Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories -- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are avaible at: https://github.com/lightChaserX/Awesome-Hetero-reIDComment: Accepted by IJCAI 2020. Project url: https://github.com/lightChaserX/Awesome-Hetero-reI

Adaptive Automated Machine Learning

The ever-growing demand for machine learning has led to the development of automated machine learning (AutoML) systems that can be used off the shelf by non-experts. Further, the demand for ML applications with high predictive performance exceeds the number of machine learning experts and makes the development of AutoML systems necessary. Automated Machine Learning tackles the problem of finding machine learning models with high predictive performance. Existing approaches incorporating deep learning techniques assume that all data is available at the beginning of the training process (offline learning). They configure and optimise a pipeline of preprocessing, feature engineering, and model selection by choosing suitable hyperparameters in each model pipeline step. Furthermore, they assume that the user is fully aware of the choice and, thus, the consequences of the underlying metric (such as precision, recall, or F1-measure). By variation of this metric, the search for suitable configurations and thus the adaptation of algorithms can be tailored to the user’s needs. With the creation of a vast amount of data from all kinds of sources every day, our capability to process and understand these data sets in a single batch is no longer viable. By training machine learning models incrementally (i.ex. online learning), the flood of data can be processed sequentially within data streams. However, if one assumes an online learning scenario, where an AutoML instance executes on evolving data streams, the question of the best model and its configuration remains open. In this work, we address the adaptation of AutoML in an offline learning scenario toward a certain utility an end-user might pursue as well as the adaptation of AutoML towards evolving data streams in an online learning scenario with three main contributions: 1. We propose a System that allows the adaptation of AutoML and the search for neural architectures towards a particular utility an end-user might pursue. 2. We introduce an online deep learning framework that fosters the research of deep learning models under the online learning assumption and enables the automated search for neural architectures. 3. We introduce an online AutoML framework that allows the incremental adaptation of ML models. We evaluate the contributions individually, in accordance with predefined requirements and to state-of-the- art evaluation setups. The outcomes lead us to conclude that (i) AutoML, as well as systems for neural architecture search, can be steered towards individual utilities by learning a designated ranking model from pairwise preferences and using the latter as the target function for the offline learning scenario; (ii) architectual small neural networks are in general suitable assuming an online learning scenario; (iii) the configuration of machine learning pipelines can be automatically be adapted to ever-evolving data streams and lead to better performances

Change Factors in Enterprise 2.0 Initiatives: A multi-case comparison

The growing importance of Enterprise 2.0 is not adequately reflected in research on its implementation. This study contributes to understanding the change factors specific to Enterprise 2.0 initiatives. It draws upon grounded theory to compare sixteen case studies, integrates the findings in the context of socio-technical change and discusses similarities and differences compared to the field of ERP. The resulting change factors specific to Enterprise 2.0 initiatives can support practitioners in avoiding pitfalls of change management and present a starting point for researchers to empirically investigate change in Enterprise 2.0 initiatives

Structuring the Front-End of Innovation: An Empirical Analysis of the Role of Perceived Contextual Factors on Intended Applications and Proficiency of Innovation Fields

The global economy has gained momentum in recent years, with advances in technology and digitalization leading to shorter product life cycles, increased competition, and transformed industries. These circumstances call for the need for constant innovation. Organizations are required to act and adapt quickly to technological changes, dynamic markets, competitive threats, and rapidly altering customer needs, without losing focus of their established business. Two notions are important for organizations in this setting: (1) reaching ambidexterity and (2) structuring the front-end of innovation. Ambidextrous companies, which own the ability to balance between innovation activities that exploit current competencies (exploitation) and those that explore new competencies (exploration), are more successful than companies which concentrate on only one of these activities (Gibson & Birkinshaw, 2004; He & Wong, 2004; Jansen, Van Den Bosch, & Volberda, 2006; Katila & Ahuja, 2002; C. Kim, Song, & Nerkar, 2012). However, both exploration and exploitation require the allocation of resources, causing a trade-off, which makes it difficult to perform the combination of both (Greve, 2007; Levinthal & March, 1993). Previous research does not focus on how organizations can adapt their innovation activities in order to reach ambidexterity (Cantarello, Martini, & Nosella, 2012; Judge & Blocker, 2008; Z. Wei, Yi, & Guo, 2014). Managing innovations poses an increasingly daunting task for organizations, demanding different requirements regarding the innovation management process. Managing innovation through a structured innovation process facilitates the creation and planning of innovation to transform ideas into marketable products. The first stage of this process – the front-end of innovation – is of significant meaning, since activities in the front-end of innovation are strongly linked to innovation success (Dwyer & Mellor, 1991; Markham, 2013; Moenart, De Meyer, Souder, & Deschoolmeester, 1995; Reid & de Brentani, 2004). The creation of value and competitive advantage takes primarily place in the front-end of innovation, and the actual costs of mismanagement can only be discovered at later stages (Markham, 2013; Reid & de Brentani, 2004; P. Smith & Reinertsen, 1991). A concept to foster ambidexterity and structure the front-end of innovation described mainly by practitioners are so-called innovation fields (Cooper, Edgett, & Kleinschmidt, 2004; Crawford, 1980; Hambrick & Fredrickson, 2001; Khurana & Rosenthal, 1998; Reid & de Brentani, 2004; Talke, Salomo, & Rost, 2010). Innovation fields establish guidelines that determine search strategy, scope, depth, and locus of innovation search by setting search boundaries. Literature describes different types of applications for innovation fields such as strategic purposes, ideation, lifting synergies, technology intelligence and portfolio extension. With innovation fields, organizations (1) can structure the front-end of innovation and align corporate objectives to innovation activities and (2) have an instrument at hand to facilitate the shift of resources and to prioritize innovation activities according to the balance between exploitation and exploration, thereby fostering ambidexterity. However, research on innovation fields is scarce, thus, the objective of this dissertation is to examine how and why perceived contextual factors influence the intended application and perceived proficiency of innovation fields in the front-end of innovation. The theoretical foundation is based on the theory of organizational learning. A research framework is derived from acknowledged literature, focusing on (1) strategic orientation, (2) organizational context and (3) external environment as main contextual factors influencing the intended application of innovation fields. An explorative research design is followed, composed of an embedded single case study design using a mixed-methods approach. As a case, a corporate R&D division of a Germany-based company is selected. First, a qualitative study with semi-structured interviews is conducted, followed by a quantitative survey to get a more comprehensive picture of the role of perceived contextual factors influencing intended innovation field applications and proficiency. Based on the underlying empirical research, distinct differences regarding perceived contextual factors and their influence on intended innovation field applications and proficiency have been identified. Notably, the perceived contextual factors vary across the different types of applications for innovation fields. Overall, the strategic orientation and external environment have a strong influence on the intended innovation field applications and proficiency, while organizational context only play a minor role. Furthermore, the findings substantiate the use of different types of applications for innovation fields in the front-end of innovation. This study contributes to theory by creating a research framework linking perceived contextual factors to intended innovation field applications and proficiency. Finally, this dissertation delivers a comprehensive description of innovation field applications. The findings enhance the existing body of knowledge regarding innovation research, specifically regarding the front-end of innovation and innovation fields as well as organizational learning. Besides the advancement of scientific knowledge, managerial implications are drawn for the application of innovation fields in a corporate context

Implementation of IT service management in Australia: case studies focusing on organisational change strategies

In order to provide better Information Technology (IT) services to their customers, organisations are increasingly implementing the IT Infrastructure Library (ITIL). ITIL describes processes, procedures, tasks, and checklists that can be applied by an organisation for establishing integration with its own strategies, delivering value, and maintaining a minimum level of competency. It allows the organisation to establish a baseline from which it can plan, implement, and measure improvements as well as compliance. Whilst the promise of ITIL implementation is compelling, many implementations do not achieve the anticipated outcomes. The extant literature provides very little information as to how ITIL is implemented or the organisational change strategies that organisations use to implement it. To find a point of comparison, study of Enterprise Resource Planning (ERP) and Business Process Reengineering (BPR) projects have found that there are similarities in this change processes with those observed during ITIL implementations. Research into the implementation of ERP and BPR has found that applying a formal change management strategy, incorporating a Socio-Technical Systems (STS) approach, can assist in the likelihood of a successful implementation. This exploratory research was conducted using a multiple case study approach to investigate the organisational change strategies that organisations have applied to ITIL implementation. This research also sought to identify if using STS as an approach to organisational change can influence the success of ITIL implementations. In this research, one particular STS model, the Leavitt Diamond, was applied in order to investigate the inter-relationships between the STS components and how they are affected during the implementation of ITIL. This research found that organisations were not deliberately selecting and applying an organisational change strategy during implementation. Interestingly though, without knowing it or doing so consciously, the organisations adopted similar organisational change approaches and types for their ITIL implementations. All eight of the organisations studied adopted a planned change approach with a phased ITIL implementation. The research found that although none of the organisations deliberately applied an STS approach to their ITIL implementations, they did without realising, apply an STS approach. The ITIL implementation was found to require greater effort to be applied to the people component of the STS; followed by process, technology and structure. The research also found that an appropriate amount of effort was necessary for each of the individual STS components; rather than an equal effort per component. This research has developed new insights into organisational change strategies and ITIL implementations that had not previously been explored. Through this research, an ITIL STS Model of Organisational Change has been produced that provides a plan and overview of the primary decisions to be made, with the resultant actions, in response to changes to the STS components. This research provides ITIL practitioners, for the first time, information about organisational change strategies as they have been applied to successful ITIL implementations; as well as a model that may assist with developing their own organisational change strategies