Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

Review of Point of Interest Recommendation Systems in Location-Based Social Networks

Point of interest recommendation is recently one of the hotspots in the field of location-based social networks and recommendation systems. Understanding the research status of the point of interest recommendation in location-based social networks can provide a direction for the next step of work. The recent literatures of the point of interest recommendation systems are analyzed. Firstly, the definition is introduced, and the difference from traditional recommendation is discussed from three aspects: influencing factors, recommendation approaches and existing problems. Secondly, the general framework of the point of interest recommendation is proposed, which includes data sources, recommendation approaches and evaluation. Based on this framework, the various influencing factors are introduced, the current recommendation algorithms are generalized, and the evaluation metrics are summarized. Meanwhile, the representative works are analyzed, the research contents and characteristics of each type of methods are summarized in detail, and their advantages and limitations are evaluated. Finally, the challenges and potential directions for possible extensions in this filed are summarized and prospected, and the future research trends and development directions are concluded

Hierarchical Multi-Clue Modelling for POI Popularity Prediction with Heterogeneous Tourist Information

© 1989-2012 IEEE. Predicting the popularity of Point of Interest (POI) has become increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarcity of POI's information, which tendentiously confines the recommendation to popular scene spots, and ignores the unpopular attractions with potentially precious values. In this paper, we propose a novel approach, termed Hierarchical Multi-Clue Fusion (HMCF), for predicting the popularity of POIs. Specifically, in order to cope with the problem of data sparsity, we propose to comprehensively describe POI using various types of user generated content (UGC) (e.g., text and image) from multiple sources. Then, we devise an effective POI modelling method in a hierarchical manner, which simultaneously injects semantic knowledge as well as multi-clue representative power into POIs. For evaluation, we construct a multi-source POI dataset by collecting all the textual and visual content of several specific provinces in China from four main-stream tourism platforms during 2006 to 2017. Extensive experimental results show that the proposed method can significantly improve the performance of predicting the attractions' popularity as compared to several baseline methods

User-centred design of smartphone augmented reality in urban tourism context.

Exposure to new and unfamiliar environments is a necessary part of nearly everyone’s life. Effective communication of location-based information through various locationbased service interfaces (LBSIs) became a key concern for cartographers, geographers, human-computer interaction (HCI) and professional designers alike. Much attention is directed towards Augmented Reality (AR) interfaces. Smartphone AR browsers deliver information about physical objects through spatially registered virtual annotations and can function as an interface to (geo)spatial and attribute data. Such applications have considerable potential for tourism. Recently, the number of studies discussing the optimal placement and layout of AR content increased. Results, however, do not scale well to the domain of urban tourism, because: 1) in any urban destination, many objects can be augmented with information; 2) each object can be a source of a substantial amount of information; 3) the incoming video feed is visually heterogeneous and complex; 4) the target user group is in an unfamiliar environment; 5) tourists have different information needs from urban residents. Adopting a User-Centred Design (UCD) approach, the main aim of this research project was to make a theoretical contribution to design knowledge relevant to effective support for (geo)spatial knowledge acquisition in unfamiliar urban environments. The research activities were divided in four (iterative) stages: (1) theoretical, (2) requirements analysis, (3) design and (4) evaluation. After critical analysis of existing literature on design of AR, the theoretical stage involved development of a theoretical user-centred design framework, capturing current knowledge in several relevant disciplines. In the second stage, user requirements gathering was carried out through a field quasi experiment where tourists were asked to use AR browsers in an unfamiliar for them environment. Qualitative and quantitative data were used to identify key relationships, extend the user-centred design framework and generate hypotheses about effective and efficient design. In the third stage, several design alternatives were developed and used to test the hypotheses through a laboratory-based quantitative study with 90 users. The results indicate that information acquisition through AR browsers is more effective and efficient if at least one element within the AR annotation matches the perceived visual characteristics or inferred non-visual attributes of target physical objects. Finally, in order to ensure that all major constructs and relationships are identified, qualitative evaluation of AR annotations was carried out by HCI and GIS domain-expert users in an unfamiliar urban tourism context. The results show that effective information acquisition in urban tourism context will depend on the visual design and delivered content through AR annotations for both visible and non-visible points of interest. All results were later positioned within existing theory in order to develop a final conceptual user-centred design framework that shifts the perspective towards a more thorough understanding of the overall design space for mobile AR interfaces. The dissertation has theoretical, methodological and practical implications. The main theoretical contribution of this thesis is to Information Systems Design Theory. The developed framework provides knowledge regarding the design of mobile AR. It can be used for hypotheses generation and further empirical evaluations of AR interfaces that facilitate knowledge acquisition in different types of environments and for different user groups. From a methodological point of view, the described userbased studies showcase how a UCD approach could be applied to design and evaluation of novel smartphone interfaces within the travel and tourism domain. Within industry the proposed framework could be used as a frame of reference by designers and developers who are not familiar with knowledge acquisition in urban environments and/or mobile AR interfaces

Monitoring, modelling and managing urban growth in Alexandria, Egypt using remote sensing and GIS

Alexandria is the second largest urban governorate in Egypt and has seen significant urban growth in its modern and contemporary history. This study investigates the urban growth phenomenon in Alexandria, Egypt using the integration of remote sensing and GIS. The study has revealed some significant findings that can help in understanding the current and future trends of urban growth in Alexandria. For demographic analysis, growth rates dropped off between 1976 and 1996. In the same manner, Alexandria's population decreased from 6.33% of total country in 1976 to 5.6% in 1996. Family size and crowding rates are declining as well. Moreover, the role of internal migration has changed and the city sends out more population than it receives. In addition, there is a clear decline in population density in the city's core, while city fringes have witnessed increases in their density. For physical expansion, Alexandria experienced a long history of deterioration from the end of the Roman era until the French expedition's departure in the beginning of the 19`" century. Alexandria began to revive again from the first half of the 19`n century during Mohamed Ali era up to date. The city expanded in all available directions. Therefore, the side effects of urban growth commenced to develop in some parts such as informal housing on the cultivated land in the east and southeast of the city. The urban physical expansion and change were detected using Landsat satellite images. The satellite images of years 1984 and 1993 were first georeferenced, achieving a very small RMSE that provided high accuracy data for satellite image analysis. Then, the images were classified using a tailored classification scheme with accuracy of 93.82% and 95.27% for 1984 and 1993 images respectively. This high accuracy enabled detecting land use/cover changes with high confidence using a postclassification comparison method. One of the most important findings here is the loss of cultivated land in favour of urban expansion. If the current loss rates continued, 75% of green lands would be lost by year 2191. These hazardous rates call for an urban growth management policy that can preserve such valuable resources to achieve sustainable urban development. The starting point of any management programme will be based on the modelling of the future growth. Modelling techniques can help in defining the scenarios of urban growth. In this study, the SLEUTH urban growth model was applied to predict future urban expansion in Alexandria until the year 2055. The application of this model in Alexandria of Egypt with its different environmental characteristics is the first application outside USA and Europe. The results revealed that future urban growth would continue in the edges of the current urban extent, which means the cultivated lands in the east and the southeast of the city will continue to lose more day by day from their area. To deal with this crisis, there is a serious need for a comprehensive urban growth management programme that based on the best practices in similar situations. Good urban governance, public participation, using GIS and remote sensing, and decentralisation (among others) are found to be the most important principles for such programme.EThOS - Electronic Theses Online ServiceEgyptian Government, Ministry of Higher EducationGBUnited Kingdo

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges