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IEEE Open Journal of the computer Society 2025年

作者： Tran, Van-Nhan Choi, Piljoo Le, Hoanh-Su Lee, Suk-Hwan Kwon, Ki-Ryong Pukyong National University Department of Artificial Intelligence Convergence Busan48547 Korea Republic of University of Economics and Law Falcuty of Information Systems Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Dong-A University Division of Computer and AI Engineering Busan Korea Republic of

Diffusion-based generative models have significantly advanced the field of image synthesis, presenting additional challenges regarding the integrity and authenticity of digital images. Consequently, the identification of ai-generated images has become a critical problem in image forensics. However, there is a lack of literature addressing the detection of images generated by diffusion models. In this paper, our focus is on developing a model capable of detecting images generated through both GAN techniques and diffusion models. We propose DiffCoR, a novel detection method for identifying ai-generated images. It consists of two main modules: Stable Diffusion Processing (SDP) and Image Representation Learning (IRL). The SDP module uses a pre-trained Stable Diffusion model to reconstruct input images via reverse diffusion and captures subtle manipulations through reconstruction discrepancies. The IRL module applies self-supervised learning with Latent Consistency Loss (LCL) to extract robust, invariant features, ensuring consistent latent representations across augmented views. We also incorporate frequency domain analysis using Discrete Fourier Transform (DFT) to enhance manipulation detection. Additionally, we introduce ForensicsImage, a publicly available dataset of over 400,000 real and ai-generated images from LSUN-Bedroom, CelebA-HQ, CelebDFv2, and various diffusion models. Experiments on ForensicsImage and GenImage show that DiffCoR achieves state-of-the-art performance, with strong cross-dataset generalization, making it suitable for real-world use in digital forensics, content verification, and social media moderation. © 2020 IEEE.

关键词： Self-supervised learning

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Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case

arXiv

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arXiv 2020年

作者： Wollenstein-Betech, Salomón Muise, Christian Cassandras, Christos G. Paschalidis, Ioannis Ch. Khazaeni, Yasaman Dept. of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA United States School of Computing Queen’s University KingstonON Canada IBM Research AI CambridgeMA United States

Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controller’s decision given the state of the system. For this, we use simulated historical state-action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads. Copyright © 2020, The Authors. All rights reserved.

关键词： Controllers

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Understanding vehicular routing behavior with location-based service data

arXiv

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arXiv 2023年

作者： Xu, Yanyan Di Clemente, Riccardo González, Marta C. MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China Department of Civil & Environmental Engineering MIT CambridgeMA02139 United States Department of City and Regional Planning University of California Berkeley BerkeleyCA94720 United States Energy Analysis & Environmental Impacts Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Centre for Advanced Spatial Analysis University College London LondonWC1E 6BT United Kingdom Department of Computer Science University of Exeter ExeterEX4 4NR United Kingdom Department of Civil and Environmental Engineering University of California Berkeley BerkeleyCA94720 United States

Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation. © 2023, CC BY-NC-ND.

关键词： Location based services

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FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices

arXiv

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arXiv 2022年

作者： Wehbi, Osama Arisdakessian, Sarhad Wahab, Omar Abdel Otrok, Hadi Otoum, Safa Mourad, Azzam Guizani, Mohsen Cyber Security Systems and Applied AI Research Center Department of CSM Lebanese American University Lebanon Department of Computer and Software Engineering Polytechnique Montréal MontrealQC Canada Khalifa University Abu Dhabi United Arab Emirates College of Technological Innovation Zayed University Dubai United Arab Emirates Division of Science New York University Abu Dhabi United Arab Emirates Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server’s side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. Based on our simulation findings, our strategy surpasses the VanillaFL selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model. © 2022, CC BY-NC-SA.

关键词： Game theory

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Towards a fast steady-state visual evoked potentials (SSVEP) brain-computer interface (BCI)

arXiv

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arXiv 2020年

作者： Wai, Aung Aung Phyo Zhang, Yangsong Guo, Heng Chi, Ying Zhang, Lei Hua, Xian-Sheng Lee, Seong Whan Guan, Cuntai School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Computer Science and Technology Southwest University of Science and Technology China Health-AI Division DAMO Academy Alibaba Group Holding Limited Department of Brain and Cognitive Engineering Korea University Korea Republic of

Steady-state visual evoked potentials (SSVEP) brain-computer interface (BCI) provides reliable responses leading to high accuracy and information throughput. But achieving high accuracy typically requires a relatively long time window of one second or more. Various methods were proposed to improve sub-second response accuracy through subject-specific training and calibration. Substantial performance improvements were achieved with tedious calibration and subject-specific training;resulting in user's discomfort. Real-world SSVEP applications demand training-free fast SSVEP frequency recognition with improved performance and usability. So, we propose a training-free method by combining spatial-filtering and temporal alignment (CSTA) to recognize SSVEP responses in sub-second response time. CSTA exploits linear correlation and non-linear similarity between steady-state responses and stimulus templates with complementary fusion to achieve desirable performance improvements. We evaluated the performance of CSTA in terms of accuracy and Information Transfer Rate (ITR) in comparison with both training-based and training-free methods using two SSVEP data-sets. We observed that CSTA achieves the maximum mean accuracy of 97.43 ± 2.26 % and 85.71 ± 13.41% with four-class and forty-class SSVEP data-sets respectively in sub-second response time in offline analysis. CSTA yields significantly higher mean performance (p Copyright © 2020, The Authors. All rights reserved.

关键词： Beamforming

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Disentanglement of intrinsic and extrinsic side-jump scattering induced spin Hall effect in N-implanted Pt

引用

Physical Review B 2023年第6期107卷 064402-064402页

作者： Utkarsh Shashank Yoji Nakamura Yu Kusaba Takafumi Tomoda Razia Nongjai Asokan Kandasami Rohit Medwal Rajdeep Singh Rawat Hironori Asada Surbhi Gupta Yasuhiro Fukuma Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka 820-8502 Japan Materials Science Division Inter University Accelerator Centre Aruna Asaf Ali Marg New Delhi 110067 India Department of Physics and Centre for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand 248007 India Department of Physics Indian Institute of Technology Kanpur Kanpur 208016 India Natural Sciences and Science Education National Institute of Education Nanyang Technological University 637616 Singapore Graduate School of Sciences and Technology for Innovation Yamaguchi University 2-16-1 Tokiwadai Ube 755-8611 Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu 808-0196 Japan

The rapidly evolving utilization of spin Hall effect (SHE) arising from spin-orbit coupling in 5d transition metals and alloys has made giant strides in the development of designing low-power, robust, and nonvolatile magnetic memory. Recent studies, on incorporating nonmetallic lighter elements such as oxygen, nitrogen, and sulfur into 5d transition metals, have shown an enhancement in dampinglike torque efficiency θDL due to the modified SHE, but the mechanism behind this enhancement is not clear. In this paper, we study θDL at different temperatures (100–293 K) to disentangle the intrinsic and extrinsic side-jump scattering induced SHE in N-implanted Pt. We observe a crossover of intrinsic to extrinsic side-jump mechanism as the implantation dose increases from 2×1016 to 1×1017ions/cm2. A sudden decrease in the intrinsic spin Hall conductivity is counterbalanced by the increase in the extrinsic side-jump induced SHE efficiency. These results conclude that studying θDL as a function of implantation dose, and as a function of temperature, is important to understand the physical mechanism contributing to SHE.

关键词： Spin Hall effect Spin-orbit coupling Spin-orbit torque Spintronics Thin films Ferromagnetic resonance Ion implantation

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Statistical modeling: The three cultures

arXiv

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arXiv 2020年

作者： Daoud, Adel Dubhashi, Devdatt Department of Sociology and Work Science University of Gothenburg Gothenburg Sweden Center for Population and Development Studies Harvard University Cambridge United States Data Science and AI division Department of Computer Science and Engineering Gothenburg Sweden

Two decades ago, Leo Breiman (2001) identified two cultures for statistical modeling. The data modeling culture (DMC) refers roughly to practices aiming to conduct statistical inference, βĜ, on one or several quantities of interest. The algorithmic modeling culture (AMC) refers to practices defining a machine-learning (ML) procedure that generates accurate predictions, ŶŶ, about an event of interest (outcome). Breiman argued that statisticians should give more attention to AMC than to DMC, because AMC provides novel procedures for prediction. While twenty years later, DMC has perhaps lost some of its dominant role in statistics because of the data-science revolution, we observe that this culture is still the modus operandi—the leading practice of a group—in the natural and social sciences. DMC is the modus operandi because of the influence of the established scientific method, called the hypothetico-deductive scientific method. Despite the incompatibilities of AMC with the hypothetico-deductive scientific method, among some research groups, AMC and DMC cultures mix intensely. We argue that this mixing has formed a fertile spawning pool for a mutated culture: a hybrid modeling culture (HMC) where prediction and inference have fused into new procedures where they reinforce one another. This article identifies key characteristics of HMC, thereby facilitating the scientific endeavor and fueling the evolution of statistical cultures towards better practices. By better, we mean increasingly reliable, valid, and efficient statistical practices in analyzing causal relationships. In combining inference and prediction, the result of HMC is that the distinction between ŶŶ and βĜ—taken to its limit—melts away. We qualify our "melting-away" argument by describing three HMC practices, where each practice captures an aspect of the scientific cycle: ML for causal inference, ML for data acquisition, and ML for theory prediction. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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The wealth of nations and the health of populations: A quasi-experimental design of the impact of sovereign debt crises on child mortality

arXiv

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arXiv 2020年

作者： Daoud, Adel Department of Sociology and Work Science University of Gothenburg Gothenburg Sweden Center for Population and Development Studies Harvard University Cambridge United States The Data Science and AI division Department of Computer Science and Engineering Gothenburg Sweden

The wealth of nations and the health of populations are intimately strongly associated, yet the extent to which economic prosperity (GDP per capita) causes improved health remains disputed. The purpose of this article is to analyze the impact of sovereign debt crises (SDC) on child mortality, using a sample of 57 low- and middle-income countries surveyed by the Demographic and Health Survey between the years 1990 and 2015. These surveys supply 229 household data and containing about 3 million childbirth history records. This focus on SDC instead of GDP provides a quasi-experimental moment in which the influence of unobserved confounding is less than a moment analyzing the normal fluctuations of GDP. This study measures child mortality at six thresholds: neonatal, under-one (infant), under-two, under-three, under-four, and under-five mortality. Using a machine-learning (ML) model for causal inference, this study finds that while an SDC causes an adverse yet statistically insignificant effect on neonatal mortality, all other child mortality group samples are adversely affected between a probability of 0.12 to 0.14 (all statistically significant at the 95-percent threshold). Through this ML, this study also finds that the most important treatment heterogeneity moderator—in the entire adjustment set—is whether a child is born in a low-income country. Copyright © 2020, The Authors. All rights reserved.

关键词： Health

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Parameters of Chronic Kidney Disease to Identify Outpatients at Increased Risk for COVID-19 Mortality: A Cohort Study of UK Biobank Participants

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The Open Urology and Nephrology Journal 2021年 14卷 26-28页

作者： Anusua Trivedi W. Conrad Liles Nicholas Becker Catherine Egan Juan Lavista Ferres Aaron Lee Pavan K. Bhatraju University of Washington School of Medicine Seattle United States AI for Good Research Microsoft United States Department of Medicine and Sepsis Center of Research Excellence University of Washington (SCORE-UW) Seattle United States University of Washington Computer Science and Engineering United States Moorfields Eye Hospital NHS Foundation Trust United Kingdom University of Washington Division of Pulmonary Critical Care and Sleep Medicine Seattle United States

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Summary: Chicago Quantum Exchange (CQE) Pulse-level Quantum Control Workshop

arXiv

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arXiv 2022年

作者： Smith, Kaitlin N. Ravi, Gokul Subramanian Alexander, Thomas Bronn, Nicholas T. Carvalho, Andre Cervera-Lierta, Alba Chong, Frederic T. Chow, Jerry M. Cubeddu, Michael Hashim, Akel Jiang, Liang Lanes, Olivia Otten, Matthew J. Schuster, David I. Gokhale, Pranav Earnest, Nathan Galda, Alexey Department of Computer Science University of Chicago United States IBM Quantum IBM T. J. Watson Research Center United States Q-CTRL Australia Department of Chemistry Department of Computer Science University of Toronto Canada Aliro Technologies Inc. United States Department of Physics UC Berkeley United States Pritzker School of Molecular Engineering University of Chicago United States HRL Laboratories LLC United States Super.tech India Computational Science Division Argonne National Laboratory United States James Franck Institute University of Chicago United States Menten AI Inc. United States

Quantum information processing holds great promise for pushing beyond the current frontiers in computing. Specifically, quantum computation promises to accelerate the solving of certain problems, and there are many opportunities for innovation based on applications in chemistry, engineering, and finance. To harness the full potential of quantum computing, however, we must not only place emphasis on manufacturing better qubits, advancing our algorithms, and developing quantum software. To scale devices to the fault tolerant regime, we must refine device-level quantum control. On May 17-18, 2021, the Chicago Quantum Exchange (CQE) partnered with IBM Quantum and *** to host the Pulse-level Quantum Control Workshop. At the workshop, representatives from academia, national labs, and industry addressed the importance of fine-tuning quantum processing at the physical layer. The purpose of this report is to summarize the topics of this meeting for the quantum community at large. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum computers

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