pwc-archive/papers-with-abstracts huggingface dataset:
Transfer Learning for E-commerce Query Product Type Prediction
Authors: Anna Tigunova, Thomas Ricatte, Ghadir Eraisha
Date: 2024-09-20 00:00:00
Tasks: Prediction, Transfer Learning, Type prediction

Getting a good understanding of the customer intent is essential in e-commerce search engines. In particular, associating the correct product type to a search query plays a vital role in surfacing correct products to the customers. Query product type classification (Q2PT) is a particularly challenging task because search queries are short and ambiguous, the number of existing product categories is extremely large, spanning thousands of values. Moreover, international marketplaces face additional challenges, such as language and dialect diversity and cultural differences, influencing the interpretation of the query. In this work we focus on Q2PT prediction in the global multilocale e-commerce markets. The common approach of training Q2PT models for each locale separately shows significant performance drops in low-resource stores. Moreover, this method does not allow for a smooth expansion to a new country, requiring to collect the data and train a new locale-specific Q2PT model from scratch. To tackle this, we propose to use transfer learning from the highresource to the low-resource locales, to achieve global parity of Q2PT performance. We benchmark the per-locale Q2PT model against the unified one, which shares the training data and model structure across all worldwide stores. Additionally, we compare locale-aware and locale-agnostic Q2PT models, showing the task dependency on the country-specific traits. We conduct extensive quantiative and qualitative analysis of Q2PT models on the large-scale e-commerce dataset across 20 worldwide locales, which shows that unified locale-aware Q2PT model has superior performance over the alternatives.
FAIR GPT: A virtual consultant for research data management in ChatGPT
Authors: Renat Shigapov, Irene Schumm
Date: 2024-09-20 00:00:00
Tasks: Fairness, Hallucination, Management

FAIR GPT is a first virtual consultant in ChatGPT designed to help researchers and organizations make their data and metadata compliant with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. It provides guidance on metadata improvement, dataset organization, and repository selection. To ensure accuracy, FAIR GPT uses external APIs to assess dataset FAIRness, retrieve controlled vocabularies, and recommend repositories, minimizing hallucination and improving precision. It also assists in creating documentation (data and software management plans, README files, and codebooks), and selecting proper licenses. This paper describes its features, applications, and limitations.
Augmenting the Interpretability of GraphCodeBERT for Code Similarity Tasks
Authors: Jorge Martinez-Gil
Date: 2024-09-20 00:00:00
Tasks: Clone Detection

Assessing the degree of similarity of code fragments is crucial for ensuring software quality, but it remains challenging due to the need to capture the deeper semantic aspects of code. Traditional syntactic methods often fail to identify these connections. Recent advancements have addressed this challenge, though they frequently sacrifice interpretability. To improve this, we present an approach aiming to improve the transparency of the similarity assessment by using GraphCodeBERT, which enables the identification of semantic relationships between code fragments. This approach identifies similar code fragments and clarifies the reasons behind that identification, helping developers better understand and trust the results. The source code for our implementation is available at https://www.github.com/jorge-martinez-gil/graphcodebert-interpretability.
Assessment of the Financial Competitiveness of Publicly Listed Indian Real Estate Companies Using the Entropy Method
Authors: Ritij Saini, Aditya Deora, Kirtesh Gadiya
Date: 2024-10-09 00:00:00

The real estate sector is one of the key drivers of India's national economy, contributing about 7.3\% to the GDP. As the market evolves, more players enter, and government policies become more stringent, Indian real estate companies face increasing competition. Improving financial competitiveness is crucial for the survival and growth of these companies. This paper presents a financial competitiveness evaluation index system for Indian-listed real estate companies, covering profitability, solvency, and operational capacity. Using key financial ratios and a scoring system, the financial competitiveness of various companies was evaluated, revealing that companies with high scores have strong profitability and operational capacity. In contrast, those with lower scores struggle with solvency and working capital.
Ensured: Explanations for Decreasing the Epistemic Uncertainty in Predictions
Authors: Helena Löfström, Tuwe Löfström, Johan Hallberg Szabadvary
Date: 2024-10-07 00:00:00

This paper addresses a significant gap in explainable AI: the necessity of interpreting epistemic uncertainty in model explanations. Although current methods mainly focus on explaining predictions, with some including uncertainty, they fail to provide guidance on how to reduce the inherent uncertainty in these predictions. To overcome this challenge, we introduce new types of explanations that specifically target epistemic uncertainty. These include ensured explanations, which highlight feature modifications that can reduce uncertainty, and categorisation of uncertain explanations counter-potential, semi-potential, and super-potential which explore alternative scenarios. Our work emphasises that epistemic uncertainty adds a crucial dimension to explanation quality, demanding evaluation based not only on prediction probability but also on uncertainty reduction. We introduce a new metric, ensured ranking, designed to help users identify the most reliable explanations by balancing trade-offs between uncertainty, probability, and competing alternative explanations. Furthermore, we extend the Calibrated Explanations method, incorporating tools that visualise how changes in feature values impact epistemic uncertainty. This enhancement provides deeper insights into model behaviour, promoting increased interpretability and appropriate trust in scenarios involving uncertain predictions.
Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decomposition
Authors: Serena Grazia De Benedictis, Grazia Gargano, Gaetano Settembre
Date: 2024-10-03 00:00:00
Proceeding: Journal of Computational Mathematics and Data Science 2024 10
Tasks: Brain Tumor Classification, Brain Tumor Segmentation, Dimensionality Reduction, Image Enhancement, MRI segmentation, Tensor Decomposition, Topological Data Analysis

The advent of artificial intelligence in medical imaging has paved the way for significant advancements in the diagnosis of brain tumors. This study presents a novel ensemble approach that uses magnetic resonance imaging (MRI) to identify and categorize common brain cancers, such as pituitary, meningioma, and glioma. The proposed workflow is composed of a two-fold approach: firstly, it employs non-trivial image enhancement techniques in data preprocessing, low-rank Tucker decomposition for dimensionality reduction, and machine learning (ML) classifiers to detect and predict the type of brain tumor. Secondly, persistent homology (PH), a topological data analysis (TDA) technique, is exploited to extract potential critical areas in MRI scans. When paired with the ML classifier output, this additional information can help domain experts to identify areas of interest that might contain tumor signatures, improving the interpretability of ML predictions. When compared to automated diagnoses, this transparency adds another level of confidence and is essential for clinical acceptance. The performance of the system was quantitatively evaluated on a well-known MRI dataset, with an overall classification accuracy of 97.28% using an extremely randomized trees model. The promising results show that the integration of TDA, ML, and low-rank approximation methods is a successful approach for brain tumor identification and categorization, providing a solid foundation for further study and clinical application.
A Predictive and Optimization Approach for Enhanced Urban Mobility Using Spatiotemporal Data
Authors: Shambhavi Mishra, T. Satyanarayana Murthy
Date: 2024-10-07 00:00:00
Tasks: Management

In modern urban centers, effective transportation management poses a significant challenge, with traffic jams and inconsistent travel durations greatly affecting commuters and logistics operations. This study introduces a novel method for enhancing urban mobility by combining machine learning algorithms with live traffic information. We developed predictive models for journey time and congestion analysis using data from New York City's yellow taxi trips. The research employed a spatiotemporal analysis framework to identify traffic trends and implemented real-time route optimization using the GraphHopper API. This system determines the most efficient paths based on current conditions, adapting to changes in traffic flow. The methodology utilizes Spark MLlib for predictive modeling and Spark Streaming for processing data in real-time. By integrating historical data analysis with current traffic inputs, our system shows notable enhancements in both travel time forecasts and route optimization, demonstrating its potential for widespread application in major urban areas. This research contributes to ongoing efforts aimed at reducing urban congestion and improving transportation efficiency through advanced data-driven methods.
Improved PCRLB for radar tracking in clutter with geometry-dependent target measurement uncertainty and application to radar trajectory control
Authors: Yifang Shi, Yu Zhang, Linjiao Fu, Dongliang Peng, Qiang Lu, Jee Woong Choi, Alfonso Farina
Date: 2024-10-08 00:00:00

In realistic radar tracking, target measurement uncertainty (TMU) in terms of both detection probability and measurement error covariance is significantly affected by the target-to-radar (T2R) geometry. However, existing posterior Cramer-Rao Lower Bounds (PCRLBs) rarely investigate the fundamental impact of T2R geometry on target measurement uncertainty and eventually on mean square error (MSE) of state estimate, inevitably resulting in over-conservative lower bound. To address this issue, this paper firstly derives the generalized model of target measurement error covariance for bistatic radar with moving receiver and transmitter illuminating any type of signal, along with its approximated solution to specify the impact of T2R geometry on error covariance. Based upon formulated TMU model, an improved PCRLB (IPCRLB) fully accounting for both measurement origin uncertainty and geometry-dependent TMU is then re-derived, both detection probability and measurement error covariance are treated as state-dependent parameters when differentiating log-likelihood with respect to target state. Compared to existing PCRLBs that partially or completely ignore the dependence of target measurement uncertainty on T2R geometry, proposed IPCRLB provides a much accurate (less-conservative) lower bound for radar tracking in clutter with geometry-dependent TMU. The new bound is then applied to radar trajectory control to effectively optimize T2R geometry and exhibits least uncertainty of acquired target measurement and more accurate state estimate for bistatic radar tracking in clutter, compared to state-of-the-art trajectory control methods.
Non-overshooting output shaping for switched linear systems under arbitrary switching using eigenstructure assignment
Authors: Kai Wulff, Maria Christine Honecker, Robert Schmid, Johann Reger
Date: 2024-10-09 00:00:00

We consider the analytical control design for a pair of switched linear multiple-input multiple-output (MIMO) systems that are subject to arbitrary switching signals. A state feedback controller design method is proposed to obtain an eigenstructure assignment that ensures that the closed-loop switched system is globally asymptotically stable, and the outputs achieve the non-overshooting tracking of a step reference. Our analysis indicates whether non-overshooting or even monotonic tracking is achievable for the given system and considered outputs and provides a choice of possible eigenstructures to be assigned to the constituent subsystems. We derive a structural condition that verifies the feasibility of the chosen assignment. A constructive algorithm to obtain suitable feedback matrices is provided, and the method is illustrated with numerical examples.
Automated Retail Billing: Streamlining Checkout with QR Codes and Object Tracking Using YOLOv8 and DeepSORT
Authors: Bishwambhar Dahal, Sirjana Bhatta, Sujana Acharya, Apsara Shrestha, Praches Acharya
Date: 2024-10-05 00:00:00
Proceeding: International Journal of Science, Engineering and Technology 2024 10
Tasks: Object Tracking

In the contemporary retail landscape, long checkout queues and the issuance of expired products present significant challenges to operational efficiency. To address these issues and enhance the billing process, we propose an innovative solution that automates billing while effectively managing sales data. Our system features a conveyor belt mechanism activated by a touch sensor, where products, each with unique QR codes, are placed. A camera captures live video of the conveyor belt, enabling real-time detection and decoding of these QR codes, along with immediate alerts for any expired products identified. The system generates a comprehensive bill detailing product names, IDs, and prices, while securely storing scanned data in a database for in-depth sales and profit analysis, complemented by graphical visualizations. Registered customers receive a PDF copy of their bill via email through the Simple Mail Transfer Protocol (SMTP), enhancing their overall experience. By employing the You Only Look Once version 8 (YOLOv8) model alongside the Deep Simple Online and Realtime Tracking (DeepSORT) algorithm, the system ensures precise object tracking and accurate scanning of each product. The Raspberry Pi serves as the core component of the system, managing the integration of advanced hardware and software. This solution significantly improves the efficiency and accuracy of the billing process, offering a holistic approach to modern retail management.
Unsupervised representation learning with long-term dynamics for skeleton based action recognition
Authors: Nenggan Zheng, Jun Wen, Risheng Liu, Liangqu Long, Jianhua Dai, Zhefeng Gong
Date: 2018-04-26 00:00:00
Proceeding: AAAI 2018 4
Tasks: Action Recognition, Representation Learning, Self-supervised Skeleton-based Action Recognition, Skeleton Based Action Recognition

In recent years, skeleton based action recognition is becoming an increasingly attractive alternative to existing video-based approaches, beneficial from its robust and comprehensive 3D information. In this paper, we explore an unsupervised representation learning approach for the first time to capture the long-term global motion dynamics in skeleton sequences. We design a conditional skeleton inpainting architecture for learning a fixed-dimensional representation, guided by additional adversarial training strategies. We quantitatively evaluate the effectiveness of our learning approach on three well-established action recognition datasets. Experimental results show that our learned representation is discriminative for classifying actions and can substantially reduce the sequence inpainting errors.
Training Stiff Neural Ordinary Differential Equations with Implicit Single-Step Methods
Authors: Colby Fronk, Linda Petzold
Date: 2024-10-08 00:00:00

Stiff systems of ordinary differential equations (ODEs) are pervasive in many science and engineering fields, yet standard neural ODE approaches struggle to learn them. This limitation is the main barrier to the widespread adoption of neural ODEs. In this paper, we propose an approach based on single-step implicit schemes to enable neural ODEs to handle stiffness and demonstrate that our implicit neural ODE method can learn stiff dynamics. This work addresses a key limitation in current neural ODE methods, paving the way for their use in a wider range of scientific problems.
Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials
Authors: Sifan Wang, Tong-Rui Liu, Shyam Sankaran, Paris Perdikaris
Date: 2024-09-23 00:00:00
Tasks: Computational Efficiency, Transfer Learning

Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods. In this work, we introduce the Micromechanics Transformer ({\em Micrometer}), an artificial intelligence (AI) framework for predicting the mechanical response of heterogeneous materials, bridging the gap between advanced data-driven methods and complex solid mechanics problems. Trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, Micrometer can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of microstructures, material properties under any loading conditions and We demonstrate the accuracy and computational efficiency of Micrometer through applications in computational homogenization and multiscale modeling, where Micrometer achieves 1\% error in predicting macroscale stress fields while reducing computational time by up to two orders of magnitude compared to conventional numerical solvers. We further showcase the adaptability of the proposed model through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in mechanical analysis of solid materials. Our work represents a significant step towards AI-driven innovation in computational solid mechanics, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.
VisDiff: SDF-Guided Polygon Generation for Visibility Reconstruction and Recognition
Authors: Rahul Moorthy, Jun-Jee Chao, Volkan Isler
Date: 2024-10-07 00:00:00
Tasks: Representation Learning

The ability to capture rich representations of combinatorial structures has enabled the application of machine learning to tasks such as analysis and generation of floorplans, terrains, images, and animations. Recent work has primarily focused on understanding structures with well-defined features, neighborhoods, or underlying distance metrics, while those lacking such characteristics remain largely unstudied. Examples of these combinatorial structures can be found in polygons, where a small change in the vertex locations causes a significant rearrangement of the combinatorial structure, expressed as a visibility or triangulation graphs. Current representation learning approaches fail to capture structures without well-defined features and distance metrics. In this paper, we study the open problem of Visibility Reconstruction: Given a visibility graph $G$, construct a polygon $P$ whose visibility graph is $G$. We introduce VisDiff, a novel diffusion-based approach to generate polygon $P$ from the input visibility graph $G$. The main novelty of our approach is that, rather than generating the polygon's vertex set directly, we first estimate the signed distance function (SDF) associated with the polygon. The SDF is then used to extract the vertex location representing the final polygon. We show that going through the SDF allows VisDiff to learn the visibility relationship much more effectively than generating vertex locations directly. In order to train VisDiff, we create a carefully curated dataset. We use this dataset to benchmark our method and achieve 26% improvement in F1-Score over standard methods as well as state of the art approaches.
SC-Bench: A Large-Scale Dataset for Smart Contract Auditing
Authors: Shihao Xia, Mengting He, Linhai Song, Yiying Zhang
Date: 2024-10-08 00:00:00

There is a huge demand to ensure the compliance of smart contracts listed on blockchain platforms to safety and economic standards. Today, manual efforts in the form of auditing are commonly used to achieve this goal. ML-based automated techniques have the promise to alleviate human efforts and the resulting monetary costs. However, unlike other domains where ML techniques have had huge successes, no systematic ML techniques have been proposed or applied to smart contract auditing. We present SC-Bench, the first dataset for automated smart-contract auditing research. SC-Bench consists of 5,377 real-world smart contracts running on Ethereum, a widely used blockchain platform, and 15,975 violations of standards on Ehereum called ERCs. Out of these violations, 139 are real violations programmers made. The remaining are errors we systematically injected to reflect the violations of different ERC rules. We evaluate SC-Bench using GPT-4 by prompting it with both the contracts and ERC rules. In addition, we manually identify each violated rule and the corresponding code site (i.e., oracle) and prompt GPT-4 with the information asking for a True-or-False question. Our results show that without the oracle, GPT-4 can only detect 0.9% violations, and with the oracle, it detects 22.9% violations. These results show the potential room for improvement in ML-based techniques for smart-contract auditing.
Data Quality Issues in Vulnerability Detection Datasets
Authors: Yuejun Guo, Seifeddine Bettaieb
Date: 2024-10-08 00:00:00
Tasks: Vulnerability Detection

Vulnerability detection is a crucial yet challenging task to identify potential weaknesses in software for cyber security. Recently, deep learning (DL) has made great progress in automating the detection process. Due to the complex multi-layer structure and a large number of parameters, a DL model requires massive labeled (vulnerable or secure) source code to gain knowledge to effectively distinguish between vulnerable and secure code. In the literature, many datasets have been created to train DL models for this purpose. However, these datasets suffer from several issues that will lead to low detection accuracy of DL models. In this paper, we define three critical issues (i.e., data imbalance, low vulnerability coverage, biased vulnerability distribution) that can significantly affect the model performance and three secondary issues (i.e., errors in source code, mislabeling, noisy historical data) that also affect the performance but can be addressed through a dedicated pre-processing procedure. In addition, we conduct a study of 14 papers along with 54 datasets for vulnerability detection to confirm these defined issues. Furthermore, we discuss good practices to use existing datasets and to create new ones.