Google Earth Engine: Planetary-scale geospatial analysis for everyone

We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.

/pdf/yago-a-core-of-semantic-knowledge-13k3nmj6l2.pdf

Yago: a core of semantic knowledge

Social Network Analysis

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

The present report is submitted in response to General Assembly resolution 61/106, by which the Assembly adopted the Convention on the Rights of Persons with Disabilities and the Optional Protocol thereto. As requested by the Assembly, a brief overview of the status of the Convention as at 3 August 2007 is presented. The report also contains a brief description of technical arrangements on staff and facilities made necessary for the effective performance of the functions of the Conference of States Parties and the Committee under the Convention and the Optional Protocol, and a description on the progressive implementation of standards and guidelines for the accessibility of facilities and services of the United Nations system. Recent efforts of the United Nations and agencies to disseminate information on the Convention and the Optional Protocol are also described.

わかりやすい障害者の権利条約 : 知的障害のある人の権利のために = Convention on the rights of persons with disabilities

Spatio-Textual Data

Abstract With the widespread use of networked and geo-positioned mobile devices, e.g., smartphones, Spatial Crowdsourcing (SC), which refers to the assignment of location-based tasks to moving workers, is drawing increasing attention. One of the critical issues in SC is task assignment that allocates tasks to appropriate workers. We propose and study a novel SC problem, namely Coalition-based Task Assignment (CTA), where the spatial tasks (e.g., home improvement and furniture installation) may require more than one worker (forming a coalition) to cooperate to maximize the overall rewards of workers. We design a greedy and an equilibrium-based CTA approach. The greedy approach forms a set of worker coalitions greedily for performing tasks and uses an acceptance probability to identify high-value task assignments. In the equilibrium-based approach, workers form coalitions in sequence and update their strategies (i.e., selecting a best-response task), to maximize their own utility (i.e., the reward of the coalition they belong to) until a Nash equilibrium is reached. Since the equilibrium obtained is not unique and optimal in terms of total rewards, we further propose a simulated annealing scheme to find a better Nash equilibrium. To achieve fair task assignments, we optimize the framework to distribute rewards fairly among workers in a coalition based on their marginal contributions and give workers who arrive first at the SC platform highest priority. Extensive experiments demonstrate the efficiency and effectiveness of the proposed methods on real and synthetic data. 

/pdf/coalition-based-task-assignment-with-priority-aware-fairness-22tdxfww.pdf

Coalition-based task assignment with priority-aware fairness in spatial crowdsourcing

—Geo-social networks offer opportunities for the mar- keting and promotion of geo-located services. In this setting, we explore a new problem, called Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors (MaxInfBR k NN). The objective is to ﬁnd a set of points of interest (POIs), which are geo-textually and socially attractive to social inﬂuencers who are expected to largely promote the POIs through online inﬂuence propagation. In other words, the problem is to detect an optimal set of POIs with the largest word-of-mouth (WOM) marketing potential. This functionality is useful in various real- life applications, including social advertising, location-based viral marketing, and personalized POI recommendation. However, solving MaxInfBR k NN with theoretical guarantees is challenging, because of the prohibitive overheads on BR k NN retrieval in geo-social networks, and the NP and #P-hardness in searching the optimal POI set. To achieve practical solutions, we present a framework with carefully designed indexes, efﬁcient batch BR k NN processing algorithms, and alternative POI selection policies that support both approximate and heuristic solutions. Extensive experiments on real and synthetic datasets demonstrate the good performance of our proposed methods.

Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors in Geo-Social Networks

Movement paths are used widely in intelligent transportation and smart city applications. To serve such applications, path representation learning aims to provide compact representations of paths that enable efficient and accurate operations when used for different downstream tasks such as path ranking and travel cost estimation. In many cases, it is attractive that the path representation learning is lightweight and scalable; in resource-limited environments and under green computing limitations, it is essential. Yet, existing path representation learning studies focus on accuracy and pay at most secondary attention to resource consumption and scalability. We propose a lightweight and scalable path representation learning framework, termed LightPath, that aims to reduce resource consumption and achieve scalability without affecting accuracy, thus enabling broader applicability. More specifically, we first propose a sparse auto-encoder that ensures that the framework achieves good scalability with respect to path length. Next, we propose a relational reasoning framework to enable faster training of more robust sparse path encoders. We also propose global-local knowledge distillation to further reduce the size and improve the performance of sparse path encoders. Finally, we report extensive experiments on two real-world datasets to offer insight into the efficiency, scalability, and effectiveness of the proposed framework.

LightPath: Lightweight and Scalable Path Representation Learning

We demonstrate Crowd Rank Eval, a novel framework for the evaluation of ranking functions for top-k spatial keyword queries. The framework enables researchers to study hypotheses regarding ranking functions. Crowd Rank Eval uses crowd sourcing for synthesizing results to top-k queries and is able to visualize the results and to compare them to the results obtained from ranking functions, thus offering insight into the ranking functions.

Christian S. Jensen

Papers

Spatio-Textual Data

Coalition-based task assignment with priority-aware fairness in spatial crowdsourcing

Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors in Geo-Social Networks

LightPath: Lightweight and Scalable Path Representation Learning

CrowdRankEval: A Ranking Function Evaluation Framework for Spatial Keyword Queries