pwc-archive/papers-with-abstracts huggingface dataset:
Real-Time Mine Road Boundary Detection and Tracking for Autonomous Truck
Authors: Xiaowei Lu1, 2, Yunfeng Ai1and Bin Tian3, *
Date: 2020-01-01 00:00:00
Proceeding: https://www.mdpi.com/journal/sensors 2020 1
Tasks: Autonomous Driving, Boundary Detection

Abstract: Road boundary detection is an important part of the perception of autonomous driving.
It is difficult to detect road boundaries of unstructured roads because there are no curbs. There are no
clear boundaries on mine roads to distinguish areas within the road boundary line and areas outside
the road boundary line. This paper proposes a real-time road boundary detection and tracking
method by a 3D-LIDAR sensor. The road boundary points are extracted from the detected elevated
point clouds above the ground point cloud according to the spatial distance characteristics and the
angular features. Road tracking is to predict and update the boundary point information in real-time,
in order to prevent false and missed detection. The experimental verification of mine road data shows
the accuracy and robustness of the proposed algorithm.
Powershell malware detection method based on features combination
Authors: LIU Yue, LIU Baoxu, ZHAO Zihao, LIU Chaoge, WANG Xiaoxi, WU Xianda
Date: 2021-01-01 00:00:00
Proceeding: Journal of Cyber Security 2021 1
Tasks: Malware Detection

In recent years, powershell is widely used in APT attack due to its ease of use and high concealment. Tradi-
tional malicious code detection technology based on artificial feature extraction and machine learning method is more and
more difficult to be effective in the detection of malicious code in PowerShell. For this reason, this paper proposes a mali-
cious Powershell code detection method based on random forest features combination and deep learning. This method uses
random forest to generate new features which better characterize the original data, and uses deep neural network to build
classifiers for classification and recognition. This method can make up for the lack of experience in artificial feature engi-
neering, and characterize the original data better, so as to improve the detection effect. The experimental results in this
article show that this method has a good performance, high recall rate and accuracy rate, which can effectively detect and
identify malicious Powershell code
EPYNET: Efficient Pyramidal Network for Clothing Segmentation
Authors: Andrei De Souza Inácio, Heitor Silvério Lopes
Date: 2020-10-13 00:00:00
Proceeding: IEEE Access 2020 10
Tasks: Data Augmentation, Image Description, Segmentation, Semantic Segmentation

Soft biometrics traits extracted from a human body, including the type of clothes, hair color, and accessories, are useful information used for people tracking and identification. Semantic segmentation of these traits from images is still a challenge for researchers because of the huge variety of clothing styles, layering, shapes, and colors. To tackle these issues, we proposed EPYNET, a framework for clothing segmentation. EPYNET is based on the Single Shot MultiBox Detector (SSD) and the Feature Pyramid Network (FPN) with the EfficientNet model as the backbone. The framework also integrates data augmentation methods and noise reduction techniques to increase the accuracy of the segmentation. We also propose a new dataset named UTFPR-SBD3, consisting of 4,500 manually annotated images into 18 classes of objects, plus the background. Unlike available public datasets with imbalanced class distributions, the UTFPR-SBD3 has, at least, 100 instances per class to minimize the training difficulty of deep learning models. We introduced a new measure of dataset imbalance, motivated by the difficulty in comparing different datasets for clothing segmentation. With such a measure, it is possible to detect the influence of the background, classes with small items, or classes with a too high or too low number of instances. Experimental results on UTFPR-SBD3 show the effectiveness of EPYNET, outperforming the state-of-art methods for clothing segmentation on public datasets. Based on these results, we believe that the proposed approach can be potentially useful for many real-world applications related to soft biometrics, people surveillance, image description, clothes recommendation, and others.
OSVidCap: A Framework for the Simultaneous Recognition and Description of Concurrent Actions in Videos in an Open-Set Scenario
Authors: Andrei De Souza Inácio, Matheus Gutoski, André Eugênio Lazzaretti, Heitor Silvério Lopes
Date: 2021-09-29 00:00:00
Proceeding: IEEE Access 2021 9
Tasks: Decoder, Open Set Video Captioning, Video Captioning

Automatically understanding and describing the visual content of videos in natural language is a challenging task in computer vision. Existing approaches are often designed to describe single events in a closed-set setting. However, in real-world scenarios, concurrent activities and previously unseen actions may appear in a video. This work presents the OSVidCap, a novel open-set video captioning framework that recognizes and describes, in natural language, concurrent known actions and deal with unknown ones. The OSVidCap is based on the encoder-decoder framework and uses a detection-and-tracking-object-based mechanism followed by a background blurring method to focus on specific targets in a video. Additionally, we employ the TI3D Network with the Extreme Value Machine (EVM), which learns representations and recognizes unknown actions. We evaluate the proposed approach on the benchmark ActivityNet Captions dataset. Also, an enhanced version of the LIRIS human activity dataset was proposed by providing descriptions for each action. We also provide spatial, temporal, and caption annotations for existing unlabeled actions in the dataset - considered unknown actions in our experiments. Experimental results showed our method’s effectiveness in recognizing and describing concurrent actions in natural language and the strong ability to deal with detected unknown activities. Based on these results, we believe that the proposed approach can be potentially helpful for many real-world applications, including human behavior analysis, safety monitoring, and surveillance.
Parameter-free Geometric Document Layout Analysis
Authors: Seong-Whan Lee, Senior Member, IEEE, and Dae-Seok Ryu
Date: 2001-11-01 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2001 11
Tasks: Attribute, Document Layout Analysis, Texture Classification

Automatic transformation of paper documents into electronic documents requires geometric document layout analysis at the first stage. However, variations in character font sizes, text line spacing, and document layout structures have made it difficult to design a general-purpose document layout analysis algorithm for many years. The use of some parameters has therefore been unavoidable in previous methods. In this paper, we propose a parameter-free method for segmenting the document images into maximal homogeneous regions and identifying them as texts, images, tables, and ruling lines. A pyramidal quadtree structure is constructed for multiscale analysis and a periodicity measure is suggested to find a periodical attribute of text regions for page segmentation. To obtain robust page segmentation results, a confirmation procedure using texture analysis is applied to only ambiguous regions. Based on the proposed periodicity measure, multiscale analysis, and confirmation procedure, we could develop a robust method for geometric document layout analysis independent of character font sizes, text line spacing, and document layout structures. The proposed method was experimented with the document database from the University of Washington and the MediaTeam Document Database. The results of these tests have shown that the proposed method provides more accurate results than the previous ones.
Hyperspectral Image Classification via Sparse Representation With Incremental Dictionaries
Authors: Shujun Yang, Junhui Hou, Yuheng Jia, Shaohui Mei, and Qian Du
Date: 2019-11-06 00:00:00
Proceeding: journal 2019 11
Tasks: Classification, Hyperspectral Image Classification, image-classification, Image Classification

In this letter, we propose a new sparse representation (SR)-based method for hyperspectral image (HSI) classification, namely SR with incremental dictionaries (SRID). Our SRID boosts existing SR-based HSI classification methods significantly, especially when used for the task with extremely limited training samples. Specifically, by exploiting unlabeled pixels with spatial information and multiple-feature-based SR classifiers, we select and add some of them to dictionaries in an iterative manner, such that the representation abilities of the dictionaries are progressively augmented, and likewise more discriminative representations. In addition, to deal with large-scale data sets, we use a certainty sampling strategy to control the sizes of the dictionaries, such that the computational complexity is well balanced. Experiments over two benchmark data sets show that our proposed method achieves higher classification accuracy than the state-of-the-art methods, i.e., the overall classification accuracy can improve more than 4%.
Dual-Stream Fusion Network for Spatiotemporal Video Super-Resolution
Authors: Min-Yuan Tseng, Yen-Chung Chen, Yi-Lun Lee, Wei-Sheng Lai, Yi-Hsuan Tsai, Wei-Chen Chiu
Date: 2021-01-05 00:00:00
Proceeding: Winter Conference on Applications of Computer Vision (WACV) 2021 1
Tasks: Image Super-Resolution, Super-Resolution, Video Super-Resolution

Visual data upsampling has been an important research topic for improving the perceptual quality and benefiting various computer vision applications. In recent years, we have witnessed remarkable progresses brought by the re-naissance of deep learning techniques for video or image super-resolution. However, most existing methods focus on advancing super-resolution at either spatial or temporal direction, i.e, to increase the spatial resolution or the video frame rate. In this paper, we instead turn to discuss both directions jointly and tackle the spatiotemporal upsampling problem. Our method is based on an important observation that: even the direct cascade of prior research in spatial and temporal super-resolution can achieve the spatiotemporal upsampling, changing orders for combining them would lead to results with a complementary property. Thus, we propose a dual-stream fusion network to adaptively fuse the intermediate results produced by two spatiotemporal up-sampling streams, where the first stream applies the spatial super-resolution followed by the temporal super-resolution, while the second one is with the reverse order of cascade. Extensive experiments verify the efficacy of the proposed method against several baselines. Moreover, we investigate various spatial and temporal upsampling methods as the basis in our two-stream model and demonstrate the flexibility with wide applicability of the proposed framework.
A tutorial to set safety stock under guaranteed-service time by dynamic programming
Authors: Luis A. Moncayo-Martinez, Adan Ramirez-Lopez
Date: 2016-10-01 00:00:00
Proceeding: International Journal of Industrial and Systems Engineering 2016 10

In this paper, we provide a tutorial to solve the problem of minimising the safety stock levels under guaranteed-service time over a supply chain (SC) using the dynamic programming (DP) algorithm proposed by Graves and Willems (2000a). We solve a small instance to exemplify the steps of the DP algorithm, then we solve two bigger instances by a Java-based application. As the DP algorithm has some insights that must be explained in detail to carry it out, the novelty and helpfulness of this tutorial lies in the fact that we account for those insights which lead researchers to develop new algorithms to solve bigger instances. This problem is included in some state-of-the-art SC books but none gives details about the solution algorithm as we do in this tutorial.
Would I regret later joining this Community ? Using temporal neighborhood information for community retention in a game theoretic community detection framework
Authors: Anonymous
Date: 2021-11-24 00:00:00
Proceeding: AAAI Workshop ML4OR-22 2022 2
Tasks: Community Detection

Detection of relevant and meaningful communities from any social network is always a constant research challenge. Considering large social networks existing approaches often fail to capture perfect community partition or fail to converge fast. Inspired by nonmyopic reinforcement learning domain, in this paper, we have proposed a novel game theory based community detection algorithm which considers community retention based on temporal information as part of a node’s strategy (i.e. for a node whether their current assigned community is more profitable based upon future possibilities, or should they switch at the moment), thereby minimizing cross-community false node switches. The proposed method has the following properties (a) considers community retention for a node in the network when it considers switching to another community, (b) achieves significantly better/comparable performance w.r.t baselines in terms of metrics such as quality of partition on various real world datasets,(c) faster convergence w.r.t traditional game theoretic approach which only considers short-term utility by minimizing cross-community node switches (d) utility to interpret at each iteration for any particular node’s strategy.
SatNet: A Benchmark for Satellite Scheduling Optimization
Authors: Anonymous
Date: 2021-11-24 00:00:00
Proceeding: AAAI Workshop ML4OR-22 2022 2
Tasks: Combinatorial Optimization, Evolutionary Algorithms, Reinforcement Learning (RL), Scheduling

Satellites provide essential services such as networking and weather tracking, and the number of satellites are expected to grow rapidly in the coming years. Communications with terrestrial ground stations is one of the critical functionalities of any space mission. Satellite scheduling is a problem that has been scientifically investigated since the 1970s. A central aspect of this problem is the need to consider bandwidth resource contention and satellite visibility constraints as they require line of sight. 
Due to the combinatorial nature of the problem, prior solutions such as linear programs and evolutionary algorithms require extensive compute capabilities to output a feasible schedule for each scenario. Machine learning based scheduling can provide an alternative solution by training a model with historical data and generating a schedule quickly with model inference. We present SatNet, a benchmark for satellite scheduling optimization based on historical data from the NASA Deep Space Network. We propose formulation of the satellite scheduling problem as a Markov Decision Process and use reinforcement learning (RL) policies to generate schedules. The nature of constraints imposed by SatNet differ from other combinatorial optimization problems such as vehicle routing studied in prior literature. Our initial results indicate that RL is an alternative optimization approach that can generate candidate solutions of comparable quality to existing state-of-the-practice results. However, we also find that RL policies overfit to the training dataset and do not generalize well to new data, thereby necessitating continued research on reusable and generalizable agents.
Neural Column Generation for Capacitated Vehicle Routing
Authors: Anonymous
Date: 2021-11-24 00:00:00
Proceeding: AAAI Workshop ML4OR-22 2022 2
Tasks: Decision Making, Imitation Learning, Sequential Decision Making

The column generation technique is essential for solving linear programs with an exponential number of variables. Many important applications such as the vehicle routing problem (VRP) now require it. However, in practice, getting column generation to converge is challenging. It often ends up adding too many columns. In this work, we frame the problem of selecting which columns to add as one of sequential decision-making. We propose a neural column generation architecture that iteratively selects columns to be added to the problem. The architecture, inspired by stabilization techniques, first predicts the optimal duals. These predictions are then used to obtain the columns to add. We show using VRP instances that in this setting several machine learning models yield good performance on the task and that our proposed architecture learned using imitation learning outperforms a modern stabilization technique.
Crew Recovery Using Machine Learning and Optimization
Authors: Anonymous
Date: 2021-11-24 00:00:00
Proceeding: AAAI Workshop ML4OR-22 2022 2
Tasks: BIG-bench Machine Learning

Due to the irregular nature of flight operations, airlines need to take a range of actions to recover their aircraft and crew schedules. The limited time frames prevent airlines from using a full-scale optimization approach. Consequently, airlines typically apply recovery solutions that can be far from optimal. This study proposes a practical method that combines machine learning and optimization to find improved recovery solutions. Our procedure is based on the idea that the most effective constraints to add to the recovery models without sacrificing the solution quality, can be determined in advance by leveraging the similarities between disruptions. Our experiments show that this approach can reduce solution time significantly while still achieving high-quality solutions.
Machine Learning Based Aircraft Recovery Optimization
Authors: Anonymous
Date: 2021-11-24 00:00:00
Proceeding: AAAI Workshop ML4OR-22 2022 2
Tasks: BIG-bench Machine Learning

In this research, supervised machine learning is employed to expedite optimization. The core idea is to try to identify components of optimal solutions to new problem instances by leveraging their similarity with alternative (historical) problem instances presented in the offline model-training phase. Our approach prunes the decision space by using machine learning algorithms that are trained using the network- wide optimal decisions for similar scenarios.
10,000 optimal CVRP solutions for testing machine learning based heuristics
Authors: Anonymous
Date: 2021-11-24 00:00:00
Proceeding: AAAI Workshop ML4OR-22 2022 2
Tasks: 2D Object Detection, 3D Point Cloud Reinforcement Learning, BIG-bench Machine Learning

We introduce a benchmark of 10,000 instances with heterogeneous characteristics for the capacitated vehicle routing problem. We also provide optimal solutions for almost all of them along with a generator to produce additional training and validation data. This benchmark aims to permit a more systematic comparison of machine learning based search algorithms on this important problem. We also emit recommendations regarding the correct use of this dataset.
Application of Multi-Agent Reinforcement Learning for Battery Management in Renewable Mini-Grids
Authors: Anonymous
Date: 2021-11-24 00:00:00
Proceeding: AAAI Workshop ML4OR-22 2022 2
Tasks: Management, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Electricity is an integral part of modern society, yet globally millions of people are without access. This lack of access, coupled with increasing concern over climate change represents a serious global challenge. Distributed energy storage will likely play a large part in the future of the grid, however, battery management remains an open problem. In this work, we re-frame the battery management problem in an Operations Research (OR) context as a multi-agent newsvendor problem. We benchmark seven Multi-Agent Reinforcement Learning (MARL) algorithms and compare their performance with five popular handcrafted heuristic strategies.
We considered MARL algorithms due to their capacity to learn novel policies from data that may outperform handcrafted rule-based policies, especially as problem complexity increases. We find that all seven methods learn policies that achieve comparable results to each other and outperform a simple keep-fully-charged heuristic consistently. However, they do not consistently outperform all the heuristics considered in all the scenarios considered.
Multicrew Scheduling and Routing in Road Network Restoration Based on Deep Q-learning
Authors: Anonymous
Date: 2021-11-24 00:00:00
Proceeding: AAAI Workshop ML4OR-22 2022 2
Tasks: Q-Learning, Scheduling

Road network restoration is an important issue in the post-disaster disposal and rescue, especially when extraor-dinarily serious natural disasters (e.g., floods and earth-quakes) occur. Central to this endeavor is the problem of determining how to reasonably schedule and route the re-pair crew to quickly restore the damaged road network and establish reliable supply lines from supply nodes to demand nodes. However, most existing work focuses on the activities of the single repair crew, and rarely consid-ers the problem of continuously damaged road sections, especially for the road network with enormous demand nodes and serious damage. Consequently, this work is concentrated on multicrew scheduling and routing for the damaged road network with enormous demand nodes. Specifically, a model of multicrew scheduling and routing is first presented. Next, a road network partition strategy is first proposed to make different repair crews responsible for different subnets. Then, a deep Q-learning based mul-ticrew scheduling and routing algorithm is proposed for the damaged road network with enormous demand nodes, which utilizes the learning experience of multiple repair crews to achieve hybrid learning. Finally, experimental re-sults demonstrate that the proposed method can make re-pair crews adjust their scheduling and routing strategies according to the damaged road network and provides a useful attempt to restore the damaged road network in complex emergency scenarios of post-disaster.
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes
Authors: Anonymous
Date: 2021-11-24 00:00:00
Proceeding: AAAI Workshop ML4OR-22 2022 2
Tasks: Combinatorial Optimization, Reinforcement Learning (RL), Scheduling, Stochastic Optimization

The simultaneous stochastic optimization of mining complexes (SSOMC) is a large-scale combinatorial optimization problem that manages the extraction of materials from multiple mines and their processing using interconnected facilities. Following the work of Zarpellon et al. (2020) and Chmiela et al. (2021), to the best of our knowledge, this work proposes the first data-driven framework for heuristic scheduling in a hyper-heuristic-based solver that is fully self-managed to solve the SSOMC. The proposed learn-to-perturb (L2P) hyper-heuristic is a multi-neighborhood simulated annealing algorithm. The L2P selects the heuristic (perturbation) to apply in a self-adaptive manner using reinforcement learning (RL) to efficiently explore which local search is best suited for a particular search point. Several state-of-the-art agents have been incorporated into the proposed hyper-heuristic to better adapt the search and guide it towards better solutions. By learning from data describing the performance of heuristics, a problem-specific ordering of heuristics that collectively finds better solutions faster is obtained. The L2P is tested on several real-world mining complexes, with an emphasis on efficiency, robustness, and generalization capacity. Results show a reduction in the computational time by 30-45%.