Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification

In the last years, the number of IoT devices deployed has suffered an undoubted explosion, reaching the scale of billions. However, some new cybersecurity issues have appeared together with this development. Some of these issues are the deployment of unauthorized devices, malicious code modification, malware deployment, or vulnerability exploitation. This fact has motivated the requirement for new device identification mechanisms based on behavior monitoring. Besides, these solutions have recently leveraged Machine and Deep Learning techniques due to the advances in this field and the increase in processing capabilities. In contrast, attackers do not stay stalled and have developed adversarial attacks focused on context modification and ML/DL evaluation evasion applied to IoT device identification solutions. This work explores the performance of hardware behavior-based individual device identification, how it is affected by possible context- and ML/DL-focused attacks, and how its resilience can be improved using defense techniques. In this sense, it proposes an LSTM-CNN architecture based on hardware performance behavior for individual device identification. Then, previous techniques have been compared with the proposed architecture using a hardware performance dataset collected from 45 Raspberry Pi devices running identical software. The LSTM-CNN improves previous solutions achieving a +0.96 average F1-Score and 0.8 minimum TPR for all devices. Afterward, context- and ML/DL-focused adversarial attacks were applied against the previous model to test its robustness. A temperature-based context attack was not able to disrupt the identification. However, some ML/DL state-of-the-art evasion attacks were successful. Finally, adversarial training and model distillation defense techniques are selected to improve the model resilience to evasion attacks, without degrading its performance.
3D Reconstruction Using Low Precision Scanner

The objective of this work is to use low precision laser sensor and create reasonably precise 3D reconstructions. The 3D reconstruction is executed by combining several point clouds obtained from different viewpoints. The proposed method was developed with three main steps: point cloud registration, error compensation and surface reconstruction. The ICP algorithm is improved to execute the point cloud registration: dynamic distance threshold, weighted distance, rigid body restriction and color information. It is shown that using this improved ICP, the number of point correspondences to evaluate the quadratic error converges to a value. The quadratic error can be determined independently of scene complexity. The point cloud errors are compensated using the consensus surface algorithm with signed distance. The surface is reconstructed using the marching cubes algorithm. Several results are shown to demonstrate the reliability of the proposed method.
Choosing the Number of Topics in LDA Models - A Monte Carlo Comparison of Selection Criteria

Selecting the number of topics in LDA models is considered to be a difficult task, for which alternative approaches have been proposed. The performance of the recently developed singular Bayesian information criterion (sBIC) is evaluated and compared to the performance of alternative model selection criteria. The sBIC is a generalization of the standard BIC that can be implemented to singular statistical models. The comparison is based on Monte Carlo simulations and carried out for several alternative settings, varying with respect to the number of topics, the number of documents and the size of documents in the corpora. Performance is measured using different criteria which take into account the correct number of topics, but also whether the relevant topics from the DGPs are identified. Practical recommendations for LDA model selection in applications are derived.
Speckle-correlation imaging through a kaleidoscopic multimode fiber

Significance By reflecting light multiple times, a simple object seen through a kaleidoscope transforms into a beautiful, complex symmetrical pattern. Here, we show that this effect can actually be used the other way around to unscramble the complexity of light propagation in multimode fibers and transform highly complex measured patterns into clear images. Indeed, in usual circular-core multimode fibers, light is strongly scrambled, in the same way as in disordered scattering media. This is also true for square-core multimode fibers, but thanks to the kaleidoscopic effect, we were able to reconstruct images of fluorescent objects through the fiber without any prior knowledge about the fiber.
DGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation

Automatic font generation without human experts is a practical and significant problem, especially for some languages that consist of a large number of characters. Existing methods for font generation are often in supervised learning. They require a large number of paired data, which are labor-intensive and expensive to collect. In contrast, common unsupervised image-to-image translation methods are not applicable to font generation, as they often define style as the set of textures and colors. In this work, we propose a robust deformable generative network for unsupervised font generation (abbreviated as DGFont++). We introduce a feature deformation skip connection (FDSC) to learn local patterns and geometric transformations between fonts. The FDSC predicts pairs of displacement maps and employs the predicted maps to apply deformable convolution to the low-level content feature maps. The outputs of FDSC are fed into a mixer to generate final results. Moreover, we introduce contrastive self-supervised learning to learn a robust style representation for fonts by understanding the similarity and dissimilarities of fonts. To distinguish different styles, we train our model with a multi-task discriminator, which ensures that each style can be discriminated independently. In addition to adversarial loss, another two reconstruction losses are adopted to constrain the domain-invariant characteristics between generated images and content images. Taking advantage of FDSC and the adopted loss functions, our model is able to maintain spatial information and generates high-quality character images in an unsupervised manner. Experiments demonstrate that our model is able to generate character images of higher quality than state-of-the-art methods.
Similarity-Based Predictive Maintenance Framework for Rotating Machinery

Within smart manufacturing, data driven techniques are commonly adopted for condition monitoring and fault diagnosis of rotating machinery. Classical approaches use supervised learning where a classifier is trained on labeled data to predict or classify different operational states of the machine. However, in most industrial applications, labeled data is limited in terms of its size and type. Hence, it cannot serve the training purpose. In this paper, this problem is tackled by addressing the classification task as a similarity measure to a reference sample rather than a supervised classification task. Similarity-based approaches require a limited amount of labeled data and hence, meet the requirements of real-world industrial applications. Accordingly, the paper introduces a similarity-based framework for predictive maintenance (PdM) of rotating machinery. For each operational state of the machine, a reference vibration signal is generated and labeled according to the machine's operational condition. Consequentially, statistical time analysis, fast Fourier transform (FFT), and short-time Fourier transform (STFT) are used to extract features from the captured vibration signals. For each feature type, three similarity metrics, namely structural similarity measure (SSM), cosine similarity, and Euclidean distance are used to measure the similarity between test signals and reference signals in the feature space. Hence, nine settings in terms of feature type-similarity measure combinations are evaluated. Experimental results confirm the effectiveness of similarity-based approaches in achieving very high accuracy with moderate computational requirements compared to machine learning (ML)-based methods. Further, the results indicate that using FFT features with cosine similarity would lead to better performance compared to the other settings.
Doppler Spoofing in OFDM Wireless Communication Systems

In this paper we present a method that prevents an unauthorized receiver (URx) from correctly estimating the Doppler shift present in an orthogonal frequency division multiplexing (OFDM) wireless signal. To prevent this estimation we propose to insert an artificial frequency variation in the transmitted signal that mimics a transmitter (Tx) movement with a spoofed/fake speed. This spoofed Doppler shift does not affect data demodulation since it can be compensated at the legitimate receiver (LRx). We evaluate our method for its efficacy through simulations and we show that it offers a reliable way to protect one key element of the privacy of a wireless source, namely the speed of the transmitter.
Magnetic response of carbon nanotori: the importance of curvature and disorder

Taking into account both curvature and disorder, we study the magnetic moments of carbon nanotori in the presence of magnetic field B (perpendicular to the torus plane). Due to the intrinsic curvature effect, energy gaps of about 10 meV are obtained in those primary metallic carbon nanotori, which result in a novel magnetic response, depending on its chirality. An armchair carbon nanotorus exhibits a diamagnetic response in the vicinity of zero magnetic field, while the primary metallic zigzag nanotorus presents a much weaker paramagnetic response. This result differs substantially from previous predictions. In the weak-magnetic-field regime, it is found that the low-temperature magnetic moments show up the periodic Aharonov–Bohm (AB) oscillation, with period ΔB≈ϕ0/S, ϕ0 = h/e being the quantum flux, S the area enclosed by the torus, while at high temperature, the AB oscillation is destroyed because of the loss of phase coherence. A phase transition from diamagnetic to paramagnetic is observed by increasing the disorder strength.
Simple Physics and Integrators Accurately Reproduce Mercury Instability Statistics

The long-term stability of the solar system is an issue of significant scientific and philosophical interest. The mechanism leading to instability is Mercury’s eccentricity being pumped up so high that Mercury either collides with Venus or is scattered into the Sun. Previously, only three five-billion-year N-body ensembles of the solar system with thousands of simulations have been run to assess long-term stability. We generate two additional ensembles, each with 2750 members, and make them publicly available at https://archive.org/details/@dorianabbot. We find that accurate Mercury instability statistics can be obtained by (1) including only the Sun and the eight planets, (2) using a simple Wisdom–Holman scheme without correctors, (3) using a basic representation of general relativity, and (4) using a time step of 3.16 days. By combining our solar system ensembles with previous ensembles, we form a 9601-member ensemble of ensembles. In this ensemble of ensembles, the logarithm of the frequency of a Mercury instability event increases linearly with time between 1.3 and 5 Gyr, suggesting that a single mechanism is responsible for Mercury instabilities in this time range and that this mechanism becomes more active as time progresses. Our work provides a robust estimate of Mercury instability statistics over the next five billion years, outlines methodologies that may be useful for exoplanet system investigations, and provides two large ensembles of publicly available solar system integrations that can serve as test beds for theoretical ideas as well as training sets for artificial intelligence schemes.
Explaining the ${\mathcal{R}}(D)$ and ${\mathcal{R}}(D^{\ast})$ Anomalies in the $B-L$ Supersymmetric Standard Model with Inverse Seesaw

We investigate the ${\mathcal{R}}(D)$ and ${\mathcal{R}}(D^{\ast})$ anomalies in the context of the $B-L$ extension of the Minimal Supersymmetric Standard Model with Inverse Seesaw. We demonstrate that the lepton penguin $W^{\pm}l \bar\nu_l $ ($l=e,\mu,\tau$) mediated by CP-even/odd right-handed sneutrinos, charginos and neutralinos can account for these anomalies simultaneously.
Justification Logic and the Epistemic Contribution of Deduction

Accounting for the epistemic contribution of deduction has been a pervasive problem for logicians interested in deduction, such as, among others, Jakko Hintikka. The problem arises because the conclusion validly deduced from a set of premises is said to be “contained” in that set; because of this containment relation, the conclusion would be known from the moment the premises are known. Assuming this, it is problematic to explain how we can gain knowledge by deducing a logical consequence implied by a set of known premises. To address this problem, we offer an alternative account of the epistemic contribution of deduction as the process required to deduce a conclusion or a theorem, understanding such a process not only in terms of the number of steps in the derivation but also, more importantly, in terms of the reason for or justification for every step. That is, we do not know a proposition unless we have a justification or proof of that proposition. With this goal in mind, we develop a justification logic system which exhibits the epistemic contribution of a deductive derivation as the resulting justified formula.
πQLB: A Privacy-preserving with Integrity-assuring Query Language for Blockchain

The increase in the adoption of blockchain technology in different application domains e.g., healthcare systems, supplychain management, has raised the demand for a data query mechanism on blockchain. Since current blockchain systems lack the support for querying data with embedded security and privacy guarantees, there exists inherent security and privacy concerns on those systems. In particular, existing systems require users to submit queries to blockchain operators (e.g., a node validator) in plaintext. This directly jeopardizes users' privacy as the submitted queries may contain sensitive information, e.g., location or gender preferences, that the users may not be comfortable sharing. On the other hand, currently, the only way for users to ensure integrity of the query result is to maintain the entire blockchain database and perform the queries locally. Doing so incurs high storage and computational costs on the users, precluding this approach to be practically deployable on common light-weight devices (e.g., smartphones). To this end, this paper proposes $\pi$QLB, a query language for blockchain systems that ensures both confidentiality of query inputs and integrity of query results. Additionally, $\pi$QLB enables SQL-like queries over the blockchain data by introducing relational data semantics into the existing blockchain database. $\pi$QLB has applied the recent cryptography primitive, i.e., function secret sharing (FSS), to achieve confidentiality. To support integrity, we extend the traditional FSS setting in such a way that integrity of FSS results can be efficiently verified. Successful verification indicates absence of malicious behaviors on the servers, allowing the user to establish trust from the result. To the best of our knowledge, $\pi$QLB is the first query model designed for blockchain databases with support for confidentiality, integrity, and SQL-like queries.
A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads✱

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets. [GitHub]
Smooth Compactifications of the Abel-Jacobi Section

Abstract For $\theta $ a small generic universal stability condition of degree $0$ and A a vector of integers adding up to $-k(2g-2+n)$ , the spaces $\overline {\mathcal {M}}_{g,A}^\theta $ constructed in [AP21, HMP+22] are observed to lie inside the space $\textbf {Div}$ of [MW20], and their pullback under $\textbf {Rub} \to \textbf {Div}$ of loc. cit. to be smooth. This provides smooth and modular modifications $\widetilde {\mathcal {M}}_{g,A}^\theta $ of $\overline {\mathcal {M}}_{g,n}$ on which the logarithmic double ramification cycle can be calculated by several methods.
Lie algebras and Hamiltonian structures of multi-component Ablowitz–Kaup–Newell–Segur hierarchy

Isospectral and non-isospectral hierarchies of multi-component Ablowitz–Kaup–Newell–Segur (AKNS) are obtained from a matrix spectral problem, then by means of the zero curvature representations of the isospectral flows {Km} and non-isospectral flows {σn}, we construct the symmetries and their algebraic structures for isospectral multi-component AKNS hierarchies, demonstrate the recursive operator L is a strong and hereditary symmetry for the isospectral hierarchy. We also derive that there are implectic operator θ and symplectic operator J such that L = θJ, and discuss the multi-Hamiltonian structures and the Liouville integrability of the isospectral hierarchies.
BRST symmetry and unitarity of the Hořava theory

We present an analysis on the BRST symmetry transformations of the Horava theory under the BFV quantization, both in the nonprojectable and projectable cases. We obtain that the BRST transformations are intimately related to a particular spatial diffeomorphism along one of the ghost vector fields. We show explicitly the invariance of the quantum action and the nilpotence of the BRST transformations, using this diffeomorphism largely. The BRST symmetry is verified in the whole phase space, inside and outside the constrained surface. When restricted to the constrained surface, the BRST transformations are completely local. The consistency of the BRST symmetry is a fundamental feature of a quantum field theory, specially for renormalization. The BFV quantization is independent of the chosen gauge-fixing condition. This allows us to show the unitarity of the quantum Horava theory, covering the gauge required for renormalization.
A Survey of Diversification Metrics and Approaches in Retrieval Systems: From the Perspective of Search and Recommendation

Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers. There has been a growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, the diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems
Learning from Data Streams: An Overview and Update

The literature on machine learning in the context of data streams is vast and growing. However, many of the defining assumptions regarding data-stream learning tasks are too strong to hold in practice, or are even contradictory such that they cannot be met in the contexts of supervised learning. Algorithms are chosen and designed based on criteria which are often not clearly stated, for problem settings not clearly defined, tested in unrealistic settings, and/or in isolation from related approaches in the wider literature. This puts into question the potential for real-world impact of many approaches conceived in such contexts, and risks propagating a misguided research focus. We propose to tackle these issues by reformulating the fundamental definitions and settings of supervised data-stream learning with regard to contemporary considerations of concept drift and temporal dependence; and we take a fresh look at what constitutes a supervised data-stream learning task, and a reconsideration of algorithms that may be applied to tackle such tasks. Through and in reflection of this formulation and overview, helped by an informal survey of industrial players dealing with real-world data streams, we provide recommendations. Our main emphasis is that learning from data streams does not impose a single-pass or online-learning approach, or any particular learning regime; and any constraints on memory and time are not specific to streaming. Meanwhile, there exist established techniques for dealing with temporal dependence and concept drift, in other areas of the literature. For the data streams community, we thus encourage a shift in research focus, from dealing with often-artificial constraints and assumptions on the learning mode, to issues such as robustness, privacy, and interpretability which are increasingly relevant to learning in data streams in academic and industrial settings.
Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial Networks

—Network Intrusion Detection Systems (NIDS) are tools or software that are widely used to maintain the computer networks and information systems keeping them secure and preventing malicious traffics from penetrating into them, as they flag when somebody is trying to break into the system. Best effort has been set up on these systems, and the results achieved so far are quite satisfying, however, new types of attacks stand out as the technology of attacks keep evolving, one of these attacks are the attacks based on Generative Adversarial Networks (GAN) that can evade machine learning IDS leaving them vulnerable. This project investigates the impact of the Adversarial Attacks synthesized using real DDos attacks generated using GANs on the IDS. The objective is to discover how will these systems react towards synthesized attacks. marking the vulnerability and weakness points of these systems so we could fix them.
PanDepth: Joint Panoptic Segmentation and Depth Completion

Understanding 3D environments semantically is pivotal in autonomous driving applications where multiple computer vision tasks are involved. Multi-task models provide different types of outputs for a given scene, yielding a more holistic representation while keeping the computational cost low. We propose a multi-task model for panoptic segmentation and depth completion using RGB images and sparse depth maps. Our model successfully predicts fully dense depth maps and performs semantic segmentation, instance segmentation, and panoptic segmentation for every input frame. Extensive experiments were done on the Virtual KITTI 2 dataset and we demonstrate that our model solves multiple tasks, without a significant increase in computational cost, while keeping high accuracy performance. Code is available at https://github.com/juanb09111/PanDepth.git