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Causal perception in qestion-answering systems

Causal perception in qestion-answering systems

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2021 CHI Conference on Human Factors in computing systems: Making Waves, Combining Strengths, CHI 2021

作者： Law, Po-Ming Lo, Leo Yu-Ho Endert, Alex Stasko, John Qu, Huamin Georgia Institute of Technology United States The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781450380966

Root cause analysis is a common data analysis task. While question-answering systems enable people to easily articulate a why question (e.g., why students in Massachusetts have high ACT Math scores on average) and obtain an answer, these systems often produce questionable causal claims. To investigate how such claims might mislead users, we conducted two crowdsourced experiments to study the impact of showing diferent information on user perceptions of a question-answering system. We found that in a system that occasionally provided unreasonable responses, showing a scatterplot increased the plausibility of unreasonable causal claims. Also, simply warning participants that correlation is not causation seemed to lead participants to accept reasonable causal claims more cautiously. We observed a strong tendency among participants to associate correlation with causation. Yet, the warning appeared to reduce the tendency. Grounded in the fndings, we propose ways to reduce the illusion of causality when using question-answering systems. © 2021 acm.

关键词： Artificial intelligence

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GAN or DM? In-depth analysis and Evaluation of AI-generated Face Data for Generalizable Deepfake Detection 25

GAN or DM? In-depth Analysis and Evaluation of AI-generated ...

引用

40th Annual acm Symposium on Applied computing, SAC 2025

作者： Choi, Hyeongjun Woo, Simon S. Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400706295

Deepfake detection remains challenging, particularly when identifying deepfakes generated by unseen forgery methods. Recent studies have shown that detectors trained on forgery data from Generative Adversarial Networks (GAN) cannot generalize well on data from Diffusion Models (DM) and vice versa. As generative methods such as GAN and DM are significantly advanced for creating highly photorealistic images, it becomes crucial to develop generalized methods to detect forgeries generated from different generation methods. While research on generalizable detectors is gaining momentum, the impact of training data on detectors' generalization ability has yet to be extensively studied, especially concerning synthetic human face images. In this work, we train popular deep neural networks using face data generated by various generative models and thoroughly analyze their generalizability. Our results reveal significant differences in model performance based on the forgery method used to generate the training data. Notably, we identify specific scenarios that significantly enhance model generalization, contradicting previous research finding that models trained on DM-generated data would achieve higher generalization performance than those trained on GAN-generated data. These findings emphasize the crucial role of training data selection in enhancing the generalization capabilities of deepfake detectors. By strategically selecting and combining datasets, we can develop more robust detection systems, laying a foundation for future research in creating reliable and universal deepfake detection methods. Copyright © 2025 held by the owner/author(s).

关键词： Generative adversarial networks

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Light-GC: A Lightweight and Efficient Garbage Collection Scheme for Embedded File systems 22

Light-GC: A Lightweight and Efficient Garbage Collection Sch...

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23rd acm/IFIP International Middleware Conference (Middleware)

作者： Sun, Diansen Song, Yunlong Chai, Yunpeng Peng, Baoling Lu, Fangzhou Deng, Xiang Huawei Technol Co Ltd Shenzhen Peoples R China Renmin Univ China Beijing Peoples R China

ISBN: (纸本)9781450393409

Raw flash file systems are essential in today's cost-sensitive embedded systems and legacy embedded devices. The performance of raw flash file systems is often limited by their inefficient garbage collection (GC) due to the tight capacity of the onboard flash memory and CPU computing power. However, in real-world scenarios, we observed that the GC overhead can account for more than 85% of the total I/O time, which not only degrades the performance of the file system but also shortens the endurance of the flash chip. However, existing GC optimization used in high-end SSD or serverside file systems usually leads to high CPU and memory usage, which is not suitable for the embedded environment. In this paper, we propose LightGC, a low-complexity and high-efficiency GC scheme. We design an optimized clustering algorithm for page hotness measurement and a hot-delay GC victim selection strategy. LightGC can also be adapted to locality changes in workloads to maximize the effect of cold and heat separation. Our experimental results show that LightGC offers a relative improvement of up to 19% in GC efficiency, a 5%-15% reduction in write amplification, and a 10%-17% increase in flash memory endurance compared to other existing GC methods. In addition, driven by LightGC, we implement the UBIFS2 file system, an upgraded version of the UBIFS flash file system. We have done careful engineering design so that UBIFS2 and the original UBIFS are compatible with each other and users can switch between them freely.

关键词： Embedded filesystem garbage collection

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computing Ethics Narratives: Teaching computing Ethics and the Impact of Predictive Algorithms 21

Computing Ethics Narratives: Teaching Computing Ethics and t...

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52nd acm Technical Symposium on Computer Science Education, SIGCSE 2021

作者： Bullock, Beleicia B. Nascimento, Fernando L. Doore, Stacy A. University of Illinois Urbana-ChampaignIL United States Bowdoin College BrunswickME United States Colby College WatervilleME United States

ISBN: (纸本)9781450380621

The prevention of criminal activity has changed dramatically over the past two decades, largely due to the increased reliance on systems that provide crime data analysis. Created specifically for police, judicial sentencing, and prison applications, these systems conduct both predictive and retrospective analysis to aid decision making within the criminal justice system. Furthermore, these software platforms typically combine spatial informatics packages and advanced statistical features behind user-friendly interfaces. Recent studies have demonstrated problems with both the flawed logic within these systems' algorithms and the inherent biases in the underlying data. In this paper, we present a novel repository of computing ethics teaching modules across a variety of narrative areas. These modules and curated narratives help faculty to establish 'ethical laboratories' that can guide computer science students in improving their ethical reasoning skills as it relates to the creation of current and future technologies. First, we provide an overview of the computing Ethics Narratives (CEN) project, its narrative repository and the module framework through a sample module on predictive policing algorithms. Furthermore, we share preliminary findings from a pilot of this module, which was implemented in an intermediate algorithms course. The preliminary student and faculty feedback suggest the predictive policing module was able to help students contextualize the ethical issues around the topic, however, students recommended devoting more class time to evaluating the technical complexities of these critical systems. © 2021 acm.

关键词： Students

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Using Venom to Flip the Coin and Peel the Onion: measurement Tool and Dataset for Studying the Bitcoin - Dark Web Synergy: Data/Toolset Paper 25

Using Venom to Flip the Coin and Peel the Onion: Measurement...

引用

proceedings of the Fifteenth acm Conference on Data and Application Security and Privacy

作者： Lukas Ingmarsson Karl Duckert Karlsson Niklas Carlsson Linköping University Linköping Sweden

ISBN: (纸本)9798400714764

Bitcoin and the Dark Web present an interesting synergy that enables both legitimate anonymity and illicit activities, making it an important landscape to understand, especially as the Dark Web, with its hidden services, relies heavily on Bitcoin as a pseudonymous currency for transactions. However, a lack of scalable tools and timely datasets has limited systematic analysis of this ecosystem. To address this gap, we introduce Venom, a scalable framework for mapping Bitcoin activity on the Dark Web. Venom integrates multithreaded crawling, data extraction, and dataset generation, resulting in a comprehensive resource that allows us to easily collect snapshots of over 177,000 onion sites in roughly 24 hours. With the paper, we share both the tool and an example snapshot containing both per-site metadata and Bitcoin transaction data. Preliminary analysis reveals concentrated activity among key players and widespread content mirroring, offering new insights into the Dark Web's economic structure. Venom provides a critical resource for advancing research and monitoring in this domain.

关键词： Bitcoin

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Interactive mapping and geospatial analysis with Leafmap and Jupyter 3

Interactive mapping and geospatial analysis with Leafmap and...

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3rd acm SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science, SpatialAPI 2021 - In conjunction with the 29th International Conference on Advances in Geographic Information systems, acm SIGSPATIAL 2021

作者： Wu, Qiusheng University of Tennessee United States

ISBN: (纸本)9781450391030

Leafmap is a Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment. It is built upon several open-source packages, such as ipyleaflet and *** (for creating interactive maps), WhiteboxTools (for analyzing geospatial data), and ipywidgets (for designing interactive graphical user interface). Leafmap provides many convenient functions for loading and visualizing geospatial data with only one line of code. Users can also use the interactive user interface to load geospatial data without coding. Anyone with a web browser and Internet connection can use leafmap to perform geospatial analysis and data visualization in the cloud with minimal coding. This workshop will introduce the key features of leafmap for interactive mapping and geospatial analysis in a Jupyter environment. Attendees will learn how to leverage open-source Python packages and free cloud computing platforms for geospatial analysis and data visualization. © 2021 Owner/Author.

关键词： Python

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PanguLU: A Scalable Regular Two-Dimensional Block-Cyclic Sparse Direct Solver on Distributed Heterogeneous systems 23

PanguLU: A Scalable Regular Two-Dimensional Block-Cyclic Spa...

引用

2023 International Conference for High Performance computing, Networking, Storage and analysis, SC 2023

作者： Fu, Xu Zhang, Bingbin Wang, Tengcheng Li, Wenhao Lu, Yuechen Yi, Enxin Zhao, Jianqi Geng, Xiaohan Li, Fangying Zhang, Jingwen Jin, Zhou Liu, Weifeng SSSLab China University of Petroleum-Beijing Beijing China

ISBN: (纸本)9798400701092

Sparse direct solvers play a vital role in large-scale high performance computing in science and engineering. Existing distributed sparse direct methods employ multifrontal/supernodal patterns to aggregate columns of nearly identical forms and to exploit dense basic linear algebra subprograms (BLAS) for computation. However, such a data layout may bring more unevenness when the structure of the input matrix is not ideal, and using dense BLAS may waste many floating-point operations on zero *** this paper, we propose a new sparse direct solver called PanguLU. Unlike the multifrontal/supernodal layout, our work relies on simpler regular 2D blocking and stores the blocks in their sparse forms to avoid any extra fill-ins. Based on the sparse patterns of the blocks, a variety of block-wise sparse BLAS methods are developed and selected for higher efficiency on local GPUs. To make PanguLU more scalable, we also adjust the mapping of blocks to processes for overall more balanced workload, and propose a synchronisation-free communication strategy considering the dependencies among different sub-tasks to reduce overall latency *** on two distributed heterogeneous platforms consisting of 128 NVIDIA A100 GPUs and 128 AMD MI50 GPUs demonstrate that PanguLU achieves up to 11.70x and 17.97x speedups over the latest SuperLU_DIST, and scales up to 47.51x and 74.84x on the 128 A100 and MI50 GPUs over a single GPU, respectively. © 2023 acm.

关键词： Linear algebra

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Refining Mean-field Approximations by Dynamic State Truncation 21

Refining Mean-field Approximations by Dynamic State Truncati...

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2021 acm SIGMETRICS / International Conference on measurement and Modeling of Computer systems, SIGMETRICS 2021

作者： Randone, Francesca Bortolussi, Luca Tribastone, Mirco Imt School for Advanced Studies Lucca Lucca Italy Università Degli Studi di Trieste Trieste Italy

ISBN: (纸本)9781450380720

Mean-field models are an established method to analyze large stochastic systems with N interacting objects by means of simple deterministic equations that are asymptotically correct when N tends to infinity. For finite N, mean-field equations provide an approximation whose accuracy is model-and parameter-dependent. Recent research has focused on refining the approximation by computing suitable quantities associated with expansions of order $1/N$ and $1/N^2$ to the mean-field equation. In this paper we present a new method for refining mean-field approximations. It couples the master equation governing the evolution of the probability distribution of a truncation of the original state space with a mean-field approximation of a time-inhomogeneous population process that dynamically shifts the truncation across the whole state space. We provide a result of asymptotic correctness in the limit when the truncation covers the state space;for finite truncations, the equations give a correction of the mean-field approximation. We apply our method to examples from the literature to show that, even with modest truncations, it is effective in models that cannot be refined using existing techniques due to non-differentiable drifts, and that it can outperform the state of the art in challenging models that cause instability due orbit cycles in their mean-field equations. © 2021 Owner/Author.

关键词： Stochastic systems

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DeLiBA-K: Speeding-up Hardware-Accelerated Distributed Storage Access by Tighter Linux Kernel Integration and Use of a Modern API 24

DeLiBA-K: Speeding-up Hardware-Accelerated Distributed Stora...

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proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and analysis

作者： Babar Khan Andreas Koch Embedded Systems and Applications Group TU Darmstadt Germany

ISBN: (纸本)9798350355543

We present DeLiBA-K, an improved version of the Development of Linux Block I/O Accelerators (DeLiBA) framework. DeLiBA-K operates at the Linux kernel level, bypassing the user-space interactions of DeLiBA-1 and -2 to interact with the block and network I/O kernel stack directly. Another critical feature of DeLiBA-K is implementing and benchmarking the modern io_uring Asynchronous I/O (AIO) API within a 16nm AMD Alveo U280 FPGA I/O framework. This allows for better parallelism and reduced latency in I/O operations. Our results show significant performance gains, up to a 3.2x improvement in I/O operations per second (IOPS) and 3.45x increased throughput for synthetic workloads. Real-world applications see a 30% reduction in execution time for data-intensive tasks. DeLiBA-K has been successfully tested in an industrial environment using real workloads, demonstrating its effectiveness in large-scale enterprise environments.

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REITS: Reflective Surface for Intelligent Transportation systems 21

REITS: Reflective Surface for Intelligent Transportation Sys...

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22nd International Workshop on Mobile computing systems and Applications, HotMobile 2021

作者： Li, Zhuqi Wu, Can Wagner, Sigurd Sturm, James C. Verma, Naveen Jamieson, Kyle Princeton University United States

ISBN: (纸本)9781450383233

Autonomous vehicles are predicted to dominate the transportation industry in the foreseeable future. Safety is one of the major challenges to the early deployment of self-driving systems. To ensure safety, self-driving vehicles must sense and detect humans, other vehicles, and road infrastructure accurately, robustly, and timely. However, existing sensing techniques used by self-driving vehicles may not be absolutely reliable. In this paper, we design REITS, a system to improve the reliability of RF-based sensing modules for autonomous vehicles. We conduct theoretical analysis on possible failures of existing RF-based sensing systems. Based on the analysis, REITS adopts a multi-antenna design, which enables constructive blind beamforming to return an enhanced radar signal in the incident direction. REITS can also let the existing radar system sense identification information by switching between constructive beamforming state and destructive beamforming state. Preliminary results show that REITS improves the detection distance of a self-driving car radar by a factor of 3.63. © 2021 acm.

关键词： Autonomous vehicles

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