Hard Label Black Box Node Injection Attack on Graph Neural Networks

While graph neural networks have achieved state-of-the-art performances in many real-world tasks including graph classification and node classification, recent works have demonstrated they are also extremely vulnerable to adversarial attacks. Most previous works have focused on attacking node classification networks under impractical white-box scenarios. In this work, we will propose a non-targeted Hard Label Black Box Node Injection Attack on Graph Neural Networks, which to the best of our knowledge, is the first of its kind. Under this setting, more real world tasks can be studied because our attack assumes no prior knowledge about (1): the model architecture of the GNN we are attacking; (2): the model's gradients; (3): the output logits of the target GNN model. Our attack is based on an existing edge perturbation attack, from which we restrict the optimization process to formulate a node injection attack. In the work, we will evaluate the performance of the attack using three datasets, COIL-DEL, IMDB-BINARY, and NCI1

ProcData: An R Package for Process Data Analysis

Process data refer to data recorded in the log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents' response processes of solving the items. Process data analysis aims at enhancing educational assessment accuracy and serving other assessment purposes by utilizing the rich information contained in response processes. The R package ProcData presented in this article is designed to provide tools for processing, describing, and analyzing process data. We define an S3 class "proc" for organizing process data and extend generic methods summary and print for class "proc". Two feature extraction methods for process data are implemented in the package for compressing information in the irregular response processes into regular numeric vectors. ProcData also provides functions for fitting and making predictions from a neural-network-based sequence model. These functions call relevant functions in package keras for constructing and training neural networks. In addition, several response process generators and a real dataset of response processes of the climate control item in the 2012 Programme for International Student Assessment are included in the package

Contextualizing legal norms: a multi-dimensional view of the 2014 legal capital reform in China

This paper intends to shed light on the contentious theme of the reception of legal transplantation in the host environment, by examining the 2014 legislative reform of legal capital in China, which at least on paper imitates the enabling settings of US Revised Model Business Corporation Act (RMBCA). The paper looks at the interconnections between national-specific contextual elements, the resultant complexities, and the spillover effects of transplanted configurations in the unique Chinese socio-cultural setting, implicating the discrepancy between the ‘law in practice’ and the borrowed words ‘on the books’, and suggesting the importance of gaining a holistic understanding of ‘law’ involving the legal traditions in both the donor country and the recipient nation

Subtask analysis of process data through a predictive model

Response process data collected from human–computer interactive items contain detailed information about respondents' behavioural patterns and cognitive processes. Such data are valuable sources for analysing respondents' problem-solving strategies. However, the irregular data format and the complex structure make standard statistical tools difficult to apply. This article develops a computationally efficient method for exploratory analysis of such process data. The new approach segments a lengthy individual process into a sequence of short subprocesses to achieve complexity reduction, easy clustering and meaningful interpretation. Each subprocess is considered a subtask. The segmentation is based on sequential action predictability using a parsimonious predictive model combined with the Shannon entropy. Simulation studies are conducted to assess the performance of the new method. We use a case study of PIAAC 2012 to demonstrate how exploratory analysis for process data can be carried out with the new approach.National Science Foundation of Sri Lanka12 month embargo; first published: 01 November 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

An exploratory analysis of the latent structure of process data via action sequence autoencoders

Computer simulations have become a popular tool for assessing complex skills such as problem-solving. Log files of computer-based items record the human-computer interactive processes for each respondent in full. The response processes are very diverse, noisy, and of non-standard formats. Few generic methods have been developed to exploit the information contained in process data. In this paper we propose a method to extract latent variables from process data. The method utilizes a sequence-to-sequence autoencoder to compress response processes into standard numerical vectors. It does not require prior knowledge of the specific items and human-computer interaction patterns. The proposed method is applied to both simulated and real process data to demonstrate that the resulting latent variables extract useful information from the response processes.12 month embargo; published online: 22 May 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

High mobility Ge pMOSFETs with amorphous Si passivation: impact of surface orientation

Abstract We report the amorphous Si passivation of Ge pMOSFETs fabricated on (001)-, (011)-, and (111)-orientated surfaces for advanced CMOS and thin film transistor applications. Amorphous Si passivation of Ge is carried out by magnetron sputtering at room temperature. With the fixed thickness of Si t Si, (001)-oriented Ge pMOSFETs achieve the higher on-state current I ON and effective hole mobility μ eff compared to the devices on other orientations. At an inversion charge density Q inv of 3.5 × 1012 cm−2, Ge(001) transistors with 0.9 nm t Si demonstrate a peak μ eff of 278 cm2/V × s, which is 2.97 times higher than the Si universal mobility. With the decreasing of t Si, I ON of Ge transistors increases due to the reduction of capacitive effective thickness, but subthreshold swing and leakage floor characteristics are degraded attributed to the increasing of midgap D it