Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effectiveness of Explicit Corrective Feedback in the Second Language Classroom

Despite an increasing push for standardisation in schools, where the focus is increasingly turning to testing, language classrooms in some locations around the world (e.g., North America and Japan) are moving away from grammar-emphasized teaching filled with drills, for an approach that is more communicative (see Lee & VanPatten, 2003; Omaggio Hadley, 2000; Richards, 2006). Research suggests that exposure to native-like language is not enough for learners to achieve acquisition (White, 1991). Student, instead, require opportunities to produce language, complete with errors. For language teachers, the question is how to respond to students’ errors in the classroom, in particular to consider whether error correction is effective. Although many studies have examined this problem, the results are mixed. In order to sort out these mixed results, this study presents the results of a meta-analysis on the effects of explicit oral corrective feedback in the second language classroom. The results reveal a small effect on the ability of explicit feedback to promote language learning, although whether there is a long-term effect that leads to acquisition is not yet confirmed.
Vision Servoing Based on the Density Analysis of Texture Elements

We present a novel vision servoing method, which is fit for climbing robots and those in unstructured environments, based on texture analysis. A large textured area is the target for observation. After the definition of texture element distribution density, the relationship between the change of the density at some selected points and the camera pose is deduced. The vision servoing control law, which meets the requirement of Lyapunov stability is designed in this paper. Experiments show the effectiveness of this method.
LibID: reliable identification of obfuscated third-party Android libraries

Third-party libraries are vital components of Android apps, yet they can also introduce serious security threats and impede the accuracy and reliability of app analysis tasks, such as app clone detection. Several library detection approaches have been proposed to address these problems. However, we show these techniques are not robust against popular code obfuscators, such as ProGuard, which is now used in nearly half of all apps. We then present LibID, a library detection tool that is more resilient to code shrinking and package modification than state-of-the-art tools. We show that the library identification problem can be formulated using binary integer programming models. LibID is able to identify specific versions of third-party libraries in candidate apps through static analysis of app binaries coupled with a database of third-party libraries. We propose a novel approach to generate synthetic apps to tune the detection thresholds. Then, we use F-Droid apps as the ground truth to evaluate LibID under different obfuscation settings, which shows that LibID is more robust to code obfuscators than state-of-the-art tools. Finally, we demonstrate the utility of LibID by detecting the use of a vulnerable version of the OkHttp library in nearly 10% of 3,958 most popular apps on the Google Play Store.
Optimizing Assignment of Students to Courses based on Learning Activity Analytics

In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by diﬀerent teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by diﬀerent teachers. Therefore, mismatches often occur between students’ learning behavior patterns and teachers’ approach to teaching. As a result, students may be less satisﬁed, have a lower level of understanding of the material, and achieve less. To solve these problems, we propose a strategy to optimize the assignment of students to courses based on learning activity analytics. The contributions of this study are 1) clarifying the relationship between learning behavior pattern and teaching based on learning activity analytics using large-scale educational data, 2) optimizing the assignment of students to courses based on learning behavior pattern analytics, and 3) demonstrating the eﬀectiveness of assignment optimization via simulation experiments.
Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective

Replication of machine learning experiments can be a useful tool to evaluate how both modeling and experimental design contribute to experimental results; however, existing replication eﬀorts focus almost entirely on modeling alone. In this work, we conduct a three-part replication case study of a state-of-the-art LSTM dropout prediction model. In our ﬁrst experiment, we replicate the original authors’ methodology as precisely as possible in collaboration with the original authors. In a second experiment, we demonstrate that this initial experiment likely overestimates the generalization performance of the proposed model due to the design of its validation. In a third experiment, we attempt to achieve the previously-reported performance in a more dif-ﬁcult, but more relevant, hold-out set design by exploring a large space of model regularization conﬁgurations. We demonstrate that we can reduce overﬁtting and improve generalization performance of the model, but cannot achieve the previously-reported level of performance. This work demonstrates the importance of replication of predictive modeling experiments in education, and demonstrates how experimental design and modeling decisions can impact the extent to which model performance generalizes beyond the initial training data.
Adlib: analyzer for mobile ad platform libraries

Mobile advertising has become a popular advertising approach by taking advantage of various information from mobile devices and rich interaction with users. Mobile advertising platforms show advertisements of nearby restaurants to users using the geographic locations of their mobile devices, and also allow users to make reservations easily using their phone numbers. However, at the same time, they may open the doors for advertisements to steal device information or to perform malicious behaviors. When application developers integrate mobile advertising platform SDKs (AdSDKs) to their applications, they are informed of only the permissions required by the AdSDKs, and they may not be aware of the rich functionalities of the SDKs that are available to advertisements. In this paper, we first report that various AdSDKs provide powerful functionalities to advertisements, which are seriously vulnerable to security threats. We present representative malicious behaviors by advertisements using APIs provided by AdSDKs. To mitigate the security vulnerability, we develop a static analyzer, Adlib, which analyzes Android Java libraries that use hybrid features to enable communication with JavaScript code and detects possible flows from the APIs that are accessible from third-party advertisements to device-specific features like geographic locations. Our evaluation shows that Adlib found genuine security vulnerabilities from real-world AdSDKs.
Measuring Item Teaching Value in an Online Learning Environment

The Additive Factor Model (AFM) is a cognitive diagnostic model that can be used to predict student performance on items in a context that allows for student learning. Within AFM, skills have a learning rate, and student acquisition of a skill depends only on the number of opportunities a student has had to exercise that skill and the learning rate of that skill. Here we demonstrate an approach to measure the teaching value of individual items with respect to one another. The teaching values estimated through this approach may be useful for structuring intelligent tutoring systems and for content improvement.
Investigation the BER Characteristics of Hexagonal Multicarrier Modulation in the Presence of System Impairments

—Hexagonal Multicarrier Modulation (HMM) is based on arranging the information data in a hexagonal manner in the time-frequency plane to mitigate interference in a doubly dispersive wireless channel. This paper investigates the bit error rate characteristics of this modulation scheme theoretically in the presence of timing offset and carrier frequency offset. The simulation results show clearly that the HMM system can tolerate more offset compared with a conventional OFDM counterpart.
Internet surfing for kindergarten children

The Internet is an effective learning tool for gifted children because it allows them to independently select the areas in which they have talent. The Internet also enables children to discover and maximize their potential. However, younger children might not have a large enough vocabulary to surf the Internet, even if they are gifted. For example, children who are creatively gifted might not have exceptional reading ability. To solve this problem, a special web browser was used to generate human speech according to the words that appeared on the web pages displayed. Experiments involving about 100 kindergarteners were conducted to assess the effectiveness of our approach. This paper demonstrates the feasibility of this web browser in enabling kindergarten children aged 3–6 years old to surf the Internet.
Probabilistic Modeling of Peer Correction and Peer Assessment

Peer assessment is a promising solution for scaling up the grading of a large number of submissions. The reliability of evaluations is one of the critical issues in peer assessment; several probabilistic models have been proposed for obtaining reliable grades from peers. Peer correction is a similar framework, in which students are instructed to correct the errors in submissions from other students. Peer correction is typically performed simultaneously with peer assessment; a reviewer is instructed to correct the errors in a submission and to provide a grade to it. We observe the occasional inconsistency between a grade and the correction; for example, a reviewer provides a high grade for a submission but she corrects many errors in it. Such inconsistencies can point to unreliable reviewers. In this paper, we propose probabilistic models for peer correction, and the combination of the peer correction models and the existing peer assessment models for capturing the inconsistency to accurately estimate the reviewer reliability and the student ability. We conduct experiments using the dataset of an actual peer correction platform for language translation, and the results demonstrate that the combination of peer correction models and peer assessment models improves the accuracy of the student ability estimation.
Failure clustering without coverage

Developing and integrating software in the automotive industry is a complex task and requires extensive testing. An important cost factor in testing and debugging is the time required to analyze failing tests. In the context of regression testing, usually, large numbers of tests fail due to a few underlying faults. Clustering failing tests with respect to their underlying faults can, therefore, help in reducing the required analysis time. In this paper, we propose a clustering technique to group failing hardware-in-the-loop tests based on non-code-based features, retrieved from three different sources. To effectively reduce the analysis effort, the clustering tool selects a representative test for each cluster. Instead of analyzing all failing tests, testers only inspect the representative tests to find the underlying faults. We evaluated the effectiveness and efficiency of our solution in a major automotive company using 86 regression test runs, 8743 failing tests, and 1531 faults. The results show that utilizing our clustering tool, testers can reduce the analysis time more than 60% and find more than 80% of the faults only by inspecting the representative tests.
Affective State Prediction in a Mobile Setting using Wearable Biometric Sensors and Stylus

The role of aﬀective states in learning has recently attracted considerable attention in education research. The accurate prediction of aﬀective states can help increase the learning gain by incorporating targeted interventions that are capable of adjusting to changes in the individual aﬀective states of students. Until recently, most work on the prediction of aﬀective states has relied on expensive and stationary lab devices that are not well suited for classrooms and every-day use. Here, we present an automated pipeline capable of accurately predicting (AUC up to 0 . 86) the aﬀective states of participants solving tablet-based math tasks using signals from low-cost mobile bio-sensors. In addition, we show that we can achieve a similar classiﬁcation performance (AUC up to 0 . 84) by only using handwriting data recorded from a stylus while students solved the math tasks. Given the emerging digitization of classrooms and increased reliance on tablets as teaching tools, stylus data may be a viable alternative to bio-sensors for the prediction of aﬀective states.
Crash-avoiding program repair

Existing program repair systems modify a buggy program so that the modified program passes given tests. The repaired program may not satisfy even the most basic notion of correctness, namely crash-freedom. In other words, repair tools might generate patches which over-fit the test data driving the repair, and the automatically repaired programs may even introduce crashes or vulnerabilities. We propose an integrated approach for detecting and discarding crashing patches. Our approach fuses test and patch generation into a single process, in which patches are generated with the objective of passing existing tests, and new tests are generated with the objective of filtering out over-fitted patches by distinguishing candidate patches in terms of behavior. We use crash-freedom as the oracle to discard patch candidates which crash on the new tests. In its core, our approach defines a grey-box fuzzing strategy that gives higher priority to new tests that separate patches behaving equivalently on existing tests. This test generation strategy identifies semantic differences between patch candidates, and reduces over-fitting in program repair. We evaluated our approach on real-world vulnerabilities and open-source subjects from the Google OSS-Fuzz infrastructure. We found that our tool Fix2Fit (implementing patch space directed test generation), produces crash-avoiding patches. While we do not give formal guarantees about crash-freedom, cross-validation with fuzzing tools and their sanitizers provides greater confidence about the crash-freedom of our suggested patches.
Using Knowledge Component Modeling to Increase Domain Understanding in a Digital Learning Game

Knowledge components (KCs) deﬁne the underlying skill model of intelligent educational software, and they are critical to understanding and improving the eﬃcacy of learning technology. In this research, we show how learning curve analysis is used to ﬁt a KC model - one that was created after use of the learning technology - which can then be improved by human-centered data science methods. We analyzed data from 417 middle-school students who used a digital learning game to learn decimal numbers and decimal operations. Our initial results showed that problem types (e.g., ordering decimals, adding decimals) capture students’ performance better than underlying decimal misconceptions (e.g., longer decimals are larger). Through a process of KC model reﬁnement and domain knowledge interpretation, we were able to identify the diﬃculties that students faced in learning decimals. Based on this result, we present an instructional redesign proposal for our digital learning game and outline a framework for post-hoc KC modeling in a tutoring system. More generally, the method we used in this work can help guide changes to the type, content and order of problems in educational software.
Search-based test and improvement of machine-learning-based anomaly detection systems

Machine-learning-based anomaly detection systems can be vulnerable to new kinds of deceptions, known as training attacks, which exploit the live learning mechanism of these systems by progressively injecting small portions of abnormal data. The injected data seamlessly swift the learned states to a point where harmful data can pass unnoticed. We focus on the systematic testing of these attacks in the context of intrusion detection systems (IDS). We propose a search-based approach to test IDS by making training attacks. Going a step further, we also propose searching for countermeasures, learning from the successful attacks and thereby increasing the resilience of the tested IDS. We evaluate our approach on a denial-of-service attack detection scenario and a dataset recording the network traffic of a real-world system. Our experiments show that our search-based attack scheme generates successful attacks bypassing the current state-of-the-art defences. We also show that our approach is capable of generating attack patterns for all configuration states of the studied IDS and that it is capable of providing appropriate countermeasures. By co-evolving our attack and defence mechanisms we succeeded at improving the defence of the IDS under test by making it resilient to 49 out of 50 independently generated attacks.
Automating Classification of Nonverbal Cues from Leader Figures

Author(s): Seidel, Claudia | Advisor(s): Zhu, Song-Chun | Abstract: The need for accurate measures to evaluate and study human expression has grown exponentially in recent years, especially with the proliferation of video and image content across the Internet landscape. However, the study of nonverbal communication still often relies on the creation of data by hand, with humans manually labeling video footage or images. This thesis explores automation of the process through computer vision and machine learning, allowing for better speed and precision. The developed automated classification pipeline is run on video footage of the first and third presidential debates between Donald Trump and Hillary Clinton to gauge its accuracy. Results show the automated pipeline is viable as an easily upscaled replacement for human work, able to both accurately reproduce the results of human labeling of the footage and allow for insight into the various nonverbal idiosyncrasies of the speakers.
Discovery of Novel Naphthylphenylketone and Naphthylphenylamine Derivatives as Cell Division Cycle 25B (CDC25B) Phosphatase Inhibitors: Design, Synthesis, Inhibition Mechanism and in Vitro Efficacy against Melanoma Cell Lines.

CDC25 phosphatases play a critical role in the regulation of the cell cycle and thus represent attractive cancer therapeutic targets. We previously discovered the 4-(2-carboxybenzoyl)phthalic acid (NSC28620) as a new CDC25 inhibitor endowed with promising anticancer activity in breast, prostate and leukemia cells. Herein, we report a structure-based optimization of NSC28620, leading to the identification of a series of novel naphthylphenylketone (NPK) and naphthylphenylamine (NPA) derivatives as CDC25B inhibitors. Compounds 7j, 7i, 6e, 7f and 3 showed higher inhibitory activity than the initial lead, with Ki values in the low micromolar range. Kinetic analysis, intrinsic fluorescence studies and induced fit docking simulations provided a mechanistic understanding of the activity of these derivatives. All compounds were tested in the highly aggressive human melanoma cell lines A2058 and A375. Compound 4a potently inhibited cell proliferation and colony formation, causing an increase of the G2/M phase and a reduction of G0/G1 phase of the cell cycle in both cell lines.
Mindfulness training and systemic low-grade inflammation in stressed community adults: Evidence from two randomized controlled trials

Mindfulness interventions have garnered significant attention as a complementary health treatment for many physical and psychological conditions. While some research has shown that mindfulness training can decrease psychological and physiological stress responses, it remains unclear whether mindfulness training impacts inflammation—a predictor of poor health outcomes. In addition, little research has examined the active components of mindfulness that may drive health-related improvements. Here, we provide data from two 3-arm randomized controlled trials that examined the effect of mindfulness training on inflammation in stressed community adults. Specifically, we examined whether training individuals to have an accepting attitude towards present moment experiences is a key emotion regulation skill that can lead to decreases in inflammation. Both studies randomly assigned participants to one of three conditions: mindfulness training that taught both attention monitoring and acceptance skills (Monitor+Accept); mindfulness training teaching monitoring without the acceptance component (Monitor Only); or a control condition. Study 1 employed a novel 2-week smartphone-based intervention and Study 2 employed a standard 8-week Mindfulness-Based Stress Reduction (MBSR) intervention. We hypothesized that Monitor+Accept training would lead to reductions in the inflammatory biomarker C-Reactive Protein (CRP) compared to Monitor Only training and control groups. Contrary to this hypothesis, we found that Monitor+Accept mindfulness training did not lead to reductions in CRP. Exploratory analyses combining study subsamples, however, suggest that both mindfulness interventions may reduce CRP in populations at risk for systemic inflammation—midlife-to-older adults and individuals with high BMI. Overall, the present studies contribute significantly to the question of whether mindfulness interventions can reduce systemic markers of low-grade inflammation.
The serologic investigation and viral isolation of bluetongue virus in Shangri‐La in Southwest China

Abstract Bluetongue is an arthropod‐borne viral disease of ruminants caused by bluetongue virus (BTV). In China, BTV is relatively common in Yunnan Province with the exception of northern regions around Shangri‐La, where the average altitude is approximately 3,450 metres. Recently, the seroprevalence of BTV has been measured in yaks in Shangri‐La; therefore, this study investigated BTV infections in this area. The serological investigation in five villages in Shangri‐La showed that there were sporadic BTV infections in yaks (20 of 507 positive) during 2014 to 2017, while the seroprevalence of BTV at three goat farms in a nearby river valley was 35%–65% in 2017. Subsequently, 20 sentinel goats were kept on two separate farms in the river valley and monitored for seroconversion between May and September of 2017. Five of the sentinel animals were tested positive for antibodies to BTV by C‐ELISA during the study period, and 13 BTV isolates were isolated from ten sentinel animals. All isolates were identified as the same serotype, and the complete nucleotide sequence of one was determined. The genomic sequences showed that the isolated BTV strain belonged to serotype 21 and had approximately 99.8%–100% homology with three Indonesian BTV‐21 strains (D151, RIVS‐66 and RIVS‐60) between their coding sequences (CDSs) except for Seg4 (99.5%). Besides, our data suggested that this BTV‐21 strain might have also infected some local yaks and sheep.