Reviewerly/exHarmony huggingface dataset:
Noisy Sorting Capacity

Sorting is the task of ordering n elements using pairwise comparisons. It is well known that m = Θ(n log n) comparisons are both necessary and sufficient when the outcomes of the comparisons are observed with no noise. In this paper, we study the sorting problem in the presence of noise. Unlike the common approach in the literature which aims to minimize the number of pairwise comparisons m to achieve a given desired error probability, our goal is to characterize the maximal ratio $\frac{{n\log n}}{m}$ such that the ordering of the elements can be estimated with a vanishing error probability asymptotically. The maximal ratio is referred to as the sorting capacity. In this work, we derive upper and lower bounds on the sorting capacity. The algorithm that attains the lower bound is based on the well-known Burnashev–Zigangirov algorithm for coding over channels with feedback. By comparing with existing algorithms in the literature under the proposed framework, we show that our algorithm can achieve a strictly larger ratio asymptotically.
The Linear Capacity of Single-Server Individually-Private Information Retrieval With Side Information

This paper considers the problem of single-server Individually-Private Information Retrieval with side information (IPIR). In this problem, there is a remote server that stores a dataset of K messages, and there is a user that initially knows M of these messages, and wants to retrieve D other messages belonging to the dataset. The goal of the user is to retrieve the D desired messages by downloading the minimum amount of information from the server while revealing no information about whether an individual message is one of the D desired messages. In this work, we focus on linear IPIR schemes, i.e., the IPIR schemes in which the user downloads only linear combinations of the original messages from the server. We prove a converse bound on the download rate of any linear IPIR scheme for all K, D, M, and show the achievability of this bound for all K, D, M satisfying a certain divisibility condition. Our results characterize the linear capacity of IPIR, which is defined as the maximum achievable download rate over all linear IPIR schemes, for a wide range of values of K, D, M.
Minimizing Quantum Rényi Divergences via Mirror Descent with Polyak Step Size

Quantum information quantities play a substantial role in characterizing operational quantities in various quantum information-theoretic problems. We consider the numerical computation of four quantum information quantities: Petz-Augustin information, sandwiched Augustin information, conditional sandwiched Rényi entropy, and sandwiched Rényi information. To compute these quantities requires minimizing some order-α quantum Rényi divergences over the set of quantum states. Whereas the optimization problems are obviously convex, they violate standard bounded gradient/Hessian conditions in the literature, so existing convex optimization methods and their convergence guarantees do not directly apply. In this paper, we propose a new class of convex optimization methods called mirror descent with the Polyak step size. We prove their convergence under a weak condition, showing that they provably converge for minimizing quantum Rényi divergences. Numerical experiment results show that entropic mirror descent with the Polyak step size converges fast in minimizing quantum Rényi divergences.
Error-Erasure Decoding of Linearized Reed-Solomon Codes in the Sum-Rank Metric

Codes in the sum-rank metric have various applications in error control for multishot network coding, distributed storage and code-based cryptography. Linearized Reed-Solomon (LRS) codes contain Reed-Solomon and Gabidulin codes as subclasses and fulfill the Singleton-like bound in the sum-rank metric with equality. We propose the first known error-erasure decoder for LRS codes to unleash their full potential for multishot network coding by incorporating erasures into the known syndrome-based Berlekamp-Massey-like decoder. This allows to correct t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</inf> full errors, t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</inf> row erasures and t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</inf> column erasures up to $2{t_F} + {t_R} + {t_C} \leq n - k$ in the sum-rank metric requiring at most $\mathcal{O}\left( {{n^2}} \right)$ operations in ${\mathbb{F}_{{q^m}}},$ where n is the code’s length and k its dimension. We show how the proposed decoder can be used to correct errors in the sum-subspace metric that occur in (noncoherent) multishot network coding.
Two-Server Private Information Retrieval with Result Verification

Private information retrieval (PIR) allows a client to retrieve any block x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> from a database x = x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> x <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> such that i remains hidden from the database servers. PIR •protocols with unconditional privacy and sublinear (in n) communication complexity can be constructed assuming multiple honest-but-curious servers. This assumption however cannot be guaranteed in many real life scenarios such as using cloud servers as database servers. In this paper, we consider an information-theoretic PIR with result verification (PIR-RV) model where the servers may be dishonest (i.e., cheating) and provide incorrect answers but the client can detect the existence of cheating servers. We construct a 2-server PIR-RV protocol with communication complexity ${\mathcal{O}}({n^{1/2}}{\text{log }}p)$ where p is a parameter and controls the probability that the client fails to detect. Our idea may be extended to construct k-server PIR-RV protocols for k ≥ 3.
Minimax Rate-Distortion

We show the existence of universal, variable-rate rate-distortion codes that meet the distortion constraint almost surely and approach the rate-distortion function uniformly with respect to an unknown source distribution and a distortion measure that is only revealed to the encoder and only at runtime. If the convergence only needs to be uniform with respect to the source distribution and not the distortion measure, then we provide an explicit bound on the minimax rate of convergence. Our construction combines conventional random coding with a zero-rate uncoded transmission scheme. The proof uses exact asymptotics from large deviations, acceptance-rejection sampling, the VC dimension of distortion measures, and the identification of an explicit, code-independent, finite-blocklength quantity, which converges to the rate-distortion function, that controls the performance of the best codes.
Heterogeneous Differential Privacy via Graphs

This paper is eligible for the Jack Keil Wolf ISIT Student Paper Award. We generalize a previous framework for designing utility-optimal differentially private (DP) mechanisms via graphs, where datasets are vertices in the graph and edges represent dataset neighborhood. The boundary set contains datasets where an individual’s response changes the binary-valued query compared to its neighbors. Previous work was limited to the homogeneous case where the privacy parameter ε across all datasets was the same and the mechanism at boundary datasets was identical. In our work, the mechanism can take different distributions at the boundary and the privacy parameter ε is a function of neighboring datasets, which recovers an earlier definition of personalized DP as special case. The problem is how to extend the mechanism, which is only defined at the boundary set, to other datasets in the graph in a computationally efficient and utility optimal manner. Using the concept of strongest induced DP condition we solve this problem efficiently in polynomial time (in the size of the graph).
Multichannel ALOHA Optimization for Federated Learning With Multiple Models

Large-scale wireless sensor networks are instrumental for several Internet of Things (IoT) applications involving data analytics and machine learning. The huge data volume generated by such networks imposes a change of paradigm from centralized machine learning to decentralized. Federated Learning (FL) is a well-known type of decentralized machine learning, whose efficiency heavily depends on the use of wireless communication resources. Random Access (RA) protocols, such as ALOHA, despite their simplicity, can improve the convergence time of FL systems if multiple orthogonal channels are used. This letter considers that devices are involved in the optimization of more than one model in a FL system, and then proposes an optimum method to allocate wireless resources in a multi-channel ALOHA setup. The proposed method outperforms uniform and fully-shared channel allocations in terms of convergence time.
Curiosity-Driven Class-Incremental Learning via Adaptive Sample Selection

Modern artificial intelligence systems require class-incremental learning while suffering from catastrophic forgetting in many real-world applications. Due to the missing knowledge of past data, performance substantially degrades. Recent methods often used knowledge distillation and bias correction to avoid catastrophic forgetting caused by cognitive bias. However, since these methods mainly learn all samples indiscriminately, the model is hard to learn what it truly needs from the data stream to balance the new and old knowledge, leading to inevitable forgetting. Instead of considering each sample indiscriminately, the model should learn from its curious samples automatically. To tackle this problem, we propose a curiosity-driven class-incremental learning approach via adaptive sample selection for learning a more generalized model with fewer ineffective updates. Specifically, our method quantifies the model’s curiosity in each sample by two properties: uncertainty and novelty. Our model learns informative samples selectively during training utilizing the proposed uncertainty property, which benefits the classification decision boundary. In light of the imbalanced data, a novelty property is used to selectively optimize the model by employing dissimilar samples, endowing it with more robustness and less cognitive bias. Our method successfully reduces catastrophic forgetting and can be flexibly incorporated with other techniques. Extensive experiments and in-depth analysis on the CIFAR-100, Tiny-ImageNet and Caltech-101 datasets show that our approach outperforms competing methods for class-incremental learning in terms of preventing catastrophic forgetting.
A Combined Stripe Noise Removal and Deblurring Recovering Method for Thermal Infrared Remote Sensing Images

Remote sensing images (RSIs) have been applied to many fields such as environmental monitoring, urban planning, and military defense. All-day thermal infrared imaging observation system can finely portray the trajectory of human activities and provide data support for United Nations sustainable development planning. However, in the process of RSIs, image quality degradation containing image blurring is caused by optical system aberration, satellite platform vibration, imaging system out-of-focus, and atmospheric turbulence. Moreover, stripe noise in the image is produced by the non-uniformity of infrared sensors. Image deblurring and destriping are classical tasks in RSIs, but the above two problems are discussed separately in almost all research. i.e., denoising after adding stripe noise on clear images or deblurring under the assumption that only random noise exists. In this paper, stripe noise and blur are jointly removed from on-orbit RSIs acquired by the thermal infrared spectrometer on the SDGSAT-1 satellite, using a method based on stripe component residuals and gradient property. According to the experimental results, the performance of the proposed method is greatly improved compared with processing these two tasks separately, which can provide a valuable reference for the study of high-resolution thermal infrared RSIs recovery.
APPB: Anti-Counterfeiting and Privacy-Preserving Blockchain-Based Vehicle Supply Chains

The vehicle supply chain industry is in a stage of rapid development with the popularization of electric vehicles. However, counterfeits, also known as fake products, present a non-negligible effect on vehicle supply chains. Counterfeit vehicular parts have created significant losses in both monetary value and social terms. For example, tainted and sub-standard tires or batteries used in vehicles even have caused traffic accidents or loss of human life. Due to these dominant reasons, many researchers have focused on building anti-counterfeiting supply chain systems. More recently, several blockchain-based solutions have been presented, whereas most of them lack privacy protection. In this paper, aiming at the issue, we propose a reliable and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> nti-counterfeiting and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> rivacy- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> reserving <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> lockchain-based vehicle supply chains, called APPB. APPB is characterized by offering dual advantages, by utilizing the immutability of blockchain and keeping the privacy of product selling information and business relationship. Both security analysis and experimental results are conducted, and the results indicate that APPB can achieve privacy protection and anti-counterfeiting with acceptable efficiency.
Simultaneous fault diagnosis of proton exchange membrane fuel cell systems based on an Incremental Multi-label Classification Network

Fault diagnosis plays an important role in the operation of proton exchange membrane fuel cell (PEMFC) systems. In some certain working conditions, multiple faults can occur simultaneously. And, to the best of our knowledge, very few studies have yet to design an algorithm specifically for simultaneous fault diagnosis in PEMFC systems. Therefore, a novel simultaneous fault diagnosis algorithm, based on multi-label classifier chain named Incremental Multi-label Classification Network (IMCN), is proposed. To develop and optimize IMCN, a PEMFC model is constructed based on the commercial software AVL CURISE M to simulate data when simultaneous multiple faults occur. To further verify the generalization performance and practical effect of IMCN, a bench experiment using a high-power PEMFC system is conducted, which has similar boundary conditions as the boundary conditions embedded in simulation model. And, whether in experiment or simulation, corresponding verification methods are adopted to verify the success of simultaneous multiple faults embedding. Experimental data testing shows that, the subset accuracy, Hamming loss, Jaccard index, precision and recall of IMCN reaches 0.973, 0.029, 0.921, 0.961 and 0.956 respectively (better than Multi-Label MLP classifier, Label powerset MLP classifier, etc.), and the proposed simultaneous fault diagnosis method has achieved excellent results.
HF-SCA: Hands-Free Strong Customer Authentication Based on a Memory-Guided Attention Mechanisms

Strong customer authentication (SCA) is a requirement of the European Union Revised Directive on Payment Services (PSD2) which ensures that electronic payments are performed with multifactor authentication. While increasing the security of electronic payments, the SCA impacted seriously on the shopping carts abandonment: an Italian bank computed that 22% of online purchases in the first semester of 2021 did not complete because of problems with the SCA. Luckily, the PSD2 allows the use of transaction risk analysis tool to exempt the SCA process. In this paper, we propose an unsupervised novel combination of existing machine learning techniques able to determine if a purchase is typical or not for a specific customer, so that in the case of a typical purchase the SCA could be exempted. We modified a well-known architecture (U-net) by replacing convolutional blocks with squeeze-and-excitation blocks. After that, a memory network was added in a latent space and an attention mechanism was introduced in the decoding side of the network. The proposed solution was able to detect nontypical purchases by creating temporal correlations between transactions. The network achieved 97.7% of AUC score over a well-known dataset retrieved online. By using this approach, we found that 98% of purchases could be executed by securely exempting the SCA, while shortening the customer’s journey and providing an elevated user experience. As an additional validation, we developed an Alexa skill for Amazon smart glasses which allows a user to shop and pay online by merely using vocal interaction, leaving the hands free to perform other activities, for example driving a car.
Communication Cost of Two-Database Symmetric Private Information Retrieval: A Conditional Disclosure of Multiple Secrets Perspective

We consider the total (upload plus download) communication cost of two-database symmetric private information retrieval (SPIR) through its relationship to conditional disclosure of secrets (CDS). In SPIR, a user wishes to retrieve a message out of K messages from N non-colluding and replicated databases without learning anything beyond the retrieved message, while no individual database learns the retrieved message index. In CDS, two parties each holding an individual input and sharing a common secret wish to disclose this secret to an external party in an efficient manner if and only if their inputs satisfy a public deterministic function. As a natural extension of CDS, we introduce conditional disclosure of multiple secrets (CDMS) where two parties share multiple i.i.d. common secrets rather than a single common secret as in CDS. We show that a special configuration of CDMS is equivalent to two-database SPIR. Inspired by this equivalence, we design download cost efficient SPIR schemes using bipartite graph representation of CDS and CDMS, and determine the exact minimum total communication cost of N = 2 database SPIR for K = 3 messages when the message length is 1.
Improved Bound on the Local Leakage-resilience of Shamir’s Secret Sharing

Side-channel attacks have repeatedly falsified the assumption that cryptosystems are black boxes. Leakage-resilient cryptography studies the robustness of cryptographic constructions when an unforeseen revelation of information occurs. In this context, recently, Benhamouda, Degwekar, Ishai, and Rabin (CRYPTO–2018) motivated the study of the local leakage resilience of secret-sharing schemes against an adversary who obtains independent leakage from each secret share.Motivated by applications in secure computation, Benhamouda et al. (CRYPTO–2018) initiated the study of the local leakage resilience of Shamir’s secret-sharing scheme, an essential primitive for nearly all threshold cryptography. The objective is to achieve local leakage resilience with as small a fractional reconstruction threshold as possible. Previously, Benhamouda et al. showed that the reconstruction threshold k being at least 0.907 times the number of parties n is sufficient for Shamir’s secretsharing scheme to be resilient against arbitrary single-bit local leakage from each secret share. After that, Maji et al. (CRYPTO–2021) and Benhamouda et al. (Journal of Cryptology–2021) independently lowered this threshold to k/n ⩾ 0.8675 and k/n ⩾0.85, respectively.This paper contributes to this line of research and proves that k/n ⩾ 0.78 is sufficient. Next, motivated by applications in GMW-style leakage-resilient secure computation, our work extends this bound to a more general adversary who corrupts some parties (obtaining their entire secret shares) and obtains leakage from the remaining honest parties’ secret shares.Our technical analysis proceeds by Fourier analysis and accurately estimates an exponential sum arising in this analysis.
Orthonormal Sketches for Secure Coded Regression

In this work, we propose a method for speeding up linear regression distributively, while ensuring security. We leverage randomized sketching techniques, and improve straggler resilience in asynchronous systems. Specifically, we apply a random orthonormal matrix and then subsample in blocks, to simultaneously secure the information and reduce the dimension of the regression problem. In our setup, the transformation corresponds to an encoded encryption in an approximate gradient coding scheme, and the subsampling corresponds to the responses of the non-straggling workers; in a centralized coded computing network. We focus on the special case of the Subsampled Randomized Hadamard Transform, which we generalize to block sampling; and discuss how it can be used to secure the data.
The Posterior Distribution of Bayesian Context-Tree Models: Theory and Applications

The Context-Tree Weighting (CTW) algorithm and the accompanying collection of ideas and techniques have a long history of statistical applications in discrete time series analysis. CTW was recently revisited from a principled Bayesian statistics point of view, and a general modelling framework called Bayesian Context Trees (BCT) was introduced and found to be very effective in numerous core statistical tasks. In this work, a novel representation of the induced BCT posterior distribution on model space is derived in terms of a simple branching process, and several consequences of this are explored in theory and in practice. First, it is shown that it leads to a simple variable-dimensional Monte Carlo sampler for the joint posterior on models and parameters, which is found to be more efficient than earlier MCMC samplers for the same tasks. Then the branching process representation is used to establish the asymptotic consistency of the BCT posterior, including the derivation of an almost-sure convergence rate.
Detection and localization of image tampering in digital images with fused features

Summary In digital forensics, image tamper detection and localization have attracted increased attention in recent days, where the standard methods have limited description ability and high computational costs. As a result, this research introduces a novel picture tamper detection and localization model. Feature extraction, tamper detection, as well as tamper localization are the three major phases of the proposed model. From the input digital images, a group of features like “Scale‐based Adaptive Speeded Up Robust Features (SA‐SURF), Discrete Wavelet Transform (DWT) based Patched Local Vector Pattern (LVP) features, HoG feature with harmonic mean based PCA and MBFDF” are extracted. Then, with this extracted feature strain the “optimized Convolutional Neural Network (CNN)” will be trained in the tamper detection phase. Since it is the key decision‐maker about the presence/absence of tamper, its weighting parameters are fine‐tuned via a novel improved Sea‐lion Customized Firefly algorithm (ISCFF) model. This ensures the enhancement of detection accuracy. Once an image is recognized to have tampers, then it is essential to identify the tamper localization. In the tamper localization phase, the copy‐move tampers are localized using the SIFT features, splicing tampers are localized using the DBN and the noise inconsistency is localized with a newly introduced threshold‐based tamper localization technique. The simulation outcomes illustrate that the adopted model attains better tamper detection as well as localization performance over the existing methods.