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Notice of Violation of IEEE Publication Principles: Data Trading with a Monopoly Social Network: Outcomes Are Mostly Privacy Welfare Damaging

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IEEE Networking Letters

IEEE Networking Letters 2020年第4期2卷 185-189页

作者： Ranjan Pal Junhui Li Yixuan Wang Mingyan Liu Swades De Jon Crowcroft Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Computer Laboratory University of Cambridge Cambridge U.K. Alan Turing Institute London U.K.

Notice of Violation of IEEE Publication Principles “Data Trading with a Monopoly Social Network: Outcomes are Mostly Privacy Welfare Damaging” by Ranjan Pal, Junhui Li, Yixuan Wang, Mingyan Liu, Swades De, and Jon Crowcroft in the IEEE Networking Letters, Volume: 2, Issue: 4, October 2020 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This paper is a duplication of the original text from the paper cited below. The original text was copied with insufficient attribution and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: "Too Much Data: Prices and Inefficiencies in Data Markets" by Daron Acemoglu, Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar NBER Working Paper No. 26296, September 2019 This letter argues that data of strategic individuals with heterogeneous privacy valuations in a distributed online social network (e.g., Facebook) will be under-priced, if traded in a monopoly buyer setting, and will lead to diminishing utilitarian welfare. This result, for a certain family of online community data trading problems, is in stark contrast to a popular information economics intuition that increased amounts of end-user data signals in a data market improves its efficiency. Our proposed theory paves the way for a future (counter-intuitive) analysis of data trading oligopoly markets for online social networks (OSNs).

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A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15–1.73 GHz

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arXiv 2023年

作者： Margot, Jean-Luc Li, Megan G. Pinchuk, Pavlo Myhrvold, Nathan Lesyna, Larry Alcantara, Lea E. Andrakin, Megan T. Arunseangroj, Jeth Baclet, Damien S. Belk, Madison H. Bhadha, Zerxes R. Brandis, Nicholas W. Carey, Robert E. Cassar, Harrison P. Chava, Sai S. Chen, Calvin Chen, James Cheng, Kellen T. Cimbri, Alessia Cloutier, Benjamin Combitsis, Jordan A. Couvrette, Kelly L. Coy, Brandon P. Davis, Kyle W. Delcayre, Antoine F. Du, Michelle R. Feil, Sarah E. Fu, Danning Gilmore, Travis J. Grahill-Bland, Emery Iglesias, Laura M. Juneau, Zoe Karapetian, Anthony G. Karfakis, George Lambert, Christopher T. Lazbin, Eric A. Li, Jian H. Li, Zhuofu Liskij, Nicholas M. Lopilato, Anthony V. Lu, Darren J. Ma, Detao Mathur, Vedant Minasyan, Mary H. Muller, Maxwell K. Nasielski, Mark T. Nguyen, Janice T. Nicholson, Lorraine M. Niemoeller, Samantha Ohri, Divij Padhye, Atharva U. Penmetcha, Supreethi V. Prakash, Yugantar Qi, Xinyi Rindt, Liam Sahu, Vedant Scally, Joshua A. Scott, Zefyr Seddon, Trevor J. Shohet, Lara-Lynn V. Sinha, Anchal Sinigiani, Anthony E. Song, Jiuxu Stice, Spencer M. Tabucol, Nadine M. Uplisashvili, Andria Vanga, Krishna Vazquez, Amaury G. Vetushko, George Villa, Valeria Vincent, Maria Waasdorp, Ian J. Wagaman, Ian B. Wang, Amanda Wight, Jade C. Wong, Ella Yamaguchi, Natsuko Zhang, Zijin Zhao, Junyang Lynch, Ryan S. Department of Earth Planetary and Space Sciences University of California Los AngelesCA90095 United States Department of Physics and Astronomy University of California Los AngelesCA90095 United States National Renewable Energy Laboratory GoldenCO80401 United States Intellectual Ventures 3150 139th Ave SE BellevueWA98005 United States LXL Technology Las VegasNV89148 United States UCLA Samueli School of Engineering University of California Los AngelesCA90095 United States Department of Mathematics University of California Los AngelesCA90095 United States Department of Electrical Engineering University of California Los AngelesCA90095 United States Department of Public Affairs University of California Los AngelesCA90095 United States Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States Department of Computer Science University of California Los AngelesCA90095 United States Department of Materials Engineering University of California Los AngelesCA90095 United States UCLA College of Letters and Science University of California Los AngelesCA90095 United States Department of Mechanical Engineering University of California Los AngelesCA90095 United States Department of Aerospace Engineering University of California Los AngelesCA90095 United States Department of Neuroscience University of California Los AngelesCA90095 United States Green Bank Observatory P.O. Box 2 Green BankWV24494 United States

We conducted a search for narrowband radio signals over four observing sessions in 2020–2023 with the L-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ∼11,680 stars and planetary systems in the ∼9 arcminute beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. We also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense radio frequency interference are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ∼15 times smaller at ∼6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake Figure of Merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is detectable in our search (EIRP > 1013 W). For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5 × 1016 W) is at most 3 × 10−4. Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al. (2023). © 2023, CC BY-NC-SA.

关键词： Transmitters

Predicting the impact of graphene oxide and its shapes on the thermal efficiency of Polyalphaolefin-40 using a mathematical model for wind turbine gearboxes

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Case Studies in Thermal engineering 2025年 72卷

作者： Aamina Ahsan Amir Zanj Ahsan Zafar Najam Iqbal Adeena Fatima Wajid Khan Muhammad Zain Yousaf Baseem Khan Josep M. Guerrero State Key Laboratory of Engines (Tianjin University) School of Mechanical Engineering Tianjin University Tianjin 300350 China Advanced Wind Energy Technology Group College of Science and Engineering Flinders University Adelaide South Australia Australia Department of Electrical Engineering City University of Science & Information Technology Peshawar 25000 Pakistan School of Electrical and Information Engineering Tianjin University Tianjin 300072 China School of Microelectronics Tianjin University Tianjin 300072 China Department of Electrical and Electronic Engineering Technology University of Johannesburg Johannesburg South Africa Department of Electrical and Computer Engineering Hawassa University Hawassa Ethiopia Center for Research on Microgrids (AAU CROM) AAU Energy Aalborg University 9220 Aalborg Denmark

Introduction Gearbox reliability is a significant concern in wind turbines, as it ensures optimal rotational speed to meet generator requirements. However, gearbox failures, often resulting from inadequate lubrication and thermal inefficiencies, can severely affect system performance. Design/methodology/approach To tackle this issue, the current study explores the use and potential of nanolubricants, concentrating on the impact of nanoparticle type and their geometric shapes on heat transfer and lubrication properties. A Brinkman-type steady nanofluid flow is modeled within a channel of length d, influenced by a magnetic field. Polyalphaolefins-40 (PAO-40) oil serves as the base fluid, to which three nanoparticles —Titanium Dioxide (TiO 2 ), Graphene Oxide (GO), and Aluminum Oxide (Al 2 O 3 )—are added. The model was generalized using fractionalized Fick's and Fourier's laws; the exact solution of the model is derived through Laplace and Fourier sine transforms, and the results are expressed using the Mittag-Leffler function. Findings GO emerged as the most effective additive, with four of its geometric shapes (blade, platelet, brick, and cylinder) further investigated to assess their thermal and rheological behavior. Among the shapes tested, blade-shaped GO nanoparticles provided the highest improvement in heat transfer (31.91 %), followed by platelet (27.96 %), brick (26.56 %), and cylinder (25 %). Notably, indicates that platelet-shaped GO showed superior viscosity enhancement, which is critical for effective lubrication. Discussion These findings imply that shape-optimized GO nanoparticles can act as highly effective additives in nanolubricants, enhancing both heat transfer and lubrication within wind turbine gearboxes. This advancement could lead to more durable, efficient, and reliable renewable energy systems.

关键词： Brinkman-type nanofluid (BTF) Caputo fractional derivative (CFD) Nanoparticle shapes variation Generalized/fractionalized Fourier's law & Fick's law (FFL) Mittag-leffler function (MLF) Integral transform methods

A new online Arabic handwriting dataset for analyzing Parkinson’s disease

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A new online Arabic handwriting dataset for analyzing Parkin...

International Conference on Cyberworlds

作者： Mohammed F. Allebawi Thameur Dhieb Islem Jarraya Mohamed Neji Nouha Farhat Emna Smaoui Khadija Moalla Mariem Dammak Tarek M. Hamdani Chokri Mhiri Adel M. Alimi REGIM-Lab.: REsearch Groups in Intelligent Machines Enis University of Sfax Tunisia Faculty of Sciences of Monastir University of Monastir Tunisia Technical Institute of Babylon Al-Furat Al-Awsat Technical University (ATU) Iraq Higher Institute of Business Administration University of Gafsa Tunisia National School of Electronics and Telecommunications (Enet’Com) University of Sfax Tunisia Neurology’s Department Habib Bourguiba Hospital Sfax Tunisia Laboratory of Neurogenetics Parkinson’s Disease and Cerebrovascular Disease (LR-12-SP-19) University of Sfax Tunisia Higher Institute of Computer Science Mahdia University of Monastir Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Parkinson’s disease (PD) is a common and progressive neurodegenerative disorder with motor symptoms and a variety of non-motor symptoms. Experts regularly include handwriting as one of the Parkinsonian motor symptoms of PD and as a valuable tool that can aid in diagnosing and tracking the disease’s progression. PD patients have two periods. ‘On’ time is when levodopa is working well and your symptoms are controlled. ‘Off’ time is when levodopa is no longer working well and symptoms such as tremor, rigidity and slow movement re-emerge. To our knowledge, all existing publicly available datasets allow PD to be identified using only one period. No publicly available online handwriting datasets are dedicated to the analysis of PD using these two periods. Therefore, in this paper, we present our new online Arabic handwriting dataset for analysing PD, which we will make publicly available so that it could potentially be used for diagnosis, screening and monitoring the progression of PD. Our dataset was collected from 30 healthy controls and 30 PD patients in both “off” and “on” at the Neurology department, Habib Bourguiba Hospital, Sfax, Tunisia. All participants performed five different handwriting tasks. The tasks included drawing repetitive ellipses, a spiral, repetitive digits and Arabic word writing. We hope that our new dataset will help researchers in the early detection of Parkinson’s disease, inpatient rehabilitation and quantification of therapeutic effects.

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PersGNN: applying topological data analysis and geometric deep learning to structure-based protein function prediction

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arXiv 2020年

作者： Swenson, Nicolas Krishnapriyan, Aditi S. Buluc, Aydin Morozov, Dmitriy Yelick, Katherine Lawrence Berkeley National Laboratory Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA94720 United States

Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank in order to study the expressiveness of different structure-based prediction schemes. We present PersGNN—an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis to capture a complex set of both local and global structural features. While variations of these techniques have been successfully applied to proteins before, we demonstrate that our hybridized approach, PersGNN, outperforms either method on its own as well as a baseline neural network that learns from the same information. PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR) compared to the best individual model, as well as high F1 scores across different gene ontology categories, indicating the transferability of this approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Proteins

Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey

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arXiv 2025年

作者： Tian, Shuang Zhang, Tao Liu, Jiqiang Wang, Jiacheng Wu, Xuangou Zhu, Xiaoqiang Zhang, Ruichen Zhang, Weiting Yuan, Zhenhui Mao, Shiwen Kim, Dong In School of Software Engineering Beijing Jiaotong University Beijing100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Technology An hui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui University of Technology Anhui243002 China College of Computing and Data Science Nanyang Technological University Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

—With the rapid development of technology and the acceleration of digitalisation, the frequency and complexity of cyber security threats are increasing. Traditional cybersecurity approaches, often based on static rules and predefined scenarios, are struggling to adapt to the rapidly evolving nature of modern cyberattacks. There is an urgent need for more adaptive and intelligent defence strategies. The emergence of Large Language Model (LLM) provides an innovative solution to cope with the increasingly severe cyber threats, and its potential in analysing complex attack patterns, predicting threats and assisting real-time response has attracted a lot of attention in the field of cybersecurity, and exploring how to effectively use LLM to defend against cyberattacks has become a hot topic in the current research field. This survey examines the applications of LLM from the perspective of the cyber attack lifecycle, focusing on the three phases of defense reconnaissance, foothold establishment, and lateral movement, and it analyzes the potential of LLMs in Cyber Threat Intelligence (CTI) tasks. Meanwhile, we investigate how LLM-based security solutions are deployed and applied in different network scenarios. It also summarizes the internal and external risk issues faced by LLM during its application. Finally, this survey also points out the facing risk issues and possible future research directions in this domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters

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arXiv 2022年

作者： Lin, Mingbao Cao, Liujuan Zhang, Yuxin Shao, Ling Lin, Chia-Wen Ji, Rongrong Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China Youtu Laboratory Tencent Shanghai200233 China Fujian Key Laboratory of Sensing and Computing for Smart City Computer Science Department School of Informatics Xiamen University Xiamen361005 China Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Institute of Artificial Intelligence Xiamen University Xiamen361005 China

This paper focuses on filter-level network pruning. A novel pruning method, termed CLR-RNF, is proposed. We first reveal a "long-tail" pruning problem in magnitude-based weight pruning methods, and then propose a computation-aware measurement for individual weight importance, followed by a Cross-Layer Ranking (CLR) of weights to identify and remove the bottom-ranked weights. Consequently, the per-layer sparsity makes up of the pruned network structure in our filter pruning. Then, we introduce a recommendation-based filter selection scheme where each filter recommends a group of its closest filters. To pick the preserved filters from these recommended groups, we further devise a k-Reciprocal Nearest Filter (RNF) selection scheme where the selected filters fall into the intersection of these recommended groups. Both our pruned network structure and the filter selection are non-learning processes, which thus significantly reduce the pruning complexity, and differentiate our method from existing works. We conduct image classification on CIFAR-10 and ImageNet to demonstrate the superiority of our CLR-RNF over the state-of-the-arts. For example, on CIFAR-10, CLR-RNF removes 74.1% FLOPs and 95.0% parameters from VGGNet-16 with even 0.3% accuracy improvements. On ImageNet, it removes 70.2% FLOPs and 64.8% parameters from ResNet-50 with only 1.7% top-5 accuracy drops. Our project is at https://***/lmbxmu/CLR-RNF. © 2022, CC0.

关键词： Network layers

Entangled Sensor-Networks for Dark-Matter Searches

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PRX Quantum 2022年第3期3卷 030333-030333页

作者： Anthony J. Brady Christina Gao Roni Harnik Zhen Liu Zheshen Zhang Quntao Zhuang Department of Electrical and Computer Engineering University of Arizona Tucson Arizona 85721 USA Theoretical Physics Division Fermi National Accelerator Laboratory P.O. Box 500 Batavia Illinois 60510 USA Department of Physics University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Illinois Center for Advanced Studies of the Universe University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA School of Physics and Astronomy University of Minnesota Minneapolis Minnesota 55455 USA Department of Materials Science and Engineering University of Arizona Tucson Arizona 85721 USA J. C. Wyant College of Optical Sciences University of Arizona Tucson Arizona 85721 USA

The hypothetical axion particle (of unknown mass) is a leading candidate for dark matter (DM). Many experiments search for axions with microwave cavities, where an axion may convert into a cavity photon, leading to a feeble excess in the output power of the cavity. Recent work [Backes et al., Nature 590, 238 (2021)] has demonstrated that injecting squeezed vacuum into the cavity can substantially accelerate the axion search. Here, we go beyond and provide a theoretical framework to leverage the benefits of quantum squeezing in a network setting consisting of many sensor cavities. By forming a local sensor network, the signals among the cavities can be combined coherently to boost the axion search. Furthermore, injecting multipartite entanglement across the cavities—generated by splitting a squeezed vacuum—enables a global noise reduction. We explore the performance advantage of such a local, entangled sensor network, which enjoys both coherence between the axion signals and entanglement between the sensors. Our analyses are pertinent to next-generation DM-axion searches aiming to leverage a network of sensors and quantum resources in an optimal way. Finally, we assess the possibility of using a more exotic quantum state, the Gottesman-Kitaev-Preskill (GKP) state. Despite a constant-factor improvement in the scan time relative to a single-mode squeezed state in the ideal case, the advantage of employing a GKP state disappears when a practical measurement scheme is considered.

关键词： Dark matter Quantum metrology Quantum sensing Axions Josephson junctions Microwave techniques

Post-2000 Nonlinear Optical Materials and Measurements: Data Tables and Best Practices

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arXiv 2023年

作者： Vermeulen, Nathalie Espinosa, Daniel Ball, Adam Ballato, John Boucaud, Philippe Boudebs, Georges Campos, Cecília L.A.V. Dragic, Peter Gomes, Anderson S.L. Huttunen, Mikko J. Kinsey, Nathaniel Mildren, Rich Neshev, Dragomir Padilha, Lázaro A. Pu, Minhao Secondo, Ray Tokunaga, Eiji Turchinovich, Dmitry Yan, Jingshi Yvind, Kresten Dolgaleva, Ksenia Van Stryland, Eric W. Brussels Photonics B-PHOT Department of Applied Physics and Photonics IR-TONA Vrije Universiteit Brussel Pleinlaan 2 Brussel1050 Belgium School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada Dept. Of Electrical & Computer Engineering Virginia Commonwealth University RichmondVA23284 United States Dept of Materials Science and Engineering Clemson University ClemsonSC29634 United States Université Côte d’Azur CNRS CRHEA Rue Bernard Grégory Sophia-Antipolis06905 France Univ Angers LPHIA SFR MATRIX AngersF-49000 France Departamento de Física Universidade Federal de Pernambuco PE Recife50670-901 Brazil Dept. Of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Tampere University Photonics Laboratory Physics Unit TampereFI-33014 Finland MQ Photonics Research Centre Macquarie University NSW2109 Australia ARC Centre of Excellence for Transformative Meta-Optical Systems TMOS Department of Electronic Materials Engineering Research School of Physics Australian National University CanberraACT2601 Australia Instituto de Fisica "Gleb Wataghin" Universidade Estadual de Campinas Campinas Sao Paulo13083-970 Brazil Department of Electrical and Photonics engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Department of Physics Faculty of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku-ku Tokyo162-8601 Japan Fakultät für Physik Universität Bielefeld Universitätsstr. 25 Bielefeld33615 Germany CREOL The College of Optics and Photonics University of Central Florida OrlandoFL United States

In its 60 years of existence, the field of nonlinear optics has gained momentum especially over the past two decades thanks to major breakthroughs in material science and technology. In this article, we present a new set of data tables listing nonlinear-optical properties for different material categories as reported in the literature since 2000. The papers included in the data tables are representative experimental works on bulk materials, solvents, 0D-1D-2D materials, metamaterials, fiber waveguiding materials, on-chip waveguiding materials, hybrid waveguiding systems, and materials suitable for nonlinear optics at THz frequencies. In addition to the data tables, we also provide best practices for performing and reporting nonlinear-optical experiments. These best practices underpin the selection process that was used for including papers in the tables. While the tables indeed show strong advancements in the field over the past two decades, we encourage the nonlinear-optics community to implement the identified best practices in future works. This will allow a more adequate comparison, interpretation and use of the published parameters, and as such further stimulate the overall progress in nonlinear-optical science and applications. © 2023, CC BY.

关键词： Nonlinear optics