Reviewerly/exHarmony huggingface dataset:
Yarrpbox: Detecting Middleboxes at Internet-Scale

The end-to-end principle is one of the foundations of the original Internet architecture. This principle is put to the test by middleboxes, i.e., devices which change important parts of a packet in transit. Middleboxes can have beneficial effects, such as a lower handshake time, but also make it more difficult to deploy newly developed protocols, such as TLS 1.3 and QUIC. Therefore, it is important to have a good understanding of the middlebox ecosystem in the Internet. In this paper, we present results from a multi-faceted middlebox analysis study. We develop Yarrpbox, a tool to efficiently perform middlebox detection measurements on an Internet-scale. With Yarrpbox, we perform IPv4-wide middlebox detection and find that nearly 10% of paths are affected by a total of 5.8k middlebox devices. We perform the first IPv6 study to date, uncovering a lower prevalence of middleboxes in IPv6. Moreover, we show that the location of a vantage point can have an effect on the results, leading to up to 600 more detected middleboxes. Additionally, we characterize middleboxes by mapping them to vendors and resolving aliases. Finally, we release Yarrpbox as open-source software and make data and analysis code publicly available.
Nonlinear optical switch based on coherent perfect absorption

Coherent perfect absorption (CPA) or reflection (CPR) are methods to realize the extreme manipulation on an optical field. We propose a scheme to operate a bistable switch with convertible CPA and/or CPR. Generally, CPA and CPR occur with different input-field phases. For example, CPA is realized when two input probe beams are in phase; instead, CPR is achieved when they are out of phase. In this scheme, a CPA state can be converted to a CPR state by an incoherent field although two input fields are in phase. When we use the incoherent field as a switching field, the CPA (CPR) state is treated as the closed (open) state. As a result, the switching efficiency can theoretically reach a maximum value, i.e., η = 1. In addition, the switch can be operated in the linear regime with a weak input field, and in the nonlinear or bistable regime with a strong input field. Moreover, the efficiency of the bistable switch is sensitively dependent on the input-field intensity. It provides a potential application of this work on sensitive optical detecting.
Rensets and Renaming-Based Recursion for Syntax with Bindings Extended Version

Abstract We introduce renaming-enriched sets ( rensets for short), which are algebraic structures axiomatizing fundamental properties of renaming (also known as variable-for-variable substitution) on syntax with bindings. Rensets compare favorably in some respects with the well-known foundation based on nominal sets. In particular, renaming is a more fundamental operator than the nominal swapping operator and enjoys a simpler, equationally expressed relationship with the variable-freshness predicate. Together with some natural axioms matching properties of the syntactic constructors, rensets yield a truly minimalistic characterization of $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> -calculus terms as an abstract datatype—one involving an infinite set of unconditional equations , referring only to the most fundamental term operators: the constructors and renaming. This characterization yields a recursion principle, which (similarly to the case of nominal sets) can be improved by incorporating Barendregt’s variable convention. When interpreting syntax in semantic domains, our renaming-based recursor is easier to deploy than the nominal recursor. Our results have been validated with the proof assistant Isabelle/HOL.
Nonlocal Superconducting Single-Photon Detector

We present and theoretically analyze the performance of a nonlocal superconducting single-photon detector. The device operates due to the energy-to-phase conversion mechanism, where the energy of the absorbed single photon is transformed in a variation of the superconducting phase. Within this scope, the detector is designed in the form of a double-loop superconductor--normal-metal--superconductor ($S$-$N$-$S$) Josephson interferometer, where the detection occurs in a long $S$-$N$-$S$ junction and the readout is operated by a short $S$-$N$-$S$ junction. The variation of the superconducting phase across the readout junction is measured by recording the quasiparticle current flowing through a tunnel-coupled superconducting probe. By exploiting realistic geometry and materials, the detector is able to reveal single photons of frequency down to 10 GHz when operated at 10 mK. Furthermore, the device provides a value of the signal-to-noise ratio of up ${10}^{4}$ in the range from 10 GHz to 10 THz by selection of the magnetic flux and the bias voltage. This device will hopefully find direct application as a single-photon detector in both basic science and quantum technology, while the energy-to-phase conversion mechanism could be the basis of nonlocal readout and memory architectures for superconducting qubits.
CNN-BiLSTM based GAN for Anamoly Detection from Multivariate Time Series Data

Continuous recording of sensor data for monitoring applications does require detection of data patterns which deviate from normal condition. Detection of such events is necessary for implementing early preventive methods to improve overall system performance and potentially identify sensor failure causes. Recently, deep learning techniques based on generative models such as generative adversarial network (GAN) are proposed for anomaly detection from multiple time-series data. In this research, a variant deep learning method-based GAN is proposed for anomaly detection from multivariate time series data. Based on our proposed approach, the generator block consists of both CNN and BiLSTM blocks whilst the discriminator uses BiLSTM. To evaluate the performance of our new approach, multiple recordings from soil moisture measurement system are used to compare our proposed framework to the state-of-the-art techniques. Our proposed CNN-BiLSTM based GAN model presents an improved performance for the soil moisture recordings.
User Profile-aware Daily Activity Prediction

Human activity Prediction mechanisms are a challenging issue discussed within literature. Daily human activities are complex and are constituted of multiple actions, where each one provides important information about a person’s lifetime and routine. These mechanisms use this information as a baseline for the prediction of humans’ next activity, by decomposing and examining the sequences of all the activities a person has performed using information that has been collected by sensors. This paper proposes a Human Activity Prediction module based on a Recurrent Neural Networks (RNN) - Long Short-Term Memory (LSTM) network, for predicting the next activity of a user based on the information included in the user’s personalized activity profile. The activity profile is constituted of sequences of historical activities performed by an individual along with their demographic characteristics. Aside from the method, this paper presents also its three points evaluations, by comparing (a) its results with a Markov Chain model, (b) the effect that the number of activities included in a sequence has on the accuracy of prediction and (c) by evaluating the introduction of characteristics as input to the network alongside with the insertion of historical sequences or not.
Visualizing Invariant Features in Vision Models

Explainable AI is important for improving transparency, accountability, trust, and ethical considerations in AI systems, and for enabling users to make informed decisions based on the outputs of these systems. It provides insights into the factors that drove a particular machine learning model prediction. In the context of deep learning models, invariance refers to the property whereby diverse input transformations, such as data augmentations, result in similar feature spaces and predictions. The aim of this work is to unveil what invariant features the model has learned. We propose a method coined as Pixel Invariance, which measures the invariance of each pixel of the input. Our investigation involves an analysis of four self-supervised models, as these models are pre-trained to learn invariance to input transformations. We additionally perform quantitative evaluation measures to assess the faithfulness, reliability and confidence of the explanation map, and analyze the four self-supervised models both qualitatively and quantitatively.
Market Efficiency Improvements from Technical Developments of Decentralized Crypto Exchanges

A decentralized exchange (DEX) records every transaction on a blockchain, so it is much slower than a centralised exchange (CEX) which is only linked to a blockchain via a wallet, so no trades are recorded directly on the chain. While all CEXs use a traditional limit order book, most DEXs use a different, automated market making approach. Uniswap is the largest DEX for general crypto, especially for ether (ETH) this being the native token of the dominant smart contract blockchain, Ethereum. Using minute-level ETH spot prices, we examine the price discovery and informational efficiency of Uniswap-v2 and the recent updated automated market maker version, Uniswap-v3. We compare them with two CEXs that are dominant in ether spot trading, i.e. Coinbase and Bitstamp. We find that Uniswap-v3 is much improved relative to Uniswap- v2, even approaching Bitstamp in its price discovery ability. This is strong support for the significant financial influences of the technical advances implemented by Uniswap-v3. Yet our results still raise two concerns, first about the poor market efficiency of Uniswap-v3 and secondly about a complete change of long-memory patterns in ETH spot returns obtained from Uniswap- v3 relative to Uniswap-v2. These findings indicate there is yet further technical development required for DEXs to attract more ordinary investors. The lack of efficiency means there are many arbitrage opportunities on Uniswap, which professional traders can take advantage of at the expense of less experienced players.
EVAA—Exchange Vanishing Adversarial Attack on LiDAR Point Clouds in Autonomous Vehicles

In addition to RGB camera sensors, LiDAR (Light Detection and Ranging) plays an important role in autonomous vehicles (AVs) to perceive their surroundings. Deep neural networks (DNNs) are able to achieve cutting-edge 3D object detection and segmentation performance using LiDAR point clouds. LiDAR-enabled autonomous vehicles provide human perception by segmenting LiDAR point clouds into meaningful regions and providing semantic context to the AV user. However, the generation of point clouds to provide semantic segmentation in AVs is not reliable and secure, which may result in traffic accidents. We propose a novel adversarial attack against LiDAR point clouds in autonomous vehicles in this paper. We devised an exchange vanishing adversarial attack (EVAA) to deceive LiDAR point clouds by introducing targeted noise on specific objects (e.g., vehicles and driveways). On two autonomous driving datasets with 3D object annotations, NuScenes and PandaSet, we evaluate the performance of our proposed attack framework. We achieve an attack success rate (ASR) of ≈63% and ASR of ≈29% on both NuScenes and PandaSet datasets, respectively.
OpenFact: Factuality Enhanced Open Knowledge Extraction

Abstract We focus on the factuality property during the extraction of an OpenIE corpus named OpenFact, which contains more than 12 million high-quality knowledge triplets. We break down the factuality property into two important aspects—expressiveness and groundedness—and we propose a comprehensive framework to handle both aspects. To enhance expressiveness, we formulate each knowledge piece in OpenFact based on a semantic frame. We also design templates, extra constraints, and adopt human efforts so that most OpenFact triplets contain enough details. For groundedness, we require the main arguments of each triplet to contain linked Wikidata1 entities. A human evaluation suggests that the OpenFact triplets are much more accurate and contain denser information compared to OPIEC-Linked (Gashteovski et al., 2019), one recent high-quality OpenIE corpus grounded to Wikidata. Further experiments on knowledge base completion and knowledge base question answering show the effectiveness of OpenFact over OPIEC-Linked as supplementary knowledge to Wikidata as the major KG.
SARNas: A Hardware-Aware SAR Target Detection Algorithm via Multiobjective Neural Architecture Search

Most of the existing deep learning-based SAR target detection algorithms rely on manual experience to repeatedly adjust structures and parameters to design models suitable for specific scenarios or tasks. The implementation of the above methods is complicated, the design efficiency is low, and it is difficult to ensure the balance between accuracy and complexity. We innovatively propose a hardware-aware SAR target detection algorithm via multiobjective neural architecture search (NAS), referred to as SARNas. First, we design a flexible and efficient search space, a supernet search strategy and a subnet contribution evaluation strategy. Furthermore, we construct a new NAS loss function, called SARMI-Loss, to guide the learning of a SAR object detector that balances accuracy and computational complexity. Our SAR-Nas method can address the resource limitations of edge devices and automatically search for the optimal SAR target detector in an end-to-end manner for any deep learning-based SAR baseline model. A series of comparative experiments on three SAR image object detection datasets (SSDD, HRSID and MSAR) demonstrate the superiority of our method. The experimental results with YOLOV5 as the benchmark model show that the detection accuracy of the target detection networks automatically found by using the SARNas method on the SSDD, HRSID, and MSAR datasets can reach 98.5%, 92.8%, and 91.8% in mean average precision (mAP) with only 2.31M, 1.99M, 2.21M parameters, respectively. The number of model parameters is reduced by 88.9%, 90.46%, and 68.5%, respectively, and the inference speed is increased by 51.6%, 46.1%, and 13.9% without losing accuracy.
An Improved Sand Cat Swarm Operation and Its Application in Engineering

Meta-heuristic algorithms have emerged as a popular approach for solving engineering optimization problems. In this paper, an Improved Sand Cat Swarm Operation (ISCSO) is proposed and applied to optimize double-layer spraying path parameters. The Sand Cat Swarm Operation (SCSO) has some limitations, such as poor initial population quality, slow convergence speed, and a tendency to fall into local optima. To overcome these limitations, three improvement strategies are introduced in ISCSO. Firstly, the SPM chaotic mapping is used to enhance the initial population quality. Secondly, a nonlinear cycle adjustment strategy is introduced to balance global exploration and local exploitation, thereby accelerating the convergence speed. Finally, integrating the Immune Algorithm (IA) enables ISCSO to avoid falling into local optima, resulting in improved solution accuracy. Furthermore, we extended our experiments to include 21 low-dimensional functions and 15 test functions of LSOPs, where ISCSO was compared with seven other popular algorithms. The experimental results highlight the promising performance of ISCSO in solving different types of functions, achieving both higher solution accuracy and faster convergence speed. In particular, the effectiveness of ISCSO has been demonstrated through experiments aimed at optimizing the parameters of the double-layer spraying path. The results of these experiments further highlight the utility of ISCSO in tackling challenging optimization problems.
Online Action Detection in Surveillance Scenarios: A Comprehensive Review and Comparative Study of State-of-the-Art Multi-Object Tracking Methods

Online action detection in surveillance scenarios presents considerable challenges, particularly due to the dynamically changing environments and real-time processing requirements. Within this context, Multi-Object Tracking (MOT) serves as a critical component of the online action detection pipeline. Despite the emergence of several state-of-the-art (SOTA) object trackers in recent years, a gap remains in the comprehensive evaluation of these trackers specifically for action detection in surveillance scenarios. This paper bridges this gap by offering a thorough study of SOTA MOT trackers, aimed at determining the influential factors affecting their performance in surveillance settings and identifying the trackers optimally suited for an online action detection pipeline. For relevance and rigor, we introduce SurvTrack, a new dataset derived from a subset of VIRAT—a dataset explicitly designed for action detection tasks—but intended for object tracking. SurvTrack is utilized to assess these trackers under various conditions, including differing image resolutions and detector confidence thresholds. This study uncovers the distinctive strengths and weaknesses of each tracker, providing invaluable insights for researchers and practitioners in surveillance and action detection. Importantly, this work focuses on tracking methods within the action detection domain, underscoring the development of a tracker explicitly designed for action detection on pertinent datasets, such as VIRAT.
Hyperspectral Image Denoising Via Texture-Preserved Total Variation Regularizer

The total variation (TV) regularizer is a widely used technique in image processing tasks to model an image’s local smoothness property. Intrinsically, the TV regularizer imposes sparsity constraints on the gradient maps of the image, which inevitably weakens the image texture structure and thus affects the quality of image restoration. To alleviate this issue, we propose a novel texture-preserved total variation (TPTV) regularizer for hyperspectral image (HSI) by introducing a weighting scheme. Specifically, the weights are assigned to the gradient maps of HSI, which help slack the sparsity constraint for the pixels with large variations, thus preserving the texture structure. Additionally, we elaborate an empirical method to learn the weights adaptively from observed HSI. Then, we propose an HSI denoising method based on the TPTV regularizer. Experimental results on synthetic and real HSI illustrate the superiority of our proposed method over other state-of-the-art methods. In addition, the proposed weighting scheme can be finely embedded into other TV regularizers and protect the image texture. The experiment results also demonstrate that the denoising performance of the original method is significantly improved after embedding the weighting scheme.
SyncPCN/PSyncPCN: Payment Channel Networks without Blockchain Synchrony

Payment channel networks (PCNs) enhance the scalability of block-chains by allowing parties to conduct transactions off-chain, i.e, without broadcasting every transaction to all blockchain participants. To conduct transactions, a sender and a receiver can either establish a direct payment channel with a funding blockchain transaction or leverage existing channels in a multi-hop payment. The security of PCNs usually relies on the synchrony of the underlying blockchain, i.e., evidence of misbehavior needs to be published on the blockchain within a time limit. Alternative payment channel proposals that do not require blockchain synchrony rely on quorum certificates and use a committee to register the transactions of a channel. However, these proposals do not support multi-hop payments, a limitation we aim to overcome.
Longest Chain Consensus Under Bandwidth Constraint

Spamming attacks are a serious concern for consensus protocols, as witnessed by recent outages of a major blockchain, Solana. They cause congestion and excessive message delays in a real network due to its bandwidth constraints. In contrast, longest chain (LC), an important family of consensus protocols, has previously only been proven secure assuming an idealized network model in which all messages are delivered within bounded delay. This model-reality mismatch is further aggravated for Proof-of-Stake (PoS) LC where the adversary can spam the network with equivocating blocks. Hence, we extend the network model to capture bandwidth constraints, under which nodes now need to choose carefully which blocks to spend their limited download budget on. To illustrate this point, we show that 'download along the longest header chain', a natural download rule for Proof-of-Work (PoW) LC, is insecure for PoS LC. We propose a simple rule 'download towards the freshest block', formalize two common heuristics 'not downloading equivocations' and 'blocklisting', and prove in a unified framework that PoS LC with any one of these download rules is secure in bandwidth-constrained networks. In experiments, we validate our claims and showcase the behavior of these download rules under attack. By composing multiple instances of a PoS LC protocol with a suitable download rule in parallel, we obtain a PoS consensus protocol that achieves a constant fraction of the network's throughput limit even under worst-case adversarial strategies.
Bitcoin's Latency–Security Analysis Made Simple

Simple closed-form upper and lower bounds are developed for the security of the Nakamoto consensus as a function of the confirmation depth, the honest and adversarial block mining rates, and an upper bound on the block propagation delay. The bounds are exponential in the confirmation depth and apply regardless of the adversary's attack strategy. The gap between the upper and lower bounds is small for Bitcoin's parameters. For example, assuming an average block interval of ten minutes, a network delay bound of ten seconds, and 10% adversarial mining power, the widely used 6-block confirmation rule yields a safety violation between 0.11% and 0.35% probability.
Blockchain Nash Dynamics and the Pursuit of Compliance

We study "Nash dynamics" in the context of adversarial deviations in blockchain protocols. We introduce a formal model, within which one can assess whether the Nash dynamics can lead utility-maximizing participants to defect from the "honest" protocol operation, towards variations that exhibit one or more undesirable infractions that affect protocol security, like abstaining from participation and producing conflicting protocol histories. Blockchain protocols that lead to no such infraction states are deemed compliant. Armed with this model, we evaluate the compliance of various Proof-of-Work (PoW) and Proof-of-Stake (PoS) protocol families, under different utility functions and reward schemes, leading to the following results: i) PoW and PoS protocols exhibit different compliance behavior, depending on the lossiness of the network; ii) PoS ledgers can be compliant w.r.t. one realistic infraction (producing conflicting messages) but non-compliant (hence non-equilibria) w.r.t. others (abstaining or an attack we call selfish signing); iii) considering externalities, like exchange rate fluctuations, we quantify the benefit of economic penalties in the context of PoS protocols as mitigation for particular infractions that affect protocol security.