scholarly journals Spatial Keyword Query of Region-Of-Interest Based on the Distributed Representation of Point-Of-Interest

2019 ◽  
Vol 8 (6) ◽  
pp. 287 ◽  
Author(s):  
Zhu ◽  
Wu ◽  
Chen ◽  
Jing
Keyword(s):  
Large Scale ◽  
Rapid Development ◽  
Real Life ◽  
Region Of Interest ◽  
Environmental Information ◽  
Location Based Services ◽  
Distributed Representation ◽  
Keyword Query ◽  
Point Of Interest ◽  
Geographical Regions

The tremendous advance in information technology has promoted the rapid development of location-based services (LBSs), which play an indispensable role in people’s daily lives. Compared with a traditional LBS based on Point-Of-Interest (POI), which is an isolated location point, an increasing number of demands have concentrated on Region-Of-Interest (ROI) exploration, i.e., geographic regions that contain many POIs and express rich environmental information. The intention behind the POI is to search the geographical regions related to the user’s requirements, which contain some spatial objects, such as POIs and have certain environmental characteristics. In order to achieve effective ROI exploration, we propose an ROI top-k keyword query method that considers the environmental information of the regions. Specifically, the Word2Vec model has been introduced to achieve the distributed representation of POIs and capture their environmental semantics, which are then leveraged to describe the environmental characteristic information of the candidate ROI. Given a keyword query, different query patterns are designed to measure the similarities between the query keyword and the candidate ROIs to find the k candidate ROIs that are most relevant to the query. In the verification step, an evaluation criterion has been developed to test the effectiveness of the distributed representations of POIs. Finally, after generating the POI vectors in high quality, we validated the performance of the proposed ROI top-k query on a large-scale real-life dataset where the experimental results demonstrated the effectiveness of our proposals.

scholarly journals Improving POI Recommendation via Dynamic Tensor Completion

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jinzhi Liao ◽  
Jiuyang Tang ◽  
Xiang Zhao ◽  
Haichuan Shang
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Location Based Services ◽  
Low Rank ◽  
Tensor Structure ◽  
Tensor Completion ◽  
Tensor Method ◽  
Rank Tensor ◽  
Poi Recommendation ◽  
Novel Model

POI recommendation finds significant importance in various real-life applications, especially when meeting with location-based services, e.g., check-ins social networks. In this paper, we propose to solve POI recommendation through a novel model of dynamic tensor, which is among the first triumphs of its kind. In order to carry out timely recommendation, we predict POI by utilizing a completion algorithm based on fast low-rank tensor. Particularly, the dynamic tensor structure is complemented by the fast low-rank tensor completion algorithm so as to achieve prediction with better performance, where the parameter optimization is achieved by a pigeon-inspired heuristic algorithm. In short, our POI recommendation via the dynamic tensor method can take advantage of the intrinsic characteristics of check-ins data due to the multimode features such as current categories, subsequent categories, and temporal information as well as seasons variations are all integrated into the model. Extensive experiment results not only validate the superiority of our proposed method but also imply the application prospect in large-scale and real-time POI recommendation environment.

scholarly journals Finger Vein Recognition System Based on Convolutional Neural Network and Android

2021 ◽  
Vol 2078 (1) ◽  
pp. 012053
Author(s):  
Yangfeng Wang ◽  
Tao Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Rapid Development ◽  
Real Life ◽  
Recognition System ◽  
Finger Vein ◽  
Recognition Function ◽  
Vein Recognition ◽  
Finger Vein Recognition

Abstract With the rapid development of science and technology, biotechnology has developed rapidly. Among the many biometric technologies, finger vein technology has the characteristics of vitality, portability, and non-replicability, so it is considered to be the most promising biometric technology. However, the accuracy of finger vein recognition is affected by the collection device, the surrounding temperature and the algorithm. The flaws cannot be applied to real life on a large scale. This paper designs a finger vein recognition system based on convolutional neural network and Android, which mainly includes the following three parts. First, the system hardware includes the design of the acquisition device, the selection of the core development board and the display screen. Second, the design of the entire system software architecture is based on the MVVM architecture, which ensures low coupling of the program and is easy for later expansion and maintenance. The software includes collection function, recognition function and administrator function. Finally, a lightweight neural network is proposed for finger vein feature extraction, and proposed a storage method based on MMKV to meet the real-time performance of the system.

BDD-Based Combinatorial Keyword Query Processing under a Taxonomy Model

2012 ◽  
Vol 3 (4) ◽  
pp. 52-62
Author(s):  
Shin-ichi Minato ◽  
Nicolas Spyratos
Keyword(s):  
Digital Libraries ◽  
Large Scale ◽  
Intelligent System ◽  
Real Life ◽  
Experimental Result ◽  
Easy Access ◽  
Compact Representation ◽  
Keyword Query ◽  
Preliminary Experimental Result ◽  
Efficient Data

Digital libraries are one of the key systems for an IT society, and supporting easy access to them is an important technical issue between a human and an intelligent system. Here the authors consider a publish/subscribe system for digital libraries which continuously evaluates queries over a large repository containing document descriptions. The subscriptions, the query expressions and the document descriptions, all rely on a taxonomy that is a hierarchically organized set of keywords, or terms. The digital library supports insertion, update and removal of a document. Each of these operations is seen as an event that must be notified only to those users whose subscriptions match the document's description. In this chapter, the authors present a novel method of processing such keyword queries. Their method is based on Binary Decision Diagram (BDD), an efficient data structure for manipulating large-scale Boolean functions. The authors compile the given keyword queries into a BDD under a taxonomy model. The number of possible keyword sets can be exponentially large, but the compiled BDD gives a compact representation, and enabling a highly efficient matching process. In addition, the authors' method can deal with any Boolean combination of keywords from the taxonomy, while the previous result considered only a conjunctive keyword set. In this chapter, they describe the basic idea of their new method, and then the authors show their preliminary experimental result applying to a document set with large-scale keyword domain under a real-life taxonomy structure.

BDD-Based Combinatorial Keyword Query Processing under a Taxonomy Model

2010 ◽  
pp. 26-39
Author(s):  
Shin-ichi Minato ◽  
Nicolas Spyratos
Keyword(s):  
Digital Libraries ◽  
Large Scale ◽  
Intelligent System ◽  
Real Life ◽  
Experimental Result ◽  
Easy Access ◽  
Compact Representation ◽  
Keyword Query ◽  
Preliminary Experimental Result ◽  
Efficient Data

Digital libraries are one of the key systems for an IT society, and supporting easy access to them is an important technical issue between a human and an intelligent system. Here we consider a publish/subscribe system for digital libraries which continuously evaluates queries over a large repository containing document descriptions. The subscriptions, the query expressions and the document descriptions, all rely on a taxonomy that is a hierarchically organized set of keywords, or terms. The digital library supports insertion, update and removal of a document. Each of these operations is seen as an event that must be notified only to those users whose subscriptions match the document‘s description. In this chapter, the authors present a novel method of processing such keyword queries. Our method is based on Binary Decision Diagram (BDD), an efficient data structure for manipulating large-scale Boolean functions. The authors compile the given keyword queries into a BDD under a taxonomy model. The number of possible keyword sets can be exponentially large, but the compiled BDD gives a compact representation, and enabling a highly efficient matching process. In addition, our method can deal with any Boolean combination of keywords from the taxonomy, while the previous result considered only a conjunctive keyword set. In this chapter, they describe the basic idea of their new method, and then the authors show their preliminary experimental result applying to a document set with large-scale keyword domain under a real-life taxonomy structure.

scholarly journals Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2263
Author(s):  
Lu Huang ◽  
Hongsheng Li ◽  
Baoguo Yu ◽  
Xingli Gan ◽  
Boyuan Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Feature Detection ◽  
Turning Point ◽  
Rapid Development ◽  
Calibration Method ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Small Scale ◽  
Localization Algorithm ◽  
Positioning Accuracy

In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning.

scholarly journals An Improved Privacy-Preserving Framework for Location-Based Services Based on Double Cloaking Regions with Supplementary Information Constraints

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Li Kuang ◽  
Yin Wang ◽  
Pengju Ma ◽  
Long Yu ◽  
Chuanbin Li ◽  
...  
Keyword(s):  
Location Privacy ◽  
Rapid Development ◽  
Mobile Network ◽  
Location Based Services ◽  
Supplementary Information ◽  
Network Applications ◽  
Point Of Interest ◽  
Map Data ◽  
Location Privacy Protection ◽  
Location Point

With the rapid development of location-based services in the field of mobile network applications, users enjoy the convenience of location-based services on one side, while being exposed to the risk of disclosure of privacy on the other side. Attacker will make a fierce attack based on the probability of inquiry, map data, point of interest (POI), and other supplementary information. The existing location privacy protection techniques seldom consider the supplementary information held by attackers and usually only generate single cloaking region according to the protected location point, and the query efficiency is relatively low. In this paper, we improve the existing LBSs system framework, in which we generate double cloaking regions by constraining the supplementary information, and then k-anonymous task is achieved by the cooperation of the double cloaking regions; specifically speaking, k dummy points of fixed dummy positions in the double cloaking regions are generated and the LBSs query is then performed. Finally, the effectiveness of the proposed method is verified by the experiments on real datasets.

scholarly journals User Modeling for Point-of-Interest Recommendations in Location-Based Social Networks: The State of the Art

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Shudong Liu
Keyword(s):  
Social Networks ◽  
User Modeling ◽  
Real Life ◽  
Location Based Services ◽  
Geographical Information ◽  
Daily Movement ◽  
Spatiotemporal Information ◽  
Point Of Interest ◽  
Future Challenges ◽  
Location Based Social Networks

The rapid growth of location-based services (LBSs) has greatly enriched people’s urban lives and attracted millions of users in recent years. Location-based social networks (LBSNs) allow users to check-in at a physical location and share daily tips on points of interest (POIs) with their friends anytime and anywhere. Such a check-in behavior can make daily real-life experiences spread quickly through the Internet. Moreover, such check-in data in LBSNs can be fully exploited to understand the basic laws of humans’ daily movement and mobility. This paper focuses on reviewing the taxonomy of user modeling for POI recommendations through the data analysis of LBSNs. First, we briefly introduce the structure and data characteristics of LBSNs, and then we present a formalization of user modeling for POI recommendations in LBSNs. Depending on which type of LBSNs data was fully utilized in user modeling approaches for POI recommendations, we divide user modeling algorithms into four categories: pure check-in data-based user modeling, geographical information-based user modeling, spatiotemporal information-based user modeling, and geosocial information-based user modeling. Finally, summarizing the existing works, we point out the future challenges and new directions in five possible aspects.

scholarly journals Neural methods for effective, efficient, and exposure-aware information retrieval

2021 ◽  
Vol 55 (1) ◽  
pp. 1-2
Author(s):  
Bhaskar Mitra
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Web Search ◽  
Real Life ◽  
Inverted Index ◽  
Information Need ◽  
Product Model ◽  
Performance Improvements ◽  
Deep Model

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

scholarly journals Robots for Elderly Care in the Home: A Landscape Analysis and Co-Design Toolkit

2021 ◽  
Author(s):  
Gianluca Bardaro ◽  
Alessio Antonini ◽  
Enrico Motta
Keyword(s):  
Large Scale ◽  
Daily Life ◽  
Elderly Care ◽  
Real Life ◽  
Robotic Platform ◽  
Holistic View ◽  
Personal Assistants ◽  
Healthcare Interventions ◽  
The Impact ◽  
The Eu

AbstractOver the last two decades, several deployments of robots for in-house assistance of older adults have been trialled. However, these solutions are mostly prototypes and remain unused in real-life scenarios. In this work, we review the historical and current landscape of the field, to try and understand why robots have yet to succeed as personal assistants in daily life. Our analysis focuses on two complementary aspects: the capabilities of the physical platform and the logic of the deployment. The former analysis shows regularities in hardware configurations and functionalities, leading to the definition of a set of six application-level capabilities (exploration, identification, remote control, communication, manipulation, and digital situatedness). The latter focuses on the impact of robots on the daily life of users and categorises the deployment of robots for healthcare interventions using three types of services: support, mitigation, and response. Our investigation reveals that the value of healthcare interventions is limited by a stagnation of functionalities and a disconnection between the robotic platform and the design of the intervention. To address this issue, we propose a novel co-design toolkit, which uses an ecological framework for robot interventions in the healthcare domain. Our approach connects robot capabilities with known geriatric factors, to create a holistic view encompassing both the physical platform and the logic of the deployment. As a case study-based validation, we discuss the use of the toolkit in the pre-design of the robotic platform for an pilot intervention, part of the EU large-scale pilot of the EU H2020 GATEKEEPER project.

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