pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Financial Fraud Detection with Human-in-the-Loop Feedback and Feedback Propagation
Authors: Prashank Kadam
Date: 2024-11-07 00:00:00
Tasks: Fraud Detection

Human-in-the-loop (HITL) feedback mechanisms can significantly enhance machine learning models, particularly in financial fraud detection, where fraud patterns change rapidly, and fraudulent nodes are sparse. Even small amounts of feedback from Subject Matter Experts (SMEs) can notably boost model performance. This paper examines the impact of HITL feedback on both traditional and advanced techniques using proprietary and publicly available datasets. Our results show that HITL feedback improves model accuracy, with graph-based techniques benefiting the most. We also introduce a novel feedback propagation method that extends feedback across the dataset, further enhancing detection accuracy. By leveraging human expertise, this approach addresses challenges related to evolving fraud patterns, data sparsity, and model interpretability, ultimately improving model robustness and streamlining the annotation process.
Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatry
Authors: Soaad Hossain, James Rasalingam, Arhum Waheed, Fatah Awil, Rachel Kandiah, Syed Ishtiaque Ahmed
Date: 2024-11-07 00:00:00
Tasks: Ethics, Fairness

With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that there is little literature covering the economic aspects associated with using ML in clinical psychiatry. This study addresses this gap by specifically studying the economic implications of using ML in clinical psychiatry. In this paper, we evaluate the economic implications of using ML in clinical psychiatry through using three problem-oriented case studies, literature on economics, socioeconomic and medical AI, and two types of health economic evaluations. In addition, we provide details on fairness, legal, ethics and other considerations for ML in clinical psychiatry.
Variational Bayes Decomposition for Inverse Estimation with Superimposed Multispectral Intensity
Authors: Akinori Asahara, Yoshihiro Osakabe, Yamamoto Mitsuya, Hidekazu Morita
Date: 2024-10-29 00:00:00
Tasks: Bayesian Inference

A variational Bayesian inference for measured wave intensity, such as X-ray intensity, is proposed in this paper. The data is popular to obtain information about unobservable features of an object, such as a material sample and the components of it. The proposed method assumes particles represent the wave, and their behaviors are stochastically modeled. The inference is accurate even if the data is noisy because of a smooth prior setting. Moreover, in this paper, two experimental results show feasibility of the proposed method.
Eavesdropping on Goal-Oriented Communication: Timing Attacks and Countermeasures
Authors: Federico Mason, Federico Chiariotti, Pietro Talli, Andrea Zanella
Date: 2024-11-11 00:00:00
Tasks: Scheduling, Semantic Communication

Goal-oriented communication is a new paradigm that considers the meaning of transmitted information to optimize communication. One possible application is the remote monitoring of a process under communication costs: scheduling updates based on goal-oriented considerations can significantly reduce transmission frequency while maintaining high-quality tracking performance. However, goal-oriented scheduling also opens a timing-based side-channel that an eavesdropper may exploit to obtain information about the state of the remote process, even if the content of updates is perfectly secure. In this work, we study an eavesdropping attack against pull-based goal-oriented scheduling for the tracking of remote Markov processes. We provide a theoretical framework for defining the effectiveness of the attack and of possible countermeasures, as well as a practical heuristic that can provide a balance between the performance gains offered by goal-oriented communication and the information leakage.
InvisMark: Invisible and Robust Watermarking for AI-generated Image Provenance
Authors: Rui Xu, Mengya Hu, Deren Lei, Yaxi Li, David Lowe, Alex Gorevski, Mingyu Wang, Emily Ching, Alex Deng
Date: 2024-11-10 00:00:00
Tasks: SSIM

The proliferation of AI-generated images has intensified the need for robust content authentication methods. We present InvisMark, a novel watermarking technique designed for high-resolution AI-generated images. Our approach leverages advanced neural network architectures and training strategies to embed imperceptible yet highly robust watermarks. InvisMark achieves state-of-the-art performance in imperceptibility (PSNR$\sim$51, SSIM $\sim$ 0.998) while maintaining over 97\% bit accuracy across various image manipulations. Notably, we demonstrate the successful encoding of 256-bit watermarks, significantly expanding payload capacity while preserving image quality. This enables the embedding of UUIDs with error correction codes, achieving near-perfect decoding success rates even under challenging image distortions. We also address potential vulnerabilities against advanced attacks and propose mitigation strategies. By combining high imperceptibility, extended payload capacity, and resilience to manipulations, InvisMark provides a robust foundation for ensuring media provenance in an era of increasingly sophisticated AI-generated content. Source code of this paper is available at: https://github.com/microsoft/InvisMark.
Financial Fraud Detection using Jump-Attentive Graph Neural Networks
Authors: Prashank Kadam
Date: 2024-11-07 00:00:00
Tasks: Fraud Detection

As the availability of financial services online continues to grow, the incidence of fraud has surged correspondingly. Fraudsters continually seek new and innovative ways to circumvent the detection algorithms in place. Traditionally, fraud detection relied on rule-based methods, where rules were manually created based on transaction data features. However, these techniques soon became ineffective due to their reliance on manual rule creation and their inability to detect complex data patterns. Today, a significant portion of the financial services sector employs various machine learning algorithms, such as XGBoost, Random Forest, and neural networks, to model transaction data. While these techniques have proven more efficient than rule-based methods, they still fail to capture interactions between different transactions and their interrelationships. Recently, graph-based techniques have been adopted for financial fraud detection, leveraging graph topology to aggregate neighborhood information of transaction data using Graph Neural Networks (GNNs). Despite showing improvements over previous methods, these techniques still struggle to keep pace with the evolving camouflaging tactics of fraudsters and suffer from information loss due to over-smoothing. In this paper, we propose a novel algorithm that employs an efficient neighborhood sampling method, effective for camouflage detection and preserving crucial feature information from non-similar nodes. Additionally, we introduce a novel GNN architecture that utilizes attention mechanisms and preserves holistic neighborhood information to prevent information loss. We test our algorithm on financial data to show that our method outperforms other state-of-the-art graph algorithms.
Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
Authors: Vincent Hanke, Tom Blanchard, Franziska Boenisch, Iyiola Emmanuel Olatunji, Michael Backes, Adam Dziedzic
Date: 2024-11-02 00:00:00
Tasks: Privacy Preserving

While open Large Language Models (LLMs) have made significant progress, they still fall short of matching the performance of their closed, proprietary counterparts, making the latter attractive even for the use on highly private data. Recently, various new methods have been proposed to adapt closed LLMs to private data without leaking private information to third parties and/or the LLM provider. In this work, we analyze the privacy protection and performance of the four most recent methods for private adaptation of closed LLMs. By examining their threat models and thoroughly comparing their performance under different privacy levels according to differential privacy (DP), various LLM architectures, and multiple datasets for classification and generation tasks, we find that: (1) all the methods leak query data, i.e., the (potentially sensitive) user data that is queried at inference time, to the LLM provider, (2) three out of four methods also leak large fractions of private training data to the LLM provider while the method that protects private data requires a local open LLM, (3) all the methods exhibit lower performance compared to three private gradient-based adaptation methods for local open LLMs, and (4) the private adaptation methods for closed LLMs incur higher monetary training and query costs than running the alternative methods on local open LLMs. This yields the conclusion that, to achieve truly privacy-preserving LLM adaptations that yield high performance and more privacy at lower costs, taking into account current methods and models, one should use open LLMs.
A Survey on Data Markets
Authors: Jiayao Zhang, Yuran Bi, Mengye Cheng, Jinfei Liu, Kui Ren, Qiheng Sun, Yihang Wu, Yang Cao, Raul Castro Fernandez, Haifeng Xu, Ruoxi Jia, Yongchan Kwon, Jian Pei, Jiachen T. Wang, Haocheng Xia, Li Xiong, Xiaohui Yu, James Zou
Date: 2024-11-09 00:00:00
Tasks: Survey

Data is the new oil of the 21st century. The growing trend of trading data for greater welfare has led to the emergence of data markets. A data market is any mechanism whereby the exchange of data products including datasets and data derivatives takes place as a result of data buyers and data sellers being in contact with one another, either directly or through mediating agents. It serves as a coordinating mechanism by which several functions, including the pricing and the distribution of data as the most important ones, interact to make the value of data fully exploited and enhanced. In this article, we present a comprehensive survey of this important and emerging direction from the aspects of data search, data productization, data transaction, data pricing, revenue allocation as well as privacy, security, and trust issues. We also investigate the government policies and industry status of data markets across different countries and different domains. Finally, we identify the unresolved challenges and discuss possible future directions for the development of data markets.
FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language Models
Authors: Zhanwei Zhang, Shizhao Sun, Wenxiao Wang, Deng Cai, Jiang Bian
Date: 2024-11-05 00:00:00

Recently, there is a growing interest in creating computer-aided design (CAD) models based on user intent, known as controllable CAD generation. Existing work offers limited controllability and needs separate models for different types of control, reducing efficiency and practicality. To achieve controllable generation across all CAD construction hierarchies, such as sketch-extrusion, extrusion, sketch, face, loop and curve, we propose FlexCAD, a unified model by fine-tuning large language models (LLMs). First, to enhance comprehension by LLMs, we represent a CAD model as a structured text by abstracting each hierarchy as a sequence of text tokens. Second, to address various controllable generation tasks in a unified model, we introduce a hierarchy-aware masking strategy. Specifically, during training, we mask a hierarchy-aware field in the CAD text with a mask token. This field, composed of a sequence of tokens, can be set flexibly to represent various hierarchies. Subsequently, we ask LLMs to predict this masked field. During inference, the user intent is converted into a CAD text with a mask token replacing the part the user wants to modify, which is then fed into FlexCAD to generate new CAD models. Comprehensive experiments on public dataset demonstrate the effectiveness of FlexCAD in both generation quality and controllability. Code will be available at https://github.com/microsoft/FlexCAD.
Incentive Design with Spillovers
Authors: Krishna Dasaratha, Benjamin Golub, Anant Shah
Date: 2024-11-12 00:00:00

A principal uses payments conditioned on stochastic outcomes of a team project to elicit costly effort from the team members. We develop a multi-agent generalization of a classic first-order approach to contract optimization by leveraging methods from network games. The main results characterize the optimal allocation of incentive pay across agents and outcomes. Incentive optimality requires equalizing, across agents, a product of (i) individual productivity (ii) organizational centrality and (iii) responsiveness to monetary incentives.
A dynamic auction for multilateral collaboration
Authors: Chao Huang
Date: 2024-11-10 00:00:00

We study the problem of multilateral collaboration among agents with transferable utilities. Any group of agents can sign a contract consisting of a primitive contract and monetary transfers among the signatories. We propose a dynamic auction that finds a stable outcome when primitive contracts are gross complements for all participants.
Multidimensional Screening with Rich Consumer Data
Authors: Mira Frick, Ryota Iijima, Yuhta Ishii
Date: 2024-11-09 00:00:00

A multi-product monopolist faces a buyer who is privately informed about his valuations for the goods. As is well-known, optimal mechanisms are in general complicated, while simple mechanisms -- such as pure bundling or separate sales -- can be far from optimal and do not admit clear-cut comparisons. We show that this changes if the monopolist observes sufficiently rich data about the buyer's valuations: Now, pure bundling always outperforms separate sales; moreover, there is a sense in which pure bundling performs essentially as well as the optimal mechanism. To formalize this, we characterize how fast the corresponding revenues converge to the first-best revenue as the monopolist's data grows rich: Pure bundling achieves the same convergence rate to the first-best as optimal mechanisms; in contrast, the convergence rate under separate sales is suboptimal.
Discrete-Valued Signal Estimation via Low-Complexity Message Passing Algorithm for Highly Correlated Measurements
Authors: Tomoharu Furudoi, Takumi Takahashi, Shinsuke Ibi, Hideki Ochiai
Date: 2024-11-12 00:00:00

This paper considers a discrete-valued signal estimation scheme based on a low-complexity Bayesian optimal message passing algorithm (MPA) for solving massive linear inverse problems under highly correlated measurements. Gaussian belief propagation (GaBP) can be derived by applying the central limit theorem (CLT)-based Gaussian approximation to the sum-product algorithm (SPA) operating on a dense factor graph (FG), while matched filter (MF)-expectation propagation (EP) can be obtained based on the EP framework tailored for the same FG. Generalized approximate message passing (GAMP) can be found by applying a rigorous approximation technique for both of them in the large-system limit, and these three MPAs perform signal detection using MF by assuming large-scale uncorrelated observations. However, each of them has a different inherent self-noise suppression mechanism, which makes a significant difference in the robustness against the correlation of the observations when we apply an annealed discrete denoiser (ADD) that adaptively controls its nonlinearity with the inverse temperature parameter corresponding to the number of iterations. In this paper, we unravel the mechanism of this interesting phenomenon, and further demonstrate the practical applicability of the low-complexity Bayesian optimal MPA with ADD under highly correlated measurements.
Multidimensional Polynomial Phase Estimation
Authors: Heedong Do, Namyoon Lee, Angel Lozano
Date: 2024-11-11 00:00:00

An estimation method is presented for polynomial phase signals, i.e., those adopting the form of a complex exponential whose phase is polynomial in its indices. Transcending the scope of existing techniques, the proposed estimator can handle an arbitrary number of dimensions and an arbitrary set of polynomial degrees along each dimension; the only requirement is that the number of observations per dimension exceeds the highest degree thereon. Embodied by a highly compact sequential algorithm, this estimator exhibits a strictly linear computational complexity in the number of observations, and is efficient at high signal-to-noise ratios (SNRs). To reinforce the performance at low and medium SNRs, where any phase estimator is bound to be hampered by the inherent ambiguity caused by phase wrappings, suitable functionalities are incorporated and shown to be highly effective.
Antenna Coding Empowered by Pixel Antennas
Authors: Shanpu Shen, Kai-Kit Wong, Ross Murch
Date: 2024-11-11 00:00:00

Pixel antennas, based on discretizing a continuous radiation surface into small elements called pixels, are a flexible reconfigurable antenna technology. By controlling the connections between pixels via switches, the characteristics of pixel antennas can be adjusted to enhance the wireless channel. Inspired by this, we propose a novel technique denoted antenna coding empowered by pixel antennas. We first derive a physical and electromagnetic based communication model for pixel antennas using microwave multiport network theory and beamspace channel representation. With the model, we optimize the antenna coding to maximize the channel gain in a single-input single-output (SISO) pixel antenna system and develop a codebook design for antenna coding to reduce the computational complexity. We analyze the average channel gain of SISO pixel antenna system and derive the corresponding upper bound. In addition, we jointly optimize the antenna coding and transmit signal covariance matrix to maximize the channel capacity in a multiple-input multiple-output (MIMO) pixel antenna system. Simulation results show that using pixel antennas can enhance the average channel gain by up to 5.4 times and channel capacity by up to 3.1 times, demonstrating the significant potential of pixel antennas as a new dimension to design and optimize wireless communication systems.
Enabling ISAC in Real World: Beam-Based User Identification with Machine Learning
Authors: Umut Demirhan, Ahmed Alkhateeb
Date: 2024-11-10 00:00:00
Tasks: Deep Learning, ISAC, User Identification

Leveraging perception from radar data can assist multiple communication tasks, especially in highly-mobile and large-scale MIMO systems. One particular challenge, however, is how to distinguish the communication user (object) from the other mobile objects in the sensing scene. This paper formulates this \textit{user identification} problem and develops two solutions, a baseline model-based solution that maps the objects angles from the radar scene to communication beams and a scalable deep learning solution that is agnostic to the number of candidate objects. Using the DeepSense 6G dataset, which have real-world measurements, the developed deep learning approach achieves more than $93.4\%$ communication user identification accuracy, highlighting a promising path for enabling integrated radar-communication applications in the real world.
A Practical Validation of RIS Detection and Identification
Authors: Recep Vural, Aymen Khaleel, Majid Gerami, Ertugrul Basar
Date: 2024-11-10 00:00:00

Reconfigurable intelligent surface (RIS)-assisted communication is a key enabling technology for next-generation wireless communication networks, allowing for the reshaping of wireless channels without requiring traditional radio frequency (RF) active components. While their passive nature makes RISs highly attractive, it also presents a challenge: RISs cannot actively identify themselves to user equipments (UEs). Recently, a new method has been proposed to detect and identify RISs by letting them modulate their identities in the signals reflected from their surfaces. In this letter, we first propose a new and simpler modulation method for RISs and then validate the concept of RIS detection and identification (RIS-ID) using a real-world experimental setup. The obtained results validate the RIS-ID concept and show the effectiveness of our proposed modulation method over different operating scenarios and systems settings.
AI Multi-Agent Interoperability Extension for Managing Multiparty Conversations
Authors: Diego Gosmar, Deborah A. Dahl, Emmett Coin, David Attwater
Date: 2024-11-05 00:00:00
Tasks: Management

This paper presents a novel extension to the existing Multi-Agent Interoperability specifications of the Open Voice Interoperability Initiative (originally also known as OVON from the Open Voice Network). This extension enables AI agents developed with different technologies to communicate using a universal, natural language-based API or NLP-based standard APIs. Focusing on the management of multiparty AI conversations, this work introduces new concepts such as the Convener Agent, Floor-Shared Conversational Space, Floor Manager, Multi-Conversant Support, and mechanisms for handling Interruptions and Uninvited Agents. Additionally, it explores the Convener's role as a message relay and controller of participant interactions, enhancing both scalability and security. These advancements are crucial for ensuring smooth, efficient, and secure interactions in scenarios where multiple AI agents need to collaborate, debate, or contribute to a discussion. The paper elaborates on these concepts and provides practical examples, illustrating their implementation within the conversation envelope structure.
Diversify, Contextualize, and Adapt: Efficient Entropy Modeling for Neural Image Codec
Authors: Jun-Hyuk Kim, Seungeon Kim, Won-Hee Lee, Dokwan Oh
Date: 2024-11-06 00:00:00

Designing a fast and effective entropy model is challenging but essential for practical application of neural codecs. Beyond spatial autoregressive entropy models, more efficient backward adaptation-based entropy models have been recently developed. They not only reduce decoding time by using smaller number of modeling steps but also maintain or even improve rate--distortion performance by leveraging more diverse contexts for backward adaptation. Despite their significant progress, we argue that their performance has been limited by the simple adoption of the design convention for forward adaptation: using only a single type of hyper latent representation, which does not provide sufficient contextual information, especially in the first modeling step. In this paper, we propose a simple yet effective entropy modeling framework that leverages sufficient contexts for forward adaptation without compromising on bit-rate. Specifically, we introduce a strategy of diversifying hyper latent representations for forward adaptation, i.e., using two additional types of contexts along with the existing single type of context. In addition, we present a method to effectively use the diverse contexts for contextualizing the current elements to be encoded/decoded. By addressing the limitation of the previous approach, our proposed framework leads to significant performance improvements. Experimental results on popular datasets show that our proposed framework consistently improves rate--distortion performance across various bit-rate regions, e.g., 3.73% BD-rate gain over the state-of-the-art baseline on the Kodak dataset.