A Trust Model Based on Service Classification in Mobile Services

Internet of Things (IoT) and B3G/4G communication are promoting the pervasive mobile services with its advanced features. However, security problems are also baffled the development. This paper proposes a trust model to protect the user's security. The billing or trust operator works as an agent to provide a trust authentication for all the service providers. The services are classified by sensitive value calculation. With the value, the user's trustiness for corresponding service can be obtained. For decision, three trust regions are divided, which is referred to three ranks: high, medium and low. The trust region tells the customer, with his calculated trust value, which rank he has got and which authentication methods should be used for access. Authentication history and penalty are also involved with reasons.Comment: IEEE/ACM Internet of Things Symposium (IOTS), in conjunction with GreenCom 2010, IEEE, Hangzhou, China, December 18-20, 201

Pro- and Contra-Coalition: Governing the Rise and Fall of Creative Industrial Parks in China

A great number of creative industrial parks (CIPs) have emerged in the past two decades in China as a critical and popular approach to the adaptive reuse of abandoned industrial land in cities. However, a few vibrant CIPs have been closed in the past few years, and the sites are set to be demolished and redeveloped in a property-led manner, suggesting the fragility of CIPs as a land reuse approach. This article aims to elaborate on the institutional rationale behind such a phenomenon. Cases in Shanghai and Guangzhou are examined and presented. The key arguments include: (a) in the industrial land redevelopment process, public and private actors flexibly establish pro-coalitions and contra-coalitions to foster and close CIPs, with strategies to overcome institutional obstacles and to implement land redevelopment-pursued regulatory plans, respectively; (b) key actors forming the two coalitions overlap, such as the local government and the state-owned enterprise land occupiers, and their positions shift subject to specific circumstances; and (c) the finding of the two coalitions echoes the existing argument that there are forces beyond the growth machine driving China’s urban development and provides further insight into the explicit framework of the dual forces underneath

Interpretable Multi-View Clustering

Multi-view clustering has become a significant area of research, with numerous methods proposed over the past decades to enhance clustering accuracy. However, in many real-world applications, it is crucial to demonstrate a clear decision-making process-specifically, explaining why samples are assigned to particular clusters. Consequently, there remains a notable gap in developing interpretable methods for clustering multi-view data. To fill this crucial gap, we make the first attempt towards this direction by introducing an interpretable multi-view clustering framework. Our method begins by extracting embedded features from each view and generates pseudo-labels to guide the initial construction of the decision tree. Subsequently, it iteratively optimizes the feature representation for each view along with refining the interpretable decision tree. Experimental results on real datasets demonstrate that our method not only provides a transparent clustering process for multi-view data but also delivers performance comparable to state-of-the-art multi-view clustering methods. To the best of our knowledge, this is the first effort to design an interpretable clustering framework specifically for multi-view data, opening a new avenue in this field.Comment: 12 pages,6 figure

A Preliminary Study on the Long-Term Structural Stability of Ventilation Ducts in Cold Regions

The construction of roadways in permafrost regions modifies ground-surface conditions and consequently, negatively varies thermal stability of the underlying frozen soils. To avoid the thawing of the permafrost layer under the scenario of global warming, roadways are usually laid on a built-up embankment, which not only disperses the traffic loads to underlying layers but also minimize the thermal disturbance. In the embankment, duct ventilation, or called air duct, can be embedded to further cool the underlying permafrost. While the thermal performance of duct ventilations has been well documented, the long-term structural stability of duct ventilation remains unknown. This study examines the structural stress of ventilation ducts that are placed in harsh weather such as the Qinghai-Tibet Plateau. The ducts are currently buried in the embankment filler, with the wind-outlet and -inlet ends exposed and cantilevered out of the embankment. Field studies found that the exposed parts have plagued cracking and even failures, especially at the fixed end of the cantilevered part. Damages of these concrete ducts are attributed to cyclic freezing-thawing attack, thermally-induced stresses, moisture-induced stresses, and concrete swelling. These physical attacks are caused by the harsh weather in the Qinghai-Tibet plateau. It is recommended to insulate the exposed part of the ducts and to fabricate durable and dense concrete ducts

MESH : a flexible manifold-embedded semantic hashing for cross-modal retrieval

Hashing based methods for cross-modal retrieval has been widely explored in recent years. However, most of them mainly focus on the preservation of neighborhood relationship and label consistency, while ignore the proximity of neighbors and proximity of classes, which degrades the discrimination of hash codes. And most of them learn hash codes and hashing functions simultaneously, which limits the flexibility of algorithms. To address these issues, in this article, we propose a two-step cross-modal retrieval method named Manifold-Embedded Semantic Hashing (MESH). It exploits Local Linear Embedding to model the neighborhood proximity and uses class semantic embeddings to consider the proximity of classes. By so doing, MESH can not only extract the manifold structure in different modalities, but also can embed the class semantic information into hash codes to further improve the discrimination of learned hash codes. Moreover, the two-step scheme makes MESH flexible to various hashing functions. Extensive experimental results on three datasets show that MESH is superior to 10 state-of-the-art cross-modal hashing methods. Moreover, MESH also demonstrates superiority on deep features compared with the deep cross-modal hashing method. © 2013 IEEE

Subgraph adaptive structure-aware graph contrastive learning

Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases

OFFER: A Motif Dimensional Framework for Network Representation Learning

Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency

iCare: A Mobile Health Monitoring System for the Elderly

This paper describes a mobile health monitoring system called iCare for the elderly. We use wireless body sensors and smart phones to monitor the wellbeing of the elderly. It can offer remote monitoring for the elderly anytime anywhere and provide tailored services for each person based on their personal health condition. When detecting an emergency, the smart phone will automatically alert pre-assigned people who could be the old people's family and friends, and call the ambulance of the emergency centre. It also acts as the personal health information system and the medical guidance which offers one communication platform and the medical knowledge database so that the family and friends of the served people can cooperate with doctors to take care of him/her. The system also features some unique functions that cater to the living demands of the elderly, including regular reminder, quick alarm, medical guidance, etc. iCare is not only a real-time health monitoring system for the elderly, but also a living assistant which can make their lives more convenient and comfortable.Comment: The 3rd IEEE/ACM Int Conf on Cyber, Physical and Social Computing (CPSCom), IEEE, Hangzhou, China, December 18-20, 201

Vehicle trajectory clustering based on dynamic representation learning of internet of vehicles

With the widely used Internet of Things, 5G, and smart city technologies, we are able to acquire a variety of vehicle trajectory data. These trajectory data are of great significance which can be used to extract relevant information in order to, for instance, calculate the optimal path from one position to another, detect abnormal behavior, monitor the traffic flow in a city, and predict the next position of an object. One of the key technology is to cluster vehicle trajectory. However, existing methods mainly rely on manually designed metrics which may lead to biased results. Meanwhile, the large scale of vehicle trajectory data has become a challenge because calculating these manually designed metrics will cost more time and space. To address these challenges, we propose to employ network representation learning to achieve accurate vehicle trajectory clustering. Specifically, we first construct the k-nearest neighbor-based internet of vehicles in a dynamic manner. Then we learn the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network. Finally, using the learned vehicle vectors, vehicle trajectories are clustered with machine learning methods. Experimental results on the real-word dataset show that our method achieves the best performance compared against baseline methods. © 2000-2011 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record*

A Localization Method for the Internet of Things

Many localization algorithms and systems have been developed by means of wireless sensor networks for both indoor and outdoor environments. To achieve higher localization accuracy, extra hardware equipments are utilized by most of the existing localization solutions, which increase the cost and considerably limit the location-based applications. The Internet of Things (IOT) integrates many technologies, such as Internet, Zigbee, Bluetooth, infrared, WiFi, GPRS, 3G, etc, which can enable different ways to obtain the location information of various objects. Location-based service is a primary service of the IOT, while localization accuracy is a key issue. In this paper, a higher accuracy localization scheme is proposed which can effectively satisfy diverse requirements for many indoor and outdoor location services. The proposed scheme composes of two phases: 1) partition phase, in which the target region is split into small grids; 2) localization refinement phase, in which a higher accuracy of localization can be obtained by applying an algorithm designed in the paper. A trial system is set up to verify correctness of the proposed scheme and furthermore to illustrate its feasibility and availability. The experimental results show that the proposed scheme can improve the localization accuracy.Comment: To appear in Journal of Supercomputing. arXiv admin note: substantial text overlap with arXiv:1011.309