Medical cyber-physical systems (MCPS) are healthcare critical integration of a network of medical devices. These systems are progressively used in hospitals to achieve a continuous high-quality healthcare. The MCPS design faces numerous challenges, including inoperability, security/privacy, and high assurance in the system software. In the current work, the infrastructure of the cyber-physical systems (CPS) are reviewed and discussed. This article enriched the researches of the networked Medical Device (MD) systems to increase the efficiency and safety of the healthcare. It also can assist the specialists of medical device to overcome crucial issues related to medical devices, and the challenges facing the design of the medical device's network. The concept of the social networking and its security along with the concept of the wireless sensor networks (WSNs) are addressed. Afterward, the CPS systems and platforms have been established, where more focus was directed toward CPS-based healthcare. The big data framework of CPSs is also included.

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Medical cyber-physical systems: A survey.

Driver decisions and behaviors regarding the surrounding traffic are critical to traffic safety. It is important for an intelligent vehicle to understand driver behavior and assist in driving tasks according to their status. In this paper, the consumer range camera Kinect is used to monitor drivers and identify driving tasks in a real vehicle. Specifically, seven common tasks performed by multiple drivers during driving are identified in this paper. The tasks include normal driving, left-, right-, and rear-mirror checking, mobile phone answering, texting using a mobile phone with one or both hands, and the setup of in-vehicle video devices. The first four tasks are considered safe driving tasks, while the other three tasks are regarded as dangerous and distracting tasks. The driver behavior signals collected from the Kinect consist of a color and depth image of the driver inside the vehicle cabin. In addition, 3-D head rotation angles and the upper body (hand and arm at both sides) joint positions are recorded. Then, the importance of these features for behavior recognition is evaluated using random forests and maximal information coefficient methods. Next, a feedforward neural network (FFNN) is used to identify the seven tasks. Finally, the model performance for task recognition is evaluated with different features (body only, head only, and combined). The final detection result for the seven driving tasks among five participants achieved an average of greater than 80% accuracy, and the FFNN tasks detector is proved to be an efficient model that can be implemented for real-time driver distraction and dangerous behavior recognition.

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Identification and Analysis of Driver Postures for In-Vehicle Driving Activities and Secondary Tasks Recognition

People use online social networks to exchange information, spread ideas, learn about innovations, etc. Thus, it is important to know how information spreads through social networks. It is possible to spread information (e.g., product advertisement) to a larger number of individuals via a social network. Similarly, it is possible to minimize the spread of unwanted content (e.g., ‘false news’). The key point in both cases is to identify the most influential individuals on the social network. This problem is named as Influence Maximization (IM) problem. The IM problem focuses on finding the small subset of individuals in a social environment who influence a certain group of individuals, i.e., maximize/minimize the spreading of information. Some greedy algorithms, stochastic algorithms and evolutionary optimization algorithms have been developed to find a solution to this problem. However, these methods are not at the desired level in terms of speed or solution capability. On the other hand, although many swarm intelligence algorithms that produce fast and optimal solutions can be found in the literature, these algorithms cannot be directly applied to the IM problem because no general slope is produced on the state-space surface of the IM problem's objective function. Swarm intelligence algorithms follow the general slope over the surface to converge at the global optimum. Thus, they cannot converge to the global optimum in the IM problem. In this study, a change in the structure of the IM problem is suggested in order to tailor it to swarm intelligence algorithms and to achieve a general slope on the state-space surface of its objective function. We named this process as “reshaping”. More precisely, if a social network is envisioned as a graph and individuals as nodes, reshaping means sorting the nodes in descending order (from largest to smallest) according to the metrics under consideration (i.e., metrics that give an idea about the level of influence of an individual) and renumbering the nodes according to this order. Thus, the nodes those are close to each other in terms of level of influence become closer to each other in the state-space. This creates a general slope on the state-space surface of the objective function. This simple idea paves the way for applying all swarm intelligence algorithms to this kind of problem. The proposed approach was tested with real and synthetic graphs. The experiments employed the Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) as the swarm intelligence algorithms and PageRank and Kempe et al.’s Greedy Algorithm as benchmark methods. Experimental results showed that this approach worked well.

Using swarm intelligence algorithms to detect influential individuals for influence maximization in social networks

Influence maximization problem is to find a set of seeds in social networks such that the cascade influence is maximized. Traditional models assume that all nodes are willing to spread the influence once they are influenced, and they ignore the disparity between influence and profit of a product. In this paper, by considering the role that price plays in viral marketing, we propose price related (PR) frame that contains PR-I and PR-L models for classic independent cascade and linear threshold models, respectively, which is a pioneer work. Two pricing strategies are designed, one is binary pricing (BYC), in which the seeds are offered free samples. The other is panoramic pricing (PAP), in which the seeds are offered different discounts. Furthermore, we find that influence and profit are like two sides of the coin, high price hinders the influence propagation and to enlarge the influence some sacrifice on profit is inevitable. Based on this observation under PR frame, by adopting a parameter to denote the decision maker’s preference toward influence and profit, we propose balanced influence and profit (BIP) maximization problem. We prove the NP-hardness of BIP maximization under PR-I and PR-L model. Unlike influence maximization, the BIP objective function is not monotone. Despite the nonmonotony, we show BIP objective function is submodular under certain conditions. Two unbudgeted greedy algorithms separately, named algorithm of BYC and algorithm of PAP are devised. We conduct extensive simulations on real world data sets, test the effectiveness of our proposed parameters, compare the algorithms’ performances, and evaluate the superiority of our algorithms over existing ones.

Maximizing the Influence and Profit in Social Networks

Drivers’ decision and their corresponding behaviors are important aspects that can affect the driving safety, and it is necessary to understand the driver behaviors in real-time. In this study, an end-to-end driving-related tasks recognition system is proposed. Specifically, seven common driving activities are identified, which are normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle video device, texting, and answering mobile phone. Among these, the first four activities are regarded as normal driving tasks, while the rest three are divided into distraction group. The images are collected using a consumer range camera, namely, Kinect. In total, five drivers are involved in the naturalistic data collection. Before training the identification model, the raw images are first segmented using a Gaussian mixture model (GMM) to extract the driver region from the background. Then, a pre-trained deep convolutional neural network (CNN) model is trained to classify the behaviors, which directly takes the processed RGB images as the input and outputs the identified label. In this work, the AIexNet is selected as the pre-trained CNN model. Then, to reduce the training cost, the transfer learning mechanism is applied to the CNN model. An average of 79\% detection accuracy is achieved for the seven driving tasks. The proposed integration model can be used as a low-cost driver distraction and dangerous tasks recognition modeL.

End-to-End Driving Activities and Secondary Tasks Recognition Using Deep Convolutional Neural Network and Transfer Learning

In a social network, influence diffusion is the process of spreading innovations from user to user. An activation state identifies who are the active users who have adopted the target innovation. Given an activation state of a certain diffusion, effector detection aims to reveal the active users who are able to best explain the observed state. In this paper, we tackle the effector detection problem from two perspectives. The first approach is based on the influence distance that measures the chance that an active user can activate its neighbors. For a certain pair of users, the shorter the influence distance, the higher probability that one can activate the other. Given an activation state, the effectors are expected to have short influence distance to active users while long to inactive users. By this idea, we propose the influence-distance-based effector detection problem and provide a 3-approximation. Second, we address the effector detection problem by the maximum likelihood estimation (MLE) approach. We prove that the optimal MLE can be obtained in polynomial time for connected directed acyclic graphs. For general graphs, we first extract a directed acyclic subgraph that can well preserve the information in the original graph and then apply the MLE approach to the extracted subgraph to obtain the effectors. The effectiveness of our algorithms is experimentally verified via simulations on the real-world social network.

Shasha Li

Papers

Effector Detection in Social Networks

Effector Detection in Social Networks