VIENA2: A Driving Anticipation Dataset

Action anticipation is critical in scenarios where one needs to react before the action is finalized. This is, for instance, the case in automated driving, where a car needs to, e.g., avoid hitting pedestrians and respect traffic lights. While solutions have been proposed to tackle subsets of the driving anticipation tasks, by making use of diverse, task-specific sensors, there is no single dataset or framework that addresses them all in a consistent manner. In this paper, we therefore introduce a new, large-scale dataset, called VIENA2, covering 5 generic driving scenarios, with a total of 25 distinct action classes. It contains more than 15K full HD, 5s long videos acquired in various driving conditions, weathers, daytimes and environments, complemented with a common and realistic set of sensor measurements. This amounts to more than 2.25M frames, each annotated with an action label, corresponding to 600 samples per action class. We discuss our data acquisition strategy and the statistics of our dataset, and benchmark state-of-the-art action anticipation techniques, including a new multi-modal LSTM architecture with an effective loss function for action anticipation in driving scenarios.Comment: Accepted in ACCV 201

Find cancer early: Evaluation of a community education campaign to increase awareness of cancer signs and symptoms in people in regional Western Australians

Introduction: Cancer outcomes for people living in rural and remote areas are worse than for those living in urban areas. Although access to and quality of cancer treatment are important determinants of outcomes, delayed presentation has been observed in rural patients. Methods: Formative research with people from rural Western Australia (WA) led to the Find Cancer Early campaign. Find Cancer Early was delivered in three regions of WA, with two other regions acting as controls. Staff delivered the campaign using a community engagement approach, including promotion in local media. Television communications were not used to minimize contamination in the control regions. The campaign evaluation was undertaken at 20 months via a computer-assisted telephone interview (CATI) survey comparing campaign and control regions. The primary outcome variable was knowledge of cancer signs and symptoms. Results: Recognition and recall of Find Cancer Early and symptom knowledge were higher in the campaign regions. More than a quarter of those who were aware of the campaign reported seeing the GP as a result of their exposure. Conclusion: Despite limited use of mass media, Find Cancer Early successfully improved knowledge of cancer symptoms and possibly led to changes in behavior. Social marketing campaigns using community development can raise awareness and knowledge of a health issue in the absence of television advertising

Personalised mobile services supporting the implementation of clinical guidelines

Telemonitoring is emerging as a compelling application of Body Area Networks (BANs). We describe two health BAN systems developed respectively by a European team and an Australian team and discuss some issues encountered relating to formalization of clinical knowledge to support real-time analysis and interpretation of BAN data. Our example application is an evidence-based telemonitoring and teletreatment application for home-based rehabilitation. The application is intended to support implementation of a clinical guideline for cardiac rehabilitation following myocardial infarction. In addition to this the proposal is to establish the patient’s individual baseline risk profile and, by real-time analysis of BAN data, continually re-assess the current risk level in order to give timely personalised feedback. Static and dynamic risk factors are derived from literature. Many sources express evidence probabilistically, suggesting a requirement for reasoning with uncertainty; elsewhere evidence requires qualitative reasoning: both familiar modes of reasoning in KBSs. However even at this knowledge acquisition stage some issues arise concerning how best to apply the clinical evidence. Furthermore, in cases where insufficient clinical evidence is currently available, telemonitoring can yield large collections of clinical data with the potential for data mining in order to furnish more statistically powerful and accurate clinical evidence

The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

The Internet of Things (IoT) is a dynamic global information network consisting of internet-connected objects, such as Radio-frequency identification (RFIDs), sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organisations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. More importantly, we identify the trends, opportunities and open challenges in the industry-based the IoT solutions. Based on the application domain, we classify and discuss these solutions under five different categories: smart wearable, smart home, smart, city, smart environment, and smart enterprise. This survey is intended to serve as a guideline and conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201

Spot the Difference! Visual plagiarism in the visual arts.

Over recent years there has been considerable investment in the use of technology to identify sources of text-based plagiarism in universities. However, students of the visual arts are also required to complete numerous pieces of visual submissions for assessment, and yet very little similar work has been undertaken in the area of non-text based plagiarism detection. The Spot the Difference! project (2011-2012), funded by JISC and led by the University for the Creative Arts, seeks to address this gap by piloting the use of visual search tools developed by the University of Surrey and testing their application to support learning and teaching in the arts and specifically to the identification of visual plagiarism. Given that most commonly used search technologies rely on text, the identification and evidencing of visual plagiarism is often left to the knowledge and experience of academic staff, which can potentially result in inconsistency of detection, approach, policies and practices. This paper outlines the work of the project team, who sought to investigate the nature, scope and extent of visual plagiarism in the arts education sector

Storytelling Security: User-Intention Based Traffic Sanitization

Malicious software (malware) with decentralized communication infrastructure, such as peer-to-peer botnets, is difficult to detect. In this paper, we describe a traffic-sanitization method for identifying malware-triggered outbound connections from a personal computer. Our solution correlates user activities with the content of outbound traffic. Our key observation is that user-initiated outbound traffic typically has corresponding human inputs, i.e., keystroke or mouse clicks. Our analysis on the causal relations between user inputs and packet payload enables the efficient enforcement of the inter-packet dependency at the application level. We formalize our approach within the framework of protocol-state machine. We define new application-level traffic-sanitization policies that enforce the inter-packet dependencies. The dependency is derived from the transitions among protocol states that involve both user actions and network events. We refer to our methodology as storytelling security. We demonstrate a concrete realization of our methodology in the context of peer-to-peer file-sharing application, describe its use in blocking traffic of P2P bots on a host. We implement and evaluate our prototype in Windows operating system in both online and offline deployment settings. Our experimental evaluation along with case studies of real-world P2P applications demonstrates the feasibility of verifying the inter-packet dependencies. Our deep packet inspection incurs overhead on the outbound network flow. Our solution can also be used as an offline collect-and-analyze tool

Behavioural Evidence Analysis Applied to Digital Forensics: An Empirical Analysis of Child Pornography Cases using P2P Networks

The utility of Behavioural Evidence Analysis (BEA) has gained attention in the field of Digital Forensics in recent years. It has been recognized that, along with technical examination of digital evidence, it is important to learn as much as possible about the individuals behind an offence, the victim(s) and the dynamics of a crime. This can assist the investigator in producing a more accurate and complete reconstruction of the crime, in interpreting associated digital evidence, and with the description of investigative findings. Despite these potential benefits, the literature shows limited use of BEA for the investigation of cases of the possession and dissemination of Sexually Exploitative Imagery of Children (SEIC). This paper represents a step towards filling this gap. It reports on the forensic analysis of 15 SEIC cases involving P2P filesharing networks, obtained from the Dubai Police. Results confirmed the predicted benefits and indicate that BEA can assist digital forensic practitioners and prosecutors

Reactive Public Relations Strategies for Managing Fake News in the Online Environment

The aim of this conceptual paper is to discuss the issue of managing fake news in the online environment, from an organizational perspective, by using reactive PR strategies. First, we critically discuss the most important definitions of the umbrella term fake news, in the so-called post-truth era, in order to emphasize different challenges in conceptualizing this elusive social phenomenon. Second, employing some valuable contribution from literature, we present and illustrate with vivid examples 10 categories of fake news. Each type of fake news is discussed in the context of organizational communication. Based on existent literature, we propose a 3D conceptual model of fake news, in an organizational context. Furthermore, we consider that PR managers can use either reactive PR strategies to counteract online fake news regarding an organization, or communication stratagems to temporarily transform the organization served into a potential source of fake news. The existing typology of reactive public relations strategies from the literature allow us to discuss the challenge of using them in counteracting online fake news. Each reactive PR strategy can be a potential solution to respond to different types of online fake news. Although these possibilities seem to be extensive, in some cases, PR managers can find them ineffective. In our view, this cluster of reactive PR strategies is not a panacea for managing fake news in the online environment and different strategic approaches may be need, such as communication stratagems. In this context, communication stratagems consist in using organization as a source or as a vector for strategic creation and dissemination of online fake news, for the benefit of the organization. We conclude that within online environment PR managers can employ a variety of reactive PR strategies to counteract fake news, or different communication stratagems to achieve organizational goals

Why Are People's Decisions Sometimes Worse with Computer Support?

In many applications of computerised decision support, a recognised source of undesired outcomes is operators' apparent over-reliance on automation. For instance, an operator may fail to react to a potentially dangerous situation because a computer fails to generate an alarm. However, the very use of terms like "over-reliance" betrays possible misunderstandings of these phenomena and their causes, which may lead to ineffective corrective action (e.g. training or procedures that do not counteract all the causes of the apparently "over-reliant" behaviour). We review relevant literature in the area of "automation bias" and describe the diverse mechanisms that may be involved in human errors when using computer support. We discuss these mechanisms, with reference to errors of omission when using "alerting systems", with the help of examples of novel counterintuitive findings we obtained from a case study in a health care application, as well as other examples from the literature