Research on the mechanism of AI service satisfaction based on the culture and AI strategy perspective.

In the service industry, using artificial intelligent robots to replace service staffs in the service process has become a fashionable topic. The traditional service mode of service industry has changed a lot with the rapid development of science and technology. The artificial intelligent robot is considered as a new service carrier which entered the public’s view in recent years. Nevertheless, there are little literatures which explored on the psychological changes of consumers if service staffs are replaced by artificial intelligent robots in the service industry. Furthermore, the literatures which explore the possible impact on service quality and satisfaction if service staffs are replaced by artificial intelligent robots are insufficient as well. To fill the gap in the research of service industry, this research mainly focuses on two independent variables. The first independent variable is cultural variable (eastern culture and western culture), in this research, Chinese consumers are treated as the sample of eastern culture, American consumers are treated as the sample of western culture. The other independent variable is AI service strategy (manned strategy and unmanned strategy), manned strategy refers to the service is mainly provided by the artificial intelligent robots under the intervention of service staffs, in contrast, unmanned strategy refers to the service only provides by the robots in the whole process. The result of the research is obtained through the scenario simulation experiment, the findings show that unmanned strategy brought consumers higher fear and higher loss of control than manned strategy, furthermore, consumers under western culture feel higher degree of fear and loss of control than consumers under eastern culture when facing the services by artificial intelligent robots. This research also confirms that perceived fear, loss of control and perceived service quality influence the satisfaction of AI service. Keywords：AI robots, AI service satisfaction, AI service quality, perceived fear, loss of control, culture，manned/unmanned strateg

Direct fiber vector eigenmode multiplexing transmission seeded by integrated optical vortex emitters

Spatial modes have received substantial attention over the last decades and are used in optical communication applications. In fiber-optic communications, the employed linearly polarized modes and phase vortex modes carrying orbital angular momentum can be synthesized by fiber vector eigenmodes. To improve the transmission capacity and miniaturize the communication system, straightforward fiber vector eigenmode multiplexing and generation of fiber-eigenmode-like polarization vortices (vector vortex modes) using photonic integrated devices are of substantial interest. Here, we propose and demonstrate direct fiber vector eigenmode multiplexing transmission seeded by integrated optical vortex emitters. By exploiting vector vortex modes (radially and azimuthally polarized beams) generated from silicon microring resonators etched with angular gratings, we report data-carrying fiber vector eigenmode multiplexing transmission through a 2-km large-core fiber, showing low-level mode crosstalk and favorable link performance. These demonstrations may open up added capacity scaling opportunities by directly accessing multiple vector eigenmodes in the fiber and provide compact solutions to replace bulky diffractive optical elements for generating various optical vector beams

Correlation Analysis for Protein Evolutionary Family Based on Amino Acid Position Mutations and Application in PDZ Domain

BACKGROUND: It has been widely recognized that the mutations at specific directions are caused by the functional constraints in protein family and the directional mutations at certain positions control the evolutionary direction of the protein family. The mutations at different positions, even distantly separated, are mutually coupled and form an evolutionary network. Finding the controlling mutative positions and the mutative network among residues are firstly important for protein rational design and enzyme engineering. METHODOLOGY: A computational approach, namely amino acid position conservation-mutation correlation analysis (CMCA), is developed to predict mutually mutative positions and find the evolutionary network in protein family. The amino acid position mutative function, which is the foundational equation of CMCA measuring the mutation of a residue at a position, is derived from the MSA (multiple structure alignment) database of protein evolutionary family. Then the position conservation correlation matrix and position mutation correlation matrix is constructed from the amino acid position mutative equation. Unlike traditional SCA (statistical coupling analysis) approach, which is based on the statistical analysis of position conservations, the CMCA focuses on the correlation analysis of position mutations. CONCLUSIONS: As an example the CMCA approach is used to study the PDZ domain of protein family, and the results well illustrate the distantly allosteric mechanism in PDZ protein family, and find the functional mutative network among residues. We expect that the CMCA approach may find applications in protein engineering study, and suggest new strategy to improve bioactivities and physicochemical properties of enzymes

Graph Mining for Cybersecurity: A Survey

The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance. It is imperative to summarize existing graph-based cybersecurity solutions to provide a guide for future studies. Therefore, as a key contribution of this paper, we provide a comprehensive review of graph mining for cybersecurity, including an overview of cybersecurity tasks, the typical graph mining techniques, and the general process of applying them to cybersecurity, as well as various solutions for different cybersecurity tasks. For each task, we probe into relevant methods and highlight the graph types, graph approaches, and task levels in their modeling. Furthermore, we collect open datasets and toolkits for graph-based cybersecurity. Finally, we outlook the potential directions of this field for future research

Metal-bonded Atomic Layers of Transition Metal Carbides (MXenes)

Although two-dimensional transition metal carbides and nitrides (MXenes) have fantastic physical and chemical properties as well as wide applications, it remains challenging to produce stable MXenes due to their rapid structural degradation. Here, unique metal-bonded atomic layers of transition metal carbides with high stabilities are produced via a simple topological reaction between chlorine-terminated MXenes and selected metals, where the metals enable to not only remove Cl terminations, but also efficiently bond with adjacent atomic MXene slabs, driven by the symmetry of MAX phases. The films constructed from Al-bonded Ti$_3$C$_2$Cl$_x$ atomic layers show high oxidation resistance up to 400 degrees centigrade and low sheet resistance of 9.3 ohm per square. Coupled to the multi-layer structure, the Al-bonded Ti$_3$C$_2$Cl$_x$ film displays a significantly improved EMI shielding capability with a total shielding effectiveness value of 39 dB at a low thickness of 3.1 micron, outperforming pure Ti$_3$C$_2$Cl$_x$ film