Reviewerly/exHarmony huggingface dataset:
Studying the Practices of Logging Exception Stack Traces in Open-Source Software Projects

Logging the stack traces of runtime exceptions assists developers in diagnosing runtime failures. However, unnecessary logging of exception stack traces can have many negative impacts such as polluting log files. Unfortunately, there exist no guidelines for the logging of exception stack traces and developers usually practice it in an ad hoc manner. In this work, we perform a comprehensive study of the source code, code change history, and issue reports of ten open-source Java projects, combining quantitative and qualitative analysis, in order to understand how developers log and modify the logging of exception stack traces, their rationale for logging or not logging exception stack traces, and the factors that impact their logging of exception stack traces. We observe that logging of exception stack traces is a popular practice in open-source projects, while developers have difficulties making appropriate logging of exception stack traces in the first place. Through a qualitative analysis of 385 related issue reports, we derived recommendations for the logging of exception stack traces, such as logging of stack traces should be avoided or downgraded for user errors, normal execution, expected exceptions, in user interfaces, or when there is a security concern. Finally, based on our empirical observations, we design and extract a set of code metrics and construct models to explain the likelihood of logging an exception stack trace. Our analysis of the models indicates the important factors (e.g., the exception type and the method that throws the exception) for determining the logging of exception stack traces. Our study helps developers and researchers understand the current practices of logging exception stack traces, provides recommendations for developers to consider when determining whether to log the stack trace of an exception, and provides insights for future research and practices to derive global or company-wide guidelines for the logging of exception stack traces.
Prediction model of tumor marker examination based on fusion of CNN and bilstm

CNN can efficiently learn the local features of electronic medical record (EMR), ignoring the relevance between context semantics; The BiLSTM is the opposite. To solve this problem, this paper introduces attention mechanism and multilayer perceptron, combining CNN algorithm with BiLSTM algorithm. The attention mechanism is used to extract key information of electronic medical records, and the multi-layer perceptron splits local features learned by CNN, contextual semantic features learned by BiLSTM and key EMR features extracted by attention mechanism. This paper proposes a model of CNN-attention-BiLSTM for tumor marker detection prediction. It can be used in tumor marker detection and prediction. The experiment showed that compared with the model without attention mechanism and multilayer perceptron, the accuracy rate, recall rate and F1 value increased by 6.87%, 4.42% and 5.06% respectively. In this paper, this model is compared with traditional machine learning classification algorithms. Experimental results show that the accuracy of this model is 7.77% higher than that of Naive Bayes, which has the best classification effect, and 17.97% higher than that of SVM, which has the worst classification effect. Through experimental comparison, it can be concluded that the model proposed in this paper can better help doctors to assist decision-making to a certain extent.
About some aspects of digital ecology

The situation with the spread of disinformation in the modern information environment and the complexity of the presentation and perception of refutations caused by the effects of "lasting influence", "reverse action" and the existence of polarized communities — "echo chambers" have been analysed. The dangers of deepfakes have been considered. A new type of content marketing with the self-explanatory name clickbait has been considered. It has seriously revolutionized the way content is distributed and attracted readers' attention. The proficiency of media literacy skills by children and adults in these conditions becomes a key factor in assessing online content and detecting misinformation. And as disinformation takes on new and viral forms, teaching children and teachers how to identify it and interact with it becomes especially relevant. It is concluded that it is necessary to create and introduce a media literacy course into the training program for all subject teachers.
FISIOTERAPIA NA DISFUNÇÃO PULMONAR PÓS COVID-19 REVISÃO DE LITERATURA

Introduction: The new coronavirus of the Coronaviridae family, called COVID-19, has been demonstrating several distinct symptoms during the pandemic. Some patients have maintained sequelae after being affected by the disease, mainly pulmonary from severe to mild, and may fluctuate from the need from oxygen therapy, noninvasive mechanical ventilation (NIV) to prolonged invasive mechanical ventilation (IMV), which will lead to the need for rehabilitation during and after hospital discharge. And the physiotherapist has a very important role in pulmonary rehabilitation, acting from mild to severe cases.Justification: To identify the main respiratory dysfunctions resulting from COVID-19 and the methods of treatment, for a physiotherapeutherapy rehabilitation with higher quality and efficacy. According to PITTA, 2021 rehabilitation is indicated for all patients with some functional and respiratory impairment.Objective: to perform an analysis of the main pulmonary dysfunctions and disorders in post-covid patients 19 and the main rehabilitation programs for respiratory dysfunctions found.Methodology: This study is a bibliographic review in which scientific articles on pulmonary rehabilitation in post-covid patients 19 were used through the Virtual Health Library (VHL) in the following databases: ScieLo, MEDLINE and PubMed LILACS- Latin American and Caribbean literature on health sciences; MEDLINE- international literature on health sciences; PUB MED; Scielo- Scientific Electronic Library Online and CAPES. Published from 2019 to 2021.Results and conclusion: During the search, a result of 143 articles was obtained, of which 77 were excluded from the reading of the titles and abstracts. After a reading of the discussion and conclusion of the remaining 66 articles, 8 articles were chosen, because they met the inclusion and exclusion criteria.Studies have shown that some symptoms become persistent such as dyspnea, oxygen desaturation, cough, weakness, and fatigue. Pulmonary physical therapy rehabilitation is very important in relation to post-covid patients 19, since its sequelae alter their daily quality of life, its relevance ranges from hospital to outpatient. The physiotherapist obtains techniques ranging from bronchial hygiene maneuvers to aerobic exercises.
Design and Implementation of Intelligent Course Scheduling System Based on Simulated Artificial Algorithm

research-article Design and Implementation of Intelligent Course Scheduling System Based on Simulated Artificial Algorithm Share on Author: Liangliang Wang Hubei Three Gorges Polytechnic, China Hubei Three Gorges Polytechnic, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2110–2114https://doi.org/10.1145/3482632.3484109Online:22 November 2021Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research and Implementation of Oil Painting Virtual Reality Based on Internet of Things

The Internet of Things has been rapidly developed and updated in recent years. As an important branch of the computer field, people pay more and more attention to the application research of virtual reality technology. In recent years, virtual scene rendering technology has been the core issue of research at home and abroad, and has been widely used in education and training, aerospace industry, military exercises, urban planning and other fields. The relationship between reality and reality, as the same artistic law in the painting creation of eastern and western artists, is also a pair of contradictory unity which complement each other in the expression of artistic language. This paper mainly expounds the realization of virtual reality of oil paintings based on Internet of Things technology, which enables users to complete various remote monitoring and control while immersed in virtual reality environment. Humanized three-dimensional human-computer interaction interface allows users to get a better sense of immersion and interaction, and provides users with a more natural way to obtain physical information and virtual interaction experience.
Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion

Abstract To develop commonsense-grounded NLP applications, a comprehensive and accurate commonsense knowledge graph (CKG) is needed. It is time-consuming to manually construct CKGs and many research efforts have been devoted to the automatic construction of CKGs. Previous approaches focus on generating concepts that have direct and obvious relationships with existing concepts and lack an capability to generate unobvious concepts. In this work, we aim to bridge this gap. We propose a general graph-to-paths pretraining framework that leverages high-order structures in CKGs to capture high-order relationships between concepts. We instantiate this general framework to four special cases: long path, path-to-path, router, and graph-node-path. Experiments on two datasets demonstrate the effectiveness of our methods. The code will be released via the public GitHub repository.
The effectiveness of multimedia elements integrated into calculus learning using basical mobile application

Calculus is one branch of mathematics which is not something that students can easily learn. The BasiCal mobile application is a mobile application that provided two topics in Calculus which is Function; and Limits and Continuity. The BasiCal mobile application was designed and developed by authors to transform the learning method from conventional to digital by using multimedia elements such as audio, text, interactivity, and graphics. The purpose of multimedia elements used is to make Calculus learning more interesting and motivate students to learn. This study was designed to investigate the effectiveness of the BasiCal mobile application which was conducted on 40-degree students at a private university in Malaysia. The sample is divided into two groups which are the experimental group and the control group. The students of the experimental group were exposed to learn through the BasiCal mobile application, while the students of the control group were exposed to learn through the conventional teaching method. This study is using pre-test and post-test as the instruments to examine the students on selected topics in Calculus before and after teaching sessions. The result shows that the achievement of the BMA group of students who use multimedia elements in the BasiCal Mobile Application is more effective than the achievement of the CTM group of students who use conventional methods in calculus learning. It was found that learning Calculus with multimedia elements integrated using mobile applications is beneficial to students as a learning tool in this age of technology.
Real time driver's eye state recognition based on deep mobile learning

Abstract. Eye state recognition has been the subject of many studies due to its importance in many fields especially drowsy driver detection, which is crucial task that must be done in real time and mostly using limited hardware. These restrictions make resource consuming learning techniques such as deep learning difficult to use. Deep mobile learning seems to be a viable solution to solving this issue. In this paper, we propose a real time system based on deep mobile learning to classify the eye state, and compare its performance with classical machine learning methods. The experimental results on the Closed Eyes in the Wild (CEW) and MRL Eye Datasets show that the proposed approach outperformed the other machine learning techniques in terms of accuracy and execution time. In addition, we evaluated our system on a video dataset to demonstrate its reliability and robustness.
The Research of Anime Character Portrait Generation Based on Optimized Generative Adversarial Networks

Anime Character Portrait Generation is an interesting but a challenging job. At present, most of the existing methods are solved by using generative adversarial networks. However, the styles of anime character portrait are quite different and Generative Adversarial Networks is also prone to cause problems including mode collapse, which makes it hard to generate good anime character portrait samples. In this paper, DRAGAN model will be optimized and the improved version of the basic structure of Conv-BN-Relu-Pooling will be applied reasonably. The optimized DRAGAN is trained by using alternating gradient updates procedure to achieve high-quality generation samples. At the same time, through the experimental comparison with other generative adversarial networks of deep learning, it is finally verified that the optimized DRAGAN performs better than the experimental comparison group in terms of image visual quality, FID and gradient penalty terms. The mode collapse problem has also been alleviated finally.
PRTRすそ切り以下工場・事業場における全国市区町村での化学物質の在庫量の推計

In preparation for the leakage of chemical substances from factories and workplaces caused by disasters and accidents, it is desirable for each local government to grasp the daily stock amount of chemical substances in advance. In this study, we tried to estimate the daily stock amount of chemical substances in factories and workplaces below-threshold-condition of PRTR in municipalities nationwide.
Framework for Creating Relevant, Accessible, and Adoptable KPI Models in an Industrial Setting

The development of software for modern products with lots of interfaces, layers and stakeholders has become very complex, increasing the risk of inefficiency. Key Performance Indicators (KPIs) can be used to identify bottlenecks and problems, but the challenge is how to create KPI models, processes and dashboards that help improving the development processes and can be adopted by all the stakeholders. We introduce the RelAA Framework - a bottom-up approach for monitoring product-focused software development. The RelAA (Relevant, Accessible and Adoptable) Framework is created in an industrial setup that currently includes around 350 persons in different phases of the software life cycle. The RelAA Framework is formed by analyzing existing KPIs and tools, gathering feedback from development teams, management, business representatives, and other stakeholders, and creating intuitive ways to share information related to KPIs. The RelAA Framework itself does not define exact KPIs for the organization to adopt, but it provides a process and model how to create, document and utilize KPIs. The RelAA Framework ensures relevance, accessibility, and adoption of KPIs across stakeholders and organization.
Asymptotic stability of small bound state of nonlinear quantum walks

In this paper, we study the long time behavior of nonlinear quantum walks when the initial data is small in $l^2$. In particular, we study the case where the linear part of the quantum walk evolution operator has exactly two eigenvalues and show that the solution decomposed into nonlinear bound states bifurcating from the eigenvalues and scattering waves.
Turkuaz: The Modernization of an ERP Application with Agile Methodology in The Automotive Sector in Turkey

Thanks to the increased computing power of processors, developers can develop more complex software applications. Software developers need to follow the software design life cycle to meet consistent high standard software output. The only constant component of software projects is human. The diversity of the teams leads to different understandings during the exchange of information and communication between the teams. Using the traditional method can create inefficient processes and lower quality products. In addition, code readability, code management becomes difficult, and technical debt increases as overtime. The Agile method provides flexibility and high efficiency to teams. Teams that adopt the agile method have advantages in interaction with the customer, in rapid response to changes, and in strong communication. This paper presents the modernization of the teams experiencing the difficulties of the traditional method with the agile method with a real case study. Within the scope of modernization, it has been worked on under three main headings: changing the module-based way of doing business, reducing technical borrowing and renewing distribution strategies. With this transformation, we are able to prioritize customer requests, measure the value of the business, constantly make useful parts available, easily maintain software, and allocate time for new ideas.
The Construction and Application of Smart Classroom Teaching of College English in the Era of “Internet Plus”

In the era of Internet, the construction of smart classroom has become the emphasis of college English teaching and research. However, it requires teachers to update their own teaching ideas to carry out teaching reforms taking the advantage of the Internet. Based on this, as a reference, this paper covers the current situation of college English teaching, and analyzes the function and measures of the construction of college English smart classrooms in the era of Internet Plus.
Graphs, Matrices and Matroids

This chapter forms the basic background on graphs, matrices and matroids. We start with the definitions and notations of graphs followed by main graph types, graph operations and graph traversals in the first section. We then look at ways of defining graph in terms of matrices and conclude with a data structure called matroids which we will use to design some specific type of graph algorithms.
Approaches to Optimize Memory Footprint for Elephant Flows

The technology revisions are too quick to resist or stay apart. Most of the work, on massive and streaming data, tries to scale up the computation. The scale up and scale out comes with its own set of advantages and disadvantages. The computational hardware has already been used to its limits with best of the algorithms. Our work proposes and implements two approaches: (a) memory bound and (b) ingestion bound that optimizes memory footprint and speeds up the computation. The work compares the stated techniques on two datasets: (a) moma and (b) yelp and found up to 80% conditional optimization. The work shows results of optimization for big data stream and proves that the approach is worth implementing as compared to other state of the art techniques for big data stream aka Elephant Flows.
Distributed Temperature Monitoring System for Auto Spontaneous Combustion Based on Multiple Sensor Signals

Because the pressure and the temperature exceed the safety standard scope, causes the automobile performance to reduce greatly, finally causes the automobile self-ignition accident. In order to prevent and reduce the probability of this kind of accident, a temperature monitoring system is designed by using multiplex sensor signal and STM32 controller to monitor the temperature in real time. The SP37 sensor integrates pressure sensor and temperature sensor, and its internal controller is compatible with 8051 instruction. It also has the function of RF transmission. The system displays the temperature data in real time. When the data exceed the safety threshold, the system sends out warning sound to warn the driver to prevent the danger of spontaneous combustion. Experimental results show that the design has the advantages of good stability, high measurement accuracy and low power consumption.
Balancing Latency and Quality in Web Search

Selecting the right time budget for a search query is challenging because a proper balance between the search latency, quality and efficiency has to be maintained. State-of-the-art approaches leverage a centralized sample index at the aggregator to select the Index Serving Nodes (ISNs) to maintain quality and responsiveness. In this paper, we propose Cottage, a coordinated framework between the aggregator and ISNs for latency and quality optimization in web search. Cottage has two separate neural network models at each ISN to predict the quality contribution and latency, respectively. Then, these prediction results are sent back to the aggregator for latency and quality optimizations. The key task is integration of the predictions at the aggregator in determining an optimal dynamic time budget for identifying slow and low quality ISNs to improve latency and search efficiency. Our experiments on the Solr search engine prove that Cottage can reduce the average query latency by 54% and achieve a good P@10 search quality of 0.947.
High Density Salt and Pepper Noise Removal Algorithm Using Statistical Approach

Halder, Amiya Choudhuri, RudrajitReduction of impulse noise so as to enhance the quality of the image is a necessary preprocessing technique applied in the domain of image processing. Quite often during acquiring of an image, salt and pepper noise corrupts the image due to a faulty capturing device. Also, corrupt storage devices or noisy transmission lines can be a cause for salt and pepper noise in the image. In this paper, a statistical two-phase noise reduction adaptive technique is applied to enhance high-density noisy images. Firstly noise is removed by replacing the corrupt pixel with the median of non-noisy pixels in the adaptive neighboring window. This is followed by tuning the updated pixel for better enhancement results. Several experiments are performed on various standard images to figure out the efficiency of the proposed technique. Results confirm that the proposed technique yields better metric values than its peers, thus proving to be a robust and fault-tolerant noise reduction technique.
Selective prediction‐set models with coverage rate guarantees

The current approach to using machine learning (ML) algorithms in healthcare is to either require clinician oversight for every use case or use their predictions without any human oversight. We explore a middle ground that lets ML algorithms abstain from making a prediction to simultaneously improve their reliability and reduce the burden placed on human experts. To this end, we present a general penalized loss minimization framework for training selective prediction-set (SPS) models, which choose to either output a prediction set or abstain. The resulting models abstain when the outcome is difficult to predict accurately, such as on subjects who are too different from the training data, and achieve higher accuracy on those they do give predictions for. We then introduce a model-agnostic, statistical inference procedure for the coverage rate of an SPS model that ensembles individual models trained using K-fold cross-validation. We find that SPS ensembles attain prediction-set coverage rates closer to the nominal level and have narrower confidence intervals for its marginal coverage rate. We apply our method to train neural networks that abstain more for out-of-sample images on the MNIST digit prediction task and achieve higher predictive accuracy for ICU patients compared to existing approaches.