Reviewerly/exHarmony huggingface dataset:
Exploring Analogical Reasoning and History Use in Software Re-purposing

Today, code reuse is typically limited to programmers reusing code that does exactly what they want to do, through APIs and modules, for example. Yet, the space of existing programs that could be reused is broader. In this poster, we consider reuse through re-purposing tasks in which programmers modify an existing codebase to implement a related task. Because this process requires that users build a relationship between the source code and the target program, we examine it using analogical reasoning. To understand the process programmers take and the barriers they encounter when performing reuse through re-purposing tasks, we conducted an exploratory study involving sixteen participants completing two tasks. We find that programmers have difficulty mapping analogies related to the underlying logic of source code, but easily identify visual analogies like interface structure. We discuss how information about a code's history can be useful during reuse, and in identifying logical analogies.
Super-Resolution with Embedded Denoising via Image Frequency Separation and Convolutional Neural Network in a Prototyped Transcranial Ultrasound Brain Imaging Scanner

Medical ultrasound (US) imaging represents a remarkable technological breakthrough, offering advantages such as portability, affordability, safety, rapid imaging capabilities, and a standard diagnostic quality. The successful development of under-skull transcranial US (TUS) imaging has the potential to revolutionize the field of quantitative ultrasound diagnostics, providing a competitive alternative to current expensive diagnostic modalities like CT scans and MRI. Despite the promising aspects, researchers face significant challenges in obtaining high-resolution images with adequate tissue contrast essential for precise diagnosis and treatment planning. To overcome these limitations, innovative techniques are being explored. Among the primary obstacles, achieving high-resolution imaging beneath the adult skull remains a critical hurdle due to the high acoustic impedance of the skull, as well as scattering and absorption of signals by brain tissues. In addition, extending the imaging depth without sacrificing image quality is another area of active research, closely tied to resolution and potential image quality loss. Furthermore, the sensitivity of TUS to patient movement results in motion artifacts that substantially degrade image quality. Addressing and compensating for these motion artifacts through intelligent and robust super-resolution (SR) based analysis persist as ongoing challenges. TRUBI (Transcranial Ultrasound Brain Imaging System) is an innovative 3D transcranial ultrasound brain imaging device developed by Tessonics <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> . This cutting-edge system aims to overcome the primary limitations associated with conventional transcranial imaging, specifically the significant distorting effects caused by the human skull. Apart from the hardware design and interface software of the device in general, theoretical design, analysis and implementation of the signal and image processing units of the system is one of the main challenges. In this paper, we provide a concise overview of the convolutional neural networks (CNN) based simultaneous image SR and denoising, which holds the potential to be further developed to be incorporated into future generations of TUS imaging systems.
3D-CNN Empowered Assistive Machine Learning Model for the Hearing Impaired

According to the World Report by WHO on Hearing published in March, 2021, nearly 2.5 billion people will be deaf or hard of hearing by the year 2050. In 2020, the uncorrected hearing loss in the world costs about $1 trillion [1]. In order to communicate effectively with persons who are deaf or hard of hearing, sign language serves as an interface. Hand, finger, arm, head and facial expressions are all part of a sign language that includes gestures and symbols. Using visual descriptions, they are able to better understand the world around them and hence contribute to society. In today's world of impulsive technologies, building an assistive sign learning paradigm is a significant issue, particularly because many sign language apps are ineffective at identifying the signs they are intended to teach [2]. In the proposed framework, gestural recognition is being performed using a 3-D Convolutional Neural Network (3-D CNN) [3], [4]. Once trained, it can recognize patterns in volumetric data like videos and reliably identify the features it needs. Although, it is intended for dumb and deaf persons to use sign language to communicate with others, the proposed system seeks to comprehend some key features of sign language and convert them into text or voice using 3-D CNN. Intermediary translators are no longer needed as a result of this change. To this aim, this paper proposes a model which will enable hearing impaired persons to communicate easily with a world around them.
Comparative Analysis of The Internet of Things (IOT) in the Health Sector

The Internet of Things (IoT) technology is still the main target of the discussion since it now has a significant influence on the healthcare industry. The majority of researchers who use technologies are professors and specialists. It aids in obtaining accurate study results so that rural areas may utilize technologies as well. It offers appropriate financial gains that are substantial. Services at a reasonable cost. Today, it is crucial to advance both the therapy and pharmaceutical sectors of medicine. The level of technology aids in conducting appropriate investigation appropriate solutions. The IoT is being utilized to improve the wearable electronic technologies that are applied to provide smart healthcare services in several different methods. They can survive as a result of it. According to research, IOT in the administration of wheelchairs, mobile healthcare solutions, as well as other variables has favourably affected the improvement of healthcare services.
Dense Error Map Estimation for MRI-Ultrasound Registration in Brain Tumor Surgery Using Swin UNETR

Early surgical treatment of brain tumors is crucial in reducing patient mortality rates. However, brain tissue deformation (called brain shift) occurs during the surgery, rendering pre-operative images invalid. As a cost-effective and portable tool, intra-operative ultrasound (iUS) can track brain shift, and accurate MRI-iUS registration techniques can update pre-surgical plans and facilitate the interpretation of iUS. This can boost surgical safety and outcomes by maximizing tumor removal while avoiding eloquent regions. However, manual assessment of MRI-iUS registration results in real-time is difficult and prone to errors due to the 3D nature of the data. Automatic algorithms that can quantify the quality of inter-modal medical image registration outcomes can be highly beneficial. Therefore, we propose a novel deep-learning (DL) based framework with the Swin UNETR to automatically assess 3D-patch-wise dense error maps for MRI-iUS registration in iUS-guided brain tumor resection and show its performance with real clinical data for the first time.
Introducing CWDCMFE-MBRC Technique for Breast Cancer Detection: A Comparative Survey of Novel Approaches

Breast cancer poses a significant public health concern for countless women worldwide. Detecting breast cancer in its early stages plays a pivotal role in enhancing patient outcomes and reducing mortality rates. Various imaging techniques have been developed to aid in the early detection of breast cancer., including mammography, ultrasound, MRI, thermography, and x-ray. However, these techniques have their limitations with regard to accuracy, sensitivity, specificity, and precision. In recent years, ways of deep learning have emerged as a promising approach for breast cancer detection. Convolutional neural networks (CNNs) have demonstrated high accuracy in detecting breast cancer from medical images. However, CNNs have certain limitations, one of which is the need for substantial amounts of labeled data. This requirement can present challenges, particularly in the field of medical imaging, where acquiring a large volume of labeled data may be difficult. Moreover, CNNs suffer from a lack of interpretability, posing challenges in comprehending the decision-making process behind the network's predictions. This absence of interpretability makes it arduous to gain insights into how the network arrives at its conclusions. To address these limitations, researchers have developed alternative deep learning methods for breast cancer detection. One such method is the Complex Wavelet Deep Convolutional Multiple Feature Extraction-MBRC (CWDCMFE-MBRC) method. This method brings together the benefits of wavelet transform, deep convolutional neural networks, and multiple feature extraction to achieve high accuracy in breast cancer detection. The CWDCMFE-MBRC method involves several steps. First, the input picture is broken down into sub-bands using the complex wavelet transform. Then, the sub-bands are passed through a deep convolutional neural network to extract high-level features. Next, multiple features are extracted from the output of the neural network using PCA and LDA. Finally, the resulting characteristics are used as input to the MBRC classifier to detect breast cancer. The CWDCMFE-MBRC method has several advantages over CNNs and other traditional imaging techniques. Firstly, it achieves high accuracy in breast cancer detection while requiring less labeled data than CNNs. Additionally, the method provides interpretability through the use of multiple feature extraction techniques, making it easier to understand how the network arrives at its predictions. Deep learning methods, such as CNNs and the CWDCMFE-MBRC method, have shown promise in improving breast cancer detection accuracy. This study aims to conduct a comprehensive survey of breast cancer detection methods, including both traditional imaging techniques and deep learning methods, provide a complete snapshot of the current state of the field.
Applying Action Design Science Research to Develop a Conceptual Design for Smart Contracts in Real Estate Transaction Processes: The Potential of Blockchain Technology for Transaction Symmetry, Speed and Transparency

Commercial real estate transactions are notorious for their complexity, which stems from multiple property brokers, legal entities, illiquidity, heterogeneity, and a lack of transparency. This conceptual study proposes a novel approach to address these challenges using blockchain technology, which enables buyers and sellers to transact in an informationally symmetrical way within an open real-estate ecosystem. The proposed conceptual model employs tokenisation on the blockchain and is developed using the Design Science Research Methodology and Action Design Science Research approach. The model undergoes multiple stages of evolution, including pre-design, which is validated through expert interviews, to arrive at the final conceptual design. The study identifies six key factors that influence the application of blockchain in real estate transactions, including adoption, governance and compliance, transaction costs, transparency and immutability, security, and scalability. Overall, the study suggests that blockchain has the global potential to significantly reduce transaction costs and improve efficiency in the real estate industry, making it a promising solution for the challenges facing the sector.
Web Scraping: Huge Data Collection from Web

Web Scraping also known as data scraping means extracting the data from a particular website or web page. Extracting the data is useful for data analysis, collecting business data, real time data etc. Webpages contain a lot of information or data which is in unstructured format and cannot be used directly for data analysis and other purpose. So, after extracting the data it is converted into structured format. Extracting the data from web pages is useful but it is also illegal. We can scrap the data which is available publicly but in a limited request. Some websites may explicitly prohibit or limit scraping activities, so it is essential to respect their policies. Before scraping a website, review their terms of service, privacy policy, and any other relevant policies. Without owner's permission we cannot extract the private data from their websites. Selecting the website and conducting web scraping responsibly, we can collect desired data that can be used for various purposes, such as market research, price comparison, trend analysis, or gaining insights into consumer behavior.
Integrating Blended Learning and Collaborative Learning to Develop Reading for Main Idea for English Teaching Program Students

In the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century, reading proficiency is a critical skill, and the ability to discern the main idea is crucial for enhanced reading comprehension. However, this can be a challenging skill for ESL learners to master. It's therefore imperative to foster the development of reading skills. Techniques like blended learning and collaborative learning can be particularly effective in nurturing these skills, including the ability to identify the main ideas. This study aimed to 1) to compare learning achievements before and after learning with blended learning and collaborative learning and 2) to evaluate the ability of reading comprehension through blended learning and collaborative learning. Research instruments used in this research included lesson plans, pre-test and post-tests, and a reading evaluation form. The study was conducted with a group of 28 first-year Education students at Phranakhon Si Ayutthaya Rajabhat University in English Teaching Program. Findings demonstrated that students' aptitude in pinpointing the main ideas improved significantly after teaching via blended learning and collaborative learning methods, and they achieved a high level of proficiency in this area.
A Novel Content-Based Movie Recommendation System

Movie recommendation systems have turn out to be an essential part of the digital enjoyment landscape, supporting users navigate through sizable libraries of movies to discover content material that aligns with their possibilities. those structures employ advanced strategies together with artificial intelligence and device gaining knowledge of algorithms to investigate consumer statistics and offer customized film suggestions. The primary goal of movie recommendation systems is to decorate the film-looking revel in by means of minimizing the time and effort required to find out suitable films. This summary provides an overview of movie advice systems, highlighting their importance in the technology of digital media intake.
Subway Platform Passenger Flow Counting Algorithm Based on Feature-Enhanced Pyramid and Mixed Attention

Accurate access to real-time passenger flows on subway platforms helps to refine management in the era of networked operations. The narrow subway platforms suffer from significant crowd scale discrepancies and complex backgrounds when counting passenger flow. In the proposed passenger flow counting algorithm, the feature-enhanced pyramid structure is used to retain the channel information of deep features and eliminate the aliasing effect caused by fusion to enhance the feature representation of the original image and effectively solve the scale problem. The mixed attention mechanism suppresses background interference by capturing the global context relationship and focusing on the target area. On the ShanghaiTech Part_A dataset, the mean absolute error (MAE) and mean square error (MSE) of the proposed algorithm are 2.3% and 1.4% higher than those of the comparison algorithm, respectively. The MAE and MSE on the self-built platform dataset reach 3.1 and 5.7, respectively. The experimental results show that the accuracy of the proposed algorithm is improved and can meet the counting requirements of the subway platform scene.
Organizational Culture and Diversity Supporting Software Development

Objectives: The research problem is a lack of studies on the role of cultural awareness in the field of software engineering. There is a knowledge gap in the literature regarding how this awareness can affect productivity, software quality, and user satisfaction, as well as how it can promote a more diverse and inclusive work environment that fosters creativity and innovation. The research goal is to better understand the impact of cultural awareness in the work environment of software engineers, including Generation X and other diversity variables such as ethnicity, race, gender, age, and distinct professional roles. The study intends to discover how diversity can improve the workplace and how hybrid work structures can benefit from this process. Methods: Systematically search scientific databases such as IEEE's or ACM's for available research. Use the method Grounded Theory to devise a theory about the problem and current solutions. Results: What role does organizational culture play in fostering equity, diversity, and inclusion in software development teams?
DeepDoc: Automated Document Segregation Using Deep Learning

Document segregation has always been a significant issue as it is very tedious and time-consuming. It is something that is required almost everywhere as data is a prerequisite for manipulation, research, communication, and interpreting purposes. It is hence necessary to maintain and store it in a proper way for which document segregation plays a vital role. So, to help achieve this, the main aim is to develop a model which not only segregates the files belonging to the same document together but also helps us to distinguish whether or not given a pair of pages belong to the same document or not and this also saves manual human efforts is being developed. It is done using a standard Resnet-50 classifier and Image Processing algorithms. Phase-1 training and testing are completed for the model in which the model predicts whether or not given 2 pages belong to the same PDF. Whereas, Phase-2 segregates a given dump into their respective different folders.
Design and Development of a LMS with MVVM Architecture

A learning management system promotes elearning and can also be used in open distance e-learning. In recent times, the number of mobile devices being used by peers has increased significantly, and an LMS app has thus gained increased importance in the field of E-learning and distance learning. Moodle is another application that is being used in universities, and in this paper, there is an app based on learning management as Moodle is more on the web side, and this app will be based on MVVM architecture, which is Model View. This paper will thus provide insights into the design and development of LMS app and provide an extensive survey of development process.
BicePy: Bilingual Description of Compiler Errors in Python

The ubiquitous presence of English in programming, particularly in error messages, poses a challenge for non-native English-speaking developers. These developers frequently struggle to decipher complex error messages written in English. To address this, we introduce BicePy, an innovative VScode extension that operates in two distinct modes to cater to diverse user needs. BicePy serves as a powerful proof of concept, demonstrating the feasibility of generating context-aware translated explanations of Python error messages in Spanish. However, the utility of BicePy extends beyond this. As neural machine translation is language-agnostic, it is capable of scaling to accommodate most non-English languages. BicePy signifies an essential stride towards global programming accessibility for linguistic minorities in software engineering <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> http://zenodo.org/record/7931321#.ZF6s/z3MLrC.
Fine Grained Sentiment Analysis using Machine Learning and Deep Learning

Due to recent developments in computational algorithms like machine learning (ML) and more advance algorithm like deep learning, the area of fine-grained sentiment analysis has drawn a lot of attention in the context of natural language processing. This paper explores the domain of fine-grained sentiment analysis, with a specific focus on capturing intricate and subtle emotions in text through the utilization of ML and DL approaches. By going beyond conventional sentiment analysis, fine-grained sentiment analysis enables a more comprehensive comprehension of an individuals' sentiments. The paper provides an overview of various ML and DL techniques employed in fine-grained sentiment analysis, including Support Vector Machines, recurrent neural networks, and transformer-based architectures. Additionally, it addresses the challenges and future prospects in this field, wide aspects such as the scarcity of annotated data and the interpretability of DL models. The paper concludes by highlighting real-world applications that underscores the significance of fine-grained sentiment analysis in customer-centric industries, social media platforms, and public opinion analysis. The advancements in fine-grained sentiment analysis present promising opportunities to gain deeper insights into human emotions and opinions, thereby facilitating informed decision-making processes across diverse domains.
Imposing Object’s Trajectory and Dynamic Template Updates to Track ROIs in Ultrasound Image Sequences

This paper improves an existing object-tracking algorithm to track Regions of Interest (ROIs) in human liver ultrasound imaging sequences using a correlation filter-based Siamese neural network (advanced CFNet). Specifically, we impose object motion regularity to address a limitation of the baseline CFNet, which is losing the ROI when the object displaces and is deformed significantly. In addition, the proposed method uses a dynamic template update strategy to enable the recovery of the lost ROI. The dataset used in this study is publicly available, Challenge of Liver Ultrasound Tracking (CLUST 2015). It contains approximately 96 ultrasound sequences of the liver from different patients. We demonstrate that the proposed tracking method (Advanced CFNet) is robust for the dataset (CLUST- 2015) compared with the baseline CFNet.
Deep Architecture for Breast Cancer Detection: Using Optimized VGG16 Model and Transfer Learning

In this growing and advance era the world is suffering from many diseases and breast cancer comes at 3rd place when it comes to number of new cases estimated year year. Hence it has become significantly important for us to detect this deadly disease to prevent early demises of such patience. Since AI and ML have introduced many models which assist medical professionals in better decision making, we have experimented with various deep neural network models over BUSI dataset. In primary or first experiment we observed that model performed well when input data is normalized and split ratio is 90/10. That model produced 76.90% accuracy. In the next experiment various deep CNN with Transfer Learning [TL] have been tried and tested with the same learnings from previous experiment and VGG16 outperformed others and produced 78.21% accuracy. The learning from previous experiments have also been applied in third experiment while best performing model was further fined tuned with hyper-parameters like learning rate and Optimizers. Out of different learning rates and optimzers when the learning rate was set to. 0001 and optimizer was set to RMSprop, over VGG16 model. The model outperformed all the models constructed in this research work with 83.33% accuracy. We named it Optimized VGG16. Further the confusion matrix and ROC have also been drawn to validate and analyse our findings further.