Scheduling for Multi-Camera Surveillance in LTE Networks

Wireless surveillance in cellular networks has become increasingly important, while commercial LTE surveillance cameras are also available nowadays. Nevertheless, most scheduling algorithms in the literature are throughput, fairness, or profit-based approaches, which are not suitable for wireless surveillance. In this paper, therefore, we explore the resource allocation problem for a multi-camera surveillance system in 3GPP Long Term Evolution (LTE) uplink (UL) networks. We minimize the number of allocated resource blocks (RBs) while guaranteeing the coverage requirement for surveillance systems in LTE UL networks. Specifically, we formulate the Camera Set Resource Allocation Problem (CSRAP) and prove that the problem is NP-Hard. We then propose an Integer Linear Programming formulation for general cases to find the optimal solution. Moreover, we present a baseline algorithm and devise an approximation algorithm to solve the problem. Simulation results based on a real surveillance map and synthetic datasets manifest that the number of allocated RBs can be effectively reduced compared to the existing approach for LTE networks.Comment: 9 pages, 10 figure

A WAVELET-BASED VARIABLE CONTROL PROCEDURE FOR DETECTING PROCESS MEAN SHIFT

This paper develops a wavelet-based approach for a variable control chart, and adopts the data decomposition and linear combination techniques to detect process shifts. The Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) control charts are the most popular monitoring process graph tools. However, these charts were developed for different process situations. If a user chooses an inappropriate control chart to monitor a process, the correct control result will not be obtained. This study used the wavelet transform to develop a novel variable control procedure. First, the Haar function was used as the basis for data decomposition. Next, the linear combination technique was used to combine different resolution data through wavelet transform decomposition. Simulations were adopted to evaluate performance. An analysis showed that the detection ability of the wavelet-based variable control chart was superior to the EWMA control chart in a comparison of average run length (ARL) results

Pooling spaces associated with finite geometry

AbstractMotivated by the works of Ngo and Du [H. Ngo, D. Du, A survey on combinatorial group testing algorithms with applications to DNA library screening, DIMACS Series in Discrete Mathematics and Theoretical Computer Science 55 (2000) 171–182], the notion of pooling spaces was introduced [T. Huang, C. Weng, Pooling spaces and non-adaptive pooling designs, Discrete Mathematics 282 (2004) 163–169] for a systematic way of constructing pooling designs; note that geometric lattices are among pooling spaces. This paper attempts to draw possible connections from finite geometry and distance regular graphs to pooling spaces: including the projective spaces, the affine spaces, the attenuated spaces, and a few families of geometric lattices associated with the orbits of subspaces under finite classical groups, and associated with d-bounded distance-regular graphs

Service Failure and Recovery: The Role of Customer Forgiveness and Perceived Justice in Customers’ Coping Behaviors

This research investigated the role of customer forgiveness as the underlying mechanism of the effect of service failure severity on customers’ coping behaviors. It also investigated the moderating role of customers’ justice perceptions in the proposed model. The findings showed that customer forgiveness is essential in mending the relationships and lowering customer avoidance. Customer forgiveness was less negatively affected by service failure severity when customer perceived service providers’ recovery efforts with higher levels of justice. Additionally, this research explored the moderating effects of three dimensions of justice on the relationship between service failure severity and customer forgiveness. The findings demonstrated that the higher levels of distributive justice weakened the negative effect of service failure severity on customer forgiveness, especially when customer perceived lower levels of interactional justice. However, such effect was lessened when customer perceived higher levels of interactional justice

ShuttleSet22: Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset

In recent years, badminton analytics has drawn attention due to the advancement of artificial intelligence and the efficiency of data collection. While there is a line of effective applications to improve and investigate player performance, there are only a few public badminton datasets that can be used for researchers outside the badminton domain. Existing badminton singles datasets focus on specific matchups; however, they cannot provide comprehensive studies on different players and various matchups. In this paper, we provide a badminton singles dataset, ShuttleSet22, which is collected from high-ranking matches in 2022. ShuttleSet22 consists of 30,172 strokes in 2,888 rallies in the training set, 1,400 strokes in 450 rallies in the validation set, and 2,040 strokes in 654 rallies in the testing set with detailed stroke-level metadata within a rally. To benchmark existing work with ShuttleSet22, we test the state-of-the-art stroke forecasting approach, ShuttleNet, with the corresponding stroke forecasting task, i.e., predict the future strokes based on the given strokes of each rally. We also hold a challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAI Badminton Challenge 2023 to boost researchers to tackle this problem. The baseline codes and the dataset will be made available on https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI-Challenge-IJCAI2023.Comment: IT4PSS @ IJCAI-23 and CoachAI Badminton Challenge Track 2 @ IJCAI-23. Challenge website: https://sites.google.com/view/coachai-challenge-2023

Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton

Sports analytics has captured increasing attention since analysis of the various data enables insights for training strategies, player evaluation, etc. In this paper, we focus on predicting what types of returning strokes will be made, and where players will move to based on previous strokes. As this problem has not been addressed to date, movement forecasting can be tackled through sequence-based and graph-based models by formulating as a sequence prediction task. However, existing sequence-based models neglect the effects of interactions between players, and graph-based models still suffer from multifaceted perspectives on the next movement. Moreover, there is no existing work on representing strategic relations among players' shot types and movements. To address these challenges, we first introduce the procedure of the Player Movements (PM) graph to exploit the structural movements of players with strategic relations. Based on the PM graph, we propose a novel Dynamic Graphs and Hierarchical Fusion for Movement Forecasting model (DyMF) with interaction style extractors to capture the mutual interactions of players themselves and between both players within a rally, and dynamic players' tactics across time. In addition, hierarchical fusion modules are designed to incorporate the style influence of both players and rally interactions. Extensive experiments show that our model empirically outperforms both sequence- and graph-based methods and demonstrate the practical usage of movement forecasting.Comment: Accepted by AAAI 2022, code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/Movement\%20Forecastin

DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal

The marketplace system connecting demands and supplies has been explored to develop unbiased decision-making in valuing properties. Real estate appraisal serves as one of the high-cost property valuation tasks for financial institutions since it requires domain experts to appraise the estimation based on the corresponding knowledge and the judgment of the market. Existing automated valuation models reducing the subjectivity of domain experts require a large number of transactions for effective evaluation, which is predominantly limited to not only the labeling efforts of transactions but also the generalizability of new developing and rural areas. To learn representations from unlabeled real estate sets, existing self-supervised learning (SSL) for tabular data neglects various important features, and fails to incorporate domain knowledge. In this paper, we propose DoRA, a Domain-based self-supervised learning framework for low-resource Real estate Appraisal. DoRA is pre-trained with an intra-sample geographic prediction as the pretext task based on the metadata of the real estate for equipping the real estate representations with prior domain knowledge. Furthermore, inter-sample contrastive learning is employed to generalize the representations to be robust for limited transactions of downstream tasks. Our benchmark results on three property types of real-world transactions show that DoRA significantly outperforms the SSL baselines for tabular data, the graph-based methods, and the supervised approaches in the few-shot scenarios by at least 7.6% for MAPE, 11.59% for MAE, and 3.34% for HR10%. We expect DoRA to be useful to other financial practitioners with similar marketplace applications who need general models for properties that are newly built and have limited records. The source code is available at https://github.com/wwweiwei/DoRA.Comment: Accepted by CIKM 202

Stationary Light Pulses in Cold Atomic Media

Stationary light pulses (SLPs), i.e., light pulses without motion, are formed via the retrieval of stored probe pulses with two counter-propagating coupling fields. We show that there exist non-negligible hybrid Raman excitations in media of cold atoms that prohibit the SLP formation. We experimentally demonstrate a method to suppress these Raman excitations and realize SLPs in laser-cooled atoms. Our work opens the way to SLP studies in cold as well as in stationary atoms and provides a new avenue to low-light-level nonlinear optics.Comment: 4 pages, 4 figure