Spatial movement pattern recognition in soccer based on relative player movements

Knowledge of spatial movement patterns in soccer occurring on a regular basis can give a soccer coach, analyst or reporter insights in the playing style or tactics of a group of players or team. Furthermore, it can support a coach to better prepare for a soccer match by analysing (trained) movement patterns of both his own as well as opponent players. We explore the use of the Qualitative Trajectory Calculus (QTC), a spatiotemporal qualitative calculus describing the relative movement between objects, for spatial movement pattern recognition of players movements in soccer. The proposed method allows for the recognition of spatial movement patterns that occur on different parts of the field and/or at different spatial scales. Furthermore, the Levenshtein distance metric supports the recognition of similar movements that occur at different speeds and enables the comparison of movements that have different temporal lengths. We first present the basics of the calculus, and subsequently illustrate its applicability with a real soccer case. To that end, we present a situation where a user chooses the movements of two players during 20 seconds of a real soccer match of a 2016-2017 professional soccer competition as a reference fragment. Following a pattern matching procedure, we describe all other fragments with QTC and calculate their distance with the QTC representation of the reference fragment. The top-k most similar fragments of the same match are presented and validated by means of a duo-trio test. The analyses show the potential of QTC for spatial movement pattern recognition in soccer

A simple way to estimate similarity between pairs of eye movement sequences

We propose a novel algorithm to estimate the similarity between a pair of eye movement sequences. The proposed algorithm relies on a straight-forward geometric representation of eye movement data. The algorithm is considerably simpler to implement and apply than existing similarity measures, and is particularly suited for exploratory analyses. To validate the algorithm, we conducted a benchmark experiment using realistic artificial eye movement data. Based on similarity ratings obtained from the proposed algorithm, we defined two clusters in an unlabelled set of eye movement sequences. As a measure of the algorithm's sensitivity, we quantified the extent to which these data-driven clusters matched two pre-defined groups (i.e., the 'real' clusters). The same analysis was performed using two other, commonly used similarity measures. The results show that the proposed algorithm is a viable similarity measure

ScanGraph: A Novel Scanpath Comparison Method Using Visualisation of Graph Cliques

The article describes a new tool for analyses of eye-movement data. Many different approaches to scanpath comparison exist. One of the most frequently used approaches is String Edit Distance, where the gaze trajectories are replaced by the sequences of visited Areas of Interest. In cartographic literature, the most commonly used software for scanpath comparison is eyePatterns. During the analysis of eyePatterns functionality, we have found that tree-graph visualization of its results is not reliable. Thus, we decided to develop a new tool called ScanGraph. Its computational algorithms are modified to work better with the sequences with different lengths. The output is visualized as a simple graph, and similar groups of sequences are displayed as cliques of this graph. The article describes ScanGraph’s functionality on the example of a simple cartographic eye-tracking study. Differences of the reading strategy of a simple map between cartographic experts and novices were investigated. The paper should serve to the researchers who would like to analyze differences between groups of participants, and who would like to use our tool - ScanGraph, available at www.eyetracking.upol.cz/scangraph

Event-driven Similarity and Classification of Scanpaths

Eye tracking experiments often involve recording the pattern of deployment of visual attention over the stimulus as viewers perform a given task (e.g., visual search). It is useful in training applications, for example, to make available an expert\u27s sequence of eye movements, or scanpath, to novices for their inspection and subsequent learning. It may also be potentially useful to be able to assess the conformance of the novice\u27s scanpath to that of the expert. A computational tool is proposed that provides a framework for performing such classification, based on the use of a probabilistic machine learning algorithm. The approach was influenced by the need to compute similarity of eye fixations at single points in time, such as would be required for video stimuli. This method is also useful for eye movement analysis over static images and some interactive tasks. The algorithm employs a common qualitative omparison method, the heatmap, in a quantitative way to measure deviation from group aggregate behavior. This quantitative comparison is performed at individual events, defined by the stimulus, such as frame timestamps of video or mouseclicks of interactive tasks. The algorithm is evaluated and found to be more accurate and discriminative than existing comparison algorithms for the stimuli used in the examined experiments

Learning from Teacher's Eye Movement: Expertise, Subject Matter and Video Modeling

How teachers' eye movements can be used to understand and improve education is the central focus of the present paper. Three empirical studies were carried out to understand the nature of teachers' eye movements in natural settings and how they might be used to promote learning. The studies explored 1) the relationship between teacher expertise and eye movement in the course of teaching, 2) how individual differences and the demands of different subjects affect teachers' eye movement during literacy and mathematics instruction, 3) whether including an expert's eye movement and hand information in instructional videos can promote learning. Each study looked at the nature and use of teacher eye movements from a different angle but collectively converge on contributions to answering the question: what can we learn from teachers' eye movements? The paper also contains an independent methodology chapter dedicated to reviewing and comparing methods of representing eye movements in order to determine a suitable statistical procedure for representing the richness of current and similar eye tracking data. Results show that there are considerable differences between expert and novice teachers' eye movement in a real teaching situation, replicating similar patterns revealed by past studies on expertise and gaze behavior in athletics and other fields. This paper also identified the mix of person-specific and subject-specific eye movement patterns that occur when the same teacher teaches different topics to the same children. The final study reports evidence that eye movement can be useful in teaching; by showing increased learning when learners saw an expert model's eye movement in a video modeling example. The implications of these studies regarding teacher education and instruction are discussed.PHDEducation & PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145853/1/yizhenh_1.pd

Synthesizing Population for Travel Activity Analysis

Population synthesis is a fundamental procedure for individual-based modeling in transportation research. The population synthesis generates anonymized individuals with selected social-demographic variables that have similar statistical distributions as that of the samples from the real population. Previous studies on population synthesis focused on generating general-purpose population by fitting the joint distributions of multiple variables to their sampled distributions. In addition to fitting the joint distributions, this study focuses on generating population for travel activity analysis by considering individuals’ travel activity patterns and associated social, economic, and demographic characteristics. A person’s daily movement is a time-sequence of activities connected by travel behaviors. It can be described as vectors that include important transportation attributes such as travel distance, travel mode, activity type, activity time, and activity sequence. A multidimensional pattern vector method is used in this study to represent an individual’s daily travel activities. This method is based on the combination of time-geography, sequence alignment, and pattern vector. Using the 2001 and 2009 National Household Travel Survey (NHTS), the travel distance and activity sequence of individuals are normalized, compared, and integrated into a dissimilarity matrix. Major travel activity patterns are then examined by cluster analysis. The random forest model is applied to examine the prominent socio-demographic characteristics that correlate to the activity patterns. The prominent socio-demographic characteristics are then used to synthesize population microdata. Since the algorithm complexity of population synthesis grows exponentially with the number of attributes, the methodology used in this study can effectively reduce the computational intensity by focusing on the most important variables for travel activity analysis. This study also addresses another issue in traditional population synthesis algorithms, i.e., the probability distributions at the individual and household levels cannot be fitted simultaneously. In this study, Iterative Proportional Fitting (IPF) algorithm is used to consider the distributions at different scales and to generate synthetic population microdata with the prominent socio-demographic characteristics. The performance of the algorithm that generates synthesized population is evaluated by scatter plot and Normalized Root Mean Square Error (NRMSE) analysis. In addition, the distributions of socio-demographic attributes in the synthesized data are compared with that of variables in the observed sample dataset. The verification result indicates that the new method can produce a better population microdata. This dissertation describes how to generate a synthetic population for Milwaukee County, WI with prominent socio-demographic variables for travel activity analysis. By critically selecting the prominent socio-demographic factors, the computational intensity of population synthesis is reduced. It is also found that, by aggregating the IPF-generated weights of individuals and using them to the household level, the overall goodness-of-fit can be managed at a reasonable level and the distributions of socio-demographic factors at the individual and household levels can be fitted

Unsupervised Detection of Cell-Assembly Sequences by Similarity-Based Clustering

Neurons which fire in a fixed temporal pattern (i.e., "cell assemblies") are hypothesized to be a fundamental unit of neural information processing. Several methods are available for the detection of cell assemblies without a time structure. However, the systematic detection of cell assemblies with time structure has been challenging, especially in large datasets, due to the lack of efficient methods for handling the time structure. Here, we show a method to detect a variety of cell-assembly activity patterns, recurring in noisy neural population activities at multiple timescales. The key innovation is the use of a computer science method to comparing strings ("edit similarity"), to group spikes into assemblies. We validated the method using artificial data and experimental data, which were previously recorded from the hippocampus of male Long-Evans rats and the prefrontal cortex of male Brown Norway/Fisher hybrid rats. From the hippocampus, we could simultaneously extract place-cell sequences occurring on different timescales during navigation and awake replay. From the prefrontal cortex, we could discover multiple spike sequences of neurons encoding different segments of a goal-directed task. Unlike conventional event-driven statistical approaches, our method detects cell assemblies without creating event-locked averages. Thus, the method offers a novel analytical tool for deciphering the neural code during arbitrary behavioral and mental processes

Advanced fuzzy matching in the translation of EU texts

In the translation industry today, CAT tool environments are an indispensable part of the translator’s workflow. Translation memory systems constitute one of the most important features contained in these tools and the question of how to best use them to make the translation process faster and more efficient legitimately arises. This research aims to examine whether there are more efficient methods of retrieving potentially useful translation suggestions than the ones currently used in TM systems. We are especially interested in investigating whether more sophisticated algorithms and the inclusion of linguistic features in the matching process lead to significant improvement in quality of the retrieved matches. The used dataset, the DGT-TM, is pre-processed and parsed, and a number of matching configurations are applied to the data structures contained in the produced parse trees. We also try to improve the matching by combining the individual metrics using a regression algorithm. The retrieved matches are then evaluated by means of automatic evaluation, based on correlations and mean scores, and human evaluation, based on correlations of the derived ranks and scores. Ultimately, the goal is to determine whether the implementation of some of these fuzzy matching metrics should be considered in the framework of the commercial CAT tools to improve the translation process

A multiprocessor parallel approach to bit-parallel approximate string matching

The purpose of this project is to present with empirical results that a parallel design with the use of multiple processors can be successfully applied along with bit-parallel approximate string matching algorithms to solve practical bioinformatics problems. It will demonstrate that nearly optimal speedup can be achieved with a cluster of between two and eight workstations using MPI (Message Passing Interface), directly decreasing the total latency required to perform a string matching problem