Three-dimensional (3D) networks composing of S and N dual-doped graphene (SNG) were synthesized by a chemical vapor deposition approach using MgSO4-containing whiskers as templates and S source and ...NH3 as N source. Energy dispersive spectrometer mapping and X-ray photoelectron spectroscopy coupled with Raman analysis have revealed that S and N atoms with concentrations of 5.2 and 1.8atom%, respectively, have been substitutionally incorporated into the graphene networks via covalent bonds. The SNG, as an anode material for lithium ion batteries (LIBs), exhibits extremely high capacity (3525mAh/g at the current density of 50mA/g) and superior rate capability (870mAh/g at 1000mA/g) with excellent cycling stability (remaining a reversible capacity of 400mAh/g at 10A/g after 2500 cycles). The enhanced conductivity, the 3D porous network with many disorders and the intrinsically high Li storage capacity of S and N-doped carbon segments have led to the excellent electrode performance of the SNG networks. The effects of binder content and calendaring pressure on the electrode performance have been investigated. The full LIB with SNG as anode and LiCoO2 as cathode can afford a high reversible capability (164mAh/g at 0.2C) and good cycling stability.
Student cognitive models are playing an essential role in intelligent online tutoring for programming courses. These models capture students' learning interactions and store them in the form of a set ...of binary responses, thereby failing to utilize rich educational information in the learning process. Moreover, the recent development of these models has been focused on improving the prediction performance and tended to adopt deep neural networks in building the end-to-end prediction frameworks. Although this approach can provide an improved prediction performance, it may also cause difficulties in interpreting the student's learning status, which is crucial for providing personalized educational feedback. To address this problem, this paper provides an interpretable cognitive model named HELP-DKT, which can infer how students learn programming based on deep knowledge tracing. HELP-DKT has two major advantages. First, it implements a feature-rich input layer, where the raw codes of students are encoded to vector representations, and the error classifications as concept indicators are incorporated. Second, it can infer meaningful estimation of student abilities while reliably predicting future performance. The experiments confirm that HELP-DKT can achieve good prediction performance and present reasonable interpretability of student skills improvement. In practice, HELP-DKT can personalize the learning experience of novice learners.
With the gradual expansion of microservice architecture-based applications, the complexity of system operation and maintenance is also growing significantly. With the advent of AIOps, it is now ...possible to automatically detect the state of the system, allocate resources, warn, and detect anomalies using machine learning models. Given the dynamic nature of online workloads, the running state of a microservice system in production is constantly in flux. Therefore, it is necessary to continuously train, encapsulate, and deploy models based on the current system status for the AIOps model to dynamically adapt to the system environment. This paper proposes a model update and management pipeline framework for AIOps models in microservices systems in order to accomplish the aforementioned objectives and simplify the process. In addition, a prototype system based on Kubernetes and Gitlab is designed to provide preliminary framework implementation and validation. The system consists of three components: model training, model packaging, and model deploying. Parallelization and parameter search are incorporated into the model training procedure in order to facilitate rapid training of multiple models and automated model hyperparameter tuning. We automate the packaging and deployment process using technology for continuous integration. Experiments are conducted to validate the prototype system, and the results demonstrate the feasibility of the proposed framework. This work serves as a useful resource for constructing an integrated and streamlined AIOps model management system.
Recently, deep neural network-based cognitive models such as deep knowledge tracing have been introduced into the field of learning analytics and educational data mining. Despite an accurate ...predictive performance of such models, it is challenging to interpret their behaviors and obtain an intuitive insight into latent student learning status. To address these challenges, this paper proposes a new learner modeling framework named the EAKT, which embeds a structured cognitive model into a transformer. In this way, the EAKT not only can achieve an excellent prediction result of learning outcome but also can depict students' knowledge state on a multi-dimensional knowledge component(KC) level. By performing the fine-grained analysis of the student learning process, the proposed framework provides better explanatory learner models for designing and implementing intelligent tutoring systems. The proposed EAKT is verified by experiments. The performance experiments show that the EAKT can better predict the future performance of student learning(more than 2.6% higher than the baseline method on two of three real-world datasets). The interpretability experiments demonstrate that the student knowledge state obtained by EAKT is closer to ground truth than other models, which means EAKT can more accurately trace changes in the students' knowledge state.
Knowledge tracing models based on deep learning can achieve impressive predictive performance by leveraging attention mechanisms. However, there still exist two challenges in attentive knowledge ...tracing (AKT): First, the mechanism of classical models of AKT demonstrates relatively low attention when processing exercise sequences with shifting knowledge concepts (KC), making it difficult to capture the comprehensive state of knowledge across sequences. Second, classical models do not consider stochastic behaviors, which negatively affects models of AKT in terms of capturing anomalous knowledge states. This article proposes a model of AKT, called Enhancing Locality for Attentive Knowledge Tracing (ELAKT), that is a variant of the deep KT model. The proposed model leverages the encoder module of the transformer to aggregate knowledge embedding generated by both exercises and responses over all timesteps. In addition, it uses causal convolutions to aggregate and smooth the states of local knowledge. The ELAKT model uses the states of comprehensive KCs to introduce a prediction correction module to forecast the future responses of students to deal with noise caused by stochastic behaviors. The results of experiments demonstrated that the ELAKT model consistently outperforms state-of-the-art baseline KT models.
Learning to handle exceptions Zhang, Jian; Wang, Xu; Zhang, Hongyu ...
2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE),
12/2020
Conference Proceeding
Exception handling is an important built-in feature of many modern programming languages such as Java. It allows developers to deal with abnormal or unexpected conditions that may occur at runtime in ...advance by using try-catch blocks. Missing or improper implementation of exception handling can cause catastrophic consequences such as system crash. However, previous studies reveal that developers are unwilling or feel it hard to adopt exception handling mechanism, and tend to ignore it until a system failure forces them to do so. To help developers with exception handling, existing work produces recommendations such as code examples and exception types, which still requires developers to localize the try blocks and modify the catch block code to fit the context. In this paper, we propose a novel neural approach to automated exception handling, which can predict locations of try blocks and automatically generate the complete catch blocks. We collect a large number of Java methods from GitHub and conduct experiments to evaluate our approach. The evaluation results, including quantitative measurement and human evaluation, show that our approach is highly effective and outperforms all baselines. Our work makes one step further towards automated exception handling.
Performance auditing plays a key role in improving government performance and public accountability. This paper examines the concept of government performance auditing in the U.S. and China from a ...comparative perspective. The paper begins with a brief introduction of the origin of auditing and performance auditing. It then discusses the variances in definitions, names, and underlying values of performance auditing; describes the authorities and organizational structures of performance auditing in the two countries; and reviews the roles of performance auditing in improving government. It concludes with a discussion of challenges as well as opportunities that face government performance auditing in the U.S. and in China.
Tumor recurrence and metastasis are pressing issues of patients with colorectal cancer who receive surgery. Matrilysin-2 (MMP-26) has been proved to play an important role during invasion and ...metastasis of some human solid tumor. We aimed to investigate the clinical significance and prognostic value of matrilysin-2 in human colorectal cancer. Colorectal cancer and adjacent normal samples from 201 patients were collected. Matrilysin-2 expression level was investigated by immunohistochemistry assay, and its association with overall survival of patients was analyzed by statistical analysis. Results showed that matrilysin-2 expression level significantly elevated in colorectal cancer compared with adjacent normal specimens. Matrilysin-2 expression was also found to be associated with cancer invasion, lymph node metastasis, distant metastasis, and TNM stage. In addition, survival analysis showed that elevated matrilysin-2 expression was associated with poor overall survival of patients. Cox's proportional hazards model indicated that matrilysin-2 was an independent prognostic marker for patients with colorectal cancer. The present study found that the expression of matrilysin-2 increased in colorectal cancer and was associated with tumor progression. It also provided the first evidence that matrilysin-2 expression was an independent prognostic factor for patients with colorectal cancer, which might be a high specific biomarker for colorectal cancer.
To evaluate the left ventricular (LV) radial and longitudinal systolic function in hypertrophic cardiomyopathy (HCM) patients by 3.0 T MR.
Sixteen HCM (HCM group) and twenty normal adults (normal ...group) were examined with fast imaging employing steady-state (FIESTA) acquisition sequence of cardiac MRI. LV ejection fraction (LVEF), longitudinal shortening (LS) and fractional shortening (FS) in three standard levels were measured to analyze LV radial and longitudinal systolic function.
Asymmetric hypertrophy was detected in all HCM patients. The LVEF and FS were significantly higher while LS was significantly lower in HCM group than those in normal group (P < 0.05 or 0.01). FS at basal and middle levels were significantly higher in HCM group than in normal group (both P < 0.01). FS in apex level was similar in the two groups (P = 0.057). Pearson correlation analysis showed that LS was negatively related with the number of hypertrophy segments in HCM patients (r = -0.537, P = 0.032). But there was no correlation
To establish cardiac magnetic resonance imaging(MRI) derived left ventricular (LV) global and region function parameters in normal adults.
Twenty normal adults were examined with fast imaging ...employing steady-state(Fiesta) acquisition sequence of cardiac MRI, LV global function and LV region function were measured at basal, middle, apical level and at 16 LV segments. The regional function parameters among different levels and different segments of the same level were analyzed.
(1)LV global function: end-diastolic volume (109.17 ± 19.52) ml; end-systolic volume (37.76 ± 14.16) ml;ejection fraction (65.93 ± 7.79) %; wall thickening (83.24 ± 40.82) %; longitudinal shortening (15.51 ± 3.78) %; fractional shortening (31.78 ± 9.55) %;end-diastolic mass (95.20 ± 19.95) g. (2)LV regional function: In each LV level, there was no significant difference in end-systolic wall thickness (P > 0.05). End-diastolic wall thickness and wall thickening were similar between the middle and apical levels, but there were significant
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