Miracle-dz/UniFAR-FTUs huggingface dataset:
NFC+: Breaking NFC Networking Limits through Resonance Engineering

Current UHF RFID systems suffer from two long-standing problems: 1) miss-reading non-line-of-sight or misoriented tags and 2) cross-reading undesired, distant tags due to multi-path reflections. This paper proposes a novel system, NFC+, to overcome the fundamental challenges. NFC+ is a magnetic field reader, which can inventory standard NFC tagged objects with a reasonably long range and arbitrary orientation. NFC+ achieves this by leveraging physical and algorithmic techniques based on magnetic resonance engineering. We build a prototype of NFC+ and conduct extensive evaluations in a logistic network. Comparing to UHF RFID, we find that NFC+ can reduce the miss-reading rate from 23% to 0.03%, and cross-reading rate from 42% to 0, for randomly oriented objects. NFC+ demonstrates high robustness for RFID unfriendly media (e.g., water bottles and metal cans). It can reliably read commercial NFC tags at a distance of up to 3 meters which, for the first time, enables NFC to be directly applied to practical logistics network applications.
Risk prediction and assessment of foodborne disease based on big data

In recent years, the outbreak of foodborne diseases has been on an upward trend clearly. It is of great significance for us to predict the outbreak of foodborne diseases accurately and conduct quantitative risk assessment timely. Traditional prediction methods based on a single data source have drawbacks such as complex prediction processes and inaccurate prediction results. In this article, we figure out the scientific issues of how to improve the temporal and spatial accuracy of foodborne disease outbreak risk prediction in Beijing. Firstly, we analyze the different foodborne disease risk factors caused by the spread of water pollution in Beijing, and study the methods of collecting and preprocessing multi-source data. Then, through the comparison of different regression models and parameters tuning, we propose the use of Gradient Boosting Regression (GBR) model as a multi-source data fusion model to predict the outbreak of foodborne disease. Finally, we use the risk map to detect and predict foodborne disease outbreak in different business districts of Beijing based on visualization techniques, aiming to provide prevention and control assessment for decision-makers quickly and precisely.
Disposable botnets: examining the anatomy of IoT botnet infrastructure

Large botnets made up of Internet-of-Things (IoT) devices have been a steady presence in the threat landscape since 2016. Earlier research has found preliminary evidence that the IoT binaries and C&C infrastructure were only seen for very brief periods. It has not explained how attackers maintain control over their botnets. We present a more comprehensive analysis of the infrastructure of IoT botnets based on 23 months of data gathered via honeypots and the monitoring of botnet infrastructure. We collected 59,884 IoT malware samples, 35,494 download servers, and 2,747 C&C servers. We focuse on three dominant families: Bashlite, Mirai, and Tsunami. The picture that emerges is that of highly disposable botnets. IoT botnet are not so much maintained as reconstituted from scratch all the time. Not only are most binaries distributed for less than three days, the connection of bots to the rest of the botnet is also short-lived. To reach the C&C server, the binaries typically contain only a single hard-coded IP address or domain. The C&C servers themselves also have a short lifespan. Long-term dynamic analysis finds no mechanism for the attackers to migrate the bots to a new C&C server. In other words, bots are used only immediately after capture and then abandoned---perhaps to be recaptured again via the aggressive scanning practices that these botnets are known for. While IoT botnets appear less advanced than Windows-based botnets, the advantage of being disposable means that they are very resistant to blacklisting and C&C takedown. Most IP addresses are used only once and never seen again. The question that arises is how attackers source these addresses. We speculate that they might be abusing the IP address allocation practices of cloud providers.
Exploring the characteristics of crowdsourcing: An online observational study

This article examines the application of crowdsourcing in research studies. The aim of this study is to understand how crowdsourcing is being used in research by undertaking a content analysis of studies posted to an online site designed to facilitate crowdsourced research. While there are a number of websites that facilitate crowdsourcing, this study provides an analysis only of research studies posted on crowdcrafting.org. Characteristics of crowdsourcing, proposed by Estellés-Arolas and González-Ladrón-de-Guevara, served as the framework for the content analysis, and research projects were evaluated as to how they addressed each of the proposed criteria. This article concludes with recommendations for researchers undertaking the design and implementation of projects employing crowdsourcing.
Orderings of Data - More Than a Tripping Hazard: Visionary

As data processing techniques get more and more sophisticated every day, many of us researchers often get lost in the details and subtleties of the algorithms we are developing and far too easily seem to forget to look also at the very first steps of every algorithm: the input of the data. Since there are plenty of library functions for this task, we indeed do not have to think about this part of the pipeline anymore. But maybe we should. All data is stored and loaded into a program in some order. In this vision paper we study how ignoring this order can (1) lead to performance issues and (2) make research results unreproducible. We furthermore examine desirable properties of a data ordering and why current approaches are often not suited to tackle the two mentioned problems.
Come as You Are: Helping Unmodified Clients Bypass Censorship with Server-side Evasion

Decades of work on censorship evasion have resulted in myriad ways to empower clients with the ability to access censored content, but to our knowledge all of them have required some degree of client-side participation. Having to download and run anti-censorship software can put users at risk, and does not help the many users who do not even realize they are being censored in the first place. In this paper, we present the first purely server-side censorship evasion strategies---11 in total. We extend a recent tool, Geneva, to automate the discovery and implementation of server-side strategies, and we apply it to four countries (China, India, Iran, and Kazakhstan) and five protocols (DNS-over-TCP, FTP, HTTP, HTTPS, and SMTP). We also perform follow-on experiments to understand why the strategies Geneva finds work, and to glean new insights into how censors operate. Among these, we find that China runs a completely separate network stack (each with its own unique bugs) for each application-layer protocol that it censors. The server-side techniques we find are easier and safer to deploy than client-side strategies. Our code and data are publicly available.
Draft Genome Sequence of Streptomyces morookaense DSM 40503, an 8-Azaguanine-Producing Strain

Here, we describe the genome of Streptomyces morookaense DSM 40503, an 8-azaguanine-producing strain. The genome is the basis for future study and presents an underexplored taxonomy and biosynthetic potential, which expands our understanding of the diversity of microorganisms that produce nitrogen heterocyclic compounds. ABSTRACT Here, we describe the genome of Streptomyces morookaense DSM 40503, an 8-azaguanine-producing strain. The genome is the basis for future study and presents an underexplored taxonomy and biosynthetic potential, which expands our understanding of the diversity of microorganisms that produce nitrogen heterocyclic compounds.
Swift: Delay is Simple and Effective for Congestion Control in the Datacenter

We report on experiences with Swift congestion control in Google datacenters. Swift targets an end-to-end delay by using AIMD control, with pacing under extreme congestion. With accurate RTT measurement and care in reasoning about delay targets, we find this design is a foundation for excellent performance when network distances are well-known. Importantly, its simplicity helps us to meet operational challenges. Delay is easy to decompose into fabric and host components to separate concerns, and effortless to deploy and maintain as a congestion signal while the datacenter evolves. In large-scale testbed experiments, Swift delivers a tail latency of <50μs for short RPCs, with near-zero packet drops, while sustaining ~100Gbps throughput per server. This is a tail of <3x the minimal latency at a load close to 100%. In production use in many different clusters, Swift achieves consistently low tail completion times for short RPCs, while providing high throughput for long RPCs. It has loss rates that are at least 10x lower than a DCTCP protocol, and handles O(10k) incasts that sharply degrade with DCTCP.
High-Quality Draft Genome Sequences of the Uncultured Delta3 Endosymbiont (Deltaproteobacteria) Assembled from Metagenomes of the Gutless Marine Worm Olavius algarvensis

Here, we present two high-quality, draft metagenome-assembled genomes of deltaproteobacterial OalgDelta3 endosymbionts from the gutless marine worm Olavius algarvensis. Their 16S rRNA gene sequences share 98% identity with Delta3 endosymbionts of related host species Olavius ilvae (GenBank accession no. AJ620501) and Inanidrilus exumae (GenBank accession no. FM202060), for which no symbiont genomes are available. ABSTRACT Here, we present two high-quality, draft metagenome-assembled genomes of deltaproteobacterial OalgDelta3 endosymbionts from the gutless marine worm Olavius algarvensis. Their 16S rRNA gene sequences share 98% identity with Delta3 endosymbionts of related host species Olavius ilvae (GenBank accession no. AJ620501) and Inanidrilus exumae (GenBank accession no. FM202060), for which no symbiont genomes are available.
Assessing the quality of evolving Haskell systems by measuring structural inequality

Software metrics are used to measure the quality of a software system, and to understand the evolution of the system's quality over time. In this paper we report on an empirical study that investigates whether structural degradation in Haskell systems is related to decreasing software quality. For our study we use three metrics that measure internal attributes at the level of Haskell modules: intra-modular complexity (cohesion), inter-modular complexity (coupling), and module size. For these metrics, we calculate the Gini coefficient, which is a measure of the inequality in a distribution of values within a certain population, and the deviation of the population's central tendency from an empirically established ideal value. We develop a method to track the evolution, and measure the correlation between the calculated system-level information and post-release defects. The results show that: (1) post-release defects are significantly correlated with the degree of inequality between the size of modules, (2) the inequality measure is able to indicate significant structural shifts in Haskell source code, and (3) the deviation of a population's central tendency from an ideal value can serve as a benchmark to evaluate the structural characteristics of a Haskell system. The results, however, do not show that a combined measure for inequality and ideal value deviation increases the ability to indicate the defect proneness of Haskell source code.
SoK: why Johnny can't fix PGP standardization

Pretty Good Privacy (PGP) has long been the primary IETF standard for encrypting email, but suffers from widespread usability and security problems that have limited its adoption. As time has marched on, the underlying cryptographic protocol has fallen out of date insofar as PGP is unauthenticated on a per message basis and compresses before encryption. There have been an increasing number of attacks on the increasingly outdated primitives and complex clients used by the PGP eco-system. However, attempts to update the OpenPGP standard have failed at the IETF except for adding modern cryptographic primitives. Outside of official standardization, Autocrypt is a "bottom-up" community attempt to fix PGP, but still falls victim to attacks on PGP involving authentication. The core reason for the inability to "fix" PGP is the lack of a simple AEAD interface which in turn requires a decentralized public key infrastructure to work with email. Yet even if standards like MLS replace PGP, the deployment of a decentralized PKI remains an open issue.
Akamai DNS: Providing Authoritative Answers to the World's Queries

We present Akamai DNS, one of the largest authoritative DNS infrastructures in the world, that supports the Akamai content delivery network (CDN) as well as authoritative DNS hosting and DNS-based load balancing services for many enterprises. As the starting point for a significant fraction of the world's Internet interactions, Akamai DNS serves millions of queries each second and must be resilient to avoid disrupting myriad online services, scalable to meet the ever increasing volume of DNS queries, performant to prevent user-perceivable performance degradation, and reconfigurable to react quickly to shifts in network conditions and attacks. We outline the design principles and architecture used to achieve Akamai DNS's goals, relating the design choices to the system workload and quantifying the effectiveness of those designs. Further, we convey insights from operating the production system that are of value to the broader research community.
MasQ: RDMA for Virtual Private Cloud

RDMA communication in virtual private cloud (VPC) networks is still a challenging job due to the difficulty in fulfilling all virtualization requirements without sacrificing RDMA communication performance. To address this problem, this paper proposes a software-defined solution, namely, MasQ, which is short for "queue masquerade". The core insight of MasQ is that all RDMA communications should associate with at least one queue pair (QP). Thus, the requirements of virtualization, such as network isolation and the application of security rules, can be easily fulfilled if QP's behavior is properly defined. In particular, MasQ exploits the virtio-based paravirtualization technique to realize the control path. Moreover, to avoid performance overhead, MasQ leaves all data path operations, such as sending and receiving, to the hardware. We have implemented MasQ in the OpenFabrics Enterprise Distribution (OFED) framework and proved its scalability and performance efficiency by evaluating it against typical applications. The results demonstrate that MasQ achieves almost the same performance as bare-metal RDMA for data communication.
Fault Tolerant Service Function Chaining

Network traffic typically traverses a sequence of middleboxes forming a service function chain, or simply a chain. Tolerating failures when they occur along chains is imperative to the availability and reliability of enterprise applications. Making a chain fault-tolerant is challenging since, in the event of failures, the state of faulty middleboxes must be correctly and quickly recovered while providing high throughput and low latency. In this paper, we introduce FTC, a system design and protocol for fault-tolerant service function chaining. FTC provides strong consistency with up to f middlebox failures for chains of length f + 1 or longer without requiring dedicated replica nodes. In FTC, state updates caused by packet processing at a middlebox are collected, piggybacked onto the packet, and sent along the chain to be replicated. Our evaluation shows that compared with the state of art [51], FTC improves throughput by 2-3.5X for a chain of two to five middleboxes.
Lyra: A Cross-Platform Language and Compiler for Data Plane Programming on Heterogeneous ASICs

Programmable data plane has been moving towards deployments in data centers as mainstream vendors of switching ASICs enable programmability in their newly launched products, such as Broadcom's Trident-4, Intel/Barefoot's Tofino, and Cisco's Silicon One. However, current data plane programs are written in low-level, chip-specific languages (e.g., P4 and NPL) and thus tightly coupled to the chip-specific architecture. As a result, it is arduous and error-prone to develop, maintain, and composite data plane programs in production networks. This paper presents Lyra, the first cross-platform, high-level language & compiler system that aids the programmers in programming data planes efficiently. Lyra offers a one-big-pipeline abstraction that allows programmers to use simple statements to express their intent, without laboriously taking care of the details in hardware; Lyra also proposes a set of synthesis and optimization techniques to automatically compile this "big-pipeline" program into multiple pieces of runnable chip-specific code that can be launched directly on the individual programmable switches of the target network. We built and evaluated Lyra. Lyra not only generates runnable real-world programs (in both P4 and NPL), but also uses up to 87.5% fewer hardware resources and up to 78% fewer lines of code than human-written programs.
Determining the provenance of land parcel polygons via machine learning

An important task on land registration processes is to be able to determine the prevalent data provenance for a finalized polygon that represents a cadastral parcel, since the finalized polygon is derived by the examination of a set of initial polygons, drawn from several individual registers (databases). These registers might contain different, partially similar or conflicting information regarding the ownership, usage and polygon geometry of a cadastral parcel. In such cases, the cadastration expert either select one of of the initial geometries, or (in cases none of the initial accurately represents the finalized land parcel) creates a new geometry. Maintaining this provenance information is of high importance for further cadastration and validation/quality assessment processes; however, due to the gradual and long lasting nature of cadastration procedures, this information is absent from large parts of cadastral databases. In this paper, we present an approach for effectively classifying such land parcel polygons with respect to their provenance information. We propose a method that can produce highly accurate provenance recommendations based only on attributes derived from the geometry of a land parcel. In particular, we implement a set of spatial training features, capturing polygon properties and relations. These features are fed into several classification algorithms and are evaluated on a proprietary dataset of a cadastration company.
PaperW8: an IoT bricking ransomware proof of concept

Internet of Things (IoT) devices are used in many facets of modern life, from smart homes to smart cities, including Internet-enabled healthcare systems and industrial control systems. The prevalence and ubiquity of IoT devices makes them extremely attractive targets for malicious actors, in particular for taking control of vulnerable devices and demand ransom from their owners. The aim of this paper is twofold: to investigate the viability of a ransomware-type attack being carried out on IoT devices; and to explore what damage can be inflicted upon devices after they have been compromised. To test whether ransomware is a viable method for attacking IoT devices, we developed our own proof of concept malware for Linux-based IoT devices dubbed "PaperW8". We looked at feasible ways for infecting IoT devices, as well as potential methods for gaining control and applying persistent changes to the target device. We successfully created a proof of concept ransomware, which we tested against six vulnerable IoT devices of various brands and functions, some of which are known to have been targeted in the past but are still widely in use today. Developing this proof of concept tool allowed us to identify the main requirements for a successful ransomware attack against IoT devices. We also determined some limitations of IoT devices that may discourage attackers from developing IoT-specific ransomware, while highlighting workarounds that more determined attackers may use to overcome these obstacles. This paper has demonstrated that IoT ransomware is a credible threat. We implemented a proof of concept tool that can compromise many IoT devices of varying types. We envisage that this work can be used to assist current and future IoT developers to improve the security of their devices, and also to help security researchers in implementing more effective ransomware countermeasures, including for IoT devices.
VisualDroid: automatic triage and detection of Android repackaged applications

Considering the pervasiveness of mobile devices, malicious writers are constantly focusing their attention in developing malicious payload aimed to gather sensible information from mobile devices without user content. As a matter of fact, it is really easy for malware writers to embed malicious payloads into legitimate applications, by applying the so-called repackaging paradigm, to generate a sample with a signature unknown to anti-malware software. In this paper we propose a twofold approach for the triage and the detection of repackaged Android applications. We propose a visualization schema to assist the malware analyst in the triage of unseen applications and a set of metrics for the automatic detection of repackaged applications. Experimental results show the effectiveness of the proposed approach.
Personality effect on driving behavior

Task type may have influence on driving behavior in disasters, while personality trait may have moderating effects on driving behavior in different task types, there are few studies focus on it. In this paper, the research method of laboratory experiment and questionnaires is used to investigate the impact of altruistic task and self-interest task on driving behavior. And the personality trait is taken as the moderating variable to research the moderating effect of personality trait on driving behavior in different task types. The results show that, (1) Compared with self-interest task, altruistic task has critically significant influence on average acceleration, which indicates driver will improve the driving performance in altruistic task. (2) Personality trait of emotional stability has a moderating effect in the influence mechanism of task type on driving behavior. The results of this paper may provide scientific support to traffic evacuation and emergency management in disasters.