As digitization advances, the number of cyberattacks on enterprises increases. To counteract, cyber security culture (CSC) provides a holistic approach, shaping people’s values and beliefs in its process. The objective of this research is to determine factors that lead to the successful establishment and maintenance of a CSC, by quantitatively verifying data collected in a previous qualitative study. The established conceptual model includes four main determinants and eight hypotheses. To further evaluate these factors a survey with 55 participants from industry was conducted. The collected data were statistically analyzed with a structural equation model (SEM). The analysis confirmed two factors from the research model: ‘Awareness & Knowledge’ and ‘Continuous Development’. The results emphasize the importance for enterprises to prioritize and cultivate these key factors for the successful establishment of CSC. Continuous education, embedded in a culture characterized by learning and adaptability, is necessary to defend against potential cyber threats and vulnerabilities.

Stakeholders want quality in use requirements to be satisfied as much as possible to make their life or business activities better than ever. However, some of them should be abandoned if they are expensive. In this paper, we propose a method for analyzing the cost ratio of quality in use for a life or business system during early requirements analysis. This method enables stakeholders to sort out initially required quality in use requirements. The cost depends on the number and the complexity of operations in a system, their data and users. In this method, KAOS model is thus used because it contains goals, operations, users and data. KAOS model also enables us to represent the roles of both human and artificial systems embedded in a business system. When a part of a system should achieve some functional and quality goal, the goal should be transformed into some operations and their data respectively. The relative cost of some function or quality is thus quantified on the basis of operations, data and associations among them. Through the case study, the results of analysis seemed to meet our intuition. In addition, we found the relative cost of quality in use is not so small. This finding makes stakeholders to abandon some of quality in use in this case if they do not have enough budget and/or time.

The development of service systems leveraging Machine Learning (ML) has been actively pursued in recent years. In the development of general service systems, knowledge for effectively managing projects has been systematized. Similarly, in ML service system development projects (ML projects), knowledge such as best practices and patterns is being established. Among these, anti-patterns documenting situations that cause issues in ML projects and their corresponding solutions, are also being organized. However, knowledge like patterns and anti-patterns does not clearly define who should recognize their necessity or apply the solutions. As a result, in ML projects, where collaboration among stakeholders is critical, the utilization of such knowledge often depends on the experience and skills of the individuals involved. In this study, we propose a diffusion model for anti-patterns in ML projects and a method for representing the model. Furthermore, by representing actual anti-patterns as a diffusion model using the proposed method, we verify the effectiveness of the represented model.

This paper addresses the issue of the significant labor required to test interview dialogue systems. While interview dialogue systems are expected to be useful in various scenarios, like other dialogue systems, testing them with human users requires significant effort and cost. Therefore, testing with user simulators can be beneficial. Since most conventional user simulators have been primarily designed for training task-oriented dialogue systems, little attention has been paid to the personas of the simulated users. During development, testing interview dialogue systems requires simulating a wide range of user behaviors, but manually creating a large number of personas is labor-intensive. We propose a method that automatically generates personas for user simulators using a large language model. Furthermore, by assigning personality traits related to communication styles when generating personas, we aim to increase the diversity of communication styles in the user simulator. Experimental results show that the proposed method enables the user simulator to generate utterances with greater variation.

In the development of software systems, multiple members create models of requirement specifications under the guidance of experienced professionals. In such cases, the time taken to create models and the quality of the resulting models depends on the members’ system development experience and the characteristics of the specifications. Consequently, the cost of modeling and the quality of deliverables can make development more challenging. In this study, we identified the elements necessary to develop models of uniform quality during the process of converting specifications into the requirements analysis model defined in this study. We developed a modeling method that consists of information that should be agreed upon in advance between experienced professionals and members, and patterns of specification description analysis.

Processes of some business or life activities are sometimes changed due to some reasons, such as the emergence of new technologies and the change of the human behavior caused by a seasonal event, e.g. Christmas. Such changes are called concept drift. Detecting concept drift is useful for many reasons. For example, we can update existing out-of-date business rules. Many methods of concept drift detection in processes have been already proposed. However, most of them are a little bit complex because sliding widows should be defined on a log of business process during its analysis. We thus propose a light-weight method for its detection by using heuristic miner, which is a famous algorithm for process discovery. In our method, we simple observe the discovered model to identify the infrequent actions and transitions between actions. Our method helps us to identify several types of concept drift although some types cannot be detected. We discuss how to overcome current limitations of our method.

In this study, we consider projects for developing service systems using machine learning (ML) techniques. These projects involve collaboration between various stakeholders. Several types of models representing system architectures are introduced so that stakeholders can develop a common understanding of these projects. In addition, metamodels are constructed by combining ML service systems models to provide project practitioners with a holistic view of the projects. In certain cases, these metamodels need to be extended to incorporate other business models for the business–IT alignment used in enterprises. For such situations, an enterprise architecture-based metamodel and method for managing the metamodel are proposed in this study, which provide a holistic view of business–IT alignment for ML projects. We confirm the effectiveness of the proposed metamodel and management method through real examples.

Abstract Development of machine learning (ML) systems differs from traditional approaches. The probabilistic nature of ML leads to a more experimentative development approach, which often results in a disparity between the quality of ML models with other aspects such as business, safety, and the overall system architecture. Herein the Multi-view Modeling Framework for ML Systems (M3S) is proposed as a solution to this problem. M3S provides an analysis framework that integrates different views. It is supported by an integrated metamodel to ensure the connection and consistency between different models. To facilitate the experimentative nature of ML training, M3S provides an integrated platform between the modeling environment and the ML training pipeline. M3S is validated through a case study and a controlled experiment. M3S shows promise, but future research needs to confirm its generality.

Machine learning system analysis requires different approaches for each different task and domain. Selecting a proper set of analytic models can be challenging for a specific problem. This paper discusses the extensibility of the Multi-View Modeling Framework for ML Systems approach using process mapping and extensible metamodel. We conducted a case study to evaluate the feasibility of such extensibility by extending the approach to facilitate an activity-driven analysis for an optical character recognition system. Based on the result of the case study, we found that Multi-View Modeling Framework for ML Systems is likely to be extensible.

Modern information communications use cryptography to keep the contents of communications confidential. RSA (Rivest–Shamir–Adleman) cryptography and elliptic curve cryptography, which are public-key cryptosystems, are widely used cryptographic schemes. However, it is known that these cryptographic schemes can be deciphered in a very short time by Shor’s algorithm when a quantum computer is put into practical use. Therefore, several methods have been proposed for quantum computer-resistant cryptosystems that cannot be cracked even by a quantum computer. A simple implementation of LWE-based lattice cryptography based on the LWE (Learning With Errors) problem requires a key length of O(n2) to ensure the same level of security as existing public-key cryptography schemes such as RSA and elliptic curve cryptography. In this paper, we attacked the Ring-LWE (RLWE) scheme, which can be implemented with a short key length, with a modified LLL (Lenstra-Lenstra-Lovasz) basis reduction algorithm and investigated the trend in the degree of field extension required to generate a secure and small key. Results showed that the lattice-based cryptography may be strengthened by employing Cullen or Mersenne prime numbers as the degree of field extension.

Many different activities are performed simultaneously in the real world, and one of them contains actions, that can be utilized in another activity. If such actions are actually utilized, the activity utilizing them become more efficiently than ever. We call the activity providing such actions a contributable activity, and the one utilizing the actions a contributed activity. Our research goal is to find such contribution relationships between activities. To achieve the goal, we used a conformance checking technique in the field of process mining research. In the process mining research, logs of actions and control flow models are used, and the logs and the models are usually represented by the verb attribute in an action such as “submit”, “decide” and so on. However, we could not find some contribution relationships by using the logs and models of the verb attribute. We thus examine the usage of non-verb attributes such as a place or a tool in an action for our goal. Through a case study about elderly people care, we find non-verb attributes have some potential to achieve our goal more comprehensively than ever.

An webserver should be maintained by its experienced administrator under the firm knowledge and conviction on vulnerability measures. General administrators of novice learn the vulnerabilities with public literatures to get knowledge while unlearnt people sometimes try by the Internet articles for its affordability. However, the configuration of vulnerability countermeasures can be represented structurally. Therefore, even novices should learn more efficiently by using a learning support system originally developed those structures with the elements of vulnerability countermeasures than by learning with the literature. The system asks questions based on the learner's understanding. In the experiment, learners were divided into two groups of studying by the literature and studying by the system. Through the experiment consisted of a pre-test, learning, and post-test, the average score indicates the advantage of the system while the literature-based study shows partially advantageous. The explanatory text and feedback for items that the learners could not answer correctly need to be revised.

In this study, we consider projects for developing service systems using machine learning (ML) techniques. As ML techniques have been introduced in various domains, there is reusable knowledge on ML projects that can be employed for conducting such projects without facing major failures. The usage of such knowledge during a project has not yet been clearly described in the form of reusable knowledge such as best practices or patterns. Thus, in this study, we propose a method for collecting the ominous signs in ML projects as “bad smells” and incorporating them as a part of such reusable knowledge. We confirmed the effectiveness of the proposed method through an evaluation.

Machine learning-based system requires specific attention towards their safety characteristics while considering the higher-level requirements. This study describes our approach for analyzing machine learning safety requirements top-down from higher-level business requirements, functional requirements, and risks to be mitigated. Our approach utilizes six different modeling techniques: AI Project Canvas, Machine Learning Canvas, KAOS Goal Modeling, UML Components Diagram, STAMP/STPA, and Safety Case Analysis. As a case study, we also demonstrated our approach for lane and other vehicle detection functions of self-driving cars.

We propose a method to comprehensively find a pair of business or life activities, which can mutually contribute to each other. We assume that the activities with synchronized processes can mutually contribute to each other. A process model of an activity is thus compared to a log of another activity for measuring whether the activities can synchronize their processes to a certain extent using a conformance checking technique. The threshold of the extent is defined on the basis of a randomly generated log containing the same events as those in the compared log. We tentatively evaluated the method through a case study, and confirmed that the method seemed to be valid.

In this study, we consider projects in which systems are developed using machine learning (ML) techniques. An ML project canvas has been proposed to represent the project so that stakeholders can have a common understanding of the project. In many cases, this canvas must be constructed by business division practitioners without sufficient support from data scientists and the quality of the canvas model is dependent on the skills or experience of the practitioner. Therefore, we propose a method for constructing a project-specific project canvas model using the business–AI alignment model, and confirm the effectiveness of the method through the analysis of ML project practices.

In this study, we considered AI service systems that use artificial intelligence technologies for business functions. Presently, AI technologies are being introduced to support human activities in various business domains. Consequently, it is believed that some business tasks performed by humans will be replaced by AI technologies. In this study, we focused on such tasks and proposed an assessment method for identifying them accordingly. In this method, we defined a model that represents the business tasks executed by practitioners. We also identified the features required for each element in the task so that they can be incorporated when applying AI technologies to the business task. In addition, we introduced a model that divides the business tasks, which will be performed by practitioners for a while, into several groups. Accordingly, we can identify the types of human capabilities that will still be required in a business task in the near future. By analyzing two sample businesses, we confirmed the efficacy of the proposed method.

Machine-learning (ML) software engineering design patterns encapsulate reusable solutions to commonly occurring problems within the given contexts of ML systems and software design. These ML patterns should help develop and maintain ML systems and software from the design perspective. However, to the best of our knowledge, there is no study on the practitioners' insights on the use of ML patterns for design of their ML systems and software. Herein we report the preliminary results of a literature review and a questionnaire-based survey on ML system developers' state-of-practices with concrete ML patterns.

In this study, we consider the construction of a model for representing an artificial intelligence (AI) service system project. When developing a system using AI technologies to support a business task in a company, all project members from both business and IT divisions must have common understandings on the project before starting it. For this purpose, a business–IT alignment model for AI service systems is proposed as a business–AI alignment model. However, we need to substantiate this business–AI alignment model for each project, because it is a generic model. To address this problem, we propose a method for constructing the business– AI alignment model and apply it to a real project for developing an AI service system in a case study, and confirm that we can construct the project-specific business–AI alignment model–without support of data scientists.

In this work, we propose a model of a project to develop an artificial intelligence (AI) service system used in an office environment. Our model is based on enterprise architecture (EA) approach and consists of business layer elements, application layer elements, and motivation extensions, so that project participants from both business and IT divisions can have the same understanding of the project. By applying the proposed model to the project analysis results, we show that we can derive actionable insights for project risk management.

When customers are interested in a service or intend to buy it, they sometimes have questions on that service. In this study, we considered an inquiry system in which customers ask questions on a specific service and obtain correct information on the service. For such an inquiry system, a question-answering (Q&A) technology is needed. Many programming modules for such a technology have been developed and can be easily used for system development. In many Q&A technologies, machine-learning techniques are involved, and we need to prepare training data consisting of pairs of an answer and assumed questions. For training-data preparation, an answer set for a service should be defined as the first step and the answer set should cover all the information on the service that customers may ask about. By using a customer-behavior model and introducing a service-function model, we propose a method of effectively collecting knowledge information for an answer set on a service. Through a case study, we show that we can collect exhaustive knowledge information for an answer set with our method compared to the case in which domain experts collect knowledge information in their own way. For an actual project, we also considered an actual inquiry-system-development project, with training data obtained with the proposed method, and showed that the system covers almost all the information on the service that customers may ask after a user test.

This paper describes a novel framework for creating a parser to process and analyze texts written in a “partially structured” natural language. In many projects, the contents of document artifacts tend to be described as a mixture of formal parts (i.e. the text constructs follow specific conventions) and parts written in arbitrary free text. Formal parsers, typically defined and used to process a description with rigidly defined syntax such as program source code are very precise and efficient in processing the formal part, while parsers developed for natural language processing (NLP) are good at robustly interpreting the free-text part. Therefore, combining these parsers with different characteristics can allow for more flexible and practical processing of various project documents. Unfortunately, conventional approaches to constructing a parser from multiple small parsers were studied extensively only for formal language parsers and are not directly applicable to NLP parsers due to the differences in the way the input text is extracted and evaluated. We propose a method to configure and generate a combined parser by extending an approach based on parser combinator, the operators for composing multiple formal parsers, to support both NLP and formal parsers. The resulting text parser is based on Parsing Expression Grammars, and it benefits from the strength of both parser types. We demonstrate an application of such combined parser in practical situations and show that the proposed approach can efficiently construct a parser for analyzing project-specific industrial specification documents.

As the development of embedded systems grows in scale, it is becoming more important for engineers to share development documents such as requirements, design specifications and testing specifications, and to accurately circulate and understand the information necessary for development. Also, many defects that can be originated in the surface expression of the documents are reported through investigations of causes of defects in embedded systems development, In this paper, we highlight improper surface expressions of Japanese documents, and define quality criteria and critiquing rules to detect problems such as ambiguous expressions or omissions of information. We also carry out visual quality inspections and evaluate detection performance, correlations and working time. Then, we verify the validity of the critiquing rules we have defined and apply them to the document critiquing tool to evaluate the quality of the actual documents used in the development of embedded systems. And we quantify the quality of these documents by automatically detecting improper expression. We also apply supplemental critiquing rules to the document critiquing tool for use by non-native speakers of Japanese, and verify its efficacy at improving the quality of Japanese documents created by foreigners.

We describe a new approach to modeling and analyzing software development documents that are typically written using conventional office applications. Our approach brings automation to content extraction, quality checking and measurement of massive document artifacts that tend to be handled by labor-intensive manual work in industry today. Rather than seeking an approach based on creation or rewriting of contents using more rigid, machine-friendly representations such as standardized formal models and restricted languages, we provide a method to deal with the diversity of document artifacts by making use of project-specific formalism that exists in target documents. We demonstrate that such project-specific formalism often tends to "naturally" exist at syntactic levels, and it is possible to define a "document model", a logical data representation gained by transformation rule from the physical, syntactic structure to the logical, semantic structure. With this transformation, various quality checking rules for completeness, consistency, traceability, etc., are realized by evaluating constraints for data items in the logical structure, and measurement of these quality aspects is automated. We developed a tool to allow a user to easily define document models and checking rules, and provide the insights on transformations when defining document models for various industry specification documents written in word processor files, spreadsheets and presentations. We also demonstrate the use of natural language processing can improve document modeling and quality checking by compensating for a weakness of formalism and applying analysis to specific parts of the target documents.

Natural language is the preferred form for writing use cases. While a few linguistic techniques exist that extract or validate structured information from unstructured natural language use case, they often cannot be extended beyond their primary language. Extending linguistic analysis and automated validation capabilities across multiple languages is necessary not only for widespread industrial adoption but it helps in analyzing a collection of multilingual use cases (quite frequent in multi-national projects) that need to be aggregated. We have published a UIMA (Unstructured Information Management Architecture) based linguistic engine for analyzing English use cases. In this paper, we report on extension of our linguistic technique to Japanese and effect of such an extension on the automated requirement validation suite.