CPFinder: Finding an unknown caller's profession from anonymized mobile phone data

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones might exhibit their application preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation of an anonymized dataset of 1,282 users over a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.     

基于ARM的机载火控雷达元器件自动化检测技术研究

为解决传统技术针对机载火控雷达元器件检测吞吐率低的问题,设计基于ARM的机载火控雷达元器件自动化检测技术。确定机载火控雷达元器件自动化检测特征,基于ARM响应曲线拟合机载火控雷达元器件检测数据,表达其自动化检测特征属性值,以此实现机载火控雷达元器件自动化检测。设计实例分析,结果表明,设计检测技术检测吞吐率均在3reqs/s以上,对比传统手段具有明显的优势,能够解决传统检测技术检测吞吐率低的问题。     

Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

Link prediction has attracted wide attention among interdisciplinary researchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks. Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connected graph. However, the complexity of the real world makes the complex networks abstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start link prediction is favored as one of the most valuable subproblems of traditional link prediction. However, due to the loss of many links in the observation network, the topological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topological information from observed network becomes the key point to solve the problem of cold-start link prediction. In this paper, we propose a framework for solving the cold-start link prediction problem, a joint-weighted symmetric nonnegative matrix factorization model fusing graph regularization information, based on low-rank approximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designed graph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain each other. Finally, a unified framework for implementing cold-start link prediction is constructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validation on five real networks with attributes shows that the proposed model has very good predictive performance when predicting missing edges of isolated nodes.     

Relation network based on multi-granular hypergraphs for person re-identification

Applied Intelligence - In recent years, person re-identification (re-ID) has become a widespread research topic that focuses on retrieving target pedestrians from a set of images, typically taken...     

Study on the void reduction behaviour of porous asphalt pavement based on discrete element method

The objective of this study was to analyse the void reduction behaviour of porous asphalt mixture under load. A three-dimensional discrete element model of porous asphalt mixture based on aggregate gradation and void gradation was built in PFC3D software. The parameter of the model was obtained from creep test. The rutting test was simulated using this discrete element model. And a new method was developed to obtain and analyse the void structure in discrete element model. The simulation results were compared with one of the laboratory test. The comparative analysis indicates that, the discrete element method can be used to simulate the creep response and void reduction behaviour of porous asphalt mixture. Further research shows that porosity, effective porosity, number of connected components and section pores have a good correlation with strain of porous asphalt mixture. With the increase in strain, the proportion of section pores with diameter less than 2 mm increases. In the initial stage of loading, the void reduction is the main reason for rut increment of porous asphalt mixture. In the later stage, the void structure is almost incompressible; the lateral deformation of mixture becomes the domination factor.     

Growth and optical properties of In x Ga1−x P nanowires synthesized by selective-area epitaxy

Ternary Ⅲ-Ⅴ nanowires (NWs) cover a wide range of wavelengths in the solar spectrum and would greatly benefit from being synthesized as position-controlled arrays for improved vertical yield,reprodudbility,and tunable optical absorption.Here,we report on successful selective-area epitaxy of metal-particle-free vertical InxGa1-xP NW arrays using metal-organic vapor phase epitaxy and detail their optical properties.A systematic growth study establishes the range of suitable growth parameters to obtain uniform NW growth over a large array.The optical properties of the NWs were characterized by room-temperature cathodoluminescence spectroscopy.Tunability of the emission wavelength from 870 nm to approximately 800 nm was achieved.Transmission electron microscopy and energy dispersive X-ray measurements performed on crosssection samples revealed a pure wurtzite crystal structure with very few stacking faults and a slight composition gradient along the NW growth axis.     

Effects of redox-active interlayer anions on the oxygen evolution reactivity of NiFe-layered double hydroxide nanosheets

Nickel-iron layered double hydroxide (NiFe-LDH) nanosheets have shown optimal oxygen evolution reaction (OER) performance; however, the role of the intercalated ions in the OER activity remains unclear. In this work, we show that the activity of the NiFe-LDHs can be tailored by the intercalated anions with different redox potentials. The intercalation of anions with low redox potential (high reducing ability), such as hypophosphites, leads to NiFe-LDHs with low OER overpotential of 240 mV and a small Tafel slope of 36.9 mV/dec, whereas NiFe-LDHs intercalated with anions of high redox potential (low reducing ability), such as fluorion, show a high overpotential of 370 mV and a Tafel slope of 80.8 mV/dec. The OER activity shows a surprising linear correlation with the standard redox potential. Density functional theory calculations and X-ray photoelectron spectroscopy analysis indicate that the intercalated anions alter the electronic structure of metal atoms which exposed at the surface. Anions with low standard redox potential and strong reducing ability transfer more electrons to the hydroxide layers. This increases the electron density of the surface metal sites and stabilizes their high-valence states, whose formation is known as the critical step prior to the OER process.

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