Making sense of purpose, direction and innovation : an embedded design led innovation case study in the Australian mining industry

The mining equipment technology services sector is driven by a reactive and user-centered design approach, with a technological focus on incremental new product development. As Australia moves out of its sustained mining boom, companies need to rethink their strategic position, to become agile to stay relevant in an enigmatic market. This paper reports on the first five months on an embedded case study within an Australian, family-owned mining manufacturer. The first author is currently engaged in a longitudinal design led innovation project, as a catalyst to guide the company’s journey to design integration. The results find that design led innovation could act as a channel for highlighting and exploring company disconnections with the marketplace and offer a customer-centric catalyst for internal change. Data collected for this study is from 12 analysed semistructured interviews, a focus group and a reflective journal, over a five-month period. This paper explores limitations to design integration, and highlights opportunities to explore and leverage entrepreneurial characteristics to stay agile, broaden innovation and future-proof through the next commodity cycle in the mining industry.

Large-scale analysis of formations in soccer

Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels (e.g. strategic/tactical), effective and efficient strategies to deal with such data have yet to be deployed. A bottleneck restricting such solutions is the lack of a suitable representation (i.e. ordering of players) which is immune to the potentially infinite number of possible permutations of player orderings, in addition to the high dimensionality of temporal signal (e.g. a game of soccer last for 90 mins). Leveraging a recent method which utilizes a "role-representation", as well as a feature reduction strategy that uses a spatiotemporal bilinear basis model to form a compact spatiotemporal representation. Using this representation, we find the most likely formation patterns of a team associated with match events across nearly 14 hours of continuous player and ball tracking data in soccer. Additionally, we show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.

Predicting shot locations in tennis using spatiotemporal data

Over the past decade, vision-based tracking systems have been successfully deployed in professional sports such as tennis and cricket for enhanced broadcast visualizations as well as aiding umpiring decisions. Despite the high-level of accuracy of the tracking systems and the sheer volume of spatiotemporal data they generate, the use of this high quality data for quantitative player performance and prediction has been lacking. In this paper, we present a method which predicts the location of a future shot based on the spatiotemporal parameters of the incoming shots (i.e. shot speed, location, angle and feet location) from such a vision system. Having the ability to accurately predict future short-term events has enormous implications in the area of automatic sports broadcasting in addition to coaching and commentary domains. Using Hawk-Eye data from the 2012 Australian Open Men's draw, we utilize a Dynamic Bayesian Network to model player behaviors and use an online model adaptation method to match the player's behavior to enhance shot predictability. To show the utility of our approach, we analyze the shot predictability of the top 3 players seeds in the tournament (Djokovic, Federer and Nadal) as they played the most amounts of games.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

Swimmer localization from a moving camera

At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

Integrating time forgetting mechanisms into topic-based user interest profiling

The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

Acquiring user information needs for recommender systems

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.

Ontology-based product's reputation model

Different reputation models are used in the web in order to generate reputation values for products using uses' review data. Most of the current reputation models use review ratings and neglect users' textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And that's why the use of users' textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for users' extracted opinions about products' features from users' text. Our model uses features ontology in order to define general features and sub-features of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.

Protecting web 2.0 services from botnet exploitations

Recently, botnet, a network of compromised computers, has been recognized as the biggest threat to the Internet. The bots in a botnet communicate with the botnet owner via a communication channel called Command and Control (C & C) channel. There are three main C & C channels: Internet Relay Chat (IRC), Peer-to-Peer (P2P) and web-based protocols. By exploiting the flexibility of the Web 2.0 technology, the web-based botnet has reached a new level of sophistication. In August 2009, such botnet was found on Twitter, one of the most popular Web 2.0 services. In this paper, we will describe a new type of botnet that uses Web 2.0 service as a C & C channel and a temporary storage for their stolen information. We will then propose a novel approach to thwart this type of attack. Our method applies a unique identifier of the computer, an encryption algorithm with session keys and a CAPTCHA verification.

Improving US airport taxicab services through governance arrangements

Most airports internationally have implemented customer satisfaction programs into their operations to increase non-aeronautical revenues. In the US, taxicabs are an essential airport transport mode given the limited public transport options available. Effective airport taxicab planning can increase airport customer satisfaction levels, as well as facilitate handling increased airport passenger volumes. However, little is known on how US airports have adapted their governance practices from a traditional hierarchical to a network approach in their efforts to undertake airport taxicab planning initiatives since the deregulation of the transportation industry. Data acquired from 51 US hub airports is used to examine their existing taxicab planning practices. The findings offer how US airports can modify governance processes in their airport taxicab planning processes to better support increases in the customer satisfaction levels of airport taxicab patrons.

Performing Rural Masculinities : A Case Study of Diggers and Dealers

This chapter examines what Parpart and Zalewski (2008) label 'the man question' in terms of the rural. That is, 'how masculinity comes to be "made" as a continuing process within the social context' of rural places and spaces (Kerfoot and Knights 1993: 662). Our understanding of masculinities as discursively produced, relational, multiple and changing is given empirical force through a case study of the annual resource conference, Diggers and Dealers. The conference, held in the remote mining town of Kalgoorlie in Western Australia since 1992, is today the largest international meeting for the resource sector, attracting over 2000 attendees. Through an analysis of 120 texts related to the conference from 2006 to the present, including media repotis, blogs and conference programs and speeches, we demonstrate how, what is essentially a corporate event, is imported into the rural and constructed through the intersecting discourses of rurality, masculinity and heterosexuality. That is, though the first such meeting may have taken place spontaneously in Kalgoorlie, the delegates, and the 'skimpy' barmaids who serve patrons in their underwear or sometimes topless and are seen as central to the event, are flown into town for the conference. Kalgoorlie, as a working mining community on the edge of the deseti, provides both spectacle and conditions for the enactment of frontier masculinity not possible in the metropole.

Modernismens förespråkare : Gösta Stenman och hans konstsalong

This dissertation, based on material from Stenman s vast private archive, examines the role played by Swedish-speaking Finnish art dealer Gösta Stenman (1888-1947) and his art gallery, Stenmans Konstsalong, in the Finnish and Swedish art worlds from 1911 to 1947. This archive is examined here for the first time. The analytical framework used for this empirical study derives from Pierre Bourdieu s sociological theories. An art-sociological approach allows for the inclusion of more mechanisms at work in the art world than are typically embraced in such inquiries. This approach provides a fuller understanding of how Stenman attained his standing and central role in the art world in Finland as well as Sweden; enabling us to appreciate how he came to occupy such a prominent position in current art historical writing. All of these issues constitute new areas of research. Taking his cues from the contemporary art world of Paris, Stenman became the year 1914 a modern art dealer like no other in the Nordic countries. This dissertation represents the first academic investigation into his operations, strategies, and objectives, offering insight into not only the art dealer himself but also the functioning of the art market one of the most vital aspects of the art world. A by-product of this work, is that the modern art market in Finland is portrayed, including essential issues related to its growth and development as well as how it altered the conditions under which art could be produced, exhibited and promoted and what this entailed for the art world at large, artists and patrons alike. This first systematic analysis of the operations of Stenman s Konstsalong offers greater understanding of the art worlds of Sweden and Finland in the early twentieth century. The work also looks at how an agent of the art market could move between the fields of art in Sweden and Finland. The manner in which Stenman promoted individual artists, including his relationships with Tyko Sallinen, Helene Schjerfbeck, Juho Mäkelä, Jalmari Ruokokoski, Siri Derkert, Esther Kjerner, Eva Bagge, and many others, also falls within this purview. Stenman s contract with Sallinen from 1913 stands out as a new phenomenon in Finnish art promotion, whereby an artistic career became established via a far-sighted, strategic promotional program. The case study of Stenman s promotion of Schjerfbeck in Sweden provides evidence of the increasingly advanced nature of Stenman s strategies. The title of the dissertation, The Promoter of Modernism, attempts to convey that Stenman was the consummate modernist, modern in his thoughts, his actions, and his approach to art. Keywords: Gösta Stenman, Stenmans konstsalong, Stenmans dotter, art market, modernism, collecting, Novembergruppen, Helene Schjerfbeck, Tyko Sallinen, Juho Mäkelä, Jalmari Ruokokoski, Wäinö Aaltonen, Siri Derkert, Åke Göransson, Esther Kjerner, Eva Bagge.

Understanding data flow and security requirements in wireless Body Area Networks for healthcare

The Body Area Network (BAN) is an emerging technology that focuses on monitoring physiological data in, on and around the human body. BAN technology permits wearable and implanted sensors to collect vital data about the human body and transmit it to other nodes via low-energy communication. In this paper, we investigate interactions in terms of data flows between parties involved in BANs under four different scenarios targeting outdoor and indoor medical environments: hospital, home, emergency and open areas. Based on these scenarios, we identify data flow requirements between BAN elements such as sensors and control units (CUs) and parties involved in BANs such as the patient, doctors, nurses and relatives. Identified requirements are used to generate BAN data flow models. Petri Nets (PNs) are used as the formal modelling language. We check the validity of the models and compare them with the existing related work. Finally, using the models, we identify communication and security requirements based on the most common active and passive attack scenarios.

Orígenes, elementos determinantes y resultados de un exitoso proceso de colaboración entre competidores y otros agentes: el cluster de la Alta Cocina Vasca

[ES] El País Vasco es internacionalmente reconocido por su gastronomía y sus grandes cocineros; de hecho, es el territorio del mundo con más estrellas Michelin por kilómetro cuadrado. Esta notoriedad e imagen repercuten muy positivamente en todo el sector gastronómico y en la imagen y proyección turística del País Vasco y se ha logrado gracias a la labor sostenida de un grupo inicial de cocineros, a los que siguieron otros, que realizan importantes esfuerzos de colaboración, sin dejar de competir entre ellos (tratándose de un claro ejemplo de coopetition). El análisis de la relación entre estos grandes cocineros vascos y su entorno, permite identificar un cluster que actualmente se encuentra en fase de madurez con un futuro esperanzador y que ha arrojado importantes beneficios al sector, a cada uno de sus integrantes y a la región en su conjunto muy especialmente en términos de innovación, notoriedad y reputación. Para la realización de este trabajo se ha utilizado, además de la revisión bibliográfica y documental pertinente, una metodología cualitativa, consistente en la realización de entrevistas en profundidad a los siete cocineros fundadores y patronos del Basque Culinary Center (primera Facultad Universitaria de Estudios Gastronómicos de Europa, dependiente de la Universidad de Mondragón). El trabajo es uno de los frutos extraídos de un contrato de colaboración entre el Instituto de Economía Aplicada a la empresa de la UPV/EHU e Innobasque (Agencia Vasca para la Innovación), en el que esta última fijó tanto los objetivos de la investigación como la metodología a utilizar.