determine the best (or rational) strategy for concluding transactions that ensures maximum profit growth. Within the RMS the statistical analysis tools are used that allow to evaluate and compare the consequences and expediency of certain transactions by establishing a quantitative measure of banking risk from any transaction. Moreover, these tools make it possible to formalize various transactions and, thereby, ensure the accumulation of CB experience. To form heuristic knowledge from the history of the conclusion of various contracts, it is necessary to use and accumulate expert opinions, and, as a result, fuzzy methods of analysis and decision-making relative to banking transactions. There is a system of fuzzy models for assessing internal and external banking risks is considered. This approach involves the use a fuzzy cognitive model, based on which cause-effect relations for assessing banking risks at all levels of the hierarchy of their detailing are described using the Fuzzy Inference Systems. Qualitative evaluation criteria for the areas of localization of banking operations and external influences are used as inputs and outputs.