Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks...
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ISBN:
(数字)9798331510886
ISBN:
(纸本)9798331510893
Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.
A testbed-based approach to the evaluation of fault-tolerant distributed computing schemes is discussed. The approach is based on experimental incorporation of system structuring and design techniques into real-time d...
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A testbed-based approach to the evaluation of fault-tolerant distributed computing schemes is discussed. The approach is based on experimental incorporation of system structuring and design techniques into real-time distributed computing testbeds centered around tightly coupled microcomputer networks. The effectiveness of this approach has been confirmed through some experiments conducted in the author's laboratory. Primary advantages of the testbed-based approach include the relatively high accuracy of the data obtained on timing and logical complexity as well as the relatively high degree of assurance that can be obtained on the practical effectiveness of the scheme evaluated. This paper discusses various design issues encountered in the course of establishing the basic microcomputer network testbed facilities and augmenting them to support some experiments conducted. The shortcomings of the testbeds that have been recognized are also discussed together with the desired extensions of the testbeds. Some of the desired extensions are beyond the state of the art in microcomputer network implementation. [ABSTRACT FROM AUTHOR]
We consider the problem of Bayesian inference for bi-variate data observed in time but with observation times which occur non-synchronously. In particular, this occurs in a wide variety of applications in finance, suc...
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An approach to coordination of cooperating concurrent processes, each capable of error direction and recovery, is presented. Error detection, rollback, and retry in a process are specified by a well-structured languag...
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An approach to coordination of cooperating concurrent processes, each capable of error direction and recovery, is presented. Error detection, rollback, and retry in a process are specified by a well-structured language construct called recovery block. Recovery points of processes must be properly coordinated to prevent a disastrous avalanche of process rollbacks. The approach relies on an intelligent processor system (that runs processes) capable of establishing and discarding the recovery points of interacting processes in a well coordinated manner such that a process never makes two consecutive rollbacks without making a retry between the two, and every process rollback becomes a minimum-distance rollback. Following a discussion of the underlying philosophy of the author's approach, basic rules of reducing storage and time overhead in such a processor system are discussed. Examples are drawn from the systems in which processes communicate through monitors.
Achieving precise panoptic segmentation heavily relies on pixel-wise instance annotations, but obtaining such datasets is costly. Unsupervised instance segmentation (UIS) eliminates annotation requirements but struggl...
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Achieving precise panoptic segmentation heavily relies on pixel-wise instance annotations, but obtaining such datasets is costly. Unsupervised instance segmentation (UIS) eliminates annotation requirements but struggles with adjacent instance merging and single-instance fragmentation, largely due to the limitations of DINO-based backbones, which lack strong instance separation cues. Weakly-supervised panoptic segmentation (WPS) reduces annotation costs by using sparse labels (e.g., points, boxes), yet these annotations remain expensive and introduce human bias and boundary errors, limiting performance. To address these challenges, we propose DiffEGG (Diffusion-Driven EdGe Generation), a fully annotation-free method that extracts instance-aware features from pretrained diffusion models to generate precise instance edge maps. DiffEGG fundamentally differs from prior UIS and WPS approaches by leveraging pretrained diffusion models as a backbone. Unlike DINO-based UIS methods, diffusion models inherently capture fine-grained, instance-aware features, enabling more precise boundary delineation. DiffEGG further enhances edge connectivity through self-supervised fine-tuning, progressively improving instance separation. For WPS, DiffEGG completely eliminates annotation costs and human bias by operating without any form of manual supervision, addressing the key limitations of prior best methods. Additionally, we introduce RIP, a post-processing technique that fuses DiffEGG’s edge maps with segmentation masks in a task-agnostic manner. This broad applicability allows DiffEGG to be seamlessly integrated into various segmentation frameworks. When applied to UIS, DiffEGG and RIP achieve an average +4.4 AP improvement over prior best UIS methods. When combined with weakly-supervised semantic segmentation (WSS), DiffEGG enables WPS without instance annotations, outperforming prior best point-supervised WPS methods by +1.7 PQ. These results demonstrate that DiffEGG’s edge maps serv
Motivation: Accurately predicting drug-target protein interactions (DTI) is a cornerstone of drug discovery, enabling the identification of potential therapeutic compounds. Sequence-based prediction models, despite th...
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This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity i...
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Detecting stress is crucial for preventing long-term health issues, and recent research, particularly using the WESAD (WEarable Stress and Affect Detection) dataset, has made significant advances in this area. Deep Ne...
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Multidatabases integrate the data from preexisting, heterogeneous local databases into a distributed environment, and present global users with transparent methods to use the total information in the system. This chap...
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Multidatabases integrate the data from preexisting, heterogeneous local databases into a distributed environment, and present global users with transparent methods to use the total information in the system. This chapter describes the taxonomy of global information-sharing systems, multidatabase issues, multidatabse design choices, analysis of existing multidatabase systems, and the position of multidatabase systems in the taxonomy. Multidatabase language systems have no constraints on system size, but the tradeoff for achieving this is a multiple-data-source user paradigm and a more complex user interface. Multidatabase issues, such as site autonomy, differences in data representation, and concurrency control are also discussed in the chapter. Site autonomy is a major strength of multidatabases, yet it is also a major constraint on the global system design. Differences in data representation are many and varied, and resolving these differences is a major concern of the global system design. Global updates are a major current restriction in many systems, and the focus of much current research. To make multidatabase systems more useful and efficient, some deficiencies in existing prototypes must be resolved before they can evolve into commercial products, such as global concurrency control, global security, maintenance and administration, and error control and recovery. Multidatabase systems should begin to have a larger impact in the information-processing world as more powerful systems to become generally available in the near future.
Using the finite-difference time-domain method, three-dimensional scattering patterns are computed for cells containing multiple organelles. The scattering cross section and average cosine of the scattering angle are ...
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Using the finite-difference time-domain method, three-dimensional scattering patterns are computed for cells containing multiple organelles. The scattering cross section and average cosine of the scattering angle are computed for cells as a function of volume fraction of melanin granules and mitochondria, Results show that small organelles play a significant role in light scattering from cells, and the volume fraction of organelles affects both the total amount of scattered light and the angular distribution of scattered light.
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