653 research outputs found
RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement
Extreme learning machine (ELM) as an emerging branch of shallow networks has
shown its excellent generalization and fast learning speed. However, for
blended data, the robustness of ELM is weak because its weights and biases of
hidden nodes are set randomly. Moreover, the noisy data exert a negative
effect. To solve this problem, a new framework called RMSE-ELM is proposed in
this paper. It is a two-layer recursive model. In the first layer, the
framework trains lots of ELMs in different groups concurrently, then employs
selective ensemble to pick out an optimal set of ELMs in each group, which can
be merged into a large group of ELMs called candidate pool. In the second
layer, selective ensemble is recursively used on candidate pool to acquire the
final ensemble. In the experiments, we apply UCI blended datasets to confirm
the robustness of our new approach in two key aspects (mean square error and
standard deviation). The space complexity of our method is increased to some
degree, but the results have shown that RMSE-ELM significantly improves
robustness with slightly computational time compared with representative
methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential
framework to solve robustness issue of ELM for high-dimensional blended data in
the future.Comment: Accepted for publication in Mathematical Problems in Engineering,
09/22/201
Secure Hot Path Crowdsourcing with Local Differential Privacy under Fog Computing Architecture
Crowdsourcing plays an essential role in the Internet of Things (IoT) for
data collection, where a group of workers is equipped with Internet-connected
geolocated devices to collect sensor data for marketing or research purpose. In
this paper, we consider crowdsourcing these worker's hot travel path. Each
worker is required to report his real-time location information, which is
sensitive and has to be protected. Encryption-based methods are the most direct
way to protect the location, but not suitable for resource-limited devices.
Besides, local differential privacy is a strong privacy concept and has been
deployed in many software systems. However, the local differential privacy
technology needs a large number of participants to ensure the accuracy of the
estimation, which is not always the case for crowdsourcing. To solve this
problem, we proposed a trie-based iterative statistic method, which combines
additive secret sharing and local differential privacy technologies. The
proposed method has excellent performance even with a limited number of
participants without the need of complex computation. Specifically, the
proposed method contains three main components: iterative statistics, adaptive
sampling, and secure reporting. We theoretically analyze the effectiveness of
the proposed method and perform extensive experiments to show that the proposed
method not only provides a strict privacy guarantee, but also significantly
improves the performance from the previous existing solutions.Comment: This paper appears in IEEE Transactions on Services Computing.
https://doi.org/10.1109/TSC.2020.303933
Investigation and protection of fishery resources in the middle of Bohai Sea
In May and October 2017, 12 stations were set up in the Central Bohai Sea for fishery resources investigation. The results show that there are many dominant species in this area, and the inshore fishery resources are higher than those in the open sea because of the abundant nutrients from land, the high density of zooplankton and the food of swimming animals. In order to effectively protect the fishery resources in the Central Bohai Sea, this paper puts forward some suggestions, such as strengthening the protection propaganda, scientific and reasonable fishing, and strengthening the management of marine environment
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning
There has been significant attention devoted to the effectiveness of various
domains, such as semi-supervised learning, contrastive learning, and
meta-learning, in enhancing the performance of methods for noisy label learning
(NLL) tasks. However, most existing methods still depend on prior assumptions
regarding clean samples amidst different sources of noise (\eg, a pre-defined
drop rate or a small subset of clean samples). In this paper, we propose a
simple yet powerful idea called \textbf{NPN}, which revolutionizes
\textbf{N}oisy label learning by integrating \textbf{P}artial label learning
(PLL) and \textbf{N}egative learning (NL). Toward this goal, we initially
decompose the given label space adaptively into the candidate and complementary
labels, thereby establishing the conditions for PLL and NL. We propose two
adaptive data-driven paradigms of label disambiguation for PLL: hard
disambiguation and soft disambiguation. Furthermore, we generate reliable
complementary labels using all non-candidate labels for NL to enhance model
robustness through indirect supervision. To maintain label reliability during
the later stage of model training, we introduce a consistency regularization
term that encourages agreement between the outputs of multiple augmentations.
Experiments conducted on both synthetically corrupted and real-world noisy
datasets demonstrate the superiority of NPN compared to other state-of-the-art
(SOTA) methods. The source code has been made available at
{\color{purple}{\url{https://github.com/NUST-Machine-Intelligence-Laboratory/NPN}}}.Comment: accepted by AAAI 202
Detection and Genetic Analysis of Porcine Bocavirus
Porcine Bocavirus (PBoV) has been reported to be associated with postweaning multisystemic wasting syndrome and pneumonia in pigs. In this study, a survey was conducted to evaluate the prevalence of PBoV in slaughter pigs, sick pigs, asymptomatic pigs and classical swine fever virus (CSFV) eradication plan herds in five provinces of China (Henan, Liaoning, Shandong, Hebei and Tianjin) by means of PCR targeting NS1 gene of PBoV. Among the total of 403 tissue samples, 11.41% were positive for PBoV. The positive rates of spleen (20.75%) and inguinal lymph node (27.18%) are higher than those of other organs. PCR products of twenty PBoV positive samples from slaughter pigs were sequenced for phylogenetic analysis. The result revealed that PBoV could be divided into 6 groups (PBoV-a~PBoV-f). All PBoV sequenced in this study belong to PBoV-a–PBoV-d with 90.1% to 99% nucleotide identities. Our results exhibited significant genetic diversity of PBoV and suggested a complex prevalence of PBoV in Chinese swine herds. Whether this diversity of PBoV has a significance to pig production or even public health remains to be further studied
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Photocatalytic nitrogen reduction to ammonia: Insights into the role of defect engineering in photocatalysts
Engineering of defects in semiconductors provides an effective protocol for improving photocatalytic N2 conversion efficiency. This review focuses on the state-of-the-art progress in defect engineering of photocatalysts for the N2 reduction toward ammonia. The basic principles and mechanisms of thermal catalyzed and photon-induced N2 reduction are first concisely recapped, including relevant properties of the N2 molecule, reaction pathways, and NH3 quantification methods. Subsequently, defect classification, synthesis strategies, and identification techniques are compendiously summarized. Advances of in situ characterization techniques for monitoring defect state during the N2 reduction process are also described. Especially, various surface defect strategies and their critical roles in improving the N2 photoreduction performance are highlighted, including surface vacancies (i.e., anionic vacancies and cationic vacancies), heteroatom doping (i.e., metal element doping and nonmetal element doping), and atomically defined surface sites. Finally, future opportunities and challenges as well as perspectives on further development of defect-engineered photocatalysts for the nitrogen reduction to ammonia are presented. It is expected that this review can provide a profound guidance for more specialized design of defect-engineered catalysts with high activity and stability for nitrogen photochemical fixation
Effect of L-Arginine or L-Lysine on the Quality of Duck Meat Patties during Freeze-thaw Cycles
In this study, the effects of L-arginine or L-lysine on the quality of duck meat patties during repeated freeze-thaw cycles were studied to provide a theoretical basis for the application of L-arginine or L-lysine as cryoprotectant in meat products. L-arginine or L-lysine was added in the marinating process of duck meat patties, and the prepared duck meat patties was treated with freeze-thaw cycles. The texture, cooking loss, color, pH, total volatile base nitrogen (TVB-N), thiobarbituric reactive substances (TBARS), low-field nuclear magnetic resonance, and microstructure were measured to evaluate the quality of duck meat patties. The results showed that with the increase of freeze-thaw cycles, the hardness, springiness, cohesiveness, chewiness, a* value, pH and P21 of duck meat patties in the blank group decreased significantly (P<0.05), while the cooking loss, TVB-N value and TBARS value increased significantly (P<0.05). After five freeze-thaw cycles, L-arginine or L-lysine significantly inhibited the deterioration of duck meat patties quality (P<0.05), and the cooking loss of duck meat patties in L-arginine group was 13.23% and 6.93% higher than those in blank group and sodium tripolyphosphate (STP) group, respectively (P<0.05). In addition, after five freeze-thaw cycles, the TVB-N value and TBARS value of L-arginine group were 41.92% and 63.47% lower than those of blank group (P<0.05), respectively, which were the lowest among the four groups. Therefore, the L-arginine or L-lysine treatment could effectively inhibit spoilage, the oxidation of fat, improve water retention, and maintain good quality characteristics of duck meat patties
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