Verifying baselines for crisis event information classification on Twitter

Social media are rich information sources during and in the aftermath of crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained which can assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it is rare that code, trained models, or exhaustive parametrisation details are made openly available. Thus, even confirmation of self-reported performance is problematic; authoritatively determining the state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper seeks to make 3 contributions: 1) the replication and results confirmation of a leading (and generalisable) technique; 2) testing straightforward modifications of the technique likely to improve performance; and 3) the extension of the technique to a novel and complimentary type of crisis-relevant information to demonstrate it’s generalisability

Dynamic Data Mining: Methodology and Algorithms

Supervised data stream mining has become an important and challenging data mining task in modern organizations. The key challenges are threefold: (1) a possibly infinite number of streaming examples and time-critical analysis constraints; (2) concept drift; and (3) skewed data distributions. To address these three challenges, this thesis proposes the novel dynamic data mining (DDM) methodology by effectively applying supervised ensemble models to data stream mining. DDM can be loosely defined as categorization-organization-selection of supervised ensemble models. It is inspired by the idea that although the underlying concepts in a data stream are time-varying, their distinctions can be identified. Therefore, the models trained on the distinct concepts can be dynamically selected in order to classify incoming examples of similar concepts. First, following the general paradigm of DDM, we examine the different concept-drifting stream mining scenarios and propose corresponding effective and efficient data mining algorithms. • To address concept drift caused merely by changes of variable distributions, which we term pseudo concept drift, base models built on categorized streaming data are organized and selected in line with their corresponding variable distribution characteristics. • To address concept drift caused by changes of variable and class joint distributions, which we term true concept drift, an effective data categorization scheme is introduced. A group of working models is dynamically organized and selected for reacting to the drifting concept. Secondly, we introduce an integration stream mining framework, enabling the paradigm advocated by DDM to be widely applicable for other stream mining problems. Therefore, we are able to introduce easily six effective algorithms for mining data streams with skewed class distributions. In addition, we also introduce a new ensemble model approach for batch learning, following the same methodology. Both theoretical and empirical studies demonstrate its effectiveness. Future work would be targeted at improving the effectiveness and efficiency of the proposed algorithms. Meantime, we would explore the possibilities of using the integration framework to solve other open stream mining research problems

Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

Persistence of, and interrelation between, horizontal and vertical technology alliances.

The authors explore to what extent there is persistence in, and interrelation between, alliance strategies with different partner types (customers, suppliers, competitors). In a panel data set of innovation-active firms in the Netherlands from 1996 to 2004, the authors find persistence in alliance strategies with all three types of partners, but customer alliance strategies are more persistent than supplier alliance strategies and competitor alliance strategies. A positive interrelation between customer and supplier alliance strategies and a high persistence of joint supplier and customer alliance strategies are consistent with the advantages of value chain integration in innovation efforts. Prior engagement in horizontal (competitor) alliances increases the propensity to engage in vertical alliance strategies, but this effect occurs only with a longer lag. Overall, the authors’ findings suggest that alliance strategies with different partner types are both heterogeneous in persistence and (temporally) interrelated. This suggests that intertemporal relationships between different types of alliances may be as important as their simultaneous relationship in alliance portfolios.

Persistence of and interrelation between horizontal and vertical technology alliances

We examine how and to what extent the propensity to be engaged in alliances with different partner types (suppliers, customers and competitors) depends on prior alliance engagement with partner firms of the same type (persistence) and prior engagement in alliances with the other partner types (interrelation). We derive hypotheses from a combined competence and governance view of collaboration, and test these on an extensive panel dataset of innovation-active Dutch firms during 1996-2004. We find persistence in alliance engagement of all three types of partners, but customer alliances are more persistent than supplier alliances. Most persistent are joint supplier and customer alliances, which we attribute to the advantages of value chain integration in innovation processes. Positive interrelation also exists in vertical alliances, as immediate past customer alliances increase the propensity to engage in supplier alliances and vice versa. On the other hand, while prior engagement in horizontal (competitor) alliances increases the propensity to engage in vertical alliances, this effect only occurs with a longer lag. Overall, our findings are highly supportive of the idea that alliance engagement with different partner types is heterogeneous but interrelated. Our analysis suggests that the inter-temporal relationship between different types of alliances may be as important as their simultaneous relationship in alliance portfolios.R&D collaboration, technological partnerships, innovation, path dependency

Dynamic Document Annotation for Efficient Data Retrieval

Document annotation is considered as one of the most popular methods, where metadata present in document is used to search documents from a large text documents database. Few application domains such as scientific networks, blogs share information in a large amount is usually in unstructured data text documents. Manual annotation of each document becomes a tedious task. Annotations facilitate the task of finding the document topic and assist the reader to quickly overview and understand document. Dynamic document annotation provides a solution to such type of problems. Dynamic annotation of documents is generally considered as a semi-supervised learning task. The documents are dynamically assigned to one of a set of predefined classes based on the features extracted from their textual content. This paper proposes survey on Collaborative Adaptive Data sharing platform (CADS) for document annotation and use of query workload to direct the annotation process. A key novelty of CADS is that it learns with time the most important data attributes of the application, and uses this knowledge to guide the data insertion and querying