At manufacturing sites, mass customization is expanding along with the increasing variety of customer needs. This situation leads to complications in production planning for the factory manager, and production plans are likely to change suddenly at the manufacturing site. Because such sudden fluctuations in production often occur, it is particularly difficult to optimize the parts supply operations in these production processes. As a solution to such problems, Industry 4.0 has expanded to promote the use of digital technologies at manufacturing sites; however, these solutions can be expensive and time-consuming to introduce. Therefore, not all factory managers are favorable toward introducing digital technology. In this study, we propose a method to support parts supply operations that decreases work stagnation and fluctuation without relying on the experience of workers who supply parts in the various production processes. Furthermore, we constructed a system that is inexpensive and easy to introduce using both LPWA and BLE communications. The purpose of the system is to level out work in in-process logistics. In an experiment, the proposed method was introduced to a manufacturing site, and we compared how the workload of the site's workers changed. The experimental results show that the proposed method is effective for workload leveling in parts supply operations.

Companies listed on the stock exchange are required to share their annual reports with the U.S. Securities and Exchange Commission (SEC) within the first three months following the fiscal year. These reports, namely 10-K Filings, are presented to public interest by the SEC through an Electronic Data Gathering, Analysis, and Retrieval database. 10-K Filings use standard file formats (xbrl, html, pdf) to publish the financial reports of the companies. Although the file formats propose a standard structure, the content and the meta-data of the financial reports (e.g. tag names) is not strictly bound to a pre-defined schema. This study proposes a data collection and data preprocessing method to semantify the financial reports and use the collected data for further analysis (i.e. machine learning). The analysis of eight different datasets, which were created during the study, are presented using the proposed data transformation methods. As a use case, based on the datasets, five different machine learning algorithms were utilized to predict the existence of the corresponding company in the S&P 500 index. According to the strong machine learning results, the dataset generation methodology is successful and the datasets are ready for further use.

We propose GConvLoc, a WiFi fingerprinting-based indoor localization method utilizing graph convolutional networks. Using the graph structure, we can consider the fingerprint data of the reference points and their location labels in addition to the fingerprint data of the test point at inference time. Experimental results show that GConvLoc outperforms baseline methods that do not utilize graphs.

Aiming at the different abilities of the defogging algorithms in different fog concentrations, this paper proposes a fog image classification algorithm for a small and imbalanced sample dataset based on a convolution neural network, which can classify the fog images in advance, so as to improve the effect and adaptive ability of image defogging algorithm in fog and haze weather. In order to solve the problems of environmental interference, camera depth of field interference and uneven feature distribution in fog images, the CutBlur-Gauss data augmentation method and focal loss and label smoothing strategies are used to improve the accuracy of classification. It is compared with the machine learning algorithm SVM and classical convolution neural network classification algorithms alexnet, resnet34, resnet50 and resnet101. This algorithm achieves 94.5% classification accuracy on the dataset in this paper, which exceeds other excellent comparison algorithms at present, and achieves the best accuracy. It is proved that the improved algorithm has better classification accuracy.

Annealing machines such as quantum annealing machines and semiconductor-based annealing machines have been attracting attention as an efficient computing alternative for solving combinatorial optimization problems. They solve original combinatorial optimization problems by transforming them into a data structure called an Ising model. At that time, the bit-widths of the coefficients of the Ising model have to be kept within the range that an annealing machine can deal with. However, by reducing the Ising-model bit-widths, its minimum energy state, or ground state, may become different from that of the original one, and hence the targeted combinatorial optimization problem cannot be well solved. This paper proposes an effective method for reducing Ising model's bit-widths. The proposed method is composed of two processes: First, given an Ising model with large coefficient bit-widths, the shift method is applied to reduce its bit-widths roughly. Second, the spin-adding method is applied to further reduce its bit-widths to those that annealing machines can deal with. Without adding too many extra spins, we efficiently reduce the coefficient bit-widths of the original Ising model. Furthermore, the ground state before and after reducing the coefficient bit-widths is not much changed in most of the practical cases. Experimental evaluations demonstrate the effectiveness of the proposed method, compared to existing methods.

Memory-efficient Internet Protocol (IP) lookup with high speed is essential to achieve link-speed packet forwarding in IP routers. The rapid growth of Internet traffic and the development of optical link technologies have made IP lookup a major performance bottleneck in core routers. In this paper, we propose a new IP route lookup architecture based on hardware called Prefix-Route Trie (PR-Trie), which supports both IPv4 and IPv6 addresses. In PR-Trie, we develop a novel structure called Overlapping Hybrid Trie (OHT) to perform fast longest-prefix-matching (LPM) based on Multibit-Trie (MT), and a hash-based level matching query used to achieve only one off-chip memory access per lookup. In addition, the proposed PR-Trie also supports fast incremental updates. Since the memory complexity in MT-based IP lookup schemes depends on the level-partitioning solution and the data structure used, we develop an optimization algorithm called Bitmap-based Prefix Partitioning Optimization (BP2O). The proposed BP2O is based on a heuristic search using Ant Colony Optimization (ACO) algorithms to optimize memory efficiency. Experimental results using real-life routing tables prove that our proposal has superior memory efficiency. Theoretical performance analyses show that PR-Trie outperforms the classical Trie-based IP lookup algorithms.

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%.

In this work, we propose a new automatic speech recognition (ASR) system based on feature learning and an end-to-end training procedure for air traffic control (ATC) systems. The proposed model integrates the feature learning block, recurrent neural network (RNN), and connectionist temporal classification loss to build an end-to-end ASR model. Facing the complex environments of ATC speech, instead of the handcrafted features, a learning block is designed to extract informative features from raw waveforms for acoustic modeling. Both the SincNet and 1D convolution blocks are applied to process the raw waveforms, whose outputs are concatenated to the RNN layers for the temporal modeling. Thanks to the ability to learn representations from raw waveforms, the proposed model can be optimized in a complete end-to-end manner, i.e., from waveform to text. Finally, the multilingual issue in the ATC domain is also considered to achieve the ASR task by constructing a combined vocabulary of Chinese characters and English letters. The proposed approach is validated on a multilingual real-world corpus (ATCSpeech), and the experimental results demonstrate that the proposed approach outperforms other baselines, achieving a 6.9% character error rate.

Multimodal named entity recognition (MNER) is the task of recognizing named entities in multimodal context. Existing methods focus on utilizing co-attention mechanism to discover the relationships between multiple modalities. However, they still have two deficiencies: First, current methods fail to fuse the multimodal representations in a fine-grained way, which may bring noise of visual modalities. Second, current methods ignore bridging the semantic gap between heterogeneous modalities. To solve the above issues, we propose a novel MNER method with bottleneck fusion and contrastive learning (BFCL). Specifically, we first incorporate the transformer-based bottleneck fusion mechanism, subsequently, information between different modalities can only be exchanged through several bottleneck tokens, thus reducing the noise propagation. Then we propose two decoupled image-text contrastive losses to align the unimodal representations, making the representations of semantically similar modalities closer, while the representations of semantically different modalities farther away. Experimental results demonstrate that our method is competitive to the state-of-the-art models, and achieves 74.54% and 85.70% F1-scores on Twitter-2015 and Twitter-2017 datasets, respectively.

Why and how do people view lyrics? Although various lyrics-based music systems have been proposed, this fundamental question remains unexplored. Better understanding of lyrics viewing behavior would be beneficial for both researchers and music streaming platforms to improve their lyrics-based systems. Therefore, in this paper, we investigate why and how people view lyrics, especially when they listen to music on a smartphone. To answer “why,” we conduct a questionnaire-based online user survey involving 206 participants. To answer “how,” we analyze over 23 million lyrics request logs sent from the smartphone application of a music streaming service. Our analysis results suggest several reusable insights, including the following: (1) People have high demand for viewing lyrics to confirm what the artist sings, more deeply understand the lyrics, sing the song, and figure out the structure such as verse and chorus. (2) People like to view lyrics after returning home at night and before going to sleep rather than during the daytime. (3) People usually view the same lyrics repeatedly over time. Applying these insights, we also discuss application examples that could enable people to more actively view lyrics and listen to new songs, which would not only diversify and enrich people's music listening experiences but also be beneficial especially for music streaming platforms.

Common schedulers for long-term running services that perform task-level optimization fail to accommodate short-living batch processing (BP) jobs. Thus, many efficient job-level scheduling strategies are proposed for BP jobs. However, the existing scheduling strategies perform time-consuming objective optimization which yields non-negligible scheduling delay. Moreover, they tend to assign BP jobs in a centralized manner to reduce monetary cost and synchronization overhead, which can easily cause resource contention due to the task co-location. To address these problems, this paper proposes TEBAS, a time-efficient balance-aware scheduling strategy, which spreads all tasks of a BP job into the cluster according to the resource specifications of a single task based on the observation that computing tasks of a BP job commonly possess similar features. The experimental results show the effectiveness of TEBAS in terms of scheduling efficiency and load balancing performance.

To overcome the challenge in micro-expression recognition that it only emerge in several small facial regions with low intensity, some researchers proposed facial region partition mechanisms and introduced group sparse learning methods for feature selection. However, such methods have some shortcomings, including the complexity of region division and insufficient utilization of critical facial regions. To address these problems, we propose a novel Group Sparse Reduced Rank Tensor Regression (GSRRTR) to transform the fearure matrix into a tensor by laying blocks and features in different dimensions. So we can process grids and texture features separately and avoid interference between grids and features. Furthermore, with the use of Tucker decomposition, the feature tensor can be decomposed into a product of core tensor and a set of matrix so that the number of parameters and the computational complexity of the scheme will decreased. To evaluate the performance of the proposed micro-expression recognition method, extensive experiments are conducted on two micro expression databases: CASME2 and SMIC. The experimental results show that the proposed method achieves comparable recognition rate with less parameters than state-of-the-art methods.

We study a new transistor-level side-channel leakage caused by charges trapped in between stacked transistors namely residual electric charges (RECs). Building leakage models is important in designing countermeasures against side-channel attacks (SCAs). The conventional work showed that even a transistor-level leakage is measurable with a local electromagnetic measurement. One example is the current-path leak [1], [2]: an attacker can distinguish the number of transistors in the current path activated during a signal transition. Addressing this issue, Sugawara et al. proposed to use a mirror circuit that has the same number of transistors on its possible current paths. We show that this countermeasure is insufficient by showing a new transistor-level leakage, caused by RECs, not covered in the previous work. RECs can carry the history of the gate's state over multiple clock cycles and changes the gate's electrical behavior. We experimentally verify that RECs cause exploitable side-channel leakage. We also propose a countermeasure against REC leaks and designed advanced encryption standard-128 (AES-128) circuits using IO-masked dual-rail read-only memory with a 180-nm complementary metal-oxide-semiconductor (CMOS) process. We compared the resilience of our AES-128 circuits against EMA attacks with and without our countermeasure and investigated an RECs' effect on physically unclonable functions (PUFs). We further extend RECs to physically unclonable function. We demonstrate that RECs affect the performance of arbiter and ring-oscillator PUFs through experiments using our custom chips fabricated with 180- and 40-nm CMOS processes*.

The BGPsec protocol, which is an extension of the border gateway protocol (BGP) for Internet routing known as BGPsec, uses digital signatures to guarantee the validity of routing information. However, the use of digital signatures in routing information on BGPsec causes a lack of memory in BGP routers, creating a gaping security hole in today's Internet. This problem hinders the practical realization and implementation of BGPsec. In this paper, we present APVAS (AS path validation based on aggregate signatures), a new protocol that reduces the memory consumption of routers running BGPsec when validating paths in routing information. APVAS relies on a novel aggregate signature scheme that compresses individually generated signatures into a single signature. Furthermore, we implement a prototype of APVAS on BIRD Internet Routing Daemon and demonstrate its efficiency on actual BGP connections. Our results show that the routing tables of the routers running BGPsec with APVAS have 20% lower memory consumption than those running the conventional BGPsec. We also confirm the effectiveness of APVAS in the real world by using 800,000 routes, which are equivalent to the full route information on a global scale.

The problem of Isomorphism of Polynomials (IP problem) is known to be important to study the security of multivariate public key cryptosystems, one of the major candidates of post-quantum cryptography, against key recovery attacks. In these years, several schemes based on the IP problem itself or its generalization have been proposed. At PQCrypto 2020, Santoso introduced a generalization of the problem of Isomorphism of Polynomials, called the problem of Blockwise Isomorphism of Polynomials (BIP problem), and proposed a new Diffie-Hellman type encryption scheme based on this problem with Circulant matrices (BIPC problem). Quite recently, Ikematsu et al. proposed an attack called the linear stack attack to recover an equivalent key of Santoso's encryption scheme. While this attack reduced the security of the scheme, it does not contribute to solving the BIPC problem itself. In the present paper, we describe how to solve the BIPC problem directly by simplifying the BIPC problem due to the conjugation property of circulant matrices. In fact, we experimentally solved the BIPC problem with the parameter, which has 256 bit security by Santoso's security analysis and has 72.7bit security against the linear stack attack, by about 10 minutes.

Identity-based encryption with equality test (IBEET) is a generalization of the traditional identity-based encryption (IBE) and public key searchable encryption, where trapdoors enable users to check whether two ciphertexts of distinct identities are encryptions of the same plaintext. By definition, IBEET cannot achieve indistinguishability security against insiders, i.e., users who have trapdoors. To address this issue, IBEET against insider attacks (IBEETIA) was later introduced as a dual primitive. While all users of IBEETIA are able to check whether two ciphertexts are encryptions of the same plaintext, only users who have tokens are able to encrypt plaintexts. Hence, IBEETIA is able to achieve indistinguishability security. On the other hand, the definition of IBEETIA weakens the notion of IBE due to its encryption inability. Nevertheless, known schemes of IBEETIA made use of rich algebraic structures such as bilinear groups and lattices. In this paper, we propose a generic construction of IBEETIA without resorting to rich algebraic structures. In particular, the only building blocks of the proposed construction are symmetric key encryption and pseudo-random permutations in the standard model. If a symmetric key encryption scheme satisfies CCA security, our proposed IBEETIA scheme also satisfies CCA security.

The MinRank problem is investigated as a problem related to rank attacks in multivariate cryptography and the decoding of rank codes in coding theory. The Kipnis-Shamir method is one of the methods to solve the problem, and recently, significant progress has been made in its complexity estimation by Verbel et al. As this method reduces the problem to an MQ problem, which asks for a solution to a system of quadratic equations, its complexity depends on the solving degree of a quadratic system deduced from the method. A theoretical value introduced by Verbel et al. approximates the minimal solving degree of the quadratic systems in the method although their value is defined under a certain limit for the system considered. A quadratic system outside their limitation often has a larger solving degree, but the solving complexity is not always higher because it has a smaller number of variables and equations. Thus, in order to discuss the best complexity of the Kipnis-Shamir method, a theoretical value is needed to approximate the solving degree of each quadratic system deduced from the method. A quadratic system deduced from the Kipnis-Shamir method always has a multi-degree, and the solving complexity is influenced by this property. In this study, we introduce a theoretical value defined by such a multi-degree and show that it approximates the solving degree of each quadratic system. Thus, the systems deduced from the method are compared, and the best complexity is discussed. As an application, for the MinRank attack using the Kipnis-Shamir method against the multivariate signature scheme Rainbow, we show a case in which a deduced quadratic system outside Verbel et al.'s limitation is the best. In particular, the complexity estimation of the MinRank attack using the KS method against the Rainbow parameter sets I, III and V is reduced by about 172, 140 and 212 bits, respectively, from Verbel et al.'s estimation.

We present a forward-secure public-key encryption (PKE) scheme without key update, i.e. both public and private keys are immutable. In contrast, prior forward-secure PKE schemes achieve forward security by constantly updating the secret keys. Our scheme is based on witness encryption by Garg et al. (STOC 2013) and a proof-of-stake blockchain with the distinguishable forking property introduced by Goyal et al. (TCC 2017), and ensures a ciphertext cannot be decrypted more than once, thereby rendering a compromised secret key useless with respect to decryption of past ciphertext the legitimate user has already decrypted. In this work, we formalize the notion of blockchain-based forward-secure PKE, show the feasibility of constructing a forward-secure PKE scheme without key update, and discuss interesting properties of our scheme such as post-compromise security.

We propose a short signature scheme under the ring-SIS assumption in the standard model. Specifically, by revisiting an existing construction [Ducas and Micciancio, CRYPTO 2014], we demonstrate lattice-based signatures with improved reduction loss. As far as we know, there are no ways to use multiple tags in the signature simulation of security proof in the lattice tag-based signatures. We address the tag-collision possibility in the lattice setting, which improves reduction loss. Our scheme generates tags from messages by constructing a scheme under a mild security condition that is existentially unforgeable against random message attack with auxiliary information. Thus our scheme can reduce the signature size since it does not need to send tags with the signatures. Our scheme has short signature sizes of O(1) and achieves tighter reduction loss than that of Ducas et al.'s scheme. Our proposed scheme has two variants. Our scheme with one property has tighter reduction and the same verification key size of O(log n) as that of Ducas et al.'s scheme, where n is the security parameter. Our scheme with the other property achieves much tighter reduction loss of O(Q/n) and verification key size of O(n), where Q is the number of signing queries.