In this study, we consider the data compression with side information available at both the encoder and the decoder. The information source is assigned to a variable-length code that does not have to satisfy the prefix-free constraints. We define several classes of codes whose codeword lengths and error probabilities satisfy worse-case criteria in terms of side-information. As a main result, we investigate the exact first-order asymptotics with second-order bounds scaled as Θ(√n) as blocklength n increases under the regime of nonvanishing error probabilities. To get this result, we also derive its one-shot bounds by employing the cutoff operation.

In the field of machine learning security, as one of the attack surfaces especially for edge devices, the application of side-channel analysis such as correlation power/electromagnetic analysis (CPA/CEMA) is expanding. Aiming to evaluate the leakage resistance of neural network (NN) model parameters, i.e. weights and biases, we conducted a feasibility study of CPA/CEMA on floating-point (FP) operations, which are the basic operations of NNs. This paper proposes approaches to recover weights and biases using CPA/CEMA on multiplication and addition operations, respectively. It is essential to take into account the characteristics of the IEEE 754 representation in order to realize the recovery with high precision and efficiency. We show that CPA/CEMA on FP operations requires different approaches than traditional CPA/CEMA on cryptographic implementations such as the AES.

Blockchain-based Internet of Things (IoT) applications require flexible, fast, and low-power hashing hardware to ensure IoT data integrity and maintain blockchain network confidentiality. However, existing hashing hardware poses challenges in achieving high performance and low power and limits flexibility to compute multiple hash functions with different message lengths. This paper introduces the flexible and energy-efficient crypto-processor (FECP) to achieve high flexibility, high speed, and low power with high hardware efficiency for blockchain-based IoT applications. To achieve these goals, three new techniques are proposed, namely the crypto arithmetic logic unit (Crypto-ALU), dual buffering extension (DBE), and local data memory (LDM) scheduler. The experiments on ASIC show that the FECP can perform various hash functions with a power consumption of 0.239-0.676W, a throughput of 10.2-3.35Gbps, energy efficiency of 4.44-14.01Gbps/W, and support up to 8916-bit message input. Compared to state-of-art works, the proposed FECP is 1.65-4.49 times, 1.73-21.19 times, and 1.48-17.58 times better in throughput, energy efficiency, and energy-delay product (EDP), respectively.

The war between cyber attackers and security analysts is gradually intensifying. Owing to the ease of obtaining and creating support tools, recent malware continues to diversify into variants and new species. This increases the burden on security analysts and hinders quick analysis. Identifying malware families is crucial for efficiently analyzing diversified malware; thus, numerous low-cost, general-purpose, deep-learning-based classification techniques have been proposed in recent years. Among these methods, malware images that represent binary features as images are often used. However, no models or architectures specific to malware classification have been proposed in previous studies. Herein, we conduct a detailed analysis of the behavior and structure of malware and focus on PE sections that capture the unique characteristics of malware. First, we validate the features of each PE section that can distinguish malware families. Then, we identify PE sections that contain adequate features to classify families. Further, we propose an ensemble learning-based classification method that combines features of highly discriminative PE sections to improve classification accuracy. The validation of two datasets confirms that the proposed method improves accuracy over the baseline, thereby emphasizing its importance.

This paper presents an FPGA implementation of real-time high dynamic range (HDR) synthesis, which expresses a wide dynamic range by combining multiple images with different exposures using image pyramids. We have implemented a pipeline that performs streaming processing on images without using external memory. However, implementation for high-resolution images has been difficult due to large memory usage for line buffers. Therefore, we propose an image compression algorithm based on adaptive differential pulse code modulation (ADPCM). Compression modules based on the algorithm can be easily integrated into the pipeline. When the image resolution is 4K and the pyramid depth is 7, memory usage can be halved from 168.48% to 84.32% by introducing the compression modules, resulting in better quality.

Homomorphic encryption (HE) is public key encryption that enables computation over ciphertexts without decrypting them. To overcome an issue that HE cannot achieve IND-CCA2 security, the notion of keyed-homomorphic encryption (KH-PKE) was introduced (Emura et al., PKC 2013), which has a separate homomorphic evaluation key and can achieve stronger security named KH-CCA security. The contributions of this paper are twofold. First, recall that the syntax of KH-PKE assumes that homomorphic evaluation is performed for single operations, and KH-CCA security was formulated based on this syntax. Consequently, if the homomorphic evaluation algorithm is enhanced in a way of gathering up sequential operations as a single evaluation, then it is not obvious whether or not KH-CCA security is preserved. In this paper, we show that KH-CCA security is in general not preserved under such modification, while KH-CCA security is preserved when the original scheme additionally satisfies circuit privacy. Secondly, Catalano and Fiore (ACM CCS 2015) proposed a conversion method from linearly HE schemes into two-level HE schemes, the latter admitting addition and a single multiplication for ciphertexts. In this paper, we extend the conversion to the case of linearly KH-PKE schemes to obtain two-level KH-PKE schemes. Moreover, based on the generalized version of Catalano-Fiore conversion, we also construct a similar conversion from d-level KH-PKE schemes into 2d-level KH-PKE schemes.

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).

Multi-signatures have seen renewed interest due to their application to blockchains, e.g., BIP 340 (one of the Bitcoin improvement proposals), which has triggered the proposals of several new schemes with improved efficiency. However, many previous works have a “loose” security reduction (a large gap between the difficulty of the security assumption and breaking the scheme) or depend on strong idealized assumptions such as the algebraic group model (AGM). This makes the achieved level of security uncertain when instantiated in groups typically used in practice, and it becomes unclear for developers how secure a given scheme is for a given choice of security parameters. Thus, this leads to the question “what kind of schemes can we construct that achieves tight security based on standard assumptions?”. In this paper, we show a simple two-round tightly-secure pairing-based multi-signature scheme based on the computation Diffie-Hellman problem in the random oracle model. This proposal is the first two-round multi-signature scheme that achieves tight security based on a computational assumption and supports key aggregation. Furthermore, our scheme reduce the signature bit size by 19% compared with the shortest existing tightly-secure DDH-based multi-signature scheme. Moreover, we implemented our scheme in C++ and confirmed that it is efficient in practice; to complete the verification takes less than 1[ms] with a total (computational) signing time of 13[ms] for under 100 signers. The source code of the implementation is published as OSS.

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.

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.

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.

Synthesis problems on multiplayer non-zero-sum games (MG) with multiple environment players that behave rationally are the problems to find a good strategy of the system and have been extensively studied. This paper concerns the synthesis problems on stochastic MG (SMG), where a special controller other than players, called nature, which chooses a move in its turn randomly, may exist. Two types of synthesis problems on SMG exist: cooperative rational synthesis problem (CRSP) and non-cooperative rational synthesis problem (NCRSP). The rationality of environment players is modeled by Nash equilibria, and CRSP is the problem to decide whether there exists a Nash equilibrium that gives the system a payoff not less than a given threshold. Ummels et al. studied the complexity of CRSP for various classes of objectives and strategies of players. CRSP fits the situation where the system can make a suggestion of a strategy profile (a tuple of strategies of all players) to the environment players. However, in real applications, the system may rarely have an opportunity to make suggestions to the environment, and thus CRSP is optimistic. NCRSP is the problem to decide whether there exists a strategy σ0 of the system satisfying that for every strategy profile of the environment players that forms a 0-fixed Nash equilibrium (a Nash equilibrium where the system's strategy is fixed to σ0), the system obtains a payoff not less than a given threshold. In this paper, we investigate the complexity of NCRSP for positional (i.e. pure memoryless) strategies. We consider ω-regular objectives as the model of players' objectives, and show the complexity results of the problem for several subclasses of ω-regular objectives. In particular, the problem for terminal reachability (TR) objectives is shown to be Σp2-complete.

Following the formulation of Support Vector Regression (SVR), we consider a regression analogue of soft margin optimization over the feature space indexed by a hypothesis class H. More specifically, the problem is to find a linear model w ∈ ℝH that minimizes the sum of ρ-insensitive losses over all training data for as small ρ as posssible, where the ρ-insensitive loss for a single data (xi, yi) is defined as max{|yi - ∑h whh(xi)| - ρ, 0}. Intuitively, the parameter ρ and the ρ-insensitive loss are defined analogously to the target margin and the hinge loss in soft margin optimization, respectively. The difference of our formulation from SVR is two-fold: (1) we consider L1-norm regularization instead of L2-norm regularization, and (2) the feature space is implicitly defined by a hypothesis class instead of a kernel. We propose a boosting-type algorithm for solving the problem with a theoretically guaranteed convergence rate under a natural assumption on the weak learnability.

In Variant 4 of the one-way trading game [El-Yaniv, Fiat, Karp, and Turpin, 2001], a player has one dollar at the beginning and wants to convert it to yen only by one-way conversion. The exchange rate is guaranteed to fluctuate between m and M, and only the maximum fluctuation ratio φ = M/m is informed to the player in advance. The performance of an algorithm for this game is measured by the competitive ratio. El-Yaniv et al. derived the best possible competitive ratio over all algorithms for this game. However, it seems that the behavior of the best possible algorithm itself has not been explicitly described. In this paper we reveal the behavior of the best possible algorithm by solving a linear optimization problem. The behavior turns out to be quite different from that of the best possible algorithm for Variant 2 in which the player knows m and M in advance.

Software development companies must consider employees' job satisfaction and turnover intentions. To explain the related factors, this study focused on future perspective index (FPI). FPI was assumed to relate positively to satisfaction and negatively to turnover. In the analysis, we compared the FPI with existing factors that are considered to be related to job satisfaction. We discovered that the FPI was promising for enhancing explanatory power, particularly when analyzing satisfaction.

Code search is a task to retrieve the most relevant code given a natural language query. Several recent studies proposed deep learning based methods use multi-encoder model to parse code into multi-field to represent code. These methods enhance the performance of the model by distinguish between similar codes and utilizing a relation matrix to bridge the code and query. However, these models require more computational resources and parameters than single-encoder models. Furthermore, utilizing the relation matrix that solely relies on max-pooling disregards the delivery of word alignment information. To alleviate these problems, we propose a combined alignment model for code search. We concatenate the multi-code fields into one sequence to represent code and use one encoding model to encode code features. Moreover, we transform the relation matrix using trainable vectors to avoid information losses. Then, we combine intra-modal and cross-modal attention to assign the salient words while matching the corresponding code and query. Finally, we apply the attention weight to code/query embedding and compute the cosine similarity. To evaluate the performance of our model, we compare our model with six previous models on two popular datasets. The results show that our model achieves 0.614 and 0.687 Top@1 performance, outperforming the best comparison models by 12.2% and 9.3%, respectively.