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Speculative Vulnerability: Uncovering the Temporalities of Vulnerability in People s Experiences of the Pandemic

Proceedings of the ACM on Human-computer Interaction 2022年第CSCW2期6卷 1-27页

作者： Seberger, John S. Obi, Ike Loukil, Mariem Liao, William Wild, David J. Patil, Sameer Department of Information Science College of Computing and Informatics Drexel University 3675 Market Street PhiladelphiaPA19104 United States Department of Computer Graphics Technology Purdue Polytechnic Institute Purdue University 401 N. Grant St. West LafayetteIN47907 United States Mediterranean Institute of Technology Les Jardins du Lac 2 Tunis1053 Tunisia Department of Informatics Luddy School of Informatics Computing and Engineering Indiana University Bloomington 901 E. Tenth Street BloomingtonIN48104 United States School of Computing University of Utah 50 Central Campus Dr. Salt Lake CityUT84112 United States

Pandemic-tracking apps may form a future infrastructure for public health surveillance. Yet, there has been relatively little exploration of the potential societal implications of such an infrastructure. In semi-structured interviews with 23 participants from India, the Middle East and North Africa (MENA), and the United States, we discussed attitudes and preferences regarding the deployment of apps that support contact tracing to contain the spread of COVID-19. Through interpretive analysis, we examined the relationship between persistent discomfort and vulnerability when using such apps. Such an examination yielded three temporal forms of vulnerability: real, anticipatory, and speculative. By identifying and defining the temporalities of vulnerability through an analysis of people's pandemic-related thoughts and experiences, we develop the overlapping discourses of humanistic infrastructure studies and infrastructural speculation. In doing so, we explore the concept of vulnerability itself and present implications for the study of vulnerability in Human-computer Interaction (HCI) and for the oversight of app-based public health surveillance. © 2022 Owner/Author.

关键词： Public health

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ViPRA-Haplo: De Novo Reconstruction of Viral Populations Using Paired End Sequencing Data

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年第3期21卷 492-500页

作者： Li, Weiling Malhotra, Raunaq Wu, Steven Jha, Manjari Rodrigo, Allen Poss, Mary Acharya, Raj Weill Cornell Medical College Institute for Computational Biomedicine New YorkNY10021 United States ScienceIO New YorkNY10100 United States National Taiwan University Department of Agronomy Taipei10617 Taiwan Pennsylvania State University School of Electrical Engineering and Computer Science University ParkPA16802 United States University of Auckland School of Biological Sciences Private Bag 92019 New Zealand University of Virginia Department of Hematology and Oncology School of Medicine CharlottesvilleVA22908 United States Indiana University Bloomington Luddy School of Informatics Computing and Engineering BloomingtonIN47405 United States

We present ViPRA-Haplo, a de novo strain-specific assembly workflow for reconstructing viral haplotypes in a viral population from paired-end next generation sequencing (NGS) data. The proposed Viral Path Reconstruction Algorithm (ViPRA) generates a subset of paths from a De Bruijn graph of reads using the pairing information of reads. The paths generated by ViPRA are an over-estimation of the true contigs. We propose two refinement methods to obtain an optimal set of contigs representing viral haplotypes. The first method clusters paths reconstructed by ViPRA using VSEARCH Deorowicz et al. 2015 based on sequence similarity, while the second method, MLEHaplo, generates a maximum likelihood estimate of viral populations. We evaluated our pipeline on both simulated and real viral quasispecies data from HIV (and real data from SARS-COV-2). Experimental results show that ViPRA-Haplo, although still an overestimation in the number of true contigs, outperforms the existing tool, PEHaplo, providing up to 9% better genome coverage on HIV real data. In addition, ViPRA-Haplo also retains higher diversity of the viral population as demonstrated by the presence of a higher percentage of contigs less than 1000 base pairs (bps), which also contain k-mers with counts less than 100 (representing rarer sequences), which are absent in PEHaplo. For SARS-CoV-2 sequencing data, ViPRA-Haplo reconstructs contigs that cover more than 90% of the reference genome and were able to validate known SARS-CoV-2 strains in the sequencing data. © 2004-2012 IEEE.

关键词： Coronavirus

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Blockchain-based Security Framework for Metaverse: A Decentralized Approach 15

Blockchain-based Security Framework for Metaverse: A Decentr...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Rajawat, Anand Singh Goyal, S.B. Solanki, RamKumar Raboaca, Maria Simona Mihaltan, Traian Candin Illes, Zoltan Verma, Chaman School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Icsi Energy Department National Valcea Romania Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca40033 Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest Hungary

ISBN: (纸本)9798350321388

People can act out fake social exchanges in the Metaverse, which is an online community. Security has become a problem in the Metaverse because there are so many online transactions and digital assets. In this thesis, a Blockchain-based security system for the Metaverse is proposed. This would be a decentralised way to keep the virtual world safe. Our system uses Blockchain's autonomous structure to make sure that all financial transactions in the Metaverse are safe, anonymous, and true. We suggest a new consensus process that combines Proof of Stake (PoS) and Proof of Authority (PoA) to make networks safer and allow them to grow. We also have a user-focused, decentralised identity management system that keeps their information safe. We also suggest a way to process Metaverse transactions safely that is based on smart contracts. Our method makes sure that transactional proof, recording, and validation are done in a safe and reliable way. We also show a reputation system that benefits people who follow the law and punishes people who break the law. Lastly, we test how well our suggested structure works with simulations and real-world tests. The results show that our way works to reduce possible security risks in the Metaverse and create a safe, scalable environment for online business. Overall, our Blockchain-based Security Framework for Metaverse: A Decentralised Approach is a good way to keep the security and privacy of digital activities in Metaverse. © 2023 IEEE.

关键词： Blockchain

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Psychometrics for Hypnopaedia-Aware Machinery via Chaotic Projection of Artificial Mental Imagery

arXiv

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arXiv 2024年

作者： Chang, Ching-Chun Gao, Kai Xu, Shuying Kordoni, Anastasia Leckie, Christopher Echizen, Isao National Institute of Informatics Chiyoda Tokyo Japan The Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan The Department of Psychology Lancaster University Lancaster United Kingdom The Department of Computing and Information Systems University of Melbourne MelbourneVIC Australia The Information and Society Research Division National Institute of Informatics The Department of Informatics Graduate University for Advanced Studies The Department of Information and Communication Engineering University of Tokyo Chiyoda Tokyo Japan

Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack involves the clandestine infiltration of a trigger during the learning process, metaphorically analogous to hypnopaedia, where ideas are implanted into a subject's subconscious mind under the state of hypnosis or unconsciousness. When activated by a sensory stimulus, the trigger evokes conditioned reflex that directs a machine to mount a predetermined response. In this study, we propose a cybernetic framework for constant surveillance of backdoors threats, driven by the dynamic nature of untrustworthy data sources. We develop a self-aware unlearning mechanism to autonomously detach a machine's behaviour from the backdoor trigger. Through reverse engineering and statistical inference, we detect deceptive patterns and estimate the likelihood of backdoor infection. We employ model inversion to elicit artificial mental imagery, using stochastic processes to disrupt optimisation pathways and avoid convergent but potentially flawed patterns. This is followed by hypothesis analysis, which estimates the likelihood of each potentially malicious pattern being the true trigger and infers the probability of infection. The primary objective of this study is to maintain a stable state of equilibrium between knowledge fidelity and backdoor vulnerability. © 2024, CC BY.

关键词： Reverse engineering

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Blockchain-based Edge-assisted Knowledge Base Management for Semantic Communication in Remote Driving

Blockchain-based Edge-assisted Knowledge Base Management for...

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International Conference on Network Protocols

作者： Yangfei Lin Celimuge Wu Muhammad Luqman Fikri Jiale Wu Jie Li Lei Zhong Yusheng Ji Department of Computer and Network Engineering University of Eletronic-communication Chofu Tokyo Japan Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China Toyota Motor Corporation Tokyo Japan National Institute of Informatics (NII) Tokyo Japan

Remote driving, an emergent technology enabling remote operation of vehicles, presents a significant challenge due to the necessity of transmitting substantial volumes of image data from the vehicle to a central server. This requirement outpaces the capacity of traditional communication methods, emphasizing the need for efficient data communication. We propose a framework using semantic communication, specifically through a semantic segmentation-based method, which reduces the communication cost by transmitting meaningful semantic information rather than bit-wise data. Addressing the challenge of inconsistencies across knowledge bases in semantic communication, we present a blockchain-based, edge-assisted knowledge base management system. This system leverages edge nodes to manage multiple, geographically and contextually diverse knowledge bases while ensuring security through blockchain's tamper-resistant nature. Furthermore, blockchain sharding is employed to manage different knowledge bases for varying tasks, thereby enhancing the blockchain's throughput. Experimental results showed a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

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Proactive aging mitigation in CGRAs through utilization-aware allocation 57

Proactive aging mitigation in CGRAs through utilization-awar...

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57th ACM/IEEE Design Automation Conference, DAC 2020

作者： Brandalero, Marcelo Lignati, Bernardo Neuhaus Carlos Schneider Beck, Antonio Shafique, Muhammad Hubner, Michael Chair of Computer Engineering Cottbus Germany Institute of Informatics Porto Alegre Brazil Institute of Computer Engineering Vienna Austria

ISBN: (纸本)9781450367257

Resource balancing has been effectively used to mitigate the long-term aging effects of Negative Bias Temperature Instability (NBTI) in multi-core and Graphics Processing Unit (GPU) architectures. In this work, we investigate this strategy in Coarse-Grained Reconfigurable Arrays (CGRAs) with a novel application-to-CGRA allocation approach. By introducing important extensions to the reconfiguration logic and the datapath, we enable the dynamic movement of configurations throughout the fabric and allow overutilized Functional Units (FUs) to recover from stress-induced NBTI aging. Implementing the approach in a resource-constrained state-of-the-art CGRA reveals 2.2× lifetime improvement with negligible performance overheads and less than 10% increase in area. © 2020 IEEE.

关键词： Negative bias temperature instability

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Quantum Meets the Minimum Circuit Size Problem 13

Quantum Meets the Minimum Circuit Size Problem

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13th Innovations in Theoretical computer Science Conference, ITCS 2022

作者： Chia, Nai-Hui Chou, Chi-Ning Zhang, Jiayu Zhang, Ruizhe Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Engineering and Applied Sciences Harvard University BostonMA United States Department of Computer Science Boston University MA United States California Institute and Technology PasadenaCA United States Department of Computer Science The University of Texas AustinTX United States

ISBN: (纸本)9783959772174

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory - its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction. © Nai-Hui Chia, Chi-Ning Chou, Jiayu Zhang, and Ruizhe Zhang;licensed under Creative Commons License CC-BY 4.0

关键词： Boolean functions

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A Preprocessor for Creation of Large-Scale Computational Applications

A Preprocessor for Creation of Large-Scale Computational App...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Victor S. Simonov Marat S. Khairetdinov dept. Computer Engineering Novosibirsk State Technical University Novosibirsk Russian Federation dept. Geophysical Informatics Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russian Federation

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The emergence of large-scale computing paradigms required the evolution of software development methods, moreover, the need to effectively transform traditionally sequential applications into parallelizable and scalable architectures. This transformation is key in harnessing the computing power of modern multicore and distributed systems. Python, with its vast ecosystem and simplicity, is becoming a powerful language that facilitates this transition. This article discusses the methodologies and development of a Python preprocessor tool aimed at converting sequential applications into forms suitable for large-scale computing environments. Through detailed analysis and implementation, we demonstrate the use of Spark functions and third-party libraries to automate this transition, making large-scale computing more accessible to a wider range of applications and developers. The tool includes a set of rules that identify different patterns of code input (for example, list-sequential looping) and transform consistent code. This article introduces a Python preprocessor designed to automate the transformation of sequential Python applications into ones that harness the power of large-scale computing environments.

关键词： Performance evaluation Codes Multicore processing Scalability Transforms computer architecture Libraries

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DECOR 2024: Where Data engineering Meets Intelligent Food and Cooking Innovation

DECOR 2024: Where Data Engineering Meets Intelligent Food an...

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International Conference on Data engineering Workshops

作者： Frederic Andres Farshad Fotouhi Gheorghita Ghinea Steve Tanimoto Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo Japan Dpt of Computer Science College of Engineering Wayne State University Detroit MI USA Department of Computer Science Brunel University London London UK Paul G. Allen School of Computer Science & Engineering University of Washington Seattle WA USA

ISBN: (数字)9798350384031

ISBN: (纸本)9798350384048

This extended abstract presents the 7th International Workshop on Data engineering Meets Intelligent Food & Cooking Recipes 2024, which will be held in conjunction with the 40th IEEE International Conference on Data engineering 2024 (ICDE). The primary aim of this workshop is to provide a comprehensive overview of the latest advancements and existing challenges at the intersection of Data engineering and Intelligent Food & Cooking Recipes.

关键词： Technological innovation Conferences Data engineering

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Federated learning: Applications, challenges and future directions

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International Journal of Hybrid Intelligent Systems 2022年第1-2期18卷 19-35页

作者： Bharati, Subrato Mondal, M. Rubaiyat Hossain Podder, Prajoy Prasath, V.B. Surya Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati OH United States Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH United States Department of Biomedical Informatics College of Medicine University of Cincinnati Cincinnati OH United States Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati OH United States

Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. Inspired by the rapid growth of FL research, this paper examines recent developments and provides a comprehensive list of unresolved issues. Several privacy methods including secure multiparty computation, homomorphic encryption, differential privacy and stochastic gradient descent are described in the context of FL. Moreover, a review is provided for different classes of FL such as horizontal and vertical FL and federated transfer learning. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis system and healthcare, which we review in this paper. We also present a comprehensive review of existing FL challenges for example privacy protection, communication cost, systems heterogeneity, unreliable model upload, followed by future research directions. © 2022 - IOS Press. All rights reserved.

关键词： Artificial intelligence computing methodologies federated learning healthcare machine learning privacy protection

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