pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Task Transfer for HLS DSE
Authors: Zijian Ding, Atefeh Sohrabizadeh, Weikai Li, Zongyue Qin, Yizhou Sun, Jason Cong
Date: 2024-08-16 00:00:00
Tasks: High-Level Synthesis, Transfer Learning

There have been several recent works proposed to utilize model-based optimization methods to improve the productivity of using high-level synthesis (HLS) to design domain-specific architectures. They would replace the time-consuming performance estimation or simulation of design with a proxy model, and automatically insert pragmas to guide hardware optimizations. In this work, we address the challenges associated with high-level synthesis (HLS) design space exploration (DSE) through the evolving landscape of HLS tools. As these tools develop, the quality of results (QoR) from synthesis can vary significantly, complicating the maintenance of optimal design strategies across different toolchains. We introduce Active-CEM, a task transfer learning scheme that leverages a model-based explorer designed to adapt efficiently to changes in toolchains. This approach optimizes sample efficiency by identifying high-quality design configurations under a new toolchain without requiring extensive re-evaluation. We further refine our methodology by incorporating toolchain-invariant modeling. This allows us to predict QoR changes more accurately despite shifts in the black-box implementation of the toolchains. Experiment results on the HLSyn benchmark transitioning to new toolchain show an average performance improvement of 1.58$\times$ compared to AutoDSE and a 1.2$\times$ improvement over HARP, while also increasing the sample efficiency by 5.26$\times$, and reducing the runtime by 2.7$\times$.
Physics-Informed Machine Learning for Grade Prediction in Froth Flotation
Authors: Mahdi Nasiri, Sahel Iqbal, Simo Särkkä
Date: 2024-08-12 00:00:00
Tasks: Physics-informed machine learning

In this paper, physics-informed neural network models are developed to predict the concentrate gold grade in froth flotation cells. Accurate prediction of concentrate grades is important for the automatic control and optimization of mineral processing. Both first-principles and data-driven machine learning methods have been used to model the flotation process. The complexity of models based on first-principles restricts their direct use, while purely data-driven models often fail in dynamic industrial environments, leading to poor generalization. To address these limitations, this study integrates classical mathematical models of froth flotation processes with conventional deep learning methods to construct physics-informed neural networks. These models demonstrated superior generalization and predictive performance compared to purely data-driven models, on simulated data from two flotation cells, in terms of mean squared error and mean relative error.
SPICED: Syntactical Bug and Trojan Pattern Identification in A/MS Circuits using LLM-Enhanced Detection
Authors: Jayeeta Chaudhuri, Dhruv Thapar, Arjun Chaudhuri, Farshad Firouzi, Krishnendu Chakrabarty
Date: 2024-08-25 00:00:00
Tasks: Anomaly Detection, Language Modelling, Large Language Model

Analog and mixed-signal (A/MS) integrated circuits (ICs) are crucial in modern electronics, playing key roles in signal processing, amplification, sensing, and power management. Many IC companies outsource manufacturing to third-party foundries, creating security risks such as stealthy analog Trojans. Traditional detection methods, including embedding circuit watermarks or conducting hardware-based monitoring, often impose significant area and power overheads, and may not effectively identify all types of Trojans. To address these shortcomings, we propose SPICED, a Large Language Model (LLM)-based framework that operates within the software domain, eliminating the need for hardware modifications for Trojan detection and localization. This is the first work using LLM-aided techniques for detecting and localizing syntactical bugs and analog Trojans in circuit netlists, requiring no explicit training and incurring zero area overhead. Our framework employs chain-of-thought reasoning and few-shot examples to teach anomaly detection rules to LLMs. With the proposed method, we achieve an average Trojan coverage of 93.32% and an average true positive rate of 93.4% in identifying Trojan-impacted nodes for the evaluated analog benchmark circuits. These experimental results validate the effectiveness of LLMs in detecting and locating both syntactical bugs and Trojans within analog netlists.
Differentially Private Publication of Electricity Time Series Data in Smart Grids
Authors: Sina Shaham, Gabriel Ghinita, Bhaskar Krishnamachari, Cyrus Shahabi
Date: 2024-08-24 00:00:00
Tasks: Time Series

Smart grids are a valuable data source to study consumer behavior and guide energy policy decisions. In particular, time-series of power consumption over geographical areas are essential in deciding the optimal placement of expensive resources (e.g., transformers, storage elements) and their activation schedules. However, publication of such data raises significant privacy issues, as it may reveal sensitive details about personal habits and lifestyles. Differential privacy (DP) is well-suited for sanitization of individual data, but current DP techniques for time series lead to significant loss in utility, due to the existence of temporal correlation between data readings. We introduce {\em STPT (Spatio-Temporal Private Timeseries)}, a novel method for DP-compliant publication of electricity consumption data that analyzes spatio-temporal attributes and captures both micro and macro patterns by leveraging RNNs. Additionally, it employs a partitioning method for releasing electricity consumption time series based on identified patterns. We demonstrate through extensive experiments, on both real-world and synthetic datasets, that STPT significantly outperforms existing benchmarks, providing a well-balanced trade-off between data utility and user privacy.
Randomization Techniques to Mitigate the Risk of Copyright Infringement
Authors: Wei-Ning Chen, Peter Kairouz, Sewoong Oh, Zheng Xu
Date: 2024-08-21 00:00:00

In this paper, we investigate potential randomization approaches that can complement current practices of input-based methods (such as licensing data and prompt filtering) and output-based methods (such as recitation checker, license checker, and model-based similarity score) for copyright protection. This is motivated by the inherent ambiguity of the rules that determine substantial similarity in copyright precedents. Given that there is no quantifiable measure of substantial similarity that is agreed upon, complementary approaches can potentially further decrease liability. Similar randomized approaches, such as differential privacy, have been successful in mitigating privacy risks. This document focuses on the technical and research perspective on mitigating copyright violation and hence is not confidential. After investigating potential solutions and running numerical experiments, we concluded that using the notion of Near Access-Freeness (NAF) to measure the degree of substantial similarity is challenging, and the standard approach of training a Differentially Private (DP) model costs significantly when used to ensure NAF. Alternative approaches, such as retrieval models, might provide a more controllable scheme for mitigating substantial similarity.
NeR-VCP: A Video Content Protection Method Based on Implicit Neural Representation
Authors: Yangping Lin, Yan Ke, Ke Niu, Jia Liu, Xiaoyuan Yang
Date: 2024-08-20 00:00:00
Tasks: Model Compression, NER

With the popularity of video applications, the security of video content has emerged as a pressing issue that demands urgent attention. Most video content protection methods mainly rely on encryption technology, which needs to be manually designed or implemented in an experience-based manner. To address this problem, we propose an automatic encryption technique for video content protection based on implicit neural representation. We design a key-controllable module, which serves as a key for encryption and decryption. NeR-VCP first pre-distributes the key-controllable module trained by the sender to the recipients, and then uses Implicit Neural Representation (INR) with a (pre-distributed) key-controllable module to encrypt plain video as an implicit neural network, and the legal recipients uses a pre-distributed key-controllable module to decrypt this cipher neural network (the corresponding implicit neural network). Under the guidance of the key-controllable design, our method can improve the security of video content and provide a novel video encryption scheme. Moreover, using model compression techniques, this method can achieve video content protection while effectively mitigating the amount of encrypted data transferred. We experimentally find that it has superior performance in terms of visual representation, imperceptibility to illegal users, and security from a cryptographic viewpoint.
Robust Image Classification: Defensive Strategies against FGSM and PGD Adversarial Attacks
Authors: Hetvi Waghela, Jaydip Sen, Sneha Rakshit
Date: 2024-08-20 00:00:00
Tasks: Adversarial Defense, image-classification, Image Classification

Adversarial attacks, particularly the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) pose significant threats to the robustness of deep learning models in image classification. This paper explores and refines defense mechanisms against these attacks to enhance the resilience of neural networks. We employ a combination of adversarial training and innovative preprocessing techniques, aiming to mitigate the impact of adversarial perturbations. Our methodology involves modifying input data before classification and investigating different model architectures and training strategies. Through rigorous evaluation of benchmark datasets, we demonstrate the effectiveness of our approach in defending against FGSM and PGD attacks. Our results show substantial improvements in model robustness compared to baseline methods, highlighting the potential of our defense strategies in real-world applications. This study contributes to the ongoing efforts to develop secure and reliable machine learning systems, offering practical insights and paving the way for future research in adversarial defense. By bridging theoretical advancements and practical implementation, we aim to enhance the trustworthiness of AI applications in safety-critical domains.
Meta-Learning for Federated Face Recognition in Imbalanced Data Regimes
Authors: Arwin Gansekoele, Emiel Hess, Sandjai Bhulai
Date: 2024-08-13 00:00:00
Tasks: Face Recognition, Fairness, Federated Learning, Meta-Learning

The growing privacy concerns surrounding face image data demand new techniques that can guarantee user privacy. One such face recognition technique that claims to achieve better user privacy is Federated Face Recognition (FRR), a subfield of Federated Learning (FL). However, FFR faces challenges due to the heterogeneity of the data, given the large number of classes that need to be handled. To overcome this problem, solutions are sought in the field of personalized FL. This work introduces three new data partitions based on the CelebA dataset, each with a different form of data heterogeneity. It also proposes Hessian-Free Model Agnostic Meta-Learning (HF-MAML) in an FFR setting. We show that HF-MAML scores higher in verification tests than current FFR models on three different CelebA data partitions. In particular, the verification scores improve the most in heterogeneous data partitions. To balance personalization with the development of an effective global model, an embedding regularization term is introduced for the loss function. This term can be combined with HF-MAML and is shown to increase global model verification performance. Lastly, this work performs a fairness analysis, showing that HF-MAML and its embedding regularization extension can improve fairness by reducing the standard deviation over the client evaluation scores.
Measuring Human Contribution in AI-Assisted Content Generation
Authors: Yueqi Xie, Tao Qi, Jingwei Yi, Xiyuan Yang, Ryan Whalen, Junming Huang, Qian Ding, Yu Xie, Xing Xie, Fangzhao Wu
Date: 2024-08-27 00:00:00

With the growing prevalence of generative artificial intelligence (AI), an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI.
As Biased as You Measure: Methodological Pitfalls of Bias Evaluations in Speaker Verification Research
Authors: Wiebke Hutiri, Tanvina Patel, Aaron Yi Ding, Odette Scharenborg
Date: 2024-08-24 00:00:00
Tasks: Speaker Verification

Detecting and mitigating bias in speaker verification systems is important, as datasets, processing choices and algorithms can lead to performance differences that systematically favour some groups of people while disadvantaging others. Prior studies have thus measured performance differences across groups to evaluate bias. However, when comparing results across studies, it becomes apparent that they draw contradictory conclusions, hindering progress in this area. In this paper we investigate how measurement impacts the outcomes of bias evaluations. We show empirically that bias evaluations are strongly influenced by base metrics that measure performance, by the choice of ratio or difference-based bias measure, and by the aggregation of bias measures into meta-measures. Based on our findings, we recommend the use of ratio-based bias measures, in particular when the values of base metrics are small, or when base metrics with different orders of magnitude need to be compared.
Uncertainty Quantification in Alzheimer's Disease Progression Modeling
Authors: Wael Mobeirek, Shirley Mao
Date: 2024-08-13 00:00:00
Tasks: Ensemble Learning, Prognosis, Uncertainty Quantification, Variational Inference

With the increasing number of patients diagnosed with Alzheimer's Disease, prognosis models have the potential to aid in early disease detection. However, current approaches raise dependability concerns as they do not account for uncertainty. In this work, we compare the performance of Monte Carlo Dropout, Variational Inference, Markov Chain Monte Carlo, and Ensemble Learning trained on 512 patients to predict 4-year cognitive score trajectories with confidence bounds. We show that MC Dropout and MCMC are able to produce well-calibrated, and accurate predictions under noisy training data.
People over trust AI-generated medical responses and view them to be as valid as doctors, despite low accuracy
Authors: Shruthi Shekar, Pat Pataranutaporn, Chethan Sarabu, Guillermo A. Cecchi, Pattie Maes
Date: 2024-08-11 00:00:00
Tasks: Large Language Model, valid

This paper presents a comprehensive analysis of how AI-generated medical responses are perceived and evaluated by non-experts. A total of 300 participants gave evaluations for medical responses that were either written by a medical doctor on an online healthcare platform, or generated by a large language model and labeled by physicians as having high or low accuracy. Results showed that participants could not effectively distinguish between AI-generated and Doctors' responses and demonstrated a preference for AI-generated responses, rating High Accuracy AI-generated responses as significantly more valid, trustworthy, and complete/satisfactory. Low Accuracy AI-generated responses on average performed very similar to Doctors' responses, if not more. Participants not only found these low-accuracy AI-generated responses to be valid, trustworthy, and complete/satisfactory but also indicated a high tendency to follow the potentially harmful medical advice and incorrectly seek unnecessary medical attention as a result of the response provided. This problematic reaction was comparable if not more to the reaction they displayed towards doctors' responses. This increased trust placed on inaccurate or inappropriate AI-generated medical advice can lead to misdiagnosis and harmful consequences for individuals seeking help. Further, participants were more trusting of High Accuracy AI-generated responses when told they were given by a doctor and experts rated AI-generated responses significantly higher when the source of the response was unknown. Both experts and non-experts exhibited bias, finding AI-generated responses to be more thorough and accurate than Doctors' responses but still valuing the involvement of a Doctor in the delivery of their medical advice. Ensuring AI systems are implemented with medical professionals should be the future of using AI for the delivery of medical advice.
Misrepresented Technological Solutions in Imagined Futures: The Origins and Dangers of AI Hype in the Research Community
Authors: Savannah Thais
Date: 2024-08-08 00:00:00

Technology does not exist in a vacuum; technological development, media representation, public perception, and governmental regulation cyclically influence each other to produce the collective understanding of a technology's capabilities, utilities, and risks. When these capabilities are overestimated, there is an enhanced risk of subjecting the public to dangerous or harmful technology, artificially restricting research and development directions, and enabling misguided or detrimental policy. The dangers of technological hype are particularly relevant in the rapidly evolving space of AI. Centering the research community as a key player in the development and proliferation of hype, we examine the origins and risks of AI hype to the research community and society more broadly and propose a set of measures that researchers, regulators, and the public can take to mitigate these risks and reduce the prevalence of unfounded claims about the technology.
Exploiting Formal Concept Analysis for Data Modeling in Data Lakes
Authors: Anes Bendimerad, Romain Mathonat, Youcef Remil, Mehdi Kaytoue
Date: 2024-08-11 00:00:00
Tasks: Data Visualization

Data lakes are widely used to store extensive and heterogeneous datasets for advanced analytics. However, the unstructured nature of data in these repositories introduces complexities in exploiting them and extracting meaningful insights. This motivates the need of exploring efficient approaches for consolidating data lakes and deriving a common and unified schema. This paper introduces a practical data visualization and analysis approach rooted in Formal Concept Analysis (FCA) to systematically clean, organize, and design data structures within a data lake. We explore diverse data structures stored in our data lake at Infologic, including InfluxDB measurements and Elasticsearch indexes, aiming to derive conventions for a more accessible data model. Leveraging FCA, we represent data structures as objects, analyze the concept lattice, and present two strategies-top-down and bottom-up-to unify these structures and establish a common schema. Our methodology yields significant results, enabling the identification of common concepts in the data structures, such as resources along with their underlying shared fields (timestamp, type, usedRatio, etc.). Moreover, the number of distinct data structure field names is reduced by 54 percent (from 190 to 88) in the studied subset of our data lake. We achieve a complete coverage of 80 percent of data structures with only 34 distinct field names, a significant improvement from the initial 121 field names that were needed to reach such coverage. The paper provides insights into the Infologic ecosystem, problem formulation, exploration strategies, and presents both qualitative and quantitative results.
UAV's Rotor Micro-Doppler Feature Extraction Using Integrated Sensing and Communication Signal: Algorithm Design and Testbed Evaluation
Authors: Jiachen Wei, Dingyou Ma, Feiyang He, Qixun Zhang, Zhiyong Feng, Zhengfeng Liu, Taohong Liang
Date: 2024-08-29 00:00:00
Tasks: Integrated sensing and communication, ISAC

With the rapid application of unmanned aerial vehicles (UAVs) in urban areas, the identification and tracking of hovering UAVs have become critical challenges, significantly impacting the safety of aircraft take-off and landing operations. As a promising technology for 6G mobile systems, integrated sensing and communication (ISAC) can be used to detect high-mobility UAVs with a low deployment cost. The micro-Doppler signals from UAV rotors can be leveraged to address the detection of low-mobility and hovering UAVs using ISAC signals. However, determining whether the frame structure of the ISAC system can be used to identify UAVs, and how to accurately capture the weak rotor micro-Doppler signals of UAVs in complex environments, remain two challenging problems. This paper first proposes a novel frame structure for UAV micro-Doppler extraction and the representation of UAV micro-Doppler signals within the channel state information (CSI). Furthermore, to address complex environments and the interference caused by UAV body vibrations, the rotor micro-Doppler null space pursuit (rmD-NSP) algorithm and the feature extraction algorithm synchroextracting transform (SET) are designed to effectively separate UAV's rotor micro-Doppler signals and enhance their features in the spectrogram. Finally, both simulation and hardware testbed demonstrate that the proposed rmD-NSP algorithm enables the ISAC base station (BS) to accurately and completely extract UAV's rotor micro-Doppler signals. Within a 0.1s observation period, ISAC BS successfully captures eight rotations of the DJI M300 RTK UAV's rotor in urban environments. Compared to the existing AM-FM NSP and NSP signal decomposition algorithms, the integrity of the rotor micro-Doppler features is improved by 60%.
Many-Worlds Inverse Rendering
Authors: Ziyi Zhang, Nicolas Roussel, Wenzel Jakob
Date: 2024-08-13 00:00:00
Tasks: Inverse Rendering

Discontinuous visibility changes remain a major bottleneck when optimizing surfaces within a physically-based inverse renderer. Many previous works have proposed sophisticated algorithms and data structures to sample visibility silhouettes more efficiently. Our work presents another solution: instead of differentiating a tentative surface locally, we differentiate a volumetric perturbation of a surface. We refer this as a many-worlds representation because it models a non-interacting superposition of conflicting explanations (worlds) of the input dataset. Each world is optically isolated from others, leading to a new transport law that distinguishes our method from prior work based on exponential random media. The resulting Monte Carlo algorithm is simpler and more efficient than prior methods. We demonstrate that our method promotes rapid convergence, both in terms of the total iteration count and the cost per iteration.