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.     

无线传感器网络中移动sink节点的路径规划

在无线传感器网络中,大量感知数据汇集到sink节点的采集方法会导致sink节点附近的节点能量耗尽,造成能量空洞。针对该问题,利用移动的sink节点进行数据收集是一种解决方法,其中移动sink的路径规划成为一个重要的问题。提出了一个移动sink路径规划算法,将无线传感器中随机分布的节点划分为不同的子区域,寻找sink节点移动的最佳转向点,最终得到最优的移动路径,以实现无线传感器网络生命周期最大化。仿真实验表明,与现有方案相比,该算法能显著延长网络的生命周期。     

An experiential account of phantom vibration syndrome

A small handful of recent studies have addressed a phenomenon known as “phantom vibration syndrome”. This refers to when phone users perceive their device to vibrate, indicating that a call or text is incoming, when in fact the phone did not vibrate at all. Though these studies show that most users do not find phantom vibration hallucinations to be very bothersome, they also find that a large majority of users experience this phenomenon. This paper explores what the striking prevalence of phantom vibration syndrome means for our contemporary relationships with technology. I begin with a review of the theories purporting to explain these data, which largely rely on particular understandings of the brain. Next I develop an alternative theory of phantom vibration syndrome based on insights from the philosophical tradition of phenomenology. This account considers the ways users develop bodily and perceptual habits regarding how the phone is understood and used. By critically contrasting these different theories, we can refine our questions about what the high prevalence of phantom vibration syndrome implies about our contemporary technological situation.     

Mapping gender and marital roles on customer delight by value perception for mobile technology in India

The Indian telephony industry is a diverse platform for product and service in customer value perception. The present study organizes and synthesizes the varied research streams for developing a customer value delight (CVD) framework for marketing decisions like segmentation and targeting. The conceptual framework is developed by using ten-value predictor variables and customer delight through divergent viewpoints identified from previous studies. The significance of the segregation proposition is tested through discriminant analysis in three different dimension runs i.e. delight, gender, and marital category. The scholarship behind the study provides insight into an imperative issue of segregating customer delight by perceived value predictors in categories. The perceptual mapping and vector magnitude analysis of value predictors were performed to club customers in two categories of Mid-Town (low/moderate delight) and Extremes (high delight). The analysis rigorously supports the discriminating power of perceived price, perceived benefit, and self-congruity. The salience of study unlocks psychographic and demographic perceptual maps of customer delight with its perceived value predictors for strategic and tactful planning in the mobile industry.     

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.