Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for man...
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Deploying AI systems in public institutions can have far-reaching consequences for many people, making it a matter of public interest. Providing opportunities for stakeholders to come together, understand these system...
We consider the equity and fairness of curricula derived from Knowledge Tracing models. We begin by defining a unifying notion of an equitable tutoring system as a system that achieves maximum possible knowledge in mi...
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Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep lea...
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Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.
Akin to neuroplasticity in human brains, the plasticity of deep neural networks enables their quick adaption to new data. This makes plasticity particularly crucial for deep Reinforcement Learning (RL) agents: Once pl...
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作者：
Qiu, RanZhao, ShengrongLiang, Hu
Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China
Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science
Shandong Provincial Key Laboratory of Computer Networks Jinan China
Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and ...
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Spatial crowdsourcing(SC)is a popular data collection paradigm for numerous *** the increment of tasks and workers in SC,heterogeneity becomes an unavoidable difficulty in task *** researches only focus on the single-...
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Spatial crowdsourcing(SC)is a popular data collection paradigm for numerous *** the increment of tasks and workers in SC,heterogeneity becomes an unavoidable difficulty in task *** researches only focus on the single-heterogeneous task ***,a variety of heterogeneous objects coexist in real-world SC *** dramatically expands the space for searching the optimal task allocation solution,affecting the quality and efficiency of data *** this paper,an aggregation-based dual heterogeneous task allocation algorithm is put *** investigates the impact of dual heterogeneous on the task allocation problem and seeks to maximize the quality of task completion and minimize the average travel *** problem is first proved to be ***,a task aggregation method based on locations and requirements is built to reduce task ***,a time-constrained shortest path planning is also developed to shorten the travel distance in a *** that,two evolutionary task allocation schemes are ***,extensive experiments are conducted based on real-world datasets in various *** with baseline algorithms,our proposed schemes enhance the quality of task completion by up to 25% and utilize 34% less average travel distance.
This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactiv...
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This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.
Among exotic reptiles, bearded dragons (Pogona) are popular pets. As ectotherms, bearded dragons require special food and controlled environments, including temperature, humidity, ultraviolet light, and cleanliness. T...
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作者：
Liu, XiaojingJiang, XuesongYi, Fengge
Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China
Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science
Shandong Provincial Key Laboratory of Computer Networks Jinan China
The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singula...
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