Size Of Candidate Set Research Articles

Jointly estimating the most likely driving paths and destination locations with incomplete vehicular trajectory data

With an ever-increasing deployment density of probe and fixed sensors, massive vehicular trajectory data is available and show a promising foundation to improve the observability of dynamic traffic demand pattern. However, due to technical and privacy issues, the raw trajectories are not always complete and the paths and destinations between discontinuous trajectory nodes are usually missing. This paper proposes a probabilistic method to jointly reconstruct the missing driving path and destination location of vehicles with incomplete trajectory data. One problem-specific HMM-structured model incorporating spatial and temporal analysis (ST-HMM) is constructed to define the matching probability between observed data and possible movement. Two algorithms, namely candidate set generation and best-match search algorithms, are developed to seek the most possible one as matching result. It can implement end-to-end processing from incomplete trajectory data to complete and connective paths and destinations for the target vehicle. The proposed method is tested based on field-test data and city-wide road network. Compared with two benchmark methods, the proposed method improved the matching accuracy in terms of both path identification and destination inference. Additionally, sensitivity analyses on the size of training dataset and candidate set were performed. We believe that experiment results of these sensitivity analyses can help to provide guidance on data sensing and candidate generation.

CSD-RkNN: reverse k nearest neighbors queries with conic section discriminances

The reverse k nearest neighbors (RkNN) query is a prominent yet time-consuming spatial query used in facility siting, influential domain analysis, potential customer analysis, etc. Its aim is to identify all points that consider the query point as one of their k closest points. However, when k is relatively large (e.g. k = 1000), existing RkNN techniques often struggle to provide acceptable response times (within a few seconds). To address this issue, we propose a verification approach called conic section discriminance (CSD). This method serves to determine whether points belong to the RkNN set. With CSD, only a small fraction of candidates require costly k nearest neighbors (kNN) queries for verification, while the rest can be rapidly verified with O(1) complexity. Furthermore, we propose a Voronoi-based candidate generation approach to curtail the candidate set size. By leveraging the VoR-tree structure, we integrate these two approaches to form a novel RkNN algorithm named CSD-RkNN. A comprehensive set of experiments is conducted to compare CSD-RkNN with Slice as the state-of-the-art RkNN algorithm, and VR-RkNN as the original RkNN algorithm on VoR-tree. The results indicate that CSD-RkNN consistently outperforms the other two algorithms, especially when k is relatively large.

An Accurate Matching Query Method of Natural Language Knowledge Graph Based on Hierarchical Graph Topological Sequence

In recent years, although the application of knowledge graph in natural language processing has made some progress, there are still some key problems to be solved, especially the matching query problem in natural language knowledge graph. Since the basic data model of knowledge graph is graph, the matching query problem in natural language knowledge graph is usually transformed into graph matching query problem. However, at present, the traditional graph matching technology applied in knowledge graph consumes too much time and has low query efficiency, which cannot meet the needs of users for large-scale natural language knowledge graph query. Based on the full analysis of the defects of the traditional graph matching technology applied in the knowledge graph, according to the characteristics of the natural language knowledge graph, in order to improve the query efficiency, we propose an accurate matching query method of graph hierarchical topological sequence based on the graph model of knowledge graph. Through experiment analysis, compared with the traditional graph model matching algorithm applied in knowledge graph query, this method can quickly filter the unqualified knowledge graph candidate sets, effectively reduce the number of knowledge graph candidate sets, and make it have more advantages in matching efficiency and time performance. In addition, compared with two algorithms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GIndex</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FG-Index</i> , this method has better performance in index construction time, average size of candidate set and average running time of online update.

Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics.

Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC–MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model.

Privacy Leakage via De-Anonymization and Aggregation in Heterogeneous Social Networks

Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS first leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that NHDS significantly outperforms the prior schemes. Finally, we perform an empirical study on privacy leakage arising from cross-network aggregation based on four real-world social network datasets. Our findings show that 39.9 percent more information is disclosed through de-anonymization and the de-anonymized ratio is 84 percent. The detailed privacy leakage of user demographics and interests is also examined, which demonstrates the practicality of the identified privacy leakage issue.

Multiple Decision Aided Successive Interference Cancellation Receiver for NOMA Systems

Non-orthogonal multiple access (NOMA) is one of the critical technologies in fifth generation systems. Successive interference cancellation (SIC) is a typical solution of multiple users detection in NOMA systems. However, conventional SIC suffers from error propagation. To alleviate this problem, in this letter, a multiple decision SIC (MD-SIC) strategy is introduced. Instead of choosing one codeword for each user in traditional SIC detection, the proposed algorithm introduces multiple codewords as the candidates to combat the error propagation. Furthermore, a threshold is designed to control the size of candidate set for each user. It helps the MD-SIC to achieve a better tradeoff between performance and complexity. The simulation results indicate that the MD-SIC algorithm obviously outperforms the conventional SIC with a little additional computational complexity.

An evaluation of constrained randomization for the design and analysis of group-randomized trials.

In group-randomized trials, a frequent practical limitation to adopting rigorous research designs is that only a small number of groups may be available, and therefore, simple randomization cannot be relied upon to balance key group-level prognostic factors across the comparison arms. Constrained randomization is an allocation technique proposed for ensuring balance and can be used together with a permutation test for randomization-based inference. However, several statistical issues have not been thoroughly studied when constrained randomization is considered. Therefore, we used simulations to evaluate key issues including the following: the impact of the choice of the candidate set size and the balance metric used to guide randomization; the choice of adjusted versus unadjusted analysis; and the use of model-based versus randomization-based tests. We conducted a simulation study to compare the type I error and power of the F-test and the permutation test in the presence of group-level potential confounders. Our results indicate that the adjusted F-test and the permutation test perform similarly and slightly better for constrained randomization relative to simple randomization in terms of power, and the candidate set size does not substantially affect their power. Under constrained randomization, however, the unadjusted F-test is conservative, while the unadjusted permutation test carries the desired type I error rate as long as the candidate set size is not too small; the unadjusted permutation test is consistently more powerful than the unadjusted F-test and gains power as candidate set size changes. Finally, we caution against the inappropriate specification of permutation distribution under constrained randomization. An ongoing group-randomized trial is used as an illustrative example for the constrained randomization design.

L2OR: Low-Cost Low-Complexity Opportunistic Routing for Wireless Sensor Networks

Opportunistic routing can greatly improve energy efficiency in wireless sensor networks (WSNs). An important challenge is the design of a coordination protocol to implement an effective relay selection and prioritization scheme to minimize expected cost. Many existing opportunistic routing protocols often assumed an ideal link-layer, and thus the computed forwarder list may be suboptimal. Worth noting that, the computation complexity of optimal result grows rapidly with size of candidate set when the nonideal link-layer conditions are taken into account. It is, however, not feasible for energy and processing capability constrained WSNs. To address this problem, we propose a low-cost low-complexity opportunistic routing (L2OR) protocol to achieve an energy-efficient data delivery over WSNs. Our research work begins with a thorough investigation and understanding of properties of forwarder list under nonideal link-layer conditions. On this basis, a low-complexity heuristic algorithm is designed to efficiently find a reasonable choice of forwarder list, with an objective of reducing the search space to the size of candidate set. We conduct theoretical analysis and a set of simulation experiments to evaluate the performance of our L2OR. It is shown that, when comparing to typical energy-aware opportunistic protocol, the L2OR can achieve performance improvement in terms of data transmission efficiency as well as energy consumption.

Outlier detection on uncertain data based on local information

Based on local information: local density and local uncertainty level, a new outlier detection algorithm is designed in this paper to calculate uncertain local outlier factor (ULOF) for each point in an uncertain dataset. In this algorithm, all concepts, definitions and formulations for conventional local outlier detection approach (LOF) are generalized to include uncertainty information. The least squares algorithm on multi-times curve fitting is used to generate an approximate probability density function of distance between two points. An iteration algorithm is proposed to evaluate K–η–distance and a pruning strategy is adopted to reduce the size of candidate set of nearest-neighbors. The comparison between ULOF algorithm and the state-of-the-art approaches has been made. Results of several experiments on synthetic and real data sets demonstrate the effectiveness of the proposed approach.

Performance Optimization of Tomlinson-Harashima Precoder with Tilted Constellation

An algorithm is proposed to optimize performance of a novel technique providing a great performance improvement over Tomlinson-Harashima precoder (THP), referred to as THP with tilted constellation (THP-TC). Proposed algorithm deals with a problem that THP-TC has faced with, the optimization of size of angular candidate set under a constant noise power condition, which is valid for the most applications of THP-TC including multiuser MIMO applications. As a result, proposed algorithm facilitates THP-TC to reach its best compromise between the transmit power reduction and the symbol error increase caused by the estimation process of tilted angle at the receiver.

A novel prediction model based on hierarchical characteristic of web site

Internet has developed in a rapid way in the recent 10 years,and the information of web site has also been increasing fast. Predicting web user’s behavior becomes a crucial issue following the purposes like increasing the user’s browsing speed efficiently, decreasing the user’s latency as well as possible and reducing the loading of web server. In this paper, we propose an efficient prediction model, two-level prediction model (TLPM), using a novel aspect of natural hierarchical property from web log data. TLPM can decrease the size of candidate set of web pages and increase the speed of predicting with adequate accuracy. The experiment results prove that TLPM can highly enhance the performance of prediction when the number of web pages is increasing.

The new interpretation of support vector machines on statistical learning theory

This paper is concerned with the theoretical foundation of support vector machines (SVMs). The purpose is to develop further an exact relationship between SVMs and the statistical learning theory (SLT). As a representative, the standard C-support vector classification (C-SVC) is considered here. More precisely, we show that the decision function obtained by C-SVC is just one of the decision functions obtained by solving the optimization problem derived directly from the structural risk minimization principle. In addition, an interesting meaning of the parameter C in C-SVC is given by showing that C corresponds to the size of the decision function candidate set in the structural risk minimization principle.

Model selection for support vector machines via uniform design

The problem of choosing a good parameter setting for a better generalization performance in a learning task is the so-called model selection. A nested uniform design (UD) methodology is proposed for efficient, robust and automatic model selection for support vector machines (SVMs). The proposed method is applied to select the candidate set of parameter combinations and carry out a k-fold cross-validation to evaluate the generalization performance of each parameter combination. In contrast to conventional exhaustive grid search, this method can be treated as a deterministic analog of random search. It can dramatically cut down the number of parameter trials and also provide the flexibility to adjust the candidate set size under computational time constraint. The key theoretic advantage of the UD model selection over the grid search is that the UD points are “ far more uniform”and “ far more space filling” than lattice grid points. The better uniformity and space-filling phenomena make the UD selection scheme more efficient by avoiding wasteful function evaluations of close-by patterns. The proposed method is evaluated on different learning tasks, different data sets as well as different SVM algorithms.

A hybrid post-processing system for offline handwritten Chinese script recognition

In the recognition of offline handwritten Chinese scripts, contextual post-processing plays a vital role in improving accuracy. In this paper, we systematically analyze the key factors that have an impact on the performance of contextual post-processing: statistical language models (LMs), candidate confidence, candidate set size, and search strategy. We then present a hybrid post-processing system, which integrates various kinds of information available. Next, we investigate seven LMs, four estimation methods of candidate confidence and different size of candidate set, and illustrate their influence on the performance of contextual post-processing in detail. Experimental results justify that the performance of the LMs are affected by training corpora size, smoothing method, and model pruning, and that lower perplexity correlates with a high accuracy. Comparing different estimation methods of candidate confidence shows that, it is vital to the contextual post-processing. We also show that allowing the correct characters to be captured in a limited number of candidates is extremely important for obtaining good post-processing performance. By adopting the hybrid post-processing, we can obtain high accuracy while paying attention to post-processing speed and memory space at the same time. It is shown that the average recognition accuracy of three Chinese scripts (about 66,000 characters in total) can reach 97.65%, which means 87% error correction rate in comparison with the 81.58% average accuracy before post-processing. In the end, we give some proposals for choosing a proper post-processing method for real script recognition tasks.

Scalable parallel data mining for association rules

The authors propose two new parallel formulations of the Apriori algorithm (R. Agrawal and R. Srikant, 1994) that is used for computing association rules. These new formulations, IDD and HD, address the shortcomings of two previously proposed parallel formulations CD and DD. Unlike the CD algorithm, the IDD algorithm partitions the candidate set intelligently among processors to efficiently parallelize the step of building the hash tree. The IDD algorithm also eliminates the redundant work inherent in DD, and requires substantially smaller communication overhead than DD. But IDD suffers from the added cost due to communication of transactions among processors. HD is a hybrid algorithm that combines the advantages of CD and DD. Experimental results on a 128-processor Cray T3E show that HD scales just as well as the CD algorithm with respect to the number of transactions, and scales as well as IDD with respect to increasing candidate set size.