Abstract Modern enterprise systems are likely to have a very long life. Their specifications therefore need to employ mechanisms that allow them to evolve during their lifetime; where they exploit generic components, these must be adaptable for use in novel situations. The paper looks at some of the issues that arise from this requirement, and how the exploitation of domain-specific language technologies in the tool-chain can assist in maintaining consistency of the specification as a whole. First, it reviews the final state of the family of standards supporting the ODP Enterprise Language, which is intended to handle this kind of application. In particular, it looks at the way the framework for defining policies can be used to accommodate changing requirements during the lifetime of an evolving system. It also looks at the way the idea of deontic tokens enables factoring out of the management of obligations from the basic behaviour of interacting system components. It then proposes a roadmap for building tools that can be used to unify the constraints from different areas of concern into a single specification. The approach taken is to exploit the power of domain-specific languages (DSLs) to allow designers in the various areas of concern to provide their input in terms natural to them. Finally, it looks at the way this approach promotes the establishment of a robust tool-chain capable of handling the evolution and scalability of enterprise systems. The paper uses a running example from the e-health domain to show how specific areas identified in the e-health standards can lead to language definitions, and so to tooling, that can be used to manage unified, system-wide specifications.

Phishing attacks reached a record high in 2022, as reported by the Anti-Phishing Work Group, following an upward trend accelerated during the pandemic. Attackers employ increasingly sophisticated tools in their attempts to deceive unaware users into divulging confidential information. Recently, the research community has turned to the utilization of screenshots of legitimate and malicious websites to identify the brands that attackers aim to impersonate. In the field of Computer Vision, convolutional neural networks (CNNs) have been employed to analyze the visual rendering of websites, addressing the problem of phishing detection. However, along with the development of these new models, arose the need to understand their inner workings and the rationale behind each prediction. Answering the question, “How is this website attempting to steal the identity of a well-known brand?” becomes crucial when protecting end-users from such threats. In cybersecurity, the application of explainable AI (XAI) is an emerging approach that aims to answer such questions. In this article, we propose VORTEX, a phishing website detection solution equipped with the capability to explain how a screenshot attempts to impersonate a specific brand. We conduct an extensive analysis of XAI methods for the phishing detection problem and demonstrate that VORTEX provides meaningful explanations regarding the detection results. Additionally, we evaluate the robustness of our model against Adversarial Example attacks. We adapt these attacks to the VORTEX architecture and evaluate their efficacy across multiple models and datasets. Our results show that VORTEX achieves superior accuracy compared to previous models, and learns semantically meaningful patterns to provide actionable explanations about phishing websites. Finally, VORTEX demonstrates an acceptable level of robustness against adversarial example attacks.

This research aims at automatic clustering from a large-scale malware specimen set by constructing a phylogenetic tree. In our previous work, we proposed a scalable method for constructing a phylogenetic tree with a clustering algorithm. In this paper, we will introduce the current progress of our work. We are trying to improve our algorithm to achieve higher clustering accuracy and we are using a much larger IoT malware set containing 182,838 malware specimens to evaluate our method.

Most packets arriving in the darknet (or network telescope), which is unused IP address space on the Internet, are related to indiscriminate scanning and attack activities. In recent years, the number of indiscriminate scanning attacks observed on the darknet has increased in diversity and quantity. In our earlier study, we proposed a framework called Dark-TRACER that detects anomalies in spatiotemporal pattern synchronization by using darknet data, with the aim being early detection of malware-caused indiscriminate scanning attacks. Although Dark-TRACER has achieved an average of 126.4 days earlier threat detection, we have not been able to determine whether there is a relationship between the early detections and the actual threats. Hence, in this paper, we perform a cross-checking analysis to identify if any information links the detections and the actual threats. As a result, we confirmed the validity of our early threat detection framework by showing, e.g., that more than 60% of unique hosts overlapped in large-scale threats. In addition, we outline four future studies to address the issue that the present Dark-TRACER has many false-positive alerts. Lastly, the darknet data used in our research has been made publicly available.

Most IoT malware is variants generated by editing and reusing parts of the functions based on publicly available source codes. In our previous study, we proposed a method to estimate the functions of a specimen using the Function Call Sequence Graph (FCSG), which is a directed graph of execution sequence of function calls. In the FCSG-based method, the subgraph corresponding to a malware functionality is manually created and called a signature-FSCG. The specimens with the signature-FSCG are expected to have the corresponding functionality. However, this method cannot detect the specimens with a slightly different subgraph from the signature-FSCG. This paper found that these specimens were supposed to have the same functionality for a signature-FSCG. These specimens need more flexible signature matching, and we propose a graph embedding technique to realize it.

We had previously proposed a supervised Laplacian eigenmap for visualization (SLE-ML) that can handle multi-label data. In addition, SLE-ML can control the trade-off between the class separability and local structure by a single trade-off parameter. However, SLE-ML cannot transform new data, that is, it has the “out-of-sample” problem. In this paper, we show that this problem is solvable, that is, it is possible to simulate the same transformation perfectly using a set of linear sums of reproducing kernels (KSLE-ML) with a nonsingular Gram matrix. We experimentally showed that the difference between training and testing is not large; thus, a high separability of classes in a low-dimensional space is realizable with KSLE-ML by assigning an appropriate value to the trade-off parameter. This offers the possibility of separability-guided feature extraction for classification. In addition, to optimize the performance of KSLE-ML, we conducted both kernel selection and parameter selection. As a result, it is shown that parameter selection is more important than kernel selection. We experimentally demonstrated the advantage of using KSLE-ML for visualization and for feature extraction compared with a few typical algorithms.

Many social phenomena are triggered by public opinion that is formed in the process of opinion exchange among individuals. To date, from the engineering point of view, a large body of work has been devoted to studying how to manipulate individual opinions so as to guide public opinion towards the desired state. Recently, Abebe et al. (KDD 2018) have initiated the study of the impact of interventions at the level of susceptibility rather than the interventions that directly modify individual opinions themselves. For the model, Chan et al. (The Web Conference 2019) designed a local search algorithm to find an optimal solution in polynomial time. However, it can be seen that the solution obtained by solving the above model might not be implemented in real-world scenarios. In fact, as we do not consider the amount of changes of the susceptibility, it would be too costly to change the susceptibility values for agents based on the solution.

Kernel regression problem with autocorrelation prior is discussed in this paper. We revealed the optimal model of the kernel ridge regression in terms of the expected generalization error under the assumed autocorrelation prior. This result agrees with the optimal model of the Gaussian process regression, whose optimality is specified by the conditional expectation by a given set of training samples. We also proved that the minimizer of the expected cross-validation criterion is reduced to the optimal model, which gives a novel aspect of nonasymptotic theoretical justification of the cross-validation technique in the kernel regression problem.

Abstract The studies of browsing behavior have gained increasing attention in web analysis for providing better service. Most of the conventional approaches focus on simple indices such as average dwell time and conversion rate. These indices make similar evaluations to websites even if their features are significantly different. Moreover, such statistical indices are not sensitive to the dynamics of users’ interests. In this paper, we propose a new framework for measuring a website’s attractiveness that takes into account both the distribution and dynamics of users’ interests. Within the framework, we define a new index for the website, called Attractiveness Factor, which evaluates the degree of users’ attention. It consists of three procedures: First, we capture the transition of users’ interests during browsing by solving a nonnegative matrix factorization and constrained network flow problems. To accommodate multiple types of interests of a user, we applied a soft clustering as opposed to a hard clustering to model attributes of users and websites. Second, for each website, the feature of each cluster is obtained by fitting the dwell time distribution with Weibull distribution. Finally, we calculate Attractiveness Factor of a website by applying the results of clustering and fitting. Attractiveness Factor depends on the distribution of the dwell time of users interested in the website, which reflects the change of interest of users. Numerical experiments with real web access data of Yahoo Japan News are conducted by solving extremely large-scale optimization problems. They show that Attractiveness Factor captures more exceptional information about browsing behavior more effectively than well-used indices. Attractive factors give low ratings to category pages; however, it can assign high ratings to websites that attract many people, such as hot topic news about the 2018 FIFA World Cup, Japan’s new imperial era’ REIWA,’ and North Korea—the United States Hanoi Summit. Moreover, we demonstrate that Attractiveness Factor can detect the tendency of users’ attention to each website at a given time interval of the day.

One of the most critical problems in bike-sharing services is a bicycle repositioning problem, which is how service providers must relocate their bicycles to maintain the quality of service. In this paper, we propose an end-to-end approach for the bike repositioning problem, which realizes the operator-feasible repositioning plan with cooperation among multiple trucks. Our proposed algorithm consists of three procedures. First, we predict the number of rented and returned bicycles at each station with a deep learning based on the bicycle usage information. Second, we determine the optimal number of bicycles to satisfy the availability of each station by solving an integer optimization problem. Finally, we solve the vehicle routing problem formulated as another integer optimization problem. Based on our algorithm, service operators can actually perform a relocation task based with a reference to the truck capacity, routes, and the number of bicycles to be loaded and unloaded. We demonstrate the applicability of our algorithm in the real world through numerical experiments on the real bicycle data of a Japanese company.

Cyber-Physical Systems (CPSs) are attracting significant attention from a number of industries, including social infrastructure, manufacturing, retail, among others. We can easily gather big datasets of people and transportation movements by utilizing camera and sensor technologies, and create new industrial applications by optimizing and simulating social mobility in the cyberspace. In this paper, we develop the system which automatically performs a series of processes, including object detection, multiple object tracking, and mobility optimization. The mobility of humans and objects is one of the essential components in the real world. Therefore, our system can be widely applied to various application fields. Our major contributions to this paper are remarkable performance improvement of multiple object tracking and building the new mobility optimization engine. In the former, we improve the multiple object tracker using K-Shortest Paths (KSP), which achieves significant data reduction and acceleration by specifying and deleting unnecessary nodes. Numerical experiments show that our proposed tracker is over three times faster than the original KSP tracker while keeping the accuracy. We formulate the mobility optimization problem as the SATisfiability problem (SAT) and the Integer Programming problem (IP) in the latter. Numerical experiments demonstrate that the total transit time can be reduced from 30 s to 10 s. We discuss the characteristics of solutions obtained by the two formulations. We can finally select the appropriate optimization method according to the constraints of calculation time and accuracy for real applications.

The kernel-induced sampling theorem enables us to determine whether the sampling theorem holds or not for a reproducing kernel Hilbert space and a given set of sampling points. However, it is not easy to specifically calculate the necessary and sufficient condition formula except in some special cases, since it includes the inverse of an infinite dimensional Gramian matrix. In this paper, we discuss the kernel-induced sampling theorem restricted to a translation-invariant reproducing kernel Hilbert space with uniform sampling; and introduce an alternative necessary and sufficient condition formula, in which the inverse of the Gramian matrix is explicitly treated, by incorporating the theory of Laurent operators.

In this paper, we propose a practical approach to evacuation planning by utilizing network flow and deep learning algorithms. In recent years, large amounts of data are rapidly being stored in the cloud system, and effective data utilization for solving real-world problems is required more than ever. Hierarchical Data Analysis and Optimization System (HDAOS) enables us to select appropriate algorithms according to the degree of difficulty in solving problems and a given time for the decision-making process, and such selection helps address real-world problems. In the field of emergency evacuation planning, however, the Lexicographically Quickest Flow (LQF) algorithm has an extremely long computation time on a large-scale network, and is therefore not a practical solution. For Osaka city, which is the second-largest city in Japan, we must solve the maximum flow problems on a large-scale network with over 8.3M nodes and 32.8M arcs for obtaining an optimal plan. Consequently, we can feed back nothing to make an evacuation plan. To solve the problem, we utilize the optimal solution as training data of a deep Convolutional Neural Network (CNN). We train a CNN by using the results of the LQF algorithm in normal time, and in emergencies predict the evacuation completion time (ECT) immediately by the well-learned CNN. Our approach provides almost precise ECT, achieving an average regression error of about 2%. We provide several techniques for combining LQF with CNN and addressing numerous movements as CNN's input, which has rarely been considered in previous studies. Hodge decomposition also demonstrates that LQF is efficient from the standpoint of the total distance traveled by all evacuees, which reinforces the validity of the method of utilizing the LQF algorithm for deep learning.

Theoretical validity of empirical error minimization in multiple kernel regressors is discussed in this paper. Generalization error of a kernel machine is usually evaluated by the induced norm of the difference between an unknown true function and an estimated one in an appropriate reproducing kernel Hilbert space. It is well known that empirical error minimization also achieves the minimum generalization error in single kernel regressors. However, it is not clarified whether or not that is true for multiple kernel regressors. Moreover, possibility of constructing the minimizer of the generalization error by a given training date set is not also clarified. In this paper, we give negative conclusions for these problems through theoretical analyses on the generalization error of multiple kernel regressors and also give an example by popular Gaussian kernels.

In this paper, a sampling theorem for bandpass signals with uniformly spaced sampling points is discussed. We firstly show that a function space consisting of all functions with a specific bandpass property is a reproducing kernel Hilbert space and also give a closed-form of the corresponding reproducing kernel. Moreover, on the basis of the framework of the kernel-induced sampling theorem, we give a simple perfect reconstruction formula for the bandpass signals by uniformly spaced sampling points with the bandpass Nyquist rate, which is defined as twice the signal bandwidth, for the cases that the maximum frequency of the signals is identical to bandwidth multiplied by some positive integer.

A cycling wheel chair (CWC) is a pedal-driven wheelchair developed as a personal transportation device for patients with hemiplegia after cerebral stroke. The CWC enables the patient to travel much faster than an ordinary wheel chair and is effective for prevention of disuse syndrome in lower limbs. However, the patients often have impairment of cognitive function as well as motor function. It is dangerous for such patients to ride on the CWC on outdoor roads. To cope with this problem, a virtual reality system for the CWC named “Virtual CWC” was developed. The Virtual CWC can provide safe and space-saving training and test environment for the patients. In the present study, a new scenario of the Virtual CWC has been developed to test the patient's cognitive function with respect to navigation as well as driving skills. The normal subjects' “homing vectors” obtained after riding on the Virtual CWC in three kinds of routes were analyzed, and compared between a young group (26 normal subjects; age 23.9 ± 2.5) and an elderly group (14 normal subjects; age 69.9 ± 4.1). As a result, the effect of cooperation between pedaling and steering could be found in the angular error of homing vectors in the case of the elderly group.

Learning based on kernel machines is widely known as a powerful tool for various fields of information science including signal processing such as function estimation from finite sampling points. One of central topics of kernel machines is model selection, especially selection of a kernel or its parameters. In our previous works, we investigated the generalization error of a model space itself corresponding to a selected kernel in kernel regressors. In this paper, we discuss the generalization error in a model space corresponding to a selected kernel in kernel regressors; and prove that the variance of a learning result is reduced when we adopt a kernel corresponding to a larger reproducing kernel Hilbert space.

One of central topics of kernel machines in the field of machine learning is a model selection, especially a selection of a kernel or its parameters. In our previous work, we discussed a class of kernels forming a class of nested reproducing kernel Hilbert spaces with an invariant metric; and proved that the kernel corresponding to the smallest reproducing kernel Hilbert space, including an unknown true function, gives the optimal model. In this paper, we consider a class of kernels forming a class of nested reproducing kernel Hilbert spaces whose metrics are not always invariant and show that a similar result to the invariant case is not obtained by providing a counter example using a class of Gaussian kernels.

One of central topics of kernel machines in the field of machine learning is a model selection, especially a selection of a kernel or its parameters. In our previous work, we discussed a class of kernels whose corresponding reproducing kernel Hilbert spaces have an invariant metric and proved that the kernel corresponding to the smallest reproducing kernel Hilbert space, including an unknown true function, gives the optimal model. However, discussions for properties that make the metrics of reproducing kernel Hilbert spaces invariant are insufficient. In this paper, we show a necessary and sufficient condition that makes the metrics of reproducing kernel Hilbert spaces invariant.

A relationship between generalization error and training samples in kernel regressors is discussed in this paper. The generalization error can be decomposed into two components. One is a distance between an unknown true function and an adopted model space. The other is a distance between an estimated function and the orthogonal projection of the unknown true function onto the model space. In our previous work, we gave a framework to evaluate the first component. In this paper, we theoretically analyze the second one and show that a larger set of training samples usually causes a larger generalization error.

In this paper, we propose a new ICA-based BSS algorithm including estimation of sources' probability density functions (PDFs) to adapt the nonlinear activation function to various noise conditions. In the proposed method, closed-form second-order ICA is introduced as a computational-cost-efficient preprocessing to extract sources' PDFs, which is beneficial for real-time application. Compared with various type of conventional ICAs, e.g., fixed activation-function type and ML-based type, our proposed algorithm can give a faster and higher convergence. Based on the proposed source-adaptive ICA, we show a real-time noise reduction results under diffuse noise environment. Also we can demonstrate our recently developed hands-free robot spoken dialogue system via real-time ICA.

Noise suppression by linear filters for a time series is discussed. We propose a method for jointly estimating signal and noise correlation matrices by incorporating steering vectors of the noise or eigenvectors of the noise correlation matrix as well as steering vectors of the target signals. Our estimates bring us two significant advantages. One is reduction of computational cost in obtaining the Wiener filter since the Wiener post filter, which is combined to the minimum variance distortionless response filter (MVDRF), is no longer needed with the estimates of signal and noise correlation matrices. The other is an improvement of the performance of the MVDRF since we can construct the regularized version of it with an estimate of the noise correlation matrix.

In this paper, an optimum insertion/deletion point selection algorithm for fractional sample rate conversion (SRC) is proposed. The direct insertion/deletion technique achieves low complexity and low power consumption as compared to the other fractional SRC methods. Using a multiple set insertion/deletion technique is efficient for reduction of distortion caused by the insertion/deletion step. When the conversion factor is (N ±¿)/N, the number of possible patterns of insertion/deletion points and the number of combinations for multiple set inserters/deleters grow as ¿ increases. The proposed algorithm minimizes the distortion due to SRC by selecting the patterns and the combinations.

In this paper, first, we propose a computational-cost efficient blind source separation combining closed-form 2nd-order independent component analysis (ICA) and nonclosed-form higher-order ICA. The closed-form solution of the 2nd-order ICA has been recently presented by one of the authors. This finding motivates us to combine the closed-form 2nd-order ICA and higher-order ICA, where the preceding closed-form ICA produces a good initial value and the following higher-order ICA updates the separation filters from the advantageous status. Secondly, we utilize the proposed architecture to address an essential question that which type of statistics is more beneficial to ICA among non-stationarity and non-Gaussianity. This can be conducted owing to the attractive property that the closed-form ICA can provide a good estimate of the theoretical upper limitation of the separation performance among 2nd-order ICAs without suffering from poor-convergence problems. Experimental results reveal that the non-Gaussianity-based ICA can outperform the non-stationarity-based ICA.

An innovative application of adaptive thresholding is used for calcification regions detection in intravascular ultrasound images. A priori knowledge about the acoustic shadow that usually follows the calcification regions is used as discriminant of other bright regions of the image. Tests were carried out with 20 in vivo coronary artery images obtained from different patients. This proposed algorithm presented specificity of 88% and sensitivity of 84%. A ROC curve, whose AUC was equal to 0.87, was plotted for evaluation of the algorithm performance

The aim of "blind source separation" is to recover mutually independent unknown source signals from observations obtained through an unknown linear mixture system. Simultaneous diagonalization of correlation matrices (second-order statistics) of observations is one of the resolutions, when the unknown source signals are non-stationary. Although it is trivial that the true separation matrix simultaneously diagonalizes all the correlation matrices, it is not well investigated whether a simultaneous diagonalizer of the correlation matrices is always a separation matrix. In this paper, we give explicit solutions of simultaneous diagonalizers of the correlation matrices and we also clarify the condition that the solutions always achieve the blind source separation

In this paper, a new method of direction of arrival (D.O.A.) estimation with environmental noise, whose spatial correlation matrix is singular, is proposed. In D.O.A. estimation, identification of signal and noise subspaces plays a very important role. The identification process can be achieved by (generalized) eigenvalue decomposition of the spatial correlation matrix of observations (with respect to that of noise), if these spatial correlation matrices are non-singular. However, these mathematical tools cannot be applied to the problems in which the spatial correlation matrices are singular. The main idea of this work deeply depends on identification of proper and improper eigenvectors of the spatial correlation matrix of noise with respect to that of observations. The results of computer simulations are also presented to verify the efficacy of the proposed method.

In this paper, firstly, processes of classical inference are reviewed as granular reasoning from a point of view of reconstructing Kripke-style models with granularity. The essential point of the reconstruction is that some possible worlds are amalgamated to generate granules of worlds and vice versa. It is also called zoom reasoning systems. Then, the idea is applied for fuzzy reasoning processes by considering fuzzily granularized possible worlds. There linguistic truth values with linguistic hedges can be naturally introduced.

INTAP (Interoperability Technology Association for Information Processing, Japan), CBOP (Consortium for Business Object Promotion), MEDIS-DC (Medical Information System Development Center) and JAHIS (Japanese Association of Healthcare Information Systems Industry) have completed the initial phase of the Japanese Healthcare Domain Reference Enterprise Model project. The goal of the project is to define a reference healthcare domain model using an ISO/IEC and ITU-T standardized RM-ODP framework. The initial phase focused on modeling the enterprise aspect of hospital information systems in Japan. RM-ODP Enterprise Viewpoint Language and UML were used as the key languages. The paper presents the results of the initial phase of the project, and discusses issues faced during model development.

To diagnose the lung cancer as to determine if it has malignant or benign nature, it is important to understand the spatial relationship among the abnormal nodule and other pulmonary organs. But the lung field has very complicated structure, so it is difficult to understand the connectivity of the pulmonary organs using thin-section CT images. This method consists of two parts. The first is the classification of the pulmonary structure based on the anatomical information. The second is the quantitative analysis that is then applicable to differential diagnosis, such as differentiation of malignant or benign abnormal tissue.

In multivariate statistical methods, it is important to identify influential observations for a reasonable interpretation of the data structure. In this paper, we propose a method for identifying influential data in the fuzzy C-means (FCM) algorithm. To investigate such data, we consider a perturbation of the data points and evaluate the effect of a perturbation. As a perturbation, we consider two cases: one is the case in which the direction of a perturbation is specified and the other is the case in which the direction of a perturbation is not specified. By computing the change in the clustering result of FCM when given data points are slightly perturbed, we can look for data points that greatly affect the result. Also, we confirm an efficacy of the proposed method by numerical examples.