Reviewerly/exHarmony huggingface dataset:
Towards Demystifying the Impact of Dependency Structures on Bug Locations in Deep Learning Libraries

Background: Many safety-critical industrial applications have turned to deep learning systems as a fundamental component. Most of these systems rely on deep learning libraries, and bugs of such libraries can have irreparable consequences. Aims: Over the years, dependency structure has shown to be a practical indicator of software quality, widely used in numerous bug prediction techniques. The problem is that when analyzing bugs in deep learning libraries, researchers are unclear whether dependency structures still have a high correlation and which forms of dependency structures perform the best. Method: In this paper, we present a systematic investigation of the above question and implement a dependency structure-centric bug analysis tool: Depend4BL, capturing the interaction between dependency structures and bug locations in deep learning libraries. Results: We employ Depend4BL to analyze the top 5 open-source deep learning libraries on Github in terms of stars and forks, with 279,788 revision commits and 8,715 bug fixes. The results demonstrate the significant differences among syntactic, history, and semantic structures, and their vastly different impacts on bug locations. Their combinations have the potential to further improve bug prediction for deep learning libraries. Conclusions: In summary, our work provides a new perspective regarding to the correlation between dependency structures and bug locations in deep learning libraries. We release a large set of benchmarks and a prototype toolkit to automatically detect various forms of dependency structures for deep learning libraries. Our study also unveils useful findings based on quantitative and qualitative analysis that benefit bug prediction techniques for deep learning libraries.
Data Mining Performance Assessment of Regional Development Planning Agency Employees Using Bayesian Classifier Algorithm

Performance is the success or work achievement of a person or group in an organization in completing work. Performance is the goals and targets given, performance appraisal is carried out. The results of employee performance consist of two, namely good or bad. These results are used as indicators in making decisions for giving rewards or punishments. This study aims to measure the level of employee performance with targets very good, good, quite good, less good and bad. The data used in this study is employee data. The assessment criteria are the results of the best employee assessment at the Regional Development Planning Agency in June 2022. The method used in determining employee performance is the Bayesian Classifier Algorithm. This algorithm uses the concept of classification. The data that is processed is first classified and followed by the analysis process in producing employee performance. This study uses training data as many as 43 records then the assessment is used as testing data. The results of the analysis of employee performance appraisal using the Bayesian Classifier Algorithm that the algorithm succeeded in classifying employee performance in accordance with the objectives of this study very well.
Uniqueness of system integration scheme of artificial intelligence technology in fractional differential mathematical equation

Abstract At present, various branches of mathematics have developed very mature, but they still lack an internal structural correlation. They are like “islands” in the universe. If we can find the universal conversion relationship of the three basic mathematical structures of geometry, arithmetic and logic, it will greatly expand the field of mathematics. The ultimate goal of mathematics in the past is to pursue its preciseness, while the ultimate goal of mathematics in the future is to pursue its unity. Fractional differential equation is a generalization of integer order calculus. It discusses the theory of any non integer order differential and integral equation. The rise of artificial intelligence technology just provides the most powerful laboratory for the unity of mathematics. To explore the mystery of the brain and achieve the ultimate goal of artificial intelligence technology, the key is to make a major breakthrough in basic mathematical research and find a basic theoretical system describing the mutual mapping and transformation of different mathematical paradigms. The unity of mathematics will undoubtedly become the forefront of the development of mathematics in the future, which will be the core of the research on the new generation of intelligent computer and artificial intelligence technology.
Non-IID Learning

Real-life AI systems are non-IID, i.e., their variables are unlikely independent and drawn from the same distribution. Instead, non-IIDness is a common characteristic and complexity of real-life systems, where variables, objects, and subsystems are coupled/interactive and heterogeneous. This issue highlights this important theme on Non-IID Learning with six feature articles. In addition, four columns highlight expert opinions on beyond i.i.d., trustworthy AI, data-driven predictive maintenance, and secrets for data science deployments, respectively.
Assistência de enfermagem à saúde da pessoa amputada

Objective: to identify the importance of Nursing care to the amputated patient. Methods: this is an integrative review study, which allows the synthesis of multiple published studies. Results: no hospital before the nurse's procedure should carry out all the client's history, exams examined, identify and treat any health problem that may hinder the verification of the surgical act and emotionally support the patient and family, in the face of loss of the limb, explaining the procedure and rehabilitation, the nurse seeks to observe and guide regarding the changes that must be made at the patient's home, in nursing consultations, physical and psychosocial adaptation must be evaluated and continue performing the physical examination and regular diagnostic tests Conclusion: in view of the above, it is concluded that the nurse is in charge of offering assistance, promoting a better quality of life, in aspects related to its realization as well as family and social relationships, in addition to emotional issues and daily difficulties. Assistance to amputee patients encompasses a range of factors where the professional must have a holistic view of this individual.
Recognition and Prediction Method of Music and Dance Movements Based on G-ResNet SPP and Attention Mechanism

Aiming at the problems of difficult action recognition and low prediction accuracy in the process of music and dance movement. This paper proposes a music and dance motion recognition and prediction method based on G-ResNet SPP (Spatial Pyramid Pooling) and attention mechanism, so as to improve the accuracy of motion feature recognition. Firstly, the action recognition model is described, and the related theoretical basis and the construction of sampling function and weight function in action recognition are described. Secondly, the recognition method of the encoder-decoder design framework is described; finally, a group of music and dance sequences are used to verify the system, and the results show that the recognition effect of the G-ResNet + APP + ATTEN model has better performance in different experimental data sets.
Beyond Instance-Dependent Noise: Generalizing Noisy Label Modeling to Multiple Labeler Dependence

&lt;p&gt;Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as class-conditional transition matrices. More recent work on instance-dependent noise models are more realistic; however, these models assume a single generative process for label noise across the entire dataset, which does not accurately capture the real-world labeling dynamics observed in modern dataset collection. We propose a new model of label noise, which we call "labeler-dependent noise (LDN)." LDN extends and generalizes instance-dependent noise to multiple labelers, based on the observation that modern datasets are typically constructed via distributed data gathering and labeling methods. Furthermore, inspired by recent research in social learning theory, we introduce a modular learning framework under our proposed labeler-dependent noise model called "labeler-aware learning", in which each labeler is estimated and compared against the others during the learning process. Our experimental results demonstrate how previous state-of-the-art approaches for learning from noisy labels are unable to handle the general model of label noise, while our labeler-aware learning framework remains robust even when large fractions of labelers are spammers.&lt;/p&gt;
PROBLEM BASED MOBILE LEARNING IN BIOCHEMISTRY: AN INTERVENTIONAL STUDY IN PHASE I MBBS STUDENTS

Objectives: The objectives of the study were (1) to compare the effectiveness of problem-based learning (PBL) in classroom and problem-based mobile learning (PBML) using a mobile application and (2) to study the perception of students to PBML and PBL in classroom. Methods: After getting clearance from institutional ethics committee, an interventional study was conducted for a period of 2 months among 250 Phase IMBBS students of Government Medical College Kozhikode, Kerala. Students who gave consent for the study were divided into two groups, namely, R-1 and R-2, and analyzed for the effectiveness of PBML as a teaching learning method in comparison with PBL in classroom. Perception of students toward use of PBML and PBL as teaching learning method was collected using validated questionnaire. Results: It was observed from the study that in all the sessions post-test mean scores were significantly higher than pre-test scores in both PBL and PBML. Before the intervention there was no significant difference in the pre-test mean scores in topics done by PBL and PBML. After intervention the only the post-test mean scores significantly improved. When the total post score scores of PBL and PBML compared, it was found that PBML mean score is significantly higher than PBL mean score. In the student’s perception analysis, it was found that both methods are effective in concept building, stimulating, helpful in passing exams, and development of problem-learning skills. Conclusion: From the study finding, we can conclude that both PBL and PBML are effective modes of teaching applied biochemistry for Phase I MBBS students. PBML is more effective than PBL in teaching applied biochemistry in Phase I MBBS students. Perception analysis shows that interaction with the students and doubt clearance is more effectuated with PBL than PBML.
The Use of Panel Data Regression to Identify Students Reading Interest in the Library

Library visit is an indicator of college academic literacy. The current problem was a decrease on the students’ visit and read at the library of STMIK Amik Riau. Based on the visit data in the library, there was a reduction of the students’ visit for each year, as well as books lent by the students has decreased significantly. The purpose of this study was to identify the correlation between the library facilities and the library services on the students’ reading interest at the library of STMIK Amik Riau by using Panel Data. This study used qualitative and quantitative approach. The population of this study were the visitors at STMIK Amik Riau Library that chosen by using simple random sampling. The data were collected through observation, interviews, questionnaires and documentation. The method used to identify the interest on the students’ to read in the library was Panel Data regression. The procedures of this method were to determine the Panel Data Regression Model Estimation, the selection of Panel Data Regression Model (Estimated Technique) which includes the F Statistical Test (Chow Test), Hausman Test, and Lagrange Multiplier Test. Furthermore, conducted testing the Classical Assumptions (Multicollinearity and Heteroscedasticity) which includes the Multicollinearity test and heteroscedasticity test, followed by a feasibility test (Goodness of Fit) with the F-test hypothesis and Partial t-test. The result of this study showed that there was a correlation between the library facilities and the library services on the students’ reading interest at library of STMIK Amik Riau.
The students' difficulties in understanding the authentic news items materials in the listening class

This objective of the research is to identify the difficulties faced by the students in understanding the authentic listening materials of the news item. The participants of the research are the second-semester undergraduate Students of the English Education Department (EED) in Universitas Nasional Karangturi Semarang 2020/2021. The research method applied in this research setting is the descriptive method. Six students are participating in this research. To collect the data of the research, a questionnaire was used. The data collected was analyzed descriptively by identifying the responses of the questionnaire to identify further the difficulties faced by the research participants. The result of the research shows that the participants, majorly, face difficulties in understanding each part of the authentic news item materials. The difficulties faced by participants are caused by the rate or speed of the native speakers’ talk, the participants’ vocabulary unfamiliarity, lack of vocabulary mastery, and listening strategy.
Learning Flexible and Fair Data Representations

In this paper, we consider the problem of learning fair data representations that can be used for some downstream utility task in the vendor-user setting. We propose splitting the latent space between sensitive and non-sensitive latent variables where maximum mean discrepancy (MMD) is used to induce statistical independence between sensitive and non-sensitive latent variables. The non-sensitive latent representations can then be used for utility task by the user and achieve group and sub-group fairness with respect to multiple sensitive attributes. We perform extensive experiments and compare the proposed method against various representation learning methods proposed earlier in the literature. Our quantitative results and visualizations show that the proposed method produces representations that are able to achieve better or comparable performance at the utility task while simultaneously achieving sub-group and group fairness.
Contrastive Graph-Based Recommendation with Domain-Relevant Augmentation and Task-Oriented Loss Function

Contrastive learning (CL) has seen rapid growth in the field of graph-based recommendation. Two typical strategies to adopt CL to the graph-based recommendation are employing node or edge dropping to augment the original graph and training the model under a joint optimization. We argue that this domain-agnostic augmentation can corrupt the graphs’ underlying properties, and the joint learning strategy requires additional costs. This paper proposes a new Contrastive graph-based Recommendation framework with Domain-relevant augmentation and Tasl-oriented loss function (CRDT). Specifically, we propose a domain-related data augmentation especially designed for alleviating the long tail problem and a task-oriented loss function unifying the CL task and the main task of the recommendation. Unlike most other CL tasks, CRDT does not require pre-training ampersand fine-tuning, joint learning, negative sampling. Extensive experiments on various real-world datasets demonstrate the superiority of CRDT.
Improving Students’ Reading Ability in Guessing Word Meaning from Context through Guided Discovery Learning: A Classroom Action Research on the Eleventh Grade Students of SMAN 1 Sumpiuh in the Academic Year 2019/2020

This action research aimed to investigate how Guided Discovery Learning can be implemented to improve the students’ ability in guessing word meanings from context as well as the strengths and weaknesses of the method when used in class. The research participants were the students of SMA Negeri 1 Sumpiuh, XI Science 4. The researcher implemented two cycles in this research and four steps for each cycle; planning, action, observation, and reflection. The data were obtained from several techniques, including tests, observation, questionnaires, and interviews. To analyse the quantitative data, the researcher applied descriptive statistics, comprising the following dimensions: highest and lowest scores and means. The researcher analysed the improvement of the students’ ability through interviews and questionnaires to analyse the qualitative data. The research findings show that the student problems related to difficult words could be solved by predicting their meanings from context. This skill could help the students understand and comprehend the text easily without depending on the dictionary or asking the meanings of the unknown words to other people. Therefore, a certain method was needed to develop the students’ skills in guessing word meanings from context, one of which is Guided Discovery Learning (GDL). In conclusion, using GDL allowed the students to discover signal words to determine the meaning of the unknown words. However, GDL has its strengths and weaknesses, although it is recommended for teachers, particularly when teaching reading and vocabulary.
IOS-Based Mobile Service Ordering Application Using Wireframe and Figma

Technological developments are increasingly growing rapidly so that everyone cannot avoid these technological advances. The technology in question covers all aspects of life, therefore here we create an application that is expected to help the community in finding tenants for IOS-based services, with the aim of helping people who are difficult in terms of finding service providers who want to be hired to help with their daily homework day.
Using Reading Questioning Answering (RQA) Model to Improve Student Reading Skill During Online Learning

This research is motivated by the lack of reading interest in students at MTS Khazanah Kebajikan which affects online learning activeness in increasing reading interest, student understanding and students' critical thinking skills. As we already know, online learning makes learning less effective because students can easily answer all questions from the internet without reading in detail, but there are also students who are hampered in terms of facilities, technology and networks. This study uses a descriptive qualitative method based on information data collection, interviews and observations about the problems or difficulties of students during online learning and especially in increasing students' reading interest. The main target of this research is students at MTS Khazanah Kebajikan, where students lack reading interest during online learning. With the RQA model it can be seen how high the level of students 'reading interest is during online learning and can optimize students' understanding at school.&#x0D; Keyword: Education, Online Learning, Reading Interest, RQA
The Effect of Parenting Education on Mothers Towards Knowledge and Attitude of Mothers of Toddler About Growing

The role of the family, especially the mother in raising children, is very important in determining the growth and development of children. In order for parents to be able to carry out their functions properly, parents need to understand the level of child development, assess the growth or development of children, and have a strong motivation to advance the growth and development of children. The results of the study on parenting education show that the skills of parents in carrying out the parenting function are still low Syakrani, 2010 the causes include the absence of parenting programs, low understanding and awareness of parents about the importance and influence of motherhood on children's growth and development, and lack of information about the importance of parenting. child care. Shochib, 2010 explains that the knowledge and attitudes of parents in providing parenting to their children are influenced by the lack of information about ways to achieve a healthy life which can lead to deviations in parenting patterns for children. This study aims to determine the effect of parenting education on mothers of infants under five on the knowledge and attitudes of mothers of infants under five about growth and development. The research method used in this study is a quasi-experimental one-group pretest-post test design. The population of this research is mothers of infants under five in the Cikalang Village, Tasikmalaya City. The subjects of this study were mothers of infants under the age of five in the Cikalang sub-district who were selected using the purposive sampling method. The sample selected as the respondent is determined based on the criteria. Total 70 0 people. univariate analysis to determine the frequency distribution. The results of the data normality test showed that the data distribution was not normal, so the bivariate test used the Wilcoxon test. The results of the study obtained knowledge of mothers of infants under five about growth and development of infants under five before parenting education, the highest level of mother knowledge was in the less category (67.1%), while after attending parenting education the mother's level of knowledge was in the medium category (60%) and the good category (40%). Prior to parenting education, the mother's attitude was mostly in the bad category (64.3%), while after attending parenting education the mother's attitude was mostly in the good category (87.1%). The results of the different test were known as the Asymp.Sig value. (2-tailed) of 0.000 &lt;0.05, which means that H0 is rejected. It can be concluded that there is an influence of parenting education on the knowledge and attitudes of mothers of infants under five about the growth and development of infants under five.
NonReference Mapping Net

It is very challenging to collect images under low light conditions due to low photon count. The exposure time is short and the number of photons is so rare that the objects in the image cannot be recognized and the noise will be amplified. However, the long exposure time is almost impossible to achieve in daily shooting. In response to these challenges, many researchers have proposed a lot of methods to solve the above problems, but most of the methods are only for specific lighting conditions. In recent years, some methods for training deep learning models based on a large amount of images are used for low-light image enhancement, but many methods require strictly paired images. This paper proposes a new idea, no reference mapping network, it only needs images collected in dark scenes. NRM-Net is a pixel-wise nonlinear mapping network, which realizes pixel-wise adjustment of low-light images in a high dynamic range. The realization of this goal is mainly attributed to our carefully designed high-order nonlinear mapping function and a set of loss functions.