In the real world, the spread of various epidemics can be modelled using the SEIR disease transmission model. Early detection and prevention of susceptible individuals is an effective method of controlling the spread of infectious viruses. This paper presents a study that detects susceptible community on a hospital contact network, which encompasses patients, nurses, doctors and managers. The goal of our work is to identify susceptible communities with patients and healthcare workers, and analyse the independent contact networks of various roles to determine the high-influence nodes within the hospital contact network. If these high-influence nodes are part of the susceptible community, they should be the focus of observation to prevent the spread of the virus. The proposed model combines social network analysis method and machine learning method with the disease transmission model. This study employs the classic overlapping community detection method CPM for community detection and the PageRank algorithm to rank node influence. The experimental results, obtained from real-world hospital contact networks over a 4-day period, demonstrate the effectiveness of the model.

Cohesive subgraph mining on attributed social networks is attracting much attention in the realm of graph mining and analysis. Most existing studies on cohesive subgraph mining over attributed social networks neglect the fairness of attributes, which lead to difficulties in deploying responsible applications. Toward this end, this article formulates a new problem by introducing fairness into cliques model to mine the absolute fair cliques from attributed social networks. Specifically, this article adopts formal concept analysis (FCA) methodology to represent the given attributed social network, and extracts a set of special attributed equiconcepts to further return the absolute fair maximal cliques. Then, we develop an efficient absolute fair cliques detection algorithm AFCMiner for the cases of single-dimensional attributed social networks, multivalued attributed social networks, as well as multidimensional attributed social networks. Extensive experiments are conducted for demonstrating that the proposed AFCMiner algorithm can significantly reduce the time for finding absolute fair cliques with the correctness guarantee. Finally, a case study is also presented for uncovering the usefulness of our model.

Abstract IT technology and traditional industries have been combined recently, resulting in IT convergence technology in various fields. Through convergence with the automobile, pedestrian detection technology, in particular, is used in the autonomous navigation control service of autonomous vehicles and also applied in various fields such as intelligent CCTV and robot recognition technology. For pedestrian detection, hierarchical classification and feature vector were used in early stage, and deep learning is under active progress. However, since deep learning for pedestrian detection is time-consuming for processing a large volume of image data, it requires a lot of computing resources, and hence building such a system is very expensive. Therefore, in this paper we shall present a distributed deep learning platform which can easily build a cluster, and execute deep learning process in the distributed cloud environment, while achieving performance improvement in various ways. Our platform provides a convenient interface for easily and efficiently executing the deep learning process in a distributed environment by providing a multilayered system architecture. Our system builds and utilizes computing power in easy and efficient way by leveraging container technique, so-called OS-level virtualization, rather than traditional hypervisor-based virtualization. In our system, we improve the whole performance by exploiting both of data and parameter parallelisms at once and reduce the synchronization overhead by exploiting asynchronous communication for parameter updates. Also, we propose an efficient resource allocation scheme for parameter servers and slaves which can improve the performance from the experiment.

Mobile edge computing (MEC) is providing a promising solution for augmenting the computing and storage capacity of mobile devices by exploiting the available resources at the network edge. Among the various Internet of Things (IoT) applications, MEC could help us to narrow the gap between the requirements of IoT applications and the limited resources of IoT devices and to achieve the energy-efficient communication and computing. Importantly, the upper and edge infrastructure of cloud computing should effectively collaborate for executing the complex tasks which are requested by mobile users. In particular, community cloud computing, as a novel computational model for a specific community with common concerns (such as security, compliance, and jurisdiction), can make full use of the spare resources of networked computers to provide the facilities so that the community gains services from them. However, how to allocate the subtasks into community clouds and edge community clouds (cloudlets) is becoming a critical challenge. To tackle this challenge, this paper first proposes a two-layer multi-community-cloud/cloudlet social collaborative paradigm, called 2L-MC3for MEC. Further, we formulate a problem on tasks allocation in community clouds/cloudlets by jointly taking task offloading, tasks and clouds profiles into account. To address this problem, we devise a bi-level programming model for tasks allocation. Extensive simulations are conducted for demonstrating that the proposed approach can achieve the relative global performance for satisfying the each metric comparing to the other approaches.

ABSTRACT This paper presents a robust and secure hybrid connectivity framework for ubiquitous convergence in a home environment. The proposed hybrid connectivity framework is based on various consumer electronics and mobile devices with wireless interfaces such as IEEE 802.15.4 (e.g., radio frequency for consumer electronics (RF4CE) or ZigBee) and IEEE 802.11. In particular, this framework involves an RF4CE‐based WiFi zero‐configuration scheme. It simplifies connectivity by means of secure key distribution and a simple WiFi configuration involving WiFi‐capable devices. To validate our framework, we analyzed the system and security performance using real hardware prototypes. Copyright © 2011 John Wiley & Sons, Ltd.

The purpose of this study was to study the effects of 12 weeks of circuit weight training and aerobic exercise on body composition, physical fitness, and pulse wave velocity in obese collegiate women. Twelve obese collegiate women were randomly assigned either to an exercise training group (TG) or control group (CG). The main exercise program was composed of an approximately 40–65 min session of circuit weight training (resistance training and aerobic exercise) as well as jogging at an intensity of 50–70% of the age-predicted heart rate reserve. The circuit weight training program was made by Korean Institute of Sport Science and was modified as needed for obese collegiate women. All analyses were performed using SPSS and all data was reported as mean ± standard deviation (SD). Significant differences between groups were determined using a two-way repeated measures analysis of variance (ANOVA) with a post hoc test (Tukey method). Statistical significance was accepted for all tests at a value of p < 0.05. The results indicated that after the 12-week intervention, there were no significant changes in body weight, % body fat, or WC in either group. There was a significant interaction of time by group with respect to body weight ( p < 0.05), % body fat ( p < 0.01), and WC ( p < 0.01) and there was a significant change in back strength between the TG before beginning the program and the TG after having completed the program ( p < 0.01). There was also a significant interaction of time by group with respect to back strength ( p < 0.01), grip strength ( p < 0.05), sit and reach ( p < 0.01), sargent-jump ( p < 0.01), and the one leg balance with eyes closed ( p < 0.01); however, these differences were not statistically significant between groups. Further, there was a significant interaction of time by group with respect to the 1,200 m run for cardiopulmonary endurance ( p < 0.01); however, this difference was not statistically significant between the TG pre and TG post. In addition, there was a significant in sit-ups ( p < 0.01) and the 1,200 m run ( p < 0.01) between the TG and CG. There was no significant difference in side-steps between the TG and CG. Further, there were no significant differences in the pulse wave velocity, RPP, SBP, DBP, and MAP between the TG and CG. In conclusion, circuit weight training and aerobic exercise had favorable effects on the occurrence of obesity and physical fitness in obese collegiate women.

The intelligent pervasive network environment is thing which invisible computer that is not shown linked mutually through network so that user may use computer always is been pervasive. As a number of pervasive network services are available and smart environment is expanding into ubiquitous computing environment, we need to protect intelligent pervasive environment system from illegal accesses and a variety of threats. Intelligent pervasive environment is exposed to various cyber attacks of Internet, involves hacking, malicious codes, worms, viruses, DoS attacks, and eavesdropping since it is connected to Internet. So in this paper, we propose authentication in a intelligent pervasive environment security for guaranteeing reliability and availability including authentication, authorization system. We proposed S/Key based authentication, scheme for secure remote access in intelligent pervasive environments. Furthermore, we describes smart environment authentication which is the basic and essential element in the pervasive network security. And our device authentication concept can offer pervasive network service users convenience and security.

This paper proposes a restoration scheme based on mobility learning and prediction in the presence of the failure of mobility databases in personal communication systems (PCSs). In PCSs, mobility databases must maintain the current location information of users to provide a fast connection for them. However, the malfunction of mobility databases may cause some location information to be lost. As a result, without an explicit restoration procedure, incoming calls to users may be rejected. Therefore, an explicit restoration scheme against the malfunction of mobility databases is needed to guarantee continuous service availability to users. Introducing mobility learning and prediction into the restoration process allows systems to locate users after a failure of mobility databases. In failure-free operations, the movement patterns of users are learned by a neuro-fuzzy inference system (NFIS). After a failure, an inference process of the NFIS is initiated and the users' future location is predicted. This is used to locate lost users after a failure. This proposal differs from previous approaches using a checkpoint because it does not need a backup process nor additional storage space to store checkpoint information. In addition, simulations show that our proposal can reduce the cost needed to restore the location records of lost users after a failure when compared to the checkpointing scheme.