Journaling is a well-established practice for reflecting on life events and constructing meaning; yet, finding concrete meaning from them remains challenging. Recent advances in generative AI demonstrate the potential to generate materials that support reflection, suggesting opportunities to assist meaning-making in journaling. However, prior approaches often center on ambiguous outputs and reflections of a single experience, leaving unexplored how concrete interpretations from AI can help self-reflection in journaling. In this study, we designed Augmentiary, an LLM-based journaling system that suggests candidate interpretations of experiences with concrete meaning while preserving users’ agency and voice, based on insights from a formative study with eight journal writers. We then conducted a four-week deployment study with 25 participants. Our findings show that AI’s interpretive feedback helped connect fragmented experiences and supported self-understanding through comparison with their own thoughts. Moreover, tensions between user agency and constructive reflection were revealed. We conclude by discussing design implications for AI-supported systems for fostering meaning-making without replacing users’ thinking.

In this paper, we present TubeLens, a system designed to support YouTube users in reflecting on how recommendation algorithms perceive and represent their interests. TubeLens invites users to engage with their algorithmic selves through self-portraits accompanied by dispositional keywords and explanations, creating space to consider how algorithmic experiences might be interpreted and potentially reshaped over time. Rather than positioning users as passive recipients of recommendations, TubeLens foregrounds users’ agency in questioning and making sense of algorithmic influence on their media consumption. We conducted an exploratory user study with 22 participants to examine users’ experiences with TubeLens. Our findings suggest that algorithmic self-portraits can surface gaps between perceived and algorithmic selves, supporting self-awareness and agentic awareness, while also revealing tensions around privacy and social comparison. This work offers initial insights into how interactive representations of algorithmic profiles can support reflective engagement with algorithmic systems and inform the design of future identity-oriented interfaces.

Local journalism plays a vital role in community life by connecting citizens to local news and supporting public discussion. However, the global spread of Local News Desertification—the decline of reliable local media—has weakened civic deliberation, shared knowledge, and community attachment. Citizen-Participatory Journalism enables citizens to discover and share local news. Still, their participatory agency in reporting local news that matters to their lives is controlled by the journalists’ normative authority. This pictorial frames this tension as a Participatory Design (PD) problem. Through a multi-stage PD process, we propose a citizen–AI participatory journalism platform that enables citizens to actively report local news. The platform combines AI-assisted news article creation with a multi-actor review structure to support responsible reporting. Based on this platform and its underlying model, this pictorial contributes a framework for Community News Resilience, which aims to democratize local information ecosystems through human–AI collaboration in future participatory journalism.

Advances in Generative AI (GenAI) enable unexpected creation in visual images. In fashion design, this capability has intensified demand for creativity support tools where fast-paced trends challenge fixation and drive exploration of novel creative directions. While prior work has explored interfaces that align designer intent with GenAI outputs, we still lack an empirical understanding of how fashion designers define, seek, and utilize AI-generated surprise as a valuable resource and actionable design direction rather than random noise. We address this gap through a qualitative study combining semi-structured interviews with 20 fashion professionals and a design workshop with 12 graduate students. We conceptualized surprise as a strategy that can be designed into GenAI-powered visualization tools to support traceable exploration, contextual grounding, and controllable variation across ideation stages. This work (1) reframes surprise as a designable mechanism or resource for co-creative interaction, (2) provides empirical insights into how fashion designers can utilize AI-generated surprise in the early stage of design, and (3) translates these insights into actionable guidance for building GenAI-driven visualization tools for fashion and related creative domains from a human-centered AI perspective.

Multi-agent AI systems are increasingly prevalent across digital environments, yet their social influence dynamics remain underexplored beyond basic compliance. This study investigates how different multi-agent configurations affect human decision-making through compliance and conversion mechanisms. We conducted a controlled experiment with 127 participants interacting with three LLM-powered agents across three conditions: Majority (all agents opposing participant), Minority (one dissenting agent), and Diffusion (gradual spread of minority position). Participants completed normative and informative tasks while reporting stance and confidence at five time points. Results demonstrate distinct influence patterns by condition and task type. In informative tasks, majority consensus drove largest immediate opinion changes, while minority dissent showed potential for delayed but deeper attitude shifts consistent with conversion-like processes. The diffusion condition revealed how temporal dynamics serve as persuasive signals. These findings extend social psychology theories to human-AI interaction, highlighting risks of synthetic consensus manipulation and opportunities for structured dissent to promote critical thinking.

Copyright policies play a pivotal role in protecting the intellectual property of creators and companies in creative industries. The advent of cost-reducing technologies, such as generative AI, in these industries calls for renewed attention to the role of these policies. This paper studies competition in a market of creatively differentiated products and the competitive and welfare effects of copyright protection. A common feature of products with creative elements is that their key attributes (e.g., images and text) are unstructured and thus high-dimensional. We focus on a stylized design product, fonts, and use data from the world's largest online marketplace for fonts. We construct neural network embeddings to quantify unstructured attributes and measure the visual similarity. We show that this measure closely aligns with actual human perception. Based on this measure, we empirically find in a descriptive analysis that competitions occur locally in the visual characteristics space. We then develop a structural model for supply and demand that integrate the embeddings. On the supply side, our model describes firms' location choices within the visual characteristics space as well as pricing and entry decisions. A copyright policy is modeled as imposing restrictions on the area of possible choices in the characteristics space, providing local protection to right holders. On the demand side, we characterize consumers' heterogeneous preferences for visual attributes, focusing on recovering substitution patterns across different designs. Overall, the estimated demand model reveals that the substitution patterns are effectively explained by the visual similarity. The estimated supply-side model indicates that the firm's development costs are low when mimicking close competitors and increase as products become more visually differentiated. This suggests the existence of a mimicking externality: the presence of a design product reduces the fixed costs of visually similar entrants. Through counterfactual analyses, we find that local copyright protection can enhance consumer welfare when products are relocated, and the interplay between copyright and cost-reducing technologies is essential in determining an optimal policy for social welfare. We believe that the embedding analysis and empirical models introduced in this paper can be applicable to a range of industries where unstructured data captures essential features of products and markets.

Group dance, a sub-genre characterized by intricate motions made by a cohort of performers in tight synchronization, has a longstanding and culturally significant history and, in modern forms such as cheerleading, a broad base of current adherents. However, despite its popularity, learning group dance routines remains challenging. Based on the prior success of interactive systems to support individual dance learning, this paper argues that group dance settings are fertile ground for augmentation by interactive aids. To better understand these design opportunities, this paper presents a sequence of user-centered studies of and with amateur cheerleading troupes, spanning from the formative (interviews, observations) through the generative (an ideation workshop) to concept validation (technology probes and speed dating). The outcomes are a nuanced understanding of the lived practice of group dance learning, a set of interactive concepts to support those practices, and design directions derived from validating the proposed concepts. Through this empirical work, we expand the design space of interactive dance practice systems from the established context of single-user practice (primarily focused on gesture recognition) to a multi-user, group-based scenario focused on feedback and communication.

This paper investigates an end-to-end multiuser precoding in massive multiple-input multiple-output (MIMO) system. We propose a novel neural network architecture which exploits an appropriate human expert knowledge in wireless communication domain. The employed domain knowledge addresses the inter-user interference. In particular, the concept of a leakage is adopted to decouple the sum-rate maximization problem by transforming the input to the decoder neural network (NN) at base station (BS). In contrast with the existing NN architecture, the proposed architecture is proven to be scalable to the number of user equipments (UEs) as the common NN model (parameter set) can be employed for the different numbers of UEs. Numerical results demonstrate that the proposed scalable decoder NN architecture can learn the distribution of the inter-user interference effectively.

COVID-19 has prompted a rise in telecommuting practices in most companies worldwide. Meanwhile, companies are struggling to cope with the new and evolving security threats in telecommuting using old control methods. Specifically, there is an increased danger of hacking attacks in telecommuting environments. Furthermore, corporate concerns regarding telecommuting security have led to a questioning of existing control methods that no longer seem adequate. Significant research has been conducted on the factors that improve the effectiveness of corporate security policies, such as formal control, informal control, and extrarole behaviors. However, these studies did not consider telecommuting environments, which surged after the COVID-19 outbreak. Telecommuting loosens the physical control over employees and eliminates the collegial environment in which employees encourage each other to protect system information. This study determined how the factors that influence the effectiveness of existing information security policies behave in a telecommuting environment. Our study shows that specification and mandatoriness are the most important factors for an effective telecommuting security policy. We conclude that this sudden change in the working environment has rendered existing security controls obsolete, and specification and mandatoriness are likely to receive increasingly more attention in the growing field of telecommuting security policy.

Creating agile and responsive characters from a collection of unorganized human motion has been an important problem of constructing interactive virtual environments. Recently, learning-based approaches have successfully been exploited to learn deep network policies for the control of interactive characters. The agility and responsiveness of deep network policies are influenced by many factors, such as the composition of training datasets, the architecture of network models, and learning algorithms that involve many threshold values, weights, and hyper-parameters. In this paper, we present a novel teacher-student framework to learn time-critically responsive policies, which guarantee the time-to-completion between user inputs and their associated responses regardless of the size and composition of the motion databases. We demonstrate the effectiveness of our approach with interactive characters that can respond to the user's control quickly while performing agile, highly dynamic movements.

Biometric devices play an integral role in consumer’s daily life, providing a seamless environment. However, it is essential to measure the usability of biometrics, owing to the elements of biometrics satisfying both usability and security. This study redefines the elements of biometrics pertaining to usability determined in previous studies and adds elements of psychological relevance, such as privacy concerns. To organize the interrelated usability structure systemically, this paper applies the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) to derive the usability structure. Thereupon, the established structure is applied in the clustered weighted Analytical Network Processes (ANP) to generate the proposed usability evaluation model. By these methods, the pertinent relationships between the factors are clarified and the weight of each element is determined. In the empirical study, 106 students measured usability of the fingerprint recognition system, iris recognition system, and facial recognition system employing our usability evaluation model. The results of this model generate the quantitative score of usability for biometric systems and suggest strategies to increase the score. The proposed usability evaluation model can comprehensively assist usability practitioners to evaluate biometric systems.

As smartphones such as mobile devices become popular, malicious attackers are choosing them as targets. The risk of attack is steadily increasing as most people store various personal information such as messages, contacts, and financial information on their smartphones. Particularly, the vulnerabilities of the installed operating systems (e.g., Android, iOS, etc.) are trading at a high price in the black market. In addition, the development of the Internet of Things (IoT) technology has created a hyperconnected society in which various devices are connected to one network. Therefore, the safety of the smartphone is becoming an important factor to remotely control these technologies. A typical attack method that threatens the security of such a smartphone is a method of inducing installation of a malicious application. However, most studies focus on the detection of malicious applications. This study suggests a method to evaluate threats to be installed in the Android OS environment in conjunction with machine learning algorithms. In addition, we present future direction from the cyber threat intelligence perspective and situational awareness, which are the recent issues.

Financial fraud under IoT environment refers to the unauthorized use of mobile transaction using mobile platform through identity theft or credit card stealing to obtain money fraudulently. Financial fraud under IoT environment is the fast-growing issue through the emergence of smartphone and online transition services. In the real world, a highly accurate process of financial fraud detection under IoT environment is needed since financial fraud causes financial loss. Therefore, we have surveyed financial fraud methods using machine learning and deep learning methodology, mainly from 2016 to 2018, and proposed a process for accurate fraud detection based on the advantages and limitations of each research. Moreover, our approach proposed the overall process of detecting financial fraud based on machine learning and compared with artificial neural networks approach to detect fraud and process large amounts of financial data. To detect financial fraud and process large amounts of financial data, our proposed process includes feature selection, sampling, and applying supervised and unsupervised algorithms. The final model was validated by the actual financial transaction data occurring in Korea, 2015.

In the era of Internet of Things (IoT), impact of social media is increasing gradually. With the huge progress in the IoT device, insider threat is becoming much more dangerous. Trying to find what kind of people are in high risk for the organization, about one million of tweets were analyzed by sentiment analysis methodology. Dataset made by the web service “Sentiment140” was used to find possible malicious insider. Based on the analysis of the sentiment level, users with negative sentiments were classified by the criteria and then selected as possible malicious insiders according to the threat level. Machine learning algorithms in the open-sourced machine learning software “Weka (Waikato Environment for Knowledge Analysis)” were used to find the possible malicious insider. Decision Tree had the highest accuracy among supervised learning algorithms and K-Means had the highest accuracy among unsupervised learning. In addition, we extract the frequently used words from the topic modeling technique and then verified the analysis results by matching them to the information security compliance elements. These findings can contribute to achieve higher detection accuracy by combining individual’s characteristics to the previous studies such as analyzing system behavior.

We present an approach that learns to act from raw motion data for interactive character animation. Our motion generator takes a continuous stream of control inputs and generates the character's motion in an online manner. The key insight is modeling rich connections between a multitude of control objectives and a large repertoire of actions. The model is trained using Recurrent Neural Network conditioned to deal with spatiotemporal constraints and structural variabilities in human motion. We also present a new data augmentation method that allows the model to be learned even from a small to moderate amount of training data. The learning process is fully automatic if it learns the motion of a single character, and requires minimal user intervention if it deals with props and interaction between multiple characters.

Abstract The behavioral structure of human movements is imposed by multiple sources, such as rules, regulations, choreography, habits, and emotion. Our goal is to identify the behavioral structure in a specific application domain and create a novel sequence of movements that abide by structure‐building rules. To do so, we exploit the ideas from formal language, such as rewriting rules and grammar parsing, and adapted those ideas to synthesize the three‐dimensional animation of multiple characters. The structured motion synthesis using motion grammars is formulated in two layers. The upper layer is a symbolic description that relates the semantics of each individual's movements and the interaction among them. The lower layer provides spatial and temporal contexts to the animation. Our multi‐level MCMC (Markov Chain Monte Carlo) algorithm deals with the syntax, semantics, and spatiotemporal context of human motion to produce highly‐structured, animated scenes. The power and effectiveness of motion grammars are demonstrated in animating basketball games from drawings on a tactic board. Our system allows the user to position players and draw out tactical plans, which are animated automatically in virtual environments with three‐dimensional, full‐body characters.

A conductor provides a single unified vision of how to interpret and perform music. However, perceiving a conductor's musical intention and expression is quite challenging as they convey information to performers with subtle, nuanced, and highly individualized gestures. This artwork visualizes the conductor's gestures in order to give the audience a better understanding of its expressivity. To represent the expressivity of the gestures, we created motion profiles over eight frames, at 30 frames per second, and compared them to previously modeled gestures using three motion factors, called Weight, Space and Time from related concepts in Laban Movement Analysis (LMA). Based on this, we have created a real-time, interactive visualization that is driven by the motion factor parameters. The visualization receives the input video stream, and it is transformed into a representation of the three motion factors extracted from the real-time conducting gestures.

Abstract Markerless human pose recognition using a single‐depth camera plays an important role in interactive graphics applications and user interface design. Recent pose recognition algorithms have adopted machine learning techniques, utilizing a large collection of motion capture data. The effectiveness of the algorithms is greatly influenced by the diversity and variability of training data. We present a new sampling method that resamples a collection of human motion data to improve the pose variability and achieve an arbitrary size and level of density in the space of human poses. The space of human poses is high dimensional, and thus, brute‐force uniform sampling is intractable. We exploit dimensionality reduction and locally stratified sampling to generate either uniform or application specifically biased distributions in the space of human poses. Our algorithm learns to recognize such challenging poses as sitting, kneeling, stretching, and doing yoga using a remarkably small amount of training data. The recognition algorithm can also be steered to maximize its performance for a specific domain of human poses. We demonstrate that our algorithm performs much better than the Kinect software development kit for recognizing challenging acrobatic poses while performing comparably for easy upright standing poses. Copyright © 2015 John Wiley & Sons, Ltd.

Sharing copyright protected content without the copyright holder’s permission is illegal in many countries. Regardless, the number of illegal file sharing using BitTorrent continues to grow and most of file sharers and downloader are unconcerned legal action to transfer copywrite-protected files. However, it is difficult to gather enough probative evidence to prosecute illegal file sharers in criminal court and/or sued for damages in civil court. Further, there is a lack of research on investigation techniques to reveal illegal BitTorrent sharers. This is because the role of the server in BitTorrent networks has been changed compared to servers in conventional P2P networks. As a result, it is difficult to apply previous investigation processes for investigation of conventional P2P networks to the investigation of suspected illegal file sharing using BitTorrent. This paper proposes a methodology for the investigation of illegal file sharers using BitTorrent networks through the use of a P2P digital investigation process.

Biped controller design pursues two fundamental goals; simulated walking should look human-like and robust against perturbation while maintaining its balance. Normal gait is a pattern of walking that humans normally adopt in undisturbed situations. It has previously been postulated that normal gait is more energy efficient than abnormal or impaired gaits. However, it is not clear whether normal gait is also superior to abnormal gait patterns with respect to other factors, such as stability. Understanding the correlation between gait and stability is an important aspect of biped controller design. We studied this issue in two sets of experiments with human participants and a simulated biped. The experiments evaluated the degree of resilience to external pushes for various gait patterns. We identified four gait factors that affect the balance-recovery capabilities of both human and simulated walking. We found that crouch gait is significantly more stable than normal gait against lateral push. Walking speed and the timing/magnitude of disturbance also affect gait stability. Our work would provide a potential way to compare the performance of biped controllers by normalizing their output gaits and improve their performance by adjusting these decisive factors.

The Wikipedia, one of the richest human knowledge repositories on the Internet, has been developed by collective intelligence. To gain insight into Wikipedia, one asks how initial ideas emerge and develop to become a concrete article through the online collaborative process? Led by this question, the author performed a microscopic observation of the knowledge creation process on the recent article, "Fukushima Daiichi nuclear disaster." The author collected not only the revision history of the article but also investigated interactions between collaborators by making a user-paragraph network to reveal an intellectual intervention of multiple authors. The knowledge creation process on the Wikipedia article was categorized into 4 major steps and 6 phases from the beginning to the intellectual balance point where only revisions were made. To represent this phenomenon, the author developed a visaphor (digital visual metaphor) to digitally represent the article's evolving concepts and characteristics. Then the author created a dynamic digital information visualization using particle effects and network graph structures. The visaphor reveals the interaction between users and their collaborative efforts as they created and revised paragraphs and debated aspects of the article.

In many application areas, such as animation for pre-visualizing movie sequences and choreography for dance or other types of performance, only a high-level description of the desired scene is provided as input, either written or verbal. Such sparsity, however, lends itself well to the creative process, as the choreographer, animator or director can be given more choice and control of the final scene. Animating scenes with multi-character interactions can be a particularly complex process, as there are many different constraints to enforce and actions to synchronize. Our novel 'generate-and-rank' approach rapidly and semi-automatically generates data-driven multi-character interaction scenes from high-level graphical descriptions composed of simple clauses and phrases. From a database of captured motions, we generate a multitude of plausible candidate scenes. We then efficiently and intelligently rank these scenes in order to recommend a small but high-quality and diverse selection to the user. This set can then be refined by re-ranking or by generating alternatives to specific interactions. While our approach is applicable to any scenes that depict multi-character interactions, we demonstrate its efficacy for choreographing fighting scenes and evaluate it in terms of performance and the diversity and coverage of the results.

The forthcoming Internet of Things – an intelligent collaboration of resource‐limited static/mobile devices that are embedded in the daily lives of users – poses new challenges to security and end‐user privacy. One of the most challenging problems is how to thwart replica attacks. Once a device is captured physically by an attacker or contaminated by malicious code, it can be reprogrammed and the secret information inside can be duplicated into a large number of replicas that maliciously occupy the network and collect users private information without authorisation. In this study, the authors focus on studying the detection of replicas in mobile applications; which can consist of resource‐limited static devices attached to mobile entities. Most existing solutions for detecting replicas have been designed for static devices, and cannot be immediately applied to mobile devices because of their unique properties, in particular, their node mobility. The authors present two efficient methods for detecting replicas in mobile devices. Through performance evaluations, they find that the proposed methods provide highly accurate replica detection with nearly zero detection errors. Furthermore, the distributed and cooperative strategy of the methods significantly reduces the energy required to detect replicas while providing faster replica detection than the existing solutions.