pwc-archive/papers-with-abstracts huggingface dataset:
Query-specific Variable Depth Pooling via Query Performance Prediction towards Reducing Relevance Assessment Effort
Authors: Debasis Ganguly, Emine Yilmaz
Date: 2023-04-23 00:00:00
Tasks: Retrieval

Due to the massive size of test collections, a standard practice in IR evaluation is to construct a 'pool' of candidate relevant documents comprised of the top-k documents retrieved by a wide range of different retrieval systems - a process called depth-k pooling. A standard practice is to set the depth (k) to a constant value for each query constituting the benchmark set. However, in this paper we argue that the annotation effort can be substantially reduced if the depth of the pool is made a variable quantity for each query, the rationale being that the number of documents relevant to the information need can widely vary across queries. Our hypothesis is that a lower depth for the former class of queries and a higher depth for the latter can potentially reduce the annotation effort without a significant change in retrieval effectiveness evaluation. We make use of standard query performance prediction (QPP) techniques to estimate the number of potentially relevant documents for each query, which is then used to determine the depth of the pool. Our experiments conducted on standard test collections demonstrate that this proposed method of employing query-specific variable depths is able to adequately reflect the relative effectiveness of IR systems with a substantially smaller annotation effort.
Enabling knowledge discovery in natural hazard engineering datasets on DesignSafe
Authors: Chahak Mehta, Krishna Kumar
Date: 2023-04-21 00:00:00
Tasks: Knowledge Graphs

Data-driven discoveries require identifying relevant data relationships from a sea of complex, unstructured, and heterogeneous scientific data. We propose a hybrid methodology that extracts metadata and leverages scientific domain knowledge to synthesize a new dataset from the original to construct knowledge graphs. We demonstrate our approach's effectiveness through a case study on the natural hazard engineering dataset on ``LEAP Liquefaction'' hosted on DesignSafe. Traditional lexical search on DesignSafe is limited in uncovering hidden relationships within the data. Our knowledge graph enables complex queries and fosters new scientific insights by accurately identifying relevant entities and establishing their relationships within the dataset. This innovative implementation can transform the landscape of data-driven discoveries across various scientific domains.
Credit Card Fraud Detection Using Convolutional Neural Networks
Authors: Kang Fu, Dawei Cheng, Yi Tu, and Liqing Zhang
Date: 2016-10-16 00:00:00
Proceeding: International Conference on Neural Information Processing 2016 10
Tasks: Fraud Detection

Credit card is becoming more and more popular in financial transactions, at the same time frauds are also increasing. Conventional methods use rule-based expert systems to detect fraud behaviors, neglecting diverse situations, extreme imbalance of positive and negative samples. In this paper, we propose a CNN-based fraud detection framework, to capture the intrinsic patterns of fraud behaviors learned from labeled data. Abundant transaction data is represented by a feature matrix, on which a convolutional neural network is applied to identify a set of latent patterns for each sample. Experiments on real-world massive transactions of a major commercial bank demonstrate its superior performance compared with some state-of-the-art methods.
Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection
Authors: Dawei Cheng, Sheng Xiang, Chencheng Shang, Yiyi Zhang, Fangzhou Yang, Liqing Zhang
Date: 2020-04-03 00:00:00
Proceeding: AAAI 2020 4
Tasks: Fraud Detection

Credit card fraud is an important issue and incurs a considerable cost for both cardholders and issuing institutions. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant patterns in fraudulent behavior. Therefore, in this work, we propose a spatial-temporal attention-based neural network (STAN) for fraud detection. In particular, transaction records are modeled by attention and 3D convolution mechanisms by integrating the corresponding information, including spatial and temporal behaviors. Attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. Afterward, we conduct extensive experiments on real-word fraud transaction dataset, the result shows that STAN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud post-analysis; the result demonstrates the effectiveness of our proposed method in both detecting suspicious transactions and mining fraud patterns.
Graph Neural Network for Fraud Detection via Spatial-Temporal Attention
Authors: Dawei Cheng, Xiaoyang Wang, Ying Zhang, Liqing Zhang
Date: 2020-09-23 00:00:00
Proceeding: TKDE 2020 9
Tasks: Fraud Detection, Graph Neural Network

Card fraud is an important issue and incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant fraudulent behavior patterns in the online detection system. Therefore, in this work, we propose a spatial-temporal attention-based graph network (STAGN) for credit card fraud detection. In particular, we learn the temporal and location-based transaction graph features by a graph neural network first. Afterward, we employ the spatial-temporal attention on top of learned tensor representations, which are then fed into a 3D convolution network. The attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. After that, we conduct extensive experiments on the real-world card transaction dataset. The result shows that STAGN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud detection and knowledge discovery; the result demonstrates its superiority in detecting suspicious transactions, mining spatial and temporal fraud hotspots, and uncovering fraud patterns. The effectiveness of the proposed method in other user behavior-based tasks is also demonstrated. Finally, in order to tackle the challenges of big data, we integrate our proposed STAGN into the fraud detection system as the predictive model and present the implementation detail of each module in the system.
Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture
Authors: Davoud Saadati, Omid Nejati Manzari, Sattar Mirzakuchaki
Date: 2023-04-22 00:00:00
Tasks: Decoder, Diagnostic, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Medical image segmentation is crucial for the development of computer-aided diagnostic and therapeutic systems, but still faces numerous difficulties. In recent years, the commonly used encoder-decoder architecture based on CNNs has been applied effectively in medical image segmentation, but has limitations in terms of learning global context and spatial relationships. Some researchers have attempted to incorporate transformers into both the decoder and encoder components, with promising results, but this approach still requires further improvement due to its high computational complexity. This paper introduces Dilated-UNet, which combines a Dilated Transformer block with the U-Net architecture for accurate and fast medical image segmentation. Image patches are transformed into tokens and fed into the U-shaped encoder-decoder architecture, with skip-connections for local-global semantic feature learning. The encoder uses a hierarchical Dilated Transformer with a combination of Neighborhood Attention and Dilated Neighborhood Attention Transformer to extract local and sparse global attention. The results of our experiments show that Dilated-UNet outperforms other models on several challenging medical image segmentation datasets, such as ISIC and Synapse.
Knowledge-aware attentional neural network for review-based movie recommendation with explanations
Authors: Yun Liu, Jun Miyazaki
Date: 2022-09-04 00:00:00
Proceeding: Neural Computing and Applications 2022 9
Tasks: Movie Recommendation, Recommendation Systems, Word Embeddings

In this paper, we propose a knowledge-aware attentional neural network (KANN) for dealing with movie recommendation
tasks by extracting knowledge entities from movie reviews and capturing understandable interactions between users and
movies at the knowledge level. In most recommendation systems, review information is already widely utilized to uncover
the explicit preferences of users for items, especially for domains including movie recommendations, music recommendations,
and book recommendations, as reviews are full of knowledge entities relevant to the domain. When processing
review information, current methods usually use word embeddings to represent reviews for modeling users and items. As a
result, they may split the meaning of a phrase, and thereby induce erroneous predictions. Moreover, most methods capture
high-order interactions between users and items after obtaining latent low-dimensional representations, which means they
cannot discover understandable interactions or provide knowledge-level explanations. By incorporating knowledge graph
representation into movie recommendation tasks, the proposed KANN can not only capture the inner attention among user
(movie) reviews but also compute the outer attention values between users and movies before generating corresponding
latent vector representations. These characteristics enable the explicit preferences of users for movies to be learned and
understood. We test our model on two datasets (IMDb and Amazon) for the movie rating prediction task and the clickthrough
rate prediction task and show that it outperforms some of the existing state-of-the-art models and gains outstanding
prediction performances in cases with a very small amount of reviews. Furthermore, we demonstrate the high explainability
of the proposed KANN by visualizing the interaction between users and movies through a case study. Our results
and analyses highlight the relatively high effectiveness and reliability of KANN for movie recommendation tasks.
altiro3D: Scene representation from single image and novel view synthesis
Authors: E. Canessa, L. Tenze
Date: 2023-04-02 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Novel View Synthesis

We introduce altiro3D, a free extended library developed to represent reality starting from a given original RGB image or flat video. It allows to generate a light-field (or Native) image or video and get a realistic 3D experience. To synthesize N-number of virtual images and add them sequentially into a Quilt collage, we apply MiDaS models for the monocular depth estimation, simple OpenCV and Telea inpainting techniques to map all pixels, and implement a 'Fast' algorithm to handle 3D projection camera and scene transformations along N-viewpoints. We use the degree of depth to move proportionally the pixels, assuming the original image to be at the center of all the viewpoints. altiro3D can also be used with DIBR algorithm to compute intermediate snapshots from a equivalent 'Real (slower)' camera with N-geometric viewpoints, which requires to calibrate a priori several intrinsic and extrinsic camera parameters. We adopt a pixel- and device-based Lookup Table to optimize computing time. The multiple viewpoints and video generated from a single image or frame can be displayed in a free-view LCD display.
Rediscovering Hashed Random Projections for Efficient Quantization of Contextualized Sentence Embeddings
Authors: Ulf A. Hamster, Ji-Ung Lee, Alexander Geyken, Iryna Gurevych
Date: 2023-03-13 00:00:00
Tasks: Quantization, Sentence, Sentence Classification, Sentence Embeddings

Training and inference on edge devices often requires an efficient setup due to computational limitations. While pre-computing data representations and caching them on a server can mitigate extensive edge device computation, this leads to two challenges. First, the amount of storage required on the server that scales linearly with the number of instances. Second, the bandwidth required to send extensively large amounts of data to an edge device. To reduce the memory footprint of pre-computed data representations, we propose a simple, yet effective approach that uses randomly initialized hyperplane projections. To further reduce their size by up to 98.96%, we quantize the resulting floating-point representations into binary vectors. Despite the greatly reduced size, we show that the embeddings remain effective for training models across various English and German sentence classification tasks that retain 94%--99% of their floating-point.
IDLS: Inverse Depth Line based Visual-Inertial SLAM
Authors: Wanting Li, Shuo Wang, Yongcai Wang, Yu Shao, Xuewei Bai, Deying Li
Date: 2023-04-23 00:00:00
Tasks: Descriptive, Visual Localization

For robust visual-inertial SLAM in perceptually-challenging indoor environments,recent studies exploit line features to extract descriptive information about scene structure to deal with the degeneracy of point features. But existing point-line-based SLAM methods mainly use Pl\"ucker matrix or orthogonal representation to represent a line, which needs to calculate at least four variables to determine a line. Given the numerous line features to determine in each frame, the overly flexible line representation increases the computation burden and comprises the accuracy of the results. In this paper, we propose inverse depth representation for a line, which models each extracted line feature using only two variables, i.e., the inverse depths of the two ending points. It exploits the fact that the projected line's pixel coordinates on the image plane are rather accurate, which partially restrict the line. Using this compact line presentation, Inverse Depth Line SLAM (IDLS) is proposed to track the line features in SLAM in an accurate and efficient way. A robust line triangulation method and a novel line re-projection error model are introduced. And a two-step optimization method is proposed to firstly determine the lines and then to estimate the camera poses in each frame. IDLS is extensively evaluated in multiple perceptually-challenging datasets. The results show it is more accurate, robust, and needs lower computational overhead than the current state-of-the-art of point-line-based SLAM methods.
SketchXAI: A First Look at Explainability for Human Sketches
Authors: Zhiyu Qu, Yulia Gryaditskaya, Ke Li, Kaiyue Pang, Tao Xiang, Yi-Zhe Song
Date: 2023-04-23 00:00:00
Proceeding: CVPR 2023 1
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Sketch Recognition

This paper, for the very first time, introduces human sketches to the landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as a ``human-centred'' data form, represents a natural interface to study explainability. We focus on cultivating sketch-specific explainability designs. This starts by identifying strokes as a unique building block that offers a degree of flexibility in object construction and manipulation impossible in photos. Following this, we design a simple explainability-friendly sketch encoder that accommodates the intrinsic properties of strokes: shape, location, and order. We then move on to define the first ever XAI task for sketch, that of stroke location inversion SLI. Just as we have heat maps for photos, and correlation matrices for text, SLI offers an explainability angle to sketch in terms of asking a network how well it can recover stroke locations of an unseen sketch. We offer qualitative results for readers to interpret as snapshots of the SLI process in the paper, and as GIFs on the project page. A minor but interesting note is that thanks to its sketch-specific design, our sketch encoder also yields the best sketch recognition accuracy to date while having the smallest number of parameters. The code is available at \url{https://sketchxai.github.io}.
GamutMLP: A Lightweight MLP for Color Loss Recovery
Authors: Hoang M. Le, Brian Price, Scott Cohen, Michael S. Brown
Date: 2023-04-23 00:00:00
Proceeding: CVPR 2023 1

Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing, this color-rich representation is transformed and clipped to fit within the small-gamut standard RGB (sRGB) color space, representing only 30% of visible colors. Recovering the lost color information is challenging due to the clipping procedure. Inspired by neural implicit representations for 2D images, we propose a method that optimizes a lightweight multi-layer-perceptron (MLP) model during the gamut reduction step to predict the clipped values. GamutMLP takes approximately 2 seconds to optimize and requires only 23 KB of storage. The small memory footprint allows our GamutMLP model to be saved as metadata in the sRGB image -- the model can be extracted when needed to restore wide-gamut color values. We demonstrate the effectiveness of our approach for color recovery and compare it with alternative strategies, including pre-trained DNN-based gamut expansion networks and other implicit neural representation methods. As part of this effort, we introduce a new color gamut dataset of 2200 wide-gamut/small-gamut images for training and testing. Our code and dataset can be found on the project website: https://gamut-mlp.github.io.
Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval
Authors: Mehdi Rafiei, Alexandros Iosifidis
Date: 2023-04-23 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Using a discriminative representation obtained by supervised deep learning methods showed promising results on diverse Content-Based Image Retrieval (CBIR) problems. However, existing methods exploiting labels during training try to discriminate all available classes, which is not ideal in cases where the retrieval problem focuses on a class of interest. In this paper, we propose a regularized loss for Variational Auto-Encoders (VAEs) forcing the model to focus on a given class of interest. As a result, the model learns to discriminate the data belonging to the class of interest from any other possibility, making the learnt latent space of the VAE suitable for class-specific retrieval tasks. The proposed Class-Specific Variational Auto-Encoder (CS-VAE) is evaluated on three public and one custom datasets, and its performance is compared with that of three related VAE-based methods. Experimental results show that the proposed method outperforms its competition in both in-domain and out-of-domain retrieval problems.
Analysis of Recent Trends in Face Recognition Systems
Authors: Krishnendu K. S
Date: 2023-04-23 00:00:00
Tasks: Face Recognition

With the tremendous advancements in face recognition technology, face modality has been widely recognized as a significant biometric identifier in establishing a person's identity rather than any other biometric trait like fingerprints that require contact sensors. However, due to inter-class similarities and intra-class variations, face recognition systems generate false match and false non-match errors respectively. Recent research focuses on improving the robustness of extracted features and the pre-processing algorithms to enhance recognition accuracy. Since face recognition has been extensively used for several applications ranging from law enforcement to surveillance systems, the accuracy and performance of face recognition must be the finest. In this paper various face recognition systems are discussed and analysed like RPRV, LWKPCA, SVM Model, LTrP based SPM and a deep learning framework for recognising images from CCTV. All these face recognition methods, their implementations and performance evaluations are compared to derive the best outcome for future developmental works.
Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models
Authors: Jie Shao, Wei Yao, Puzuo Wang, Zhiyi He, Lei Luo
Date: 2023-04-23 00:00:00
Tasks: Point Cloud Segmentation, Segmentation

Developments in three-dimensional real worlds promote the integration of geoinformation and building information models (BIM) known as GeoBIM in urban construction. Light detection and ranging (LiDAR) integrated with global navigation satellite systems can provide geo-referenced spatial information. However, constructing detailed urban GeoBIM poses challenges in terms of LiDAR data quality. BIM models designed from software are rich in geometrical information but often lack accurate geo-referenced locations. In this paper, we propose a complementary strategy that integrates LiDAR point clouds with as-designed BIM models for reconstructing urban scenes. A state-of-the-art deep learning framework and graph theory are first combined for LiDAR point cloud segmentation. A coarse-to-fine matching program is then developed to integrate object point clouds with corresponding BIM models. Results show the overall segmentation accuracy of LiDAR datasets reaches up to 90%, and average positioning accuracies of BIM models are 0.023 m for pole-like objects and 0.156 m for buildings, demonstrating the effectiveness of the method in segmentation and matching processes. This work offers a practical solution for rapid and accurate urban GeoBIM construction.
Automatized marine vessel monitoring from sentinel-1 data using convolution neural network
Authors: Surya Prakash Tiwari, Sudhir Kumar Chaturvedi, Subhrangshu Adhikary, Saikat Banerjee, Sourav Basu
Date: 2023-04-23 00:00:00
Tasks: Decision Making, object-detection, Object Detection

The advancement of multi-channel synthetic aperture radar (SAR) system is considered as an upgraded technology for surveillance activities. SAR sensors onboard provide data for coastal ocean surveillance and a view of the oceanic surface features. Vessel monitoring has earlier been performed using Constant False Alarm Rate (CFAR) algorithm which is not a smart technique as it lacks decision-making capabilities, therefore we introduce wavelet transformation-based Convolution Neural Network approach to recognize objects from SAR images during the heavy naval traffic, which corresponds to the numerous object detection. The utilized information comprises Sentinel-1 SAR-C dual-polarization data acquisitions over the western coastal zones of India and with help of the proposed technique we have obtained 95.46% detection accuracy. Utilizing this model can automatize the monitoring of naval objects and recognition of foreign maritime intruders.