dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods and reproducibility Our benchmarks were run on a Dell T730 computer with two Intel Xeon CPUs E5-2643 v3 @ 3. 40 GHz and 128 GB memory used as a detector simulator. The 100 Gb s À1 network card is a Mellanox ConnectX-5 EN in a PCIe generation 3 Â 16 interface, which is directly connected by an optical fiber to the analysis computer. This network card behaves similarly to a standard NIC for TCP-UDP/IP but decodes RoCE datagrams and transparently offloads the Linux kernel (4. 16. 0-0. bpo. 2-amd64) and the CPU. The Mellanox Technologies stack in use is OFED-4. 4-1. 0. 0. The data-receiver computer is a Dell T740 (4. 16. 0-0. bpo. 2-amd64) with two Intel(R) Xeon(R) Gold 6134 CPUs @ 3. 20 GHz and 192 GB memory, and a ConnectX-5 EN and OFED-4. 6-1. 0. 1. The GPU is a NVIDIA Quadro P6000 with 24 GB memory in a PCIe generation 3 Â 16 slot located on the same root complex as the RNIC.
Test images were extracted from a lysozyme dataset provided by  Dectris (2019)  to advertise the EIGER 4M detector for protein crystallography. Invalid pixels were removed from the images. All frames were processed to add a random pedestal and to apply a gain factor, and the gain bits have been set accordingly. Three sets of data were generated for different detector sizes: 512 Â 1024 pixels, 2048 Â 2048 pixels and 128 Â 8246 pixels (JUNGFRAU datagram format, four lines of 1024 pixels). All the datasets contain images and pedestals. Application and GPU code are available from the ESRF GitLab repository, including Python scripts to produce datasets and measurements. A small subset of the code relies on an ESRF internally developed library LIBDANCE for communication and control of embedded systems but access requests from the scientific community are welcome.
Acknowledgements Thanks to  Marie Ruat  and  Paolo Busca  from the  ESRF Detector Group ,  Shibom Basu  from  EMBL  and  Bernd Schmitt 's team at  PSI  for sharing early data from the JUNGFRAU detector. Daniele De Sanctis  in charge of the upgrade of the ID29 serial crystallography line provided us with valuable input on their project. We would also like to thank  Alejandro Homs  and  Samuel Debionne  and the  ESRF-LIMA team  for their deep knowledge of detectors and integration in the Linux kernel. Wassim Mansour  has made several fruitful preliminary works on FPGA RoCEv2 implementation in detector heads. Nothing could have been carried out without Pablo Fajardo who initiated the  RASHPA  project during the  CRISP EU  project and pursued in the context of the  Horizon 2020   EUCALL  project (work package  WP5 , ultra-fast data acquisition UFDAC).
In this paper, we study the stability of the public IP addresses a user device uses to communicate with our server. Over time, a same device communicates with our server using a set of distinct IP addresses, but we find that devices reuse some of their previous IP addresses for long periods of time. We call this IP address retention and, the duration for which an IP address is retained by a device, is named the IP address retention period. We present an analysis of 34,488 unique public IP addresses collected from 2,230 users over a period of 111 days and we show that IP addresses remain a prime vector for online tracking. 87 % of participants retain at least one IP address for more than a month and 45 % of ISPs in our dataset allow keeping the same IP address for more than 30 days. Furthermore, we also detect the presence of cycles of IP addresses in a user's history and highlight their potential to be abused to infer traits of the user behaviour, as well as mobility traces.
Indeed, while mobile network IP addresses are commonly shared by multiple users because of carrier-grade NAT  [18] , it is less the case of residential IP addresses. Even though most Internet Service Providers (ISP) provide dynamic IP addresses, they still remain the same for long time, as long as the user does not turn off their WiFi router for long enough. Nevertheless, no large-scale longitudinal study has been conducted on the duration for which such IP addresses are retained by users and its implications in terms of privacy. Our study leverages a dataset of public IP addresses collected over a period of 111 days from 5,443 users. The IP addresses were collected using two browser extensions advertised on the AmIUnique website. 1 Using this dataset, we study the stability of the public IP addresses a user's device uses to communicate with our server.
Then, we derive cycles of long-lived IP addresses for each user, which shows potential to discriminate and track users. Our intuition is that, given the portable nature of personal devices, IP cycles reflect human behavior and can be used as a proxy to infer other information, like user routines. Our evaluation also shows that even simple metrics, such as the Jaccard similarity between sets of IP addresses, provide unique and stable information that could be used by trackers, and could be also be used for respawning cookies. In summary, this paper reports on the following results: (1) 87 % of users have at least one IP address that was retained for more than 30 days, (2) Among the 10 countries that contributed the most IP addresses to our dataset, the Netherlands shows the highest average IP address retention period of 36.
96 days, (3) 93 % of users have a distinct set of long-lived IP addresses, that is, the set of IP addresses with a retention period at least 30 days proves to be highly discriminating, (4) 20 % of users have at least one cycle of long-lived IP addresses that lasts for more than 30 days. 1 https://amiunique. org/ The remainder of this paper is organized as follows. Section 2 describes the background and related Work. Section 3 details our input dataset and how we cleaned it. Section 4 goes over our methodology and the analysis of our dataset. Section 5 discusses our results and some of the privacy implications. Section 6 concludes the paper.
They also studied the volatility of dynamic IP addresses and they showed that over 30 % of dynamic IP addresses were shared by more than one user within 1 to 3 days. Despite the very large dataset, the IP address is only collected when a user logs in, and important information, like the ID of the device or any IP changes between two user logins, is lost. As we propose in this paper, we believe that better understanding the stability of IP addresses requires a finer-grained dataset that can associate IP addresses to specific devices, and retrieve IP address changes rapidly. Maier et al. [19]  studied the dominant characteristics of residential broadband traffic in 2009. On a DSL network, they found that 50 % of IP addresses were assigned to residential routers at least twice in 24 hours, whereas 1-5% of IP addresses were reassigned almost 10 times a day.
[30]  showed that the IP addresses along with browsing history of a user can be used effectively for cross-device tracking. However, their dataset is quite small with only 126 users and 1,994 IP addresses. Their objective was to study cross-device tracking, but they do not study the stability of IP addresses, how they are assigned, how portable devices, like laptops and cellphones, cycle through IP addresses when moving across networks. We show the existence of IP address cycles, that is, IP addresses that the devices use to connect to the Internet over long periods of time. We argue that this is a proxy to the user's behavior, inherent in the way modern, portable, personal devices are used, and can reveal information on the user daily routine. De Montjoye et al. [7] , in a study in 2013, showed that human mobility traces are highly unique.
They showed that 95 % of individuals from a dataset of 1. 5 million individuals can be uniquely identified if their location is specified hourly. The results of this study highlight the impact of learning an individual daily routine. In this paper, we focus on studying i) the stability of IP addresses, including variations among countries, ii) the uniqueness of a set of IP addresses assigned to a user device, and finally, iii) we show the presence of cycles of IP addresses allocated to a user device, which has privacy implications, as suggested by De Montjoye et al. [7].
INPUT DATASET The dataset we study contains 41,566 unique IP addresses from 5,443 browser instances and has been collected between June 6, 2019 and September 25, 2019 using the AmIUnique browser extensions for Chrome and Firefox. 2  AmIUnique  [2]  is a website that aims to study browser fingerprinting. To better understand stability of fingerprints over time, AmIUnique also provides two browser extensions, available for Chromium-based and Gecko-based browsers. Our dataset focuses on desktop devices, as these extensions are not supported by mobile browsers. Users are fully aware that these extensions collect data about their fingerprint, the timestamp and the IP address, since it is the main purpose of the extension and is clearly stated when the users install it. Every four hours, the extension sends a browser fingerprint and IP address to the AmIUnique servers.
The AmIUnique initiative, including the online website and the associated extensions, obtained the agreement from the IRB to collect such information. We used the Maxmind GeoLite2 database  3  to gather geolocation data on the collected IP addresses. Our dataset of 41,566 IP addresses originates from 131 countries, with the majority of them coming from either Europe or North America. Figure  1  reports on the Top 10 countries contributing IP addresses to our dataset and Germany is the highest represented one with 8,352 distinct IP addresses. We also gather the ISP (Internet Service Provider) associated with the IP addresses in our dataset from the website Who is my ISP? 4  for this purpose. Our dataset of 41,566 IP addresses are assigned and owned by 3,087 ISPs. Figure  2  reports on the Top 10 ISPs contributing users to our dataset-i.
e. , those ISPs which are used by the most number of users in our dataset. Figure  3  summarizes the evolution of the number of active users for our extensions, together with the number of collected IPs over time. It is important to note that the x-axis reports browser instances, and not users: a user can have the extension installed in multiple browsers and we do not try to link those two installations to a single user. Furthermore, the extension assigns a unique identifier at install time and, if users reinstall the extension, a unique identifier is generated again and we consider it as a new instance. Nevertheless, for simplification purposes, we use the term user interchangeably with browser instance in the rest of the paper. We can observe from the graph that, even though the number of extension users remains somewhat stable over the course of three months, the number of IP addresses collected per month decreases.
This is because we get a stable number of new users every month, which are present for only a few days, thus, we remove these users from our study for better quality of results. Dataset cleaning. We remove users that contribute IP addresses for less than 60 days from our dataset, as they do not contribute to our objective of studying stability of IP address over long durations. After removing these users from the dataset, we are left with 2,230 users and 34,488 distinct IP addresses in the dataset.
Stability & Uniqueness of IP Addresses 4. 1. 1 Stability. We specifically want to answer the following question: How long does a device retain the same IP address on a given network? Devices can obtain different IP addresses when connected to different networks (e. g. , home, office, leisure networks). When reconnecting to a previous network, more often than not, the device is assigned its previously used IP address. We call this duration the retention period. The IP addresses collected in our dataset are public IP addresses from desktop and laptop devices, and it is entirely possible that the user physically moves to a different location and connects to a different network. Hence, if we observe a change of IP address for a user at two timestamps, it does not necessarily mean that the IP address for the user has changed on a given network.
Thus, we simply calculate the retention period of a specific IP address irrespective of the fact that the user had a different IP address somewhere in the middle. We compute the retention period of each IP address for every user in the dataset by finding the time between the first and the last time the IP address appeared in our dataset for that user. Retention period of an IP address. The average retention period of an IP address is 9. 3 days, whereas 760 IP addresses (~2 %) were retained for more than 100 days. ~70 % of users (1569) had at least 1 IP address that was retained for more than 2 months. Figure  4  depicts a CDF plot for the retention period of IP addresses in days. From this graph, one can observe that the green line describes the overall retention period of IP addresses. We can see that most IP addresses are retained for a very short periods of time, for example, 90 % of IP addresses are retained for less than 10 days.
However, when we plot the average retention of IP addresses per-user (red line), we observe that IP addresses are retained, on average, for longer periods. This is because, although the majority of IP addresses are short-lived, users also have very  long-lived IP addresses. This makes it apparent that the users have both short-lived and long-lived IP addresses. Intuitively, when users are traveling or connected to a WiFi hot-spot, these IP addresses are retained for a short period of time, whereas the IP addresses at home and work are retained for longer durations. Short-lived and long-lived IP addresses. To further understand the distribution of such short-and long-lived IP addresses for each user, we compute the number of IP addresses retained by a user for less than 1 week, 1 week, 1 month and 2 months in our dataset.
135 users (6 %) have at least one IP address that lasted for the entire duration of the dataset (111 days). Figure  5   About 77 % of all the distinct IP addresses in our dataset have a retention period of less than a week while 7. 1 % of IP addresses are retained for more than 2 months. Furthermore, only 22 users in our dataset do not have any IP address retained for more than a week, whereas 99 % (2, 208) of users have at least one IP address retained more than a week, ~87 % (1, 948) users have at least one IP that was retained for at least one month, and 70 % (1, 569) users have at least one IP address that was retained for more than 2 months. Impact of country on retention period. We also study how the retention period varies per country. We gather country information using Maxmind's geolite database and compute the average retention period of each IP address per country.
Padmanabhan et al. [23]  reported that some ISPs renew IP addresses after a fixed duration, ranging from one day to two weeks, and even more. Here, we want to see if we can observe similar behaviors in our dataset. By querying the whoismyisp. org website, we identified 3,087 distinct ISPs and we plotted, in Figure  7 , the maximum observable retention period for each of them. We consider the maximum period we were able to identify in our dataset, per ISP, as the lower bounds of the worst case scenario in terms of IP retention for that ISP. This is because we collected data over a limited amount of time and this might not reveal the complete behavior of IP assignments; the  Retention Period (days)  longest retention periods may indeed be higher. We can see from the figure that about 45 % of ISPs in our dataset have at least 1 IP address that was retained for at least 30 days.
Moreover, there are 392 ISPs for which the maximum retention period is more than 100 days. This shows that a significant portion of ISPs all over the world suffer from the issue of assigning long-lived IP addresses, making these users susceptible to IP-based tracking. Furthermore, in Figure  8 , we present the average retention periods of the top 10 ISPs contributing the highest number of users in our dataset. In conclusion, we observe significant differences between ISPs. It remains to be seen if these results generalize at a larger scale with a more representative numbers of users and ISPs per country. Amount of changes in IP addresses. Finally, we also observed several cases wherein, after a change in IP address, the new IP address is very similar to the previously assigned address. IPv4 addresses are made of only 4 octets, and we observed several cases where only the last octet of the IP address changed.
For instance, assume a user had an IP address 89. 158. 242. 220 that changed to 89. 158. 242. 120, while on the same network. This occurs often because ISPs generally assign IPs from a relatively small range. To measure the prevalence of such changes in our dataset, we calculate the average retention period of each user by ignoring the last octet of every IP address in our dataset. Thus, IP 89. 158. 242. 120 simply becomes 89. 158. 242 for further calculations of retention period. The average retention period more than doubles from 9. 3 days to 19. 51 days after ignoring the last octet of the IP address. Thus, showing that even in cases when IP addresses change, they often do not change completely, and retain much of their previous information. Our analyses and observations show that users have both shortand long-lived IP addresses.
Multiple users can share the same IP address behind a NAT router. For instance, in our dataset, there are 1,046 users who share 1 IP with another user. However, we observed in our dataset that there are multiple IP addresses that are reused and retained for a long duration by a user. The chances that two users connect to the same network at multiple locations and share multiple IP addresses is quite low. Thus, set of IP addresses retained by a user over a long duration can be considered to be unique, as well as stable, for a long duration. To evaluate this hypothesis, we compute a pair-wise comparison between the set of long-lived (greater than 30 days) IP addresses for each user in our dataset and compute the Jaccard similarity to verify if a set of IP addresses can be used for discriminating different users.
We use the following formula to calculate the Jaccard similarity between 2 users A and B with IP A and IP B being the sets of IP addresses used by the corresponding users. J (A, B) = IP A ∩ IP B IP A ∪ IP B This results in us performing 2,485,335 comparisons for 2,230 users in our dataset. From this pair-wise comparison, we find that the Jaccard similarity is zero for 99. 97 % of pairs of users in our dataset. We further observed that ~93 % of users have a unique set of longlived IP addresses. Thus, showing the high uniqueness offered by sets of IP addresses assigned to a user. Furthermore, we also observed a perfect Jaccard similarity of 100 % for 44 pairs of users. After manually analyzing the browser fingerprints of these users, we came to the conclusion that these users are actually the same, but are marked as different.
Presence of Cycles With our dataset, we have observed that the list of distinct IP addresses is enough to identify, and potentially track, our user base over time. We also observed that there are users in our dataset who exhibit connectivity patterns in their IP addresses, with one IP in the morning and the other at night, often repeated over long durations. We define these repeated patterns as cycles. In Section 4. 1. 1, we showed that there are both short-lived and long-lived IP addresses for each user. In this section, we only look at those cycles, which are made of IP addresses with retention periods greater than a month, since they are more interesting in terms of tracking a user's routine. We observed 443 users in our dataset who have at least 1 cycle, repeating every week (frequency of 1 week) and lasting more than a month.
Impact on Privacy. Our hypothesis is that the IP address cycles present in our database can be attributed to a simple cause: the user's behavior and daily routines. As we have shown in Section 4. 1. 1, IP addresses are stable over time. If we observe the same IP address multiple times for a given user over long durations, and this IP address is interleaved by other IP addresses, this likely means that the user reconnected through the same network, from the same physical location as before. In essence, the user came back to the same network. These cycles then present two major privacy problems, as we describe next. Insight into the user routine. First, it can give an adversary incredible insight into someone's weekly or daily routine. If we observe the same set of addresses during the day and a different one during the night, we can deduce when the user is at work or at home.
From our dataset of IP addresses, we observe that 5, 633 of them are always present in either day or night. Furthermore, we also detected 58 users in our dataset, which have IP cycles that resemble mobility traces. Their IP address changes every morning, remains the same until the evening, and the same cycle continues the next day. We also saw differences for these users over weekends, where the IP address that was observed between morning and evening was not present for these users on Saturdays and Sundays. It is intuitive to think that the reason behind this could be that these IP addresses are assigned to the user's device at work. If one is able to acquire fine-grain data, identifying cycles can even help predict where the user will be next. In 2015, Libert et al. crawled the Alexa Top 1M websites and found that 78.
DISCUSSION While the current tracking ecosystem exploits numerous ways to track users online, particularly with the use of UUIDs in persistent storage (e. g. , cookies), not enough attention has been given to tracking using IP addresses. In this study, we looked at the stability of public IP address and found that long-lived addresses are stable and can enable long-term tracking of users. IP addresses are retained, and reused, and can form cycles that could potentially allow inferring information about a user behavior or routine. These findings are comparable to what can be seen with mobility traces. In a study published in 2013, De Montjoye et al. [7]  showed that human mobility traces are highly unique. They found that 95 % of individuals from a dataset of 1. 5 million individuals can be uniquely identified if their location is specified hourly.
This would mitigate most of the long-term impacts of IP tracking and prevent trackers from building IP address cycles that can give insight into users' lives. Threats to Validity Our dataset is inevitably biased as we collected data from desktop users who visited the AmIUnique website and installed our extension. Our findings are intended to be challenged with a larger population and data coming from mobile devices. Moreover, we do not have concrete details on the renewal policies of different ISPs that would confirm and put into perspective the retention periods we observed in our study. Yet, our results show that IP-based tracking is still very much alive. With both longlived IP addresses and the presence of cycles, most users in our dataset can be tracked just because of the network environments they connect to.
Our analysis shows that 93 % of users in our dataset had a unique set of long-lived IP addresses (minimum retention period of 30 days) that remain stable over time, presenting a bleak picture of the state of online tracking. Despite much effort to fight stateless and stateful tracking mechanisms, such as cookies, cookie syncing or browser fingerprinting, the role of IP addresses in tracking remains significant and, in regards to recent large-scale research studies, an overlooked threat to online privacy. Based on these preliminary findings, we believe more research is required to get a better picture of the risks involved in IP address tracking.