Romantic couples often face challenges in navigating complex emotions and relationship dynamics through verbal communication alone, which can limit opportunities for deeper connection and understanding. To address this, we present an AI-mediated collaborative drawing system that enables couples to engage in structured drawing activities while analyzing their interactions. Inspired by interviews with art therapists, our system integrates behavioral data collection, AI-generated questions, and a comprehensive report synthesizing multimodal evidence. We conducted a user study involving 20 couples (N = 40) to evaluate the system’s effectiveness. Our findings demonstrate that the system fosters self-reflection, partner understanding, and relational awareness with high user acceptance. Participants highlighted the value of non-verbal communication as a unique pathway for gaining relational insight and deeper mutual understanding. Our work contributes design implications for AI-mediated relationship tools that position AI as a facilitator, providing accessible and creative avenues for couples to explore relational patterns and strengthen communication.

Journaling can potentially serve as an effective method for autistic adolescents to improve narrative skills. However, its text-centric nature and high executive functioning demands present barriers to practice. We present Autiverse, an AI-guided multimodal journaling app for tablets that scaffolds daily narratives through conversational prompts and visual supports. Autiverse elicits key details of an adolescent-selected event through a stepwise dialogue with peer-like, customizable AI and composes them into an editable four-panel comic strip. Through a two-week deployment study with 10 autistic adolescent-parent dyads, we examine how Autiverse supports autistic adolescents to organize their daily experience and emotion. Our findings show Autiverse scaffolded adolescents’ coherent narratives, while enabling parents to learn additional details of their child’s events and emotions. Moreover, the customized AI peer created a comfortable space for sharing, fostering enjoyment and a strong sense of agency. Drawing on these results, we discuss implications for adaptive scaffolding across autism profiles, socio-emotionally appropriate AI peer design, and balancing autonomy with parental involvement.

Psychiatric symptom identification on social media aims to infer fine-grained mental health symptoms from user-generated posts, allowing a detailed understanding of users' mental states. However, the construction of large-scale symptom-level datasets remains challenging due to the resource-intensive nature of expert labeling and the lack of standardized annotation guidelines, which in turn limits the generalizability of models to identify diverse symptom expressions from user-generated text. To address these issues, we propose SynSym, a synthetic data generation framework for constructing generalizable datasets for symptom identification. Leveraging large language models (LLMs), SynSym constructs high-quality training samples by (1) expanding each symptom into sub-concepts to enhance the diversity of generated expressions, (2) producing synthetic expressions that reflect psychiatric symptoms in diverse linguistic styles, and (3) composing realistic multi-symptom expressions, informed by clinical co-occurrence patterns. We validate SynSym on three benchmark datasets covering different styles of depressive symptom expression. Experimental results demonstrate that models trained solely on the synthetic data generated by SynSym perform comparably to those trained on real data, and benefit further from additional fine-tuning with real data. These findings underscore the potential of synthetic data as an alternative resource to real-world annotations in psychiatric symptom modeling, and SynSym serves as a practical framework for generating clinically relevant and realistic symptom expressions.

Reliable relevance modeling and ranking are pivotal to legal information retrieval (IR), where graded relevance judgments are indispensable yet prohibitively expensive to acquire. Despite their importance, expert-validated graded relevance benchmarks remain scarce, especially for non-English statutory-law jurisdictions where citation practices differ fundamentally from common-law paradigms. We introduce KCC, a publicly available and reusable benchmark resource for legal IR in Korean civil law. KCC is constructed from 38,372 publicly accessible court decisions and comprises 2,942 query–candidate case pairs annotated with expert-validated four-level relevance labels. These labels jointly capture factual similarity and legal reasoning, explicitly addressing the absence of systematic citation links in statutory-law systems. To facilitate reproducibility, we provide a detailed description of the dataset construction pipeline, annotation protocol, relevance criteria, and inter-annotator agreement statistics validated by legal professionals. The annotation guidelines are designed to be adaptable to other statutory-law and non-English legal IR settings, enabling future extensions beyond the current dataset. We further present a set of reference benchmark experiments using representative retrieval approaches, including traditional IR models, neural rankers, and LLM-based prompting methods, to indicate recommended usage and evaluation practices with KCC. We believe that KCC provides a long-term resource for the research community to study relevance modeling, ranking behavior, and evaluation methodology in domain-specific legal IR scenarios beyond English and common-law traditions.

Automated ophthalmic report generation aims to reduce the diagnostic burden on retinal specialists by producing clinically accurate and standardized descriptions from medical imaging. However, current research predominantly remains fundus-centric and rarely exploits OCT-derived spatial evidence, limiting clinical transparency by obscuring which anatomical regions drive diagnostic decisions. To address these limitations, we propose LASOR (Lesion-Aware Segmentation-Guided Ophthalmic Report Generation), which extracts multi-scale features to robustly capture both small focal abnormalities and broader anatomical structures, generating reliable segmentation masks as spatial priors for report generation. Specifically, we utilize a lesion-aware patch weighting module to emphasize abnormal regions and leverage a curated instruction dataset incorporating spatial mask information to enhance the diagnostic capabilities of the proposed model. In addition, we introduce a mask-guided cross-modal consistency loss that strengthens vision-language alignment between pathological regions and their diagnostic descriptions. Extensive experiments on a retinal OCT dataset that includes twenty pathological conditions exhibit state-of-the-art performance, underscoring LASOR’s potential to advance clinically transparent ophthalmic report generation systems.

The proliferation of highly realistic deepfake videos threatens public trust and the integrity of digital information. However, detecting sophisticated deepfakes requires analysis beyond surface-level visual artifacts. We propose Harmonizing Action Units with Temporal-contextual Embeddings (HAUTE), integrating physiological muscle dynamics with holistic semantic context through adaptive attention mechanisms. HAUTE captures temporal Action Unit coordination patterns and high-level contextual embeddings, enabling the model to reveal synthesis-induced inconsistencies imperceptible to isolated modalities. Extensive experiments demonstrate state-of-the-art performance with strong cross-dataset adaptability, particularly on commercial tool-based high-quality deepfakes, advancing trustworthy content verification for web ecosystems.

Sketch-based drawing assessments in art therapy are commonly used to understand the cognitive and psychological states of individuals. In conjunction with self-report measures, drawing assessments serve to enhance insights into an individual's psychological state. However, interpreting the drawing assessments is labor-intensive and substantially reliant on the experience of the art therapists. While a few automated approaches for analyzing drawing-based assessments have been proposed to remedy this issue, they mostly rely on existing object detection methods, where complex drawing attributes cannot be accurately decoded. To overcome these challenges, we propose a novel and comprehensive Draw-A-Person-in-the-Rain (DAPR) analysis system, CheckDAPR, which utilizes a Multimodal Large Language Model (MLLM) with object detection methods for in-depth evaluation. Our experimental results show the promising performance of CheckDAPR and its ability to reduce analysis time for art therapists, indicating its potential to aid professionals in art therapy.

The rapid development and prevalence of generative AI have made it easy for people to create high-quality deepfake images and videos, but their abuses have also increased exponentially. To mitigate potential social disruption, it is crucial to quickly detect the authenticity of each deepfake content hidden in a sea of information. While researchers have worked on developing deep learning-based methods, the deepfake datasets utilized in these studies are far from the real world in terms of their qualities; most popular deepfake datasets are human-distinguishable. To address this problem, we present a novel deepfake dataset, HiDF, a high-quality and human-indistinguishable deepfake dataset consisting of 62K images and 8K videos. HiDF is a meticulously curated dataset that includes diverse subjects that have undergone rigorous quality checks. A comparison of the quality between HiDF and existing deepfake datasets demonstrates that HiDF is human-indistinguishable. Hence, it can be a valuable benchmark dataset for deepfake detection tasks. Data and code (https://github.com/DSAIL-SKKU/HiDF) are publicly available for future deepfake detection research.

Precise retina Optical Coherence Tomography (OCT) image classification and segmentation are important for di-agnosing various retinal diseases and identifying specific regions. Alongside comprehensive lesion identification, re-ducing the predictive uncertainty of models is crucial for improving reliability in clinical retinal practice. However, existing methods have primarily focused on a limited set of regions identified in OCT images and have often faced challenges due to aleatoric and epistemic uncertainty. To address these issues, we propose CAMEL (Confidence-Aware Multi-task Ensemble Learning), a novel frame-work designed to reduce task-specific uncertainty in multi-task learning. CAMEL achieves this by estimating model confidence at both pixel and image levels and leveraging confidence-aware ensemble learning to minimize the un-certainty inherent in single-model predictions. CAMEL demonstrates state-of-the-art performance on a compre-hensive retinal OCT image dataset containing annotations for nine distinct retinal regions and nine retinal diseases. Furthermore, extensive experiments highlight the clini-cal utility of CAMEL, especially in scenarios with mini-mal regions, significant class imbalances, and diverse re-gions and diseases. Our code is publicly available at: https://github.com/DSAIL-SKKU/CAMEL.

In this paper, we propose PracticeDAPR, an AI-based education-supported system for beginners in DAPR assessment practice. As professional identity is considered a pivotal goal in art therapy education, it is important to help beginners not to experience difficulties in professional identity development. Therefore, we designed the proposed system to provide the following three factors, which are closely associated with the professional identity formation of beginners in art therapy: (i) performance improvement, (ii) anxiety reduction, and (iii) self-efficacy enhancement. To this end, we adopt online peer-to-peer learning as the foundational learning approach. In addition, by introducing AI as a mentor, we let users not only interact with their peers but also experience AI assistance. The user study targeting graduate students in art therapy was conducted with both quantitative and qualitative methods. In general, users reported positive experiences with PracticeDAPR. The results of the structural equation model analysis showed that perceived usefulness is an important contributor to the three factors, highlighting the effectiveness of online peer-to-peer learning with the AI mentor. Furthermore, by deriving the results that intention to use can be promoted by performance improvement, it is demonstrated that PracticeDAPR can consistently help the development of the professional identity, which is not easily established in a short period of time. Discussion and implications are provided in relation to using AI and online peer-to-peer learning to support current art therapy education.

The utilization of automated depression detection significantly enhances early intervention for individuals experiencing depression. Despite numerous proposals on automated depression detection using recorded clinical interview videos, limited attention has been paid to considering the hierarchical structure of the interview questions. In clinical interviews for diagnosing depression, clinicians use a structured questionnaire that includes routine baseline questions and follow-up questions to assess the interviewee's condition. This paper introduces HiQuE (Hierarchical Question Embedding network), a novel depression detection framework that leverages the hierarchical relationship between primary and follow-up questions in clinical interviews. HiQuE can effectively capture the importance of each question in diagnosing depression by learning mutual information across multiple modalities. We conduct extensive experiments on the widely-used clinical interview data, DAIC-WOZ, where our model outperforms other state-of-the-art multimodal depression detection models and emotion recognition models, showcasing its clinical utility in depression detection.

As the explainability of mental disorder detection models has become important, symptombased methods that predict disorders from identified symptoms have been widely utilized.However, since these approaches focused on the presence of symptoms, the context of symptoms can be often ignored, leading to missing important contextual information related to detecting mental disorders.Furthermore, the result of disorder detection can be vulnerable to errors that may occur in identifying symptoms.To address these issues, we propose a novel framework that detects mental disorders by leveraging symptoms and their context while mitigating potential errors in symptom identification.In this way, we propose to use large language models to effectively extract contextual information and introduce an uncertainty-aware decision fusion network that combines predictions of multiple models based on quantified uncertainty values.To evaluate the proposed method, we constructed a new Korean mental health dataset annotated by experts, named Ko-MOS.Experimental results demonstrate that the proposed model accurately detects mental disorders even in situations where symptom information is incomplete.

The increasing utilization of newly emerging networks (e.g., private-5G) across industries underscores the need for accurate traffic prediction to manage network resources effectively. However, rapidly emerging networks face challenges in accurate prediction due to limited training data at the early stage and fluctuation in traffic load at the maintenance stage. In response, we propose ISOML (Inter-Service Online Meta-Learning), a novel traffic prediction pipeline designed for newly emerging networks. ISOML utilizes meta-learning to address data scarcity and employs the EWC (Elastic Weight Consolidation) for online learning to learn dynamics of traffic patterns. Experimental validation in real-world datasets demonstrates the efficacy of ISOML in predicting traffic for emerging network environments.

Daeun Lee, Hyolim Jeon, Sejung Son, Chaewon Park, Ji hyun An, Seungbae Kim, Jinyoung Han. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Sketch-based drawing assessments are useful in understanding individuals' cognitive and psychological states, such as cognitive impairment or mental disorders. Hence, these assessments have been developed and applied on a large scale, such as in schools and workplaces, to screen individuals who may require further clinical examination. However, the interpretation of a large number of drawing assessments solely relies on human experts, requiring much time and cost. To address this issue, we introduce a novel scene-level sketch dataset, SceneDAPR, which can be used to automatically analyze the drawing assessment, Draw-A-Person-in-the-Rain (DAPR), a popular psychological drawing assessment used for identifying stressful experiences and coping behavior. The proposed dataset consists of 6,420 objects depicted in 1,399 scene sketches drawn by humans, along with detailed supplementary information about the participants. SceneDAPR includes free-hand drawings from different age groups: children & adolescents, adults, and seniors. Leveraging the proposed SceneDAPR, we develop a web-based drawing assessment system. The extensive experiments demonstrate that our system shows a robust performance across the different age groups in the object detection task as well as a considerable performance compared to human experts. We believe that the proposed new sketch dataset can be used to develop an automatic system for psychological drawing assessments, which can support human experts by reducing the time and cost of analyzing the drawing assessments for a large population. SceneDAPR and experimental code are available at https://github.com/DSAIL-SKKU/SceneDAPR.

Sketch-based drawing assessments in art therapy are widely used to understand individuals’ cognitive and psychological states, such as cognitive impairments or mental disorders. Along with self-reported measures based on questionnaires, psychological drawing assessments can augment information regarding an individual’s psychological state. Interpreting drawing assessments demands significant time and effort, particularly for large groups such as schools or companies, and relies on the expertise of art therapists. To address this issue, we propose an artificial intelligence (AI)-based expert support system called AlphaDAPR to support art therapists and psychologists in conducting large-scale automatic drawing assessments. In Study 1, we first investigated user experience in AlphaDAPR . Through surveys involving 64 art therapists, we observed a substantial willingness (64.06% of participants) in using the proposed system. Structural equation modeling highlighted the pivotal role of explainable AI in the interface design, affecting perceived usefulness, trust, satisfaction, and intention to use. However, our interviews unveiled a nuanced perspective: while many art therapists showed a strong inclination to use the proposed system, they also voiced concerns about potential AI limitations and risks. Since most concerns arose from insufficient trust, which was the focal point of our attention, we conducted Study 2 with the aim of enhancing trust. Study 2 delved deeper into the necessity of clear communication regarding the division of roles between AI and users for elevating trust. Through experimentation with another 26 art therapists, we demonstrated that clear communication enhances users’ trust in our system. Our work not only highlights the potential of AlphaDAPR to streamline drawing assessments but also underscores broader implications for human-AI collaboration in psychological domains. By addressing concerns and optimizing communication, we pave the way for a symbiotic relationship between AI and human expertise, ultimately enhancing the efficacy and accessibility of psychological assessment tools.

The emergence of hyper-realistic deepfake videos has raised significant concerns regarding their potential misuse. However, prior research on deepfake detection has primarily focused on image-based approaches, with little emphasis on video. With the advancement of generation techniques enabling intricate and dynamic manipulation of entire faces as well as specific facial components in a video sequence, capturing dynamic changes in both global and local facial features becomes crucial in detecting deepfake videos. This paper proposes a novel sequential attentive face embedding, SAFE, that can capture facial dynamics in a deepfake video. The proposed SAFE can effectively integrate global and local dynamics of facial features revealed in a video sequence using contrastive learning. Through a comprehensive comparison with the state-of-the-art methods on the DFDC (Deepfake Detection Challenge) dataset and the FaceForensic++ benchmark, we show that our model achieves the highest accuracy in detecting deepfake videos on both datasets.

Aphasia, a language disorder resulting from brain damage, requires accurate identification of specific aphasia types, such as Broca's and Wernicke's aphasia, for effective treatment.However, little attention has been paid to developing methods to detect different types of aphasia.Recognizing the importance of analyzing co-speech gestures for distinguish aphasia types, we propose a multimodal graph neural network for aphasia type detection using speech and corresponding gesture patterns.By learning the correlation between the speech and gesture modalities for each aphasia type, our model can generate textual representations sensitive to gesture information, leading to accurate aphasia type detection.Extensive experiments demonstrate the superiority of our approach over existing methods, achieving stateof-the-art results (F1 84.2%).We also show that gesture features outperform acoustic features, highlighting the significance of gesture expression in detecting aphasia types.We provide the codes for reproducibility purposes 1 .

As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influencer marketing, but it is challenging to find the right influencers among hundreds of millions of social media users. In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time. To represent the posting behaviors and social relations, the graph convolutional neural networks are applied to model influencers with heterogeneous networks during different historical periods. By learning the network structure with the embedded node features, InfluencerRank can derive informative representations for influencers at each period. An attentive recurrent neural network finally distinguishes highly effective influencers from other influencers by capturing the knowledge of the dynamics of influencer representations over time. Extensive experiments have been conducted on an Instagram dataset that consists of 18,397 influencers with their 2,952,075 posts published within 12 months. The experimental results demonstrate that InfluencerRank outperforms existing baseline methods. An in-depth analysis further reveals that all of our proposed features and model components are beneficial to discover effective influencers.

Sketch-based drawing assessments in art therapy are widely used to understand individuals’ cognitive and psychological states, such as cognitive impairment or mental disorders. Along with self-report measures based on a questionnaire, psychological drawing assessments can augment information about an individual psychological state. However, the interpretation of the drawing assessments requires much time and effort, especially in a large-scale group such as schools or companies, and depends on the experience of the art therapists. To address this issue, we propose an AI-based expert support system, AlphaDAPR, to support art therapists and psychologists in conducting a large-scale automatic drawing assessment. Our survey results with 64 art therapists showed that 64.06% of the participants indicated a willingness to use the proposed system. The results of structural equation modeling highlighted the importance of explainable AI embedded in the interface design to affect perceived usefulness, trust, satisfaction, and intention to use eventually. The interview results revealed that most of the art therapists show high levels of intention to use the proposed system while expressing some concerns about AI’s possible limitations and threats as well. Discussion and implications are provided, stressing the importance of clear communication about the collaborative role of AI and users.

Bipolar disorder (BD) is closely associated with an increased risk of suicide. However, while the prior work has revealed valuable insight into understanding the behavior of BD patients on social media, little attention has been paid to developing a model that can predict the future suicidality of a BD patient. Therefore, this study proposes a multi-task learning model for predicting the future suicidality of BD patients by jointly learning current symptoms. We build a novel BD dataset clinically validated by psychiatrists, including 14 years of posts on bipolar-related subreddits written by 818 BD patients, along with the annotations of future suicidality and BD symptoms. We also suggest a temporal symptom-aware attention mechanism to determine which symptoms are the most influential for predicting future suicidality over time through a sequence of BD posts. Our experiments demonstrate that the proposed model outperforms the state-of-the-art models in both BD symptom identification and future suicidality prediction tasks. In addition, the proposed temporal symptom-aware attention provides interpretable attention weights, helping clinicians to apprehend BD patients more comprehensively and to provide timely intervention by tracking mental state progression.

As the importance of information assets increases, employees are increasingly required to comply with organisational policies for information security (InfoSec). Since security-related demands lead to stressful situations, employees are likely to bypass security policies to perform their tasks. Extant literature does not sufficiently address the detrimental factors of information system security policies (ISSP). This study investigates employees’ emotional reactions to ISSP compliance from the perspectives of technostress and coping. The aim of this study is to identify behaviour antecedents of frustration in the context of InfoSec and provide factors for mitigating the negative effects of frustration on ISSP compliance intentions. This study followed a survey approach and conducted structural equation modelling using the WarpPLS program to examine its research model and hypotheses. The survey respondents comprised employees who used an enterprise digital rights management system. The results demonstrated that frustration negatively affected employees’ intentions to comply with ISSP, but these negative effects of frustration decreased when autonomy was granted. Further, this study provides critical new insights on ISSP compliance from an emotional perspective.

Knowledge management (KM) has emerged as an operational and strategic organisational tool. However, the influence of recent cyber regulations (CR) and security policies (SP) on the usefulness of KM, are absent from the literature. This study focuses on the intersection of knowledge management and knowledge fortification. The study employs survey data from a sample of firms and utilises partial least squares (PLS) with extensive post hoc analyses, to examine the influence of CR and SP on KM core processes (acquisition, sharing, and utilisation) and subsequently on the operational effectiveness and strategic performance of firms. Contrary to prior research, we found that external knowledge sharing negatively affected performance. These findings support our assertion that KM processes are tied to contingencies that change the relationship between KM outcomes.

Detecting depression based on non-verbal behaviors has received great attention. However, most prior work on detecting depression mainly focused on detecting depressed individuals in laboratory settings, which are difficult to be generalized in practice. In addition, little attention has been paid to analyzing the non-verbal behaviors of depressed individuals in the wild. Therefore, in this paper, we present a multimodal depression dataset, D-Vlog, which consists of 961 vlogs (i.e., around 160 hours) collected from YouTube, which can be utilized in developing depression detection models based on the non-verbal behavior of individuals in real-world scenario. We develop a multimodal deep learning model that uses acoustic and visual features extracted from collected data to detect depression. Our proposed model employs the cross-attention mechanism to effectively capture the relationship across acoustic and visual features, and generates useful multimodal representations for depression detection. The extensive experimental results demonstrate that the proposed model significantly outperforms other baseline models. We believe our dataset and the proposed model are useful for analyzing and detecting depressed individuals based on non-verbal behavior.

The purpose of this study is to examine the effects of Confucian values, social fear and organizational knowledge sharing culture on the knowledge sharing intention of Information Systems (IS) professionals in an East Asian context. We surveyed 187 professionals working in IS in South Korean companies and applied partial least squares structural equation modeling for testing our hypotheses. The results show that Confucian values negatively influence knowledge sharing intention by increasing social fear. Conversely, knowledge sharing culture enhances knowledge sharing intention directly, and by mitigating the negative effects of Confucian values and social fear. Thus, while individuals’ reluctance to share knowledge has been conceptualized as a rational cost-benefit process in a Western context, our study shows that negative emotions and the underlying values, which stimulate these emotions, influence knowledge sharing intention in an East Asian context.

This study proposes Magic Brush, an AI-based application for dementia prevention targeting middle-aged individuals who may be at risk for mild cognitive impairment (MCI) or dementia. In order to prevent dementia effectively at home, it is important to strengthen their intrinsic motivation. Promoting motivation is a critical issue in designing computerized cognitive therapy (CCT) for sustainability. Guided by self-determination theory, three main factors of intrinsic motivation were utilized as the main themes for the development: autonomy, competence, and relatedness. Especially, we focused on demotivating factors of low competence which are commonly shared among the elderly and developed a magic brush function equipped with neural style transfer technology to help disconnect the link between low competence and demotivation. The user study (n=35) targeting individuals aged over 50 was conducted with both quantitative and qualitative methods. In general, users reported positive experiences with Magic Brush. The results of Structural Equation Modeling analysis showed that intrinsic motivation is an important contributor to the intention to use together with perceived usefulness. Intrinsic motivation can be promoted by AI therapist likability and perception of one's own performance. Discussion and implications are provided in relation to using AI technology to promote motivation.

Influencer marketing has become a key marketing method for brands in recent years. Hence, brands have been increasingly utilizing influencers’ social networks to reach niche markets, and researchers have been studying various aspects of influencer marketing. However, brands have often suffered from searching and hiring the right influencers with specific interests/topics for their marketing due to a lack of available influencer data and/or limited capacity of marketing agencies. This paper proposes a multimodal deep learning model that uses text and image information from social media posts (i) to classify influencers into specific interests/topics (e.g., fashion, beauty) and (ii) to classify their posts into certain categories. We use the attention mechanism to select the posts that are more relevant to the topics of influencers, thereby generating useful influencer representations. We conduct experiments on the dataset crawled from Instagram, which is the most popular social media for influencer marketing. The experimental results show that our proposed model significantly outperforms existing user profiling methods by achieving 98% and 96% accuracy in classifying influencers and their posts, respectively. We release our influencer dataset of 33,935 influencers labeled with specific topics based on 10,180,500 posts to facilitate future research.

The Fourth Industrial Revolution become a major global topic around, and as a result, universities in Korea have been carrying out structural reforms in preparation of a highly technological future. Engineering colleges, particularly, are participating in the reform by supporting interdisciplinary education programs that combine knowledge and experience across a variety of areas. This study attempts to examine the effects of interdisciplinary education for engineering students by observing students' competencies before and after the program. We studied the effects of education over three years, finding improvements in competencies due to interdisciplinary education every year. Among the 12 competencies we developed, leadership significantly improved during all five semesters. Analytical thinking, interpersonal competency: professional attitude, global mindedness, and creative attitudes also improved across numerous semesters. Future studies can elaborate on competencies based on our findings. Further research is needed on the development of interdisciplinary courses and teaching methods aiming to improve competencies.

Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest. However, existing work mainly focuses on modeling popularity using a single metric such as the total number of likes or shares. In this work, we propose Diffusion-LSTM, a memory-based deep recurrent network that learns to recursively predict the entire diffusion path of an image through a social network. By combining user social features and image features, and encoding the diffusion path taken thus far with an explicit memory cell, our model predicts the diffusion path of an image more accurately compared to alternate baselines that either encode only image or social features, or lack memory. By mapping individual users to user prototypes, our model can generalize to new users not seen during training. Finally, we demonstrate our model»s capability of generating diffusion trees, and show that the generated trees closely resemble ground-truth trees.

Understanding socio-economic systems in MMORPGs can provide an important implication on how people participate in the economy and how people interact with each other. In this paper, we model the socio-economic system of an Aion, a popular MMORPG, as a multi-layer graph. Using the dataset consisting of 94,870 users and their activity records spanning three months, we examine how economic activities are associated with social interactions, and find that social interactions like participating in a party or exchanging messages are highly correlated with the trade activities. We also find that virtual economy in Aion is heavily inclined to a small number of upper-class userswho play a crucial role in virtual economy. Our analysis on the upper-class users reveals that a significant portion of them reach at the max-level and tend to either (i) have many social interactions with others or (ii) play extremely much time with no social activity. We also reveal that there are some low-level upper-class users who gain much money but hardly socialize with others. Lastly, we show how upper-class users who are at low-levels, play the game extremely much more than others, or rarely interact with other users, are associated with the Real Money Trade (RMT), which may be an illegal behavior that gathers in-game money for exchanging into real-world money. We reveal that more than half of total money exchanged through the trade are associated with the upper-class users who involve in the RMT.

Various Online Social Network (OSN) based applications depend on the interactions between users to disseminate information and recruit more users. The temporal evolution of adoption or cascade process of new products, applications or ideas is important to advertisers, OSN operators and application developers. Interactions between users are represented by massive directed graphs, so graph sampling methods were proposed to capture their properties. Existing graph sampling methods, such as a simple random walk, however, are ill- suited for capturing this and other dynamic properties of the graph. We propose ForestStream, a measurement method that relies on a combination of sampling and streaming with the goal of capturing the statistical properties of cascades in OSN graphs. We demonstrate our method's accuracy over existing methods in inferring the cascade statistics, with a low memory usage.

Fog computing is an emerging architecture that aims to run applications on multiple devices that lie on a continuum from cloud servers to personal user smartphones. These architectures allow applications to optimize over the information stored at and functionalities run on each device, based on individual device capabilities. We demonstrate the benefits of this approach for mobile video streaming. Existing HAS (HTTP adaptive streaming) techniques often suffer from problems like unstable video quality and suboptimal resource utilization. We find that a lack of coordination prevents both clientand network-side HAS techniques from solving them. However, our fog approach can exploit existing telecommunication APIs, which expose network capabilities to applications, in order to coordinate between clients and the network. Our coordinated HAS solution, FLARE, optimizes the total utility of all clients in a cell while maintaining stable video quality and supporting user- and device-specific needs. We implement FLARE on a commodity LTE femtocell and use the implementation to conduct the first comparison of HAS players on an LTE femtocell. By conducting extensive experiments using the ns-3 simulator, we also demonstrate that FLARE (i) enhances the average video bitrate, (ii) achieves stable video quality, and (iii) balances the throughput of simultaneous video and data flows, compared to other representative HAS solutions.

With rapid improvements in communication technologies and infrastructure, the Internet of Things (IoT) has become a promising sector within the global information and communication technology industry. Various fields are employing the concept of IoT for their traditional products and services for convenient use by consumers. In the construction industry, the majority of leading companies and organizations are using IoT technologies in a smart home environment. However, only few studies have focused on the user experience of IoT technologies in such an environment. Thus, the current study explores the key determinants of user acceptance of IoT technologies in a smart home environment, and investigates a research model integrated with five potential user factors and a technology acceptance model. The results of the collected data, which were investigated using a structural equation modeling method, show that three positive motivations, compatibility, connectedness, and control, and a negative hindrance, cost, are significant determinants of the technology acceptance behavior of users. The current study can serve as a foundation for future studies on improving IoT technologies in a smart home environment by considering the user experience.

Event-based social networks (EBSNs) are increasingly popular since they provide platforms on which online and offline activities are combined. Despite the increasing interest in EBSNs, little research has paid attention to the privacy issues coming from the unique features of EBSNs; the on-site information of users is highly relevant to real lives. In this paper, we try to investigate privacy leakages in Meetup, one of the most popular EBSN service. More specifically, we answer what private information can be inferred from the site's publicly available data. To this end, we conduct a measurement study by crawling webpages from Meetup containing 240K groups, 8.9M users, 27M group affiliations and 78M topical interests. By analyzing the dataset, we find that LGBT status of users, which is one of the most sensitive privacy information, can be predicted with 93% accuracy. Finally we discuss the cause of the privacy leakage on EBSNs and its possible ensuing damages.

To achieve growth in the user base of online social networks--(OSN) based applications, word-of-mouth diffusion mechanisms, such as user-to-user invitations, are widely used. This article characterizes the adoption and cascading process of OSN-based applications that grow via user invitations. We analyze a detailed large-scale dataset of a popular Facebook gifting application, iHeart, that contains more than 2 billion entries of user activities generated by 190 million users during a span of 64 weeks. We investigate (1) how users invite their friends to an OSN-based application, (2) how application adoption of an individual user can be predicted, (3) what factors drive the cascading process of application adoptions, and (4) what are the good predictors of the ultimate cascade sizes. We find that sending or receiving a large number of invitations does not necessarily help to recruit new users to iHeart. We also find that the average success ratio of inviters is the most important feature in predicting an adoption of an individual user, which indicates that the effectiveness of inviters has strong predictive power with respect to application adoption. Based on the lessons learned from our analyses, we build and evaluate learning-based models to predict whether a user will adopt iHeart. Our proposed model that utilizes additional activity information of individual users from other similar types of gifting applications can achieve high precision (83%) in predicting adoptions in the target application (i.e., iHeart). We next identify a set of distinctive features that are good predictors of the growth of the application adoptions in terms of final population size. We finally propose a prediction model to infer whether a cascade of application adoption will continue to grow in the future based on observing the initial adoption process. Results show that our proposed model can achieve high precision (over 80%) in predicting large cascades of application adoptions. We believe our work can give an important implication in resource allocation of OSN-based product stakeholders, for example, via targeted marketing.

Knowledge sharing and organizational citizenship behavior (OCB) among project team members are crucial for project success due to the particularities of information systems (IS) projects. Bonding social capital is known to increase knowledge sharing and collaboration. However, the influence of bridging social capital on knowledge sharing is unclear. The interplay between bridging and bonding social capital is especially interesting in a collectivistic society such as South Korea where in-group ties are particularly strong. In this study, we examine the effect of the sub-dimensions of the above constructs (i.e., bonding, bridging, OCB) on knowledge sharing. The results suggest that bonding and citizenry behavior improves knowledge sharing. Bridging improves knowledge sharing by increasing organizational citizenry behavior. Unexpectedly, the effect of citizenship behavior on knowledge sharing differs from the effect of its sub-dimensions. Only helping others is positively related to knowledge sharing. Similarly, the sub-dimensions of bridging and bonding influence knowledge sharing and citizenship behavior differently than the first-order constructs. For example, shared team mental model positively affects OCB, but has no direct influence on knowledge sharing. Conversely, shared vision is the only sub-dimension of bridging that positively affects OCB. Thus, the results suggest that project managers should facilitate shared project vision and team mental model to encourage team members’ citizenship behavior and control the potential adverse influence of bridging social capital.

This paper demystifies the adoption and cascading process of OSN-based applications that grow via user invitations. We analyze a detailed large-scale dataset of a popular Facebook gifting application, iHeart, that contains more than 2 billion entries of user activities generated by 190 million users during a span of 64 weeks. We investigate: (1) how users invite their friends to an OSN-based application, (2) what factors drive the cascading process of application adoptions, and (3) what are the good predictors of the ultimate cascade sizes. We find that sending or receiving a large number of invitations does not necessarily help to recruit new users to iHeart. We also identify a set of distinctive features that are good predictors of the growth of the application adoptions in terms of final population size. Finally, based on the insights learned from our analyses, we propose a prediction model to infer whether a cascade of application adoption will continue to grow in the future based on observing the initial adoption process. Results show our proposed model can achieve high precision (over 80%) in iHeart as well as in another OSN-based gifting application, Hugged.

Pinterest, a popular social curating service where people collect, organize, and share content (pins in Pinterest), has gained great attention in recent years. Despite the increasing interest in Pinterest, little research has paid attention to how people collect, manage, and share pins in Pinterest. In this paper, to shed insight on such issues, we study the following questions. How do people collect and manage pins by their tastes in Pinterest? What factors do mainly drive people to share their pins in Pinterest? How do the characteristics of users (e.g., gender, popularity, country) or properties of pins (e.g., category, topic) play roles in propagating pins in Pinterest? To answer these questions, we have conducted a measurement study on patterns of pin curating and sharing in Pinterest. By keeping track of all the newly posted and shared pins in each category (e.g., animal, kids, women's fashion) from June 5 to July 18, 2013, we built 350 K pin propagation trees for 3 M users. With the dataset, we investigate: (1) how users collect and curate pins, (2) how users share their pins and why, and (3) how users are related by shared pins of interest. Our key finding is that pin propagation in Pinterest is mostly driven by pin's properties like its topic, not by user's characteristics like her number of followers. We further show that users in the same community in the interest graph (i.e., representing the relations among users) of Pinterest share pins (i) in the same category with 94% probability and (ii) of the same URL where pins come from with 89% probability. Finally, we explore the implications of our findings for predicting how pins are shared in Pinterest.

Understanding the group characteristics in MMORPGs is important in user behavior studies since people tend to gather together and form groups due to their inherent nature. In this paper, we analyze the group activities of users in Aion, one of the largest MMORPGs, based on the records of the activities of 94,497 users. In particular, we focus on (i) how social interactions within a group differ from the ones across groups, (ii) what makes a group rise, sustain, or fall, (iii) how group members join and leave a group, and (iv) what makes a group end. We first find that structural patterns of social interactions within a group are more likely to be close-knit and reciprocative than the ones across groups. We also observe that members in a rising group (i.e., the number of members increases) are more cohesive, and communicate with more evenly within the group than the ones in other groups. Our analysis further reveals that if a group is not cohesive, not actively communicating, or not evenly communicating among members, members of the group tend to leave.

We conduct comprehensive measurements on the current practice of content bundling to understand the structural patterns of torrents and the participant behaviors of swarms on one of the largest BitTorrent portals: The Pirate Bay. From the datasets of the 120K torrents and 14.8M peers, we investigate what constitutes torrents and how users participate in swarms from the perspective of bundling, across different content categories: Movie, TV, Porn, Music, Application, Game and E-book. In particular, we focus on: (1) how prevalent content bundling is, (2) how and what files are bundled into torrents, (3) what motivates publishers to bundle files, and (4) how peers access the bundled files. We find that over 72% of BitTorrent torrents contain multiple files, which indicates that bundling is widely used for file sharing. We reveal that profit-driven BitTorrent publishers who promote their own web sites for financial gains like advertising tend to prefer to use the bundling. We also observe that most files (94%) in a bundle torrent are selected by users and the bundle torrents are more popular than the single (or non-bundle) ones on average. Overall, there are notable differences in the structural patterns of torrents and swarm characteristics (i) across different content categories and (ii) between single and bundle torrents.

BitTorrent has achieved a great success in the field of peer-to-peer (P2P) file sharing. Although BitTorrent allows peers to share files efficiently and scalably, it shows inefficiency when a client participates in multiple torrents where each of them concurrently competing for the limited link bandwidth. In this paper, we propose a new bandwidth allocation algorithm, which greedily increases the bandwidth consumption for downloading, to reduce file transfer time considering the current download/upload status. To compensate overall performance degradation resulting from our greedy allocation, we suggest modifying the choking algorithm of BitTorrent to consider the ratio of seeders and leechers in each torrent. Through comprehensive experiments, we validate the performance gain of the proposed scheme over original BitTorrent in a mix of WiFi and Ethernet testbed and large scale public torrents.

Surveillance systems are developed to enhance security and safety by constantly observing locations of interest. Although those systems can observe scenes from each camera separately, it is difficult to keep track of any moving target across different cameras. This paper firstly proposes Video Diary Service (VDS) to solve this problem. VDS is an automatic diary service, which makes it possible to keep track of users' lives. In addition, VDS can identify social networking relationships among the users while each camera is watching multiple users. By exploiting these properties of VDS, we extend VDS into a new surveillance system called S-VDS. We also illustrate a few application scenarios where the proposed system can enhance security and safety.

Despite the increasing interest in content bundling in BitTorrent systems, there are still few empirical studies on the bundling practice in real BitTorrent communities. In this paper, we conduct comprehensive measurements on one of the largest BitTorrent portals: The Pirate Bay. From the torrents data set collected for 38 days from April to May, 2010, we study how prevalent bundling is and how many files are bundled in a torrent, across different types of contents shared: Movie, Porn, TV, Music, Application, E-book, and Game.

This paper proposes a service differentiation architecture using virtualization of mobile femtocell. Network-prefix division multiple access (NDMA) is proposed to enable mobile devices inside a moving space to access the Internet with different service demands. With the NDMA scheme, our architecture provides multiple virtual mobile networks for diverse services which are optimized under different network conditions. We experimented on the operation of the proposed architecture with testbed implementation. The result shows the ability of multiple degrees of QoS over each virtual mobile network with additional benefits of personalization, mobility management aggregation, and so on.

While the Internet was designed with host-oriented networking applications, recent Internet statistics show that content-oriented traffic has become more and more dominant. Even though content-oriented networking, which tries to resolve this discordance, has received increasing attention, there have been few comprehensive and quantitative studies on how to realize a content-oriented networking architecture. In this paper, we focus on the design alternatives of the content-oriented networking architecture and evaluate their performance: (i) how to locate contents, (ii) how to cache contents, and (iii) how to deliver contents. There are two major infrastructure alternatives in substantiating these mechanisms: a tree and a distributed hash table (DHT).We carry out comprehensive simulation experiments to compare these alternatives in terms of content transfer latency, cache effectiveness, and failure resilience.