scholarly journals Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Xiaohuan Shan ◽  
Haihai Li ◽  
Chunjie Jia ◽  
Dong Li ◽  
Baoyan Song
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Original Data ◽  
The State ◽  
Average Performance ◽  
Query Graph ◽  
Subgraph Query ◽  
Data Graph ◽  
Partition Method ◽  
The Impact

Interesting subgraph query aims to find subgraphs that are isomorphic to the given query graph from a data graph and rank the subgraphs according to their interestingness scores. However, the existing subgraph query approaches are inefficient when dealing with large-scale labeled data graph. This is caused by the following problems: (i) the existing work mainly focuses on unweighted query graphs, while ignoring the impact of query constraints on query results. (ii) Excessive number of subgraph candidates or complex joins between nodes in the subgraph candidates reduce the query efficiency. To solve these problems, this paper proposes an intelligent solution. Firstly, an Isotype Structure Graph Compression (ISGC) strategy is proposed to compress similar nodes in a graph to reduce the size of the graph and avoid unnecessary matching. Then, an auxiliary data structure Supergraph Topology Feature Index (STFIndex) is designed to replace the storage of the original data graph and improve the efficiency of an online query. After that, a partition method based on Edge Label Step Value (ELSV) is proposed to partition the index logically. In addition, a novel Top-K interest subgraph query approach is proposed, which consists of the multidimensional filtering (MDF) strategy, upper bound value (UBV) (Size-c) matching, and the optimizational join (QJ) method to filter out as many false subgraph candidates as possible to achieve fast joins. We conduct experiments on real and synthetic datasets. Experimental results show that the average performance of our approach is 1.35 higher than that of the state-of-the-art approaches when the query graph is unweighted, and the average performance of our approach is 2.88 higher than that of the state-of-the-art approaches when the query graph is weighted.

scholarly journals Large-scale Semantic Parsing without Question-Answer Pairs

2014 ◽  
Vol 2 ◽  
pp. 377-392 ◽  
Author(s):  
Siva Reddy ◽  
Mirella Lapata ◽  
Mark Steedman
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Graph Matching ◽  
State Of The Art ◽  
The State ◽  
Semantic Parsing ◽  
Matching Problem ◽  
Weak Supervision ◽  
Benchmark Datasets

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.

A Comprehensive Taxonomy of Dynamic Texture Representation

2023 ◽  
Vol 55 (1) ◽  
pp. 1-39
Author(s):  
Thanh Tuan Nguyen ◽  
Thanh Phuong Nguyen
Keyword(s):  
Large Scale ◽  
Environmental Changes ◽  
State Of The Art ◽  
The State ◽  
Future Research ◽  
Research Activities ◽  
Potential Applications ◽  
Benchmark Datasets ◽  
Negative Impacts ◽  
Made In

Representing dynamic textures (DTs) plays an important role in many real implementations in the computer vision community. Due to the turbulent and non-directional motions of DTs along with the negative impacts of different factors (e.g., environmental changes, noise, illumination, etc.), efficiently analyzing DTs has raised considerable challenges for the state-of-the-art approaches. For 20 years, many different techniques have been introduced to handle the above well-known issues for enhancing the performance. Those methods have shown valuable contributions, but the problems have been incompletely dealt with, particularly recognizing DTs on large-scale datasets. In this article, we present a comprehensive taxonomy of DT representation in order to purposefully give a thorough overview of the existing methods along with overall evaluations of their obtained performances. Accordingly, we arrange the methods into six canonical categories. Each of them is then taken in a brief presentation of its principal methodology stream and various related variants. The effectiveness levels of the state-of-the-art methods are then investigated and thoroughly discussed with respect to quantitative and qualitative evaluations in classifying DTs on benchmark datasets. Finally, we point out several potential applications and the remaining challenges that should be addressed in further directions. In comparison with two existing shallow DT surveys (i.e., the first one is out of date as it was made in 2005, while the newer one (published in 2016) is an inadequate overview), we believe that our proposed comprehensive taxonomy not only provides a better view of DT representation for the target readers but also stimulates future research activities.

Precise No-Reference Image Quality Evaluation Based on Distortion Identification

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-21
Author(s):  
Chenggang Yan ◽  
Tong Teng ◽  
Yutao Liu ◽  
Yongbing Zhang ◽  
Haoqian Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Large Scale ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Gaussian White Noise ◽  
The State ◽  
Reference Image

The difficulty of no-reference image quality assessment (NR IQA) often lies in the lack of knowledge about the distortion in the image, which makes quality assessment blind and thus inefficient. To tackle such issue, in this article, we propose a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation. In the first step, we employ the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types, i.e., Gaussian white noise (WN), Gaussian blur (GB), jpeg compression (JPEG), and jpeg2000 compression (JP2K). Specifically, the deep neural network is trained on the large-scale Waterloo Exploration database, which ensures the robustness and high performance of distortion classification. In the second step, after determining the distortion type of the image, we then design a specific approach to quantify the image distortion level, which can estimate the image quality specially and more precisely. Extensive experiments performed on LIVE, TID2013, CSIQ, and Waterloo Exploration databases demonstrate that (1) the accuracy of our distortion classification is higher than that of the state-of-the-art distortion classification methods, and (2) the proposed NR IQA method outperforms the state-of-the-art NR IQA methods in quantifying the image quality.

scholarly journals Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval

2019 ◽  
Vol 33 ◽  
pp. 176-183 ◽  
Author(s):  
Chao Li ◽  
Cheng Deng ◽  
Lei Wang ◽  
De Xie ◽  
Xianglong Liu
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Storage Cost ◽  
Common Representation ◽  
Benchmark Datasets ◽  
Query Efficiency ◽  
Hash Codes

In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Benefiting from deep leaning, continuously compelling results in cross-modal retrieval community have been achieved. However, existing deep cross-modal hashing methods either rely on amounts of labeled information or have no ability to learn an accuracy correlation between different modalities. In this paper, we proposed Unsupervised coupled Cycle generative adversarial Hashing networks (UCH), for cross-modal retrieval, where outer-cycle network is used to learn powerful common representation, and inner-cycle network is explained to generate reliable hash codes. Specifically, our proposed UCH seamlessly couples these two networks with generative adversarial mechanism, which can be optimized simultaneously to learn representation and hash codes. Extensive experiments on three popular benchmark datasets show that the proposed UCH outperforms the state-of-the-art unsupervised cross-modal hashing methods.

scholarly journals RDFuzz: Accelerating Directed Fuzzing with Intertwined Schedule and Optimized Mutation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jiaxi Ye ◽  
Ruilin Li ◽  
Bin Zhang
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Exploration And Exploitation ◽  
Balance Problem ◽  
Evaluation Strategy ◽  
Testing Schedule ◽  
Available Resources

Directed fuzzing is a practical technique, which concentrates its testing energy on the process toward the target code areas, while costing little on other unconcerned components. It is a promising way to make better use of available resources, especially in testing large-scale programs. However, by observing the state-of-the-art-directed fuzzing engine (AFLGo), we argue that there are two universal limitations, the balance problem between the exploration and the exploitation and the blindness in mutation toward the target code areas. In this paper, we present a new prototype RDFuzz to address these two limitations. In RDFuzz, we first introduce the frequency-guided strategy in the exploration and improve its accuracy by adopting the branch-level instead of the path-level frequency. Then, we introduce the input-distance-based evaluation strategy in the exploitation stage and present an optimized mutation to distinguish and protect the distance sensitive input content. Moreover, an intertwined testing schedule is leveraged to perform the exploration and exploitation in turn. We test RDFuzz on 7 benchmarks, and the experimental results demonstrate that RDFuzz is skilled at driving the program toward the target code areas, and it is not easily stuck by the balance problem of the exploration and the exploitation.

scholarly journals Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3603
Author(s):  
Dasol Jeong ◽  
Hasil Park ◽  
Joongchol Shin ◽  
Donggoo Kang ◽  
Joonki Paik
Keyword(s):  
Large Scale ◽  
Body Shape ◽  
State Of The Art ◽  
The State ◽  
Whole Body ◽  
Distinctive Features ◽  
Common Features ◽  
Siamese Network ◽  
Art Methods ◽  
Triplet Loss

Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. In this paper, we present a novel attentive learning-based Siamese network for person Re-ID. Unlike existing methods, we designed an attention module and attention loss using the properties of the Siamese network to concentrate attention on common and distinctive features. The attention module consists of channel attention to select important channels and encoder-decoder attention to observe the whole body shape. We modified the triplet loss into an attention loss, called uniformity loss. The uniformity loss generates a unique attention map, which focuses on both common and discriminative features. Extensive experiments show that the proposed network compares favorably to the state-of-the-art methods on three large-scale benchmarks including Market-1501, CUHK03 and DukeMTMC-ReID datasets.

Researching Telework

2000 ◽  
pp. 255-276 ◽  
Author(s):  
Nicole B. Ellison
Keyword(s):  
Organizational Culture ◽  
State Of The Art ◽  
The State ◽  
Future Research ◽  
Scholarly Literature ◽  
Home Based ◽  
The Family ◽  
The Individual ◽  
The Impact

This chapter examines the state of the art in telework research. The author reviews the most central scholarly literature examining the phenomenon of telework (also called home-based work or telecommuting) and develops a framework for organizing this body of work. She organizes previous research on telework into six major thematic concerns relating to the definition, measurement, and scope of telework; management of teleworkers; travel-related impacts of telework; organizational culture and employee isolation; boundaries between “home” and “work” and the impact of telework on the individual and the family. Areas for future research are suggested.

scholarly journals Local Feature-Aware Siamese Matching Model for Vehicle Re-Identification

2020 ◽  
Vol 10 (7) ◽  
pp. 2474
Author(s):  
Honglie Wang ◽  
Shouqian Sun ◽  
Lunan Zhou ◽  
Lilin Guo ◽  
Xin Min ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Feature Matching ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
The State ◽  
Local Feature ◽  
Public Security ◽  
Matching Model ◽  
Image Deformation

Vehicle re-identification is attracting an increasing amount of attention in intelligent transportation and is widely used in public security. In comparison to person re-identification, vehicle re-identification is more challenging because vehicles with different IDs are generated by a unified pipeline and cannot only be distinguished based on the subtle differences in their features such as lights, ornaments, and decorations. In this paper, we propose a local feature-aware Siamese matching model for vehicle re-identification. A local feature-aware Siamese matching model focuses on the informative parts in an image and these are the parts most likely to differ among vehicles with different IDs. In addition, we utilize Siamese feature matching to better supervise our attention. Furthermore, a perspective transformer network, which can eliminate image deformation, has been designed for feature extraction. We have conducted extensive experiments on three large-scale vehicle re-ID datasets, i.e., VeRi-776, VehicleID, and PKU-VD, and the results show that our method is superior to the state-of-the-art methods.

Introduction

2017 ◽  
Author(s):  
Ellen F. Steinberg ◽  
Jack H. Prost
Keyword(s):  
Large Scale ◽  
Shape Change ◽  
The State ◽  
Food Preparation ◽  
Rural And Urban ◽  
Production And Consumption ◽  
Economic Practices ◽  
The Impact

This introductory chapter provides an overview of the book's main themes. This book explores the state, shape, change, and evolution of Midwestern Jewish cuisine through time. It tracks geographically based culinary recipes and changes made to them through time by presenting and analyzing ones from Midwestern Jewish sources, both kosher and non-kosher. It documents the availability of fruits, vegetables, and other comestibles throughout the Midwest that impacted how and what Jews cooked; and considers the effect of improved preservation and transportation on rural and urban Jewish foodways. Then, it examines the impact on Jewish foodways—the cultural, social, and economic practices relating to the production and consumption of food—of large-scale immigration, relocation, and Americanization efforts during the nineteenth and early twentieth centuries, paying special attention to the attempts of social and culinary reformers to modify traditional Jewish food preparation and ingredients.

Replicative Senescence-Associated Gene Expression Changes In Human MSPCs Independent of Genomic Variations

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 4775-4775
Author(s):  
Katharina Schallmoser ◽  
Christina Bartmann ◽  
Eva Rohde ◽  
Simone Bork ◽  
Christian Guelly ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Culture ◽  
Bone Marrow ◽  
Large Scale ◽  
Replicative Senescence ◽  
Genomic Stability ◽  
The State ◽  
Efficient Treatment ◽  
The Impact

Abstract Abstract 4775 Background: Based on promising experimental studies with mesenchymal stem and progenitor cells (MSPCs) multiple clinical trials have been initiated. In previous studies we have observed genomic stability of MSPCs after efficient short-term expansion in a humanized GMP compliant system with pooled human platelet lysate (pHPL) replacing fetal bovine serum (FBS) as the cell culture supplement (Schallmoser K. and Strunk D., Journal of Visualized Experiments (32) DOI: 10.3791/1523, 2009). Notably, depending on culture protocols, an extensive propagation with highly variable cell culture duration may be necessary to yield enough MSPCs for therapy. The decline in proliferation rates of MSPCs in the course of the different long-term expansion procedures may indicate a propensity for replicative senescence which may hamper long term functionality in vivo. We have therefore initiated a molecular profiling of senescence-associated regulated genes to determine the state of senescence before MSPC transplantation. Methods: Human bone marrow-derived MSPCs were cultured following a highly efficient two-passage protocol (primary culture of unseparated bone marrow and subsequent large scale expansion; Schallmoser K. et al., Tissue Engineering 14:185-196, 2008) compared to conventional serial passaging in three different growth conditions with regularly more then four passages to obtain comparable final cell numbers. Culture media were either supplemented with FBS in different concentrations or pHPL. Gene expression changes were tested by microarray analysis and selected targets were reanalyzed by quantitative real-time PCR. The genomic stability of MSPCs after long-term culture was determined by array comparative genomic hybridization (CGH). Results: Despite high proliferation rate large scale expanded MSPCs showed genomic stability in array CGH. Long-term MSPC growth induced similar gene expression changes in MSPCs irrespective of isolation and expansion conditions. In particular, genes involved in cell differentiation, apoptosis and cell death were up-regulated, whereas genes involved in mitosis and proliferation were down-regulated. Furthermore, overlapping senescence-associated gene expression changes were found in all MSPC preparations. The genomic copy number variations detected in MSPCs of early and late passages in all culture conditions did not coincide with differentially expressed genes. Conclusion: Our data indicate that MSPC expansion can induce gene expression changes independent of isolation and FBS-supplemented as well as FBS-free expansion conditions. A panel of genes will be presented that might offer a practicable approach to assess MSPC quality with regard to the state of replicative senescence in advance of therapeutic application. Determining the impact of senescence acquired during cell expansion on the therapeutic potential of MSCPs for both immune modulation and organ regeneration may help to develop more efficient treatment strategies. Disclosures: No relevant conflicts of interest to declare.

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