Social cars : sensing, gathering, sharing and conveying social cues to road users

Intelligent Transport Systems (ITS) resembles the infrastructure for ubiquitous computing in the car. It encompasses a) all kinds of sensing technologies within vehicles as well as road infrastructure, b) wireless communication protocols for the sensed information to be exchanged between vehicles (V2V) and between vehicles and infrastructure (V2I), and c) appropriate intelligent algorithms and computational technologies that process these real-time streams of information. As such, ITS can be considered a game changer. It provides the fundamental basis of new, innovative concepts and applications, similar to the Internet itself. The information sensed or gathered within or around the vehicle has led to a variety of context-aware in-vehicular technologies within the car. A simple example is the Anti-lock Breaking System (ABS), which releases the breaks when sensors detect that the wheels are locked. We refer to this type of context awareness as vehicle/technology awareness. V2V and V2I communication, often summarized as V2X, enables the exchange and sharing of sensed information amongst cars. As a result, the vehicle/technology awareness horizon of each individual car is expanded beyond its observable surrounding, paving the way to technologically enhance such already advanced systems. In this chapter, we draw attention to those application areas of sensing and V2X technologies, where the human (driver), the human’s behavior and hence the psychological perspective plays a more pivotal role. The focal points of our project are illustrated in Figure 1: In all areas, the vehicle first (1) gathers or senses information about the driver. Rather than to limit the use of such information towards vehicle/technology awareness, we see great potential for applications in which this sensed information is then (2) fed back to the driver for an increased self-awareness. In addition, by using V2V technologies, it can also be (3) passed to surrounding drivers for an increased social awareness, or (4), pushed even further, into the cloud, where it is collected and visualized for an increased, collective urban awareness within the urban community at large, which includes all city dwellers.

Grouping people in social networks using a weighted multi-constraints clustering method

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0.

Analyzing the effectiveness of graph metrics for anomaly detection in online social networks

Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Experimental results on real-world data collected from online social networks show that the majority of users typically have friends who are friends themselves, whereas anomalous users’ graphs typically do not follow this common rule. Empirical analysis also shows that the relationship between average betweenness centrality and edges identifies anomalies more accurately than other approaches.

Higher degree research at Australian Universities : Responding to diversity in engineering and information technology

BACKGROUND There is increasing enrolment of international students in the Engineering and Information Technology disciplines and anecdotal evidence of a need for additional understanding and support for these students and their supervisors due to differences both in academic and social cultures. While there is a growing literature on supervisory styles and guidelines on effective supervision, there is little on discipline-specific, cross-cultural supervision responding to the growing diversity. In this paper, we report findings from a study of Engineering and Information technology Higher Degree Research (HDR)students and supervision in three Australian universities. PURPOSE The aim was to assess perceptions of students and supervisors of factors influencing success that are particular to international or culturally and linguistically diverse (CaLD) HDR students in Engineering and Information technology. DESIGN/METHOD Online survey and qualitative data was collected from international and CaLD HDR students and supervisors at the three universities. Bayesian network analysis, inferential statistics, and qualitative analysis provided the main findings. RESULTS Survey results indicate that both students and supervisors are positive about their experiences, and do not see language or culture as particularly problematic. The survey results also reveal strong consistency between the perceptions of students and supervisors on most factors influencing success. Qualitative analysis of critical supervision incidents has provided rich data that could help improve support services. CONCLUSIONS In contrast with anecdotal evidence, HDR completion data from the three universities reveal that international students, on average, complete in shorter time periods than domestic students. The analysis suggests that success is linked to a complex set of factors involving the student, supervision, the institution and broader community.

Social signal processing for pain monitoring using a hidden conditional random field

utomatic pain monitoring has the potential to greatly improve patient diagnosis and outcomes by providing a continuous objective measure. One of the most promising methods is to do this via automatically detecting facial expressions. However, current approaches have failed due to their inability to: 1) integrate the rigid and non-rigid head motion into a single feature representation, and 2) incorporate the salient temporal patterns into the classification stage. In this paper, we tackle the first problem by developing a “histogram of facial action units” representation using Active Appearance Model (AAM) face features, and then utilize a Hidden Conditional Random Field (HCRF) to overcome the second issue. We show that both of these methods improve the performance on the task of pain detection in sequence level compared to current state-of-the-art-methods on the UNBC-McMaster Shoulder Pain Archive.

The Effects of Personality in a Social Context

The contextuality of changing attitudes makes them extremely difficult to model. This paper scales up Quantum Decision Theory (QDT) to a social setting, using it to model the manner in which social contexts can interact with the process of low elaboration attitude change. The elements of this extended theory are presented, along with a proof of concept computational implementation in a low dimensional subspace. This model suggests that a society's understanding of social issues will settle down into a static or frozen configuration unless that society consists of a range of individuals with varying personality types and norms.

Privacy by Design : Does it matter for social networks?

Privacy is an important component of freedom and plays a key role in protecting fundamental human rights. It is becoming increasingly difficult to ignore the fact that without appropriate levels of privacy, a person’s rights are diminished. Users want to protect their privacy - particularly in “privacy invasive” areas such as social networks. However, Social Network users seldom know how protect their own privacy through online mechanisms. What is required is an emerging concept that provides users legitimate control over their own personal information, whilst preserving and maintaining the advantages of engaging with online services such as Social Networks. This paper reviews “Privacy by Design (PbD)” and shows how it applies to diverse privacy areas. Such an approach will move towards mitigating many of the privacy issues in online information systems and can be a potential pathway for protecting user’s personal information. The research has posed many questions in need of further investigation for different open source distributed Social Networks. Findings from this research will lead to a novel distributed architecture that provides more transparent and accountable privacy for the users of online information systems.

Empower me : social innovation design for homeless families : collective design creativity

Design Creativity has largely been explored as an individual expression of design cognition rather than as the collective manifestation of interaction in context. Recent approaches to design with an emphasis on co-design suggest that the problem-solution space co-evolves through social interaction. Socially Responsive Design for Social Innovation constitutes the most recent and perhaps the most promising domain of application for design thinking practices that emphasize collaborative innovation. In this paper, we describe the ideation of a service design solution for homeless families (Em.power.me), developed through consultation with a range of stakeholders over a three month period. This service design innovation aimed to visualise how such a service would operate and identify the potential benefits for all stakeholders. We focus here on the phases leading to the ideation of the service design.

Social Media Sites as Online Information Grounds : An exploratory study of the ways in which Information Technology (IT) professionals experience social media

Social media tools are often the result of innovations in Information Technology and developed by IT professionals and innovators. Nevertheless, IT professionals, many of whom are responsible for designing and building social media technologies, have not been investigated on how they themselves use or experience social media for professional purposes. This study will use Information Grounds Theory (Pettigrew, 1998) as a framework to study IT professionals’ experience in using social media for professional purposes. Information grounds facilitates the opportunistic discovery of information within social settings created temporarily at a place where people gather for a specific purpose (e.g., doctors’ waiting rooms, office tea rooms etc.), but the social atmosphere stimulates spontaneous sharing of information (Pettigrew, 1999). This study proposes that social media has the qualities that make it a rich information grounds; people participate from separate “places” in cyberspace in a synchronous manner in real-time, making it almost as dynamic and unplanned as physical information grounds. There is limited research on how social media platforms are perceived as a “place,” (a place to go to, a place to gather, or a place to be seen in) that is comparable to physical spaces. There is also no empirical study on how IT professionals use or “experience” social media. The data for this study is being collected through a study of IT professionals who currently use Twitter. A digital ethnography approach is being taken wherein the researcher uses online observations and “follows” the participants online and observes their behaviours and interactions on social media. Next, a sub-set of participants will be interviewed on their experiences with and within social media and how social media compares with traditional methods of information grounds, information communication, and collaborative environments. An Evolved Grounded Theory (Glaser, 1992) approach will be used to analyse tweets data and interviews and to map the findings against the Information Ground Theory. Findings from this study will provide foundational understanding of IT professionals’ experiences within social media, and can help both professionals and researchers understand this fast-evolving method of communications.

Social media metrics : are we measuring business value?

Over the past few years many organizations that directly or indirectly interact with consumers have invested heavily into a social media presence. As a consequence some success indicators are openly available to users of many social media platforms, such as the number of fans (or followers, members, visitors and others) or the amount of content(tweets, images, shares or other content). Many organizations additionally track their social activities internally to understand audience reach, consumer influence, brand image, consumer preference or other key metrics that make sense for a business. However, most of the immediately available social media success metrics are activity-based and many organizations are struggling with establishing a direct relationship to business success. This paper systematically reviews some of the common social media metrics/ratings used by organisations, critically analyse its business value and identify gaps formulating research questions for empirical study and concluding with recommendations and suggestions for future research.

Digital social signal processing - theorectical underpinning and research agenda

Organizations make increasingly use of social media in order to compete for customer awareness and improve the quality of their goods and services. Multiple techniques of social media analysis are already in use. Nevertheless, theoretical underpinnings and a sound research agenda are still unavailable in this field at the present time. In order to contribute to setting up such an agenda, we introduce digital social signal processing (DSSP) as a new research stream in IS that requires multi-facetted investigations. Our DSSP concept is founded upon a set of four sequential activities: sensing digital social signals that are emitted by individuals on social media; decoding online data of social media in order to reconstruct digital social signals; matching the signals with consumers’ life events; and configuring individualized goods and service offerings tailored to the individual needs of customers. We further contribute to tying loose ends of different research areas together, in order to frame DSSP as a field for further investigation. We conclude with developing a research agenda.

Recommending people in social networks using data mining

This thesis improves the process of recommending people to people in social networks using new clustering algorithms and ranking methods. The proposed system and methods are evaluated on the data collected from a real life social network. The empirical analysis of this research confirms that the proposed system and methods achieved improvements in the accuracy and efficiency of matching and recommending people, and overcome some of the problems that social matching systems usually suffer.

Information quality in social media : a conceptual model

Social Media (SM) is increasingly being integrated with business information in decision making. Unique characteristics of social media (e.g. wide accessibility, permanence, global audience, recentness, and ease of use) raise new issues with information quality (IQ); quite different from traditional considerations of IQ in information systems (IS) evaluation. This paper presents a preliminary conceptual model of information quality in social media (IQnSM) derived through directed content analysis and employing characteristics of analytic theory in the study protocol. Based in the notion of ‘fitness for use’, IQnSM is highly use and user centric and is defined as “the degree to which information is suitable for doing a specified task by a specific user, in a certain context”. IQnSM is operationalised as hierarchical, formed by the three dimensions (18 measures): intrinsic quality, contextual quality and representational quality. A research plan for empirically validating the model is proposed.

Gelatine : making coworking places gel for better collaboration and social learning

Public libraries and coworking spaces seek for means to facilitate peer collaboration, peer inspiration and cross-pollination of skills and creativity. However, social learning, inspiration and collaboration between coworkers do not come naturally. In particular in (semi-) public spaces, the behavioural norm among unacquainted coworkers is to work in individual silos without taking advantage of social learning or collaboration opportunities. This paper presents results from a pilot study of ‘Gelatine’ – a system that facilitates shared encounters between coworkers by allowing them to digitally ‘check in’ at a work space. Gelatine displays skills, areas of interest, and needs of currently present coworkers on a public screen. The results indicate that the system amplifies users’ sense of place and awareness of other coworkers, and serves as an interface for social learning through exploratory, opportunistic and serendipitous inspirations, as well as through helping users identify like-minded peers for follow-up face-to-face encounters. We discuss how Gelatine is perceived by users with different pre-entry motivations, and discuss users’ challenges as well as non-use of the system.