Interactive Messages Research Articles

A Data-Driven Approach to Discovering Process Choreography

Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments.

Design and Implementation of Chopper Control System for HIAF

The chopper control system which is synchronized by the White Rabbit timing network is designed and implemented to meet the requirements of the chopper of ion source for the High-Intensity Heavy Ion Accelerator Facility (HIAF). The hardware system consists of timing modulation controller, waveform acquisition, parameter setting, and remote control of the chopper power supply. The software includes low-level drivers and top-level applications which are based on Experimental Physics and Industrial Control System (EPICS). The combination of hardware and software completes the interaction of timing messages with the White Rabbit network, waveform acquisition, the setting of timing modulation parameters in several running modes, and remote control of the chopper power supply, etc. The test results show that the system can achieve timing modulation with a resolution of 8 ns and waveform acquisition of embedded EPICS IOC (input/output controller) at a 125 MHz sample rate. The design, implementation, and test of the chopper control system are described in this paper.

Pagerank Algorithms and Multi-Particle Environments Using Pettingzoo: Network Simulation and Validation

This study introduces and investigates two novel algorithms, namely Topic-Specific Crawling PageRank and Motif-Based PageRank, along with their corresponding application scenarios. The former algorithm focuses on targeted web crawling, refining the collection of web pages relevant to specific topics. The latter algorithm addresses higher-order relationships in citation networks, enhancing insights into researcher influence. These algorithms are evaluated within a Multi-Particle Environment, showcasing their capability to simulate necessary scenarios and data for PageRank algorithms. However, the environment's realism is limited due to its use of virtual data. The environment offers controllability through the PettingZoo resource library, providing an efficient experimentation platform. While the Multi-Particle Environment displays potential and advantages, its deviation from real-world data should be carefully considered. The experimental methodology involves constructing a network of agents and their message interactions within a simulated environment. The resulting network undergoes the PageRank algorithm, and the resulting ranking aligns with the expected outcome, confirming the Multi-Particle Environment's compatibility with the algorithm. These experiments furnish empirical evidence for the Multi-Particle Environment's adaptability to algorithmic simulations and real-world scenarios. This research contributes to a deeper comprehension of algorithm behavior in diverse and dynamic settings, paving the way for further advancements in the field

Baseball Injury Prevention Through a Community Outreach Initiative.

Sports-related craniofacial injuries account for 10% to 42% of facial fractures in the US pediatric population, with the incidence of injury peaking at ages 6 to 7 and 12 to 14. The National Survey of Children's Health reported ~5.6 million children between the ages of 6 and 17 participated in baseball in 2021. National Electronic Injury Surveillance System reported 861,456 injuries resulting from baseball and 511,117 injuries resulting from softball in 2021, with ~500,000 of those injuries being to the face/head/neck. While participation in baseball and other youth sports has tremendous physical and mental health benefits, facial and other athletic injuries are concerning beyond the pain and disability caused by the incident itself. Severe injury can result in lost time from school as well as other social activities. Players and their families often experience financial strain as a result of physical trauma as well. The use of protective equipment has decreased the incidence of emergency department visits due to baseball-related trauma, especially mouthguards. However, adherence to using mouthguards is relatively low in baseball, with over 80% of players reporting not being told by a coach to wear mouthpieces. Youth from low-income areas often lack financial means to provide protective equipment and health insurance in the case of injury. The authors, along with the University of Miami Miller School of Medicine Division of Plastic and Reconstructive Surgery and Miami Marlins RBI Program, created an outreach program by which over 400 mouthguards were given to players and caretakers at inner-city baseball and softball programs. Two community outreach events took place in South Florida. One event was held at a University of Miami Men's Baseball Game, and another was held on the opening day of the Miami Marlins RBI Program youth baseball league. In addition to handing out protective equipment and informational pamphlets, a question and answers session was held to discuss injury prevention. In conclusion, our community safety initiative created an interactive dialogue and message to hundreds of youth baseball players about the importance of protective gear to prevent injuries.

Exercise behaviours and motivation after a first psychotic episode: A digital intervention.

Research has demonstrated that participation in aerobic exercise can have significant beneficial effects across both physical and mental health domains for individuals who are in the early phase of schizophrenia. Despite these notable benefits of exercise, deficits in motivation and a lack of methods to increase engagement are significant barriers for exercise participation, limiting these potentially positive effects. Fortunately, digital health tools have the potential to improve adherence to an exercise program. The present study examined the role of motivation for exercise and the effects of an automated digital text messaging program on participation in an aerobic exercise program. A total of 46 first-episode psychosis participants from an ongoing 12-month randomized clinical trial (Enhancing Cognitive Training through Exercise Following a First Schizophrenia Episode (CT&E-RCT)) were included in an analysis to examine the efficacy of motivational text messaging. Personalized motivational text message reminders were sent to participants with the aim of increasing engagement in the exercise program. We found that participants with higher levels of intrinsic motivation to participate in a text messaging program and in an exercise intervention completed a higher proportion of individual, at-home exercise sessions. In a between groups analysis, participants who received motivational text messages, compared to those who did not, completed a higher proportion of at-home exercise sessions. These results indicate the importance of considering a person's level of motivation for exercise and the potential utility of using individualized and interactive mobile text messaging reminders to increase engagement in aerobic exercise in the early phase of psychosis. We emphasize the need for understanding how individualized patient preferences and needs interplay between intrinsic motivation and digital health interventions for young adults.

Hybrid Scheduling of Tasks and Messages for TSN-Based Avionics Systems

Time-Sensitive Networking (TSN) has been widely considered as a promising networking candidate for avionics systems due to its capability of deterministic communication. In such TSN-based avionics systems, the network scheduling enables the timely transmission of messages. However, this is insufficient to satisfy the real-time requirements of functions, since functions involve the chain-execution of several tasks where messages only serve for the inter-task communication. In order to enhance the functionality of TSN-based avionics systems, scheduling should be extended from the network level to the system level to coordinate message transmission with task execution. Then, how to efficiently implement the hybrid scheduling of tasks and messages becomes an important issue. In this paper, we construct a novel hybrid scheduling framework for TSN-based avionics systems, which consists of system consistency constraints, in-loop function delay calculation, and two hybrid scheduling algorithms. Consistency constraints restrict the unexpected interaction of messages and tasks for hybrid scheduling to guarantee the system-level determinism. Function delay is the end-to-end delay of the task chain, and its calculation provides the optimization objective for hybrid scheduling indicated by two metrics, namely reaction delay and age delay. Scheduling algorithms improve solving efficiency and functional performance through the incremental strategies of message dynamic ordering and task-message rescheduling. Experimental results verify that, compared with exiting scheduling work which considers the dependency of messages on tasks, our work can complete complex scheduling for large systems even with hundreds of functions, and can reduce function delays by 69% in reaction delay and 37% in age delay.

Analysis of Patient-Physician Electronic Messaging Interactions in the Allergy/Immunology Department in an Academic Setting

Analysis of Patient-Physician Electronic Messaging Interactions in the Allergy/Immunology Department in an Academic Setting

Pemaknaan Pesan Dalam Perilaku Celebrity Worship Fandom Carat Di Aplikasi Weverse

This research aims to analyze the interpretation of interaction messages in Carat's celebrity worship behavior towards Seventeen on the Weverse application. The research method employed is qualitative with a phenomenological approach. Data was collected through in-depth interviews with Carat users on Weverse in Indonesia, direct observation on the Weverse application, and consultations with experts for data validity. The results of the study indicate that Carat interprets Seventeen's messages based on their expectations or happiness, creating a sense of closeness or a relationship beyond being mere fans and their idols. These findings align with symbolic interaction theory and reflect an intense-personal level in celebrity worship behavior.

User-Centered Design to Develop Automated Short Message Service Messaging for Patient Support of Rheumatic Heart Disease Secondary Antibiotic Prophylaxis.

The Active Community Case Management Platform is a cloud-based technology developed to facilitate rheumatic heart disease case management by health care providers. This study aimed to design and pilot an automated short message service (SMS) intervention to support secondary prophylaxis adherence. We developed a concise library of messages to support secondary antibiotic prophylaxis. The SMS intervention used TextIT, an interface that enables users to send out interactive SMS messages at scale. The message bank was piloted in a cohort of 50 patients with rheumatic heart disease randomized into 2 groups. Group 1 received standard support (nurse-led/Active Community Case Management Platform). Group 2 received standard support plus SMS intervention for 2 months in the Lira and Gulu districts of Northern Uganda. We collected qualitative data on participants' experiences and assessed treatment adherence. Using a sequential user-centered process consisting of 4 phases (phases 1-4), we developed a message bank (n=43) deployed during our pilot study. There were no between-group differences in treatment adherence or acceptance. Interviews of participants indicated that the intervention was viewed positively. A total of 75% of SMS recipients responded to the messages, and 25% called the study staff to acknowledge receipt of text messages. This study has successfully developed a bank of SMS messages to support secondary antibiotic prophylaxis adherence. We have demonstrated the feasibility and acceptability of SMS technology in rheumatic heart disease care management. Future work will include integrating automated SMS into the Active Community Case Management Platform and a larger study of integrated SMS to reduce health care worker burden for patient support and improve adherence to secondary antibiotic prophylaxis.

Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures.

U.S. and Japanese Consumer Attitudes Toward Tailored and Targeted Communication with Human and Chatbot Agents

Consumers in the United States (n = 290) and Japan (n = 221) participated in 2 × 2 experiments designed to test effects of agent type (online human or chatbot) and communication strategy (targeted or tailored) on attitudes toward messages and agents. In both national samples, tailored messages were more effective in positively affecting attitudes. In addition, perceived humanness was found to mediate the relationships between agent type and attitudes such that when human or artificial agents are perceived as more human in text exchanges, perceived humanness in turn increases attitudes toward both the agents and messages. Hypothesized effects based on individualism-collectivism were not found. Theoretical and practical implications for designing interactive message strategy via conversational chatbots in an era of artificial intelligence (AI) and global communication are discussed.

I'm not upset–I get it: Effects of co-workers' stress cues on help-seekers' social diction and empathy in telecommuting

In both remote and physical work environments, it is commonplace for help-seeking messages to be rejected by other colleagues. This paper investigates how signifying co-workers’ stress status would influence the social diction and empathy of help-seekers in the context of rejection. 36 participants were recruited to perform help-seeking tasks with virtual co-workers via a professional mobile messaging application (Trillian). Their device was tailored with a vibrotactile mechanism (TacStatus), which could signify different emotional states of the co-workers: no-cue, relaxed, normal, and stressful. Independent sample Friedman nonparametric tests were conducted to analyze the social diction and empathy of the participants in their messages for help-seeking and responses to the co-workers’ rejection. This study revealed that stress cues have observable impacts on the social diction and empathy of help-seekers. Stressful and relaxed cues were found to evidently shape the social diction of help-seekers. When faced with a relaxed co-worker, the help-seeker felt disappointed and unaccepted after being rejected. By contrast, when confronted with a stressful cue, help-seekers tended to exhibit relatively more positive emotions after been rejected. This study attempts to reveal the mechanism through which stress cues influence professional messaging interactions and collaboration. The findings could provide implications for the design of socio-emotional cues in the context of messaging.

A blockchain-based scheme for edge–edge collaboration management in time-sensitive networking

Edge–edge collaboration is the main collaboration mode for edge computing (EC) in time-sensitive networking (TSN). This distributed collaboration mode, in which the computing work is implemented collaboratively by the edge computing nodes (ECNs) without the involvement of cloud servers, poses challenges to collaboration management. However, most existing methods of ECN collaboration only focus on the scheduling of computing resources rather than management process design and rarely consider the transparency, security, and fair distribution guarantee mechanism of collaboration benefits. To address this issue, we propose a blockchain-based and decentralized management scheme for ECNs autonomous collaboration in TSN. First, we design an edge–edge collaboration management framework and its workflow based on blockchain for EC in TSN (TECChain), which combines blockchain with TSN technology and EC paradigm. To solve the consensus problem in TECChain, the proof of diligence (PoD) and delegated PoD (DPoD) consensus mechanisms are designed. Furthermore, by leveraging the high-precision synchronous clock foundation of TSN, we propose two time-slot-driven consensus algorithms, named sequential decision-making based on DPoD (S-DPoD) and Byzantine fault tolerance based on DPoD (BFT-DPoD) respectively. Theoretical analysis results show that the proposed algorithms perform better in terms of security, transactions per second (TPS), energy saving, etc., compared to other algorithms. Comparison experiments reveal that S-DPoD consumes fewer interactive messages and has higher Byzantine fault tolerance and success rate of consensus than BFT-DPoD, but BFT-DPoD has lower transaction verification latency and larger TPS.

Smart contract generation for inter‐organizational process collaboration

SummaryCurrently, inter‐organizational process collaboration (IOPC) has been widely used in the design and development of distributed systems that support business process execution. Blockchain‐based IOPC can establish trusted data sharing among participants, attracting more and more attention. The core of such study is to translate the graphical model (e.g., BPMN) into program code called smart contract that can be executed in the blockchain environment. In this context, a proper smart contract plays a vital role in the correct implementation of block‐chain‐based IOPC. In fact, the quality of graphical model affects the smart contract generation. Problematic models (e.g., deadlock) will result in incorrect contracts (causing unexpected behaviors). To avoid this undesired implementation, this article explores to generate smart contracts by using the verified formal model as input instead of graphical model. Specifically, we introduce a prototype framework that supports the automatic generation of smart contracts, providing an end‐to‐end solution from modeling, verification, translation to implementation. One of the cores of this framework is to provide a CSP#‐based formalization for the BPMN collaboration model from the perspective of message interaction. This formalization provides precise execution semantics and model verification for graphical models, and a verified formal model for smart contract generation. Another novelty is that it introduces a syntax tree‐based translation algorithm to directly map the formal model into a smart contract. The required formalism, verification, and translation techniques are transparent to users without imposing additional burdens. Finally, a set of experiments shows the effectiveness of the framework.

Digital Health Interventions to Enhance Tuberculosis Treatment Adherence: Scoping Review.

Digital health technologies are widely used for disease management, with their computing platforms, software, and sensors being used for health care. These technologies are developed to manage chronic diseases and infectious bacterial diseases, including tuberculosis (TB). This study aims to comprehensively review the literature on the use of digital health interventions (DHIs) for enhancing TB treatment adherence and identify major strategies for their adoption. We conducted a literature search in the PubMed, Cochrane Library, Ovid Embase, and Scopus databases for relevant studies published between January 2012 and March 2022. Studies that focused on web-based or mobile phone-based interventions, medication adherence, digital health, randomized controlled trials, digital interventions, or mobile health and ubiquitous health technology for TB treatment and related health outcomes were included. We identified 27 relevant studies and classified them according to the intervention method, a significant difference in treatment success, and health outcomes. The following interventions were emphasized: SMS text messaging interventions (8/27, 30%), medicine reminders (6/27, 22%), and web-based direct observation therapy (9/27, 33%). Digital health technology significantly promoted disease management among individuals and health care professionals. However, only a few studies addressed 2-way communication therapies, such as interactive SMS text messaging and feedback systems. This scoping review classified studies on DHIs for patients with TB and demonstrated their potential for the self-management of TB. DHIs are still being developed, and evidence on the impact of digital technologies on enhancing TB treatment adherence remains limited. However, it is necessary to encourage patients' participation in TB treatment and self-management through bidirectional communication. We emphasize the importance of developing a communication system.

Chatbot ads with a human touch: A test of anthropomorphism, interactivity, and narrativity

Powered by artificial intelligence (AI), chatbots are increasingly capable of simulating human-like conversations. But, is this desirable for strategic communications? Will chatbots be more persuasive if they are more human-like, not only in their appearance but also in their interaction and delivery of advertising content? We explored these questions with a 2 (chatbot profile: human-like vs. machine-like) x 2 (message interactivity: high vs. low) x 2 (ad type: narrative vs. factual) experiment (N = 414). Data reveal that high message interactivity fosters positive attitudes toward the chatbot and the ad by mitigating violated expectancy. Narrative ads promote chatbot advertising through perceived transportation. A three-way interaction revealed that when a chatbot is machine-like in appearance, higher interactivity and adoption of a narrative style of delivery serve to increase ad persuasiveness by heightening social presence. Theoretical and practical implications for chatbot advertising are discussed.

Research on the mechanism of short video information interaction behavior of college students with psychological disorders based on grounded theory

BackgroundThe utilization of short videos by individuals often leads to the emergence of information exchange behavior. Previous studies have shown that certain students with psychological disorders exhibit addictive tendencies towards short video-related software. Therefore, it is essential to address the psychology and behavior of college students with psychological disorders while engaging with short videos. This study aims to explore the mechanism of short video information interaction behavior among college students with psychological disorders.MethodsWe conducted semi-structured interviews with 30 college students afflicted by psychological disorders in a prefecture-level city in Henan Province, China from September to December 2022. Based on the Grounded theory, we encoded 30 text materials across three levels to explore the mechanism of short video information interaction behavior among college students with psychological disorders, and subsequently build a model framework.ResultsThe findings of this study suggest that college students with psychological disorders exhibit negative cognition tendencies that can lead to strongly negative emotions, excacerbated by a lack of social support. These adverse factors collectively drive the consumption of short video content in this demographic, providing a virtual environment where they can fulfill their unmet social needs. Therefore, the mechanism governing short video messages interaction among college students with psychological disorders encompasses negative cognitive tendencies, negative emotions, lack of social support, post-video-watching behaviors, and the gratification of social needs within the confines of a virtual environment.ConclusionsThis study comprehensively analyzes the motivation and complexity of college students with psychological disorders in short video interaction. Although short videos provide this group with some ways of self-expression and emotional support, they still have a negative impact on their physical and mental health. The short video interaction of college students with psychological disorders is affected by many factors, including their negative cognitive tendencies, negative emotions, lack of social support, post-video-watching behaviors, and the gratification of social needs within the confines of a virtual environment. These findings deepened our understanding to the mechanism of short video information interaction behavior among college students with psychological disorders, also provided us with guidance on facilitating the proper use of short video and maintaining the mental health. In future researches, researchers can discuss more about intervention measures to help this demographic cope with the challenges from short video interaction.

RAGraph: A Region-Aware Framework for Geo-Distributed Graph Processing

In many global businesses of multinational enterprises, graph-structure data is usually geographically distributed in different regions to support low-latency services. Geo-distributed graph processing suffers from the Wide Area Networks (WANs) with scarce and heterogeneous bandwidth, thus essentially differs from traditional distributed graph processing. In this paper, we propose RAGraph, a Region-Aware framework for geo-distributed graph processing. At the core of RAGraph, we design a region-aware graph processing framework that allows advancing inefficient global updates locally and enables sensible coordination-free message interactions. RAGraph also contains an adaptive hierarchical message interaction engine to switch interaction modes adaptively based on network heterogeneity and fluctuation, and a discrepancy-aware message filtering strategy to filter important messages. Finally, the experiments show that RAGraph can achieve 1.69X - 40.53X speedup and 20.9% - 97% WAN cost reduction compared with state-of-the-art systems.

Communicating the benefits of quitting smoking on mental health increases motivation to quit in people with anxiety and/or depression

BackgroundAlthough smoking rates have declined over time, this decline has not been observed among those with mental health concerns. It is therefore important to develop effective messaging to support quitting in this population. MethodsWe conducted an online experiment with 419 adults who smoke cigarettes daily. Participants with, or without a lifetime history of anxiety and/or depression were randomized to view a message focused on the benefits of quitting smoking on mental or physical health. Participants then reported motivation to quit smoking, mental health concerns about quitting, and perceived effectiveness of the message. ResultsParticipants with a lifetime history of anxiety and/or depression who saw the message focused on the benefits of quitting smoking on mental health reported greater motivation to quit than when they saw a message focused on the benefits to physical health. This was not replicated when examining current symptoms instead of lifetime history. Pre-existing beliefs that smoking improves one’s mood were greater in those experiencing current symptoms and in those with a lifetime history of anxiety and/or depression. There was no main or interaction (message type X mental health status) effect of message type received on mental health related concerns about quitting. ConclusionsThis study is one of the first to evaluate a smoking cessation message with content specifically targeted to those with mental health concerns about quitting smoking. Additional work is needed to determine how to best target those with mental health concerns with messages focused on the benefits of quitting on mental health.

Legal knowledge management for prosecutors based on judgment prediction and error analysis from indictments

Legal AI aims to provide improved knowledge management services based on legal documents. Existing legal judgment prediction datasets mainly use court verdicts. However, for prosecutors, the use of indictments for judgment predictions can help detecting inconsistencies between predictions and prosecution, providing prosecutors with more accurate references to laws and charges through error analysis. In this study, we collect a dataset called TWLJP, which contains 342,754 indictments. We compared three possible messaging passing architectures among the law, regulation, and accusation cause prediction tasks, i.e. independent, topological, and interactive. The result shows that interactive message passing among the three tasks achieved the best Macro-F1 performance of 95.2 %, 79.62 %, and 65.84 % for laws, regulations, and accusation cause prediction, respectively. We further improve the prediction of accusation cause from 8.8 % macro-F1 to 62.3 % for underperformed accusation causes via Prompt-Based Learning. Finally, in view of the situation where the charge prediction are written in various ways, we adopted a lenient approach to assess the accusation and improved the accusation performance to 77.2 %.