608 research outputs found
Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing
Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers’ mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers’ competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit. IEE
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources
Given a controversial target such as ``nuclear energy'', argument mining aims
to identify the argumentative text from heterogeneous sources. Current
approaches focus on exploring better ways of integrating the target-associated
semantic information with the argumentative text. Despite their empirical
successes, two issues remain unsolved: (i) a target is represented by a word or
a phrase, which is insufficient to cover a diverse set of target-related
subtopics; (ii) the sentence-level topic information within an argument, which
we believe is crucial for argument mining, is ignored. To tackle the above
issues, we propose a novel explainable topic-enhanced argument mining approach.
Specifically, with the use of the neural topic model and the language model,
the target information is augmented by explainable topic representations.
Moreover, the sentence-level topic information within the argument is captured
by minimizing the distance between its latent topic distribution and its
semantic representation through mutual learning. Experiments have been
conducted on the benchmark dataset in both the in-target setting and the
cross-target setting. Results demonstrate the superiority of the proposed model
against the state-of-the-art baselines.Comment: 10 pages, 3 figure
BMAD: Benchmarks for Medical Anomaly Detection
Anomaly detection (AD) is a fundamental research problem in machine learning
and computer vision, with practical applications in industrial inspection,
video surveillance, and medical diagnosis. In medical imaging, AD is especially
vital for detecting and diagnosing anomalies that may indicate rare diseases or
conditions. However, there is a lack of a universal and fair benchmark for
evaluating AD methods on medical images, which hinders the development of more
generalized and robust AD methods in this specific domain. To bridge this gap,
we introduce a comprehensive evaluation benchmark for assessing anomaly
detection methods on medical images. This benchmark encompasses six reorganized
datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT,
chest X-ray, and digital histopathology) and three key evaluation metrics, and
includes a total of fourteen state-of-the-art AD algorithms. This standardized
and well-curated medical benchmark with the well-structured codebase enables
comprehensive comparisons among recently proposed anomaly detection methods. It
will facilitate the community to conduct a fair comparison and advance the
field of AD on medical imaging. More information on BMAD is available in our
GitHub repository: https://github.com/DorisBao/BMA
Prognostic nomogram for bladder cancer with brain metastases: a National Cancer Database analysis.
BACKGROUND: This study aimed to establish and validate a nomogram for predicting brain metastasis in patients with bladder cancer (BCa) and assess various treatment modalities using a primary cohort comprising 234 patients with clinicopathologically-confirmed BCa from 2004 to 2015 in the National Cancer Database.
METHODS: Machine learning method and Cox model were used for nomogram construction. For BCa patients with brain metastasis, surgery of the primary site, chemotherapy, radiation therapy, palliative care, brain confinement of metastatic sites, and the Charlson/Deyo Score were predictive features identified for building the nomogram.
RESULTS: For the original 169 patients considered in the model, the areas under the receiver operating characteristic curve (AUC) were 0.823 (95% CI 0.758-0.889, P \u3c 0.001) and 0.854 (95% CI 0.785-0.924, P \u3c 0.001) for 0.5- and 1-year overall survival respectively. In the validation cohort, the nomogram displayed similar AUCs of 0.838 (95% CI 0.738-0.937, P \u3c 0.001) and 0.809 (95% CI 0.680-0.939, P \u3c 0.001), respectively. The high and low risk groups had median survivals of 1.91 and 5.09 months for the training cohort and 1.68 and 8.05 months for the validation set, respectively (both P \u3c 0.0001).
CONCLUSIONS: Our prognostic nomogram provides a useful tool for overall survival prediction as well as assessing the risk and optimal treatment for BCa patients with brain metastasis
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