Reviewerly/exHarmony huggingface dataset:
Transparent Complexity by Goals

Making explicit the teleological structure of e-government can contribute to its understanding and reduce its complexity. E-government can be viewed from distinct perspectives—those of authorities and citizens, and also from conception and construction. This view of administrative and legal informatics requires paradigmatic changes in the development of e-government. We think that currently dominant normative thinking is not enough to solve specific problems of e-government. Here, a new concept of legal teleology is required. We propose to supplement norms, and even structural parts of a whole legal system, with teleological relations. This will form a separate structural layer of legal-knowledge representation and metadata of legal documents. Proposed notation A te→ B contains three elements: a basic element A, a target element B, and a teleological relation te→.
Legal Taboos

We propose to formalise a taboo as a prohibition on speaking (in general, on informing). Three levels of norms are distinguished. First are basic prohibitions, Forbidden X. These are norms which prohibit basic actions, Norm(¬X). Second-level norms comprise primary taboos which prohibit information about facts or fakes, Norm(¬Inf(X)), but permit them to happen. Third-level norms comprise meta-taboos, which prohibit information that a primary taboo exists, Norm(¬Inf(Norm(¬Inf(X)))). The message also is that a taboo on the essential causes A of an effect E can be officially camouflaged with a fake relationship between certain facts B and E.
Content Meaning and Institutional Meaning of a Legal Act

We distinguish two entities: an act and its legal meaning. We treat the legal meaning (henceforth institutional meaning) as an abstract objective entity. In this chapter, this entity is also viewed from an information systems perspective. Representing the institutional meaning in computers (legal machines) is a research question for future. We consider operations that strengthen or lessen the institutional meaning. Our idea is to link institutional meaning (which appears in Ought) with its representation (which appears inspiepr146 Is). Suppose a process is performed to modify the meaning of a legal act (including its content and its institutional meaning). Our idea is to link the process with events (institutional facts) which lead to this modification. Thus the modification in Ought is related with the modification of representation in Is. In this way we relate the modification in Ought with events in Is but do not reduce Ought to Is.
Extended Legal Thesaurus: Legal Terms as a Modally Indifferent Substrate

We hold that the modes of obligation, in the same way as legal terms, constitute the subject matter of a legal thesaurus. Moreover, we propose to consider three more relations. These are three types of weak relations: dialectical relations, context relations and metaphorical relations. They augment the five types of strong logical relations of synonymy, semi-synonymy, antonymy, hypernymy/hyponymy and thematic relations. (A hyponym is a word or phrase whose semantic field is included within that of another word, its hypernym. For example, there is the hyponymic relationship between red and colour (see Wikipedia, https://en.wikipedia.org/wiki/Hyponymy_and_hypernymy ).) We begin with combinations of ought modes, which result in obligation, permission, liberty and vetum. Then we explore the types of norms by combining structural parts such as condition, ought, which includes subject, modus, action, and object, and also purpose (telos).
Designing an Augmented Reality Authoring Tool to Support Complex Tasks. A Design Science Study Using Cognitive Load Theory

Despite the potential of augmented reality (AR) to support and guide complex industrial tasks, the technology is still not broadly applied. One possible reason for this is that the creation of AR content is highly complex and requires programming skills and deep spatial knowledge. AR authoring tools can help address this complexity by enabling non-developers to create self-sufficient AR content. Therefore, this paper proposes a theory-driven design for AR authoring tools that allows non-developers to create self-sufficient AR-based instructions to support complex tasks. Based on ten interviews with experts working with AR authoring tools and a following focus group with eight participants, we propose three design principles for future AR authoring tools in the engineering context. These design principles are instantiated in two prototypes of different richness and evaluated in an experiment with 23 students. Our study shows that the cognitive load is slightly increased when using the extensive AR authoring tool, but it also shows that significantly better results can be achieved with the extensive AR authoring tool. We contribute by providing design principles for AR authoring tools for creating AR-based instructions, which extend the existing AR authoring research in the industrial context.
Inquiry Frameworks for Research Question Scoping in DSR: A Realization for ChatGPT

Research Question (RQ) Scoping refers to defining and refining a research question before conducting research. This step is crucial for ensuring the relevance and focus of the study, particularly in Design Science Research (DSR), where problems and solutions develop gradually. Literature reviews are a traditional method for comprehending the problem and determining key questions; however, they can be time-consuming and not worth it at the onset when lashing out in the dark. NLP chatbots such as ChatGPT can serve as a cost-effective alternative to clearing the way due to their capability to interact with users in a natural language-based manner and provide intuitive responses. The requirements for RQ Scoping extend beyond conversational support to include a framework for a sustained and iterative scoping process. An inquiry framework is necessary to guide and assist students and supervisors in fully harnessing the potential of NLP chatbots. This work incorporates ChatGPT into an inquiry framework for RQ Scoping, with mind maps as the visualization and the 5 Why technique as the inquiry strategy. Contributions include Design Principles, an IT artifact, and a Technology Acceptance Model evaluation (n = 9). Regarding perceived usefulness, the results indicate agreement on the intervention’s effectiveness in maintaining focus. However, there is less enthusiasm for mind maps as a communication tool. Perceived ease of use was also positive but revealed concerns about the query templates used by the framework.
What I Do not Know Will Hurt Me—Mental Models and Risk Perception

This chapter deals with the crucial role, mental models play, in accidents and disasters. A mental model can be regarded as constructs or abstractions in the mind. These can be intuitive, based on beliefs and social consensus, or based on science. Experts have more refined mental models and consistent training plays a vital role in evolution of the mental models and is discussed in this chapter. It is argued that the evolution of risk perception is dependent on the continuous refinement of mental models.
Designing Pedagogical Conversational Agents for Achieving Common Ground

As educational organizations face difficulties in providing personalized learning material or individual learning support., pedagogical conversational agents (PCAs) promise individualized learning for students. However, the problem of conversational breakdowns of PCAs and consequently poor learning outcomes still exist. Hence, effective and grounded communication between learners and PCAs is fundamental to improving learning processes and outcomes. As understanding each other and the conversational grounding is crucial for conversations between humans and PCAs, we propose common ground theory as a foundation for designing a PCA. Conducting a design science research project, we propose theory-motivated design principles and instantiate them in a PCA. We evaluate the utility of the artifact with an experimental study in higher education to inform the subsequent design iterations. We contribute design knowledge on conversational agents in learning settings, enabling researchers and practitioners to develop PCAs based on common ground research in education and providing avenues for future research. Thereby, we can secure further understanding of learning processes based on grounding communication.
Design of an Augmented Reality App for Primary School Students Which Visualizes Length Units to Promote the Conversion of Units

Insight and understanding of the structure of units can be considered one of mathematics’ most important learning areas, as it is needed in everyday life. It is essential in everyday tasks, such as trading goods, which requires dealing with monetary values and, e.g., lengths, weights, volumes, or area units. In addition, students in higher education after primary school show problems with understanding units by confusing units, especially in advanced STEM subjects, e.g., physics. The paper shows how Augmented Reality (AR) technology can be used to gain insight into understanding units by visualizing units of length using an AR app. The design of the AR app will be presented, which was developed after a theoretical grounding and practical testing with an existing AR measuring tool on the app market. Design Science Research (DSR) will be used to develop a suitable content learning environment using the AR app. The learning environment will then be tested with students again and disseminated in international workshops for teachers.
The Crypto Multiplier

The exchange rates of cryptocurrencies are highly volatile. This paper provides insight into the source of this volatility by developing the concept of a “crypto multiplier,” which measures the equilibrium response of a cryptocurrency’s market capitalization to aggregate inflows and outflows of investors’ funds. The crypto multiplier takes high values when a large share of a cryptocurrency’s coins is held as an investment rather than being used as a means of payment. Empirical evidence shows that the number of coins held for the purpose of making payments is rather small for major cryptocurrencies suggesting large crypto multipliers. The analysis explains why announcements by large investors, celebrity endorsements or financial crises can result in substantial price movements.
Multi-objective Searching on Multimodal Transportation Networks*

Urban transportation involves multiple modes of public transportation such as trains, streetcars, and buses. In Tokyo, timetables are often organized to take into account the connections between different modes of transportation, and transportation between points is assumed to be a multi-modal transportation network. In general, route search services allow users to set multiple objectives, such as the minimum number of transfers and the lowest fare, in addition to the fastest arrival time, and users select routes considering these multiple objective functions. In this study, we propose Trans-Net, which models multimodal transportation networks, and a solution method for multi-objective optimization problems on Trans-Net.
Towards Strong Psychological Equivalence in Nartural Language Understanding

Natural language understanding of strong psychological equivalence was attempted to approach based on a mental image model and some interim progress was reported. The application system is designed to understand User's utterances in text and respond in text or animation. Its responses in text were compared with those of human participants in a psychological experiment and showed globally good agreement with them and locally some interesting disagreement that was also the case among them.
Examination of the relation between affective content of images and gaze behavior

Saliency maps show the likelihood of each pixel on the image being looked at. They are often computed considering a neutral human subject, who is considered to be simply observing the image without any particular motivation (e.g, searching, registering etc.) or feelings (e.g, excited, fearful etc.). In this study, we focus on the emotional aspect and investigate whether there is any need to adjust these saliency maps to account for the emotions that they may induce in the viewers. To that end, we choose a set of images from an emotional image data set and display them to human subjects. We register their eye gaze and compute how well empirical gaze data matches the saliency maps. We quantify this based on two distribution-based (AUC, IG) and two location-based saliency metrics (CC and SIM). We see that although there are some parallels in some of these metrics, the evidence is not strong enough to claim a significant contribution due to affective content.
Data Processing Tools for Graph Data Modelling Big Data Analytics

Any Big Data scenario eventually reaches scalability concerns for several factors, often storage or computing power related. Modern solutions have been proven to be effective in multiple domains and have automated many aspects of the Big Data pipeline. In this paper, we aim to present a solution for deploying event-based automated data processing tools for low code environments that aim to minimize the need for user input and can effectively handle common data processing jobs, as an alternative to distributed solutions which require language specific libraries and code. Our architecture uses a combination of a network exposed service with a cluster of “Data Workers” that handle data processing jobs effectively without requiring manual input from the user. This system proves to be effective at handling most data processing scenarios and allows for easy expandability by following simple patterns when declaring any additional jobs.
Investigation of the relation between task engagement and eye gaze

In human-computer interaction, it is important that the users sustain their engagement during cognitively demanding tasks. To that end, the system needs first to estimate the level of engagement and detect the declines and then to trigger a support mechanism to recover engagement. In that respect, many studies in the literature propose estimating engagement from facial images. Although these studies have proved that facial landmarks, especially those relating to the eyes (ocular landmarks), serve useful in the estimation of task engagement, they are subject to criticism, since it may make the user uncomfortable to be constantly watched. In addition, the video data needs to be handled very carefully due to privacy issues. In that respect, this study investigates whether it is possible to estimate the level of engagement from anonymous data, specifically from eye gaze. This sort of data is considered to be closely related to the changes in ocular landmark locations and therefore has the potential to involve similar qualities to those of ocular landmarks. In addition, since it is not possible to identify the individuals from gaze fixations, it is not privacy sensitive. Moreover, it is also expected to help relieve users' discomfort due to constant observation and also prevent modifications in their behavior due to their awareness of being observed.
Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry

Barriers to the delivery of journalistic content to suitable media outlets present difficulties to both journalists and publishing houses. These may take the form of barriers to the identification of key individuals to whom the content is relevant, and who may be influential within their domain topic. To address this problem, we present a methodological approach to automated recommender systems for journalists or content-writers, which enables the identification of social media accounts, based upon information retrieval techniques. Firstly, this work investigates three concepts, which to our knowledge have yet to be applied to such a domain; firstly, how user-defined descriptions, commonly associated with user profiles on the Twitter platform, may impact upon the perceived accuracy of recommendations. Secondly, the application of a full-text query facility to accept journalistic-content queries. Finally, an investigation into time-decay, as part of the recommendation-ranking methodology. This results in a proposed novel hybrid weighting methodology, which provides advantages to baseline information retrieval techniques, and demonstrates the influence of temporal decay ranking upon model performance. As part of this work, a vectorised tweet dataset is provided, entailing 390,750 unique tweets, for use in wider research. Finally, we demonstrate an implementation in a real-world commercial setting, and provide an anonymised data set to assist in future research into this domain.
Improved Topic-bound Caption Generation with VGG-19, Batch Normalization, and Subword-based Tokenization

This paper presents a performance comparison of Topic-bound caption generation regarding object recognition and tokenizer. Current state-of-the-art caption generation systems accomplish a high level of fluency and rich expression of a caption, but those systems do not generate multiple captions from an image depending on the objects in the image. We previously developed a topic-bound caption generation with an object in an image appropriately focused. However, the topic-bound caption generation system generates less appropriate captions than state-of-the-art approaches. Additionally, generated captions tend to include <UNK> tokens, which denotes unknown words, because of an out-ot-vocabulary problem. To improve these drawbcks, we propose three improvements: the use of VGG19 instead of VGG16 for image encoding, batch normalization, and subword-based tokenization to reduce <UNK> tokens. The evaluation experiment shows that the employment of these three techniques improved the quality of output captions
Developing a web application for RBSC-based solution of the subset selection problem

In this article, we introduce an application, which implements the RBSC-SubGen algorithm on a web platform in an easy-to-use manner. Originally, Furuya et al. proposed this algorithm and demonstrated it on an sample scenario, where a pair of vocabulary decks are constructed with a desired difficulty relation out of a large corpus. In addition to such applications, RBSC-SubGen can be used in a broad range of applications. For instance, studies which require the recruitment of a representative set of human subjects (e.g. drug testing, consumer surveys) may benefit from this method in sampling from the population. However, the deployment of the algorithm in non-technical fields such as medicine or social science may be difficult, since the publicly available algorithm implementation target users skilled in software development. In that respect, with the proposed web application the accessibility and disponibility of the algorithm by users from non-technical fields are expected to be facilitated considerably.
Evolutionary Acquisition of CNN Architecture by Thermodynamical Genetic Algorithm

Convolutional Neural Network (CNN) is a method that has been used for many years in the field of computer vision. In recent years, CNN has become huge and complex in proportion to the performance of computers and the difficulty of problems. As a result, the expertise and time required to build a better CNN architecture have increased, making it difficult to easily find a good CNN by hand or design for each problem. Therefore, it is important to optimize the CNN architecture automatically. On the other hand, the Genetic Algorithm (GA) is known as a method to optimize various problems based on the evolutionary process efficiently, and the evolutionary acquisition approach to CNN architecture using GA has attracted much attention. In this study, we propose the Thermodynamical Genetic Algorithmic Convolutional Neural Networks (tdgaCNN) based on two methods: gaCNN, one of the architecture search methods for CNN by GA, and Thermodynamical Genetic Algorithm (TDGA), one of the methods for GA. This method achieves better CNN architecture acquisition by emphasizing the importance of maintaining population diversity during the GA search phase. tdgaCNN achieved a prediction accuracy of up to 94.64% in experiments on the Fashion MNIST dataset. 12 existing algorithms' predictions compared to the accuracy, tdgaCNN outperformed 9 other methods, including other CNN architecture search methods.
Using Framework of Software Lifecycle Process in Preparing and Improving Remote Class Materials

Integrations of information processing technology into education and expectations for DX in education fields are increasing. Under these circumstances, the importance of not only the ability to utilize software-intensive systems in education but also to develop and improve them is also increasing. In order to effectively integrate the progress of educational technology and continuously promote DX of educational activities, knowledges and skills of software engineering play an important role as they are necessary to effectively develop, maintain, and operate software-intensive education systems as conventional ones. Especially, we focus on the knowledges and skills related to software process and process improvement. In the same way as software systems have life cycle processes, educational software systems also have life cycle processes. In this paper, we discuss the effectiveness of introducing the concept of software lifecycle process in preparing remote class materials with software-intensive education systems and improving their quality, cost, and delivery. We also discuss the effectiveness of knowledges and skills related to software process improvement in addition to establishment based on PSP, Personal Software Process for software engineers.
Speed of Concept Drift

We present a brute-force approach to analyze con-cept drift behind time sequence data. This approach, named SELECT, searches for the optimal length of training data to minimize error metrics. In other words, SELECT searches for the start point of a new concept from the input sequence. Unlike many related works, SELECT does not require a pre-specified error threshold to detect drift. This paper analyzes the effect of concept drift speed on the performance of SELECT and previous representative methods. The experimental results show that SELECT can improve the model performance. In addition, SELECT can detect concept drift earlier than a typical method that uses a pre-specified error threshold. These results show the effectiveness of the brute-force approach to analyzing concept drift.
Automatic Astrophotography Editing Method Using Deep Learning

Constellation detection in astrophotography is a difficult task because the stars appear very different depending on the photographer and the equipment, and because there are many similar alignments of stars. Although there are some previous studies on constellation detection, they either require a long time to totally match the bright stars in the astrophotography with the major stars that make up each constellation, or they match constellations that are known in advance in the astrophotograph, which is not very practical. Therefore, we propose a method using deep learning that can detect constellations more efficiently than total matching, even when the constellations in the astrophotogra-phy are not known in advance. Specifically, the object detection network that has been trained on the characteristics of each constellation by original data sets detects the rough location of each constellation, and from there, fine point matching is used to accurately detect the location of the constellation. As a result, the proposed method can detect constellations in the test images with 80 percent accuracy, and the average detection time was approximately 1 seconds in CPU time. This confirms that the proposed method is accurate and efficient. In addition, based on this method, we aim to realize automatic editing for astrophotography, such as complementing stars that are not in the astrophotography and adding color information to stars.