Reviewerly/exHarmony huggingface dataset:
Protocol Efficiency Using Multiple Level Encoding in Quantum Secure Direct Communication Protocol

One of the objectives of information security is to maintain the confidentiality and integrity of the information by ensuring that information is transferred in a way that is secure from any listener or attacker. There was no comparison experiment conducted in earlier studies regarding different level encoding performance towards the multiphoton technique. In Quantum Secure Direct Communication (QSDC), when unpolarized light enters into the polarizer, the light value will be changed into a different value when it hit the Half Wave Plate (HWP) along the quantum communication channel. The multiphoton technique in the earlier study is particular to transmission time for data transfer encoding and extra time for polarizers to change polarization angles, both of which contribute to longer transmission times. With four different sizes of qubits, the three simulation experiments are carried out using Python coding with 2,4 and 8 levels of encoding. Experiment results demonstrate that the most efficient average photon transmission derived from 18 qubit size ranges from 98.71% to 98.73% depending on encoding level. With 18 qubit size, the four-level encoding result has the highest average efficiency, followed by the eight-level and two-level encodings, respectively. 4-level encoding exhibits the highest average photon efficiency between 2 and 8-level encoding.
Energy-Efficient Dynamic Asynchronous Federated Learning in Mobile Edge Computing Networks

To break data silos and address the challenge of green communication, federated learning (FL) is widely used at network edges to train deep learning models in mobile edge computing (MEC) networks. However, many existing FL algorithms do not fully consider the dynamic environment, resulting in slower convergence of the model and larger training energy consumption. In this paper, we design a dynamic asynchronous federated learning (DAFL) model to improve the efficiency of FL in MEC networks. Specifically, we dynamically choose a certain number of mobile devices (MDs) by their arrival order to participate in the global aggregation at each epoch. Meanwhile, we analyze the energy consumption model of local update and upload update, and formulate the problem as a dynamic sequential decision problem to minimize the energy consumption, which is NP-hard. To address it, we propose an energy-efficient algorithm based on deep reinforcement learning named DDAFL, to intelligently determine the number of MDs participating in global aggregation according to the state of MEC networks at each epoch. Compared with baseline schemes, the proposed algorithm can significantly reduce energy consumption and accelerate model convergence.
Quantum Annealing Approach for Selective Traveling Salesman Problem

Quantum computing has paved a new way for faster and more efficient solutions to large-scale, real-world optimization problems that are challenging for classical computing systems. For instance, selective traveling salesman problem (sTSP) that is famous in such fields as logistic optimization and has attracted increasing attention from the research community, however, is known as an NP-Hard problem. Solving the sTSP is, therefore, extremely complex because the optimization function potentially comes with an exponential number of variables that cannot be solved in polynomial time in general. To this end, we propose a quantum annealing framework for time-bounded and near-optimal solutions for the sTSP, overcoming hardware limits of near-term quantum devices. In particular, we put forth an efficient Hamiltonian (QUBO) to encode the complex decision-making for the sTSP on noisy intermediate-scale quantum (NISQ) annealer. Furthermore, experimental results we obtained on the D-Wave 2000 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> quantum hardware demonstrate that the optimal solutions for several instances can be attained.
Performance Analysis of Quantum Error-Correcting Surface Codes over Asymmetric Channels

One of the main challenge for an efficient implementation of quantum information technologies is how to counteract quantum noise. Quantum error correcting codes are therefore of primary interest for the evolution towards quantum computing and quantum Internet. We here analyze the performance of surface codes, one of the most important class for practical implementations, on both symmetric and asymmetric quantum channels. We derive approximate expressions, confirmed by simulations, to evaluate the performance of surface codes and of XZZX codes, and provide a metric to assess the advantage of codes with respect to uncoded systems. Our findings allow to characterize the performance by means of analytical formulas of surface codes, like, for example, the [[13, 1, 3]], the [[23, 1, 3/5]], the [[33, 1, 3/7]], and the [[41, 1, 5]] surface codes.
Sparse Estimation in Finite Mixture of Accelerated Failure Time and Mixture of Regression Models with R Package fmrs

Variable selection in large-dimensional data has been extensively studied in different settings over the past decades. In a recent article, Shokoohi et. al. [29, DOI:10.1214/18-AOAS1198] proposed a method for variable selection in finite mixture of accelerated failure time regression models for studies on time-to-event data to capture heterogeneity within the population and account for censoring. In this paper, we introduce the fmrs package, which implements the variable selection methodology for such models. Furthermore, as a byproduct, the fmrs package facilitates variable selection in finite mixture regression models. The package also incorporates a tuning parameter selection mechanism based on component-wise bic. Commonly used penalties, such as Least Absolute Shrinkage and Selection Operator, and Smoothly Clipped Absolute Deviation, are integrated into fmrs. Additionally, the package offers an option for non-mixture regression models. The C language is chosen to boost the optimization speed. We provide an overview of the fmrs principles and the strategies employed for optimization. Hands-on illustrations are presented to help users get acquainted with fmrs. Finally, we apply fmrs to a lung cancer dataset and observe that a two-component mixture model reveals a subgroup with a more aggressive form of the disease, displaying a lower survival time.
Aggregating Credences into Beliefs: Threshold-Based Approaches

Binarizing belief aggregation tackles the problem of aggregating individuals’ probabilistic beliefs on logically connected propositions into the group’s binary beliefs. One common approach to associating probabilistic beliefs with binary beliefs would be applying thresholds to probabilities. This paper aims to introduce and classify a range of threshold-based binarizing belief aggregation rules while characterizing them based on different forms of monotonicity and other properties.
A Characterization of Lewisian Causal Models

An important component in the interventionist account of causal explanation is an interpretation of counterfactual conditionals as statements about consequences of hypothetical interventions. The interpretation receives a formal treatment in the framework of functional causal models. In Judea Pearl's influential formulation, functional causal models are assumed to satisfy a "unique-solution" property; this class of Pearlian causal models includes the ones called recursive. Joseph Halpern showed that every recursive causal model is Lewisian, in the sense that from the causal model one can construct a possible worlds model in David Lewis's well-known semantics that satisfies the exact same formulas in a certain language. Moreover, he demonstrated that some Pearlian (non-recursive) models are not Lewisian in this sense. This raises the question regarding the exact contour of Lewisian causal models. In this paper, we provide a characterization of the class of Lewisian causal models and a complete axiomatization with respect to this class. Our results have philosophically interesting consequences, two of which are especially worth noting. First, the class of Stalnakerian causal models, a subclass of Lewisian causal models, is precisely the class of Pearlian models that do not contain any cycle of counterfactual dependence (in a sense of counterfactual dependence akin to Lewis's famous relation between distinct events). Second, a more natural class of causal models is actually a superclass of Lewisian causal models, the logic of which respects only weak centering rather than centering.
A Product of Shape and Sequence Abstractions

Traditional separation logic-based shape analyses utilize inductive summarizing predicates so as to capture general properties of the layout of data-structures, to verify accurate manipulations of, e.g., various forms of lists or trees. However, they also usually abstract away contents properties, so that they may only verify memory safety and invariance of data-structure shapes. In this paper, we introduce a novel abstract domain to describe sequences of values of unbounded size, and track constraints on their length and on extremal values contained in them. We define a reduced product of such a sequence abstraction together with an existing shape abstraction so as to infer both shape and contents properties of data-structures. We report on the implementation of the sequence domain, its integration into a static analyzer for C code, and we evaluate its ability to verify partial functional correctness properties for list and tree algorithms.
A Formal Framework to Measure the Incompleteness of Abstract Interpretations

In program analysis by abstract interpretation, backward-completeness represents no loss of precision between the result of the analysis and the abstraction of the concrete execution, while forward-completeness stands for no imprecision between the concretization of the analysis result and the concrete execution. Program analyzers satisfying one of the two properties (or both) are considered precise. Regrettably, as for all approximation methods, the presence of false-alarms is most of the time unavoidable and therefore we need to deal somehow with incompleteness of both. To this end, a new property called partial completeness has recently been formalized as a relaxation of backward-completeness allowing a limited amount of imprecision measured by quasi-metrics. However, the use of quasi-metrics enforces distance functions to adhere precisely the abstract domain ordering, thus not suitable to be used to weaken the forward-completeness property which considers also abstract domains that are not necessarily based on Galois Connections. In this paper, we formalize a weaker form of quasi-metric, called pre-metric, which can be defined on all domains equipped with a pre-order relation. We show how this newly defined notion of pre-metric allows us to derive other pre-metrics on other domains by exploiting the concretization and, when available, the abstraction maps, according to the information and the corresponding level of approximation that we want to measure. Finally, by exploiting pre-metrics as our imprecision meter, we introduce the partial forward/backward-completeness properties.
Domain Precision in Galois Connection-Less Abstract Interpretation

The ever growing pervasiveness of software systems in modern days technology results in an increasing need of software/program correctness proofs. The latter, allow developers to spot software failures before production, hence preventing potentially catastrophic repercussions on our society, as in the case of safety-critical infrastructures. Unfortunately, correctness proofs may fail (even when software is actually correct) due to program analysis imprecision: program analysis sacrifices precision in order to gain decidability. In standard abstract interpretation-based static analyses, such imprecision is “measured” in terms of completeness of the chosen observation (i.e., of the chosen abstract domain) w.r.t. the programming language semantics. In this setting, fixed the language language, it is crucial to have decidable techniques to determine whether the chosen abstraction is sufficiently precise to analyze the program under consideration. In this paper, we characterize abstract domain precision from a novel point of view, providing a formal framework for characterizing and (statically) verifying abstract domain precision, that can be adopted also in the case of “weakened”, i.e., Galois Connection-less, static analysis frameworks. Distinctive examples adopting such frameworks are the Convex Polyhedra and Automata domains, for which standard approaches to reason about analysis precision (i.e., completeness) cannot be applied.
Personalized Graph Federated Learning With Differential Privacy

This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device or cluster-specific models while maintaining the privacy of every individual device. The proposed approach exploits similarities among different models to provide a more relevant experience for each device, even in situations with diverse data distributions and disproportionate datasets. Furthermore, to ensure a secure and efficient approach to collaborative personalized learning, we study a variant of the PGFL implementation that utilizes differential privacy, specifically zero-concentrated differential privacy, where a noise sequence perturbs model exchanges. Our mathematical analysis shows that the proposed privacy-preserving PGFL algorithm converges to the optimal cluster-specific solution for each cluster in linear time. It also reveals that exploiting similarities among clusters could lead to an alternative output whose distance to the original solution is bounded and that this bound can be adjusted by modifying the algorithm's hyperparameters. Further, our analysis shows that the algorithm ensures local differential privacy for all clients in terms of zero-concentrated differential privacy. Finally, the effectiveness of the proposed PGFL algorithm is showcased through numerical experiments conducted in the context of regression and classification tasks using some of the National Institute of Standards and Technology's (NIST's) synthetic datasets, namely, MNIST, and MedMNIST.
A Cost and Power Feasibility Analysis of Quantum Annealing for NextG Cellular Wireless Networks

In order to meet mobile cellular users' ever-increasing data demands, today's 4 G and 5 G wireless networks are designed mainly with the goal of maximizing spectral efficiency. While they have made progress in this regard, controlling the carbon footprint and operational costs of such networks remains a long-standing problem among network designers. This paper takes a long view on this problem, envisioning a NextG scenario where the network leverages quantum annealing for cellular baseband processing. We gather and synthesize insights on power consumption, computational throughput and latency, spectral efficiency, operational cost, and feasibility timelines surrounding quantum annealing technology. Armed with these data, we project the quantitative performance targets future quantum annealing hardware must meet in order to provide a computational and power advantage over CMOS hardware, while matching its whole-network spectral efficiency. Our quantitative analysis predicts that with 82.32 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s problem latency and 2.68 M qubits, quantum annealing will achieve a spectral efficiency equal to CMOS while reducing power consumption by 41 kW (45% lower) in a Large MIMO base station with 400 MHz bandwidth and 64 antennas, and a 160 kW power reduction (55% lower) using 8.04 M qubits in a CRAN setting with three Large MIMO base stations.
A Fast and Low Distortion Image Steganography Framework Based on Nature-Inspired Optimizers

Nowadays, secret information is susceptible to numerous hacks while being shared through traditional correspondence channels.Unlike other methods of securing data, steganography offers the most comprehensive security.Several algorithms have been developed to determine the optimal embedding capacity of every pixel to enhance steganography techniques.Concealing secret bits is not the only challenging aspect of this operation; improving the embedding capacity of secret data is also important, as is maintaining an acceptable visual quality after concealment.A new framework is proposed for concealing a secret text in the spatial domain by partitioning the host image into some non-overlapping blocks and classifying each block as edged or smooth using the Whale Optimization Algorithm (WOA).Different WOA objective functions can be utilized to determine each pixel's embedding capacity based on its intensity.For smooth blocks, the fitness function (with low objective values) is used, and for edge blocks, the cost function (with high objective values) is used instead.Additionally, several combinations of scanning order and starting point for each block in the host image are determined to reduce embedding distortion.WOA can be used to determine the best combination of them.Minimum replacement error (MRE) and compensated grayscale changes (CGSC) modules further reduce embedded distortion and artifacts.Accordingly, the recommended technique produces a peak signal-to-noise ratio (PSNR) of 62.44 dB, compared to 43.15 dB using the benchmark algorithm Multiple Pixel-value Adjustment (MPA) with an encoding function algorithm.Due to its superior performance, the recommended framework outperforms existing frameworks.
<i>R</i> <sup>3</sup>—Rescale, Rotate, and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyper-Chaotic Systems

This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Chua system and the hyper-chaotic nature of the Chen system, the algorithm carries out rescaling, rotation, and randomization on the target image. The intrinsic unpredictability and sensitivity to initial conditions of these chaotic systems endow the encryption algorithm with an expansive key space of 25208. This feature not only bolsters its resilience against brute-force attacks but also magnifies its overall security profile. The algorithm’s efficiency is evident in its rapid computational speed and lean resource consumption, making it suitable for real-time applications. To gauge its robustness, a battery of rigorous tests and analyses, spanning differential attacks, statistical attacks, and brute-force assaults, were employed. The results validate its ability to resist a diverse spectrum of threats. With its expansive key space, exceptional efficiency, and sturdy defenses, the proposed algorithm emerges as a potential cornerstone for safeguarding digital images in arenas like secure communication, data storage, and multimedia transmission. In sum, this research pushes the boundaries of high-security image encryption methodologies, catering to the burgeoning demands of the digital age.
A Hybrid Music Recommendation Model Based on Personalized Measurement and Game Theory

Music recommendation algorithms, from the perspective of real-time, can be classified into two categories: offline recommendation algorithms and online recommendation algorithms.To improve music recommendation accuracy, especially for the new music (users have no historic listening records on it), and real-time recommendation ability, and solve the interest drift problem simultaneously, we propose a hybrid music recommendation model based on personalized measurement and game theory.This model can be separated into two parts: an offline recommendation part (OFFLRP) and an online recommendation part (ONLRP).In the offline part, we emphasize users personalization.We introduce two metrics named user pursue-novelty degree (UPND) and music popularity (MP) to improve the traditional items-based collaborative filtering algorithm.In the online part, we try to solve the interest drift problem, which is a thorny problem in the offline part.We propose a novel online recommendation algorithm based on game theory.Experiments verify that the hybrid music recommendation model has higher new music recommendation accuracy, decent dynamical personalized recommendation ability, and real-time recommendation capability, and can substantially mitigate the problem of interest drift.
A Time-Area-Efficient and Compact ECSM Processor over GF (p)

The elliptic curve scalar multiplication (ECSM) is the core of elliptic curve cryptography (ECC), which directly determines the performance of ECC. In this paper, a novel time-area-efficient and compact design of a 256-bit ECSM processor over GF (p) for the resource-constrained device is proposed, where p can be selected flexibly according to the application scenario. A compact and efficient 256-bit modular adder/subtractor and an improved 256-bit Montgomery multiplier are designed. We select Jacobian coordinates for point doubling and mixed Jacobian-affine coordinates for point addition. We have improved the binary expansion algorithm to reduce 75% of the point addition operations. The clock consumption of each module in this architecture is constant, which can effectively resist side-channel attacks. Reuse technology is adopted in this paper to make the overall architecture more compact and efficient. The design architecture is implemented on Xilinx Kintex-7 (XC7K325T-2FFG900I), consuming 1439 slices, 2 DSPs, and 2 BRAMs. It takes about 7.9 ms at the frequency of 222.2 MHz and 1763k clock cycles to complete once 256-bit ECSM operation over GF (p).
Improved differential fault attack against SIMECK based on two-bit fault model

Low-end embedded systems gain specific advantages from using lightweight block ciphers, such as the SIMECK cipher, first introduced in 2015. However, the public nature of SIMECK has led to risks of potential fault attacks. Our study scrutinizes the differential fault attack (DFA) on SIMECK. It considers the different models of byte fault, in which the ability to obtain the fault location and the cracking efficiency of the round key are studied. Both theoretical and experimental testing demonstrate the ability to discover SIMECK's master key by inducing byte faults at LT−5, with a hamming weight of two and a bit distance of five, denoted as a two-bit fault. In the cases of SIMECK 48/96 and 64/128, an attacker can pinpoint the master key with 35.15 and 33.18 faults, respectively. Our method diverges from previous methodologies by introducing the fault in an earlier round and only one round, decreasing the number of faulty ciphertexts needed. Thus, this model is more practice.