How to Catch when Proxies Lie: Verifying the Physical Locations of Network Proxies with Active Geolocation

Internet users worldwide rely on commercial network proxies both to conceal their true location and identity, and to control their apparent location. Their reasons range from mundane to security-critical. Proxy operators offer no proof that their advertised server locations are accurate. IP-to-location databases tend to agree with the advertised locations, but there have been many reports of serious errors in such databases. In this study we estimate the locations of 2269 proxy servers from ping-time measurements to hosts in known locations, combined with AS and network information. These servers are operated by seven proxy services, and, according to the operators, spread over 222 countries and territories. Our measurements show that one-third of them are definitely not located in the advertised countries, and another third might not be. Instead, they are concentrated in countries where server hosting is cheap and reliable (e.g. Czech Republic, Germany, Netherlands, UK, USA). In the process, we address a number of technical challenges with applying active geolocation to proxy servers, which may not be directly pingable, and may restrict the types of packets that can be sent through them, e.g. forbidding traceroute. We also test three geolocation algorithms from previous literature, plus two variations of our own design, at the scale of the whole world

Deep interest shifting network with meta embeddings for fresh item recommendation

Nowadays, people have an increasing interest in fresh products such as new shoes and cosmetics. To this end, an E-commerce platform Taobao launched a fresh-item hub page on the recommender system, with which customers can freely and exclusively explore and purchase fresh items, namely, the New Tendency page. In this work, we make a first attempt to tackle the fresh-item recommendation task with two major challenges. First, a fresh-item recommendation scenario usually faces the challenge that the training data are highly deficient due to low page views. In this paper, we propose a deep interest-shifting network (DisNet), which transfers knowledge from a huge number of auxiliary data and then shifts user interests with contextual information. Furthermore, three interpretable interest-shifting operators are introduced. Second, since the items are fresh, many of them have never been exposed to users, leading to a severe cold-start problem. Though this problem can be alleviated by knowledge transfer, we further babysit these fully cold-start items by a relational meta-Id-embedding generator (RM-IdEG). Specifically, it trains the item id embeddings in a learning-to-learn manner and integrates relational information for better embedding performance. We conducted comprehensive experiments on both synthetic datasets as well as a real-world dataset. Both DisNet and RM-IdEG significantly outperform state-of-the-art approaches, respectively. Empirical results clearly verify the effectiveness of the proposed techniques, which are arguably promising and scalable in real-world applications

Seasonal variation of atmospheric elemental carbon aerosols at Zhongshan Station, East Antarctica

Elemental carbon (or black carbon) (EC or BC) aerosols emitted by biomass burning and fossil fuel combustion could cause notable climate forcing. Southern Hemisphere biomass burning emissions have contributed substantially to EC deposition in Antarctica. Here, we present the seasonal variation of EC determined from aerosol samples acquired at Zhongshan Station (ZSS), East Antarctica. The concentration of EC in the atmosphere varied between 0.02 and 257.81 ng·m−3 with a mean value of 44.87±48.92 ng·m−3. The concentration of EC aerosols reached its peak in winter (59.04 ng·m−3) and was lowest (27.26 ng·m−3) in summer. Back trajectory analysis showed that biomass burning in southern South America was the major source of the EC found at ZSS, although some of it was derived from southern Australia, especially during winter. The 2019–2020 Australian bush fires had some influence on EC deposition at ZSS, especially during 2019, but the contribution diminished in 2020, leaving southern South America as the dominant source of EC

A Street-Level IP Geolocation Method Based on Delay-Distance Correlation and Multilayered Common Routers

The geographical locations of smart devices can help in providing authentication information between multimedia content providers and users in 5G networks. The IP geolocation methods can help in estimating the geographical location of these smart devices. The two key assumptions of existing IP geolocation methods are as follows: (1) the smallest relative delay comes from the nearest host; (2) the distance between hosts which share the closest common routers is smaller than others. However, the two assumptions are not always true in weakly connected networks, which may affect accuracy. We propose a novel street-level IP geolocation algorithm (Corr-SLG), which is based on the delay-distance correlation and multilayered common routers. The first key idea of Corr-SLG is to divide landmarks into different groups based on relative-delay-distance correlation. Different from previous methods, Corr-SLG geolocates the host based on the largest relative delay for the strongly negatively correlated groups. The second key idea is to introduce the landmarks which share multilayered common routers into the geolocation process, instead of only relying on the closest common routers. Besides, to increase the number of landmarks, a new street-level landmark collection method called WiFi landmark is also presented in this paper. The experiments in one province capital city of China, Zhengzhou, show that Corr-SLG can improve the geolocation accuracy remarkably in a real-world network

Estimating Socioeconomic Status via Temporal-Spatial Mobility Analysis -- A Case Study of Smart Card Data

The notion of socioeconomic status (SES) of a person or family reflects the corresponding entity's social and economic rank in society. Such information may help applications like bank loaning decisions and provide measurable inputs for related studies like social stratification, social welfare and business planning. Traditionally, estimating SES for a large population is performed by national statistical institutes through a large number of household interviews, which is highly expensive and time-consuming. Recently researchers try to estimate SES from data sources like mobile phone call records and online social network platforms, which is much cheaper and faster. Instead of relying on these data about users' cyberspace behaviors, various alternative data sources on real-world users' behavior such as mobility may offer new insights for SES estimation. In this paper, we leverage Smart Card Data (SCD) for public transport systems which records the temporal and spatial mobility behavior of a large population of users. More specifically, we develop S2S, a deep learning based approach for estimating people's SES based on their SCD. Essentially, S2S models two types of SES-related features, namely the temporal-sequential feature and general statistical feature, and leverages deep learning for SES estimation. We evaluate our approach in an actual dataset, Shanghai SCD, which involves millions of users. The proposed model clearly outperforms several state-of-art methods in terms of various evaluation metrics. Comment: 9 pages, double column, IEEE ICCCN 201

On-the-fly treatment of temperature dependent cross sections in the unresolved resonance region in RMC code

With the rapid development of computational power and parallel algorithms, Monte Carlo method has been widely investigated and used in neutron transport simulations due to its advantages in geometry modelling and usage of continuous energy point-wise cross sections. Monte Carlo codes can also be coupled with thermal-hydraulics codes to consider feedbacks. One of the most important aspects of thermal-hydraulics feedback is the detailed temperature distribution, which results in the demands of updates of temperature dependent cross sections in neutron transport simulations. For thermal reactors such as PWR and HTGR, two neutron energy regions should be carefully considered: the resolved resonance region (RRR) and the thermal energy region, especially the resolved resonance region. Besides, the temperature dependence of cross sections in the unresolved resonance energy region (URR) is also important for fast reactors and some experimental critical assemblies. In this paper, the on-the-fly temperature treatment of cross sections in the RRR was implemented in RMC code. The on-the-fly method for the URR was also proposed and implemented in RMC code, which was also combined with the on-the-fly temperature treatments for resolved resonance energy. The proposed method was applied to Monte Carlo transport and depletion calculations. The accuracy and efficiency are compared for different combinations of methods of RRR and URR. The results show that the on-the-fly treatment has high efficiency and satisfactory fidelity. The probability tables with equiprobable bands were also developed to reduce the memory consumption, with keeping the same accuracy of original probability tables