In industrial practice, many bugs in commercial mobile apps manifest as self-conflicts of data presented in the GUI (Graphical User Interface). Such data inconsistency bugs can bring confusion to the users and deterio...
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ISBN:
(纸本)9798400702174
In industrial practice, many bugs in commercial mobile apps manifest as self-conflicts of data presented in the GUI (Graphical User Interface). Such data inconsistency bugs can bring confusion to the users and deteriorate user experiences. they are a major target of industrial testing practice. However, due to the complication and diversity of GUI implementation and data presentation (e.g., the ways to present the data in natural language), detecting data inconsistency bugs is a very challenging task. It still largely relies on manual efforts. To reduce such human efforts, we proposed AutoConsis, an automateddata inconsistency testing tool we designed for Meituan. one of the largest E-commerce providers with over 600 million transacting users. AutoConsis can automatically analyze GUI pages via a multi-modal deep-learning model and extract target data from textual phrases leveraging LLMs (Large Language Models). Withthese extracted data, their inconsistencies can then be detected. We evaluate the design of AutoConsis via a set of ablation experiments. Moreover, we demonstrate the effectiveness of AutoConsis when applying it to real-world commercial mobile apps with eight representative cases.
Network traffic patterns in high-performance computing systems can impact application performance by inducing costly re-transmissions or consuming memory bandwidth. Emerging SmartNIC technologies present new opportuni...
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ISBN:
(纸本)9798350370621
Network traffic patterns in high-performance computing systems can impact application performance by inducing costly re-transmissions or consuming memory bandwidth. Emerging SmartNIC technologies present new opportunities for addressing these issues and optimizing network performance by providing a platform for the intelligent utilization of network resources through machine learning models of network traffic. We analyze traffic data from scientific applications and proxies and propose lightweight approaches to modelling network traffic based on dynamic (i.e., rolling) linear regression and supplement it with random forest classifiers for additional accuracy, reaching 90% or higher.
Music has become an indispensable element of human life. Its rhythms, melodies, and harmonies resonate deeply within us, touching our emotions and echoing our sentiments. In recent years, music emotion sentiment class...
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ISBN:
(纸本)9798350351194;9798350351187
Music has become an indispensable element of human life. Its rhythms, melodies, and harmonies resonate deeply within us, touching our emotions and echoing our sentiments. In recent years, music emotion sentiment classifications in different languages have been studied. However, to be best of our knowledge, Turkish music has not been explored sufficiently using intelligent tools. We explore machine learning algorithms to classify Turkish music audio excerpts into distinct mood categories. We use two datasets: the Turkish Music Emotion (TME) dataset and the Turkish Emotional Voice database (TurEV-DB) dataset. the Gradient Boosting, XGBoost, CatBoost, Random Forest, Decision Tree, and Gaussian Naive Bayes machine learning algorithms are used for training and testing the learners. Our case study results demonstrate that the CatBoost learner has the best overall performance with an Area Under the ROC Curve of 0.948 and Accuracy of about 82% for the TME dataset, and an Area Under the ROC Curve of 0.989 and Accuracy of about 90% for the TurEV-DB dataset. In the context of the two datasets, the top two learners are CatBoost followed by XGBoost. the six learners, to the best of our knowledge, have not been explored elsewhere withthese two datasets, making this work a unique addition to the related literature and state-of-the-art.
this study proposes an automateddata mining framework based on autoencoders and experimentally verifies its effectiveness in feature extraction and data dimensionality reduction. through the encoding-decoding structu...
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Xiaomai is an intelligent tutoring system (ITS) designed to help Chinese college students in learning advanced mathematics and preparing for the graduate school math entrance exam. this study investigates two distinct...
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ISBN:
(纸本)9783031643019;9783031643026
Xiaomai is an intelligent tutoring system (ITS) designed to help Chinese college students in learning advanced mathematics and preparing for the graduate school math entrance exam. this study investigates two distinctive features within Xiaomai: the incorporation of free-response questions with automatic feedback and the metacognitive element of reflecting on self-made errors. An experiment was conducted to evaluate the impact of these features on mathematics learning. One hundred and twenty college students were recruited and randomly assigned to four conditions: (1) multiple-choice questions without reflection, (2) multiple-choice questions with reflection, (3) free-response questions without reflection, and (4) free-response questions with reflection. Students in the multiple-choice conditions demonstrated better practice performance and learning outcomes compared to their counterparts in the free-response conditions. Additionally, the incorporation of error reflection did not yield a significant impact on students' practice performance or learning outcomes. these findings indicate that current design of free-response questions and the metacognitive feature of error reflection do not enhance the efficacy of the math ITS. this study highlights the need for redesign or enhancement of Xiaomai to optimize its effectiveness in facilitating advanced mathematics learning.
this paper focuses on risk estimation problems of urban traffic accidents using deep learning approaches. there are two major challenges in the previous studies. the first challenge is the data imbalance problem that ...
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ISBN:
(数字)9781665468800
ISBN:
(纸本)9781665468800
this paper focuses on risk estimation problems of urban traffic accidents using deep learning approaches. there are two major challenges in the previous studies. the first challenge is the data imbalance problem that occurs numerous zeros in input data and can negatively affect the risk estimation results. the second challenge lies in neglecting the road environmental factors in risk estimation, which are also essential in causing traffic accidents. In order to address the aforementioned two problems, this study developed a hierarchical deep learning-based model with mobility and road environment data for estimating the risk of urban traffic accidents. the experiment results indicate the proposed method outperforms other existing models. the suggested method can be applied to the traffic warning system to assist people to avoid traffic accidents and further used in traffic accident prediction.
Global interest in complementary and alternative medicine has increased in recent years, with Kampo medicine in Japan gaining greater trust and use. Detailed patient interviews are essential in Kampo medicine, as the ...
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the aim of the next release problem (NRP) is to decide the most suitable subset of candidate requirements to include in the software system's upcoming version. To advance the automation degree of contemporary NRP ...
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ISBN:
(纸本)9798350351194;9798350351187
the aim of the next release problem (NRP) is to decide the most suitable subset of candidate requirements to include in the software system's upcoming version. To advance the automation degree of contemporary NRP solutions, we investigate in this paper the cluster hypothesis where we balance the to-be-released candidates withthe already implemented features. Clustering the balanced set gives rise to a fully automatic NRP solution, using only the features' natural language descriptions and a project's release history. Our experiments on a total of 1,296 requirements from four real-world systems' 78 NRP instances show that k-means best fulfills the cluster hypothesis, and also outperforms the zero-shot learning capability of large language models (LLMs) in solving the NRP.
this work investigates how tutoring discourse interacts with students' proximal knowledge to explain and predict students' learning outcomes. Our work is conducted in the context of high-dosage human tutoring ...
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ISBN:
(纸本)9783031642982;9783031642999
this work investigates how tutoring discourse interacts with students' proximal knowledge to explain and predict students' learning outcomes. Our work is conducted in the context of high-dosage human tutoring where 9th-grade students (N = 1080) attended small group tutorials and individually practiced problems on an intelligent Tutoring System (ITS). We analyzed whether tutors' talk moves and students' performance on the ITS predicted scores on mathlearning assessments. We trained Random Forest Classifiers (RFCs) to distinguish high and low assessment scores based on tutor talk moves, student's ITS performance metrics, and their combination. A decision tree was extracted from each RFC to yield an interpretable model. We found AUCs of 0.63 for talk moves, 0.66 for ITS, and 0.77 for their combination, suggesting interactivity among the two feature sources. Specifically, the best decision tree emerged from combining the tutor talk moves that encouraged rigorous thinking and students' ITS mastery. In essence, tutor talk that encouraged mathematical reasoning predicted achievement for students who demonstrated high mastery on the ITS, whereas tutors' revoicing of students' mathematical ideas and contributions was predictive for students with low ITS mastery. Implications for practice are discussed.
Withthe development of deep learning, the accuracy of load forecasting performs better and better on large sample datasets. However, there are areas in real life where there is only limited data, which is a challenge...
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