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

An Improved Robust Fractal Image Compression Based on M-Estimator

In this paper, a robust fractal image compression method based on M-estimator is presented. The proposed method applies the M-estimator to the parameter estimation in the fractal encoding procedure using Huber and Tukey’s robust statistics. The M-estimation reduces the influence of the outliers and makes the fractal encoding algorithm robust to the noisy image. Meanwhile, the quadtree partitioning approach has been used in the proposed methods to improve the efficiency of the encoding algorithm, and some unnecessary computations are eliminated in the parameter estimation procedures. The experimental results demonstrate that the proposed method is insensitive to the outliers in the noisy corrupted image. The comparative data shows that the proposed method is superior in both the encoding time and the quality of retrieved images over other robust fractal compression algorithms. The proposed algorithm is useful for multimedia and image archiving, low-cost consumption applications and progressive image transmission of live images, and in reducing computing time for fractal image compression

An Improved Robust Fractal Image Compression Based on M-Estimator

In this paper, a robust fractal image compression method based on M-estimator is presented. The proposed method applies the M-estimator to the parameter estimation in the fractal encoding procedure using Huber and Tukey’s robust statistics. The M-estimation reduces the influence of the outliers and makes the fractal encoding algorithm robust to the noisy image. Meanwhile, the quadtree partitioning approach has been used in the proposed methods to improve the efficiency of the encoding algorithm, and some unnecessary computations are eliminated in the parameter estimation procedures. The experimental results demonstrate that the proposed method is insensitive to the outliers in the noisy corrupted image. The comparative data shows that the proposed method is superior in both the encoding time and the quality of retrieved images over other robust fractal compression algorithms. The proposed algorithm is useful for multimedia and image archiving, low-cost consumption applications and progressive image transmission of live images, and in reducing computing time for fractal image compression

A Novel Color Image Encryption Algorithm Using Coupled Map Lattice with Polymorphic Mapping

Some typical security algorithms such as SHA, MD4, MD5, etc. have been cracked in recent years. However, these algorithms have some shortcomings. Therefore, the traditional one-dimensional-mapping coupled lattice is improved by using the idea of polymorphism in this paper, and a polymorphic mapping–coupled map lattice with information entropy is developed for encrypting color images. Firstly, we extend a diffusion matrix with the original 4 × 4 matrix into an n × n matrix. Then, the Huffman idea is employed to propose a new pixel-level substitution method, which is applied to replace the grey degree value. We employ the idea of polymorphism and select f(x) in the spatiotemporal chaotic system. The pseudo-random sequence is more diversified and the sequence is homogenized. Finally, three plaintext color images of 256×256×3, “Lena”, “Peppers” and “Mandrill”, are selected in order to prove the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm has a large key space, better sensitivity to keys and plaintext images, and a better encryption effect

Synthesis and Characterization of Rh/B–TNTs as a Recyclable Catalyst for Hydroformylation of Olefin Containing –CN Functional Group

The TiO2-based nanotubes (TNTs, B–TNTs) of different surface acidities and their supported Rh catalysts were designed and synthesized. The catalysts were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectrometer (XPS), tempera–ture–programmed desorption of ammonia (NH3–TPD), atomic emission spectrometer (ICP), and Brunauer–Emmett–Tellerv (BET) surface-area analyzers. Images of SEM and TEM showed that the boron-decorated TiO2 nanotubes (B–TNTs) had a perfect multiwalled tubular structure; their length was up to hundreds of nanometers and inner diameter was about 7 nm. The results of NH3-TPD analyses showed that B–TNTs had a stronger acid site compared with TNTs. For Rh/TNTs and Rh/B–TNTs, Rh nanoparticles highly dispersed on B–TNTs were about 2.79 nm in average diameter and much smaller than those on TNTs, which were about 4.94 nm. The catalytic performances of catalysts for the hydroformylation of 2-methyl-3-butennitrile (2M3BN) were also evaluated, and results showed that the existence of B in Rh/B–TNTs had a great influence on the catalytic performance of the catalysts. The Rh/B–TNTs displayed higher catalytic activity, selectivity for aldehydes, and stability than the Rh/TNTs

Alkali and Alkaline Earth Cation-Decorated TiO₂ Nanotube-Supported Rh Catalysts for Vinyl Acetate Hydroformylation

Alkali and alkaline earth cation-decorated TiO2 nanotube (TNT)-supported rhodium catalysts were synthesized and characterized by inductively-coupled plasma optical emission spectrometer, surface characterization analyzer, X-ray diffraction, transmission electron microscopy, X-ray photoelectron spectroscopy, and Fourier transforming infrared spectrum, respectively. Their catalytic performances were evaluated by the hydroformylation of vinyl acetate. Results showed that both the conversion rate of vinyl acetate and selectivity for aldehyde were improved after Rh/TNTs were modified by alkali or alkali-earth cations. Such improved selectivity for aldehyde might be attributed to the presence of alkali or alkaline earth cations which enhanced CO adsorption, while the high conversion rate of vinyl acetate was likely due to the proper interaction of Lewis acid⁻base between cations modified TNTs and vinyl acetate

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. © 2020 Copyright held by the owner/author(s)

Social lens: searching and browsing communities by content and interaction

Community analysis is an important task in graph mining. Most of the existing community studies are community detection, which aim to find the community membership for each user based on the user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited applications. This motivates us to consider systematically profiling the communities and thereby developing useful community-level applications. In this paper, we introduce a novel concept of community profiling, upon which we build a SocialLens system1 to enable searching and browsing communities by content and interaction. We deploy SocialLens on two social graphs: Twitter and DBLP. We demonstrate two useful applications of SocialLens, including interactive community visualization and profile-aware community ranking

Titanate Nanotube-Supported Au–Rh Bimetallic Catalysts: Characterization and Their Catalytic Performances in Hydroformylation of Vinyl Acetate

A series of titanate nanotube-supported metal catalysts (M/TNTs, M = Rh, Au orAu–Rh) were facilely synthesized. The effects of different Au contents, reduction processes and sequence of loading metals on their catalytic performances in the hydroformylation of vinyl acetate were comparatively investigated. The results showed that some Au and Rh formed bimetallic particles. Furthermore, the presence of Au in catalysts could significantly improve the selectivity of reaction for aldehydes. Compared with the monometallic catalysts (Rh0.33/TNTs-1 and Au0.49/TNTs-2), the resultant bimetallic catalysts exhibited significantly higher selectivity for aldehydes as well as higher TOF values in the hydroformylation of vinyl acetate. Among them, Au0.52/Rh0.32/TNTs-12 displayed the best catalytic performance. The corresponding selectivity for aldehydes was as high as 88.67%and the turnover frequency (TOF) reached up to 3500 h−1. In addition, for the reduction of Rh3+ and Au3+ ions, the photo-reduction and ethanol-reduction were the optimal techniques under the present conditions, respectively

Preparation and Characterization of Rh/MgSNTs Catalyst for Hydroformylation of Vinyl Acetate: The Rh⁰ was Obtained by Calcination

A simple and practical Rh-catalyzed hydroformylation of vinyl acetate has been synthesized via impregnation-calcination method using silicate nanotubes (MgSNTs) as the supporter. The Rh0 (zero valent state of rhodium) was obtained by calcination. The influence of calcination temperature on catalytic performance of the catalysts was investigated in detail. The catalysts were characterized in detail by X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectrometer (XPS), atomic emission spectrometer (ICP), and Brunauer⁻Emmett⁻Teller (BET) surface-area analyzers. The Rh/MgSNTs(a2) catalyst shows excellent catalytic activity, selectivity and superior cyclicity. The catalyst could be easily recovered by phase separation and was used up to four times