Biblio

Artificial intelligence creation comes into fashion and has brought unprecedented challenges to intellectual property law. In order to study the viewpoints of AI creation copyright ownership from professionals in different institutions, taking the papers of AI creation on CNKI from 2016 to 2021, we applied orthogonal design and analysis of variance method to construct the dataset. A kernel-SVM classifier with different kernel methods in addition to some shallow machine learning classifiers are selected in analyzing and predicting the copyright ownership of AI creation. Support vector machine (svm) is widely used in statistics and the performance of SVM method is closely related to the choice of the kernel function. SVM with RBF kernel surpasses the other seven kernel-SVM classifiers and five shallow classifier, although the accuracy provided by all of them was not satisfactory. Various performance metrics such as accuracy, F1-score are used to evaluate the performance of KSVM and other classifiers. The purpose of this study is to explore the overall viewpoints of AI creation copyright ownership, investigate the influence of different features on the final copyright ownership and predict the most likely viewpoint in the future. And it will encourage investors, researchers and promote intellectual property protection in China.

This research studies the effect of a countdown timer and a count-up timer on the keystroke pattern of the student and finds out whether changing the timer type changes the keystroke pattern. It also points out which timer affects more students in a timer environment during exams. We used two hypothesis testing statistical Algorithms, namely, the Two-Sample T-Test and One-way ANOVA Test, for analysis to identify the effect of different times our whether significant differences were found in the keystroke pattern or not when different timers were used. The supporting results have been found with determines that timer change can change the keystroke pattern of the student and from the study of hypothesis testing, different students result from different types of stress when they are under different timer environments.

The objective of this study is to explore changes of trust by a situation where drivers need to intervene. Trust in automation is a key determinant for appropriate interaction between drivers and the system. System transparency and types of system failure influence shaping trust in a supervisory control. Subjective ratings of trust were collected to examine the impact of two factors: system transparency (Detailed vs. Less) and system failure (by Limits vs. Malfunction) in a driving simulator study in which drivers experienced a partially automated vehicle. We examined trust ratings at three points: before and after driver intervention in the automated vehicle, and after subsequent experience of flawless automated driving. Our result found that system transparency did not have significant impacts on trust change from before to after the intervention. System-malfunction led trust reduction compared to those of before the intervention, whilst system-limits did not influence trust. The subsequent experience recovered decreased trust, in addition, when the system-limit occurred to drivers who have detailed information about the system, trust prompted in spite of the intervention. The present finding has implications for automation design to achieve the appropriate level of trust.

Malware scanning of an app market is expected to be scalable and effective. However, existing approaches use either syntax-based features which can be evaded by transformation attacks or semantic-based features which are usually extracted by performing expensive program analysis. Therefor, in this paper, we propose a lightweight graph-based approach to perform Android malware detection. Instead of traditional heavyweight static analysis, we treat function call graphs of apps as social networks and perform social-network-based centrality analysis to represent the semantic features of the graphs. Our key insight is that centrality provides a succinct and fault-tolerant representation of graph semantics, especially for graphs with certain amount of inaccurate information (e.g., inaccurate call graphs). We implement a prototype system, MalScan, and evaluate it on datasets of 15,285 benign samples and 15,430 malicious samples. Experimental results show that MalScan is capable of detecting Android malware with up to 98% accuracy under one second which is more than 100 times faster than two state-of-the-art approaches, namely MaMaDroid and Drebin. We also demonstrate the feasibility of MalScan on market-wide malware scanning by performing a statistical study on over 3 million apps. Finally, in a corpus of dataset collected from Google-Play app market, MalScan is able to identify 18 zero-day malware including malware samples that can evade detection of existing tools.