Distributing attention across multiple social worlds

Being a member of both local and global teams requires constant distribution and re-distribution of attention, engagement, and intensive communication over synchronous and asynchronous channels with remote and local partners. We explore in this paper the increasing number of social worlds such participants distribute their attention to, how this affects their level of engagement and attention, and how the workspace, collaboration technologies, and interaction modes afford and constrain the communicative events. The use of information and collaboration technologies (ICT) shapes and reshapes work spaces, processes, and social interactions among team members, and team members reshape ICT and the way it is used. We use as a testbed the AEC Global Teamwork course established at Stanford in 1993 in collaboration with universities worldwide and provide examples from two sites—Stanford PBL Lab, California USA and Chalmers University, Goteborg Sweden. We used temporal analysis and qualitative methods of inquiry in order to study participants’ attention, how they used the site and the means, i.e., their social and material resources, to accomplish their interaction needs, and how they engaged throughout project reviews.     

Using shared representations to improve coordination and intent inference

In groupware, users must communicate about their intentions and aintain common knowledge via communication channels that are explicitly designed into the system. Depending upon the task, generic communication tools like chat or a shared whiteboard may not be sufficient to support effective coordination. We have previously reported on a methodology that helps the designer develop task specific communication tools, called coordinating representations, for groupware systems. Coordinating representations lend structure and persistence to coordinating information. We have shown that coordinating representations are readily adopted by a user population, reduce coordination errors, and improve performance in a domain task. As we show in this article, coordinating representations present a unique opportunity to acquire user information in collaborative, user-adapted systems. Because coordinating representations support the exchange of coordinating information, they offer a window onto task and coordination-specific knowledge that is shared by users. Because they add structure to communication, the information that passes through them can be easily exploited by adaptive technology. This approach provides a simple technique for acquiring user knowledge in collaborative, user-adapted systems. We document our application of this approach to an existing groupware system. Several empirical results are provided. First, we show how information that is made available by a coordinating representation can be used to infer user intentions. We also show how this information can be used to mine free text chat for intent information, and show that this information further enhances intent inference. Empirical data shows that an automatic plan generation component, which is driven by information from a coordinating representation, reduces coordination errors and cognitive effort for its users. Finally, our methodology is summarized, and we present a framework for comparing our approach to other strategies for user knowledge acquisition in adaptive systems.     

Understanding motivated publics during disasters: Examining message functions,frames, and styles of social media influentials and followers

There has been a growing body of research regarding how organizations and general publics communicate on social media during crises. Integrating the uses and gratifications perspective, the social‐mediated crisis communication model, and the framing approach, our study offers a consolidated framework explaining how and why motivated social media publics communicate during crises. Namely, we examined whether and how influentials and followers employed different message functions through communicative devices including frames and styles. A content analysis was conducted on 800 tweets sampled from influentials and followers discussing the 2017 Ariana Grande concert bombing disaster. Our results (N = 800) suggested that influentials and followers adopted distinct communicative functions on social media during the disaster. Influentials engaged in information sharing and support exchange, whereas followers engaged in opinion expression and emotional coping. Influentials and followers also adopted specific frames and styles to achieve these communicative functions. Our findings can help crisis communicators understand the needs, concerns, and communication features of different publics and construct effective messages to reach them.     

Beyond Bandwidth: Dimensions of Connection in Interpersonal Communication

Computer-mediated communication (CMC) is a keystone of computer-supported collaborative work. Current CMC theory utilizes an information channel metaphor in which media vary according to how well they afford the transfer of messages in the channel, i.e., bandwidth. This paper draws attention to a different aspect of communication argued to be equally important: a relation between people that defines a state of communicative readiness in which fruitful communication is likely. Drawing on research on instant messaging (Nardi et al., 2000) and face to face communication (Nardi et al., 2002; Nardi and Whittaker, 2003), as well as related literature, three dimensions of connection that activate readiness are proposed: affinity, commitment, and attention. These dimensions comprise a field of connection between dyads. A field of connection is conceptualized as a labile, multidimensional space in which the values of the dimensions vary according to the history of communicative activity. Affinity, commitment, and attention are constantly monitored, negotiated, and managed through social bonding, expression of commitment, and capture of attention. The management of fields of connection requires significant interactional work to sustain communication over time.     

Microplanning with Communicative Intentions: The SPUD System

The process of microplanning in natural language generation (NLG) encompasses a range of problems in which a generator must bridge underlying domain‐specific representations and general linguistic representations. These problems include constructing linguistic referring expressions to identify domain objects, selecting lexical items to express domain concepts, and using complex linguistic constructions to concisely convey related domain facts. In this paper, we argue that such problems are best solved through a uniform, comprehensive, declarative process. In our approach, the generator directly explores a search space for utterances described by a linguistic grammar. At each stage of search, the generator uses a model of interpretation, which characterizes the potential links between the utterance and the domain and context, to assess its progress in conveying domain‐specific representations. We further address the challenges for implementation and knowledge representation in this approach. We show how to implement this approach effectively by using the lexicalized tree‐adjoining grammar (LTAG) formalism to connect structure to meaning and using modal logic programming to connect meaning to context. We articulate a detailed methodology for designing grammatical and conceptual resources which the generator can use to achieve desired microplanning behavior in a specified domain. In describing our approach to microplanning, we emphasize that we are in fact realizing a deliberative process of goal‐directed activity. As we formulate it, interpretation offers a declarative representation of a generator's communicative intent. It associates the concrete linguistic structure planned by the generator with inferences that show how the meaning of that structure communicates needed information about some application domain in the current discourse context. Thus, interpretations are plans that the microplanner constructs and outputs. At the same time, communicative intent representations provide a rich and uniform resource for the process of NLG. Using representations of communicative intent, a generator can augment the syntax, semantics, and pragmatics of an incomplete sentence simultaneously, and can work incrementally toward solutions for the various problems of microplanning.     

Rumor detection with self-supervised learning on texts and social graph

Rumor detection has become an emerging and active research field in recent years. At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth. However, existing works on rumor detection fall short in modeling heterogeneous information, either using one single information source only (e.g., social network, or post content) or ignoring the relations among multiple sources (e.g., fusing social and content features via simple concatenation).Therefore, they possibly have drawbacks in comprehensively understanding the rumors, and detecting them accurately. In this work, we explore contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better. Technically, we supplement the main supervised task of detection with an auxiliary self-supervised task, which enriches post representations via post self-discrimination.Specifically, given two heterogeneous views of a post (i.e., representations encoding social patterns and semantic patterns), the discrimination is done by maximizing the mutual information between different views of the same post compared to that of other posts. We devise cluster-wise and instance-wise approaches to generate the views and conduct the discrimination, considering different relations of information sources. We term this framework as self-supervised rumor detection (SRD). Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.     

Interdisciplinary communication medium for collaborative conceptual building design

Collaborative conceptual design involves intensive cross-disciplinary communication of design concepts and decisions. Difficulty in producing and expressing such information leads to extensive delays, miscommunication and confusion, which often have an impact upon the quality of the final design and the time required to achieve design concensus. Computer tools provide little support for the special needs for representation and reasoning posed by cross-disciplinary communication in collaborative conceptual building design. By building upon design theory, literature and observations for a case study of an actual building design project, we identify and devise computational strategies for addressing these needs. Our objective is to help improve the communication among design team members. Our test case focuses on the communication between architects and structural engineers. We propose a conceptual framework for interdisciplinary communication to support collaborative conceptual design and present a prototype called Interdisciplinary Communication Medium (ICM). Our conceptualization suggests that designers propose a shared form model, interpret the form model into discipline models, critique the discipline form models to derive behavior and compare it to function, and explain the results to other members of the team. We present this propose-interpret-critique-explain paradigm as a communication cycle for collaborative conceptual building design. We explore and test the conceptualization by modeling it with an experimental software prototype, ICM, that integrates graphic representations and AI reasoning about, the evolving building design. ICM provides a graphic environment as the central interface to reasoning tools to support collaborative design.     

Kolmogorov Complexity and Information Theory. With an Interpretation in Terms of Questions and Answers

We compare the elementary theories of Shannon information and Kolmogorov complexity, the extent to which they have a common purpose, and wherethey are fundamentally different. We discuss and relate the basicnotions of both theories: Shannon entropy, Kolmogorov complexity, Shannon mutual informationand Kolmogorov (``algorithmic') mutual information. We explainhow universal coding may be viewed as a middle ground betweenthe two theories. We consider Shannon's rate distortion theory, whichquantifies useful (in a certain sense) information.We use the communication of information as our guiding motif, and we explain howit relates to sequential question-answer sessions.     

Tension between perceived collocation and actual geographic distribution in project teams

This paper describes an exploratory comparative study of knowledge workers and their challenges in high tech global project teams. More specifically we focus on the tension between perceived collocation and actual geographical distributed project work as a function of: (1) the demand to distribute and shift attention in multi-teaming, (2) virtuality i.e. number of virtual teams participants engage in, (3) the continuous adjustment and re-adjustment to new places they perform their activity, and (4) the collaboration technologies they use. We present the methodology for data collection that included semi-structured interviews, surveys, and on site shadowing of the project participants, and discuss the findings from the data analysis. The study is based on the bricks-bits-interaction framework. It is at the intersection of the design of physical spaces, i.e., bricks; rich digital information and collaboration technology (ICT) content, mobile devices and network infrastructures, i.e., bits, and emergent work practices, process, and new ways people behave in communicative events using the affordances of ICT augmented physical, virtual spaces and digital content, i.e., interaction.     

Will mobile video become the killer application for 3G mobile Internet? a model of media convergence acceptance

We propose and empirically test a model of user acceptance of digital media convergence. Integrating information system research, particularly the paradigm of task–technology fit and the platform–application dichotomy, and theories from media psychology and human–computer interaction, we theorize the fit between the content applications and the media platform as the key determinant of user evaluation and acceptance of digital media convergence. We empirically tested our theory in the context of mobile video entertainment services. Specifically, we hypothesize that both the duration of the video content (i.e., the characteristic of the content) and the attentional constraint of the mobile media platform (i.e., the characteristic of the media platform) jointly determine users’ attentional involvement (i.e., reflecting the fit) in the video content. Such involvement in turn influences users’ emotional enjoyment and satisfaction with the mobile video entertainment service. The empirical results support most of our hypotheses. Two key findings are: (1) in the case of media convergence, the attentional constraint of the target media platform significantly influences user experience during content delivery that may lead to the rejection of media convergence; and (2) the duration of the content interacts with both the attentional constraint of the media platform and the type of interruptions from the environment in determining attentional involvement and emotional enjoyment. We discuss the theoretical contributions and practical implications from our empirical findings.     

Consistent practices in artifact-mediated collaboration

The design of collaborative representations faces a challenge in integrating theoretical communication models with the context-sensitive and creative practices of human interaction. This paper presents results from a study that identified multiple, invariant communicative practices in how dyads appropriated flexible, paper-based media in discussions of wicked problems. These invariants, identified across media, participants and topics are a promising first step towards creating an abstract model for design that connects representational affordances and communicative functions. The authors identify areas where this model may challenge conventional design wisdom and discuss directions for further research.     

基于深度学习的跨模态检索综述

由于多模态数据的快速增长,跨模态检索受到了研究者的广泛关注,其将一种模态的数据作为查询条件检索其他模态的数据,如用户可以用文本检索图像或/和视频。由于查询及其检索结果模态表征的差异,如何度量不同模态之间的相似性是跨模态检索的主要挑战。随着深度学习技术的推广及其在计算机视觉、自然语言处理等领域的显著成果,研究者提出了一系列以深度学习为基础的跨模态检索方法,极大缓解了不同模态间相似性度量的挑战,本文称之为深度跨模态检索。本文从以下角度综述有代表性的深度跨模态检索论文,基于所提供的跨模态信息将这些方法分为3类：基于跨模态数据间一一对应的、基于跨模态数据间相似度的以及基于跨模态数据语义标注的深度跨模态检索。一般来说,上述3类方法提供的跨模态信息呈现递增趋势,且提供学习的信息越多,跨模态检索性能越优。在上述不同类别下,涵盖了7类主流技术,即典型相关分析、一一对应关系保持、度量学习、似然分析、学习排序、语义预测以及对抗学习。不同类别下包含部分关键技术,本文将具体阐述其中有代表性的方法。同时对比提供不同跨模态数据信息下不同技术的区别,以阐述在提供了不同层次的跨模态数据信息下相关技术的关注点与使用异同。为评估不同的跨模态检索方法,总结了部分代表性的跨模态检索数据库。最后讨论了当前深度跨模态检索待解决的问题以及未来的研究方向。     

Optimizing social media message dissemination problem for emergency communication

Social media is increasingly being used as a communication bridge between government, emergency responders and managers, and the general public in extreme events. Passing information through social media channels enables individuals to send and receive content in real-time and without limitation of location and geography. While the use of social media in extreme event situations has become prevalent, there is often little strategy involved in message dissemination and too little understanding of the effects that underlying online social networks have on message distribution. In this study, we introduce a formal model for social media message dissemination in social networks through time. Our proposed model includes emphasis on single and multiple message scenarios and examines key communication characteristics in the development of more intentional and targeted social messaging strategies. We present a detailed experimental design on randomly generated networks and real-world sub-networks of the Twitter social graph and discuss our findings. We also include a Tabu Search procedure for solving single-message problem and discuss its potential value for large-scale problems in real-world applications.     

Integrating new information and communication technologies in a group decision support system

We view group decision making as a collaborative process, where decision makers can establish a common belief on the dimensions of the problem by following a series of well-defined communicative actions. Having first defined these actions, this paper reports on the exploitation of recent advances in information and communication technology, which can be used to: (i) remove the communication impediments among spatially dispersed decision makers; (ii) efficiently elicit and represent the domain of knowledge; (iii) develop efficient mechanisms to structure and consistently maintain the decision analysis; and (iv) automate the decision making process itself. Automation concerns coherence and consistency checking, detection of contradictions, truth maintenance, and information retrieval techniques.     

Multi-source shared nearest neighbours for multi-modal image clustering

Shared Nearest Neighbours (SNN) techniques are well known to overcome several shortcomings of traditional clustering approaches, notably high dimensionality and metric limitations. However, previous methods were limited to a single information source whereas such methods appear to be very well suited for heterogeneous data, typically in multi-modal contexts. In this paper, we propose a new technique to accelerate the calculation of shared neighbours and we introduce a new multi-source shared neighbours scheme applied to multi-modal image clustering. We first extend existing SNN-based similarity measures to the case of multiple sources and we introduce an original automatic source selection step when building candidate clusters. The key point is that each resulting cluster is built with its own optimal subset of modalities which improves the robustness to noisy or outlier information sources. We experiment our method in the scope of multi-modal search result clustering, visual search mining and subspace clustering. Experimental results on both synthetic and real data involving different information sources and several datasets show the effectiveness of our method.     

Kontextualisierung der Medienwahl mit Hilfe von Kommunikationsgenres

Value creation is increasingly organised in virtualised settings requiring effective computer-mediated communication. While media choice has been a topic of interest in Information Systems for some time, corresponding media choice theories exhibit a range of shortcomings with regard to applicability in context. Since the theories try to generalise across social contexts, their key constructs are rather abstract and underspecified with regard to application. Against this backdrop we present an approach for contextualising media choice using genre analysis. Genre analysis aims at identifying communication patterns (genres) in social communities (e. g. teams) as a structured overview of existing team communication. By juxtaposing requirements of the identified genres and media characteristics, we are able to propose a new set of media for improving team communication. We illustrate the application of our approach with a case example.     

Patterns for emerging application integration scenarios: A survey

The discipline of enterprise application integration (EAI) enables the decoupled communication between (business) applications, and thus became a cornerstone of today’s IT architectures. In 2004, the book by Hohpe and Woolf on Enterprise Integration Patterns (EIP) provided a fundamental collection of messaging patterns, denoting the building blocks of many EAI system implementations. Since then, multiple new trends and a broad range of new application scenarios have emerged, e. g., cloud and mobile computing, multimedia streams. These developments ultimately lead to conceptual changes and challenges such as larger data volumes (i. e., message sizes), a growing number of messages (i. e., velocity) and communication partners, and even more diverse message formats (i. e., variety). However, the research since 2004 focused on isolated EAI solutions, and thus a broader and integrated analysis of solutions and new patterns is missing. In this survey, we summarize new trends and application scenarios which serve as a frame to structure our survey of academic research on EIP, existing systems for EAI and also to classify integration patterns from these sources. We evaluate recently developed integration solutions and patterns in the context of real-world integration scenarios. Finally, we derive and summarize remaining challenges and open research questions.     

A review on communication protocols for autonomous unmanned aerial vehicles for inspection application

The communication system is a critical part of the system design for the autonomous Unmanned Aerial Vehicle (UAV). It has to address different considerations, including efficiency, reliability and mobility of the UAV. In addition, a multi-UAV system requires a communication system to assist information sharing, task allocation and collaboration in a team of UAVs. In this paper, we review communication solutions for supporting a team of UAVs while considering an application in the power line inspection industry. We provide a review of candidate wireless communication technologies for supporting communication in UAV applications. Performance measurements and UAV-related channel modeling of those candidate technologies are reviewed. A discussion of current technologies for building UAV mesh networks is presented. We then analyze the structure, interface and performance of robotic communication middleware, ROS and ROS2. Based on our review, the features and dependencies of candidate solutions in each layer of the communication system are presented.