Ramp secret sharing is a variant of secret sharing which can achieve better information ratio than perfect schemes by allowing some partial information on a secret to leak out. Strongly secure ramp schemes can control the amount of leaked information on the components of a secret. In this paper, we reduce the construction of strongly secure ramp secret sharing for general access structures to a linear algebraic problem. As a result, we show that previous results on strongly secure network coding imply two linear transformation methods to make a given linear ramp scheme strongly secure. They are explicit or provide a deterministic algorithm while the previous methods which work for any linear ramp scheme are non-constructive. In addition, we present a novel application of strongly secure ramp schemes to symmetric PIR in a multi-user setting. Our solution is advantageous over those based on a non-strongly secure scheme in that it reduces the amount of communication between users and servers and also the amount of correlated randomness that servers generate in the setup.

This paper considers error-correction for information in array design, i.e., two-dimensional design such as QR-codes. The error model is multi deletion/substitution/erasure errors. Code construction for the errors and an application of the code are provided. The decoding technique uses an error-locator for deletion codes.

An authentication code (A-code) is a two-party message authentication code in the information-theoretic security setting. One of the variants of A-codes is a multi-receiver authentication code (MRA-code), where there are a single sender and multiple receivers and the sender can create a single authenticator so that all receivers accepts it unless it is maliciously modified. In this paper, we introduce a multi-designated receiver authentication code (MDRA-code) with information-theoretic security as an extension of MRA-codes. The purpose of MDRA-codes is to securely transmit a message via a broadcast channel from a single sender to an arbitrary subset of multiple receivers that have been designated by the sender, and only the receivers in the subset (i.e., not all receivers) should accept the message if an adversary is absent. This paper proposes a model and security formalization of MDRA-codes, and provides constructions of MDRA-codes.

A side channel attack is a means of security attacks that tries to restore secret information by analyzing side-information such as electromagnetic wave, heat, electric energy and running time that are unintentionally emitted from a computer system. The side channel attack that focuses on the running time of a cryptosystem is specifically named a “timing attack”. Timing attacks are relatively easy to carry out, and particularly threatening for tiny systems that are used in smart cards and IoT devices because the system is so simple that the processing time would be clearly observed from the outside of the card/device. The threat of timing attacks is especially serious when an attacker actively controls the input to a target program. Countermeasures are studied to deter such active attacks, but the attacker still has the chance to learn something about the concealed information by passively watching the running time of the target program. The risk of passive timing attacks can be measured by the mutual information between the concealed information and the running time. However, the computation of the mutual information is hardly possible except for toy examples. This study focuses on three algorithms for RSA decryption, derives formulas of the mutual information under several assumptions and approximations, and calculates the mutual information numerically for practical security parameters.

Methods that embed data into printed images and retrieve data from printed images captured using the camera of a mobile device have been proposed. Evaluating these methods requires printing and capturing actual embedded images, which is burdensome. In this paper, we propose a method for reducing the workload for evaluating the performance of data embedding algorithms by simulating the degradation caused by printing and capturing images using generative adversarial networks. The proposed method can represent various captured conditions. Experimental results demonstrate that the proposed method achieves the same accuracy as detecting embedded data under actual conditions.

Electromagnetic scattering in a coaxial cable having two flanges and concentric grooves is studied. The associated Weber-Orr transform is used to represent electromagnetic fields in an infinitely long cavity, and the mode-matching method is used to enforce boundary continuity. S-parameters obtained by our approach are compared with the reference solutions, and the characteristics are discussed when geometric parameters are varied. The results show that the proposed model provides cost effective and accurate solutions to the problem.

Crosstalk between lines plays an important role in the transmission of signal. Hence it is of great significance to establish the transmission lines model accurately to evaluate factors affecting crosstalk coupling between lines and to improve the anti-interference capability of the system. As twisted-pair line is widely used for its unique twist structure which improves the anti-interference performance of cables, this paper presents a method of constructing transmission lines model of the shielded twisted-pair line (STP) with two twisted pairs based on S-parameters. Firstly, the transmission lines model of STP with one twisted pair is established. The establishment of distributed capacitance matrix of this model depends on the dielectric constant of insulation layer that surrounds a conductor, but the dielectric constant is often unknown. In this respect, a method to obtain the distributed capacitance matrix based on the S-parameters of this model is proposed. Due to twisting, there is a great deal of variability between the distribution parameters along the length of the STP. As the spatial distribution of conductors in the cross-section of twisted-pair line vary along with the cable length, the distribution parameters matrices also change as they move. The cable is divided into several segments, and the transmission lines model of STP is obtained with the cascade of each segment model. For the STP with two twisted pairs, the crosstalk between pairs is analyzed based on the mixed mode S-parameters. Combined with the transmission lines model of STP with one twisted pair, that of STP with two twisted pairs is obtained. The terminal response voltage can be calculated from the transmission lines model and cable terminal conditions. The validity of the transmission lines model is verified by the consistency between the terminal responses calculated by the model and by the simulated. As the theoretical and simulation results are compatible, the modeling method for the STP with two twisted pairs can be used for the STP with more twisted pairs. In practical engineering application, S-parameters and mixed mode S-parameters can be obtained by testing. That means the transmission lines model of STP can be established based on the test results.

Aiming at the problem of the deterioration of the contact performance caused by the wear debris generated during the fretting wear of the electrical connector, low-frequency fretting wear experiments were carried out on the contacts of electrical connectors, the accumulation and distribution of the wear debris were detected by the electrical capacitance tomography technology; the influence of fretting cycles, vibration direction, vibration frequency and vibration amplitude on the accumulation and distribution of wear debris were analyzed; the correlation between characteristic value of wear debris and contact resistance value was studied, and a performance degradation model based on the accumulation and distribution of wear debris was built. The results show that fretting wear and performance degradation are the most serious in axial vibration; the characteristic value of wear debris and contact resistance are positively correlated with the fretting cycles, vibration frequency and vibration amplitude; there is a strong correlation between the sum of characteristic value of wear debris and the contact resistance value; the prediction error of ABC-SVR model of fretting wear performance degradation of electrical connectors constructed by the characteristic value of wear debris is less than 6%. Therefore, the characteristic value of wear debris in contact subareas can quantitatively describe the degree of fretting wear and the process of performance degradation.

Register automaton (RA) is an extension of finite automaton by adding registers storing data values. RA has good properties such as the decidability of the membership and emptiness problems. Linear temporal logic with the freeze quantifier (LTL↓) proposed by Demri and Lazić is a counterpart of RA. However, the expressive power of LTL↓ is too high to be applied to automatic verification. In this paper, we propose a subclass of modal µ-calculus with the freeze quantifier, which has the same expressive power as RA. Since a conjunction ψ1 ∧ ψ2 in a general LTL↓ formula cannot be simulated by RA, the proposed subclass prohibits at least one of ψ1 and ψ2 from containing the freeze quantifier or a temporal operator other than X (next). Since the obtained subclass of LTL↓ does not have the ability to represent a cycle in RA, we adopt µ-calculus over the subclass of LTL↓, which allows recursive definition of temporal formulas. We provide equivalent translations from the proposed subclass of µ-calculus to RA and vice versa and prove their correctness.

Multiple context-free grammar (MCFG) is an extension of context-free grammar (CFG), which generates tuples of words. The expressive power of MCFG is between CFG and context-sensitive grammar while MCFG inherits good properties of CFG. In this paper, we introduce weighted multiple context-free grammar (WMCFG) as a quantitative extension of MCFG. Then we investigate properties of WMCFG such as polynomial-time computability of basic problems, its closure property and expressive power.

Object detection is one of the most important aspects of computer vision, and the use of CNNs for object detection has yielded substantial results in a variety of fields. However, due to the fixed sampling in standard convolution layers, it restricts receptive fields to fixed locations and limits CNNs in geometric transformations. This leads to poor performance of CNNs for slender object detection. In order to achieve better slender object detection accuracy and efficiency, this proposed detector DFAM-DETR not only can adjust the sampling points adaptively, but also enhance the ability to focus on slender object features and extract essential information from global to local on the image through an attention mechanism. This study uses slender objects images from MS-COCO dataset. The experimental results show that DFAM-DETR achieves excellent detection performance on slender objects compared to CNN and transformer-based detectors.

Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.

A new method for hiding information in digital images is proposed. Our method differs from existing techniques in that the information is hidden in a mixture of colors carefully tuned on a specific device according to the device's signal-to-luminance (gamma) characteristics. Because these reproduction characteristics differ in general from device to device and even from model to model, the hidden information appears when the cover image is viewed on a different device, and hence the hiding property is device-dependent. To realize this, we modulated a cover image using two identically-looking checkerboard patterns and switched them locally depending on the hidden information. Reproducing these two patterns equally on a different device is difficult. A possible application of our method would be secure printing where an image is allowed to be viewed only on a screen but a warning message appears when it is printed.

In this letter, we propose an energy beamforming empowered relaying scheme for a batteryless IoT network, where wireless-powered relays are deployed between the hybrid access point (HAP) and batteryless IoT devices to assist the uplink information transmission from the devices to the HAP. In particular, the HAP first exploits energy beamforming to efficiently transmit radio frequency (RF) signals to transfer energy to the relays and as the incident signals to enable the information backscattering of batteryless IoT devices. Then, each relay uses the harvested energy to forward the decoded signals from its corresponding batteryless IoT device to the HAP, where the maximum-ratio combing is used for further performance improvement. To maximize the network sum-rate, the joint optimization of energy beamforming vectors at the HAP, network time scheduling, power allocation at the relays, and relection coefficient at the users is investigated. As the formulated problem is non-convex, we propose an alternating optimization algorithm with the variable substitution and semi-definite relaxation (SDR) techniques to solve it efficiently. Specifically, we prove that the obtained energy beamforming matrices are always rank-one. Numerical results show that compared to the benchmark schemes, the proposed scheme can achieve a significant sum-rate gain.

In this paper, we propose a combined use of transformed images and vision transformer (ViT) models transformed with a secret key. We show for the first time that models trained with plain images can be directly transformed to models trained with encrypted images on the basis of the ViT architecture, and the performance of the transformed models is the same as models trained with plain images when using test images encrypted with the key. In addition, the proposed scheme does not require any specially prepared data for training models or network modification, so it also allows us to easily update the secret key. In an experiment, the effectiveness of the proposed scheme is evaluated in terms of performance degradation and model protection performance in an image classification task on the CIFAR-10 dataset.

This paper proposes a novel random access identifier (RAID)-linked receiver beamforming method for time division duplex (TDD)-based random access. When the number of receiver antennas at the base station is large in a massive multiple-input multiple-output (MIMO) scenario, the channel estimation accuracy per receiver antenna at the base station receiver is degraded due to the limited received signal power per antenna from the user terminal. This results in degradation in the receiver beamforming (BF) or antenna diversity combining and active RAID detection. The purpose of the proposed method is to achieve accurate active RAID detection and channel estimation with a reasonable level of computational complexity at the base station receiver. In the proposed method, a unique receiver BF vector applied at the base station is linked to each of the M RAIDs prepared by the system. The user terminal selects an appropriate pair comprising a receiver BF vector and a RAID in advance based on the channel estimation results in the downlink assuming channel reciprocity in a TDD system. Therefore, per-receiver antenna channel estimation for receiver BF is not necessary in the proposed method. Furthermore, in order to utilize fully the knowledge of the channel at the user transmitter, we propose applying transmitter filtering (TF) to the proposed method for effective channel shortening in order to increase the orthogonal preambles for active RAID detection and channel estimation prepared for each RAID. Computer simulation results show that the proposed method greatly improves the accuracy of active RAID detection and channel estimation. This results in lower error rates than that for the conventional method performing channel estimation at each antenna in a massive MIMO environment.

Convolutional neural network (CNN) based vehicle re-identificatioin (ReID) inevitably has many disadvantages, such as information loss caused by downsampling operation. Therefore we propose a vision transformer (Vit) based vehicle ReID method to solve this problem. To improve the feature representation of vision transformer and make full use of additional vehicle information, the following methods are presented. (I) We propose a Quadratic Split Architecture (QSA) to learn both global and local features. More precisely, we split an image into many patches as “global part” and further split them into smaller sub-patches as “local part”. Features of both global and local part will be aggregated to enhance the representation ability. (II) The Auxiliary Information Embedding (AIE) is proposed to improve the robustness of the model by plugging a learnable camera/viewpoint embedding into Vit. Experimental results on several benchmarks indicate that our method is superior to many advanced vehicle ReID methods.

Distributed array radars consist of multiple sub-arrays separated by tens to hundreds of wavelengths and can match narrow beamwidths with large-aperture, high-gain antennas. The physical independence of the sub-arrays contributes to significant structure flexibility and is one of the advantages of such radars. However, a typical problem is the grating lobes in the digital beam forming (DBF) beam pattern. Unfortunately, the need to suppress the generation of grating lobes makes the design of acceptable sub-array arrangements very difficult. A sigma-delta beam former direction of arrival (DOA) estimation method is proposed in this study to solve this problem. The proposed method performs DOA estimation by acquiring the difference signals in addition to the sum signals of all sub-arrays. The difference signal is typically used for monopulse DOA estimation in the phased array radar. The sigma-delta beamformer simultaneously has both advantages of DOA estimations using a distributed array with a large aperture length and using a sub-array that is not affected by the grating lobe. The proposed method can improve the DOA estimation accuracy over the conventional method under grating lobe situations and help the distributed array radar achieve flexibility in the sub-array arrangement. Numerical simulations are presented to verify the effectiveness of the proposed DOA estimation method.

Statical causal discovery is an approach to infer the causal relationship between observed variables whose causalities are not revealed. LiNGAM (Linear Non-Gaussian Acyclic Model), an algorithm for causal discovery, can calculate the causal relationship uniquely if the independent components of variables are assumed to be non-Gaussian. However, use-cases of LiNGAM are limited because of its O(d3x) computational complexity, where dx is the number of variables. This paper shows two approaches to accelerate LiNGAM causal discovery: SIMD utilization for LiNGAM's mathematical matrixes operations and MPI parallelization. We evaluate the implementation with the supercomputer Fugaku. Using 96 nodes of Fugaku, our improved version can achieve 17,531 times faster than the original OSS implementation (completed in 17.7 hours).

Channel state information (CSI) acquisition at the transmitter side is a major challenge in massive MIMO systems for enabling high-efficiency transmissions. To address this issue, various CSI feedback schemes have been proposed, including limited feedback schemes with codebook-based vector quantization and explicit channel matrix feedback. Owing to the limitations of feedback channel capacity, a common issue in these schemes is the efficient representation of the CSI with a limited number of bits at the receiver side, and its accurate reconstruction based on the feedback bits from the receiver at the transmitter side. Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6%-26% compared with DL-based receivers and 33%-58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.