Reviewerly/exHarmony huggingface dataset:
Metaphors of Digital Transformation

Authors:
raw_author_name: Bertrand Audrin
raw_affiliation_string: HRM and Organizational Behavior, EHL Business School in Lausanne, Switzerland

Abstract: Abstract This chapter explores metaphors used by organizational actors to understand digital transformation and digitalized organizations. Following a “pigeon-holing approach” (Örtenblad 2017), we identify and categorize two sets of metaphors. The first set of metaphors consists of the metaphors of digital transformation: that is, the metaphors that deal with change—the wave metaphor and the digit metaphor. These metaphors help us better understand different dimensions of digital transformation as a technological and organizational change process. The second set consists of the metaphors of digitalized organizations: that is, the metaphors that deal with transformed organizations. Based on Morgan’s (1986) metaphors, the authors identify three root metaphors, namely the machine metaphor, brain metaphor, and instrument of domination metaphor. They argue that using several metaphors as categories helps us identify differences between various phenomena associated with digital transformation and digitalized organizations, and offer an overview of organizational actors’ understandings of these phenomena.
Exploring the influence of anthropomorphic appearance on usage intention on online medical service robots (OMSRs): A neurophysiological study

Authors:
raw_author_name: Vincent G. Duffy
raw_affiliation_string: School of Industrial Engineering, Purdue University, West Lafayette, IN, USA.

Abstract: Online medical service robots (OMSRs) are becoming increasingly important in the medical industry, and their design has become a highly focused issue. This study investigated the neuroeconomics underlying the formation of usage intention, specifically evaluating the impact of anthropomorphic appearance and age on users' intentions to use OMSRs. Event-related potentials were used to analyze electroencephalography signals recorded from participants. This study found that OMSRs with a low anthropomorphic appearance induced larger P200 and P300 amplitudes, resulting in increased attentional resources compared to OMSRs with a moderate or high anthropomorphic appearance. OMSRs with moderate anthropomorphic appearances captured more attention and elicited larger P200 and P300 than those with high anthropomorphic appearances. Regarding age characteristics, OMSRs with senior features attracted more attention and induced larger P200 and P300 amplitudes. In terms of usage intention, compared to the others, users demonstrate a stronger usage intention towards the low anthropomorphism of OMSRs. Additionally, compared to the senior ones, users also exhibit a stronger usage intention toward a young appearance of OMSRs. These findings provide valuable insights for robot designers and practitioners to improve the appearance of OMSRs.
Shaping photons: Quantum information processing with bosonic cQED

Authors:
raw_author_name: Adrian Copetudo
raw_affiliation_string: Centre for Quantum Technologies, National University of Singapore 1 , Singapore
raw_author_name: Fernando Valadares
raw_affiliation_string: Centre for Quantum Technologies, National University of Singapore 1 , Singapore
raw_author_name: Yvonne Y. Gao
raw_affiliation_string: Centre for Quantum Technologies, National University of Singapore 1 , Singapore

Abstract: With its rich dynamics, the quantum harmonic oscillator is an innate platform for understanding real-world quantum systems and could even excel as the heart of a quantum computer. A particularly promising and rapidly advancing platform that harnesses quantum harmonic oscillators for information processing is the bosonic circuit quantum electrodynamics (cQED) system. In this article, we provide perspectives on the progress, challenges, and future directions in building a bosonic cQED quantum computer. We describe the main hardware building blocks and how they facilitate quantum error correction, metrology, and simulation. We conclude with our views of the key challenges that lie on the horizon, as well as scientific and cultural strategies for overcoming them and building a practical quantum computer with bosonic cQED hardware.
Changing Digital and Telecytology Practices post COVID-19 Comparing ASC survey results from 2016 to 2023

Authors:
raw_author_name: Diana Murro Lin
raw_affiliation_string: Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama.
raw_author_name: Min Huang
raw_affiliation_string: Department of Pathology, Fox Chase Cancer Center, Philadelphia, Pennsylvania.
raw_author_name: Uma Krishnamurti
raw_affiliation_string: Department of Pathology, Yale School of Medicine, New Haven, Connecticut.
raw_author_name: Oscar Lin
raw_affiliation_string: Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.
raw_author_name: Amy Ly
raw_affiliation_string: Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts.
raw_author_name: Liron Pantanowitz
raw_affiliation_string: Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.

Abstract: During the COVID-19 pandemic, the need for digital pathology tools became more urgent. However, there needs to be more knowledge of the use in cytology. We aimed to evaluate current digital cytology practices and attitudes and compare the results with a pre-COVID-19 American Society of Cytopathology (ASC) survey.Fourteen survey questions assessing current attitudes toward digital cytology were developed from a 2016 ASC Digital Pathology Survey. Ten new survey questions were also created to evaluate telecytology use. The survey was e-mailed to ASC members over 6 weeks in 2023.A total of 123 individuals responded (116 in 2016). Attitudes toward digital cytology were unchanged; most participants stated digital cytology is beneficial (87% 2023 versus 90% 2016). The percentage of individuals using digital cytology was unchanged (56% in 2016 and 2023). However, telecytology for rapid onsite assessment (ROSE) is now considered the best application (55% 2023 versus 31% 2016). Forty-three institutions reported using digital and telecytology tools; 40% made implementations after 2020; most did not feel that COVID-19 affected digital cytology (56%). Telecytology for ROSE is the most common application now (78%) compared with education (30%) in 2016. Limitations for implementing digital imaging in cytology included inability to focus (38%) and expense (33%).General attitudes toward digital tools by the cytology community have essentially remained the same between 2016 and now. However, telecytology for ROSE is increasingly being used, which supports a need for validation and competency guidelines.
Residual Contextual Hourglass Network for Single-Image Deraining

Authors:
raw_author_name: Weina Zhou
raw_author_name: Linhui Ye
raw_author_name: Xiu Wang

Abstract: Abstract Rain streaks could blur and distort images, significantly impacting further image processing. Single-image deraining is a hotspot and has practical application value, while most existing methods still have problems such as residual rain streaks and inadequate recovery of detail textures. To address these issues, we propose a Residual Contextual Hourglass Network (RCHNet) for single-image deraining, which could adapt to remove rain streaks in complex environments. Firstly, a contextual distillation block is presented to obtain local and global features across different scales. Further, residual downsampling block and residual upsampling block are used to maintain the residual nature of our architecture and better restore the details of the image. Finally, a dual attention mechanism is introduced to compensate for the spatial information lost by the downsampling. Extensive experiments on five synthetic datasets and a real-world dataset demonstrate that our proposed RCHNet outperforms existing state-of-the-art deraining approaches. On average across all synthetic datasets, the PSNR score of RCHNet is as high as 33.31 dB.
Towards more reliable evaluation in pedestrian detection by rethinking “ignore regions”

Authors:
raw_author_name: Xiang Li
raw_author_name: Shanshan Zhang
raw_author_name: Jian Yang

Abstract: Abstract It remains a challenging task to detect pedestrians in crowds and it needs more efforts to understand why the detectors fail. When we perform an error analysis based on the traditional evaluation strategy, we find that it produces many misleading false positives, which in fact cover occluded pedestrians. The reason for this is that we usually have two kinds of annotations in the dataset: regular pedestrians (detection targets) labeled by full-body boxes and ignored pedestrians (NOT detection targets) labeled by visible boxes. Ignored pedestrians are labeled as an additional category termed the “ignore region”. Nevertheless, our detectors always predict a full-body box for each pedestrian. This gap results in the following case: when a detector successfully predicts a full-body box for those ignored pedestrians, a false positive is triggered due to the low overlap between the predicted full-body box and the labeled visible box for the ignored pedestrian. This becomes even more harmful as the detector improves and becomes more capable of locating occluded pedestrians. To alleviate this issue, we devise a new pedestrian detection pipeline, which considers the additional visible box at both the detection and evaluation stages. During detection, we predict an extra visible box apart from the full-body box for every instance; during evaluation, we employ visible boxes instead of full-body boxes to match the “ignore region”. We apply the new pipeline to dozens of detection methods and validate the effectiveness of our pipeline in reducing the over-reporting of false positives and providing more reliable evaluation results.
Blockchain and Access Control Encryption-Empowered IoT Knowledge Sharing for Cloud-Edge Orchestrated Personalized Privacy-Preserving Federated Learning

Authors:
raw_author_name: Jianhua Li
raw_affiliation_string: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai 200240, China

Abstract: Federated learning (FL) is emerging as a powerful paradigm for distributed data mining in the context of Internet of Things (IoT) big data. It addresses privacy concerns associated with data outsourcing by enabling local data training and knowledge (i.e., model) sharing. However, simplistic local knowledge sharing can inadvertently expose user privacy to advanced attacks, such as model inversion or gradient leakage. Furthermore, achieving fine-grained and personalized privacy protection for IoT users remains a challenge. In this paper, we propose a novel solution called hierarchical blockchain-empowered cloud-edge orchestrated federated learning (HBCE-FL) to address these challenges. HBCE-FL is designed to provide secure, intelligent, and distributed data analysis for IoT users. To tackle FL’s privacy issues, we develop a multi-level access control encryption and blockchain-based approach for sharing IoT knowledge within the HBCE-FL framework. Our approach classifies IoT users into different levels based on their individual privacy requirements, enabling fine-grained privacy protection. The blockchain is employed for identity authentication, key management, and message sanitization. For scenarios involving IoT users with non-IID data, we integrate federated multi-task learning into HBCE-FL to ensure fairness, robustness, and privacy. Finally, we conduct experiments using classic MNIST and CIFAR10 datasets to validate our approach. The experimental results illustrate that HBCE-FL effectively achieves personalized privacy-preserving FL without losing IoT data availability. Regardless of whether IoT data are homogeneous or heterogeneous, our approach enhances model accuracy and convergence rates by enabling secure IoT knowledge access and sharing for IoT users.
Postdigital Teaching of Critical Thinking in Higher Education: Non-Instrumentalised Sociality and Interactivity

Authors:
raw_author_name: Erika Kerruish
raw_affiliation_string: Faculty of Business, Law and Arts, Southern Cross University, Coolangatta, Australia

Abstract: Abstract In the wake of the digital revolution, the digital teaching of critical thinking takes established forms in higher education. Its technologies are productively understood in postdigital terms as a diverse, inconsistent and cobbled-together collection of platforms and software. This paper considers the limits, problems and advantages of this messy and layered amalgam of technologies. Examining them shows that the benefits of digital critical thinking teaching are frequently bound up with digitisations’ support of sociability and interactivity. This is counterbalanced by difficulties and limits encountered in digitised teaching of critical thinking, often framed as deficiencies in students, teachers, institutions or technologies. However, following Bernard Stiegler’s work and postdigital scholarship, these distinctions can be countered to understand critical thinking technologies as performed within social, technical and psychic milieus processes. Stiegler’s emphasis on temporality allows for a critical analysis of the constraints of digital forms of sociality and interactivity. His notion of otium suggests that techniques cultivating interruptions and layering in digital critical thinking technologies engage their negativity and enable temporal zones in which reflective thought can emerge.
Robust fake-post detection against real-coloring adversaries

Authors:
raw_author_name: Khushboo Agarwal
raw_affiliation_string: IEOR, IIT Bombay, India
raw_author_name: Veeraruna Kavitha
raw_affiliation_string: IEOR, IIT Bombay, India

Abstract: We design warning mechanisms for detecting fake-post on online social networks using crowd signals without significantly affecting the real-post propagation. Building on a recent algorithm where all users assign a real or fake tag to any post, we now consider that users can exhibit adversarial or non-cooperative behavior while tagging. To analyze this system, we introduce and study the total-current population-dependent branching process with multiple death types. We highlight the limitations of the existing mechanism in handling adversaries and propose improved mechanisms that perform effectively even with limited user-specific parameter knowledge. Our theoretical findings are validated through Monte-Carlo simulations.
POBO: Safe and Optimal Resource Management for Cloud Microservices

Authors:
raw_author_name: Hengquan Guo
raw_affiliation_string: ShanghaiTech University
raw_author_name: Hongchen Cao
raw_affiliation_string: ShanghaiTech University
raw_author_name: Jingzhu He
raw_affiliation_string: ShanghaiTech University
raw_author_name: Xin Liu
raw_affiliation_string: ShanghaiTech University
raw_author_name: Yuanming Shi
raw_affiliation_string: ShanghaiTech University

Abstract: Resource management in microservices is challenging due to the uncertain latency-resource relationship, dynamic environment, and strict Service-Level Agreement (SLA) guarantees. This paper presents a Pessimistic and Optimistic Bayesian Optimization framework, named POBO, for safe and optimal resource configuration for microservice applications. POBO leverages Bayesian learning to estimate the uncertain latency-resource functions and combines primal-dual and penalty-based optimization to maximize resource efficiency while guaranteeing strict SLAs. We prove that POBO can achieve sublinear regret and SLA violation compared with the optimal resource configuration in hindsight. We have implemented a prototype of POBO and conducted extensive experiments on a real-world microservice application. Our results show that POBO can find the safe and optimal configuration efficiently, outperforming Kubernetes' built-in auto-scaling module and the state-of-the-art algorithm.
A novel low light object detection method based on the YOLOv5 fusion feature enhancement

Authors:
raw_author_name: Daxin Peng
raw_affiliation_string: College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China
raw_author_name: Wei Ding
raw_affiliation_string: Anhui Jianzhu University, Hefei, 230601, Anhui, China
raw_author_name: Tong Zhen
raw_affiliation_string: College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China

Abstract: Low-light object detection is an important research area in computer vision, but it is also a difficult issue. This research offers a low-light target detection network, NLE-YOLO, based on YOLOV5, to address the issues of insufficient illumination and noise interference experienced by target detection tasks in low-light environments. The network initially preprocesses the input image with an improvement technique before suppressing high-frequency noise and enhancing essential information with C2fLEFEM, a unique feature extraction module. We also created a multi-scale feature extraction module, AMC2fLEFEM, and an attention mechanism receptive field module, AMRFB, which are utilized to extract features of multiple scales and enhance the receptive field. The C2fLEFEM module, in particular, merges the LEF and FEM modules on top of the C2f module. The LEF module employs a low-frequency filter to remove high-frequency noise; the FEM module employs dual inputs to fuse low-frequency enhanced and original features; and the C2f module employs a gradient retention method to minimize information loss. The AMC2fLEFEM module combines the SimAM attention mechanism and uses the pixel relationship to obtain features of different receptive fields, adapt to brightness changes, capture the difference between the target and the background, improve the network's feature extraction capability, and effectively reduce the impact of noise. The AMRFB module employs atrous convolution to enlarge the receptive field, maintain global information, and adjust to targets of various scales. Finally, for low-light settings, we replaced the original YOLOv5 detection head with a decoupled head. The Exdark dataset experiments show that our method outperforms previous methods in terms of detection accuracy and performance.
From Cognitive Agents to Cognitive Systems: Theoretical, Methodological, and Empirical Developments of van Gelder's (1998) “Dynamical Hypothesis”

Authors:
raw_author_name: Tri D. Nguyen
raw_affiliation_string: Department of Psychology Arizona State University

Abstract: Abstract Over two decades have passed since the publication of van Gelder's (1998) “dynamical hypothesis.” In that paper, van Gelder proposed that cognitive agents were not digital computers—per the representational computational approach—but dynamical systems. The evolution of the dynamical hypothesis was driven by parallel advances in three areas. Theoretically , a deeper understanding of genetics, biology, neuroscience, and cognitive science inspired questions about how systems within each domain dynamically interact and extend their effects across spatiotemporal scales. Methodologically , more sophisticated and domain‐general tools allowed researchers to discover, model, and quantify system dynamics, structure, and patterns across multiple scales to generate a more comprehensive system‐level understanding of behaviors. Empirically , we can analyze a system's behavior while preserving its natural dynamics, revealing evidence that the reductionist approach leads to an incomplete understanding of the components and the overall system. Researchers have traditionally reduced a complex system into its component processes and assumed that the parts can be recombined to explain the whole. These three advances fundamentally altered our understanding of a “cognitive agent:” How their behaviors are driven by long‐range coordination across multiple processes, how the interdependent and nested structure of interacting variables produces behaviors that are greater than the sum of its parts, and how environmental constraints shape adaptive yet stable behavioral patterns.
One-Step Replica Symmetry Breaking of Random Regular NAE-SAT II

Authors:
raw_author_name: Allan Sly
raw_affiliation_string: Department of Mathematics, Princeton University, Princeton, NJ, 08544, USA

Abstract: Abstract Continuing our earlier work in Nam et al. (One-step replica symmetry breaking of random regular NAE-SAT I, arXiv:2011.14270 , 2020), we study the random regular k - nae-sat model in the condensation regime. In Nam et al. (2020), the (1 rsb ) properties of the model were established with positive probability. In this paper, we improve the result to probability arbitrarily close to one. To do so, we introduce a new framework which is the synthesis of two approaches: the small subgraph conditioning and a variance decomposition technique using Doob martingales and discrete Fourier analysis. The main challenge is a delicate integration of the two methods to overcome the difficulty arising from applying the moment method to an unbounded state space.
A Foundation for Real-time Applications onFunction-as-a-Service

Authors:
raw_author_name: Hai Duc Nguyen
raw_affiliation_string: University of Chicago
raw_author_name: Andrew A. Chien
raw_affiliation_string: University of Chicago &amp; Argonne National Laboratory

Abstract: Serverless (or Function-as-a-Service) compute model enables new applications with dynamic scaling. However, all current Serverless systems are best-effort, and as we prove this means they cannot guarantee hard real-time deadlines, rendering them unsuitable for such real-time applications. We analyze a proposed extension of the Serverless model that adds a guaranteed invocation rate to the serverless model called Real-time Serverless. This approach aims to meet real-time deadlines with dynamically allocated function invocations. We first prove that the Serverless model does not support real-time guarantees. Next, we analyze Real-time Serverless, showing it can guarantee application real-time deadlines for rate-monotonic real-time workloads. Further, we derive bounds on the required invocation rate to meet any set of workload runtimes and periods. Subsequently, we explore an application technique, pre-invocation, and show that it can reduce the required guaranteed invocation rate. We derive bounds for the feasible rate guarantee reduction, and corresponding overhead in wasted compute resources. Finally, we apply the theoretical results to improve the experience quality of a distributed virtual reality/ augmented reality application as well as simplify the application design and resource management.