Reviewerly/exHarmony huggingface dataset:
PLA-LiDAR: Physical Laser Attacks against LiDAR-based 3D Object Detection in Autonomous Vehicle

Autonomous vehicles and robots increasingly exploit LiDAR-based 3D object detection systems to detect obstacles in environment. Correct detection and classification are important to ensure safe driving. Though existing work has demonstrated the feasibility of manipulating point clouds to spoof 3D object detectors, most of the attempts are conducted digitally. In this paper, we investigate the possibility of physically fooling LiDAR-based 3D object detection by injecting adversarial point clouds using lasers. First, we develop a laser transceiver that can inject up to 4200 points, which is 20 times more than prior work, and can measure the scanning cycle of victim LiDARs to schedule the spoofing laser signals. By designing a control signal method that converts the coordinates of point clouds to control signals and an adversarial point cloud optimization method with physical constraints of LiDARs and attack capabilities, we manage to inject spoofing point cloud with desired point cloud shapes into the victim LiDAR physically. We can launch four types of attacks, i.e., naive hiding, record-based creating, optimization-based hiding, and optimization-based creating. Extensive experiments demonstrate the effectiveness of our attacks against two commercial LiDAR and three detectors. We also discuss defense strategies at the sensor and AV system levels.
One Key to Rule Them All: Secure Group Pairing for Heterogeneous IoT Devices

Pairing schemes establish cryptographic keys to secure communication among IoT devices. Existing pairing approaches that rely on trusted central entities, human interaction, or shared homogeneous context are prone to a single point of failure, have limited usability, and require additional sensors. Recent work has explored event timings observed by devices with heterogeneous sensing modalities as proof of co-presence for decentralized pairing. Yet, this approach incurs high pairing time, cannot pair sensors that sense continuous physical quantities and does not support group pairing, making it infeasible for many IoT deployments. In this paper, we design and develop IoTCupid, a secure group pairing system for IoT devices with heterogeneous sensing modalities, without requiring active user involvement. IoTCupid operates in three phases: (a) detecting events sensed by both instant and continuous sensors with a novel window-based derivation technique, (b) grouping the events through a fuzzy clustering algorithm to extract inter-event timings, and (c) establishing group keys among devices with identical inter-event timings through a partitioned group password-authenticated key exchange scheme. We evaluate IoTCupid in smart home and office environments with 11 heterogeneous devices and show that it effectively pairs all devices with only 2 group keys with a minimal pairing overhead.
Weak Fiat-Shamir Attacks on Modern Proof Systems

A flurry of excitement amongst researchers and practitioners has produced modern proof systems built using novel technical ideas and seeing rapid deployment, especially in cryptocurrencies. Most of these modern proof systems use the Fiat-Shamir (F-S) transformation, a seminal method of removing interaction from a protocol with a public-coin verifier. Some prior work has shown that incorrectly applying F-S (i.e., using the so-called "weak" F-S transformation) can lead to breaks of classic protocols like Schnorr’s discrete log proof; however, little is known about the risks of applying F-S incorrectly for modern proof systems seeing deployment today.In this paper, we fill this knowledge gap via a broad theoretical and practical study of F-S in implementations of modern proof systems. We perform a survey of open-source implementations and find 30 weak F-S implementations affecting 12 different proof systems. For four of these—Bulletproofs, Plonk, Spartan, and Wesolowski’s VDF—we develop novel knowledge soundness attacks accompanied by rigorous proofs of their efficacy. We perform case studies of applications that use vulnerable implementations, and demonstrate that a weak F-S vulnerability could have led to the creation of unlimited currency in a private smart contract platform. Finally, we discuss possible mitigations and takeaways for academics and practitioners.
Three Birds with One Stone: Efficient Partitioning Attacks on Interdependent Cryptocurrency Networks

The biased distribution of cryptocurrency nodes across Autonomous Systems (ASes) increases the risk of spatial partitioning attacks, allowing an adversary to isolate nodes by hijacking AS prefixes. Prior works on spatial partitioning attacks have mainly focused on the Bitcoin network, showing that the prominent cryptocurrency network can be paralyzed by disrupting the physical topology through BGP hijacks.Despite the persisting threat of BGP hijacks, Bitcoin and other cryptocurrencies have not been frequently targeted, likely due to their shielded overlay topology, which limits the exposure of physical network anomalies. In this paper, we present a new perspective by examining the security of cryptocurrency networks, considering shared network resources (network interdependence). We conduct measurements extending beyond the Bitcoin network and analyze commonalities in Bitcoin, Ethereum, and Ripple node hosting patterns. We observe that all three networks are highly centralized, predominantly sharing the common ASes. We also note that among the three cryptocurrencies, Ripple does not shield its overlay topology, which can be exploited to learn about the physical network anomalies. The observed network anomalies present practical attack strategies that can be launched to target all three cryptocurrencies simultaneously. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> We supplement our analysis by surveying recent BGP attacks on high-profile ASes and recognizing a need for application-level countermeasures. We propose attack countermeasures that reduce the risk of spatial partitioning, notwithstanding the increasing centralization of nodes and network interdependence.
Disguising Attacks with Explanation-Aware Backdoors

Explainable machine learning holds great potential for analyzing and understanding learning-based systems. These methods can, however, be manipulated to present unfaithful explanations, giving rise to powerful and stealthy adversaries. In this paper, we demonstrate how to fully disguise the adversarial operation of a machine learning model. Similar to neural backdoors, we change the model’s prediction upon trigger presence but simultaneously fool an explanation method that is applied post-hoc for analysis. This enables an adversary to hide the presence of the trigger or point the explanation to entirely different portions of the input, throwing a red herring. We analyze different manifestations of these explanation-aware backdoors for gradient- and propagation-based explanation methods in the image domain, before we resume to conduct a red-herring attack against malware classification.
A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction

We study the bias introduced in Differentially-Private Stochastic Gradient Descent (DP-SGD) with clipped or normalized per-sample gradient. As one of the most popular but artificial operations to ensure bounded sensitivity, gradient clipping enables composite privacy analysis of many iterative optimization methods without additional assumptions on either learning models or input data. Despite its wide applicability, gradient clipping also presents theoretical challenges in systematically instructing improvement of privacy or utility. In general, without an assumption on globally-bounded gradient, classic convergence analyses do not apply to clipped gradient descent. Further, given limited understanding of the utility loss, many existing improvements to DP-SGD are heuristic, especially in the applications of private deep learning.In this paper, we provide meaningful theoretical analysis validated by thorough empirical results of DP-SGD. We point out that the bias caused by gradient clipping is underestimated in previous works. For generic non-convex optimization via DP-SGD, we show one key factor contributing to the bias is the sampling noise of stochastic gradient to be clipped. Accordingly, we use the developed theory to build a series of improvements for sampling noise reduction from various perspectives. From an optimization angle, we study variance reduction techniques and propose inner-outer momentum. At the learning model (neural network) level, we propose several tricks to enhance network internal normalization and BatchClipping to carefully clip the gradient of a batch of samples. For data preprocessing, we provide theoretical justification of recently proposed improvements via data normalization and (self-)augmentation.Putting these systematic improvements together, private deep learning via DP-SGD can be significantly strengthened in many tasks. For example, in computer vision applications, with an (ϵ = 8, δ = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> ) DP guarantee, we successfully train ResNet20 on CIFAR10 and SVHN with test accuracy 76.0% and 90.1%, respectively; for natural language processing, with (ϵ = 4, δ = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> ), we successfully train a recurrent neural network on IMDb data with test accuracy 77.5%.
Public Verification for Private Hash Matching

End-to-end encryption (E2EE) prevents online services from accessing user content. This important security property is also an obstacle for content moderation methods that involve content analysis. The tension between E2EE and efforts to combat child sexual abuse material (CSAM) has become a global flashpoint in encryption policy, because the predominant method of detecting harmful content—server-side perceptual hash matching on plaintext images—is unavailable.Recent applied cryptography advances enable private hash matching (PHM), where a service can match user content against a set of known CSAM images without revealing the hash set to users or nonmatching content to the service. These designs, especially a 2021 proposal for identifying CSAM in Apple’s iCloud Photos service, have attracted widespread criticism for creating risks to security, privacy, and free expression.In this work, we aim to advance scholarship and dialogue about PHM by contributing new cryptographic methods for system verification by the general public. We begin with motivation, describing the rationale for PHM to detect CSAM and the serious societal and technical issues with its deployment. Verification could partially address shortcomings of PHM, and we systematize critiques into two areas for auditing: trust in the hash set and trust in the implementation. We explain how, while these two issues cannot be fully resolved by technology alone, there are possible cryptographic trust improvements.The central contributions of this paper are novel cryptographic protocols that enable three types of public verification for PHM systems: (1) certification that external groups approve the hash set, (2) proof that particular lawful content is not in the hash set, and (3) eventual notification to users of false positive matches. The protocols that we describe are practical, efficient, and compatible with existing PHM constructions.
IPDNet: A dual convolutional network combined with image prior for single image dehazing

Dehazing based on deep learning neural networks (CNNs) has achieved remarkable results. However, the most existing dehazing CNNs perform well only on synthetic images and struggle with realistic hazy images. Moreover, training complex dehazing models is challenging due to the limited availability of realistic image datasets, leading to suboptimal performance. For this purpose, we develop a novel Image Prior Dehazing Network called IPDNet to tackle the challenge of dehazing realistic images with limited training data. The IPDNet comprises two sub-networks and a learnable fusion block. The global and local features are obtained by atmospheric scattering and direct mapping via two sub-networks with a sparse mechanism. And the learnable fusion block is settled to acquire the optimal fusion solution to improve the dehazing quality. The IPDNet offers several benefits: (1) a dual network with a learnable fusion block can effectively generate high-quality dehazed images at low computational cost; (2) an image preprocessing block based on dehazing prior can acquires the salient features of realistic hazy image, enhancing the dehazing performance on realistic hazy images; (3) the sub-network is designed by incorporating a feature attention (FA) block into the U-net structure, allowing flexible feature extraction on limited datasets. Extensive experiments on SOTS, NH-HAZE, and DENSE-HAZE datasets show that IPDNet outperforms other state-of-the-art methods on synthetic and realistic datasets. Specifically, our model achieved an improvement of approximately 0.84 dB in PSNR and 11.6% in SSIM, demonstrating its effectiveness in realistic scenarios, which contributes to improving traffic safety in adverse weather conditions.
Lightweight single-image super-resolution via multi-scale feature fusion CNN and multiple attention block

In recent years, single-image super-resolution (SISR) has acquired tremendous progress with the development of deep learning. However, the majority of SISR methods based on deep learning focus on building more complex networks, which inevitably lead to the problems of computational and memory costs. Thus, these methods may fail to be applied in real-world scenarios. To solve this problem, this paper proposes a lightweight convolution network combined with transformer for SISR named as MMSR. Specifically, an efficient convolutional neural network (CNN) based on multi-scale feature fusion is designed for local feature extraction, which is called MFF-CNN. In addition, we propose a simple and efficient multiple attention block (MAB) to further utilize the context information in features. MAB incorporates channel attention and transformer to help network obtain similar features at a long-term dependence, making full use of global information to further refine texture details. Finally, this paper provides comprehensive results for different settings of the entire network. Experimental results on common used datasets demonstrate that the proposed method can achieve better performances at the 2 $$\times $$ , 3 $$\times $$ and 4 $$\times $$ scales than other state-of-the-art lightweight methods.
International relationship management during social distancing: the role of AI-integrated social CRM by MNEs during the Covid-19 pandemic

Purpose The research empirically examines the role of artificial intelligence (AI) integrated with social customer relationship management (SCRM) in multinational enterprises (MNEs) towards international relationship management under social distancing conditions due to the COVID-19 pandemic. Design/methodology/approach The study initially undertakes pertinently focused theoretical research in the fields of international marketing, knowledge management, and customer relationship management. And, utilizing the theories of resource-based view (RBV) and dynamic capability view (DCV) theory, the study develops a theoretical model that is subsequently empirically validated through a survey and structural equation modeling. Findings The study highlights the importance and means of adopting AI-integrated social CRM by MNEs, in the context of international relationship management, under the Covid-19 social distancing conditions. The study more specifically elucidates the role and significance of MNE leadership approach and support towards the adoption of AI-integrated social CRM systems and, ultimately, performance improvement of MNEs under such conditions. Research limitations/implications The study presents insights and prescriptive explications on a topic at the heart of state-of the-art technology-based international marketing in the explicit context of the primary business-defining environment of the Covid-19 pandemic. The research provides practicable suggestions to MNEs' leadership towards the adoption of an AI-integrated social CRM system. And the study presents a unique model for international relationship management under social distancing conditions, potentially applicable during other crises. Originality/value The research is original and on a ‘fresh’ topic that combines the latest technological advancements in business (AI-integrated CRM) with the present critical business context (pandemic). The research develops a tested theoretical model that (a) is unique in its field; (b) provides a solid foundation for further research; (c) bears generic value and application during other-than-Covid-19 conditions; and (d) enhances the understanding of important fields of international marketing, including international customer relationship management and global knowledge management.
A fine-grained and multi-context-aware learning path recommendation model over knowledge graphs for online learning communities

Existing approaches to learning path recommendation for online learning communities mainly rely on the individual characteristics of users or the historical records of their learning processes, but pay less attention to the semantics of users' postings and the context. To facilitate the knowledge understanding and personalized learning of users in online learning communities, it is necessary to conduct a fine-grained analysis of user data to capture their dynamical learning characteristics and potential knowledge levels, so as to recommend appropriate learning paths. In this paper, we propose a fine-grained and multi-context-aware learning path recommendation model for online learning communities based on a knowledge graph. First, we design a multidimensional knowledge graph to solve the problem of monotonous and incomplete entity information presentation of the single layer knowledge graph. Second, we use the topic preference features of users' postings to determine the starting point of learning paths. We then strengthen the distant relationship of knowledge in the global context using the multidimensional knowledge graph when generating and recommending learning paths. Finally, we build a user background similarity matrix to establish user connections in the local context to recommend users with similar knowledge levels and learning preferences and synchronize their subsequent postings. Experiment results show that the proposed model can recommend appropriate learning paths for users, and the recommended similar users and postings are effective.
CircularE: A Complex Space Circular Correlation Relational Model for Link Prediction in Knowledge Graph Embedding

Knowledge graphs are regarded as structured knowledge bases that embody various facts coming from the real world. Their completeness is still far from satisfactory. Relational learning models in link prediction can automatically find the missing relationships between entities to increase the integrality of the knowledge bases, which form two categories purely embedding-based and hybrid embedding-based. Several models including HolE and RotatE belong to purely embedding-based with inefficient performers and few element interactions. Based on the above, this paper advances a novel Knowledge Graph Embedding relational model that leverages a circular correlation operation in the complex domain and dubs as CircularE, which increases interactions between entities and relations to a great extent by this compressed operator without expanding the dimension of space. It gives expression of the interactions between element semantics to achieve good performance in relational learning. Besides, a self-adaption adversarial negative sampling scheme is proposed on account of the KGs structure and the probability semantic of the triples. This negative sampler efficiently optimizes the knowledge representation capability of CircularE and far more than enhances the outputs of several relational original models in embedding-based. Experiments indicate that the competitive properties of CircularE on the four large-scale benchmarks of knowledge base completion tasks are superior to the state-of-the-art methods.
Analysis of single photon detectors in differential phase shift quantum key distribution

In the current research work, an analysis of differential phase shift quantum key distribution using InGaAs/InP and Silicon-APD (avalanche photodiode) as single photon detectors is performed. Various performance parameters of interest such as shifted key rate, secure key rate, and secure communication distance obtained are investigated. In this optical fiber-based differential phase shift quantum key distribution, it is observed that Si-APD under frequency conversion method at telecommunication window outperforms the InGaAs/InP APD.
Greek sign language recognition for an education platform

Abstract Recent advances in sign language (SL) technologies, along with demand for SL education, have led to increased interest in developing tools that enable automatic assessment of learners’ SL video productions, helping both students and their instructors. At the very least, such tools should perform automatic SL recognition (SLR) of non-studio quality videos in a signer-independent (SI) fashion, thus providing simple binary feedback on learners’ signing under realistic usage scenarios. Motivated by the above and the lack of any such tools for the Greek SL (GSL), we have been developing the “SL-ReDu” education platform for both receptive and productive GSL learning and student assessment. In this paper, we present our SLR module for GSL, developed for and integrated to the “SL-ReDu” system. The module incorporates state-of-the-art deep-learning based visual detection, feature extraction, and classification, operates in an SI mode on web-cam videos, and accommodates a small-size vocabulary of isolated signs and continuously fingerspelled letter sequences. We train the module on collected GSL data and demonstrate its superiority over a number of alternative SLR algorithms. We then conduct its objective evaluation within the “SL-ReDu” system and carry out a subjective evaluation of the overall platform, obtaining very satisfactory results in both.
Decomposition approaches for scheduling chronic outpatients’ clinical pathways in Answer Set Programming

Abstract Chronic patients suffering from non-communicable diseases are often enrolled into a diagnostic and therapeutic care program featuring a personalized care plan. Healthcare is mostly provided at the patient’s home, but those examinations and treatments that must be delivered at the hospital have to be explicitly booked. Booking is not trivial due to, on the one hand, the several time constraints that become particularly tight in the case of comorbidity, on the other hand, the limited availability of both staff and equipment at the hospital care units. This suggests that the scheduling of the clinical pathways for enrolled outpatients should be managed in a centralized manner, taking advantage of the fact that demand for services is known well in advance. The aim is to serve as many requests as possible (unattended requests are supplied by contracted private health facilities) in a timely manner, taking patients priority into account. Booking involves setting a date and a time for each selected health service, which is rather complex. In this work, we provide a declarative approach by encoding the problem in Answer Set Programming (ASP). In order to improve the scalability of the ASP approach, we present and compare two heuristic approaches, respectively based on service demand and time decomposition. All approaches are tested on instances of increasing size to assess scalability with respect to time horizon and number of requests.