Borrower’s default and self-disclosure of social media information in P2P lending

Background:We examine the signaling effect of borrowers’social media behavior,especially self-disclosure behavior,on the default probability of money borrowers on a peer-to-peer(P2P)lending site.Method:We use a unique dataset that combines loan data from a large P2P lending site with the borrower’s social media presence data from a popular social media site.Results:Through a natural experiment enabled by an instrument variable,we identify two forms of social media information that act as signals of borrowers’creditworthiness:(1)borrowers’choice to self-disclose their social media account to the P2P lending site,and(2)borrowers’social media behavior,such as their social network scope and social media engagement.Conclusion:This study offers new insights for screening borrowers in P2P lending and a novel usage of social media information.

A first-principles study of helium diffusion in quartz and coesite under high pressure up to 12GPa

Helium diffusion in mantle minerals is crucial for understanding mantle structure and the dynamic processes of Earth's degassing.In this paper,we report helium incorporation and the mechanism of its diffusion in perfect crystals of quartz and coesite.The diffusion pathways,activation energies(Ea),and frequency factors of helium under ambient and high pressure conditions were calculated using Density Functional Theory(DFT)and the climbing image nudged elastic band(CI-NEB)method.The calculated diffusive coefficients of He in the quartz in different orientations are:D[100]=1.24×10^(−6)exp.(−26.83 kJ/mol/RT)m^(2)/s D[010]=1.11×10^(−6)exp.(−31.60 kJ/mol/RT)m^(2)/s.and in the coesite:D[100]=3.00×10^(−7)exp.(−33.79 kJ/mol/RT)m^(2)/s D[001]=2.21×10^(−6)exp.(−18.33 kJ/mol/RT)m^(2)/s.The calculated results indicate that diffusivity of helium is anisotropic in both quartz and coesite and that the degree of anisotropy is much more pronounced in coesite.Helium diffusion behavior in coesite under high pressures was investigated.The activation energies increased with pressure:Ea[100]increased from 33.79 kJ/mol to 58.36 kJ/mol,and Ea[001]increased from 18.33 kJ/mol to 48.87 kJ/mol as pressure increased from0 GPa to 12 GPa.Our calculations showed that helium is not be quantitatively retained in silica at typical surface temperatures on Earth,which is consistent with the findings from previous studies.These results have implications for discussion of the Earth's mantle evolution and for recognition thermal histories of ultra-high pressure(UHP)metamorphic terranes.

VPN and Non-VPN Network Traffic Classification Using Time-Related Features

The continual growth of the use of technological appliances during the COVID-19 pandemic has resulted in a massive volume of data flow on the Internet,as many employees have transitioned to working from home.Furthermore,with the increase in the adoption of encrypted data transmission by many people who tend to use a Virtual Private Network(VPN)or Tor Browser(dark web)to keep their data privacy and hidden,network traffic encryption is rapidly becoming a universal approach.This affects and complicates the quality of service(QoS),traffic monitoring,and network security provided by Internet Service Providers(ISPs),particularly for analysis and anomaly detection approaches based on the network traffic’s nature.The method of categorizing encrypted traffic is one of the most challenging issues introduced by a VPN as a way to bypass censorship as well as gain access to geo-locked services.Therefore,an efficient approach is especially needed that enables the identification of encrypted network traffic data to extract and select valuable features which improve the quality of service and network management as well as to oversee the overall performance.In this paper,the classification of network traffic data in terms of VPN and non-VPN traffic is studied based on the efficiency of time-based features extracted from network packets.Therefore,this paper suggests two machine learning models that categorize network traffic into encrypted and non-encrypted traffic.The proposed models utilize statistical features(SF),Pearson Correlation(PC),and a Genetic Algorithm(GA),preprocessing the traffic samples into net flow traffic to accomplish the experiment’s objectives.The GA-based method utilizes a stochastic method based on natural genetics and biological evolution to extract essential features.The PC-based method performs well in removing different features of network traffic.With a microsecond perpacket prediction time,the best model achieved an accuracy of more than 95.02 percent in the most demanding traffic classification task,a drop in accuracy of only 2.37 percent in comparison to the entire statistical-based machine learning approach.This is extremely promising for the development of real-time traffic analyzers.

新工科背景下物联网工程专业建设存在的问题及对策

物联网工程专业属于新建的本科专业,针对该专业建设中出现的问题,从新工科建设的基本要求出发,围绕物联网工程专业的专业定位、培养目标、课程设置、实践教学、师资培养等方面进行了实践与探索,取得了一些成效。确立了更新课程教育教学基本观念,落实学生为中心(SC)、面向产出(OBE)、持续改进(CQI)的人才培养理念,为新工科背景下地方高校物联网工程专业的建设提供参考。

Instagram Mobile Application Digital Forensics

In this research,we developed a plugin for our automated digital forensics framework to extract and preserve the evidence from the Android and the IOS-based mobile phone application,Instagram.This plugin extracts personal details from Instagram users,e.g.,name,user name,mobile number,ID,direct text or audio,video,and picture messages exchanged between different Instagram users.While developing the plugin,we identified resources available in both Android and IOS-based devices holding key forensics artifacts.We highlighted the poor privacy scheme employed by Instagram.This work,has shown how the sensitive data posted in the Instagram mobile application can easily be reconstructed,and how the traces,as well as the URL links of visual messages,can be used to access the privacy of any Instagram user without any critical credential verification.We also employed the anti-forensics method on the Instagram Android’s application and were able to restore the application from the altered or corrupted database file,which any criminal mind can use to set up or trap someone else.The outcome of this research is a plugin for our digital forensics ready framework software which could be used by law enforcement and regulatory agencies to reconstruct the digital evidence available in the Instagram mobile application directories on both Android and IOS-based mobile phones.

Applying the Theory of Numerical Radius of Operators to Obtain Multi-observable Quantum Uncertainty Relations

Quantum uncertainty relations are mathematical inequalities that describe the lower bound of products of standard deviations of observables(i.e.,bounded or unbounded self-adjoint operators).By revealing a connection between standard deviations of quantum observables and numerical radius of operators,we establish a universal uncertainty relation for k observables,of which the formulation depends on the even or odd quality of k.This universal uncertainty relation is tight at least for the cases k=2 and k=3.For two observables,the uncertainty relation is a simpler reformulation of Schr?dinger’s uncertainty principle,which is also tighter than Heisenberg’s and Robertson’s uncertainty relations.

Computing the Smallest Eigenvalue of Large Ill-Conditioned Hankel Matrices

This paper presents a parallel algorithm for finding the smallest eigenvalue of a family of Hankel matrices that are ill-conditioned.Such matrices arise in random matrix theory and require the use of extremely high precision arithmetic.Surprisingly,we find that a group of commonly-used approaches that are designed for high efficiency are actually less efficient than a direct approach for this class of matrices.We then develop a parallel implementation of the algorithm that takes into account the unusually high cost of individual arithmetic operations.Our approach combines message passing and shared memory,achieving near-perfect scalability and high tolerance for network latency.We are thus able to find solutions for much larger matrices than previously possible,with the potential for extending this work to systems with greater levels of parallelism.The contributions of this work are in three areas:determination that a direct algorithm based on the secant method is more effective when extreme fixed-point precision is required than are the algorithms more typically used in parallel floating-point computations;the particular mix of optimizations required for extreme precision large matrix operations on a modern multi-core cluster,and the numerical results themselves.