Reviewerly/exHarmony huggingface dataset:
Local Communities and Their Visitors: An Interaction Design Approach

Digital age has changed the way we interact with space, allowing us to explore places all over the world without leaving home and to communicate with people instantly, regardless of distance. However, a lack of physical contact or experience of place can significantly contribute to the loss of local identity within a community and the absence of visitors. Interaction design can act significantly to promote localities, by enhancing interactions between human beings and environments (digital and non-digital). This paper aims to present opportunities for interaction design intervene in the context of a locality, by discussing how different design methods can be applied. Structured as a literature review, it revisits the main schools of thought and the scope of three different methods: persuasive techniques, design thinking and tangible interaction. It also proposes collaborative networks and connectivity as a possible focus of intervention, as the case study Route EN/N2 in Portugal suggests. We questioned whether a process that prioritizes comfort and human desires is the right practice when interaction design intervenes in territories. We also considered context mapping, facilitation techniques, and consequence anticipation, achievable through a humanity-centered design approach, as the most appropriate practices for achieving measures with greater long-term impact. This approach looks at humans not as individuals, but as part of a complex society with deep-rooted systems and issues of sustainability, inclusion, accessibility, and representation. Our goal is to promote understanding of interaction design as a behavior facilitator, able to generate new experiences for its participants and attract others, making a territory more understandable, attractive and memorable. Opening space for new interactive models, this paper analyzes the importance of creating new relationships and collaborations as a method of decoding and understanding a local community.
Apply Natural Language Processing-Chatbot on Industry 4.0

NLP, or natural language processing, is an area of artificial intelligence that has been studied for more than 50 years and allows computers to comprehend human language. NLP interprets and makes sense of spoken or written natural language inputs using AI algorithms. Data preprocessing and algorithm development, which include tasks like tokenization, parsing, lemmatization, and part-of-speech tagging, are the two fundamental aspects of NLP. This break language down into smaller parts and make an effort to comprehend the connections between them. Improved documentation, better human-machine interaction, and personal assistants that can interpret natural language are all advantages of NLP. In this paper, we will concentrate on one particular use of NLP: creating chatbots that can converse with people. NLP and programming languages like Python and JavaScript were used to create a chatbot. In order to build a better user interface, JavaScript was employed, while Python was used to implement the NLP algorithms and process the inputs in natural language. With this example, we want to show how NLP can be used to build engaging, user-friendly chatbots that can converse with people in a natural way.
Understanding the Pushbutton Revisited: From on and off to Input and Output

The button is a familiar technology that is used to control and regulate things and machines in our everyday lives. With the digitalization of the button, many possibilities for novel and innovative functions have been invented and implemented. The use of digital buttons comes with some challenges that are explored in this paper. We describe the transition between the mechanical switch to the digital switch, and specifically use the concept of familiarity to find out more about the use of digital switches in a case study of indoor lighting. The contribution of the paper is to open for the transition between mechanical buttons and digital buttons and point to some challenges that arise in everyday use.
Open-Domain Dialogue Management Framework Across Multiple Device for Long-Term Interaction

This study explores the feasibility of dialogue systems with individuality capable of providing continuous and lasting assistance via a multiple device dialogue system. A framework has been devised to manage dialogue history, allowing for the use of a singular identity across various interfaces, including chatbots and virtual avatars. This framework can summarize and save the dialogue history, which can be utilized to generate responses. The impact of dialogue history sharing on users’ interactions with a particular character across various devices was assessed for naturalness, continuity, and reliability. The results indicate that dialogue history sharing can foster more natural and continuous conversations, thereby enhancing the potential for long-term support. This research advances the proposition that a digital agent endowed with a consistent identity across multiple devices can provide personalized and sustained support to users.
Exploratory Analysis of the News in Easy Language (NiEL) Corpus to Identify Characteristic Patterns for Natural Language Processing

While comprehensive corpora are available for resource-rich languages such as English in different domains, which can be made usable for natural language processing (NLP) applications, this is not the case for resource-poor languages. Parallel or monolingual corpora must first be created and adequately processed in order to make them usable for later NLP applications. In the past, selected variants of a standard language were increasingly identified as resource-poor languages and corresponding resources were created. The German Easy Language, as a highly simplified variant of the Standard German language, can be defined as a resource-poor language, since here, too, hardly any NLP-suitable corpora are available. In this paper, we present the News in Easy Language (NiEL) corpus, a monolingual text resource for German Easy Language. By means of exploratory analysis using selected NLP tools, characteristic patterns for Easy Language can be derived at both word and sentence level. The identified patterns of Easy Language can be compared in perspective with patterns from standard language texts. Our results show that multiple tools from the NLP domain are suitable for German Easy Language as well as for German Standard Language. Features like word variance, sentence depth but also average word and sentence length can be distinguished. The features extracted in this way are suitable for the development of models, whereby initial implications for the natural language processing of Easy Language can be derived. The results form an important basis for further research in the domain of Easy Language. As a low-resource language that has been primarily analyzed intellectually, another added value of our work also lies in the implications for natural processing of plain language derived from the exploratory analysis of the corpus.
Experiences with Using Diverse Evaluation Methods for Extracting Insights via Experience Prototyping in Ambient and Civic Computing

In this paper, we report our experiences with using diverse evaluation methods in our recent studies to develop some ambient and civic computing services. Extracting insights from doing researches is essential in our daily research activities, and we usually use diverse research methods for finding new opportunities from our research activities. In our current researches, we examined to use diverse research methods so we like to report our insights extracted from using the research methods. The most important contribution of this paper is to show the importance to use multiple research methods to extract various insights from the diverse angles in conducted researches.
Analysis of Classroom Test Results for an Error-Based Problem Presentation System for Mechanics

It is possible in learning for an impasse to occur during a learner's trial-and-error process. This is a situation in which a learner cannot make progress in solving a problem because they cannot properly correct an error. In this study, we developed a learning support system for mechanics that helps learners overcome impasses by presenting auxiliary problems based on their errors. This system was implemented in a class of 86 third-year middle school students. To evaluate the problem graph implemented in the system, we analyzed the results of a test that dealt with drafting problems based on the strength of each entry in the results of the class practice. We found that this system was effective for learning gravity, normal force, the net force exerted by external forces, and frictional force from the ground. However, there were problems in learning frictional forces among objects themselves and forces propagating to other objects.
Privacy-Preserving Data Management and Provision System for Personal Data Store

In systems that manage and provide personal information, such as personal data stores (PDS), the privacy of the data stored on cloud servers is paramount. Simple adoption of cryptographic primitives such as AES does not allow re-encryption for each destination without decrypting personal data, and users are thus required to maintain different secret keys for each destination. In this paper, we propose a system that utilizes ID-based proxy re-encryption to securely manage and deliver data without sacrificing the convenience of PDS.
A Framework for Humanization Evaluation in Chatbots

Chatbots play a central role in modern software systems regarding customer services. They have the potential of applicability for different purposes. A key open research challenge is how humanizing and evaluating chatbots during their development. Humanized chatbots carry out a fluid and pleasant conversation with the user, demonstrating empathy and personality. In this article, we aim to study and develop an evaluation method that indicates the level of humanization of a chatbot under analysis. Our framework comprises two objectives and questionnaires, which are applied to establish an assessment adaptable to the different objectives of using chatbots (commercial or therapeutic, for example). We combine these questionnaires to generate evaluation metrics, which provide a humanization score for the evaluated software. Our method helps designers identify specific factors that affect users’ experience interacting with chatbots. We carry out a case study on the application of our framework and reveal the main findings about its applicability.
Crowdsourcing a More Realistic Emotional Lexicon Process

Even though sentiment analysis and emotion analysis are relatively new subfields of natural language processing, they are increasingly being used by businesses and academics. Their primary objective is to automatically capture the emotions and opinions of people as they wrote their text. Whether the text is from social media, literature, or any other form of text, it is a difficult problem. We present a novel approach by introducing the idea of capturing a range of universal primary emotions (i.e., sadness, happiness, anger, disgust, surprise, and fear). By crowdsourcing the emotion lexicon, it creates a diverse lexicon of emotions. This type of lexicon shows how many people within the same culture and language can have both convergent emotions where everyone universally every agrees that a word has a single emotion behind it (e.g., yummy = happy) and divergent emotions where there is a range of feelings for the same word (e.g., hated = angry, disgusted, and sad). Finally, we empirically prove that emotions are not binary as is often used by modern approaches to this problem (e.g., not sad ≠ happy), showing that people's emotions are complex and nuanced.
Applying the Social Robot Expectation Gap Evaluation Framework

Expectations shape our experience with the world, including our interaction with technology. There is a mismatch between what humans expect of social robots and what they are actually capable of. Expectations are dynamic and can change over time. We have previously developed a framework for studying these expectations over time in human-robot interaction (HRI). In this work, we applied the social robot expectation gap evaluation framework in an HRI scenario from a UX evaluation perspective, by analyzing a subset of data collected from a larger experiment. The framework is based on three factors of expectation: affect, cognitive processing, as well as behavior and performance. Four UX goals related to a human-robot interaction scenario were evaluated. Results show that expectations change over time with an overall improved UX in the second interaction. Moreover, even though some UX goals were partly fulfilled, there are severe issues with the conversation between the user and the robot, ranging from the quality of the interaction to the users’ utterances not being recognized by the robot. This work takes the initial steps towards disentangling how expectations work and change over time in HRI. Future work includes expanding the metrics to study expectations and to further validate the framework.
Development of Applications that Integrate Information from Diverse Systems with a Focus on Information Organization

With the COVID-19 craze, remote work and on-demand learning using Microsoft Teams and other tools at home has become popular. The problems with this include the risk of being late for class and start time due to lack of chimes, and the risk of missing assignment deadlines due to increased number of assignments and media and unmanageable workspace. To reduce these risks, the main objective of this study was to provide the ability to integrate information and notify users of assignment due dates. Developing applications that integrate information from different systems with a focus on information organization. Remote working has increased the risk of delays due to misplaced or misunderstood information. To mitigate this risk, we developed a PC application feature that intermittently notifies users when the due date of an assignment registered in the Google Calendar is approaching and allows users to stop the notification only if they correctly answer a four-choice topic selection question. These specifications were designed to reduce the risk of missing deadlines. A questionnaire survey of three respondents revealed that two had experienced the usefulness of the notification feature. The results of the survey indicated that the notification feature is effective for remote classes.
Insights from the Practical Application of a Human-Centered Design Process for the Digitalization of Maintenance in Food Industry

This paper focuses on the practical application of a human-centered design (HCD) process to redesign processes for need-based use of technological possibilities, such as intelligent assistance systems. HCD is a resource-intensive approach which requires repeated development and evaluation of design prototypes with active involvement from people. The paper provides insights from a case study on the digitalization of processes within the maintenance of production machines in the food industry, intending to develop an information platform that supports maintenance workers. Various methods were used during the platform's development, including personas and user studies. The lessons learned from the practical application of HCD can aid future applications in an industrial work context, including using different user-centered methods, observed effects on workers, and challenges that emerged due to the HCD approach.
Development and Application Study of Coding Learning Game Using Augmented Reality-Based Tangible Block Chips for Children of Low Age Groups

As the importance of coding education has recently increased, various methods are being attempted to increase learning efficiency rather than conventional education platforms. Among them, due to the educational effect and change in perception of games, research on coding education platforms that combine games and learning is being actively conducted. In particular, by solving various problem situations through games, not just games for fun, the positive effects of education using games such as logical thinking, problem-solving ability, and ability to deal with situations are currently attracted. In this study, a coding education game was developed in which the game itself becomes a coding education by integrating coding education with games. Furthermore, a computational thinking training game using an augmented reality-based tangible block chip was developed for children of low age groups who are inexperienced in mobile or PC environments. Then, after playing the coding game learning for children and parents, feedback on the user experience was investigated. A questionnaire and individual interviews were conducted on the usefulness, interest, recommendation, and overall satisfaction of a coding learning game for parents with children of the low-age group. Finally, by examining the pros and cons of a coding learning game using an augmented reality-based tangible block chip, we suggest effective coding learning for children of the low-age group.
Learning User Embeddings with Generating Context of Posted Social Network Service Texts

Embedded representations that express the user's personality are essential for personalizing the output of machine learning. However, annotating training data to learn the embedding is difficult because one cannot directly observe a person's internal personality. This paper proposes a method for learning user embedding representations from social networking service data to make language models behave with personality. The method focuses on text posted by social networking service users and obtains the user's embedded representation by learning a model that predicts and generates sentences before and after the text input to the social networking service. Evaluation experiments showed that the proposed method could learn embedded expressions that reflected the user's attributes, such as location or personality.
Artificially Intelligent and Interactive 3D Hologram

An artificially intelligent and interactive 3D holographic assistant is a cutting-edge technology that aims to provide users with an immersive and engaging experience that will revolutionize the way humans interact with machines. The system utilizes holographic technology to display a virtual assistant that can interact with users through voice commands. The holographic assistant is designed to guide users through entertaining and enriching conversations, providing them with responses to questions as a conversational agent and assistance. The system is powered by artificial intelligence algorithms that enable the holographic assistant to understand and respond to user queries in real-time. The system's 3D display technology creates a lifelike image of the assistant, making the interaction feel more natural and intuitive. The proposed system utilizes a Multi-Model system in conjunction with a Holographic view, which leverages advancements in computer graphics and multimedia technologies to enhance the way people view and interact with the virtual world. This includes incorporating technologies like augmented reality (AR) and hologram display.
Toward an AI-Collaborated Authoring Tool for Writing Flash Fiction

Flash fiction refers to short stories, usually around 1,000 words, featuring characteristics such as criticism, satire, and twists at the climax. This paper describes the design of an authoring tool prototype that leverages artificial intelligence technology to help novice writers easily create flash fiction. With our prototype, users can develop a plot outline by selecting a sequence of appropriate story units, then complete a story by filling in the text description. In addition, users can manually enter keywords as hashtags, or relevant hashtags can be automatically generated based on the user's writing. Hashtags thus act as a means of communication between the user and AI during the story-writing process. After the completion of the prototype, we plan to conduct a pilot study with amateur/professional writers to investigate the prototype's usability.
Resources and Skills for Information Design Practice

Information Design (ID) is increasingly asserting itself as a fundamental subject as it plays a vital role of making complex information easy to understand and use. In the modern world, there is a need for ID, in the way that data is organized, written, and presented so that we can all understand it. When things get too complex, when an environment defies common sense, when technical requirements are allowed to prevail over human considerations, then someone must intervene – and this is where the information designer comes in. As such, this professional presents specificities that are important to emphasize. This article intends to reflect on some important issues for the definition of ID, such as: a) to justify its interdisciplinarity, and b) to clarify two fundamental axes for its practice (Infographics and DataViz) which have distinct features and contexts. Through a comprehensive literature review, we compile reflections from several scholars and professionals in this field of study, contributing to the affirmation of ID today, focusing on exclusive specificities that need to be highlighted. Information designers can have very different backgrounds and are often part of interdisciplinary teams that allow greater control and validation of the information presented. The eternal confrontation between form and function is once again in evidence in ID through two distinctive aspects of its practice, which demand professionals with different profiles. The differentiating characterization that exists allows a more specific knowledge of what ID is, aiming at a more informed practice and better preparation for the training of professionals.
Predicting Twitter Hate User Behavior Using Big Five Personality Traits and Ensemble Machine Learning

Twitter has grown in popularity as a microblogging social media platform for people of all ages and locations to exchange and discuss important events, information, and news. Some Twitter users exhibit unique behaviors, and they use such platforms maliciously to spread hate content over social media platforms. These materials may be harmful to the mental health of people and cause suicide ideation, criminal behavior, or racism. On social media, hateful content spreads more quickly than other types of content by nature. Early detection of hate users can help to lessen its damaging effects. Different personality types exist among social media users, and these personality types influence how a person interacts with others, processes information, and makes decisions. This study determines whether personality is associated with the sharing of hate content by hate users, identifying the HIGH/LOW availability of Big Five personality traits. Meanwhile, three psychologists determine the personalities of Twitter users based on their profiles. With more than 80% accuracy for each personality trait, the ensemble approach (SVM, Random Forest, XGBoost) was used in this study to examine how the Big Five personality traits of Twitter users may be predicted using their Twitter user profiles' attributes and Twitter activity considering both English and Sinhala language Tweets in the Sri Lankan context. The findings of this study provide new empirical proof that there is a considerable relationship between people's personality traits and their harmful intentions, with neuroticism and extraversion being closely associated with hate users by raising anti-social traits in them.