Reviewerly/exHarmony huggingface dataset:
Do Pretrained Language Models Indeed Understand Software Engineering Tasks?

Artificial intelligence (AI) for software engineering (SE) tasks has recently achieved promising performance. In this article, we investigate to what extent the pre-trained language model truly understands those SE tasks such as code search, code summarization, etc. We conduct a comprehensive empirical study on a board set of AI for SE (AI4SE) tasks by feeding them with variant inputs: 1) with various masking rates and 2) with sufficient input subset method. Then, the trained models are evaluated on different SE tasks, including code search, code summarization, and duplicate bug report detection. Our experimental results show that pre-trained language models are insensitive to the given input, thus they achieve similar performance in these three SE tasks. We refer to this phenomenon as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">overinterpretation</i> , where a model confidently makes a decision without salient features, or where a model finds some irrelevant relationships between the final decision and the dataset. Our study investigates two approaches to mitigate the overinterpretation phenomenon: whole word mask strategy and ensembling. To the best of our knowledge, we are the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> to reveal this overinterpretation phenomenon to the AI4SE community, which is an important reminder for researchers to design the input for the models and calls for necessary future work in understanding and implementing AI4SE tasks.
Term Weighting in Topic Modeling

Topic modeling is a text mining technique used for automatic extraction of topics addressed in document collections. Although Latent Dirichlet Allocation (LDA) is the most preferred model in this field, some problems with its use are still unsolved. In this study, in order to contribute to the solution of these problems, the effect of using different term weights on model coherence is investigated. For this purpose, topic models using different term weights are applied to more than one scientific corpus, and model coherence scores obtained are compared with each other.
GUI for fuzzy logic self-tuning PID control and FPD+I control in a temperature plant

This work deals with the development of a Graphical User Interface (GUI) for fuzzy logic self-tuning PID adjustment and FPD+I (Proportional-derivative fuzzy+integral) control. To appreciate the advantages and disadvantages of these controllers, the GUI also has classic control options, simulation graphs, comparative graphs, and control options for the temperature plant that consists of a flat heating resistance, an infrared sensor, a fan, and an Arduino micro in slave mode. Once the identification of the plant is carried out, the tuning of the different controllers is carried out, even with experimental improvements in some cases. After obtaining the Matlab GUI and drivers, it is verified that it works properly.
Big Data Analytics for Anomaly Detection in Blockchain

The ability to make fast and automatic transactions anonymously without intermediary institutions offered by blockchain technology has made this technology widespread in the use of money transfer. With its widespread use, it has also become a preferred transfer method for illegal transactions such as tax evasion and the sale of illegal goods. This has increased the importance of anomalous transactions and anomalous user detection studies in transactions. In this study, a solution is presented that enables the detection of anomalies and anomalous users in blockchain transfers by using big data storage and data processing tools. A scalable structure has been created in order to deal with the increase in the size and frequency of blockchain transfers with the use of big data technologies. In the solution, first of all, the users making the transfers and the transfer transactions are clustered using the K-Means method. Transfer information and attributes that represent the blockchain network as a graph are used as features. After clustering, anomalies were found by measuring the distance of the elements from the cluster center. The performance of the clustering was calculated using the silhouette score. When the results are analyzed, the model that reduces the size to two with principal component analysis for clustering users showed the highest scores with a silhouette score of 0.51, and the model that reduces the size of graph-based attributes with principal component analysis for clustering transfers showed the highest scores with a silhouette score of 0.87. These models were applied to real time transactions.
EMD-GCN: graph convolution network with EM dynamic routing for skeleton-based action recognition

Behavior recognition is one of the most popular fields in computer vision, and graph convolution network for skeleton based data has been widely used in the field of action recognition, and has achieved remarkable results. Most of the previous studies focused on modeling the relationship between adjacent joint points in natural state, while ignoring the graph topology between non adjacent joint points. In this work, we propose a new graph convolution with EM dynamic routing to learn different structural features dynamically in action recognition based on human skeleton data, and cluster multiple joint points into corresponding graph topologies effectively. The proposed EMD-GC takes the initially artificially defined shared topology as the general prior knowledge of the model, then models the topology according to the specific correlation of each channel, and finally clusters the features into the corresponding topology through the GMM model. Experiments show that EMD-GCN is superior to the most advanced methods on NTU RGB+D dataset.
CA-Res2UNet++: a deep residual UNet-based method for brain tumor segmentation in multimodal MRI

In recent years, segmentation of the multimodal brain tumor image puts forward high requirements for performance. To meet the accuracy requirements, we propose a multimodal brain tumor image segmentation method based on UNet and deep residual learning, which can make full use of multimodal information in order to obtain details. The model achieves the information extraction and integration of multimodal images at different scales by introducing a multiscale feature extraction module, a coordinate attention module, and a dense atrous spatial pyramid pooling module. The results show that the proposed method can fully exploit the multi-scale context information of multimodal images, alleviate the problem of blurred edges in segmentation regions, and achieve good performance on popular evaluation metrics.
A study on the detection of breast lumps based on attentional mechanisms

Traditional networks cannot focus on which features are important when extracting features and use the same weighting for all features, so this paper investigates breast lump detection based on attention mechanisms. By adding three attention mechanisms, SE, ECA and CBAM, respectively, to the backbone network of YOLOv5s, the network's ability to extract breast lump features is enhanced for breast lump recognition. The experimental results show that adding SE or ECA or CBAM attention mechanisms alone to the YOLOv5s network, they are higher in accuracy, recall and mAP@0.5 than the original YOLOv5s network model, and the model with the addition of CBAM attention mechanism improves the accuracy, recall and mAP@0.5 by 3.1%, 2.6% and 3.1% respectively, which is better than the models with the addition of SE and ECA attention mechanisms. The proposed attention mechanism-based breast lump detection model can effectively detect breast lumps and provide help for early screening.
Image‐based communication on social coding platforms

Abstract Visual content in the form of images and videos has taken over general‐purpose social networks in a variety of ways, streamlining and enriching online communications. We are interested to understand if and to what extent the use of images is popular and helpful in social coding platforms. We mined 9 years of data from two popular software developers' platforms: the Mozilla issue tracking system, that is, Bugzilla, and the most well‐known platform for developers' Q/A, that is, Stack Overflow. We further triangulated and extended our mining results by performing a survey with 168 software developers. We observed that, between 2013 and 2022, the number of posts containing image data on Bugzilla and Stack Overflow doubled. Furthermore, we found that sharing images makes other developers engage more and faster with the content. In the majority of cases in which an image is included in a developer's post, the information in that image is complementary to the text provided. Finally, our results showed that when an image is shared, understanding the content without the information in the image is unlikely for 86.9% of the cases. Based on these observations, we discuss the importance of considering visual content when analyzing developers and designing automation tools.
Application and Issues of Blockchain Technology

The concept of blockchain technology has been spreading like wildfire since it was first proposed in the year 2008. In this research, core study is carried on blockchain working while aiming to collect all the necessary and bold information about blockchain technology available on the web. Objective of the study is to develop an understanding of the current research topic, benefits, importance, and future of blockchain from the technical perspective. Several research studies and several databases are analysed to finally conclude the content of this review study. Proposed research mainly focuses on blockchain technology and its importance.
High Accuracy Automated Differential Diagnosis using Machine Learning

This study proposes a method to automate differential diagnosis based on the symptoms provided by the patient. The aim of this work is to make it accessible to all so that even the medical practitioners can access this making their job of classifying the diagnosis easier. To achieve this, the proposed machine learning algorithm has been integrated with the designed website which can be accessed by the use from anywhere around the world. A support vector machine learning algorithm is implemented which drives the developed system in training, testing, and prediction of the diagnosis. The system provides attributes depicting the results in form of a heatmap which is based on the confusion matrix which is formed with the tests data trained and predicted. The performance of the proposed diagnostic system has been verified by using the data set with 100 entries and has given 95% accuracy.
Multimodal medical data statistical system based on machine learning algorithm

Nowadays, the value contained in big data has attracted more and more attention. How to efficiently analyze big data has become an important topic. Machine learning is one of the popular data analysis methods. However, this method is difficult to apply to the massive and continuously growing datasets in the big data environment. How to transform traditional machine learning algorithms to make them more suitable for big data environment is popular in research. This paper studies the multimodal medical data statistic under machine learning algorithm, and uses the decision tree algorithm in the machine learning algorithm to investigate and study the medical data statistics.
Multi-Tenancy Architecture for Augmented Security in Cloud Computing

Several Virtual Machines (VMs) belonging to various users transparently share physical resources in multi-tenancy cloud milieu. In these situations, implementing an effective access control system can stop unwanted access to cloud resources. In the context of multi-tenancy cloud systems, a right of entry in charge of technique is suggested in this article that offers scalable and safe access management to the cloud. By implementing such a method, malevolent tenants will be prevented from creating and transmitting unlawful traffic to the Cloud network. Although both cloud providers and customers can greatly benefit from multi-tenancy, the main barriers to use are security and privacy issues. Since resource sharing is so important to Multi-Tenancy, numerous experts have proposed several strategies to protect Multi-Tenancy.
Image Processing For LDR To HDR Image Conversion Using Deep Learning

High Dynamic Range images and High Dynamic Range displays are getting popular nowadays. To attain the benefit of the High Dynamic Range, a lot of Low Dynamic Range images are to be converted to images with better ranges. Due to the reflection, the image may lose its visibility in some specific area and such area is called a highlight. The information will not be clear for the areas in highlight. The highlight should be removed in order to increase the image's visibility. It is not easy to obtain an image having dynamic range at high level for most of the people as it requires a pricey tool which can be used to function beyond the parameters that are present in the sensor technology of the conventional camera. Many low dynamic range images are combined to create a single HDR image. In the existing system the bright regions are brightened and the dark regions are darkened and the ranges with the extended brightness are merged in order to construct an image with High Dynamic Range. For Example, while capturing a crime scene in the camera with certain details that are absent in the image captured, the criminal investigation could be handled better by reconstructing the lost details of the image using this strategy. The exposure of the image is increased along with the brightness of the pixels that are highly dense and the output image is constructed with the properties necessary for a HDR image.
Basic data analysis research in the field of animal science in a computer-based context

With the promotion of the "new agricultural science" and the development of agricultural information technology, the animal science profession is developing rapidly, and many workers in the field of animal science are asking questions and acquiring knowledge through the Internet. Named entity recognition is a core fundamental technology in the field of natural language processing, which can identify entities and obtain useful information from various types of unstructured question and answer data, and then build question and answer systems, knowledge graphs and other applications, and is used by workers in the field of animal science. Although named entity recognition has been applied in many fields of Chinese, many Chinese characters have multiple meanings, and the word vectors obtained by traditional word embedding techniques are unable to show such multiple meanings of words.
Enhancing banking performance through dynamic digital transformation capabilities and governance, risk management, and compliance: Insights from the Indonesian context

Abstract Digital transformation (DT) is a crucial strategic initiative for organizations to rectify output, adapt, and keep up with the digital economy. Dynamic capability (DC) is a widely‐used model for managing opportunities and risks from digital transformation. However, there is a lack of research examining the measurement and relationship between the primary activities of dynamic capability and DT, especially within the banking sector. Acknowledging the importance of DC, we propose this research which emphasizes the significance of sensing, seizing, and transforming aptitudes in promoting DT and improving banking performance. Within this study, we specifically investigate the role of governance, risk management, and compliance (GRC) factors. By utilizing structured equation modeling (SEM) and analyzing data from 322 respondents representing 45 banks in Indonesia, our empirical findings demonstrate that all three DC activities have a significant influence on DT, in which sensing activity has the biggest impact, followed by transforming and seizing. Furthermore, we reveal that DT acts as an intermediary between DC activities and banking performance, and GRC factors further enhance the impact of DT on organizational performance. Our research offers valuable insights to assist banks in prioritizing essential capabilities, including GRC, to enhance overall performance through dynamic digital transformation capabilities.
Image based Reversible Data Hiding using Multiple Image Layer

Reversible Data Hiding (RDH) is a widely used method to embed data into digital images while preserving the integrity of the original image. The idea behind RDH is to allow decoding of embedded data while the cover image remains unchanged. This technique is particularly useful in applications where the original image must be preserved, such as medical image processing and authentication. The main objective of RDH is to maximize the delivered power and at the same time minimize the distortion due to the delivery process. Different embedding techniques have been proposed to achieve high embedding power and low distortion. These techniques include difference expansion, histogram transformation, and prediction error expansion. These methods change the pixel values of the cover image to accommodate the hidden data. The extracted key is then used to restore the original pixel values, allowing the original image to be restored. This paper proposes a new RDH algorithm that achieves high embedding power with low distortion and high security. The proposed algorithm uses a combination of image layers, encryption and histogram shift to embed the data. The suggested technique shows promising results in terms of embedding power, image quality, and computing complexity while maintaining a low amount of distortion and low computational cost, making it appropriate for real-time applications and promising results in terms of embedding power, image quality and computational complexity. The future direction of RDH research is to explore new immersive technologies and applications in various fields such as security, forensics and multimedia communication
A Comparative Analysis of Face Detection Algorithms and Real-Time Facial Recognition

This research study analyzes the state-of-the-art research works in face detection systems, with a focus on machine learning approaches. The review covers recent studies that employ various methods to detect faces in images, such as skin colour segmentation, AdaBoost, and neural networks. Some studies are concerned with specific applications, such as monochromatic photographs or multi-view detection, while others address broader issues, such as accuracy and speed. The authors highlight the emergence of deep learning techniques in recent years, which have yielded highly accurate and efficient face detection systems. In this research paper the proposal is of a method for facial recognition using face recognition library, OpenCV and Dlib. However, there is still some need of improvement in the speed and robustness of such systems. This study reports several trends in the field, including the use of principal component analysis and active contour models, as well as the increasing reliance of machine learning.
Arduino Based Vehicle Overload Detection System for Prevention of Accidents Using ADC

Traffic accidents are increasing unprecedently in India, resulting in an irreversible societal and economical loss. Most of the heavy vehicle accidents occur due to vehicle overloading. Moreover, Vehicle overloading also results in various life-threating issue like resulting in inexplicable damage to passengers causing both health and financial crisis. The accidents due to overload reduces the life of the vehicle and its weariness and also the steering will become more difficult to operate, brakes will respond more slowly by making the vehicle less safe and comfortable to drive. This research study uses a load sensor to estimate the vehicle's weight in order to illustrate the overloading control and warning system. If the weight exceeds the permitted load, the sound and light-based warning system will alert the driver. Furthermore, the fuel system gets the overload command, which causes the engine to shut down. As a result, unless the load is reduced to the allowed range, the vehicle cannot move. The proposed overload warning will increase the vehicle safety by reducing both the loss of life and the financial burden.
IoT Enabled Hand Gesture Controlled Wheelchair for Disabled People

Technology and science are continuously evolving. Smart technology is improving people's quality of life. The world is becoming more and more intelligent. Smart homes, cars, apps, and smart technologies have made life easier and faster than it has been in decades. On the other hand, there is an increasing number of disabled people, who are unable to make use of the latest technology. The lives of those with disabilities and limited mobility continue to be challenging. The proposed gesture controlled smart wheelchair is designed to improve the disabled individuals' lives. Due to the increasing number of people with mobility impairments, smart wheelchairs, which are equipped with cutting-edge technologies like sensors, motors, and computers, are developed. These savvy wheelchairs give clients more freedom and make it simpler for them to do the things they need to do consistently. The adaptability of smart wheelchairs, their compatibility with smart home technology, and their potential to enhance disabled individuals' mobility and independence are well addressed. In addition, to that the proposed work emphasizes the necessity of smart wheelchair designs that are simpler and less expensive to ensure everyone's accessibility.
A Gesture Based Communication Tool for Mute and Hearing Impaired People

The purpose of the proposed work is to build a communication bridge for those who are deaf and dumb. The combination of flex sensor and accelerometer servers this purpose, the system detects user hand motions, which are subsequently converted into spoken words using an Arduino and a WI-FI module. For those with speech and hearing impairments, the system offers a dependable and usable communication tool that is portable and user-friendly, the system is designed to translate sign language into text and audio form, and make it easier for people to communicate with blind and deaf people too. With increased participation in social interactions and daily activities, this initiative has the potential to better the standard of living for the people with disabilities.