3,494 research outputs found
Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems
Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which
explores the tremendous data collected by mobile smart devices with prominent
spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing
Systems, temporally recruited mobile users can provide agile, fine-grained, and
economical sensing labors, however their self-interest cannot guarantee the
quality of the sensing data, even when there is a fair return. Therefore, a
mechanism is required for the system server to recruit well-behaving users for
credible sensing, and to stimulate and reward more contributive users based on
sensing truth discovery to further increase credible reporting. In this paper,
we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile
Crowdsensing Systems, which achieves credibility-driven user recruitment and
payback maximization for honest users with quality data. Via theoretical
analysis, we demonstrate the correctness of our design. The performance of our
scheme is evaluated based on extensive realworld trace-driven simulations. Our
evaluation results show that our scheme is proven to be effective in terms of
both guaranteeing sensing accuracy and resisting potential cheating behaviors,
as demonstrated in practical scenarios, as well as those that are intentionally
harsher
RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models
Recent advancements in language models (LMs) have gained substantial
attentions on their capability to generate human-like responses. Though
exhibiting a promising future for various applications such as conversation AI,
these LMs face deployment challenges on various devices due to their extreme
computational cost and unpredictable inference latency. Such varied inference
latency, identified as a consequence of uncertainty intrinsic to the nature of
language, can lead to computational inefficiency and degrade the overall
performance of LMs, especially under high-traffic workloads. Unfortunately, the
bandwidth of these uncertainty sources is extensive, complicating the
prediction of latency and the effects emanating from such uncertainties. To
understand and mitigate the impact of uncertainty on real-time
response-demanding systems, we take the first step to comprehend, quantify and
optimize these uncertainty-induced latency performance variations in LMs.
Specifically, we present RT-LM, an uncertainty-aware resource management
ecosystem for real-time inference of LMs. RT-LM innovatively quantifies how
specific input uncertainties, adversely affect latency, often leading to an
increased output length. Exploiting these insights, we devise a lightweight yet
effective method to dynamically correlate input text uncertainties with output
length at runtime. Utilizing this quantification as a latency heuristic, we
integrate the uncertainty information into a system-level scheduler which
explores several uncertainty-induced optimization opportunities, including
uncertainty-aware prioritization, dynamic consolidation, and strategic CPU
offloading. Quantitative experiments across five state-of-the-art LMs on two
hardware platforms demonstrates that RT-LM can significantly reduce the average
response time and improve throughput while incurring a rather small runtime
overhead.Comment: Accepted by RTSS 202
Electrocardiogram Baseline Wander Suppression Based on the Combination of Morphological and Wavelet Transformation Based Filtering
One of the major noise components in electrocardiogram (ECG) is the baseline wander (BW). Effective methods for suppressing BW include the wavelet-based (WT) and the mathematical morphological filtering-based (MMF)algorithms. However, the T waveform distortions introduced by the WTand the rectangular/trapezoidal distortions introduced by MMF degrade the quality of the output signal. Hence, in this study, we introduce a method by combining the MMF and WTto overcome the shortcomings of both existing methods. To demonstrate the effectiveness of the proposed method, artificial ECG signals containing a clinicalBW are used for numerical simulation, and we also create a realistic model of baseline wander to compare the proposed method with
other state-of-the-art methods commonly used in the literature. /e results show that the BW suppression effect of the proposed method is better than that of the others. Also, the new method is capable of preserving the outline of the BW and avoiding waveform distortions caused by the morphology filter, thereby obtaining an enhanced quality of ECG
Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming
Point cloud video is the most popular representation of hologram, which is
the medium to precedent natural content in VR/AR/MR and is expected to be the
next generation video. Point cloud video system provides users immersive
viewing experience with six degrees of freedom and has wide applications in
many fields such as online education, entertainment. To further enhance these
applications, point cloud video streaming is in critical demand. The inherent
challenges lie in the large size by the necessity of recording the
three-dimensional coordinates besides color information, and the associated
high computation complexity of encoding. To this end, this paper proposes a
communication and computation resource allocation scheme for QoE-driven point
cloud video streaming. In particular, we maximize system resource utilization
by selecting different quantities, transmission forms and quality level tiles
to maximize the quality of experience. Extensive simulations are conducted and
the simulation results show the superior performance over the existing scheme
DreamEdit: Subject-driven Image Editing
Subject-driven image generation aims at generating images containing
customized subjects, which has recently drawn enormous attention from the
research community. However, the previous works cannot precisely control the
background and position of the target subject. In this work, we aspire to fill
the void and propose two novel subject-driven sub-tasks, i.e., Subject
Replacement and Subject Addition. The new tasks are challenging in multiple
aspects: replacing a subject with a customized one can change its shape,
texture, and color, while adding a target subject to a designated position in a
provided scene necessitates a context-aware posture. To conquer these two novel
tasks, we first manually curate a new dataset DreamEditBench containing 22
different types of subjects, and 440 source images with different difficulty
levels. We plan to host DreamEditBench as a platform and hire trained
evaluators for standard human evaluation. We also devise an innovative method
DreamEditor to resolve these tasks by performing iterative generation, which
enables a smooth adaptation to the customized subject. In this project, we
conduct automatic and human evaluations to understand the performance of
DreamEditor and baselines on DreamEditBench. For Subject Replacement, we found
that the existing models are sensitive to the shape and color of the original
subject. The model failure rate will dramatically increase when the source and
target subjects are highly different. For Subject Addition, we found that the
existing models cannot easily blend the customized subjects into the background
smoothly, leading to noticeable artifacts in the generated image. We hope
DreamEditBench can become a standard platform to enable future investigations
toward building more controllable subject-driven image editing. Our project
homepage is https://dreameditbenchteam.github.io/
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