With the rapid expansion of the Internet of Things (IoT), ensuring robust security for resource-constrained devices has become essential. Many IoT devices operate in environments with significant security threats, necessitating lightweight yet effective cryptographic solutions. To address this need, the National Institute of Standards and Technology has selected Ascon as the standard for lightweight cryptography due to its efficient round-based processing. Since its introduction, extensive cryptanalysis and security evaluations have been conducted, including assessments of resistance to side-channel and fault attacks. Differential Fault Analysis has been applied to Ascon, with previous research introducing a two-step fault model that combines bit-flip and bit-set faults for key recovery. The previous study introduced a two-step fault model: the attacker first retrieves the lower 64 bits of the secret key with bit-flip faults and then uses bit-set/bit-reset faults to obtain the upper 64 bits of the key. However, in practice, we would not choose the bit-set or bit-reset fault depending on the target devices with a low precision in controlling the fault. In this regard, fault analysis based on bit-flip faults is preferable because it enables key-recovery attacks regardless of the bit-set or bit-reset fault. This paper proposes a new key-recovery fault attack that relies solely on bit-flip faults, eliminating the bit-set/reset fault assumptions. Additionally, we evaluate the theoretical relationship between the number of random bit-flips injected and the reduced keyspace using a probabilistic model based on the coupon collector problem. Through this approach, we assess the feasibility and complexity of our proposed attack, demonstrating its effectiveness against Ascon in a realistic adversarial setting.

With the advancement of hardware security, combined attacks incorporating side-channel analysis (SCA) and fault analysis (FA) have driven the development of combined countermeasures. However, these countermeasures often incur significant overhead. In this paper, we propose a method to reduce the randomness requirement while maintaining security claims. We demonstrate the approach with Masks & Macs (M&M), a scheme that integrates Boolean masking and MAC tag redundancy to provide protection against SCA and differential fault analysis (DFA), addressing its substantial overhead, particularly the high randomness requirement. We introduce a novel multiplicative masking scheme to partially replace Boolean masked modules, achieving a reduction of over 50% in randomness requirement with a minor increase in FPGA resource overhead and latency. Through both theoretical and practical analyses, we prove that our approach maintains the same security claims against SCA, FA, and combined attacks as the original M&M-AES. Additionally, we discuss the feasibility of low-cost countermeasures against statistical ineffective fault attacks (SIFA). This work provides a new perspective on enhancing combined countermeasures by reducing system overhead.

Confidential information held by companies and individuals is often stored on storage devices. When the migration or disposal of such devices is required, it is necessary to overwrite and erase the information from the devices. In order to avoid degradation or to reduce the erasure time, it is desirable to minimize the cost of information erasure, such as the number of overwriting locations. In this paper, we consider the case where confidential information is distributed and stored in multiple storage devices. To analyze the costs of information erasure for this setting, we consider the achievable cost region, i.e., the region of possible cost values. We then show that this region can be characterized using single-letter random variables of bounded cardinalities. Here, we assume that the confidential information is generated by a stationary memoryless source and that a common random number is available in storage devices for erasure.

Greedy-based approaches for solving nonoverlapping, unordered, type-2 square jigsaw puzzles in the literature, primarily consider single-side compatibility, which increases the number of false-positive candidate neighbors. In the assembly phase, these solvers generally resolve the issue by considering all possible combinatorial combinations of the candidate neighbors to determine an optimal match, increasing the solver's computational complexity. To address the same, this paper proposes an efficient greedy asymmetric assembly strategy with two-side verification to solve square jigsaw puzzles. More precisely, the proposed greedy strategy constructs nonoverlapping blocks via unambiguous jigsaw neighbors, such that at least two sides are always connected to the same block. The idea is to reconstruct the puzzle by optimally growing the subset of the local optimal solutions. The implementation leverages a disjoint set data structure to grow and merge blocks efficiently, achieving a space complexity of O (N) and an amortized time complexity of O (N log₂ N). Extensive experimental evaluations validate the solver's effectiveness across standard datasets via direct, neighbor, and perfect compatibility matrices. Furthermore, the algorithm demonstrates its versatility in handling diverse type-2 scenarios, including small puzzle sizes, missing pieces, and mixed puzzles. Our method achieves near-perfect precision in the construction of initial jigsaw blocks, with more than 74% sides of the total jigsaw pieces, as demonstrated by experimental analysis.

In the coded caching scheme proposed by Maddah-Ali and Niesen, we usually consider the setup where K users have respective cache memories of equal size and request an arbitrary one of N files to a server. The server multicasts a signal to all the users so that all the users can reproduce the files of their requests from the transmitted signal using the contents in their respective cache memories. Finding the memory-rate tradeoff is one of the fundamental problems in coded caching. In this paper, we consider the problem of centralized coded caching where K users have cache memories of heterogeneous sizes. We first give a new explicit construction of the coded caching scheme under a certain assumption on the cache sizes. The validity of the scheme is established theoretically. Next, we consider an extension of the scheme so that we can apply the scheme to general heterogeneous cache memories. We divide the N files into K + 1 portions and apply the proposed scheme to each portion in the most efficient way by solving a certain linear programming problem. We compare the memory-rate tradeoff of this optimized scheme with existing coded caching schemes.

This paper focuses on linear regression models with non-conjugate sparsity-inducing regularizers such as lasso and group lasso. Although the empirical Bayes approach enables us to estimate the regularization parameter, little is known on the properties of the estimators. In particular, many aspects regarding the specific conditions under which the mechanism of automatic relevance determination (ARD) occurs remain unexplained. In this paper, we derive the empirical Bayes estimators for the group lasso regularized linear regression models with limited parameters. It is shown that the estimators diverge under a specific condition, giving rise to the ARD mechanism. In addition, we demonstrate that group lasso solutions with the empirical Bayes estimators yield characteristics similar to those of the adaptive lasso, suggesting that such solutions exhibit consistency. Furthermore, we prove their consistency in variable selection. We also prove that empirical Bayes methods can produce the ARD mechanism in general regularized linear regression models and clarify the conditions under which models such as ridge, lasso, and group lasso can do.

The commutative property has been an essential characteristic in the development of public-key cryptosystems. There are essentially only two kinds of commutative polynomials: monomials and Chebyshev polynomials. By leveraging the commutative property of them, efficiently implementable public-key cryptosystems over the residue class ring ℤ_2^k have been introduced; unfortunately, however, they can be broken. Although commutative polynomials with two variables could be potential candidates for a public-key cryptosystem over the ring, the characteristics of these polynomials should be rigorously investigated. In this study, we analyzed several properties of commutative polynomials with two variables over ℤ²_2^k. More precisely, the degree period and the condition for permutation polynomials are discussed theoretically and verified experimentally. Based on the derived properties, a security analysis of a key exchange protocol using the commutative polynomials is also discussed.

Isogeny-based cryptography is one of post-quantum cryptography based on the difficulty of the isogeny problem. The central object is a one-dimensional isogeny, that is, an isogeny between elliptic curves. However, in recent years, not only one-dimensional isogenies but also two-dimensional isogenies have been used to isogeny-based cryptography. Such a two-dimensional isogeny is an isogeny between products of elliptic curves, and it is computed by decomposing to prime degree isogenies. The decomposed isogenies are called a chain of isogenies. Especially, for the decomposition, the first isogeny of the chain has the domain as a product of elliptic curves E₁ × E₂, and a point x to compute the image is of the form of x = (x⁽¹⁾ , 0_E2) ∈ E₁ × E₂ for x⁽¹⁾ ∈ E₁. In this paper, we focus on odd prime degree isogenies with the domain as a product of elliptic curves. For such an isogeny, we propose formulas and explicit algorithms based on the formulas. As a result, the computation of the image of a point (x⁽¹⁾ , 0_E2) is improved compared to the existing method. For the application, when we compute an odd degree isogeny chain, this result allows efficient computation of the dominant isogeny in the chain by placing the isogeny with the largest prime degree first. In addition, we implemented the proposed algorithm in SageMath and confirmed its improved efficiency over the existing algorithm by comparing running times.

Frequency-hopping multiple access (FHMA) systems provide advantages in reducing interference and enhancing security, but multi-access interference (MAI) remains a challenge. This letter presents new theoretical bounds for the maximum periodic Hamming correlation (PHC) and maximum periodic partial Hamming correlation (PPHC) within the low-hit-zone (LHZ) of multi-timeslot wide-gap frequency-hopping sequence (MTWGFHS) sets. Our results extend LHZ-FHS theory and address gaps in designing interference-resilient FHMA systems, contributing to more secure and robust frequency-hopping communication.

Recently, various Multiple-input multiple-output (MIMO) signal detectors based on deep learning techniques or quantum (-inspired) algorithms have been proposed to improve the detection performance compared with conventional detectors. This paper focuses on the simulated bifurcation (SB) algorithm, a quantum-inspired algorithm. This paper proposes two techniques to improve its detection performance. The first is modifying the algorithm inspired by the Levenberg-Marquardt algorithm to eliminate local minima of the maximum likelihood detection. The second is the use of deep unfolding, a deep learning technique to train the internal parameters of an iterative algorithm. We propose a deep-unfolded SB by making the update rule of SB differentiable. The numerical results show that these proposed detectors significantly improve the signal detection performance in massive MIMO systems.

If the channel matrix is unknown or there are constraints on the hardware implementation, decoding may use a different metric from the actual one. This kind of decoding is called mismatched decoding. In this paper, upper bounds on the error probability are derived for the ensemble of linear codes using decision feedback (ARQ) with mismatched decoding by Forney's rule (FR). FR uses a maximum likelihood codeword and determines whether the received sequence is decoded or the retransmission is requested. We also derive their Shulman and Feder type bounds, which give a single-letter expression of a lower bound on the error exponent.

In this study, we investigate soft binary hypothesis testing using a random sample, wherein decisions are made based on a soft test function. To evaluate this test function, we introduce two classes of tunable loss functions and define generalized type I and II errors, as well as Bayesian errors. We analyze the trade-offs between these errors and establish asymptotic results that extend the Neyman-Pearson lemma, the Chernoff-Stein lemma, and Chernoff information in classical binary hypothesis testing.

Group testing is a method for identifying defective items from a large set by performing a relatively small number of tests on subsets of items, called pools. The collection of pools are called designs. This work explores quasi-random designs, a hybrid approach that combines the practicality of random designs with the combinatorial advantages of deterministic designs. We provide a unified theoretical explanation for various pool selection criteria involving rows and columns of design matrices in the generation of quasi-random designs with constant pool sizes. By employing linear algebraic techniques, we offer insights into the essential differences between these criteria and demonstrate their efficient implementation, addressing several computational issues encountered in previous studies. Moreover, simulations show that quasi-random designs outperform traditional random designs in noiseless group testing, even with limited pool sizes, and that the criteria proposed by Hamada and Lu (ISITA 2024) deliver the best performance in most cases, achieving higher identification accuracy and greater stability compared to the other evaluated criteria.

Recommender systems have become more crucial for informed service consumption, product selection, and decision-making in the era of overabundant information and the digitized economy. Session-based systems have emerged in recent years as a new paradigm for recommender systems. Although session-based recommenders have been extensively investigated, there are currently no unified issue statements for them nor detailed explanations of their characteristics and difficulties. In this paper, deep learning with optimal feature selection approaches are used for effective feature selection and classification. Different data related to product reviews and movie reviews are considered as input for this suggested approach. Initially, these datasets are given to the count vectorizer for converting the ”message” column's text into numbers. These converted raw data's are pre-processed utilizing similarity based data filling, min-max normalization and fuzzy c-means clustering to fill the absent values, standardization and to reduce the redundant data present in the dataset. Then, the features from pre-processed data are extracted. Aquila Optimization based approach is employed in the suggested method to decrease the number of resources required to describe a large set of data. Finally, a hybrid LSTM-SVM classifier is utilized for classification purpose. In this model, the softmax unit of the LSTM is substituted through SVM to predict the five different classes based on the customers reviews. The valuation outcomes shows that the suggested approach achieves 95%, 96%, 97% of accuracy, 90%, 91%, 93% of precision, 95%, 88%, 95% of specificity rate for three various datasets. As a result, the recommended strategy is the greatest option for a successful recommendation system.

This paper presents a selective fixed-filter active noise control (ANC) system incorporating a compensation filter, based on a modified error filtered-x discrete cosine transform least mean square (Fx-DCT-LMS) algorithm. In the proposed framework, a compensation filter is cascaded with a fixed noise control filter, which is selected using a convolutional neural network (CNN). Adaptive signal processing is then applied to compensate for the discrepancy between the fixed filter and the optimal solution. The sliding discrete cosine transform (SDCT) enables filter selection using spectrograms generated from arbitrary-length sample windows, which serve as input to the CNN. Notably, since the proposed method utilizes the DCT to compute the spectrogram, it significantly reduces computational complexity compared to conventional DFT-based spectrogram generation. Moreover, by employing the DCT-LMS algorithm, the compensation filter achieves faster convergence than the conventional filtered-x normalized LMS (Fx-NLMS) algorithm. Simulation results using real-world impulse responses demonstrate that the proposed system can achieve up to 10 dB of noise reduction under varying noise conditions.

In this study, we propose Modern Complex-Valued Hopfield Network (Modern CVHN), a novel associative memory model designed for continuous data with inherent periodic structure. The model operates on a toroidal state space—constructed as the Cartesian product of complex unit circles—and performs memory encoding and retrieval via a softmax-based energy function that intrinsically incorporates periodicity. Through numerical experiments, we demonstrate that Modern CVHN achieves superior memory capacity and robustness to noise compared to both conventional Complex-Valued Hopfield Networks and Modern Hopfield Network, across discrete phase patterns and continuous periodic data. These findings underscore the effectiveness of energy-based modeling on toroidal manifolds for associative memory involving periodic structures. This approach offers a promising foundation for future applications in complex information processing tasks characterized by periodicity.

Balance control and high-speed motion present significant challenges for unicycle robots, particularly when implementing stable control under low-power actuator constraints. This paper investigates balance and locomotion problems for unicycle robots with low-power flywheels in uncertain environments. Considering the limitations of existing control methods in disturbance rejection, operational speed, and steering stability, we propose a robust control framework with high uncertainty-handling ability. The system overcomes low-power constraints and enhances disturbance rejection performance by introducing center-of-mass velocity estimation, strategic under-compensation techniques, and a priority-based power allocation algorithm. Then, by decoupling the robot dynamics into three axes, we propose a comprehensive control scheme integrating delay compensation and lightweight torque control without current detection. Experiments under various conditions confirm the effectiveness of our approach in achieving stable operation at speeds up to 1 m/s and diverse movement capabilities. We won the first prize in China's National College Student Intelligent Vehicle Competition. See video on https://youtu.be/8UNbX0LAHjY.

In the case of distributed power source fault crossing, traditional methods have limited negative sequence voltage suppression effect and are difficult to reduce the negative sequence voltage to the ideal level, which affects the stability and safety of microgrid operation. Therefore, an improved method for on-site feeder automation considering distributed power source fault crossing is proposed. This method deeply analyzes the impact of distributed power access on feeder automation, sets anti islanding protection values for fault crossing situations, and preliminarily improves the feeder automation structure. To further optimize the effect, an improvement strategy based on regional numbering is proposed, which divides the range of power grid areas connected to distributed power sources, assigns logical numbering and adjusts logical operation time, and achieves the coordinated optimization of power grid fault timing coordination logic and anti islanding protection setting strategy. The experimental results show that this method can effectively suppress the negative sequence voltage connected to the distributed power microgrid, and approach the ideal level of negative sequence voltage; At the same time, the ratio error of the distance between the fault point and the protected end to the total length of the line is less than 0.01, the total exit time is controlled within 40ms, and the risk of fault rejection/misoperation is less than 0.5%. Significantly improved the stability and fault handling capability of the power grid, providing reliable technical support for the intelligent management of the power system.

The CGL hash function is an isogeny-based hash function that computes non-backtracking paths on a supersingular isogeny graph. Since one of the problems of the CGL hash function is its relatively slow computational time, many acceleration methods have been studied, including the use of the Legendre form, radical isogenies. An algorithm for computing the CGL hash function proposed at SAC'22 has achieved acceleration of several orders of magnitude, by using 2ⁿ-isogenies for an integer n = Θ (log p), where p is characteristic of the underlying field. In this algorithm, the backtracking 2-isogeny between two consecutive 2ⁿ-isogenies must be prevented to assure the security of the hash function, which is called backtracking checks.

In this paper, we propose two algorithms to further accelerate the computation by reducing the overhead of backtracking checks. The first algorithm skips backtracking checks when unnecessary. The second one completely eliminates the need for these checks. Moreover, we implement our proposed algorithms. We perform a detailed and precise complexity analysis of our algorithms as well as previously proposed ones by program-matically counting the actual number of operations over the underlying finite field. We demonstrate that the first algorithm reduces the cost by 7.6%, 7.0%, 7.6%, 6.2% and second one by 18.9%, 17.8%, 16.7%, 16.1% compared to the original algorithm at SAC'22 for 256, 512, 1024, 1536-bit primes, respectively.

This paper is an extended version of [1]. We add the second algorithm without backtracking checks, which is faster than the first algorithm, and its efficiency is demonstrated by the implementation.

Card-based cryptography enables players to compute logical and arithmetic operations securely, such as bitwise AND and addition of integers. Several multiparty computation protocols and zero-knowledge proof protocols utilizing these secure computations have been developed as its applications. However, the realization of an efficient protocol for an arithmetic operation other than addition and subtraction remains an open problem. This paper proposes card-based protocols, based on integer commitment, for multiplication, division, and square root. Compared to general constructions for protocols for these operations based on binary integer commitment, the proposed protocols exhibit superior simplicity and efficiency. Furthermore, these protocols introduce novel applications for card-based cryptography to secure statistical data aggregation.

Assume that, given a sequence of n integers from 1 to n arranged in random order, we want to sort them, provided that the only acceptable operation is a prefix reversal, which means to take any number of integers (sub-sequence) from the left of the sequence, reverse the order of the sub-sequence, and return them to the original sequence. This problem is called “pancake sorting,” and sorting an arbitrary sequence with the minimum number of operations restricted in this way is known to be NP-hard. In this paper, we consider applying the concept of zero-knowledge proofs to the pancake sorting problem. That is, we design card-based zero-knowledge proof protocols in which a user (the prover) who knows how to sort a given sequence with ℓ operations can convince another user (the verifier) that the prover knows this information without divulging it.

This letter analyzes the average channel capacity of coherent free-space optical (FSO) systems employing multiple receive aperture for binary inputs and its simple upper bound over atmospheric turbulence-induced channels. The newly derived upper bound is based on the Taylor series and moment generating function, and it significantly reduces computational complexity by involving only a single infinite series, in contrast to the conventional double-integral expression. This analytical simplicity makes it especially appealing for performance evaluation and system design. Through numerical examples, we validate that the proposed upper bound closely approximates the exact average channel capacity across a wide range of receiver configurations and refractive index structure constants.

The covering radius of the r-th order Reed-Muller code RM (r, n), denoted by ρ (r, n), is the maximum r-th order nonlinearity of n-variable Boolean functions. Using the Fourquet-Tavernier list-decoding algorithm and the Fourquet list-decoding algorithm, we discover, among monomial Boolean functions, 11-variable Boolean functions with second-order nonlinearity 856, and we determine that the covering radius of RM (3, 8) in RM (4, 8) is 56. Besides, it is proved that the complexity of the Fourquet algorithm for list decoding RM (r, n) is linear in the length of the code 2ⁿ given the decoding radius up to the Johnson bound. In this paper, we prove that the complexity of the Fourquet algorithm is also linear in 2ⁿ in some special cases when the decoding radius is close to 2^n-r. Moreover, following from the Carlet's method, we improve the best proven lower bound on the third-order nonlinearity of monomial Boolean functions. In a word, the original idea of our work is to improve the lower bound on ρ (r, n) according to two categories as follows: for small r and n, we search an n-variable Boolean function with larger r-th order nonlinearity using a list-decoding algorithm for Reed-Muller codes; for large n, we study a class of quartic monomial Boolean functions to improve the best proven lower bound on its third-order nonlinearity.

To meet the key performance requirements of permanent magnet synchronous motor (PMSM) speed control systems―such as high-precision control, fast response, and strong anti-disturbance, a improved power exponential reaching law (IPERL) is proposed. This approach incorporates an inverse cotangent function to adaptively adjust the power term coefficient, effectively balancing the trade-off between convergence speed and sliding mode chattering. Additionally, a dynamic linear sliding mode surface is designed based on the system error, which effectively mitigates overshoot and further enhances both the dynamic and steady-state performance of the system. Finally, the effectiveness of the proposed control strategy is validated through simulations, demonstrating superior stability and convergence speed compared to conventional methods.

This paper presents a continuous-time pulse compressor (CTPC) for ultrasound imaging systems with enhanced signal intensity and axial resolution. Conventional pulse compression using a chirp signal employs matched filtering, which performs cross-correlation calculations in the digital domain. However, the calculation requires a large amount of computing resources. The proposed CTPC performs pulse compression directly in the analog domain by frequency-dependent delay. In the proposed CTPC, a second-order all-pass filter (APF) with a relatively high quality factor (Q) of 1.8 achieves a long group delay (GD) to reach a sufficiently long chirp period in the ultrasonic band. Furthermore, cascading other second-order APFs with different resonance frequencies approximately linearizes the total GD with respect to frequency. We investigated the implementation of the proposed CTPC using discrete operational amplifiers and passive components. Measurements confirmed a 6.7 dB signal enhancement from compression effects with an ideal electrical chirp signal. Further acoustic measurements using an 8 × 8 channel 2-D matrix array probe and an imaging prototype demonstrated improved signal enhancement and compression factor of 3.2.

Defect pattern detection in wafer bin maps (WBMs) is crucial for enhancing wafer quality, as it prevents the escalation of defects and the squandering of resources. To address this, we introduce a quantum-based Support Vector Machine (SVM) leveraging the quantum approximate optimization algorithm, termed QAOA_SVM. We employ Inception V3 to extract efficient and compact features from WBMs and apply QAOA_SVM for training and testing the data. When recognizing WBMs with two mixed defect types, our method outperforms the original mixup approach by over 7.4%, and achieves a 0.7% accuracy improvement compared to the Inception V3+SVM method. For mixed defect samples containing more than two types of defects (three-mixed and four-mixed), we observe gains of at least 4.2% (relative to SVM) and 0.9% (relative to Inception V3+SVM), respectively. Additionally, our method surpasses other state-of-the-art Convolutional Neural Network (CNN) methods in both single-type and mixed-type defect pattern recognition. Furthermore, QAOA_SVM requires only O(Nd) time, which is significantly more efficient than the O(N³) time complexity of traditional SVM. In summary, QAOA_SVM achieves higher accuracy with significantly reduced computational time.

This work studies the fundamental properties of self-learning observers (LOs), which simultaneously estimate states and parameters resource-efficiently. LOs have a simple structure, the capacity to estimate system uncertainties using only one algebraic equation, and decent performance. We explore the exponential convergence property of LOs in-depth and present an explicit exponential convergence rate for the first time using Halanay's inequality technique. This work further contributes by providing fewer conservative conditions, thereby decreasing the equality condition that must be satisfied in previous studies on LOs. The LO parameters are acquired by solving linear matrix inequalities (LMIs), and the rules for parameter tuning under the new constraints are provided.

This letter proposes an ANC system without an error microphone introducing primary path estimation. The proposed system enables adaptively update of a noise control filter to realize the noise reduction at a desired position without placing an error microphone at the target point, by estimating the primary path to the desired position. A computer simulation using impulse responses generated by the image method demonstrates that the proposed system achieves a noise reduction of more than 10 dB at the desired position, even when the noise source moves.

This paper proposes stable virtual sensing algorithm for active noise control with sequential online modeling of the auxiliary filter and the secondary path. The two online modelings prevents system divergence and maintain high noise reduction when there is a secondary path change such as microphone relocation. The online modeling of the secondary path adjusts mismatch in the noise-control filter. The online modeling of the auxiliary filter compensates for the mismatch therein caused by the secondary path change. A simulation result with recorded signals shows that the proposed method maintains a noise reduction of 22 dB even when a secondary-path change takes place.

The distributed space-based coherent aperture radar (DSCAR) is a radar system that utilizes a space-based platform to form a formation of radar unit (RU) for joint target detection, inspired by the concept of distributed coherent radar. However, DSCAR based on uniform linear formation (ULF) suffers from numerous grating lobe which severely impair the beamforming performance. Due to the fact that conventional grating lobe suppression method based on non-uniform spacing is not suitable for uniform formation, this letter proposes a method based on randomized angle yaw to address the issue of grating lobe suppression in DSCAR. We present the formula for the DSCAR joint pattern and use particle swarm optimization (PSO) to optimize the peak side lobe level (PSLL). Simulation results demonstrate that randomized angle yaw method has better grating suppression effect than non-uniform spacing method, and increasing the number of RUs and the number of RU antenna elements will further improve the optimal PSLL.

In this letter, we consider an asymptotic stabilization problem for a system under saturated input which has uncertain and time-varying bounds. With our gain-scaling controller, we analytically show that the controlled system is asymptotically stabilized and the domain of operation can be arbitrarily enlarged by increasing the gain-scaling factor. Then, our proposed method is extended to a class of feedforward systems. Two numerical results are provided to show the validity of our results.

Secure multiparty computation (MPC) is a cryptographic technology to perform some computation on multiple parties' input data while concealing the individual inputs from other parties. For the case of semi-honest adversaries, the security definition in Goldreich's famous book is widely used as a standard definition. In this paper, however, we point out that there is an MPC protocol where a semi-honest adversary receives only a ciphertext of one-time pad with unknown key but the protocol is not secure under the standard security definition, which may look inconsistent with the perfect secrecy of one-time pad that its ciphertext leaks no information at all. We propose a variant of the security definition that resolves this issue. We also show that a somewhat restrictive version of the Composition Theorem holds for our modified security definition.

With the expansion of industrial applications and data volumes, building a user-edge-cloud collaborative network and efficiently distributing computing tasks has emerged as a critical solution to alleviate terminal computing burdens and ensure quality of service (QoS). However, existing studies often overlook extra delays in the offloading process, including queuing, propagation, and wired transmission delays, significantly impacting task delay and offloading strategies. To this end, this paper proposes a multi-slice task offloading and resource allocation scheme for user-edge-cloud networks, targeting delay minimization while considering various delay factors. This scheme jointly optimizes offloading mode, offloading ratio, user association, and resource allocation under task delay constraints. To address the problem's non-convexity, an alternating optimization framework is employed to decompose the problem into offloading mode selection and resource allocation subproblems. Specifically, a deep reinforcement learning (DRL)-based algorithm is developed for offloading mode selection, while convex optimization techniques are applied to determine optimal offloading ratios and resource allocation. Additionally, a matching theory-based algorithm establishes optimal connections between users and base stations (BSs). Simulations validate the effectiveness of the proposed scheme, showing that the three-layer offloading mode, i.e., collaborative computing across user, edge, and cloud reduces latency compared to single-layer and two-layer modes for large-scale tasks.

Automatic Question Generation (AQG) aims to generate natural and relevant questions based on a given context and optional answers. It is a significant and challenging task in the field of natural language processing. However, existing AQG models often produce a single type of question with repetitive content, which hinders the diversity of the generated questions. In this paper, we introduce a Diversify Question Generation model based on the Diffusion Model (DQG-DM). Our model effectively incorporates latent variables and fine-grained question types to ensure both the relevance and diversity of the generated questions. Experiments conducted on two benchmark datasets demonstrate that our proposed model outperforms the state-of-the-art results.

In this letter, we construct a class of binary parity-check matrices with column weight 3 and show that these matrices have full-rank. Then we prove that the stopping distance of each binary parity-check matrix is equal to the minimum distance of the code specified by the parity-check matrix. Taken together, we obtain a new class of binary linear codes with optimal stopping redundancy.

For linear state estimation problems involving Brownian motion process noise, this paper proposes a novel adaptive Kalman filter that leverages online assessment of the power spectral density (PSD) for continuous-time dynamic noise. Unlike existing adaptive filters that estimate the entire noise covariance matrix, this work proposes to directly evaluate the noise PSD according to a analytical derivation for process noise covariance. As the key innovation, the proposed adaption scheme significantly reduces the number of scalar unknowns and results in enhanced accuracy for estimating the PSD of Brownian motion noise. As the resulted advantage, the new adaptive Kalman filter mitigates the crucial reliance on noise statistics without extra computation. Numerical examples of target tracking demonstrate the new adaptive Kalman filter's filtering adaptability, accuracy, and simplicity.

Recently, IoT (Internet-of-Things) devices are very widely used in our daily lives and their design and manufacturing are often outsourced to third parties to make them at a low cost. Meanwhile, malfunctions may be inserted into them intentionally by malicious third parties. Utilizing power waveforms measured from IoT devices is one of the effective ways to detect its anomalous behaviors. Most IoT devices regularly consume steady-state power due to the operating system and/or hardware components and we have to remove it from the total power to detect anomalous behaviors. However, the existing methods manually or semi-manually remove the steady-state power and further they utilize the pre-determined features in the power waveform to detect anomalies. Hence, they cannot well detect them automatically. In this paper, we propose a method, called Gen-Power2, to detect anomalous behaviors in IoT devices utilizing the generative machine-learning model. The proposed method generates an application power waveform by inferring the steady-state power by machine-learning from the observed total power waveform. Then, the anomalous application behaviors are detected by automatically extracting the latent features from the generated application power waveform. Experimental evaluations show that Gen-Power2 detects anomalous application behaviors successfully, while the recent state-of-the-art method cannot detect them.

Permutations on the vector spaces $\mathbb{F}_{q}^n$ are few at present. Inspired by the work of Chi, Li and Qu [1], we construct two classes of permutations with 3-homogeneous structures in trivariate form over $\mathbb{F}_{2^m}^3$. To establish their permutation properties, we formulate a system of equations and analyze it using techniques such as resultants, multivariate methods, and the method of undetermined coefficients.

Cache tiling or recursive data layouts for two-dimensional (2-D) data access has been proposed to ameliorate the poor data locality caused by conventional layouts like row-major and column-major. However, cache tiling and recursive data layouts require non-conventional address computation, which involves bit-level manipulations that are not supported in current processors, there is also a significant overhead in execution time due to software-based tiling address calculation. In this paper, we design a cache memory with hardware-based tile/line accessibility support for 2-D data access and a tile-set-based tag comparison (TSTC) scheme to optimize overall hardware scale overhead. Our technique captures the benefits of locality of the sophisticated data layouts while avoiding the cost of software-based address computation. Simulation results show the proposed method improves the performance of matrix multiplication (MM) over conventional data layout and Z-Morton order layout by reducing L1 cache, L2 cache and Translation Lookaside Buffer (TLB) misses, especially at larger matrix sizes. We implement the proposed cache with a SIMD-based data path by using 40 nm Complementary Metal-Oxide-Semiconductor (CMOS) technology. The entire hardware overhead of the proposed TSTC method was reduced to only 10% of that required for a conventional cache without performance degradation.

Indoor localization is essential for navigation, tracking, and path planning applications. Traditional systems, based on sensors like inertial devices and LiDAR, offer high accuracy but are costly and prone to cumulative errors. We propose a cost-effective multi-sensor fusion system specifically tailored for two-dimensional localization of two-wheeled mobile robots, combining Wi-Fi channel state information (CSI) and odometry, using an extended Kalman filter (EKF) and an adaptive Monte Carlo localization (CSI-AMCL) algorithm to enhance accuracy. Our innovative 1D convolutional neural network (1D-CNN) based on residual networks effectively processes CSI data, improving adaptability in complex environments by addressing the vanishing gradient issue. Our approach increases accuracy by 56% compared to Wi-Fi fingerprinting. Tests show a 20.1% improvement over WIO-EKF and a 36.3% improvement over Fusion-dhl. This demonstrates the potential of our method for enhancing multi-sensor fusion systems.

The skeleton modality provides an efficient representation of human pose. However, its lack of appearance information can lead to poor performance in tasks requiring such information. To address this, we propose a multimodal skeleton representation that integrates intermediate feature maps from a pose estimation network, called Pose Feature Map Enhanced Skeleton Representation (PFMESR). Specifically, we estimate the joint positions of the human body in the video and locate the local features related to each joint from the feature maps of the pose estimation network. These local features are then aligned and fused with the skeleton features in the action recognition network. We believe that the feature maps from the pose estimation network contain rich appearance information that complements the skeleton information. Experiments on multiple datasets demonstrate that this approach significantly improves action recognition performance and yields favorable results in the Action-Identity Recognition task, proving the effectiveness of incorporating appearance information from pose estimation feature maps. We also investigated the relationship between PFMESR's performance and sampling depth and range to explore its effectiveness under different parameters. Additionally, we validated the generality of PFMESR by applying it to various skeleton-based methods. Our method surpasses the state-of-the-art on multiple skeleton-based action recognition benchmarks, achieving accuracies of 94.6 % on the NTU RGB+D 60 cross-subject split, 97.7 % on the NTU RGB+D 60 cross-view split, and 93.1 % on the NTU RGB+D 120 cross-subject split.

For a regularized least squares estimation of discrete-valued signals, we propose a Linearly involved Generalized Moreau Enhanced (LiGME) regularizer, as a nonconvex regularizer, of designated isolated minimizers. The proposed regularizer is designed as a Generalized Moreau Enhancement (GME) of the so-called sum-of-absolute-values (SOAV) convex regularizer. Every candidate vector in the discrete-valued set is aimed to be assigned to an isolated local minimizer of the proposed regularizer while the overall convexity of the regularized least squares model is maintained. Moreover, a global minimizer of the proposed model can be approximated iteratively by using a variant of the constrained LiGME (cLiGME) algorithm. To enhance the accuracy of the proposed estimation, we also propose a pair of simple modifications, called respectively an iterative reweighting and a generalized superiorization. Numerical experiments demonstrate the effectiveness of the proposed model and algorithms in a scenario of multiple-input multiple-output (MIMO) signal detection.

This paper presents an iterative algorithm for computing an eigenvalue close to a user-specified value and its corresponding eigenvector of a nonlinear eigenvalue problem. This algorithm iterates two parts alternately. The first part is the existing algorithm called the successive approximation algorithm, where the Taylor expansion of a matrix is used to transform the nonlinear problem to the linear problem. By solving the linear problem, an approximate eigenvalue and an approximate eigenvector of the nonlinear problem are computed. The second part refines the approximate eigenvalue computed by the first part. To this end, we approximately compute the Rayleigh functional, which is the solution of the nonlinear equation defined by the approximate eigenvector, and use it as a new approximate eigenvalue. Experimental results show that a combination of the successive approximation algorithm and the Rayleigh functionals converges within fewer iterations and requires less computational time in comparison with the existing successive approximation algorithms.

There are some research projects on the analysis of traffic states by using mathematical models. In this article, we propose a new method based on a distributed constant circuit, namely an electrical circuit based on distributed elements. In this method the distributed constant circuit is regarded as a traffic circuit. The voltage on the distributed constant circuit is regarded as traffic pressure, like the previous works based on the lumped elements on the lumped constant circuit. However, these previous works have some difficulties on the definition of the traffic pressure. In virtue of the distributed elements composed of not Resistance, but Inductance and Capacitance, the input voltage does not augment unreasonably. The analogy between traffic circuit and electrical circuit becomes more reasonable. Moreover, the status of traffic light is also taken into account. The validity of our proposed method was confirmed by some simulations based on distributed constant circuits.

This paper presents two novel keyed hashing modes, KHC1 and KHC2, designed to construct hash functions that guarantee both collision resistance and pseudorandomness. These modes employ compression functions alongside unique encoding schemes, enabling efficient handling of variable-length inputs. The proposed constructions achieve collision resistance, provided that the underlying compression function satisfies the extended notion of collision resistance, which ensures that it is intractable to find distinct input pairs whose output difference falls within a small set. They are also proven to be secure pseudorandom functions (PRFs) under the assumption that the underlying compression function is a secure PRF under related-key attacks. They accept a 256-bit key as input and guarantee 128-bit security against quantum key recovery when instantiated with the SHA-256 compression function. Furthermore, we implemented KHC1 and KHC2 instantiated with the SHA-256 compression function and evaluated their performance. The results confirm that both constructions achieve the efficiency expected by the theoretical evaluation and outperform HMAC-SHA-256 for short messages.

We propose a probabilistic interpretation of the Gap Shortest Vector Problem by leveraging Södergren's analysis of random lattices. By linking shortest vector norms with the Gaussian heuristic, our study characterizes GapSVP behavior in high dimensions and informs secure parameter choices in lattice-based cryptography.

Symmetrical Z-complementary code sets (SZCCSs), as a novel type of training sequences, have demonstrated exceptional channel estimation performance when applied to generalized spatial modulation (GSM) systems. This paper addresses the current limitations in the design methods and parameter range of SZCCSs by proposing three schemes. Initially, by analyzing the complementary properties of Discrete Fourier Transform (DFT) matrix elements, we construct a class of optimal SZCCSs using the Kronecker product of unit modulus sequences and DFT matrices. Subsequently, to diversify the parameter of code quantity, we perform the Kronecker product with complete complementary codes (CCCs) and orthogonal matrices, resulting in a class of optimal SZCCSs. Also, if a sequence with low autocorrelation replace the orthogonal matrix, the resulting SZCCS exhibits low autocorrelation sidelobes outside the zero correlation zone and complete complementary cross correlation characteristics. The study offer a broad range of parameters not previously identified in the literature, significantly enriching the parameter space of SZCCSs. Simulation results validate that the proposed SZCCSs show superior resistance to multipath interference in GSM systems compared to traditional sequences, highlighting their potential advantages in channel estimation.

Private information retrieval (PIR) allows a client to obtain records from a database without revealing the retrieved index to the server. In the single-server model, it has been known that (plain) PIR is vulnerable to selective failure attacks, where a (malicious) server intends to learn information of an index by getting a client's decoded result. Recently, as one solution for this problem, Ben-David et al. (TCC 2022) proposed verifiable PIR (vPIR) that allows a client to verify that the queried database satisfies certain properties. However, the existing vPIR scheme is not practically efficient, especially when we consider the multi-query setting, where a client makes multiple queries for a server to retrieve some records either in parallel or in sequence.

In this paper, we introduce a new formalization of multi-query vPIR and provide an efficient scheme based on authenticated PIR (APIR) and succinct non-interatctive arguments of knowledge (SNARKs). More precisely, thanks to the nice property of APIR, the communication cost of our multiquery vPIR scheme is O(n · |a| + |π|), where n is the number of queries, |a| is the APIR communication size, and |π| is the SNARK proof size. That is, the communication includes only one SNARK proof. In addition to this result, to show the effectiveness of our multi-query vPIR scheme in a real-world scenario, we present a practical application of vPIR on the online certificate status protocol (OCSP) and provide a comprehensive theoretical evaluation on our scheme in this scenario. Especially in the setting of our application, we observe that integrating SNARK proofs (for verifiability) does not significantly increase the communication cost.

Key-insulated encryption (KIE) is one of the countermeasures against the exposure of secret keys in public-key cryptography. In KIE, a user can update secret keys with a helper key to ensure that even if many secret keys, where each of them corresponds to each time period, are leaked, the security for other time periods is not compromised. However, KIE does not have resilience against the partial exposure of secret keys. Although there is public key encryption resilient to such partial exposure, unlike KIE, it cannot ensure security against the exposure of a whole secret key. In this paper, we introduce leakage-resilient key-insulated encryption (LR-KIE) that satisfies resilience against both partial and whole exposure of secret keys. We show three LR-KIE schemes from any leakage-resilient identity-based encryption scheme and/or any leakage-resilient secret sharing scheme.

In this paper, we present a new classification method for the defining sets of η-constacyclic codes of length $n=\frac{q^4+1}{2}$. This classification can simplify the calculation of the dimensions of the Hermitian hulls for these codes, while deriving the numbers of sharing pre-shared maximally entangled states of the corresponding entanglement-assisted quantum error-correcting codes under various design distances.