Reviewerly/exHarmony huggingface dataset:
Towards Understanding Fixes of SonarQube Static Analysis Violations: A Large-Scale Empirical Study

Automated static analysis tools (ASATs) have become an integrated part of the software development workflow in many projects. While developers benefit from these tools to deliver quality code conforming to the pre-defined static analysis rules, it has been reported that many ASATs are underused. A number of detected violations are overlooked by developers due to false alarms or unactionable alerts. Despite of existing studies on the fixes of static analysis violations, there is still a gap in collecting and understanding the fact that some types of violations are fixed more often and/or more quickly than other types. To fill this gap, we conduct a large-scale empirical study on 56,506,892 violations from 30 active, popular, and high-quality open-source Java projects with long evolution histories. All violations were traced between adjacent revisions before we filtrated the fixed violations out of the closed ones by considering the types of source code changes that closed the violations. We identified the violation types with the highest and lowest fix rates and those that were fixed the most timely and least timely, and further investigated the possible underlying reasons for the differences in fix rate and fix time. Our findings is helpful to characterize and understand developers’ considerations when fixing violations and provide practical implications for developers, tool builders and researchers to optimize the usage and design of ASATs.
Evaluating and improving transformers pre-trained on ASTs for Code Completion

Automatic code completion is one of the most popular developer assistance features which is usually performed using static program analysis. But, studies have shown that static analysis can be riddled with false positives. Another issue with static-analysis based code completion is that the recommendation does not take into account the history/ context in which the software is operating and rely on type/ alphabetical ordering of suggestions. A recent development that has shown to be promising in this direction is the use of language models such as transformers that are trained on real-world code from Github to provide context sensitive, accurate code completion suggestions. Studies on transformer-based code completion have shown that such restrictions can be leveraged; i.e; training transformers on structural representations of code (specifically ASTs) could have a positive impact on the accuracy of code completion. To this end, the work by Kim et al. implemented and evaluated TravTrans which is based on the powerful text prediction language model GPT-2 by training on abstract syntax trees instead of treating code as plain texts. Using alternative source code representation such as AST provides the already potent language model with an additional layer of program semantic-awareness. But, TravTrans has adapted several rigid choices regarding various components of the transformer architecture like embedding sizes, sliding windows etc. TravTrans also suffers from issues related to running out of vocabulary. In this paper, we reproduce the TravTrans model and perform a deeper, fine-grained analysis of the impact of various architectural and code-level settings on the prediction. As a result of our fine-grained analysis, we also identify several aspects that need improvements like the fact that the model performs particularly poorly with code involving dictionaries and lists. We also offer solutions to a few of the issues like the out-of-vocabulary issue. Finally, our results motivates the need for a customizable-transformer architecture for coding tasks.
Minimization of Energy Consumption in Cloud

With advancement in technology and the internet Cloud computing is becoming more popular and hence the number of datacentres is increasing which causes the amount of power consumption to increase and more cooling equipment are required. How to efficiently distribute Virtual Machines (VMs) to the Physical Machines (PMs) or hosts in order to conserve power is the task. There are numerous existing energy-conscious methods which concentrate on reducing energy use in a cloud environment. Here two models, a gradient regression-based method and a maximum percentage of correlation-based method are proposed; these can significantly minimize use of energy in cloud computing. CloudSim is a simulation tool for simulating cloud computation environments. The VM migration policy has been modified to give more energy efficient results.
On the Separation of Correlation-Assisted Sum Capacities of Multiple Access Channels

The capacity of a channel characterizes the maximum rate at which information can be transmitted through the channel asymptotically faithfully. For a channel with multiple senders and a single receiver, computing its sum capacity is possible in theory, but challenging in practice because of the nonconvex optimization involved. To address this challenge, we investigate three topics in our study. In the first part, we study the sum capacity of a family of multiple access channels (MACs) obtained from nonlocal games. For any MAC in this family, we obtain an upper bound on the sum rate that depends only on the properties of the game when allowing assistance from an arbitrary set of correlations between the senders. This approach can be used to prove separations between sum capacities when the senders are allowed to share different sets of correlations, such as classical, quantum or no-signalling correlations. We also construct a specific nonlocal game to show that the approach of bounding the sum capacity by relaxing the nonconvex optimization can give arbitrarily loose bounds. Owing to this result, in the second part, we study algorithms for non-convex optimization of a class of functions we call Lipschitz-like functions. This class includes entropic quantities, and hence these results may be of independent interest in information theory. Subsequently, in the third part, we show that one can use these techniques to compute the sum capacity of an arbitrary two-sender MACs to a fixed additive precision in quasi-polynomial time. We showcase our method by efficiently computing the sum capacity of a family of two-sender MACs for which one of the input alphabets has size two. Furthermore, we demonstrate with an example that our algorithm may compute the sum capacity to a higher precision than using the convex relaxation.
Fast ACE (FACE): An Error-Bounded Approximation of Automatic Color Equalization

We present an efficient algorithm to approximate the Automatic Color Equalization (ACE) of an input color image, with an upper-bound on the introduced approximation error. The computation is based on Summed Area Tables and a carefully optimized partitioning of the plane into rectangular regions, resulting in a pseudo-linear asymptotic complexity with the number of pixels (against a quadratic straightforward computation of ACE). Our experimental evaluation confirms both the speedups and high accuracy, reaching lower approximation errors than existing approaches. We provide a publicly available reference implementation of our algorithm.
AIoT in Healthcare: A Systematic Mapping Study

The Internet of Things (IoT) infrastructure and artificial intelligence (AI) technologies are combined to create artificial intelligence of things (AIoT). AIoT in healthcare is a key factor in enabling physicians’ offices and hospitals as well as giving patients access to superior scientific facilities. To this end, we conduct a Systematic Mapping Study (SMS) to provide critical information about various applications of AIoT in healthcare. The primary goals of this research are to provide an overview of AIoT research in healthcare and to categorize AIoT research based on annual number of publications, publication venues, and journal distribution. We present some AIoT techniques used in healthcare fields in nine well-known online libraries (IEEE, Springer, Elsevier, MDPI, Taylor and Francis, Hindawi, Wiley online lib., ACM and Google). Initially, we used a number of research questions related to the issue, and when we applied them, 71 studies were produced.
Effective Recognition System of American Sign Language Alphabets using Machine Learning Classifiers, ANN and CNN

People who cannot talk are called audibly impaired, and they communicate with others through other means. The most popular method of communication is through sign language. American Sign Language (ASL) is the de-facto standard for sign languages taught globally. Automated sign language recognition tries to bridge the gap. Convolutional neural networks are the method of choice these days when it comes to the classification of multiclass images. To recognize ASL alphabets, we used a CNN, traditional machine learning classifiers, and an artificial neural network. The Sign Language MNIST dataset has a total of 34,627 image data, of which 27,455 and 7172 are training and test data respectively. Except for J and Z, the dataset comprises 24 alphabets. We used the training dataset to train our CNN, ANN, and other machine learning models. Then examined our proposed CNN model as well as other models including ANN on the test dataset to check how well they recognize ASL alphabets correctly. The traditional classifiers such as Linear Regression, Logistic Regression, Random Forest, SVM, and ANN were able to achieve an accuracy of 71.94%, 90.16%, 98.63%, 97.92%, 82.96% respectively whereas the proposed CNN model achieved 100 % of accuracy on the unseen test data.
Stacking Classifier-based Automated Insurance Fraud Detection System

During the Covid-19 pandemic, the insurance industry's digital shift quickened, resulting in a surge in insurance fraud. To combat insurance fraud, a system that securely manages and monitors insurance processes must be built by combining a machine learning classification framework with a web application. Examining and identifying fraudulent features is a frequent method of detecting fraud, but it takes a long time and can result in false results. One of these issues is addressed by the proposed solution. By digitalizing the paper-based workflow in insurance firms, this paper intends to improve the efficiency of the existing approach. This method also aimed to improve the present approach's data management by integrating a web application with a machine learning stacking classifier framework experimented on a linear regression-based iterative imputed data for detecting fraud claims and making the entire claim processing and documentation process more robust and agile.
On quantum codes from codes over Rm

Let Rm = Fq [y] = /〈ym - 1〉, where m|q - 1. In this paper, we obtain the structure of linear and cyclic codes over Rm. Also, we introduce a preserving-orthogonality Gray map from Rm to Fmq. Among the main results, we obtain the exact structure of self-orthogonal cyclic codes over Rm to introduce parameters of quantum codes from cyclic codes over Rm.
A Proposed Convolutional Neural Network for Breast Cancer Diagnoses

Breast cancer is the second greatest cause of death in women worldwide, however, early detection may result in life prolongation or even complete recovery.Breast cancer can be classified by physicians into two types: benign tumors, and malignant tumors, all of which are fatal if not treated early.Several machine-learning algorithms have been developed to help physicians make diagnostic choices, concretely a convolutional neural network is presented in this paper.The proposed system is divided into several fundamental steps.The proposed classifier is trained to distinguish between incoming tumors using a dataset of 780 images.To evaluate the classifier's performance accuracy, precision, recall, and F1-score are used.In the testing stage, the proposed method achieved an overall classification accuracy of 93 %, 93 % precision, 93 % recall, and 93 % F1-score.
Development of Electronic Worksheet Assisted by Augmented Reality to Improve Learning Outcomes and Interest in Learning Science

This research aims to: 1) produce feasible and valid electronic worksheets assisted by augmented reality that can be used to improve learning outcomes and interest in learning science; and 2) know the effectiveness of electronic worksheets assisted by augmented reality to improve learning outcomes and interest in learning science. This study used 4D model R&amp;D research. The research was conducted in SMP Madiun City with class VIII research subjects totaling 17 students. Data collection instruments include product validation and feasibility tests, pretest and posttest questions, and learning interest questionnaires. Data analysis techniques using paired T-tests. The results obtained from the material validation tests were 63 in the interval 72 ≤X ≥58.5 criteria with a very good category, while the results from the media validation tests were 65,25 in the interval 68 ≤X ≥55,25 criteria with a very good category. Based on the results of the student response questionnaire, a percentage of 68.35 in the interval 84≤X ≥68.25 criteria indicates a very good category and is suitable for use. The results of testing learning outcomes and interest in learning science with paired T-tests obtained results of 0.007 and 0.005, indicating that there were differences in learning outcomes and interest in learning science after using electronic worksheets assisted by augmented reality. Thus, it can be said that 1) electronic worksheets assisted by augmented reality have met the eligibility criteria with a very good category according to material experts and media experts, and 2) electronic worksheets assisted by augmented reality are effective in improving learning outcomes and interest in learning science.
178P Deep learning reveals spatial disorganisation of histological features in the normal breast of breast cancer patients

Germline BRCA1/2 mutation carriers have an increased risk of developing breast cancer, however the characterisation of histological features of the normal breast for these high-risk patients has not so far been performed on large-scale image collections. Hence, deep learning-based framework is proposed to identify subvisual morphometric phenotypes in normal breast tissue of women with different risk of developing cancer. Digitised 1190 whole slide images of H&E-stained breast normal tissue images from women across different age groups were collected from 5 different biobanks, i.e., healthy donors, healthy patients derived from reduction mammoplasty, healthy patients with known germline BRCA1/2 alterations derived from risk reduction mastectomy, and the contralateral/ipsilateral tissue from breast cancer patients with known germline pathogenic BRCA1/2 alterations, totalling 282 patients. To characterise the epithelial patterns, 70 WSI were manually annotated. A convolution deep learning (DL)-network was implemented to predict the chronological age group (<30y or >50y) of tiles of epithelium. K-means clustering identified specific histological age-associated features. Class activation map and cellular segmentation were used for interpretation. Our DL-based framework revealed inter and intra-variability of epithelial histological patterns in the normal breast of healthy women, underlying the physiological ageing process. The proportion of lobule types differed in normal breast tissue of <30y or >50y healthy women. In normal breast of younger women, densely packed acinar structures with a higher glandular-to-stromal ratio are predominant and display higher neighbourhood enrichment. Normal breast tissue of young breast cancer patients revealed histological tissue-ageing patterns deviating from their chronological age. Our DL-framework exposed histological patterns associated with the variation of the underlying ageing process of normal breast tissue, which may indicate early signs of cancerous in high-risk germline BRCA1/2 carriers.
CAN THE CHATGPT NEURAL NETWORK REPLACE A MARKETER'S WORK?

In the process of transforming the Internet from a global communication tool into an effective tool in the field of marketing, the industry where everything changes almost every day, and therefore requires a marketer to familiarize himself with marketing channels and chats, read online publications and develop new hard skills. This was the challenge for the fact that after GPT chat entered the market, it was almost entirely associated with the work of a marketer. Of course, it has undeniable potential: Bot can create content, generate ideas, and take over routine work. According to a survey in the US, 37% of marketing and advertising professionals said they have already used ChatGPT and other AI-based tools in their work. So, a bot can bring a lot of benefit specifically to content marketing: improving the text, creating interesting and relevant content to attract attention and attract a new audience, adapting it to the target audience, communicating with customers through round-the-clock support, automating routine tasks. In our opinion, ChatGPT is designed to help humans by performing simple and routine work, while the human beings can devote themselves to something more important and creative. The human control over the neural network proves the importance of the human factor, as the text generated by ChatGPT may not correspond to the message or interests that the company wants to convey to consumers, its data is limited to 2021, and it has no self-awareness. After all, ChatGPT is not an expert in a particular field, it can only make recommendations, and the final decision is always up to a professional. The development of artificial intelligence is causing changes in the labor market, with professions responsible for routine and monotonous duties and dangerous human labor. However, the emergence of new professions, such as: digital removalist, data detective, drone manager, chief automation officer, rewilder, human-machine teaming manager, Internet lawyer digital currency advisor, smart home design manager, and trash engineer/garbage designer all suggest that artificial intelligence will not take away jobs from people, but rather offer to replace them with more progressive areas.
Cyber bullying Detection on Social Media Using Machine Learning

Cyber bullying has become a growing concern in today's society, with more and more people turning to the internet to harass and intimidate others. Digital forensics is an essential tool for investigating cyber bullying activities, as it allows for the collection and analysis of digital evidence. However, traditional digital forensics techniques can be time-consuming and require a significant amount of human effort. In this paper, we propose the use of machine learning algorithms to aid in the investigation of cyber bullying activities. By training these algorithms on a dataset of known cyber bullying incidents, we can create a predictive model that can automatically classify new instances of cyber bullying. This can significantly reduce the time and effort required for investigations, allowing for a more efficient response to cyber bullying incidents. The challenges associated with using machine learning for cyber bullying detection, including the need for high-quality training data and the potential for bias in the algorithms. We also explore the various types of digital evidence that can be used in cyber bullying investigations, such as social media posts, emails, and instant messages. We present a case study in which we apply our proposed approach to a real-world cyber bullying incident. Our results show that the machine learning algorithm was able to accurately identify the cyber bullying activity with a high level of precision, demonstrating the potential of this approach for improving the efficiency and effectiveness of cyber bullying investigations.
Rancang Bangun Aplikasi Pembelajaran Desain Grafis Berbasis Multimedia Interaktif

Abstract− This study aims to produce a graphic design learning media that is given to class XII students. The media is very effective in providing educational information to students, especially tutorial media. Because tutorial media is a visual interactive media tool to teach students to understand graphic design faster. In addition, at the high school / vocational school level, graphic design lessons have been given, but only use Corel Draw software, even though graphic design itself does not only use Corel Draw software. There are several examples of software used in graphic design, namely Photoshop, Adobe Illustrator. The results of this study obtained a new learning method that is ready to be used to teach graphic design material so that it can increase student motivation in the process of teaching and learning activities
A ground fault detection method of substation DC system based on particle filter

In order to reduce the false alarm rate and false alarm rate of grounding fault detection in substation DC system and improve the efficiency of fault detection, a method of grounding fault detection in substation DC system based on particle filter is proposed. The wavelet threshold denoising method is used to remove the noise of the grounding fault signal of the substation DC system. The wavelet packet decomposition method is used to decompose the grounding fault signal of the substation DC system and extract the characteristics of the grounding fault signal of the substation DC system. Based on the particle filter algorithm, the residual smoothing value is used to detect the grounding fault of the DC system in the substation. The experimental results show that the failure detection rate and false alarm rate of the proposed method are low, which has a good fault detection effect.
APLIKASI MENDETEKSI JENIS HARGA ASSESORIESHANDPHONE MENGGUNAKAN AUGMENTED REALITY

Augmented reality (AR) is a technology that can display information that is virtual but presented in a real world view (2010). With this technology in the business world, adequate presentation of information can be done appropriately and accurately. The purpose of this study is to create an application that can display information on the price of goods using the camera on an Android smartphone. The method to achieve the purpose of this paper is the experimental method utilizing the Vuforia database as a place to store data on the price of goods and collect samples of existing objects as targets to be recognized by the camera to display information on the price of goods. The language used in this application is the C # programming language with a 64-bit Windows system. The results of this study are displaying information on cellphone prices by taking data from Vuforia which will be automatically displayed by the application using the camera on a smartphone.
INFORMASI JASA LAUNDRY BERBASIS WEB

Laundry service information system that is applied at Syatifa Washing House aims to facilitate service to consumers who had consumers have to come to the place while bringing their laundyan goods, with this system is no longer necessary, from picking up to returning laundry items will be done by the courier from Syatifa Washing House.&#x0D; The system used is an online website based, on this website a list of laundry goods prices is also displayed so that customers who want to use our services can find out how much the estimated price will be paid if they use laundry services at Syatifa Washing House, if agreed with the price stated by the customer will be directed to make the registration process first to the Syatifa Washing House website. After that, ordering can be done, after ordering, a courier will arrive who will pick up customers' laundry items, in this process the customer can make payments according to the terterah price after weighing by the courier, then just wait for the goods to come back to be delivered by the courier .