pwc-archive/papers-with-abstracts huggingface dataset:
Small Object Detection via Pixel Level Balancing With Applications to Blood Cell Detection
Authors: Bin Hu, Yang Liu, Pengzhi Chu, Minglei Tong, Qingjie Kong
Date: 2022-06-17 00:00:00
Proceeding: Frontiers in Physiology 2022 6
Tasks: 2D Object Detection, Cell Detection, Data Augmentation, Object, object-detection, Object Detection, Small Object Detection

Object detection technology has been widely used in medical field, such as detecting the images of blood cell to count the changes and distribution for assisting the diagnosis of diseases. However, detecting small objects is one of the most challenging and important problems especially in medical scenarios. Most of the objects in medical images are very small but influential. Improving the detection performance of small objects is a very meaningful topic for medical detection. Current researches mainly focus on the extraction of small object features and data augmentation for small object samples, all of these researches focus on extracting the feature space of small objects better. However, in the training process of a detection model, objects of different sizes are mixed together, which may interfere with each other and affect the performance of small object detection. In this paper, we propose a method called pixel level balancing (PLB), which takes into account the number of pixels contained in the detection box as an impact factor to characterize the size of the inspected objects, and uses this as an impact factor. The training loss of each object of different size is adjusted by a weight dynamically, so as to improve the accuracy of small object detection. Finally, through experiments, we demonstrate that the size of objects in object detection interfere with each other. So that we can improve the accuracy of small object detection through PLB operation. This method can perform well with blood cell detection in our experiments.
DGCN Based Solution for Entity Linking on Visual Rich Document
Authors: Shaodong Hou
Date: 2022-11-16 00:00:00
Proceeding: no publication 2022 11
Tasks: Entity Linking

Various works on entity extraction on visual rich document (VRD) have been done. However, few methods have been explored to handle entity linking problem. The difficulties come from the number of possible linking edges among entities is of square times complexity. Our approach introduces directed graph based convolutional network (DGCN) to predict relations between entities, which out performs existing methods on the FUNSD entity linking task.
Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation
Authors: Wentao Liu，Huihua Yang, Tong Tian, Zhiwei Cao, Xipeng Pan, Weijin Xu, Yang Jin, Feng Gao
Date: 2022-07-05 00:00:00
Proceeding: JBHI 2022 7
Tasks: Retinal Vessel Segmentation, Segmentation

Vessel segmentation is critical for disease diagnosis and surgical planning. Recently, the vessel segmentation method based on deep learning has achieved outstanding performance. However, vessel segmentation remains challenging due to thin vessels with low contrast that easily lose spatial information in the traditional U-shaped segmentation network. To alleviate this problem, we propose a novel and straightforward full-resolution network (FR-UNet) that expands horizontally and vertically through a multiresolution convolution interactive mechanism while retaining full image resolution. In FR-UNet, the feature aggregation module integrates multiscale feature maps from adjacent stages to supplement high-level contextual information. The modified residual blocks continuously learn multiresolution representations to obtain a pixel-level accuracy prediction map. Moreover, we propose the dual-threshold iterative algorithm (DTI) to extract weak vessel pixels for improving vessel connectivity. The proposed method was evaluated on retinal vessel datasets (DRIVE, CHASE\_DB1, and STARE) and coronary angiography datasets (DCA1 and CHUAC). The results demonstrate that FR-UNet outperforms state-of-the-art methods by achieving the highest Sen, AUC, F1, and IOU on most of the above-mentioned datasets with fewer parameters, and that DTI enhances vessel connectivity while greatly improving sensitivity.
A mixed-categorical correlation kernel for Gaussian process
Authors: P. Saves, Y. Diouane, N. Bartoli, T. Lefebvre, J. Morlier
Date: 2022-11-15 00:00:00

Recently, there has been a growing interest for mixed-categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies either by using continuous kernels (e.g., continuous relaxation and Gower distance based GP) or by using a direct estimation of the correlation matrix. In this paper, we present a kernel-based approach that extends continuous exponential kernels to handle mixed-categorical variables. The proposed kernel leads to a new GP surrogate that generalizes both the continuous relaxation and the Gower distance based GP models. We demonstrate, on both analytical and engineering problems, that our proposed GP model gives a higher likelihood and a smaller residual error than the other kernel-based state-of-the-art models. Our method is available in the open-source software SMT.
Embedding-Based Entity Alignment Using Relation Structural Similarity
Authors: Yanhui Peng; Jing Zhang; Cangqi Zhou; Jian Xu
Date: 2022-09-11 00:00:00
Proceeding: 2020 IEEE International Conference on Knowledge Graph (ICKG) 2022 9
Tasks: Entity Alignment, Entity Embeddings, Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Relation

Entity alignment aims to find entities in different knowledge graphs that semantically represent the same real-world entity. Recently, embedding-based entity alignment methods, which represent knowledge graphs as low-dimensional embeddings and perform entity alignments by measuring the similarity between entity embeddings, have achieved promising results. Most of these methods mainly focus on improving the knowledge graph embedding model or leveraging attributes to obtain more semantic information. However, the structural similarity between the two relations (considering all entities attached on the two relations) in different KGs has not been utilized in the existing methods. In this paper, we propose a novel embedding-based entity alignment method that takes the advantages of relation structural similarity. Specifically, our method first jointly learns the embeddings of two knowledge graphs in a uniform vector space, using the entity pairs regarding to the seed alignments (the alignments already known) that each shares the same embedding. Then, it iteratively computes the structural similarity between the relations in different knowledge graphs according to the seed alignments and the alignments with high reliability generated during training, which makes the embeddings of relations with high similarity closer to each other. Experimental results on five widely used real-world datasets show that the proposed approach significantly outperforms the state-of-the-art embedding-based ones for entity alignment.
CNN-based first quantization estimation of double compressed JPEG images
Authors: Sebastiano Battiato, Oliver Giudice, Francesco Guarnera, Giovanni Puglisi
Date: 2022-11-01 00:00:00
Proceeding: Journal of Visual Communication and Image Representation 2022 11
Tasks: Quantization

Multiple JPEG compressions leave artifacts in digital images: residual traces that could be exploited in forensics investigations to recover information about the device employed for acquisition or image editing software. In this paper, a novel First Quantization Estimation (FQE) algorithm based on convolutional neural networks (CNNs) is proposed. In particular, a solution based on an ensemble of CNNs was developed in conjunction with specific regularization strategies exploiting assumptions about neighboring element values of the quantization matrix to be inferred. Mostly designed to work in the aligned case, the solution was tested in challenging scenarios involving different input patch sizes, quantization matrices (both standard and custom) and datasets (i.e., RAISE and UCID collections). Comparisons with state-of-the-art solutions confirmed the effectiveness of the presented solution demonstrating for the first time to cover the widest combinations of parameters of double JPEG compressions.
PyTrack: A Map-Matching-Based Python Toolbox for Vehicle Trajectory Reconstruction
Authors: M. Tortora, E. Cordelli and P. Soda
Date: 2022-10-25 00:00:00
Proceeding: IEEE Access 2022 10

The exponential growth of IoT devices, smartphones, smartwatches, and vehicles equipped with positioning technology, such as Global Positioning System (GPS) modules, has boosted the development of location-based services for several applications in Intelligent Transportation Systems. However, the inherent error of location-based technologies makes it necessary to align the positioning trajectories to the actual underlying road network, a process known as map-matching. To the best of our knowledge, there are no comprehensive tools that allow us to model street networks, conduct topological and spatial analyses of the underlying street graph, perform map-matching processes on GPS point trajectories, and deeply analyse and elaborate these reconstructed trajectories. To address this issue, we present PyTrack, an open-source map-matching-based Python toolbox designed for academics, researchers and practitioners that integrate the recorded GPS coordinates with data provided by the OpenStreetMap, an open-source geographic information system. This manuscript overviews the architecture of the library offering a detailed description of its capabilities and modules. Besides, we provide an introductory guide to getting started with PyTrack covering the most fundamental steps of our framework. For more information on PyTrack, users are encouraged to visit the official repository at https://github.com/cosbidev/PyTrack or the official documentation at https://pytrack-lib.readthedocs.io.
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
Authors: Qianru Zhang, Zheng Wang, Cheng Long, Chao Huang, Siu-Ming Yiu, Yiding Liu, Gao Cong, Jieming Shi
Date: 2022-11-12 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Detecting anomalous trajectories has become an important task in many location-based applications. While many approaches have been proposed for this task, they suffer from various issues including (1) incapability of detecting anomalous subtrajectories, which are finer-grained anomalies in trajectory data, and/or (2) non-data driven, and/or (3) requirement of sufficient supervision labels which are costly to collect. In this paper, we propose a novel reinforcement learning based solution called RL4OASD, which avoids all aforementioned issues of existing approaches. RL4OASD involves two networks, one responsible for learning features of road networks and trajectories and the other responsible for detecting anomalous subtrajectories based on the learned features, and the two networks can be trained iteratively without labeled data. Extensive experiments are conducted on two real datasets, and the results show that our solution can significantly outperform the state-of-the-art methods (with 20-30% improvement) and is efficient for online detection (it takes less than 0.1ms to process each newly generated data point).
Model Predictive Control for Signal Temporal Logic Specifications with Time Interval Decomposition
Authors: Xinyi Yu, Chuwei Wang, Dingran Yuan, ShaoYuan Li, Xiang Yin
Date: 2022-11-15 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

In this paper, we investigate the problem of Model Predictive Control (MPC) of dynamic systems for high-level specifications described by Signal Temporal Logic (STL) formulae. Recent works show that MPC has the great potential in handling logical tasks in reactive environments. However, existing approaches suffer from the heavy computational burden, especially for tasks with large horizons. In this work, we propose a computationally more efficient MPC framework for STL tasks based on time interval decomposition. Specifically, we still use the standard shrink horizon MPC framework with Mixed Integer Linear Programming (MILP) techniques for open-loop optimization problems. However, instead of applying MPC directly for the entire task horizon, we decompose the STL formula into several sub-formulae with disjoint time horizons, and shrinking horizon MPC is applied for each short-horizon sub-formula iteratively. To guarantee the satisfaction of the entire STL formula and to ensure the recursive feasibility of the iterative process, we introduce new terminal constraints to connect each sub-formula. We show how these terminal constraints can be computed by an effective inner-approximation approach. The computational efficiency of our approach is illustrated by a case study.
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English
Authors: Abhinav Goyal, Anupam Singh, Nikesh Garera
Date: 2022-10-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), intent-classification, Intent Classification, speech-recognition, Speech Recognition

Automation of on-call customer support relies heavily on accurate and efficient speech-to-intent (S2I) systems. Building such systems using multi-component pipelines can pose various challenges because they require large annotated datasets, have higher latency, and have complex deployment. These pipelines are also prone to compounding errors. To overcome these challenges, we discuss an end-to-end (E2E) S2I model for customer support voicebot task in a bilingual setting. We show how we can solve E2E intent classification by leveraging a pre-trained automatic speech recognition (ASR) model with slight modification and fine-tuning on small annotated datasets. Experimental results show that our best E2E model outperforms a conventional pipeline by a relative ~27% on the F1 score.
Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems
Authors: Pengzhi Huang, Emre Gönültaş, Maximilian Arnold, K. Pavan Srinath, Jakob Hoydis, Christoph Studer
Date: 2022-11-15 00:00:00
Tasks: Outdoor Positioning

Localization services for wireless devices play an increasingly important role in our daily lives and a plethora of emerging services and applications already rely on precise position information. Widely used on-device positioning methods, such as the global positioning system, enable accurate outdoor positioning and provide the users with full control over what services and applications are allowed to access their location information. In order to provide accurate positioning indoors or in cluttered urban scenarios without line-of-sight satellite connectivity, powerful off-device positioning systems, which process channel state information (CSI) measured at the infrastructure base stations or access points with deep neural networks, have emerged recently. Such off-device wireless positioning systems inherently link a user's data transmission with its localization, since accurate CSI measurements are necessary for reliable wireless communication -- this not only prevents the users from controlling who can access this information but also enables virtually everyone in the device's range to estimate its location, resulting in serious privacy and security concerns. We therefore propose on-device attacks against off-device wireless positioning systems in multi-antenna orthogonal frequency-division multiplexing systems while remaining standard compliant and minimizing the impact on quality-of-service, and we demonstrate their efficacy using real-world measured datasets for cellular outdoor and wireless LAN indoor scenarios. We also investigate defenses to counter such attack mechanisms, and we discuss the limitations and implications on protecting location privacy in existing and future wireless communication systems.
Reconfigurable Intelligent Surface Aided Wireless Sensing for Scene Depth Estimation
Authors: Abdelrahman Taha, Hao Luo, Ahmed Alkhateeb
Date: 2022-11-15 00:00:00
Tasks: Depth Estimation

Current scene depth estimation approaches mainly rely on optical sensing, which carries privacy concerns and suffers from estimation ambiguity for distant, shiny, and transparent surfaces/objects. Reconfigurable intelligent surfaces (RISs) provide a path for employing a massive number of antennas using low-cost and energy-efficient architectures. This has the potential for realizing RIS-aided wireless sensing with high spatial resolution. In this paper, we propose to employ RIS-aided wireless sensing systems for scene depth estimation. We develop a comprehensive framework for building accurate depth maps using RIS-aided mmWave sensing systems. In this framework, we propose a new RIS interaction codebook capable of creating a sensing grid of reflected beams that meets the desirable characteristics of efficient scene depth map construction. Using the designed codebook, the received signals are processed to build high-resolution depth maps. Simulation results compare the proposed solution against RGB-based approaches and highlight the promise of adopting RIS-aided mmWave sensing in scene depth perception.
Blind Performance Prediction for Deep Learning Based Ultra-Massive MIMO Channel Estimation
Authors: Wentao Yu, Hengtao He, Xianghao Yu, Shenghui Song, Jun Zhang, Khaled B. Letaief
Date: 2022-11-15 00:00:00

Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance. However, the uncertainty of prevailing deep learning (DL)-based physical layer algorithms is hard to quantify due to the black-box nature of neural networks. This limitation is a major obstacle that hinders their practical deployment. In this paper, we attempt to quantify the uncertainty of an important category of DL-based channel estimators. An efficient statistical method is proposed to make blind predictions for the mean squared error of the DL-estimated channel solely based on received pilots, without knowledge of the ground-truth channel, the prior distribution of the channel, or the noise statistics. The complexity of the blind performance prediction is low and scales only linearly with the number of antennas. Simulation results for ultra-massive multiple-input multiple-output (UM-MIMO) channel estimation with a mixture of far-field and near-field paths are provided to verify the accuracy and efficiency of the proposed method.
Non-Linear Coordination Graphs
Authors: Yipeng Kang, Tonghan Wang, Xiaoran Wu, Qianlan Yang, Chongjie Zhang
Date: 2022-10-26 00:00:00
Tasks: Multi-agent Reinforcement Learning

Value decomposition multi-agent reinforcement learning methods learn the global value function as a mixing of each agent's individual utility functions. Coordination graphs (CGs) represent a higher-order decomposition by incorporating pairwise payoff functions and thus is supposed to have a more powerful representational capacity. However, CGs decompose the global value function linearly over local value functions, severely limiting the complexity of the value function class that can be represented. In this paper, we propose the first non-linear coordination graph by extending CG value decomposition beyond the linear case. One major challenge is to conduct greedy action selections in this new function class to which commonly adopted DCOP algorithms are no longer applicable. We study how to solve this problem when mixing networks with LeakyReLU activation are used. An enumeration method with a global optimality guarantee is proposed and motivates an efficient iterative optimization method with a local optimality guarantee. We find that our method can achieve superior performance on challenging multi-agent coordination tasks like MACO.
Active Learning Framework to Automate NetworkTraffic Classification
Authors: Jaroslav Pešek, Dominik Soukup, Tomáš Čejka
Date: 2022-10-26 00:00:00
Tasks: Active Learning, Classification, Traffic Classification

Recent network traffic classification methods benefitfrom machine learning (ML) technology. However, there aremany challenges due to use of ML, such as: lack of high-qualityannotated datasets, data-drifts and other effects causing aging ofdatasets and ML models, high volumes of network traffic etc. Thispaper argues that it is necessary to augment traditional workflowsof ML training&deployment and adapt Active Learning concepton network traffic analysis. The paper presents a novel ActiveLearning Framework (ALF) to address this topic. ALF providesprepared software components that can be used to deploy an activelearning loop and maintain an ALF instance that continuouslyevolves a dataset and ML model automatically. The resultingsolution is deployable for IP flow-based analysis of high-speed(100 Gb/s) networks, and also supports research experiments ondifferent strategies and methods for annotation, evaluation, datasetoptimization, etc. Finally, the paper lists some research challengesthat emerge from the first experiments with ALF in practice.
Drone-based Volume Estimation in Indoor Environments
Authors: Samuel Balula, Dominic Liao-McPherson, Stefan Stevšić, Alisa Rupenyan, John Lygeros
Date: 2022-11-15 00:00:00
Tasks: Indoor Localization, Surface Reconstruction

Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses. We propose an approach for volume estimation for autonomous systems using visual features for indoor localization and surface reconstruction from 2D-LiDAR measurements. A Gaussian Process-based model incorporates information collected from measurements given statistical prior information about the terrain, from which the volume estimate is computed. Our algorithm finds feasible trajectories which minimize the uncertainty of the volume estimate. We show results in simulation for the surface reconstruction and volume estimate of topographic data.
Design of Unmanned Air Vehicles Using Transformer Surrogate Models
Authors: Adam D. Cobb, Anirban Roy, Daniel Elenius, Susmit Jha
Date: 2022-11-11 00:00:00

Computer-aided design (CAD) is a promising new area for the application of artificial intelligence (AI) and machine learning (ML). The current practice of design of cyber-physical systems uses the digital twin methodology, wherein the actual physical design is preceded by building detailed models that can be evaluated by physics simulation models. These physics models are often slow and the manual design process often relies on exploring near-by variations of existing designs. AI holds the promise of breaking these design silos and increasing the diversity and performance of designs by accelerating the exploration of the design space. In this paper, we focus on the design of electrical unmanned aerial vehicles (UAVs). The high-density batteries and purely electrical propulsion systems have disrupted the space of UAV design, making this domain an ideal target for AI-based design. In this paper, we develop an AI Designer that synthesizes novel UAV designs. Our approach uses a deep transformer model with a novel domain-specific encoding such that we can evaluate the performance of new proposed designs without running expensive flight dynamics models and CAD tools. We demonstrate that our approach significantly reduces the overall compute requirements for the design process and accelerates the design space exploration. Finally, we identify future research directions to achieve full-scale deployment of AI-assisted CAD for UAVs.