Reviewerly/exHarmony huggingface dataset:
A Deep Looking at the Code Changes in OpenHarmony

Open-source software has attracted more and more attention from practitioners. At the same time, many Chinese tech companies are embracing open source and opting for open source projects. Most previous research has focused on international companies such as Microsoft or Google, while the actual value of open source projects by Chinese tech companies remains unclear. To address this issue, we conducted an empirical study examining patch information for HarmonyOS. We collected a total of 234,791 commits from 2008 to March 2021. We manually screened out 3,616 submissions about bug fixes. The key findings include: 1) the percentage of bugs decreases rapidly as the number of modified source files increases. 2) About 70% of all bug types of modifications contain only one hunk. 3) About 50% of the bugs can be fixed by modifying one line of code.4) The programmer did not add any files to fix more than 80% of the bugs, Also did not delete any files to fix more than 90% of bugs. Our findings provide direction for software engineering researchers and practical recommendations for software developers and Huawei Technologies.
Death Comes But Why: An Interpretable Illness Severity Predictions in ICU

Predicting the severity of an illness is crucial in intensive care units (ICUs) if a patient‘s life is to be saved. However, most methods do not consider the correlations between features in the patient‘s condition over time. Moreover, the existing prediction methods fail to provide sufficient evidence for the time-critical decisions required in such dynamic and changing environments. For ICU caregivers, the facts and reasoning behind a prediction are the most important criteria when deciding what medical actions to take. However, the current methods lack organ level predictions and reliable interpreted prediction results. They focus on either the overall physiological severity without insight for medical staff or the illness severity that lacks generalizability. The existing interpretable models only provide the feature importance but the importance cannot be used as reliable guidance of treatment. In this paper, we propose an interpretable organ failure prediction method as a benchmark on the MIMIC-III dataset. We build some state-of-the-art (SOTA) models to implement the predictions of different organs that are used in the Sequential Organ Failure Assessment (SOFA)scores. Then we interpret prediction results by introducing the counterfactual explanation techniques. The experiment results show the high performances of the predictions on coagulation, liver, nervous, and respiration failures with F1 scores of more than 0.9. Furthermore, the counterfactual explanations can also capture the key features that can prevent the organ failure trend by comparing with true normal records in two case studies.
NED-GNN: Detecting and Dropping Noisy Edges in Graph Neural Networks

Graph neural networks have become the standard learning architectures in graph-based learning and achieve great progress in real-world tasks. Existing graph neural network methods are mostly based on message passing neural network(MPNN), which aggregates messages from neighbor nodes to update representations of target nodes. The framework follows the assumption of homophily that nodes linked by edges are similar and share the same labels. In the real world, the graphs can mostly follow the assumption. However, for nodes in the graph, the connections between nodes are not always connecting two similar nodes. We regard the edges as noisy edges. Such edges will introduce noise to message passing in the training process and hurt the performance of graph neural networks. To figure out the noisy edges and alleviate their influence, we propose the framework called Noisy Edge Dropping Graph Neural Network, short as NED-GNN. By evaluating the weights between sampled negative edges and existing edges for each node, NED-GNN detects and removes noisy edges. Extensive experiments are conducted on benchmark datasets and the promising performance compared with baseline methods indicates the effectiveness of our model.
Action Models for Coalition Logic

In the paper, we study the dynamics of coalitional ability by proposing an extension of coalition logic (CL). CL allows one to reason about what a coalition of agents is able to achieve through a joint action, no matter what agents outside of the coalition do. The proposed dynamic extension is inspired by dynamic epistemic logic, and, in particular, by action models. We call the resulting logic coalition action model logic (CAML), which, compared to CL, includes additional modalities for coalitional action models. We investigate the expressivity of CAML, and provide a complexity characterisation of its model checking problem.
Uncovering the Issues Associated with AI and Other Disruptive Technology Enabled Operational Practices in Healthcare Sectors in India

Background: Advanced technologies, including artificial intelligence (AI) and other Disruptive technology, have been directly responsible for the significant changes, renovations, and enhancements that have taken place in healthcare systems around the world. In spite of the many challenges, particularly in nations still growing their economies, the healthcare industry has a significant number of opportunities. Objective: To explore the key obstacles that were encountered by the healthcare industry both during and after the introduction of AI and other Disruptive technological practices associated with Health 4.0 in the healthcare industry to uncover how these variables influence AI and other Disruptive technology adoption in healthcare sector of India. Methods: An online survey format that included standardized questionnaire data was obtained from 83 hospitals, and a total of 434 samples have been implemented for various healthcare administrative staff members by the adoption of AI and Disruptive technology. ANOVA analysis was done to confirm the hypotheses' assumptions, then descriptive statistics were done to analyze the mean value, and also EFA and CFA analysis with SEM analysis has been done in the SPSS program for numerous validity tests (version 20). Results: This research explored 15 issues that healthcare administration staff members consider barriers and through the use of EFA, only two of the three primary obstacles—"Additional workload" and "Problems with adopting technology"—have a substantial impact on the rate at which AI is adopted in the healthcare industry, as seen through the eyes of the workers in that area. Conclusion: These challenges include a high need for capital, extra investments in new technologies like the internet of things (IoT), artificial intelligence (AI), business analytics, , resistance to change from both employees and employers, the need for a skilled workforce, and the upkeep of support systems. The use of blockchain technology in India's healthcare system as a secure service for administrative workers in Health 4.0 Practices could solve data security problems.
A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment

Low-cost camera calibration is vital in air and underwater photogrammetric applications. However, various lens distortions and the underwater environment influence are difficult to be covered by a universal distortion compensation model, and the residual distortions may still remain after conventional calibration. In this paper, we propose a combined physical and mathematical camera calibration method for low-cost cameras, which can adapt to both in-air and underwater environments. The commonly used physical distortion models are integrated to describe the image distortions. The combination is a high-order polynomial, which can be considered as basis functions to successively approximate the image deformation from the point of view of mathematical approximation. The calibration process is repeated until certain criteria are met and the distortions are reduced to a minimum. At the end, several sets of distortion parameters and stable camera interior orientation (IO) parameters act as the final camera calibration results. The Canon and GoPro in-air calibration experiments show that GoPro owns distortions seven times larger than Canon. Most Canon distortions have been described with the Australis model, while most decentering distortions for GoPro still exist. Using the proposed method, all the Canon and GoPro distortions are decreased to close to 0 after four calibrations. Meanwhile, the stable camera IO parameters are obtained. The GoPro Hero 5 Black underwater calibration indicates that four sets of distortion parameters and stable camera IO parameters are obtained after four calibrations. The camera calibration results show a difference between the underwater environment and air owing to the refractive and asymmetric environment effects. In summary, the proposed method improves the accuracy compared with the conventional method, which could be a flexible way to calibrate low-cost cameras for high accurate in-air and underwater measurement and 3D modeling applications.
Contextualized Scene Knowledge Graphs for XAI Benchmarking

In order to utilize artificial intelligence (AI) safely and securely in society, explainable artificial intelligence (XAI) technology, which has the property of being able to explain the reasons why a system has reached a conclusion, is necessary. Therefore, although machine learning approaches are currently the mainstream of AI, AI technology that combines inductive machine learning and deductive knowledge utilization is expected to become necessary in the future. Currently, however, there is no dataset to evaluate both approaches properly. In this study, we constructed and refined large-scale scene graphs and event-centered knowledge graphs, and have released them as open data. While most knowledge graphs contain only simple relationships, the constructed knowledge graphs are characterized by the fact that they contain more complex relationships that reflect the real world, such as temporal, causal, and probabilistic relationships. In addition, we developed refinement methods for the actual use of the constructed knowledge graphs for inference and machine learning. We held four technical competitions in Japan for AI technologies with various explanatory possibilities, gathered methods related to inference and estimation from a wide range of IT engineers, and classified the proposed technologies. An international version of the competition is planned for FY2022. In the future, we would like to design appropriate indices and conduct objective evaluations, classifications, and systematization for the development of AI technologies with explanatory properties, especially those that combine inductive machine learning (inference) and deductive knowledge utilization (reasoning).
Preliminary study on SQLearn : Sql Query Learning Tool Using Close Ended Drag and Drop Approach

Structured Query Language (SQL) is an industry standard for writing query.Writing SQL query is one of main competencies that must be possessed when learning database.Writing an SQL query could be challenging for novice students.SQLearn is a web-based application that give several cases study and questions.Our hypothesis is learning by using SQLearn will improve the students's understanding in SQL query.SQLearn uses close ended questions approach.Close ended questions are used to minimize possibility of syntax error.The lecturer will create the open-ended questions with a predefined answers and set of draggable SQL blocks.Student can learn while answer the questions by dragging provided SQL blocks into the correct position, when students finish dragging SQL Blocks, SQLearn will check their answer automatically.Twentyeight sophomore students from Information Technology department are involved in the experiment.Pre-test and post-test are used to measure students' understanding before using and after using SQLearn.T-test analysis result shows that there is a significant difference between pre-test and post-test score where the post-test score is overachieved the pre-test score.Based on the early result analysis, a promising result occurred, where the use of SQLearn could improve the students' test score.
Cloud-Based Software Development Lifecycle: A Simplified Algorithm for Cloud Service Provider Evaluation with Metric Analysis

At present, hundreds of cloud vendors in the global market provide various services based on a customer's requirements.All cloud vendors are not the same in terms of the number of services, infrastructure availability, security strategies, cost per customer, and reputation in the market.Thus, software developers and organizations face a dilemma when choosing a suitable cloud vendor for their developmental activities.Thus, there is a need to evaluate various cloud service providers (CSPs) and platforms before choosing a suitable vendor.Already existing solutions are either based on simulation tools as per the requirements or evaluated concerning the quality of service attributes.However, they require more time to collect data, simulate and evaluate the vendor.The proposed work compares various CSPs in terms of major metrics, such as establishment, services, infrastructure, tools, pricing models, market share, etc., based on the comparison, parameter ranking, and weightage allocated.Furthermore, the parameters are categorized depending on the priority level.The weighted average is calculated for each CSP, after which the values are sorted in descending order.The experimental results show the unbiased selection of CSPs based on the chosen parameters.The proposed parameter-ranking priority level weightage (PRPLW) algorithm simplifies the selection of the best-suited cloud vendor in accordance with the requirements of software development.
ASALD: adaptive sparse augmented lagrangian deblurring of underwater images with optical priori

Owing to absorption, reflection, diffraction and, deplorable conditions, the capturing of underwater images present more challenges for the consumer. The proposed work focuses on Enhanced Augmented Lagrangian for image deblurring with additional performance-enhancing optical and sparse derivative prior to model the underwater imaging. The proposed method using Augmented Lagragian with Optical and Sparse Derivative prior is novel in the following ways: (i) usage of optical prior modeled after underwater imaging conditions, taking into account deformations and distortions that accompany the capture of underwater images (ii) sparse derivative prior helps to optimize fast convergence through regularization and faster computation with better edge preservation; (iii) the deblurring begins with the sparsest derivative beforehand and the final deblurred result achieving good dB improvements through sparse regulation. The weights with penalty and regularization ensure that each iteration through steep descent reaches a global minimum. The proposed algorithm performs better as compared to state-of-the-art approaches.
Deep Learning Based Dynamic Object Addition to Video Instances for Creating Synthetic Data

Progressing towards the age of Autonomous driving, realistic data is necessary for maximum driving scenarios is required for creating Advanced Driver-Assistance System (ADAS) models that can persevere through our widely unpredictable day-to-day travel. So we intend to generate synthetic data that can be utilized well by the ADAS models and also keep it closer to the real-world scenarios. Using Deep Learning concepts, image synthesizing methods, and video-to-video synthesizing methods together with the available real-world data we have created synthetic data. Generative Adversarial Neural Networks (GANs) based models were previously used for static level object manipulations on benchmark datasets. Here, the idea is to enhance this aspect to adding dynamic objects (like cars, pedestrians, etc.) to real-world scenarios from the Cityscapes dataset. Then by using a pre-trained Mask-RCNN, we have done the object detection on the generated synthetic data and have evaluated the output.
Face Mask Detection and Recognition with High Accuracy on Live Streaming Video Using Improved Yolo V4 and Comparing with Convolutional Neural Network

The aim of this research is to detect face masks using Convolutional Neural network (CNN) algorithm and comparing it with the Yolo v4 algorithm. The study includes two groups namely, CNN algorithm and yolo v4 algorithm. The total sample size is 40 with pretest power of 0.8. In order to evaluate how well CNN algorithm methods perform, accuracy values are calculated. Using SPSS software, CNN algorithm method was found to be 92.65% accurate while improved Yolo v4 was found to be 85.87% accurate. 0.000 p(2-tailed) is obtained for the model. Using CNN, it was proved significant improvements to performance than improved Yolo v4.
BBACTFM (Blockchain Based Accurate Contact Tracing Framework Model) for Tourism Industry

Contact tracing is, at its core, a straightforward and logical operation. The testing facility or healthcare facility that performed the test notifies the neighborhood public health department if a person tests positive for any communicable disease (such as COVID-19, omicron). The health department then contacts the sick person and asks them to list any contacts they had for a predetermined amount of time before to the test. The health department then gets in touch with these people to request a test. The process is repeated until all of the patients have tested negative if any of those patients have a positive result. Patients who test positive for a disease are typically informed of how long they must spend in quarantine and are publicly notified after they “recover.” Blockchain technology can anonymize patient records and offer higher degrees of protection to maintain the privacy of tourist health details. Since test results are connected to a visitor’s public/private key rather than their Personal ID, their name or identity cannot be determined by simply browsing or querying the blockchain. In addition, since every blockchain entry is cryptographically signed, malicious parties are unable to alter a record without also invalidating all future records. Additional controls can be added using identity access management software to restrict what data a user can read on the blockchain. The shortcomings of conventional contact tracing applications would be resolved by our proposed framework, dubbed BBACTFM (Blockchain Based Accurate Contact Tracing Framework Model for Tourism Industry).
Deep learning Indonesian chatbot using PyTorch for customer support automation

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Mutiara Auliya Khadija, Widyawan, Lukito Edi Nugroho; Deep learning Indonesian chatbot using PyTorch for customer support automation. AIP Conference Proceedings 14 February 2023; 2654 (1): 020015. https://doi.org/10.1063/5.0115529 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Topic modelling and sentiment analysis during Covid-19 pandemic response: A systematic review

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Sella Friscilla Silalahi, Hilya Mudrika Arini, Yun Prihantina Mulyani; Topic modelling and sentiment analysis during Covid-19 pandemic response: A systematic review. AIP Conf. Proc. 14 February 2023; 2654 (1): 040018. https://doi.org/10.1063/5.0117344 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Social media users privacy protection from social surveillance using Blockchain Technology

Due to rapid technology improvements, an increasing number of people are being connected to the digital world. Considering other parts of life, the internet has grown increasingly vital to Indians. More than 4.39 billion people use the internet, and almost 70% of them use social media on smartphones, tablets, laptops, and other computers. Management, staff, and users all play an important role in information security. As a result, the human has become the weakest point in the digital environment. Human understanding and behavior are essential for successful and efficient usage of technology. The human aspect can be divided into two categories: one in which humans are directly involved in the system in some way, and the other in which they are not. The end users' lack of understanding, belief, conduct, and inappropriate use of technology are the other specific variables. Users desire security, flexibility, and simplicity of use all at the same time. Finding a balance between all these criteria is extremely difficult for any business or service provider. Users must be willing to give all information, including personal and sensitive information, in order to be online. The future is expected to be data-centric and data-driven. Data, according to researchers, is the new fuel that will drive technology forward. As a result, service providers have become accustomed to organizing the data for computational and surveillance purposes. It is time for data to be protected and confidentiality to be respected. The most significant aspect of this issue is the user's concern in deciding whether to share the data. If exchanging data is required, should the customer be able to track who has accessed the data? After that, the user should have a say in who gets access to the information and who does not. So, these are the numerous issues that require research and the development of a robust solution that places data control in the hands of the user. One of the regulations that deals with data protection and user privacy is the GDPR. This regulation must be made mandatory in all countries, including India. Using blockchain technology, the authors discuss various approaches for protecting user privacy and controlling data access. The authors also attempt to propose a different strategy to resolving the privacy and security issues using blockchain technology in a modified and enhanced manner.
Animal Detection Alert System

Current issues include reducing collisions between automobiles and wildlife on the road, comparable incidents occurring in an imbalanced ecosystem, and significant public resources. This paper proposes an Animal detection system using a deep neural networks algorithm. The proposed system is used for detecting the creatures veritably directly. This system gives the motorists an alert when the creatures are being spotted in the web cameras within the region. The result shows that the YOLO learning model is more reliable than the deep literacy algorithm, to identify creatures on roads directly.