A system of zero-knowledge proofs on graph signatures has been proposed, where a graph can be signed, and the owner of the graph signature can prove a graph relation such as the connectivity and isolation of any two vertexes on the graph without disclosing all information about the graph. The correctness of the graph information is guaranteed by the signature. One of the applications is a virtualized infrastructure, where an infrastructure provider manages a distributed system, and each tenant is allocated a specific portion of this infrastructure for use. Tenants need to check with the provider that their resources are properly connected (connectivity) and that their resources are properly separated from the resources of other tenants (isolation). On the other hand, the provider cannot simply disclose the entire infrastructure topology to each tenant. Using the zero-knowledge proof system on graph signatures, both requirements can be addressed. Previously, an efficient zero-knowledge proof system on graph signatures using a bilinear-map accumulator has been proposed, where the verification time and the size of the proof data do not depend on the number of graph vertexes and edges. However, this system has two problems. First, since the proof does not include labels, it is not possible to prove the connectivity considering network bandwidth and cost. Second, since it assumes undirected graphs, it cannot handle applications on directed graphs such as network flows. In this paper, we extend the previous system and propose a zero-knowledge proof system of the connectivity for directed graphs where each edge has labels. We implemented our system on a PC using a pairing library and evaluate it by measuring the processing times. Compared to the conference version of this paper, we show the formal definitions and the security proofs of our proposed system, and add implementation-based evaluations reflecting the application to the virtualized infrastructure.

This letter studies the robust transceiver designs for multiple-input multiple-output (MIMO) interference channels with imperfect channel state information (CSI). The sum power minimization problem is considered subject to signal-to-interference-plus-noise ratio (SINR) constraint under CSI mismatch with statistical error model. Since the problem is a joint design of transmitter and receiver beamformers, an alternating approach is developed by exploiting the primal decomposition. Specifically, closed-form expressions are derived as a solution for the robust transceiver design. Numerical results are provided to demonstrate the robustness of the proposed algorithm and the performance efficiency in combating channel errors.

Precise and automatic segmentation of pulmonary lesions is crucial for assisting pulmonologists in accurate diagnosis and decision-making. Despite advances in deep learning, segmenting pulmonary nodules remains challenging due to factors like small lesions, irregular boundaries, and data imbalance. We propose an edge detail enhancement method (EDC-UNet) for pulmonary nodule segmentation, which integrates deformable convolutional layers to improve flexibility for various lesion morphologies and dilated convolution-based residual blocks to enhance feature extraction. Additionally, a Sobel-based detail supervision module in the decoder helps capture low-level spatial details, improving segmentation of blurred edges. Extensive experiments on the LIDC-IDRI and LUNA16 datasets demonstrate that EDC-UNet outperforms other models, highlighting its potential for medical image analysis.

Automatic Micro-Expression (ME) spotting is a fundamental yet essential task in Micro-Expression analysis from videos. Due to the brief and subtle of ME, the ME spotting task is challenging and the spotting performance need to be further improved. However, current works generally neglect the correlation between expression proposals in a video. In this work, we propose a two-stage relation-aware graph convolutional network (MES-RANet) to locate the temporal positions of macro- and micro-expression. First, we adopt a temporal evaluation module (TEM) to predict the frame-level probabilities from the spatial-temporal feature sequences and generate candidate proposals for the subsequent module. Specifically, in relation-aware module (RAM), we formulate video proposals as graph nodes, and proposal-proposal correlations as edges to construct graphs. Then apply the relation-aware network to model the relations among proposals and learn powerful representations for the boundary regression. Comprehensive experiment results show that MES-RANet is effective and achieves competitive performance compared with state-of-the-art methods on two public benchmark datasets CAS(ME)² and SAMM-LV. Codes are available at https://github.com/hahaluluyo/MES-RANet.

Automatic modulation recognition in low-computation edge devices under noisy environments has become increasingly critical. To address the dual challenges of lightweight design and noise resistance, this paper proposes WCTFormer, a novel lightweight network that integrates discrete wavelet transform for frequency-domain noise suppression and a Transformer for global feature extraction, enabling robust performance in low SNR conditions. Experiments on open-source datasets demonstrate that WCTFormer achieves superior recognition accuracy, with 92.40% accuracy at 0 dB SNR, requiring only 60K parameters. WCTFormer combines high recognition performance and computational efficiency, making it suitable for deployment in resource-constrained edge devices.

The stream version of asymmetric binary systems (ABS) invented by Duda is an entropy coder for information sources with a finite alphabet. It has the state parameter l of a nonnegative integer and the probability parameter p with 0 < p < 1. First we observe that the edge shift X_G associated with the stream version of ABS has the topological entropy h(X_G) = log 2. Then we define the edge shift X_H associated with output blocks from the stream version of ABS, and show that h(X_H) = h(X_G), which implies that X_G and X_H are finitely equivalent. The encoding and decoding algorithms for the stream version of ABS establish a bijection between X_G and X_H. We consider the case where p = 1/β with the golden mean β = (1 + √5)/2. Eventually we show that X_G and X_H are conjugate for l = 7, and that they are almost conjugate for l = 10.

For a given forward transmission scheme, the backward transmission in reverse direction is often wanted in practical communications. This letter proposes an optimal scheme for the backward transmission of the propagation-delay (PD) based general X channel with two receivers, which is reciprocal to forward transmission. With cyclic interference alignment, the desired messages are shown to be allocated at specified time-slots without interference, while other interfering messages are aligned in the remaining time-slots. Thus the backward transmission achieves the maximal degrees of freedom (DoF) with the reciprocal PD channel, which supports optimal bidirectional transmission of the X channel with two receivers.

Codebooks possessing optimal correlation property are utilized to differentiate signals among various users in code-division multiple-access (CDMA) systems. In this paper, a class of codebooks is proposed based on additive characters over finite fields and is verified to be optimal with respect to the Levenshtein bound. By shortening the length of the presented optimal codebooks, a kind of asymptotically optimal codebooks is obtained. To our knowledge, the presented asymptotically optimal codebooks have new parameters. Further more, these optimal and asymptotically optimal codebooks are highly desirable in synchronous Direct-Sequence CDMA systems, as they facilitate the expansion of subscriber capacity within the constrained spectral resources.

In this letter, we optimize the weight distribution of the polar codes by exchanging elements in the information set and the frozen set. Based on the work of M. Rowshan et al., we propose two improvements. First, the rows with the twice weight of the minimum weight row are usually selected by the original scheme for the added rows. By our improvement, we select rows with the largest weights such as 4 or 8 times of the minimum weight row. This modification optimizes the weight distribution of polar codes. Furthermore, if there is a case where the selected row weights are the same, the row with higher reliability is selected. Second, we consider the influence of balancing rows for the removed rows. In the original scheme, one core row is removed and then an added row is selected at each step. Our strategy is to fix the position of several added rows, and then remove the core rows or balancing rows globally. This modification can not only reduce the number of Min-Weight codewords but also optimize the channel reliability of the removed rows. Simulation results show that the decoding performance of modified codes has been greatly improved at the practical block error rate of 10^-2 - 10^-3 without changing the code rate of the polar codes.

Accurate scene representation holds practical significance for autonomous driving and virtual reality. This letter proposes a network to optimize images encoding and features learning for better scene representations. Experimental results show that this method can render high-quality novel images on both synthetic and real-world datasets.

Mode matching (MM) is a sound field reproduction method based on spherical harmonic expansion. In this study, we propose a higher-order MM method using a limited number of microphones based on the sparse equivalent method. Simulation experiments show that the proposed method achieves a significant improvement in reproduction accuracy.

Scoring competitions such as gymnastics have a particularly high demand for accuracy and fairness of judgment. In recent years, Fujitsu has collaborated with the International Gymnastics Federation and Japan Gymnastics Association to develop an “artificial intelligence gymnastics judgement system” using three-dimensional laser sensors. In this system, multiple microelectromechanical system (MEMS) mirror-type laser sensors are used for the stereoscopic measurement of gymnastics. MEMS mirrors are typically driven at the same frequency as their mechanical resonance frequency. Because the amplitude and phase greatly fluctuate with changes in resonance frequency, controlling the rotation angle to keep the amplitude and phase accurate is crucial. Herein, we developed an amplitude control method that does not use the mirror rotation angular velocity using a sin/cos amplitude model. We developed an adaptive control system for system parameter fluctuations because of changes in the resonance frequency of a MEMS mirror system. Using the developed method for the sin/cos amplitude model, we can theoretically evaluate the stability of the control system and the convergence to the target value. Through numerical simulations where this controller was applied to a high-order model with characteristics similar to those of the actual machine, we confirmed that the amplitude tracking error and convergence time were reduced even when the system parameters fluctuated significantly, demonstrating the usefulness of the proposed adaptive control system.

In this letter, we propose constructions of Type-II Even-Length binary Z-complementary pairs (EB-ZCPs) with parameters such as (3N₁N₂N₃, 3N₁N₂N₃ - 1) and ((N₁N₂)ⁿ L, (N₁N₂)ⁿ L - L + Z), respectively, where N₁, N₂, N₃ are restricted to 2^α10^β26^γ and n represents the times of iterations. The width of zero correlation zone (ZCZ) can reach or approach the theoretical upper bound, and the obtained Type-II EB-ZCPs have the low upper bound of peak-to-mean envelope power ratio (PMEPR), which can not be produced by existing constructions. Therefore, the constructions in this letter can provide more new types of ZCPs to suppress the potential asynchronous interference in broadband wireless communication systems.

A cyber-physical system (CPS) is a system where physical and information components are connected through a communication network. From the viewpoints of energy conservation and reduction of communication amount, the control input update and the sampling should be asynchronously performed only when necessary. In this paper, an event-based design method for asynchronously updating the control input and the sampling interval is proposed. A switched system is used as a mathematical model of CPSs. In the proposed method, based on the upper bound of the Lyapunov function, the update time of the control input, the switching signal, and the next sampling time are determined. As a control specification, it is guaranteed that the closed-loop system is uniformly ultimately bounded. Through a numerical example, the effectiveness of the proposed method is presented.

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.