Auto grading tool for introductory programming courses

Using automated grading tools to provide feedback to students is common in Computer Science education. The first step of automated grading is to find defects in the student program. However, finding bugs in code has never been easy. Current automated grading tools do not focus on identifying defects inside student code. Comparing computation results using a fixed set of test cases is still the most common way to determine correctness among current automated grading tools. It takes time and effort to design a good set of test cases that can test the student code thoroughly. In practice, tests used for grading are often insufficient for accurate diagnosis. Meanwhile, automated testing tools have been developing for some time. Even though it still takes some effort to apply automated testing tools to real software development, we believe that automated testing tools are ready for automated feedback generation in the classroom. The reason is that for classroom assignments, the code is relatively simple. A well understood reference implementation provided by the instructor also makes automated testing tools more effective. In this thesis, we present our utilization of industrial automated testing on student assignments in an introductory programming course. We implemented a framework to collect student codes and apply industrial automated testing to their codes. Then we interpret the results obtained from testing in a way that students can understand easily. Furthermore, we use the test results to classify erroneous student codes into different categories. Instructors can use the category information to address the most common conceptual errors efficiently. We deployed our framework on five different introductory C programming assignments here at the University of Illinois at Urbana-Champaign. The results show that the automated feedback generation framework can discover more errors inside student submissions and can provide timely and useful feedback to both instructors and students. A total of 142 missed bugs are found within 446 submissions. More than 50% of students receive their feedback within 3 minutes of submission. By doing grouping on one of the assignments with 91 submissions, two groups of student submissions of 15 and 6 are identified to have the same type of error. The average grading code setup time is estimated to be less than 8 hours for each assignment. We believe that based on the current automated testing tools, an automated feedback framework for the classroom can benefit both students and instructors, thus improving Computer Science education

A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and \#P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.Comment: 45 page

DSCom: A Data-Driven Self-Adaptive Community-Based Framework for Influence Maximization in Social Networks

Influence maximization aims to find a subset of seeds that maximize the influence spread under a given budget. In this paper, we mainly address the data-driven version of this problem, where the diffusion model is not given but needs to be inferred from the history cascades. Several previous works have addressed this topic in a statistical way and provided efficient algorithms with theoretical guarantee. However, in their settings, though the diffusion parameters are inferred, they still need users to preset the diffusion model, which can be an intractable problem in real-world practices. In this paper, we reformulate the problem on the attributed network and leverage the node attributes to estimate the closeness between the connected nodes. Specifically, we propose a machine learning-based framework, named DSCom, to address this problem in a heuristic way. Under this framework, we first infer the users' relationship from the diffusion dataset through attention mechanism and then leverage spectral clustering to overcome the influence overlap problem in the lack of exact diffusion formula. Compared to the previous theoretical works, we carefully designed empirical experiments with parameterized diffusion models based on real-world social networks, which prove the efficiency and effectiveness of our algorithm

Coarse-to-Fine Amodal Segmentation with Shape Prior

Amodal object segmentation is a challenging task that involves segmenting both visible and occluded parts of an object. In this paper, we propose a novel approach, called Coarse-to-Fine Segmentation (C2F-Seg), that addresses this problem by progressively modeling the amodal segmentation. C2F-Seg initially reduces the learning space from the pixel-level image space to the vector-quantized latent space. This enables us to better handle long-range dependencies and learn a coarse-grained amodal segment from visual features and visible segments. However, this latent space lacks detailed information about the object, which makes it difficult to provide a precise segmentation directly. To address this issue, we propose a convolution refine module to inject fine-grained information and provide a more precise amodal object segmentation based on visual features and coarse-predicted segmentation. To help the studies of amodal object segmentation, we create a synthetic amodal dataset, named as MOViD-Amodal (MOViD-A), which can be used for both image and video amodal object segmentation. We extensively evaluate our model on two benchmark datasets: KINS and COCO-A. Our empirical results demonstrate the superiority of C2F-Seg. Moreover, we exhibit the potential of our approach for video amodal object segmentation tasks on FISHBOWL and our proposed MOViD-A. Project page at: http://jianxgao.github.io/C2F-Seg.Comment: Accepted to ICCV 202

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review

Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of cancer-associated deaths. In recent years, significant progress has been achieved in basic and clinical research concerning the epidermal growth factor receptor (EGFR), and the treatment of lung adenocarcinoma has also entered a new era of individualized, targeted therapies. However, the detection of lung adenocarcinoma is usually invasive. 18F-FDG PET/CT can be used as a noninvasive molecular imaging approach, and radiomics can acquire high-throughput data from standard images. These methods play an increasingly prominent role in diagnosing and treating cancers. Herein, we reviewed the progress in applying 18F-FDG PET/CT and radiomics in lung adenocarcinoma clinical research and how these data are analyzed via traditional statistics, machine learning, and deep learning to predict EGFR mutation status, all of which achieved satisfactory results. Traditional statistics extract features effectively, machine learning achieves higher accuracy with complex algorithms, and deep learning obtains significant results through end-to-end methods. Future research should combine these methods to achieve more accurate predictions, providing reliable evidence for the precision treatment of lung adenocarcinoma. At the same time, facing challenges such as data insufficiency and high algorithm complexity, future researchers must continuously explore and optimize to better apply to clinical practice

Study on the construction deformation of a slotted shield in loess tunnels with different buried depths and large sections

Since there is no precedent for the use of slotted shield tunneling in the large section of high-speed railways in China, the relevant technological accumulation and systematic research achievements are few. Therefore, this paper provides theoretical support for loess tunnel construction decision-making through the study of slotted shields and is expected to promote the mechanization and even intelligent construction of a high-speed iron-loess tunnel. Taking the Luochuan tunnel of the Xiyan high-speed railway as the engineering background, this paper uses the numerical simulation software packages of ANSYS and FLAC3D to study the tunnel deformation (surface settlement, vault settlement, tunnel bottom uplift, and horizontal convergence) caused by the slotted shield construction in three different buried depths of 30, 40, and 50 m surrounding rock. The deformation law and mechanical characteristics of a cutter shield construction of large cross-section loess tunnels under the influence of different buried depths are put forward. Results showed that 1) the mutual interference between the working procedures can be significantly reduced by inserting the cutting tool into the soil instead of the advanced tubule before excavation; 2) the settlement in the upper part of the longitudinal axis of the tunnel is the largest; the greater the depth of the tunnel is, the smaller the surface settlement is; and 3) the horizontal deformation of the arch waist and foot of the tunnel under different buried depths is symmetrically distributed into the tunnel during the whole process of slotted shield tunneling

Staged surgical treatment for severe and rigid scoliosis

<p>Abstract</p> <p>Background</p> <p>A retrospective study of staged surgery for severe rigid scoliosis. The purpose of this study was to evaluate the result of staged surgery in treatment of severe rigid scoliosis and to discuss the indications.</p> <p>Methods</p> <p>From 1998 to 2006, 21 cases of severe rigid scoliosis with coronal Cobb angle more than 80° were treated by staged surgeries including anterior release and halo-pelvic traction as first stage surgery and posterior instrumentation and spinal fusion as second stage. Pedicle subtraction osteotomy(PSO) was added in second stage according to spine rigidity. Among the 21 patients, 8 were male and 13 female with an average age of 15.3 years (rang from 4 to 23 years). The mean pre-operative Cobb angle was 110.5° (80°-145°) with a mean spine flexibility of 13%. Radiological parameters at different operative time points were analyzed (mean time of follow-up: 51 months).</p> <p>Results</p> <p>External appearance of all patients improved significantly. The average correction rate was 65.2% (ranging from 39.8% to 79.5%) with mean correction loss of 2.23° at the end of follow-up. No decompensation of trunk has been found. Mean distance between the midline of C7 and midsacral line was 1.19 cm ± 0.51. Two patients had neurological complications: one patient had motor deficit and recovered incompletely.</p> <p>Conclusion</p> <p>Staged operation and halo-pelvic traction offer a safe and effective way in treatment of severe rigid scoliosis. Patients whose Cobb angle was more than 80° and the flexibility of the spine was less than 20% should be treated in this way, and those whose flexibility of the spine was less than 10% and the Cobb angle remained more than 70° after 1st stage anterior release and halo-pelvic traction should undergo pedicle subtraction osteotomy (PSO) in the second surgery.</p

The efficacy and safety of sodium nitroprusside in the treatment of schizophrenia: a meta-analysis

ObjectiveSchizophrenia is a serious mental disease that brings not only serious burdens to patients and their families but also serious challenges to society. More research is needed to find better drugs to treat schizophrenia. This meta-analysis investigated the efficacy and safety of sodium nitroprusside in the treatment of schizophrenia.MethodsRandomized controlled trials comparing the efficacy and safety of sodium nitroprusside in the treatment of schizophrenia were searched via English and Chinese databases. The outcomes, including the Positive and Negative Syndrome Scale (PANSS) and Brief Psychiatric Rating Scale (BPRS), were recorded. RevMan 5.3 was used for the meta-analysis.ResultsA total of six randomized controlled trials (174 patients) were included. The overall quality of the included studies was good. No statistically significant benefit of sodium nitroprusside over placebo was found when combined PANSS total and BPRS-18 (95% CI: −1.40, 0.02). Except for PANSS positive (95% CI: −1.86, −0.01), there was no significant difference in the scale score after sodium nitroprusside treatment compared with the control group in PANSS total (95% CI: −4.93, 0.23), PANSS general (95% CI: −2.53, 1.33), and PANSS negative (95% CI: −4.44, 0.89). The results of the sensitivity analysis excluding the study with clinical heterogeneity showed that sodium nitroprusside had no statistical benefit for the score of PANSS positive (95% CI: −2.19, 0.46). Moreover, there was also no significant difference in the BPRS-18 (95% CI: −3.23, −0.43).ConclusionWe conservatively believe that sodium nitroprusside does not alleviate the symptoms of schizophrenia compared with placebo. The subjects tolerated sodium nitroprusside well. Our findings provide a new idea for researchers to explore and solve the drug treatment of schizophrenia

Identification and validation of signature for prognosis and immune microenvironment in gastric cancer based on m6A demethylase ALKBH5

BackgroundN6-methyladenosine (m6A) RNA regulators play important roles in cancers, but their functions and mechanism have not been demonstrated clearly in gastric cancer (GC).MethodsIn this study, the GC samples with clinical information and RNA transcriptome were downloaded from The Cancer Genome Atlas database. The different expression genes were compared by the absolute value and median ± standard deviation. Samples with complete information were randomly divided into a training dataset and a test dataset. The differential expression genes (DEGs) between ALKBH5-low and ALKBH5-high subgroups were identified in the training dataset and constructed a risk model by Cox and least absolute shrinkage and selection operator regression. The model was testified in test datasets, overall survival (OS) was compared with the Kaplan–Meier method, and immune cell infiltration was calculated by the CIBERSORT algorithm in the low-risk and high-risk subgroups based on the model. The protein levels of ALKBH5 were detected with immunohistochemistry. The relative expression of messenger-ribonucleic acid (mRNA) was detected with quantitative polymerase chain reaction.ResultsALKBH5 was the only regulator whose expression was lower in tumor samples than that in normal samples. The low expression of ALKBH5 led to the poor OS of GC patients and seemed to be an independent protective factor. The model based on ALKBH5-regulated genes was validated in both datasets (training/test) and displayed a potential capacity to predict a clinical prognosis. Gene Ontology analysis implied that the DEGs were involved in the immune response; CIBERSORT results indicated that ALKBH5 and its related genes could alter the immune microenvironment of GC. The protein levels of ALKBH5 were verified as lowly expressed in GC tissues. SLC7A2 and CGB3 were downregulated with ALKBH5 knockdown.ConclusionsIn this study, we found that ALKBH5 might be a suppressor of GC; ALKBH5 and its related genes were latent biomarkers and immunotherapy targets