Miracle-dz/UniFAR-FTUs huggingface dataset:
Composing Dataplane Programs with μP4

Dataplane languages like P4 enable flexible and efficient packet-processing using domain-specific primitives such as programmable parsers and match-action tables. Unfortunately, P4 programs tend to be monolithic and tightly coupled to the hardware architecture, which makes it hard to write programs in a portable and modular way---e.g., by composing reusable libraries of standard protocols. To address this challenge, we present the design and implementation of a novel framework (μP4) comprising a lightweight logical architecture that abstracts away from the structure of the underlying hardware pipelines and naturally supports powerful forms of program composition. Using examples, we show how enables modular programming. We present a prototype of the compiler that generates code for multiple lower-level architectures, including Barefoot's Tofino Native Architecture. We evaluate the overheads induced by our compiler on realistic examples.
SoK: exploring the state of the art and the future potential of artificial intelligence in digital forensic investigation

Multi-year digital forensic backlogs have become commonplace in law enforcement agencies throughout the globe. Digital forensic investigators are overloaded with the volume of cases requiring their expertise compounded by the volume of data to be processed. Artificial intelligence is often seen as the solution to many big data problems. This paper summarises existing artificial intelligence based tools and approaches in digital forensics. Automated evidence processing leveraging artificial intelligence based techniques shows great promise in expediting the digital forensic analysis process while increasing case processing capacities. For each application of artificial intelligence highlighted, a number of current challenges and future potential impact is discussed.
Detection of methylated BCAT1 and IKZF1 in stage II/III rectal cancer receiving chemoradiation.

602 Background: In liquid biopsy, analyses of circulating tumor DNA (ctDNA) in blood have been used as a tool in cancer screening, identifying tumor mutations and monitoring treatment responses/recurrences. Aberrant methylation of some genes have been associated with cancer. Two genes, BCAT1 and IKZF1, are highly methylated and specific to colorectal cancer (CRC) in blood. Detection of these two methylated genes in post-surgery samples also correlates with higher chance of recurrence. Here we investigated the presence of methylated BCAT1 and IKZF1 at multiple time-points for each patient. Methods: Plasma was extracted from blood within 4 hours of the blood collection. ctDNA were extracted from plasma and bisulphite converted using commercially available kits. Real-time qPCR was used to analyze the converted DNA in three replicates and deemed positive if at least one replicate detected either methylated BCAT1 or IKZF1. Quantification of both genes were determined using separate gene-specific standard curves. Results: Nine Stage II/III rectal cancer (RC) patients was enrolled into the study, with one pre-treatment blood draw and several subsequent draws collected throughout the treatment cycles. five patients tested positive for at least one of the methylated genes prior to treatment. Four of these five patients showed an immediate drop in detection for the assayed genes after one cycle of chemotherapy and either remains at low concentration or is undetectable through the rest of the treatment cycles. These patients either have partial or complete response to the treatment regimen. One patient with continuous high levels of methylated BCAT1 and IKZF1 post-treatment have recurred two months after surgery. Conclusions: Methylated BCAT1 and/or IKZF1 were detected in over 50% of the Stage II/III RC patients pre-treatment. This assay is sensitive to the effect of chemotherapy regimen. A significant number of these patients showed a partial/complete response to their treatment regimen and/or reduction in tumor burden, which is reflected in the loss of detection of these two genes. This study shows that this assay may be a viable tool to pair with Imaging for assessment of patient response to treatment regimen.
Switch Code Generation Using Program Synthesis

Writing packet-processing programs for programmable switch pipelines is challenging because of their all-or-nothing nature: a program either runs at line rate if it can fit within pipeline resources, or does not run at all. It is the compiler's responsibility to fit programs into pipeline resources. However, switch compilers, which use rewrite rules to generate switch machine code, often reject programs because the rules fail to transform programs into a form that can be mapped to a pipeline's limited resources---even if a mapping actually exists. This paper presents a compiler, Chipmunk, which formulates code generation as a program synthesis problem. Chipmunk uses a program synthesis engine, SKETCH, to transform high-level programs down to switch machine code. However, naively formulating code generation as program synthesis can lead to long compile times. Hence, we develop a new domain-specific synthesis technique, slicing, which reduces compile times by 1-387x and 51x on average. Using a switch hardware simulator, we show that Chipmunk compiles many programs that a previous rule-based compiler, Domino, rejects. Chipmunk also produces machine code with fewer pipeline stages than Domino. A Chipmunk backend for the Tofino programmable switch shows that program synthesis can produce machine code for high-speed switches.
Boosted molecular mobility during common chemical reactions

Reactions give solvents a kick During a chemical reaction, the reorganization of solvent molecules not directly in contact with reactants and products is normally viewed as a simple diffusion response. Wang et al. studied molecular diffusion in six common reactions—including the copper-catalyzed click reaction and the Diels-Alder reaction—with pulsed-field gradient nuclear magnetic resonance. They observed a boost in mobility relative to Brownian diffusion that was stronger for the catalyzed reactions that were studied. The mobilities for the click reaction were verified with a microfluidic gradient method. They argue that energy release produces transient translational motion of reacting centers that mechanically perturbs solvent molecules. Science, this issue p. 537 Common organic reactions mechanically perturb solvents and increase molecular mobility beyond simple Brownian diffusion. Mobility of reactants and nearby solvent is more rapid than Brownian diffusion during several common chemical reactions when the energy release rate exceeds a threshold. Screening a family of 15 organic chemical reactions, we demonstrate the largest boost for catalyzed bimolecular reactions, click chemistry, ring-opening metathesis polymerization, and Sonogashira coupling. Boosted diffusion is also observed but to lesser extent for the uncatalyzed Diels-Alder reaction, but not for substitution reactions SN1 and SN2 within instrumental resolution. Diffusion coefficient increases as measured by pulsed-field gradient nuclear magnetic resonance, whereas in microfluidics experiments, molecules in reaction gradients migrate “uphill” in the direction of lesser diffusivity. This microscopic consumption of energy by chemical reactions transduced into mechanical motion presents a form of active matter.
Learning Trajectory Routing with Graph Neural Networks

With the widespread application of vehicle GPS equipment, more and more trajectory data are becoming available. Traditional research on trajectory-based route planning uses trajectory data to build a weighted graph, and then obtains routes using classic graph theory-based search algorithms, which has difficulty in estimating multiple preferences. The Recurrent Graph Network (RGN) [1] is a graph neural network, which solves the shortest path problem as a classification problem, i.e., given arbitrary (source, destination) pair, classifying whether the nodes and edges in the graph should be labeled as "on the shortest path connecting the source and destination". Since the RGN model has non-linear feature representation ability and multiple feature fusion ability, this paper considers modifying it for the trajectory-based route planning. However, the actual trajectory data is highly sparse and non-uniform distributed, so that learning trajectory routing in large road networks is a few-shot learning task. To address the aforementioned challenges, this paper proposes the Long Short-Term Memory Graph Network (LSTM-GN) model. The LSTM-GN utilizes a deep LSTM-style message passing process to update various features including comprehensive node-level features, edge-level features and graph-level features, achieving trajectory routing patterns representation based on fewer samples. Additionally, the LSTM-GN needs to get over the difficulty of severe class imbalance and high computational complexity. This paper addresses the above challenges from two perspectives. Firstly, this paper proposes the subgraph filtering algorithms to narrow learning space. Secondly, this paper designs a dynamic weighted loss function to strengthen the positive class. This paper conducts experiments on the real-world trajectory dataset, and the main evaluation metric F1-score of LSTM-GN exceeds the RGN model by 19.18%.
The effects of reciprocal cross on inheritance of DNA methylation in cotton ( Gossypium hirsutum )

DNA methylation plays an important role for regulation of gene expression. To study the inheritance of DNA methylation, we selected two F 1 plant population by reciprocal cross with two cotton lines Zongcaixuan No.1 and HY428, and analyzed the variations of DNA methylation levels and patterns in F 1 generations by methylation sensitive amplified fragment length polymorphism (MSAP) technique with 54 primer combinations. The results show that most cytosine methylated patterns are conservatively inherited from parents. The numbers of variant sites are less in F 1 generation. According to the number of sites individually inherited from female and male parents, the different choice of female and male parents made a big distinction between the sites inherited from female parent and the sites inherited from the male parent. For reciprocal cross F113 and F132, the number for sites of cytosine methylation patterns inherited from the female parent are far more than that from the male parent, which may be closely related to cytoplasmic inheritance. Key words : Methylation sensitive amplified fragment length polymorphism (MSAP), DNA methylation, cytoplasmic inheritance, reciprocal cross, cotton.
A Low Latency and Consistent Cellular Control Plane

5G networks aim to provide ultra-low latency and higher reliability to support emerging and near real-time applications such as augmented and virtual reality, remote surgery, self-driving cars, and multi-player online gaming. This imposes new requirements on the design of cellular core networks. A key component of the cellular core is the control plane. Time to complete control plane operations (e.g. mobility handoff, service establishment) directly impacts the delay experienced by end-user applications. In this paper, we design Neutrino, a cellular control plane that provides users an abstraction of reliable access to cellular services while ensuring lower latency. Our testbed evaluations based on real cellular control traffic traces show that Neutrino provides an improvement in control procedure completion times by up to 3.1x without failures, and up to 5.6x under control plane failures, over existing cellular core proposals. We also show how these improvements translate into improving end-user application performance: for AR/VR applications and self-driving cars, Neutrino performs up to 2.5x and up to 2.8x better, respectively, as compared to existing EPC.
Jointly Leveraging Intent and Interaction Signals to Predict User Satisfaction with Slate Recommendations

Detecting and understanding implicit measures of user satisfaction are essential for enhancing recommendation quality. When users interact with a recommendation system, they leave behind fine grained traces of interaction signals, which contain valuable information that could help gauging user satisfaction. User interaction with such systems is often motivated by a specific need or intent, often not explicitly specified by the user, but can nevertheless inform on how the user interacts with, and the extent to which the user is satisfied by the recommendations served. In this work, we consider a complex recommendation scenario, called Slate Recommendation, wherein a user is presented with an ordered set of collections, called slates, in a specific page layout. We focus on the context of music streaming and leverage fine-grained user interaction signals to tackle the problem of predicting user satisfaction. We hypothesize that user interactions are conditional on the specific intent users have when interacting with a recommendation system, and highlight the need for explicitly considering user intent when interpreting interaction signals. We present diverse approaches to identify user intents (interviews, surveys and a quantitative approach) and identify a set of common intents users have in a music streaming recommendation setting. Additionally, we identify the importance of shared learning across intents and propose a multi-level hierarchical model for user satisfaction prediction that leverages user intent information alongside interaction signals. Our findings from extensive experiments on a large scale real world data demonstrate (i) the utility of considering different interaction signals, (ii) the role of intents in interpreting user interactions and (iii) the interplay between interaction signals and intents in predicting user satisfaction.
A Simple Method for Finding Local Sequence Similarities

Conventional database search programs for finding local similarities in protein and DNA sequences, such as the one based on the Smith-Waterman algorithm, FASTA, and BLAST, can contain subregions having high similarity, low similarity, and even no similarity. We propose a simple method for finding significant local sequence similarity regions, where the alignment results of two sequences are graphed as integrated scores calculated along the aligned sequences using the match, mismatch, and gap penalty scores. This method has been used to find local similarity subregions in alignment results obtained by BLAST or the Smith-Waterman algorithm. Potential applications for finding domain structures and the characteristic sequence patterns are also shown. The dynamic programming algorithm, originally developed by Needleman and Wunsch [1] and modified by Smith and Waterman [2], is the most widely used and accepted method for finding local similarities between two DNA or protein sequences. Since this method is very time consuming, rapid sequence comparison algorithms, such as FASTA [3] and BLAST [4], have been developed. These methods align two sequences and calculate similarity scores subject to the match, mismatch, and gap penalties, the latter not being allowed in the BLAST algorithm. Alignment is performed in the region from a score of zero to the maximum, where the scores can have any value larger than zero and smaller than the maximum. Therefore, it can contain subregions having no similarities as long as the above-mentioned condition is satisfied. 1(株)日 立製作所 中央研究 所 、東京都国 分寺市 東恋が窪1. 280 2東 京大学 医科学研 究所 ヒ トゲ ノム解析 セ ンター、 東京 都港区 白金台4-611
Confiance: detecting vulnerabilities in Java Card applets

This study focuses on automatically detecting wrong implementations of specifications in Java Card programs, without any knowledge on the source code or the specification itself. To achieve this, an approach based on Natural Language Processing and machine-learning is proposed. First, an oracle gathering methods with similar semantics in groups, is created. This focuses on evaluating our approach performances during the neighborhood discovery. Based on the groups of similar methods automatically retrieved, the anomaly detection relies on the Control Flow Graph of programs of these groups. In order to benchmark our approach's ability to detect vulnerabilities, an oracle of anomaly is created. This oracle knows every anomaly the approach should automatically retrieve. Both the neighborhood discovery and the anomaly detection steps are benchmarked. This approach is implemented in a tool: Confiance, and it is compared to another machine-learning tool for automatic vulnerability detection. The results expose the better performances of Confiance to detect vulnerabilities in open-source programs available online.
Generalizing the phishing principle: analyzing user behavior in response to controlled stimuli for IT security awareness assessment

Capturing behavioral data to assess users' IT security awareness is state of the art. However, recording the click rate on a company wide phishing test for IT security awareness measurement does not suffice. Perceivable artifacts, that the user might be exposed to during an attack, are manifold. We introduce a framework that allows capturing user's responses to such artifacts similar to phishing tests. A field study among 259 users shows, that the expected effect of a well-established IT security awareness intervention can be demonstrated using arbitrary artifacts. It also shows that this intervention may impair the probability of a user reporting the sighting of an artifact and therefore impair an organization's capability to detect such events and possibly decrease overall security.
Death of a Monopoly?

Hunot and Flavell discuss recent findings in Nature by Joza et al. that point to the existence of a new sort of cell death. Cell death is required during the development of an organism to remove portions of the body that will not be needed; this cell death usually requires destructive enzymes called caspases to perform the final demolition of the cell. But cavitation, an early developmental process in the mouse embryo that requires cell death, has now been suggested to occur through a caspase-independent pathway that instead uses a mitochondrial protein called AIF (apoptosis-inducing factor).
Annulus: A Dual Congestion Control Loop for Datacenter and WAN Traffic Aggregates

Cloud services are deployed in datacenters connected though high-bandwidth Wide Area Networks (WANs). We find that WAN traffic negatively impacts the performance of datacenter traffic, increasing tail latency by 2.5x, despite its small bandwidth demand. This behavior is caused by the long round-trip time (RTT) for WAN traffic, combined with limited buffering in datacenter switches. The long WAN RTT forces datacenter traffic to take the full burden of reacting to congestion. Furthermore, datacenter traffic changes on a faster time-scale than the WAN RTT, making it difficult for WAN congestion control to estimate available bandwidth accurately. We present Annulus, a congestion control scheme that relies on two control loops to address these challenges. One control loop leverages existing congestion control algorithms for bottlenecks where there is only one type of traffic (i.e., WAN or datacenter). The other loop handles bottlenecks shared between WAN and datacenter traffic near the traffic source, using direct feedback from the bottleneck. We implement Annulus on a testbed and in simulation. Compared to baselines using BBR for WAN congestion control and DCTCP or DCQCN for datacenter congestion control, Annulus increases bottleneck utilization by 10% and lowers datacenter flow completion time by 1.3-3.5x.
Beyond the mega-data center: networking multi-data center regions

The difficulty of building large data centers in dense metro areas is pushing big cloud providers towards a different approach to scaling: multiple smaller data centers within tens of kilometers of each other, comprising a "region". We show that networking this small number of nearby sites with each other is a surprisingly challenging and multi-faceted problem. We draw out the operational goals and constraints of such networks, and highlight the design trade-offs involved using data from Microsoft Azure's regions. Our analysis of the design space shows that network topologies that achieve lower latency and allow greater flexibility in data center placement are, unfortunately, encumbered by their much greater cost and complexity. We thus present and demonstrate a novel optical-circuit-switched architecture, Iris, that lowers these cost and complexity barriers, making a richer topology design space more accessible to operators of regional networks. With Iris, topologies which, in comparison to a simple hub-and-spoke topology can increase the area in which a new DC can be placed by 2-5x, can be implemented at a cost within 1.1x of the simple hub-and-spoke topology, and 7x cheaper than a natural packet-switched network.
ASAINT: a spy App identification system based on network traffic

Spy app is a class of malware for mobile devices that allows an adversary to steal sensitive information. Detecting spy apps is challenging because they do not rely on classic malware techniques, for instance, they use standard services to store stolen data, and do not perform privileges escalation on the victim phone. Thus, their behavior is generally closer to the benign apps and poses new challenges for their detection. In this paper, we propose ASAINT: A Spy App Identification System based on Network Traffic. To the best of our knowledge, ASAINT is the first system capable of detecting spy apps in a network without any physical or software control of the victim mobile device. Core of our approach is a wide range of non-intrusive network detection methods designed by studying several popular spy apps. We test ASAINT on a self-collected dataset containing network traffic from both spy and benign applications, either on Android and iOS. Our result is an F1-score of 0.85 on average, that confirms the effectiveness of ASAINT. Moreover, our analysis provides a methodological classification of the exfiltration strategies used by spy apps in different operating systems. In sum, our work gives new and practical insights about the detection of modern spy apps, paving the way for future research in detecting this class of malware.
SOK: cryptocurrency networking context, state-of-the-art, challenges

Cryptocurrencies such as Bitcoin are realized using distributed systems and hence critically rely on the performance and security of the interconnecting network. The requirements on these networks and their usage, however can differ significantly from traditional communication networks, with implications on all layers of the protocol stack. This paper is motivated by these differences, and in particular by the observation that many fundamental design aspects of these networks are not well-understood today. In order to support the networking community to contribute to this emerging application domain, we present a structured overview of the field, from topology and neighbor discovery to block and transaction propagation. In particular, we provide the context, highlighting differences and commonalities with traditional networks, review the state-of-the-art, and identify open research challenges. Our paper can hence also be seen as a call-to-arms to improve the foundation on top of which cryptocurrencies are built.