Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

Correlation between motion of source bender element and feedback signal induced by self-monitoring

Since the introduction of bender element tests to soil testing, the reliability of the estimated travel time has been the most serious problem. The author has previously shown a potential solution whereby removing the response of the bender element subsystem from the whole response could dramatically improve the accuracy of the travel time estimation. In order to lay the foundation for estimating the response of the bender element subsystem, this paper examines the correlation between the displacements of the element and the induced feedback signals by employing a self-monitoring element. The response of the self-monitoring element is modeled as a transfer function involving two internal transfer functions that relate the input signals to the displacements and the displacements to the feedback signals, respectively. Using a laser displacement sensor, the displacements are directly measured through the entire surface and reveal the three-dimensional bending motion of the element oscillating in both longitudinal and width directions. The feedback signals are similar to, but inconsistent with, the tip displacements, and an attempt is made to correct the feedback signals. Finally, a conclusion is given on the potential for estimating the response of the bender element subsystem using the self-monitoring element.     

Controlling the slides: Does clicking help adults learn?

When utilizing screen media as an educational platform, maintaining control over one's experience may lead to more successful learning outcomes. In the current work, adults learned four new action sequences, each via a different slideshow type. The computer advanced slides automatically, but each version had a different pausing mechanism: (1) free pause (viewers could click the mouse at any point to pause the show), (2) subgoal pause (show paused after subgoals, viewer clicked to continue), (3) timed pause (show paused every 20 slides, viewer clicked to continue), and (4) no pause (no viewer interaction). Participants completed a written memory test, live performance test, cognitive load measures, and satisfaction measures. Results indicated that memory recall was significantly lower in the no pause version when compared to the versions with pause capability. Also, over half of participants reported that the no pause version was their least favorite format to learn from. Conversely, over half of participants selected the free pause as their favorite slideshow format, and participants reported that they felt most in control of the free pause version. These reports occurred in spite of only one-quarter of all participants actually using the click-to-pause feature in the free pause slideshow. Perhaps the mindset of being in control, rather than the pausing itself, increased likeability of the program. This research has implications for program design and education, pointing to flexible pacing features being helpful in enhancing users' enjoyment of the program and ability to extract novel information.     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

两阶段领域自适应学习

针对领域自适应问题中源域和目标域的联合分布差异最小化问题,提出两阶段领域自适应学习方法.在第一阶段考虑样本标签和数据结构的判别信息,通过学习一个共享投影变换,使投影后的共享空间中边缘分布的差异最小.第二阶段利用源域标记数据和目标域非标记数据学习一个带结构风险的自适应分类器,不仅能最小化源域和目标域条件分布差异,还能进一步保持源域和目标域边缘分布的流形一致性.在3个基准数据集上的实验表明,文中方法在平均分类准确率和Kappa系数两项评价指标上均表现较优.     

Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time

We study a two-agent scheduling problem in a two-machine permutation flowshop with learning effects. The objective is to minimize the total completion time of the jobs from one agent, given that the maximum tardiness of the jobs from the other agent cannot exceed a bound. We provide a branch-and-bound algorithm for the problem. In addition, we present several genetic algorithms to obtain near-optimal solutions. Computational results indicate that the algorithms perform well in either solving the problem or efficiently generating near-optimal solutions.     

Deep Integration of Innovation and Entrepreneurship (InE) Education in Chinese University Classrooms

Recently, InE has been regarded as a popular education strategy in Chinese universities. However, problems have been exposed in the adoption of InE, for example, in InE courses and competitions. The purpose of this paper is to provide a possible solution to the problems, which is to organize effective InE courses by integrating InE with Inter-Course-level Problem-Based Learning (ICPBL). A detailed case is demonstrated by an ICPBL elective course design with deep integration of InE in the teaching, learning, and assessments. This paper contributes to a new curriculum design for promoting InE education in practically for Chinese universities.     

Models for solar radiation prediction based on different measurement sites

The modeling of solar radiation for forecasting its availability is a key tool for managing photovoltaic (PV) plants and, hence, is of primary importance for energy production in a smart grid scenario. However, the variability of the weather phenomena is an unavoidable obstacle in the prediction of the energy produced by the solar radiation conversion. The use of the data collected in the past can be useful to capture the daily and seasonal variability, while measurement of the recent past can be exploited to provide a short term prediction. It is well known that a good measurement of the solar radiation requires not only a high class radiometer, but also a correct management of the instrument. In order to reduce the cost related to the management of the monitoring apparatus, a solution could be to evaluate the PV plant performance using data collected by public weather station installed near the plant. In this paper, two experiments are conducted. In the first, the plausibility of the short term prediction of the solar radiation, based on data collected in the near past on the same site is investigated. In the second experiment, the same prediction is operated using data collected by a public weather station located at ten kilometers from the solar plant. Several prediction techniques belonging from both computational intelligence and statistical fields have been challenged in this task. In particular, Support Vector Machine for Regression, Extreme Learning Machine and Autoregressive models have been used and compared with the persistence and the k-NN predictors. The prediction accuracy achieved in the two experimental conditions are then compared and the results are discussed.     

YSZ/MoS2 self-lubricating coating fabricated by thermal spraying and hydrothermal reaction

Thermal sprayed ceramic coatings have extensively been used in components to protect them against friction and wear. However, the poor lubricating ability severely limits their application. Herein, yttria-stabilized zirconia (YSZ)/MoS₂ composite coatings were successfully fabricated on steel substrate with the combination of thermal spraying technology and hydrothermal reaction. Results show that the synthetic MoS₂ powders are composed of numbers of ultra-thin sheets (about 7 ~ 8?nm), and the sheet has obvious lamellar structure. After vacuum impregnation and hydrothermal reaction, numbers of MoS₂ powders, look like flowers, generate inside the plasma sprayed YSZ coating. Moreover, the growing point of the MoS₂ flower is the intrinsic micro-pores of YSZ coating. The friction and wear tests under high vacuum environment indicate that the composite coating has an extremely long lifetime (>?100,000 cycles) and possesses a low friction coefficient less than 0.1, which is lower by about 0.15 times than that of YSZ coating. Meanwhile, the composite shows an extremely low wear rate (2.30?×?10^?7 mm³ N^?1 m^?1) and causes slight wear damage to the counterpart. The excellent lubricant and wear-resistant ability are attributed to the formation of MoS₂ transfer films and the ultra-smooth of the worn surfaces of hybrid coatings.