R2DE: a NLP approach to estimating IRT parameters of newly generated questions

The main objective of exams consists in performing an assessment of students' expertise on a specific subject. Such expertise, also referred to as skill or knowledge level, can then be leveraged in different ways (e.g., to assign a grade to the students, to understand whether a student might need some support, etc.). Similarly, the questions appearing in the exams have to be assessed in some way before being used to evaluate students. Standard approaches to questions' assessment are either subjective (e.g., assessment by human experts) or introduce a long delay in the process of question generation (e.g., pretesting with real students). In this work we introduce R2DE (which is a Regressor for Difficulty and Discrimination Estimation), a model capable of assessing newly generated multiple-choice questions by looking at the text of the question and the text of the possible choices. In particular, it can estimate the difficulty and the discrimination of each question, as they are defined in Item Response Theory. We also present the results of extensive experiments we carried out on a real world large scale dataset coming from an e-learning platform, showing that our model can be used to perform an initial assessment of newly created questions and ease some of the problems that arise in question generation

Transcribing Content from Structural Images with Spotlight Mechanism

Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal structure should also be preserved. Existing image recognition methods mainly work on images with simple content (e.g., text lines with characters), but are not capable to identify ones with more complex content (e.g., structured symbols), which often follow a fine-grained grammar. To this end, in this paper, we propose a hierarchical Spotlight Transcribing Network (STN) framework followed by a two-stage "where-to-what" solution. Specifically, we first decide "where-to-look" through a novel spotlight mechanism to focus on different areas of the original image following its structure. Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly. Moreover, we propose two implementations on the basis of STN, i.e., STNM and STNR, where the spotlight movement follows the Markov property and Recurrent modeling, respectively. We also design a reinforcement method to refine the framework by self-improving the spotlight mechanism. We conduct extensive experiments on many structural image datasets, where the results clearly demonstrate the effectiveness of STN framework.Comment: Accepted by KDD2018 Research Track. In proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18

Low Temperature Oxidation Experiments and Kinetics Model of Heavy Oil

Air injection is an effective technique for improved oil recovery. For a typical heavy oil sample, the effects of temperature on the oxidation characteristics were studied by low temperature oxidation (LTO) experiments. Kinetic parameters such as activation energy, frequency factor (pre-exponential factor) and reaction order are determined by using Arrhenius Equation. These parameters provide a theoretical basis for numerical simulation of LTO taking place during air injection in heavy oil reservoirs. The results of LTO experiments show that heavy oil has good low temperature oxidation properties and LTO reaction rate is mainly related to temperature, oxygen partial pressure and properties of crude oil. In the experimental temperature range, the oxidation reaction can effectively consume oxygen and at the same time produce large amount of CO2.Key words: Air injection; Low temperature oxidation; Kinetics model (70-150 oC

Improving Simulations of the Upper Ocean by Inclusion of Surface Waves in the Mellor-Yamada Turbulence Scheme

The Mellor-Yamada turbulence closure scheme, used in many ocean circulation models, is often blamed for overly high simulated surface temperature and overly low simulated subsurface temperature in summer due to insufficient vertical mixing. Surface waves can enhance turbulence kinetic energy and mixing of the upper ocean via wave breaking and nonbreaking-wave-turbulence interaction. The influences of wave breaking and wave-turbulence interaction on the Mellor-Yamada scheme and upper ocean thermal structure are examined and compared with each other using one-dimensional and three-dimensional ocean circulation models. Model results show that the wave-turbulence interaction can effectively amend the problem of insufficient mixing in the classic Mellor-Yamada scheme. The behaviors of the Mellor-Yamada scheme, as well as the simulated upper ocean thermal structure, are significantly improved by adding a turbulence kinetic energy production term associated with wave-turbulence interaction. In contrast, mixing associated with wave breaking alone seems insufficient to improve significantly the simulations as its effect is limited to the very near-surface layers. Therefore, the effects of wave-turbulence interaction on the upper ocean are much more important than those of wave breaking

Effects Of Formation Stress On Logging Measurements

We show both theoretically and experimentally how stress concentrations affect the velocity field around a borehole surrounded by a formation with intrinsic ortohombic anisotropy. When F[subscript x] = F[subscript y], no extra anisotropy is induced, however, isotropic stress concentrations are developed in the neighborhood of the borehole. Extra anisotropy is induced only when F[subscript x] ≠ F[subscript y], and the level of induced anisotropy is affected by the intrinsic anisotropy of the formation. Experiments show that monopole acoustic waves are more sensitive to properties in the neighborbood of the borehole than dipole waves. However, only dipole logging can determine the direction of anisotropy. A combination of monopole and dipole logging may lead to a better investigation of intrinsic as well as induced anisotropy of the formation.Massachusetts Institute of Technology. Borehole Acoustics and Logging ConsortiumMassachusetts Institute of Technology. Earth Resources Laboratory. Reservoir Delineation Consortiu

Identifiable Cognitive Diagnosis with Encoder-decoder for Modelling Students' Performance

Cognitive diagnosis aims to diagnose students' knowledge proficiencies based on their response scores on exam questions, which is the basis of many domains such as computerized adaptive testing. Existing cognitive diagnosis models (CDMs) follow a proficiency-response paradigm, which views diagnostic results as learnable embeddings that are the cause of students' responses and learns the diagnostic results through optimization. However, such a paradigm can easily lead to unidentifiable diagnostic results and the explainability overfitting problem, which is harmful to the quantification of students' learning performance. To address these problems, we propose a novel identifiable cognitive diagnosis framework. Specifically, we first propose a flexible diagnostic module which directly diagnose identifiable and explainable examinee traits and question features from response logs. Next, we leverage a general predictive module to reconstruct response logs from the diagnostic results to ensure the preciseness of the latter. We furthermore propose an implementation of the framework, i.e., ID-CDM, to demonstrate the availability of the former. Finally, we demonstrate the identifiability, explainability and preciseness of diagnostic results of ID-CDM through experiments on four public real-world datasets

Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective

Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functional theory (DFT) in physics are proved to be time-consuming for predicting large number of molecules. Recently, the machine learning methods, which consider much rule-based information, have also shown potentials for this issue. However, the complex inherent quantum interactions of molecules are still largely underexplored by existing solutions. In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction. Specifically, we represent each molecule as a graph to preserve its internal structure. Moreover, the well-designed hierarchical graph neural network directly extracts features from the conformation and spatial information followed by the multilevel interactions. As a consequence, the multilevel overall representations can be utilized to make the prediction. Extensive experiments on both datasets of equilibrium and off-equilibrium molecules demonstrate the effectiveness of our model. Furthermore, the detailed results also prove that MGCN is generalizable and transferable for the prediction.Comment: The 33rd AAAI Conference on Artificial Intelligence (AAAI'2019), Honolulu, USA, 201

Neural Cognitive Diagnosis for Intelligent Education Systems

Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts. Existing approaches usually mine linear interactions of student exercising process by manual-designed function (e.g., logistic function), which is not sufficient for capturing complex relations between students and exercises. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex exercising interactions, for getting both accurate and interpretable diagnosis results. Specifically, we project students and exercises to factor vectors and leverage multi neural layers for modeling their interactions, where the monotonicity assumption is applied to ensure the interpretability of both factors. Furthermore, we propose two implementations of NeuralCD by specializing the required concepts of each exercise, i.e., the NeuralCDM with traditional Q-matrix and the improved NeuralCDM+ exploring the rich text content. Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability

Model Inversion Attacks against Graph Neural Networks

Many data mining tasks rely on graphs to model relational structures among individuals (nodes). Since relational data are often sensitive, there is an urgent need to evaluate the privacy risks in graph data. One famous privacy attack against data analysis models is the model inversion attack, which aims to infer sensitive data in the training dataset and leads to great privacy concerns. Despite its success in grid-like domains, directly applying model inversion attacks on non-grid domains such as graph leads to poor attack performance. This is mainly due to the failure to consider the unique properties of graphs. To bridge this gap, we conduct a systematic study on model inversion attacks against Graph Neural Networks (GNNs), one of the state-of-the-art graph analysis tools in this paper. Firstly, in the white-box setting where the attacker has full access to the target GNN model, we present GraphMI to infer the private training graph data. Specifically, in GraphMI, a projected gradient module is proposed to tackle the discreteness of graph edges and preserve the sparsity and smoothness of graph features; a graph auto-encoder module is used to efficiently exploit graph topology, node attributes, and target model parameters for edge inference; a random sampling module can finally sample discrete edges. Furthermore, in the hard-label black-box setting where the attacker can only query the GNN API and receive the classification results, we propose two methods based on gradient estimation and reinforcement learning (RL-GraphMI). Our experimental results show that such defenses are not sufficiently effective and call for more advanced defenses against privacy attacks.Comment: Accepted by TKDE. arXiv admin note: substantial text overlap with arXiv:2106.0282