In this paper, we consider a distributed method for constrained optimization problems that incorporates self-triggered communication. Each agent cooperatively searches for an optimal solution by exchanging estimates over a communication network among agents. Local communications are sporadically conducted to ensure that the error between the current and last triggered estimates is within a predefined threshold. The next trigger time is computed at the current trigger time in a self-triggered manner. After the information exchange, the estimate is iteratively updated by a consensus-based dual decomposition algorithm. We show that the dual estimates of agents asymptotically converge to an optimal solution under a diminishing and summable stepsize condition. Simulation results show that the proposed self-triggered algorithm can reduce the overall number of communications compared to time-triggered approaches.

Existing Two-dimensional Direction-of-Arrival (2-D DOA) estimation methods of multipath signals using uniform planar array (UPA) face challenges related to high hardware costs and high computational complexity. In this letter, we exploit the current advances in Dynamic Metasurface Antennas (DMA) to propose a new 2-D DOA estimation method of multipath signals using DMA. The proposed method first employs DMA with fewer RF chains to design a space-time isomeric scheme, which acquire equivalent multi-dimensional received signals by rapidly changing the pattern within a single pilot symbol period. Then, the array data are accurately reconstructed using a generalized inverse matrix algorithm. After that, we obtain two standard linear array data by summing over the rows data and the columns data, respectively. The corresponding angles of each multipath are estimated by the Method of Direction Estimation (MODE) algorithm. Finally, the elevation and azimuth angles are obtained by trigonometric function calculation according to the geometric relationship. Simulation results illustrate that the proposed method reduces computational complexity and verifies its effectiveness using DMA, demonstrating that DMA with fewer RF chains can achieve the same estimation performance as the UPA.

Pulmonary nodule imaging diagnosis is in a leading position in the field of deep learning research, but few can really be deployed and promoted. In this study, we summarize the reasons that hinder the deployment of research results, and develop a pulmonary nodule diagnostic model using 1015 cases of CT (Computed Tomography) images and diagnostic image reports from Yantai Affiliated Hospital of Binzhou Medical University, where the LIDC-IDRI dataset was used for external testing. Our model includes three paths: a physician diagnostic path developed by extracting and statistical analysis of high-frequency terms in diagnostic image reports, an AI (Artificial Intelligence) diagnostic path developed by training CT images, and a human-computer collaborative diagnostic path developed by the hypergraph convolutional neural network (HGCN). The results show that both in the internal test set (AUC of 0.9745) and in the external test set (AUC of 0.9694), the human-computer collaborative path achieves optimal results, which confirms that our model can combine the experience of physicians with the computational power of AI to achieve more accurate and reliable diagnosis; in addition, the easy-to-access input data and the github-shared code also increase the possibility of model deployment.

In 2022, Wang et al. proposed the multivariate signature scheme SNOVA as a UOV variant over the non-commutative ring of l × l matrices over 𝔽_q. This scheme has small public key and signature size and is a second round candidate of NIST PQC additional digital signature project. Recently, Ikematsu and Akiyama, and Li and Ding show that the core matrices of SNOVA with v vinegar-variables and o oil-variables are regarded as the representation matrices of UOV with lv vinegar-variables and lo oil-variables over 𝔽_q, and thus we can apply existing key recovery attacks as a plain UOV. In this article, we propose a method that reduces SNOVA to smaller UOV with v vinegar-variables and o oil-variables over 𝔽_q^l. As a result, we show that the previous first round parameter sets at l = 2 do not meet the NIST PQC security levels. We also confirm that the present parameter sets are secure from existing key recovery attacks with our approach.

Difference distribution table (DDT) plays an important role in studying the cryptographic properties of (n, n)-functions, (n, n)-functions with the same DDT are called DDT-equivalent. In this paper, we give one sufficient and necessary condition on DDT-equivalent according to autocorrelation distributions at first, and obtain some methods about new DDT-equivalent based on old DDT-equivalent. Finally, a construction algorithm on DDT-equivalent is present, we fully give DDT-equivalent for all balanced (3, 3)-functions.

This letter proposes an optimal scheme for the backward transmission of the propagation-delay (PD) based X channels with two transmitters and arbitrary receivers, which has reciprocal PD channel of the given forward transmission. Cyclic interference alignment is used to maximize the degrees of freedom (DoF), where the interference messages are shown to be aligned into one time-slot. This work helps support optimal bidirectional transmission of the discussed X channels.

Public key encryption with equality test, proposed by Yang et al. (CT-RSA 2010), allows anyone to check whether two ciphertexts of distinct public keys are encryptions of the same plaintext or not using trapdoors, and identity-based encryption with equality test (IBEET) is its identity-based variant. As a variant of IBEET, IBEET against insider attacks (IBEETIA) was proposed by Wu et al. (ACISP 2017), where a token is defined for each identity and is used for encryption. Lee et al. (ACISP 2018) and Duong et al. (ProvSec 2019) proposed IBEETIA schemes constructed by identity-based encryption (IBE) related complexity assumptions. Later, Emura and Takayasu (IEICE Transactions 2023) demonstrated that symmetric key encryption and pseudo-random permutations are sufficient to construct IBEETIA which is secure in the previous security definition. These results suggest us to explore a condition of IBEETIA that requires to employ IBE-related complexity assumptions. In this paper, we demonstrate a sufficient condition that IBEETIA implies IBE. We define one-wayness against chosen-plaintext/ciphertext attacks for the token generator (OW-TG- CPA/CCA) and for token holders (OW-TH-CPA/CCA), which were not considered in the previous security definition. We show that OW-TG-CPA secure IBEETIA with additional conditions implies OW-CPA secure IBE, and show that Lee et al. and Duong et al. schemes provide the OW-TG-CPA security. On the other hand, we propose a generic construction of OW-TH-CCA secure IBEETIA from public key encryption. Our results suggest a design principle to efficiently construct IBEETIA without employing IBE-related complexity assumptions.

Dosun-Fuwari is one of Nikoli's pencil puzzles. It is known that the generalized Dosun-Fuwari puzzle is NP-complete. Due to the inherent difficulty of the puzzle, solvers may often question whether a solution exists. Such questions highlight the need for a method that can verify the existence of a solution without revealing it, thereby preserving the puzzle's challenge. In this paper, we propose a physical zero-knowledge proof protocol for the Dosun-Fuwari puzzle, which can be executed using 4mn + 2n cards. Here, m × n is the size of the instance of the puzzle.

ChaCha is a stream cipher that has been adopted in TLS1.3 and is widely used around the world. Therefore, any vulnerability in ChaCha has a significant global impact, making the security analysis of its permutation a critical issue. Currently, no analysis has successfully extended beyond 8 rounds of ChaCha, and reducing the computational complexity for fewer rounds remains a challenge for future research. The primary methods of analyzing ChaCha include differential analysis, which examines the relationship between input and output differences; linear analysis, based on linear approximations; and Differential-Linear analysis, a combination of both approaches. The computational complexity of Differential-Linear analysis depends heavily on the linear bias. Therefore, we focus on increasing the linear bias and aim to reduce the computational complexity by deriving a linear approximation with a larger bias. To achieve this, we first reduce the number of linear rounds to 3 or 3.5 in order to increase the bias. Then, we derive the linear approximation between 4 or 4.25 and 7 rounds of ChaCha and identify the corresponding input and output differences. Next, to further increase the number of analysis rounds, we extend the linear approximation derived from 7-round ChaCha analysis. We analyze the 7.25-round ChaCha Permutation with computational complexity of 2^182.57 and 2^104.20. In addition we perform Differential-Linear analysis for 7.5-round ChaCha with computational complexity of 2^222.54 and 2^132.18. Although our analysis is a distinguisher, it can be extended to a key recovery attacks or differential analysis by considering final adition, which would have a significant on the overall security analysis of ChaCha.

Privacy preservation in the learning phase of machine learning poses considerable challenges. Two main approaches are commonly used to address these challenges: adding noise to machine learning model parameters to improve accuracy, and using noisy data during the learning process to enhance privacy. Recently, the Scalable Unified Privacy-preserving Machine Learning framework (SUPM) has emerged as a promising solution, effectively balancing privacy and accuracy by integrating privacy protection across the stages of dimension reduction, training, and testing. This paper introduces a novel method that optimizes privacy budget allocation by assigning budgets to various attributes based on their relevance to the target attribute. This approach improves accuracy while minimizing the reduction of relevant attributes. When incorporated into SUPM, our algorithm enhances both accuracy and privacy preservation. We evaluate its performance using logistic regression and support vector machines as the underlying machine learning models, demonstrating its effectiveness in retaining accuracy and maintaining attribute integrity. Additionally, we compare our approach with other uneven privacy budget allocation methods, such as Markov-kRR, confirming the superiority of our technique. We further examine the specific conditions under which our method proves particularly effective for certain datasets.

This letter introduces an efficient concatenation scheme using a novel discriminative mechanism for polar codes with Reed-Solomon (RS) codes. An efficient RS-polar soft information exchange process is proposed, employing the novel discriminative mechanism to reduce the number of RS codewords requiring decoding. Additionally, a low-complexity error and erasure decoding (EED) algorithm is utilized for RS codes in the scheme. These two measures collectively reduce overall complexity. Simulation results show that without compromising decoding performance, for RS-polar (8192,2732) scheme, at a signal to noise ratio of 2.0dB, the proposed scheme reduces the average number of decoded RS codewords by 70.66%.

Neural interactions under optimal excitatory/inhibitory (E/I) balance are among the most crucial mechanisms for realizing cognitive functions. Among the phenomena supported by this mechanism, the duration of a phenomenon known as perceptual alternation exhibits two representative characteristics: nondeterminism and the long-tailed property at the level of a large neural population. However, even in a system consisting of a single pair of excitatory and inhibitory neurons, called chaos-chaos intermittency (CCI), a similar intermittent alternation of neural activity emerges, involving intermittent transitions between multiple isolated attractors. We hypothesized that the characteristics of CCI dynamics in local excitatory-inhibitory neural circuits can describe the nondeterminism and long-tailed properties observed at a broad hierarchical level. We evaluated the changes in nondeterminism and long-tailed properties under different E/I balance conditions to test this hypothesis. First, we validated the determinism of two types of dynamics: 1) transitions between attractors and 2) behavior within attractors. This evaluation was performed using iterated amplitude-adjusted Fourier transform and multi-scale entropy analysis. Next, we characterize the long-tailed properties of the alternations. These properties were evaluated while gradually shifting the parameters from attractor-merging bifurcation. These results indicate that while behavior within attractors demonstrate determinism across all conditions, transitions between attractors lose nondeterminism as the predominance of excitatory neuron increases. Furthermore, the duration histograms lose their long-tailed properties as excitatory neurons become dominant. Consequently, the disappearance of determinism and long-tailed properties co-occurs, and the coexistence of nondeterminism and long-tailed properties is realized within specific domains of the E/I balance. This discovery contributes to our understanding of the importance of an optimal E/I balance for maintaining the characteristics of interactions between excitatory and inhibitory neurons.

In this work, we present an infinite family of quadratic APN functions in bivariate form, which extends one of the two families of APN functions constructed by Li et al. in [IEEE TIT 68(7), 4761-4769 (2022)]. We show that for n = 10 from our construction, we can obtain APN functions CCZ-inequivalent to those belonging to known infinite families of APN functions.

Imaging technology has revolutionized the printing industry, enhancing efficiency, quality, and versatility in various printing processes, including graphic applications on 2D and 3D objects, as well as additive manufacturing (AM) or 3D printing - directly building 3D objects layer-by-layer from digital model. This paper exemplifies the applications of imaging technologies in various printing processes of 2D and 3D printing applications. In the realm of packaging printing on 2D substrates, the roles of imaging technologies are highlighted through applications in quantifying paper topography (surface roughness), ink absorption, and dynamic interactions between the print plate, the ink, and the substrate. Regarding AM or 3D printing, imaging technology is of fundamental importance enabling the entire process from digitalization of the object with 3D scanning, CAD design and visualization, to in-situ (real time) monitoring of manufacturing process, and post-production quality inspection, e.g. revealing the relationship between the variation of mechanical strength of 3D printed objects with its pore characteristics have been provided.

In this letter, we introduce a novel and straightforward moment generating function (MGF) expression of α-κ-μ fading channels by leveraging the generalized hypergeometric series. Moreover, by utilizing the derived MGF, we further present the bit error rate (BER) approximate expressions for various modulation schemes. Some numerical results are provided to verify the accuracy of our analytical expressions.

A fully-stretching piecewise-linear (FSPL) chaotic map and a threshold function can generate a Markov binary sequence having exponentially vanishing auto-correlations. Based on the conditions of FSPL maps, we discuss bounds on the auto-correlations of the Markov binary sequences.

This study presents a recording system utilizing an array of eight hyperdirectional microphones designed for personal three-dimensional (3D) sound field reproduction via wave front synthesis. The recording positions of the hyperdirectional microphones were identified through impulse response measurement, enabling microphone array construction. To evaluate the localization performance of the constructed microphone array, the impulse responses were measured, replay sounds were synthesized, and the localization experiment was performed. Results demonstrated that the developed recording system outperformed ambisonic microphone, a standard conventional 3D sound field recording, in localization accuracy.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can achieve full-space coverage of the signal compared to conventional reconfigurable intelligent surface (RIS). However, in existing works, the problem of energy efficiency optimization for multiple STAR-RISs-aided multi-user communication systems with the coupled phase-shift (CPS) constraint remains unsolved. To this end, this letter proposes a novel joint optimization framework for solving this problem. To overcome this complex nonlinear, nonconvex issue, we decompose the main problem into two individual subproblems: phase-shift optimization, and base station (BS) beamforming design. Penalty dual decomposition (PDD)-based and successive convex approximation (SCA) methods are employed to solve the two subproblems in the alternating optimization process. Simulation outcomes demonstrate that the proposed approach surpasses both centralized deployment strategies and traditional RIS-assisted systems.

Cell-free massive MIMO systems serve users through geographically distributed access points (APs). However, if all APs provide services to each user, it will result in high power consumption and low energy efficiency. To solve this problem, we propose a user equipment (UE) priority criterion and an AP selection scheme. The criterion requires the system to prioritize serving more important UEs in order of priority. Moreover, a bidirectional restricted method was employed in the AP selection scheme. The outcomes of simulations indicate that the proposed scheme outperforms related works in both spectral efficiency and energy efficiency.

Sequences and combinatorics, particularly in coding theory, have seen increasing attention in recent years, especially in the study of difference systems of sets (DSSs) and partitioned difference families (PDFs) also known as zero-difference balanced (ZDB) functions. This paper has two main objectives: first, to propose a new, generic construction method for optimal DSSs using PDFs, and second, to introduce a direct method for constructing PDFs over cyclic groups. This direct construction, combined with a recursive approach, can produce desirable PDFs that may be used to generate DSSs leading comma-free codes with relatively high code rates. Some of the DSSs produced through this new construction are optimal when compared to known theoretical bounds.

The Agile Earth Observation Satellite Constellation Mission Planning (AEOSCMP) problem focuses on optimizing target selection and scheduling for multiple satellites to maximize global observation rewards while adhering to resource constraints. To tackle this challenging task, this letter employs the Multi-Agent Transformer (MAT) to convert the joint policy search problem into a sequential decision-making process, optimizing observation policies through the attention mechanism. This approach could provide a theoretical guarantee of monotonic improvement during online training, ensuring consistent and reliable performance enhancements. Experimental results demonstrate that MAT achieves superior observation efficiency compared to state-of-the-art Multi-Agent Reinforcement Learning (MARL) methods.

Aggregate signatures without the bilinear map is a challenging and important problem in aspects of both practical and theoretical cryptology. In order to construct an aggregate signature which does not use the bilinear map, it is general to restrict some functionality of aggregate signatures or to employ strong cryptographic assumptions. The aggregate signature with the pre-communication (ASwPC) is one of the variants of aggregate signatures to achieve the security from a standard cryptographic assumption without the bilinear map. The ASwPC requires signers to interact with each other to share a temporary randomness before they determine their messages to be signed. After the pre-communication, each signer can start the signing process individually. An instantiation of ASwPC is given based on the discrete logarithm (DL) assumption, and its security is proven in the random oracle and the knowledge of secret key (KOSK) model via a loose security reduction.

In this paper, we aim to construct a new ASwPC scheme whose security is proven via a tight security reduction. We employ the DDH assumption rather than the DL assumption. The combination of the property of the decisional assumption and that of the KOSK model enables us to apply the lossy key technique even in the case of ASwPC. Then we can prove the security of our scheme with a tight security reduction.

Quantum signal processing (QSP), which is a general technique for construction of 1-qubit rotation operators, is a strong framework for quantum algorithm design. We consider a scenario of information-theoretically secure computation for QSP: n parties who have private angle parameters make the evaluator perform the QSP from the sum of their angles in a privacy-preserving manner through noninteractive communication. In this scenario, we construct a private simultaneous messages (PSM) protocol for QSP, named Private QSP (PQSP). As an application, we construct an efficient PSM protocol for symmetric Boolean functions with an evaluator of 1-qubit workspace by adapting PQSP to the 1-qubit quantum algorithm for symmetric Boolean functions of Maslov et al. [1]. We also show that the technique of PQSP works for the 1-qubit program for Boolean functions of Cosentino et al. [2]. We construct an efficient PSM protocol for Boolean functions computable by O(log n)-depth circuits with an evaluator of 1-qubit writable workspace from their 1-qubit program [3]. Such efficient PSM protocols with space-bounded classical evaluators have not been known so far.

Attribute-based credential (ABC) system allows a user to anonymously prove his/her attributes to a verifier, using a credential issued by an issuer. In the conventional ABC systems, since the verifier knows who is the issuer from the attribute proof, the information on the issuer can reveal some attributes of the user. Thus, an issuer-hiding ABC was recently proposed, where the issuer's ID (and the issuer's public key) is hidden. However, in the previous system, the verifier decides the accepted issuers and issues the signatures on the issuers' public keys to each user in advance, and thus the computational and communication costs depending on the number of issuers are required whenever an issuer is revoked. In this paper, we propose an issuer-revocable issuer-hiding ABC system that is extended from the previous system. In the proposed system, the verifier generates and issues the signatures of the issuers' public keys once. Then, whenever an issuer is revoked, the verifier sends only a short revocation list using a pairing-based accumulator to each user.

The audio-integrated active noise control (AIANC) system is a special type of active noise control (ANC) system that aims to suppress noise while broadcasting audio. Although the nonlinear effect in ANC systems has been studied for many years, current research on AIANC systems still focuses on linear approaches, which cannot match the practical systems well and will lead to performance degradation. This paper proposes a new nonlinear AIANC method based on the psychoacoustic gated convolutional recurrent network (PGCRN). First, a new AIANC system model is developed based on the gated convolutional recurrent network (GCRN). Then a psychoacoustic model is introduced to the loss function of the GCRN to improve the audio quality. Finally, a new automatic gain control (AGC) method based on the MFCC cosine similarity and the XE-NLMS algorithm is proposed for the new AIANC system to improve audio quality under low signal-to-noise rate (SNR) conditions. Experimental results show that the proposed AIANC method outperforms several conventional methods in different noisy and nonlinear environments.

Forward security is a fundamental requirement in searchable encryption, where a newly generated ciphertext is not allowed to be searched by previously generated trapdoors. However, forward security is somewhat overlooked in the public key encryption with keyword search (PEKS) context and there are few proposals, whereas forward security has been stated as a default security notion in the (dynamic) symmetric searchable encryption (SSE) context. In this paper, we propose a generic construction of forward secure public key authenticated encryption with keyword search (FS-PAEKS) from PAEKS. In addition to PAEKS, we employ 0/1 encodings proposed by Lin et al. (ACNS 2005). We also show that the Jiang et al.'s FS-PAEKS scheme (The Computer Journal 2023) does not provide forward security. Our generic construction is quite simple, and it can also be applied to construct forward secure public key encryption with keyword search (FS-PEKS). Our generic construction yields a comparably efficient FS-PEKS scheme compared to the previous scheme. Moreover, it eliminates the hierarchical structure (Abdalla et al. (JoC 2016)) or attribute-based feature (Zeng et al. (IEEE Transactions on Cloud Computing 2022)) of the previous generic constructions which is meaningful from a feasibility perspective.

An effective method for virtualized network function forwarding graph (VNF-FG) placing based on graph attention network (GAT) and reinforcement learning (RL) is proposed for complex services and dynamic network conditions. We formulate a VNF-FG placing optimization problem, and designed a GAT-based RL agent to recognize graph structure and obtain placing policy. Experiments prove the method effectiveness.

APN functions provide the optimal resistance to differential attacks. In 2022, Li et al. [IEEE TIT 68(7), 4761-4769 (2022)] constructed an infinite family of quadratic APN functions over $\mathbb{F}_{2^{2m}}$ with $\gcd(3,m)=1$ in the bivariate form $F(x,y)=(x^{3}+xy^{2}+y^{3}+xy,x^{5}+x^{4}y+y^{5}+xy+x^{2}y^{2})$. In this work, we theoretically prove that functions in a more general form $F'(x,y)=(x^{2^k+1}+xy^{2^k}+y^{2^k+1}+\sum_{i=0}^{k-1}(xy)^{2^i}, x^{2^{2k}+1}+x^{2^{2k}}y+y^{2^{2k}+1}+\sum_{i=0}^{k-1}(xy+(xy)^{2^k})^{2^i})$ are CCZ-inequivalent to APN power functions on $\mathbb{F}_{2^{2m}}$ with $\gcd(3k,m)=1$.

The integration of numerous IoT devices into the distribution network supports coordinated control of grid-connected devices, but the complex topology of distribution networks and uneven base station distribution result in weak coverage and uneven load, leading to poor end-to-end latency performance. While IoT heterogeneous integrated networking technologies enable low-latency access for many power devices, challenges like communication resource competition and slow optimization under uncertain network conditions remain. To address these issues, this paper proposes a joint optimization model for relay scheduling, data compression, and time scheduling, aiming to minimize average end-to-end latency. A two-stage edge-end cooperative resource optimization algorithm based on Lyapunov optimization theory is proposed. In the first stage, a relay scheduling algorithm using relay device connectivity and queue delay-aware ascending price matching optimizes scheduling by dynamically adjusting channel matching costs based on connectivity and queue backlogs. The second stage introduces a delay deviation-aware adaptive particle swarm optimization to optimize time scheduling and data compression, achieving fast convergence. The relay scheduling preferences are updated based on the final objective function value. Simulation results demonstrate the method's effectiveness in reducing latency, improving network performance, and efficiently utilizing network resources.

We propose a zero-order-hold (ZOH) triggered prescribed-time controller for a chain of integrators. Our proposed controller derives the state into the arbitrarily small ball around the origin at the prescribed-time irrespective of the initial conditions while the control input is only updated discretely. We carry out the rigorous system analysis using Razumikhin theorem to prove the boundness of the state and control input. We give simulation results to illustrate the validity of our control method.

Global routing is one of the most crucial steps for design closure in the physical design of VLSI. This paper proposes a routing algorithm, called UEO algorithm, for generalized channels to achieve a small local congestion which is mainly dedicated to global routing for CMOS circuits designed to match 3D bonding technology. In our generalized channel formulation, due to tight global horizontal routing capacity, the connection of a net is restricted to a single-trunk Steiner tree. Routing algorithms proposed for the generalized channel so far achieve a small total vertical wire length while achieving the minimum number of used tracks, but they do not take a local vertical congestion into account, and the completion of detailed routing may suffer from a large local vertical congestion. The proposed UEO algorithm iteratively determines the assignment of trunks of nets based on the net priority proposed in this paper to achieve a small local vertical congestion. In experiments, it is confirmed that UEO achieves a small local vertical congestion, and that this work contributes to achieve design closure of routing design for 3D VLSI.

Multi-attribute decision-making (MADM) is crucial in various fields. Most of MADM methods based on attribute values primarily handle scenarios where attribute information is expressed as an exact value. With the wide application of methods, the problem of uncertainty arises gradually, leading to attribute values that are not single values. The paper constructs a three-way decision (TWD) model based on probabilistic dominance relation (PDR) under interval-valued intuitionistic fuzzy number (IVIFN). This model is designed to address imprecise information by utilizing IVIFN. Meanwhile, we innovatively introduce attribute dominance degree to construct loss function matrix, which makes full use of interval number information. Moreover, we propose a novel method to calculate conditional probability under PDR, thus constructing a novel TWD model that assists decision makers in ranking and classifying. Finally, this paper demonstrates the reliability of the model in ranking through Spearman rank correlation coefficient (SRCC). Furthermore, the experimental result on UCI dataset shows that the model has great classification capabilities, with a 3.1% reduction in the average error rate.

Direction of arrival (DOA) tracking on multiple moving targets in the far field for distributed sensor arrays is an important research direction. The results of multi-node DOA estimation are usually directly fused in the face of different signal-to-noise ratios (SNR) of each node, which will lead to deterioration of estimation performance. This paper proposes a DOA tracking algorithm for information fusion between distributed array nodes. Firstly, the unscented information filtering results, including status vectors and information matrices, are presented at each node. Then, by using the average consensus (AC) algorithm, the status vector and information matrix of each node are fused to provide a DOA fusion result, which fully considers the accuracy of each node. Theoretical analysis and numerical simulation results show that the algorithm has low computational complexity and can achieve good and robust DOA tracking performance at low SNRs.

Differential privacy is used to guarantee privacy protection and has become the de facto standard for privacy protection data analysis. The (α, ε) -Rényi differential privacy ((α, ε) -RDP), which is based on the Rényi divergence, has been proposed as a relaxation of the ε-differential privacy (ε-DP). The Rényi divergence is a generalization of the Kullback-Leibler divergence. The f-divergence, on the other hand, is also a generalization of the Kullback-Leibler divergence, where f: [0, ∞) → ℝ is a convex function satisfying f (1) = 0. Hence, we can consider differential privacy based on the f-divergence in the same manner as the Rényi differential privacy. This paper introduces (f, ε) -differential privacy ((f, ε) -DP) based on the f-divergence. We prove a novel composition theorem of an adaptive composition of n mechanisms all satisfying ε-DP. To derive this result, the following three propositions play an important role: (i) a probability preservation inequality via the f-divergence; (ii) a composition of two (f, ε) -DP; (iii) a relationship between the ε-DP and the (f, ε) -DP. Numerical examples show that there are cases where the proposed composition theorem is tighter than the previous composition theorems.

To address the challenges of low detection accuracy resulting from occlusion and scale variation in complex traffic scenarios, as well as the high computational complexity and large model parameters associated with traditional methods, this paper proposes a Lightweight Cross-Scale Feature Fusion Algorithm. Firstly, we design the Lightweight Cross-Scale Feature Fusion Module (LCFM), which incorporates an improved internal fusion block to facilitate interactive feature fusion. This design enhances the model's adaptability to occlusion and scale change while reducing the number of input feature channels to make the model more lightweight. Furthermore, by integrating Squeeze-and-Excitation (SE) attention with multi-branch convolution operations from the Inception structure, the model can more accurately capture multi-scale object features. Additionally, Linear Deformable Convolution (LDConv) is employed to adaptively handle shape changes through offset learning, thereby reducing computational redundancy and improving the model's overall adaptability.

A Hopfield network is a mathematical model of a spin glass system, a theory of stochastic physics. This binary model has been extended to many high-dimensional models. A persistent challenge in these models has been determining the activation function and stability conditions. In this paper, we propose an algebraic Hopfield network (AHN), which encompasses most extensions of classic Hopfield networks. In AHNs, weights act as operators on neuron outputs in weighted sum inputs. Here we provide the activation function and stability conditions for AHNs, offering a foundation for developing novel Hopfield network models.

EMV 3-D Secure is an authentication service mainly to identify and verify cardholders for card-not-present (CNP) transactions over the Internet. EMV 3-D Secure services are provided by international credit card brands such as Visa, Mastercard and American Express, and its protocol is specified by EMVCo. There are known existing works on evaluating security of several versions of 3-D Secure, such as a formal verification using Casper/FDR2 for the old specification (3-D Secure 1.0) and a spoofing attack using reverse engineering on risk assessment indicators for the current specification, EMV 3-D Secure (3-D Secure 2.0). However, there is no security verification of EMV 3-D Secure based on its protocol specification. Formal methods are known as methods that can verify security with high fidelity to the protocol specification and have been actively researched in recent years. In this paper, we verify the security of EMV 3-D Secure using ProVerif, an automated security verification tool for cryptographic protocols. First, one of the difficulties we faced is to correctly extract the detailed protocol structure from the entire specification that is written by natural language over 400 pages. Based on the extracted protocol structure, we formalize Challenge Flow for authentication by secret information under three environments (App-based (default-sdk), App-based (split-sdk), and Browser-based) in the latest version 2.3.1.1, which are specified for the purpose of identity verification in CNP transactions. We then verify the confidentiality and resistance to off-line dictionary attacks of secret information, the authenticity and the resistance to replay attacks against both man-in-the-middle attacks and colluding attacks with relay servers. As verification results, we show that Challenge Flow satisfies all of the above security requirements. Furthermore, we discuss the necessity of the unilateral authenticated channel between the cardholder and the card issuer assumed in the EMV 3-D Secure specification, and show that if we use a public channel instead of a unilateral authenticated channel, Challenge Flow still satisfies security requirements. It indicates that the protocol can be more efficient than the specification without reducing security.

We propose a new method to compute the joint reliability importance which is a useful index for reliability design. The key idea is to apply a special matrix to the computation of the marginal reliability importance. The computational complexity of the existing algorithm for computing the joint reliability importance in terms of each pair of components is the product of the square of the number of components and the computational complexity of computing the reliability of the system. However, we found that a reduction in order to the product of the number of components and the computational complexity of computing the reliability of the system is possible if the system can be represented by a special type of directed graph or any combinatorial model when the sum of disjoint products method is used to compute the reliability of the system.

In conventional fault diagnostic methods, supervised learning-based approaches may not be applicable to practical systems because of the extensive requirements for labeled data. Moreover, conventional approaches have not adequately addressed the challenges posed by sparsely labeled and imbalanced datasets. To address these limitations, we propose a semi-supervised fault diagnostic method based on graph convolutional networks with generative adversarial networks. Distinct from conventional methods, the proposed method instructs a discriminator to extract features from labeled and unlabeled data. The discriminator is employed to construct a similarity matrix to enhance the efficacy of graph-based methods. A graph-based classifier with a discriminator can efficiently perform fault diagnosis without requiring data augmentation. The fault diagnostic methods were evaluated in terms of their classification accuracy to validate the superiority of the proposed method. The simulation results confirm that the proposed method can improve classification accuracy by up to 66% compared with conventional methods.

We investigate the computational complexity of a simple one-dimensional origami problem. We are given a paper strip P of length n + 1 and fold it into unit length by creasing at unit intervals. Consequently, we have several paper layers at each crease in general. The number of paper layers at each crease is called the crease width at the crease. For a given mountain-valley assignment of P, in general, there are exponentially many ways of folding the paper into unit length consistent with the assignment. It is known that the problem of finding a way of folding P to minimize the maximum crease width of the folded state is NP-complete. In this study, we investigate a related paper-folding problem. For any given folded state of P, each crease has its mountain-valley assignment and crease-width assignment. Then, can we retrieve the folded state uniquely when only partial information about these assignments is given? We introduce this natural problem as the crease-retrieve problem, for which there are a number of variants depending on the information given about the assignments. In this paper, we show that some cases are polynomial-time solvable and that some cases are strongly NP-complete.

In this paper we first define (t, s)-completely independent spanning trees, which is a generalization of completely independent spanning trees. A set of t spanning trees of a graph is (t, s)-completely independent if, for any pair of vertices u and v, among the set of t paths from u to v in the t spanning trees, at least s ≤ t paths are internally disjoint. By (t, s)-completely independent spanning trees, one can ensure any pair of vertices can communicate each other even if at most s - 1 vertices break down. We prove that every maximal planar graph has a set of (3, 2)-completely independent spanning trees, every tri-connected planar graph has a set of (3, 2)-completely independent spanning trees, and every 3D grid graph has a set of (3, 2)-completely independent spanning trees. Also one can compute them in linear time.

This work introduces the dueling dice problem, which is a variant of the multi-armed dueling bandit problem. A die is a set of m arms in this problem, and the goal is to find the best set of m arms from n arms (m ≤ n) by an iteration of dueling dice. In a round, the learner arbitrarily chooses two dice α ⊆ [n] and β ⊆ [n] and lets them duel, where she roles dice α and β, observes a pair of arms i ∈ α and j ∈ β, and receives a probabilistic result X_i,j ∈ {0, 1}. This paper investigates the sample complexity of an identification of the Condorcet winner die, and gives an upper bound O(nh^-2(log log h^-1 + log nm²γ^-1)m log m) where h is a gap parameter and γ is an error parameter. Our problem is closely related to the dueling teams problem by Cohen et al. 2021. We assume a total order of the strength over arms similarly to Cohen et al. 2021, which ensures the existence of the Condorcet winner die, but we do not assume a total order of the strength over dice unlike Cohen et al. 2021.

The performance of phase-aware speech enhancement has improved dramatically in recent years. Combined with complex convolutions, deep complex U-Net and deep complex convolution recurrent network (DCCRN) have achieved superior performance in monaural phase-aware speech enhancement. However, these methods optimize the models with loss only in the time domain and ignore the global correlations along the frequency axis that capture the harmonic information between frequency bands. Also, the algorithms based on self-attention exhibit high computational complexity. To strike the balance between performance and computational cost, we propose a new monaural phase-aware method in the time-frequency domain on the deep complex U-Net structure. Specifically, this proposed method incorporates a dual-path recurrent neural network (DPRNN) block in the bottleneck to model both frequency-domain correlation and time-domain correlation. Additionally, attention modules are implemented between the complex encoder and decoder layers. This introduces a more effective way of enhancing the representation of the model, rather than directly concatenating their outputs. Finally, a post-processing module is introduced to mitigate the over-suppression of speech and residual noise. We conduct ablation studies to validate the effectiveness of the dual-path method and the post-processing module. Also, compared to several recent speech enhancement models, the proposed algorithm demonstrates remarkable improvements in terms of objective metrics.

In this paper, a hybrid active and passive (HAP) multiple-input multiple-output (MIMO) radar network is considered, where target returns from both active radar transmitters and illuminators of opportunity (IOs) are employed to complete target detection. With consideration for the active radar power limitation and the total available number constraint for the IOs, the joint discrete power allocation and antenna selection for target detection in HAP MIMO radar is studied. A game-theoretic framework is proposed to solve the problem where the target probability of detection (PD) of the HAP MIMO radar is utilized to build a common utility. The formulated discrete game is proven to be a potential game that possesses at least one pure strategy Nash equilibrium (NE) and an optimal strategy profile that maximizes the PD of HAP radar. The properties of the formulated game, including the feasibility, existence and optimality of NE, are also analyzed. The proposed game's pure strategy NE is determined to be an optimal scheme under certain conditions. An iterative algorithm is then designed to achieve the pure strategy NE. The designed algorithm's convergence and complexity are discussed. It is demonstrated that the designed algorithm can achieve almost optimal target detection performance while maintaining low complexity. Under certain conditions, the designed algorithm can obtain optimal performance.

If scattered X-rays carry information that is independent of that is carried by primary X-rays, the accuracy of attenuation coefficients estimated using both primary and scattered X-rays is expected to be better than that estimated using only primary X-rays. However, because scattered X-rays cannot be easily introduced into conventional X-ray computed tomography (CT), the issue has gained scant attention. This study demonstrates theoretically that the measurement of scattered X-rays improves the accuracy of reconstruction in CT, even in a photoelectric absorption scenario. Here, the CT geometry was simplified for a system that targeted a homogeneous thin cylinder, retaining the necessary configuration. Furthermore, we constructed a mathematical model termed the π-junction model. This model is an extension of the T-junction model used in one of our previous studies. It addresses the photoelectric effect, which was not considered in the T-junction model. The variance in the estimation of the attenuation coefficients of this model from the measurements of both primary and scattered photons was evaluated as the Cramer-Rao lower bound. Both the theory and numerical experiments using Monte Carlo simulation showed that the accuracy of estimating the attenuation coefficient could be improved by measuring the scattered X-rays together with the primary X-rays, even in the presence of photoelectric absorption. This result provides a basis for the superiority of using scattered X-rays.

This study addresses the issue of designing false target waveforms for radar tracking and proposes an intelligent radar-tracking interference waveform design method, the MMISCKF algorithm. The study introduces the residual constraint function of the square root cubature Kalman filter, distance-velocity coupling constraint function, and improved genetic algorithm and designs interference effectiveness evaluation indicators. Comparative experiments show that the MMISCKF interference algorithm can effectively avoid suppression by radar anti-interference methods and achieve radar-tracking loss faster than uniform acceleration towing. The theoretical analysis and experimental results demonstrate that the proposed MMISCKF interference algorithm is a fast and effective radar-tracking false target deception method holding theoretical and engineering significance.

Recently, the practical applications of advanced cryptographic protocols, such as Multi-Party Computation (MPC), Fully Homomorphic Encryption (FHE), and Zero Knowledge Proofs (ZKP), have spurred the development of a series of new symmetric encryption primitives. These novel symmetric encryption primitives, referred to as Arithmetization-Oriented (AO) ciphers, aim to minimize the number of field multiplications in large finite fields, including 𝔽_2n or 𝔽_p. In order to evaluate the algebraic degrees of AO ciphers over 𝔽_2n, the general monomial prediction technique was proposed by Cui et al. at ASIACRYPT 2022. However, when using their searching tool to evaluate the algebraic degrees of AO ciphers with complex affine layers, the efficiency is low, preventing solutions within a predetermined timeframe. In this study, we extend the propagation rules of monomials for field-based operations and present an automatic searching tool based on Mixed Integer Linear Programming (MILP) and Boolean Satisfiability (SAT) Problem for evaluating the upper bound of the algebraic degrees. Moreover, to accurately calculate the algebraic degrees of monomials in the SAT model, we improve the sequence encoding method, enabling it to accurately determine whether the monomials of degree d exist in the output. We apply our new searching tool to various AO ciphers, including Chaghri, MiMC, and Ciminion. For Chaghri, we compare our results with the Coefficient Grouping technique proposed by Liu et al. at EUROCRYPT 2023, and our results yield tighter upper bounds compared to Liu et al.'s findings. Additionally, we evaluate the algebraic degrees of Chaghri and MiMC with arbitrary complex affine layers and obtain tighter bounds compared to the results from Liu et al. at CRYPTO 2023. Regarding Ciminion, we have observed that starting from the 4-th round, the upper bound on the algebraic degrees for each round actually 1 degree lower than the previous bound. Our searching tool enables a more precise evaluation of the algebraic degrees of AO ciphers, contributing to a deeper understanding of the design and analysis of such primitives.

Many researchers have proposed optimization methods to reduce the computational complexity of the Euclidean projection onto check polytope in the alternating direction method of multipliers (ADMM) decoding for Low-Density Parity-Check (LDPC) codes. Existing the sparse affine projection algorithm (SAPA) projects the vector to be projected onto an _χ-dimension affine hull and the dimension _χ is fixed, resulting in deteriorating decoding performance. In this letter, bisection method assisted affine projection algorithm is proposed to determine the correct projection dimension for each the vector to be projected with the bisection method iterative algorithm. Simulation results show that the proposed algorithm can improve the accuracy of projection results by 68.2%. The FER performance of the proposed algorithm is almost the same as that of the exact projection algorithm, and compared with the sparse affine projection algorithm (SAPA), it can improve the FER performance by 0.14dB as well as save average number of iterations by 3.2%.

This paper presents a novel reduced-reference image quality assessment (RR IQA) method from monocular dynamic scene images for neural radiance fields (NeRF). Despite recent advancement in NeRF, evaluating the performance of NeRF models remains challenging due to the difficulty associated with obtaining ground truth viewpoint images for dynamic scenes. Collecting such ground truth images for NeRF model evaluation typically requires capturing the target scene from multiple synchronized cameras, which is labor-intensive. To address this issue, we propose a novel RR IQA metric called amplitude-dissimilarity (AMDIS), which focuses on evaluating NeRF models without requiring ground truth viewpoint images. The key idea behind AMDIS is that the differences between two near-viewpoint images are mainly absorbed in the phase components.

Thus, AMDIS evaluates NeRF models by measuring the dissimilarity between the Fourier amplitude components of the training and synthesized images. Because AMDIS only uses the training and synthesized images, the corresponding ground truth viewpoint images are not required for the evaluation. The experimental results demonstrate that the proposed AMDIS is strongly correlated with major full-reference IQA methods that directly use ground truth viewpoint images.

In recent years, the construction industry has been advancing construction DX and ICT construction. These efforts assume the availability of a communication environment, which may be challenging in certain terrains, such as canyons or underground. Self-position estimation, indispensable for the autonomous operation of construction machinery, is also a critical topic. In this study, multiple airborne markers with local coordinates measured by surveying instruments were detected in images acquired by a 360-degree camera mounted on a construction machine to estimate the machine's self-position.