The present study proposes a scheme in which variable-length orthogonal codes generated by combining inverse discrete Fourier transform matrices over a finite field multiplex user data into a multiplexed sequence and its sequence forms one or a plural number of codewords for Reed-Solomon coding. The proposed scheme realizes data multiplexing, error correction coding, and multi-rate transmitting at the same time. This study also shows a design example and its performance analysis of the proposed scheme.

Adversarial examples (AEs), which cause misclassification by adding subtle perturbations to input images, have been proposed as an attack method on image-classification systems using deep neural networks (DNNs). Physical AEs created by attaching stickers to traffic signs have been reported, which are a threat to traffic-sign-recognition DNNs used in advanced driver assistance systems. We previously proposed an attack method for generating a noise area on images by superimposing an electrical signal on the mobile industry processor interface and showed that it can generate a single adversarial mark that triggers a backdoor attack on the input image. Therefore, we propose a misclassification attack method n DNNs by creating AEs that include small perturbations to multiple places on the image by the fault injection. The perturbation position for AEs is pre-calculated in advance against the target traffic-sign image, which will be captured on future driving. With 5.2% to 5.5% of a specific image on the simulation, the perturbation that induces misclassification to the target label was calculated. As the experimental results, we confirmed that the traffic-sign-recognition DNN on a Raspberry Pi was successfully misclassified when the target traffic sign was captured with. In addition, we created robust AEs that cause misclassification of images with varying positions and size by adding a common perturbation. We propose a method to reduce the amount of robust AEs perturbation. Our results demonstrated successful misclassification of the captured image with a high attack success rate even if the position and size of the captured image are slightly changed.

In recent years, cyber attacks against infrastructure have become more serious. Unfortunately, infrastructures with vulnerable remote management devices, which allow attackers to control the infrastructure, have been reported. Targeted attacks against infrastructure are conducted manually by human attackers rather than automated scripts. Here, open questions are how often the attacks against such infrastructure happen and what attackers do after intrusions. In this empirical study, we observe the accesses, including attacks and security investigation activities, using the customized infrastructure honeypot. The proposed honeypot comprises (1) a platform that easily deploys real devices as honeypots, (2) a mechanism to increase the number of fictional facilities by changing the displayed facility names on the WebUI for each honeypot instance, (3) an interaction mechanism with visitors to infer their purpose, and (4) tracking mechanisms to identify visitors for long-term activities. We implemented and deployed the honeypot for 31 months. Our honeypot observed critical operations, such as changing configurations of a remote management device. We also observed long-term access to WebUI and Telnet service of the honeypot.

Multivariate public key cryptosystems (MPKC) are constructed based on the problem of solving multivariate quadratic equations (MQ problem). Among various multivariate schemes, UOV is an important signature scheme since it is underlying some signature schemes such as MAYO, QR-UOV, and Rainbow which was a finalist of NIST PQC standardization project. To analyze the security of a multivariate scheme, it is necessary to analyze the first fall degree or solving degree for the system of polynomial equations used in specific attacks. It is known that the first fall degree or solving degree often relates to the Hilbert series of the ideal generated by the system. In this paper, we study the Hilbert series of the UOV scheme, and more specifically, we study the Hilbert series of ideals generated by quadratic polynomials used in the central map of UOV. In particular, we derive a prediction formula of the Hilbert series by using some experimental results. Moreover, we apply it to the analysis of the reconciliation attack for MAYO.

Public key authenticated encryption with keyword search (PAEKS) has been proposed, where a sender's secret key is required for encryption, and a trapdoor is associated with not only a keyword but also the sender. This setting allows us to prevent information leakage of keyword from trapdoors. Liu et al. (ASIACCS 2022) proposed a generic construction of PAEKS based on word-independent smooth projective hash functions (SPHFs) and PEKS. In this paper, we propose a new generic construction of PAEKS, which is more efficient than Liu et al.'s in the sense that we only use one SPHF, but Liu et al. used two SPHFs. In addition, for consistency we considered a security model that is stronger than Liu et al.'s. Briefly, Liu et al. considered only keywords even though a trapdoor is associated with not only a keyword but also a sender. Thus, a trapdoor associated with a sender should not work against ciphertexts generated by the secret key of another sender, even if the same keyword is associated. That is, in the previous definitions, there is room for a ciphertext to be searchable even though the sender was not specified when the trapdoor is generated, that violates the authenticity of PAKES. Our consistency definition considers a multi-sender setting and captures this case. In addition, for indistinguishability against chosen keyword attack (IND-CKA) and indistinguishability against inside keyword guessing attack (IND-IKGA), we use a stronger security model defined by Qin et al. (ProvSec 2021), where an adversary is allowed to query challenge keywords to the encryption and trapdoor oracles. We also highlight several issues associated with the Liu et al. construction in terms of hash functions, e.g., their construction does not satisfy the consistency that they claimed to hold.

Identity-based encryption with equality test (IBEET) is a variant of identity-based encryption (IBE), in which any user with trapdoors can check whether two ciphertexts are encryption of the same plaintext. Although several lattice-based IBEET schemes have been proposed, they have drawbacks in either security or efficiency. Specifically, most IBEET schemes only satisfy selective security, while public keys of adaptively secure schemes in the standard model consist of matrices whose numbers are linear in the security parameter. In other words, known lattice-based IBEET schemes perform poorly compared to the state-of-the-art lattice-based IBE schemes (without equality test). In this paper, we propose a semi-generic construction of CCA-secure lattice-based IBEET from a certain class of lattice-based IBE schemes. As a result, we obtain the first lattice-based IBEET schemes with adaptive security and CCA security in the standard model without sacrificing efficiency. This is because, our semi-generic construction can use several state-of-the-art lattice-based IBE schemes as underlying schemes, e.g. Yamada's IBE scheme (CRYPTO'17).

We propose how to homomorphically evaluate arbitrary univariate and bivariate integer functions such as division. A prior work proposed by Okada et al. (WISTP'18) uses polynomial evaluations such that the scheme is still compatible with the SIMD operations in BFV and BGV schemes, and is implemented with the input domain ℤ257. However, the scheme of Okada et al. requires the quadratic numbers of plaintext-ciphertext multiplications and ciphertext-ciphertext additions in the input domain size, and although these operations are more lightweight than the ciphertext-ciphertext multiplication, the quadratic complexity makes handling larger inputs quite inefficient. In this work, first we improve the prior work and also propose a new approach that exploits the packing method to handle the larger input domain size instead of enabling the SIMD operation, thus making it possible to work with the larger input domain size, e.g., ℤ215 in a reasonably efficient way. In addition, we show how to slightly extend the input domain size to ℤ216 with a relatively moderate overhead. Further we show another approach to handling the larger input domain size by using two ciphertexts to encrypt one integer plaintext and applying our techniques for uni/bivariate function evaluation. We implement the prior work of Okada et al., our improved version of Okada et al., and our new scheme in PALISADE with the input domain ℤ215, and confirm that the estimated run-times of the prior work and our improved version of the prior work are still about 117 days and 59 days respectively while our new scheme can be computed in 307 seconds.

Secure two-party computation is a cryptographic tool that enables two parties to compute a function jointly without revealing their inputs. It is known that any function can be realized in the correlated randomness (CR) model, where a trusted dealer distributes input-independent CR to the parties beforehand. Sometimes we can construct more efficient secure two-party protocol for a function g than that for a function f, where g is a restriction of f. However, it is not known in which case we can construct more efficient protocol for domain-restricted function. In this paper, we focus on the size of CR. We prove that we can construct more efficient protocol for a domain-restricted function when there is a “good” structure in CR space of a protocol for the original function, and show a unified way to construct a more efficient protocol in such case. In addition, we show two applications of the above result: The first application shows that some known techniques of reducing CR size for domain-restricted function can be derived in a unified way, and the second application shows that we can construct more efficient protocol than an existing one using our result.

This paper considers the problem of balancing traceability and anonymity in designated verifier signatures (DVS), which are a kind of group-oriented signatures. That is, we propose claimable designated verifier signatures (CDVS), where a signer is able to claim that he/she indeed created a signature later. Ordinal DVS does not provide any traceability, which could indicate too strong anonymity. Thus, adding claimability, which can be seen as a sort of traceability, moderates anonymity. We demonstrate two generic constructions of CDVS from (i) ring signatures, (non-ring) signatures, pseudorandom function, and commitment scheme, and (ii) claimable ring signatures (by Park and Sealfon, CRYPTO'19).

Recently, multivariate time-series data has been generated in various environments, such as sensor networks and IoT, making anomaly detection in time-series data an essential research topic. Unsupervised learning anomaly detectors identify anomalies by training a model on normal data and producing high residuals for abnormal observations. However, a fundamental issue arises as anomalies do not consistently result in high residuals, necessitating a focus on the time-series patterns of residuals rather than individual residual sizes. In this paper, we present a novel framework comprising two serialized anomaly detectors: the first model calculates residuals as usual, while the second one evaluates the time-series pattern of the computed residuals to determine whether they are normal or abnormal. Experiments conducted on real-world time-series data demonstrate the effectiveness of our proposed framework.

Non-autoregressive generation has attracted more and more attention due to its fast decoding speed. Latent alignment objectives, such as CTC, are designed to capture the monotonic alignments between the predicted and output tokens, which have been used for machine translation and sentence summarization. However, our preliminary experiments revealed that CTC performs poorly on document abstractive summarization, where a high compression ratio between the input and output is involved. To address this issue, we conduct a theoretical analysis and propose Hierarchical Latent Alignment (HLA). The basic idea is a two-step alignment process: we first align the sentences in the input and output, and subsequently derive token-level alignment using CTC based on aligned sentences. We evaluate the effectiveness of our proposed approach on two widely used datasets XSUM and CNNDM. The results indicate that our proposed method exhibits remarkable scalability even when dealing with high compression ratios.

Pedestrian attribute recognition (PAR) aims to recognize a series of a person's semantic attributes, e.g., age, gender, which plays an important role in video surveillance. This paper proposes a multi-correlation graph convolutional network named MCGCN for PAR, which includes a semantic graph, visual graph, and synthesis graph. We construct a semantic graph by using attribute features with semantic constraints. A graph convolution is employed, based on prior knowledge of the dataset, to learn the semantic correlation. 2D features are projected onto visual graph nodes and each node corresponds to the feature region of each attribute group. Graph convolution is then utilized to learn regional correlation. The visual graph nodes are connected to the semantic graph nodes to form a synthesis graph. In the synthesis graph, regional and semantic correlation are embedded into each other through inter-graph edges, to guide each other's learning and to update the visual and semantic graph, thereby constructing semantic and regional correlation. On this basis, we use a better loss weighting strategy, the suit_polyloss, to address the imbalance of pedestrian attribute datasets. Experiments on three benchmark datasets show that the proposed approach achieves superior recognition performance compared to existing technologies, and achieves state-of-the-art performance.

Composing choreography is challenging because it involves numerous iterative refinements. According to our video analysis and interviews, choreographers typically need to imagine dancers' movements to revise drafts on paper since testing new movements and formations with actual dancers takes time. To address this difficulty, we present an interactive group-dance simulation interface, DanceUnisoner, that assists choreographers in composing a group dance in a simulated environment. With DanceUnisoner, choreographers can arrange excerpts from solo-dance videos of dancers throughout a three-dimensional space. They can adjust various parameters related to the dancers in real time, such as each dancer's position and size and each movement's timing. To evaluate the effectiveness of the system's parametric, visual, and interactive interface, we asked seven choreographers to use it and compose group dances. Our observations, interviews, and quantitative analysis revealed their successful usage in iterative refinements and visual checking of choreography, providing insights to facilitate further computational creativity support for choreographers.

A user wearing a Head-Mounted Display (HMD) is likely to feel isolated when sharing virtual reality (VR) experiences with Non-HMD users in the same physical space due to not being able to see the real space outside the virtual world. This research proposes a method for an HMD user to recognize the Non-HMD users' gaze and attention via a projector attached to the HMD. In the proposed approach, the projected HMD user's view is filtered darker than default, and when Non-HMD users point controllers towards the projected view, the filter is removed from a circular area for both HMD and Non-HMD users indicating which region the Non-HMD users are viewing. We conducted two user studies showing that the Non-HMD users' gaze can be recognized with the proposed method, and investigated the preferred range for the alpha value and the size of the area for removing the filter for the HMD user.

Text-based communication, such as text chat, is commonly employed in various contexts, both professional and personal. However, it lacks the rich emotional cues present in verbal and visual forms of communication, such as facial expressions and tone of voice, making it more challenging to convey emotions and increasing the likelihood of misunderstandings. In this study, we focused on typefaces as emotional cues employed in text-based communication and investigated the influence of font design on impression formation and decision-making through two experiments. The results of the experiments revealed the relationship between Japanese typeface design and impression formation, and indicated that advice presented in a font evoking an impression of high confidence was more likely to be accepted than advice presented in a font evoking an impression of low confidence.

Depression is a prevalent mental disorder affecting a significant portion of the global population, leading to considerable disability and contributing to the overall burden of disease. Consequently, designing efficient and robust automated methods for depression detection has become imperative. Recently, deep learning methods, especially multimodal fusion methods, have been increasingly used in computer-aided depression detection. Importantly, individuals with depression and those without respond differently to various emotional stimuli, providing valuable information for detecting depression. Building on these observations, we propose an intra- and inter-emotional stimulus transformer-based fusion model to effectively extract depression-related features. The intra-emotional stimulus fusion framework aims to prioritize different modalities, capitalizing on their diversity and complementarity for depression detection. The inter-emotional stimulus model maps each emotional stimulus onto both invariant and specific subspaces using individual invariant and specific encoders. The emotional stimulus-invariant subspace facilitates efficient information sharing and integration across different emotional stimulus categories, while the emotional stimulus specific subspace seeks to enhance diversity and capture the distinct characteristics of individual emotional stimulus categories. Our proposed intra- and inter-emotional stimulus fusion model effectively integrates multimodal data under various emotional stimulus categories, providing a comprehensive representation that allows accurate task predictions in the context of depression detection. We evaluate the proposed model on the Chinese Soochow University students dataset, and the results outperform state-of-the-art models in terms of concordance correlation coefficient (CCC), root mean squared error (RMSE) and accuracy.

This study discusses the feasibility to estimate the concentration level of Japanese document workers using computer. Based on the previous findings that dual-task scenarios increase reaction time, we hypothesized that the Kana-Kanji conversion confirmation time (KKCCT) would increase due to the decrease in cognitive resources allocated to the document task, i.e. the level of concentration on the task at hand. To examine this hypothesis, we conducted a set of experiments in which sixteen participants copied Kana text by typing and concurrently converted it into Kanji under three conditions: Normal, Dual-task, and Mental-fatigue. The results suggested the feasibility that KKCCT increased when participants were less concentrated on the task due to subtask or mental fatigue. These findings imply the potential utility of using confirmation time as a measure of concentration level in Japanese document workers.

Chained Block is one of Nikoli's pencil puzzles. We study the computational complexity of Chained Block puzzles. It is shown that deciding whether a given instance of the Chained Block puzzle has a solution is NP-complete.

This paper proposes graph linear notations and an extension of them with regular expressions. Graph linear notations are a set of strings to represent labeled general graphs. They are extended with regular expressions to represent sets of graphs by specifying chosen parts for selections and repetitions of certain induced subgraphs. Methods for the conversion between graph linear notations and labeled general graphs are shown. The NP-completeness of the membership problem for graph regular expressions is proved.

The influence of the gate voltage or base pair ratio modulation on the λ-DNA FET performance was examined. The result of the gate voltage modulation indicated that the captured electrons in the guanine base of the λ-DNA molecules greatly influenced the Id-Vd characteristics, and that of the base pair ratio modulation indicated that the tendency of the conductivity was partly clarified by considering the activation energy of holes and electrons and the length and numbers of the serial AT or GC sequences over which the holes or electrons jumped. In addition, the influence of the dimensionality of the DNA molecule on the conductivity was discussed theoretically.