Unsupervised Domain Adaptation (UDA) has been proven effective in extracting domain-invariant features, facilitating successful model generalization from the training user to the new user in sensor-based Human Activity Recognition (HAR). However, such extraction of the domain-invariant representation that only focuses on aligning global features cannot be robustly applied to Multi-sensor-based HAR (MHAR) due to the diverse distributions inherent in different sensors. In this paper, we propose CONtrastive SENsor eaxciTation (CONSENT), a novel unsupervised domain adaptation framework for cross-person MHAR. CONSENT considers both sensor-specific feature alignment and global feature alignment. At the sensor-specific feature level, we propose an effective contrastive loss to ensure consistent distributions among identical sensors across domains without applying any pre-defined augmentations. Meanwhile, to further minimize domain discrepancy at the global feature level, we design a simple but effective excitation mechanism, which motivates the model to pay more attention to sensors that are critical for activity recognition. In this way, the model is encouraged to learn representations that are robust to sensor-specific information. Empirical results demonstrate that CONSENT achieves state-of-the-art performance on three public MHAR datasets.

Conducting Unsupervised Domain Adaptation (UDA) on Time Series (TS) data is an important and challenging task. The majority of works in this area focus on extracting domain-invariant representations from the data, often achieved by employing constraints such as Maximum Mean Discrepancy (MMD) or introducing an adversarial opponent. However, this extraction process is non-trivial due to the non-linear and non-stationary relationships existing between different timestamps. Moreover, when reasoning on higher level features which consists multiple timestamps, the presence of noise and variations in timescales across diverse classes and domains further complicates the extraction of stable and reliable features. Therefore, the core challenge lies in overcoming these complexities to adaptively extract effective features with multiscale information, which is capable of representing the non-trivial relationships within temporal data.In this paper, we propose a novel framework named MSEM designed to extracting domain-invariant representations through scale-awareness feature extraction and fusion. Initially, we use multiscale group convolution on TS input data to obtain semantic information from various time length with diverse receptive fields. Subsequently, a multiscale mix-up module seamlessly integrates TS data from both domains at the feature level, with respect to its information in both short and long terms. With the help of mix-up loss, the entire framework enhances the domain adversarial network’s ability to further manipulate the learned representation, ensuring its domain-invariance while preserving optimal scale information for classification. This results in a more robust fused feature, contributing to improved performance. Extensive experimental studies not only demonstrate its state-of-the-art performance across five real-world datasets, but also verify the effectiveness of our method.

In air-water wireless optical communication, the optical signal may be completely reflected at the sea surface, which will cause signal interruption and seriously affect communication quality. Therefore, this paper analyzes the signal interruption probability and duration caused by total reflection at different wind speeds and transmitter inclination angles. The bit error rate (BER) of L-ary pulse position modulation (L-PPM) across sea surface under different L values is studied in an attempt to find a suitable modulation method to optimize the BER of the system. The BER optimization effect is apparent when the wind speed is low and L=6. However, the optimization effect of L-PPM on BER decreases with the increase in wind speed.

Abstract Heatmap‐based regression (HBR) methods have dominated for a long time in the face alignment field while these methods need complex design and post‐processing. In this study, the authors propose an end‐to‐end and simple enough coordinate‐based regression (CBR) method called Dynamic Deformable Transformer (DDT) for face alignment. Unlike general pre‐defined landmark queries, DDT uses Dynamic Landmark Queries (DLQs) to query landmarks' classes and coordinates together. Besides, DDT adopts a deformable attention mechanism rather than a regular attention mechanism which has a faster convergence speed and lower computational complexity. Experiment results on three mainstream datasets 300W, WFLW, and COFW demonstrate DDT exceeds the state‐of‐the‐art CBR methods by a large margin and is comparable to the current state‐of‐the‐art HBR methods with much less computational complexity.

In clinical diagnosis, multimodal medical image fusion is meaningful and necessary, for the reason that some diseases need to be diagnosed in combination with the situation of different tissues of patients. Spiral computed tomography (CT) realizes the high precision and smooth reconstruction of bone tissue, while it can not represent the color and texture information in soft tissue reconstruction with high accuracy. The face scan precisely records the color and shape of the maxillofacial region. The diagnosis of some diseases (like cavernous hemangioma and jaw deformity caused by idiopathic condylar resorption) needs to combine the information of maxillofacial soft tissue and bone, so it is of great significance to fuse spiral CT and face scan images. In this article, a novel intelligent Internet of Things scene is proposed: a multimodal medical images acquisition and fusion system, and by combining CT machine and face scan equipment, the CT and face scan of patients can be synchronously collected. Deep point neural networks are used to extract feature points and a threshold iterative closest point algorithm performing registration with deep feature points and contributed region segmentation is applied. Finally, high-precision fused modal data is output at the mobile terminal to facilitate diagnosis and analysis and improve the efficiency of doctor–patient communication. Quantitative experiments show promising results, and clinical experiments prove that our method enables patients and doctors to better understand the state of an illness and improves the efficiency of doctor–patient communication.

While being a lasting theme, privacy, security, and trust (PST) has been increasingly important in recent days due to the pervasive (but more prone) computation infrastructure and deep (but more intrusive) data analytics, and has been hugely demanded from governments, companies, and individuals. This workshop aims at providing a forum for researchers, practitioners and developers from different background areas such as computational intelligence, data privacy and cyber security, trust management, cloud computing, edge computing, Internet of Things, big data analytics, machine learning and data mining, knowledge discovery to exchange the latest experience, research ideas and synergic research and development on fundamental issues and applications about privacy, security and trust issues in computational intelligence.

Recently, biometric identification has been extensively used for border control. Some face recognition systems have been designed based on Internet of Things. But the rich personal information contained in face images can cause severe privacy breach and abuse issues during the process of identification if a biometric system has compromised by insiders or external security attacks. Encrypting the query face image is the state-of-the-art solution to protect an individual’s privacy but incurs huge computational cost and poses a big challenge on time-critical identification applications. However, due to their high computational complexity, existing methods fail to handle large-scale biometric repositories where a target face is searched. In this article, we propose an efficient privacy-preserving face recognition scheme based on clustering. Concretely, our approach innovatively matches an encrypted face query against clustered faces in the repository to save computational cost while guaranteeing identification accuracy via a novel multi-matching scheme. To the best of our knowledge, our scheme is the first to reduce the computational complexity from O(M) in existing methods to approximate O (√ M ), where M is the size of a face repository. Extensive experiments on real-world datasets have shown the effectiveness and efficiency of our scheme.

Big sensing data is commonly encountered from various surveillance or sensing systems. Sampling and transferring errors are commonly encountered during each stage of sensing data processing. How to recover from these errors with accuracy and efficiency is quite challenging because of high sensing data volume and unrepeatable wireless communication environment. While Cloud provides a promising platform for processing big sensing data, however scalable and accurate error recovery solutions are still need. In this paper, we propose a novel approach to achieve fast error recovery in a scalable manner on cloud. This approach is based on the prediction of a recovery replacement data by making multiple data sources based approximation. The approximation process will use coverage information carried by data units to limit the algorithm in a small cluster of sensing data instead of a whole data spectrum. Specifically, in each sensing data cluster, a Euclidean distance based approximation is proposed to calculate a time series prediction. With the calculated time series, a detected error can be recovered with a predicted data value. Through the experiment with real world meteorological data sets on cloud, we demonstrate that the proposed error recovery approach can achieve high accuracy in data approximation to replace the original data error. At the same time, with MapReduce based implementation for scalability, the experimental results also show significant efficiency on time saving.

With the advent of IoT (Internet of Things) age, considerable web services are emerging rapidly in service communities, which places a heavy burden on the target users' service selection decisions. In this situation, various techniques, e.g., collaborative filtering (i.e., CF) is introduced in service recommendation to alleviate the service selection burden. However, traditional CF-based service recommendation approaches often assume that the historical user-service quality data is centralized, while neglect the distributed recommendation situation. Generally, distributed service recommendation involves inevitable message communication among different parties and hence, brings challenging efficiency and privacy concerns. In view of this challenge, a novel privacy-preserving distributed service recommendation approach based on Locality-Sensitive Hashing (LSH), i.e., DistSRLSH is put forward in this paper. Through LSH, DistSRLSH can achieve a good tradeoff among service recommendation accuracy, privacy-preservation and efficiency in distributed environment. Finally, through a set of experiments deployed on WS-DREAM dataset, we validate the feasibility of our proposal in handling distributed service recommendation problems.

Big sensing data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity. Cloud computing provides a promising platform for big sensing data processing and storage as it provides a flexible stack of massive computing, storage, and software services in a scalable manner. Current big sensing data processing on Cloud have adopted some data compression techniques. However, due to the high volume and velocity of big sensing data, traditional data compression techniques lack sufficient efficiency and scalability for data processing. Based on specific on-Cloud data compression requirements, we propose a novel scalable data compression approach based on calculating similarity among the partitioned data chunks. Instead of compressing basic data units, the compression will be conducted over partitioned data chunks. To restore original data sets, some restoration functions and predictions will be designed. MapReduce is used for algorithm implementation to achieve extra scalability on Cloud. With real world meteorological big sensing data experiments on U-Cloud platform, we demonstrate that the proposed scalable compression approach based on data chunk similarity can significantly improve data compression efficiency with affordable data accuracy loss.

Summary Big data is one of the most referred key words in recent information and communications technology industry. As the new‐generation distributed computing platform, cloud environments offer high efficiency and low cost for data‐intensive storage and computation for big data applications. Cloud resources and services are available in pay‐as‐you‐go mode, which brings extraordinary flexibility and cost‐effectiveness as well as minimal investments in their own computing infrastructure. However, these advantages come at a price—people no longer have direct control over their own data. Based on this view, data security becomes a major concern in the adoption of cloud computing. Authenticated key exchange is essential to a security system that is based on high‐efficiency symmetric‐key encryptions. With virtualisation technology being applied, existing key exchange schemes such as Internet key exchange become time consuming when directly deployed into cloud computing environment, especially for large‐scale tasks that involve intensive user–cloud interactions, such as scheduling and data auditing. In this paper, we propose a novel hierarchical key exchange scheme, namely hierarchical key exchange for big data in cloud, which aims at providing efficient security‐aware scheduling and auditing for cloud environments. In this novel key exchange scheme, we developed a two‐phase layer‐by‐layer iterative key exchange strategy to achieve more efficient authenticated key exchange without sacrificing the level of data security. Both theoretical analysis and experimental results demonstrate that when deployed in cloud environments with diverse server layouts, efficiency of the proposed scheme is dramatically superior to its predecessors cloud computing background key exchange and Internet key exchange schemes. Copyright © 2014 John Wiley & Sons, Ltd.

Cloud computing that provides elastic computing and storage resource on demand has become increasingly important due to the emergence of “big data”. Cloud computing resources are a natural fit for processing big data streams as they allow big data application to run at a scale which is required for handling its complexities (data volume, variety and velocity). With the data no longer under users' direct control, data security in cloud computing is becoming one of the most concerns in the adoption of cloud computing resources. In order to improve data reliability and availability, storing multiple replicas along with original datasets is a common strategy for cloud service providers. Public data auditing schemes allow users to verify their outsourced data storage without having to retrieve the whole dataset. However, existing data auditing techniques suffers from efficiency and security problems. First, for dynamic datasets with multiple replicas, the communication overhead for update verifications is very large, because each update requires updating of all replicas, where verification for each update requires O(log n ) communication complexity. Second, existing schemes cannot provide public auditing and authentication of block indices at the same time. Without authentication of block indices, the server can build a valid proof based on data blocks other than the blocks client requested to verify. In order to address these problems, in this paper, we present a novel public auditing scheme named MuR-DPA. The new scheme incorporated a novel authenticated data structure (ADS) based on the Merkle hash tree (MHT), which we call MR-MHT. To support full dynamic data updates and authentication of block indices, we included rank and level values in computation of MHT nodes. In contrast to existing schemes, level values of nodes in MR-MHT are assigned in a top-down order, and all replica blocks for each data block are organized into a same replica sub-tree. Such a configuration allows efficient verification of updates for multiple replicas. Compared to existing integrity verification and public auditing schemes, theoretical analysis and experimental results show that the proposed MuR-DPA scheme can not only incur much less communication overhead for both update verification and integrity verification of cloud datasets with multiple replicas, but also provide enhanced security against dishonest cloud service providers.

Cloud computing provides promising scalable IT infrastructure to support various processing of a variety of big data applications in sectors such as healthcare and business. Data sets like electronic health records in such applications often contain privacy-sensitive information, which brings about privacy concerns potentially if the information is released or shared to third-parties in cloud. A practical and widely-adopted technique for data privacy preservation is to anonymize data via generalization to satisfy a given privacy model. However, most existing privacy preserving approaches tailored to small-scale data sets often fall short when encountering big data, due to their insufficiency or poor scalability. In this paper, we investigate the local-recoding problem for big data anonymization against proximity privacy breaches and attempt to identify a scalable solution to this problem. Specifically, we present a proximity privacy model with allowing semantic proximity of sensitive values and multiple sensitive attributes, and model the problem of local recoding as a proximity-aware clustering problem. A scalable two-phase clustering approach consisting of a t-ancestors clustering (similar to k-means) algorithm and a proximity-aware agglomerative clustering algorithm is proposed to address the above problem. We design the algorithms with MapReduce to gain high scalability by performing data-parallel computation in cloud. Extensive experiments on real-life data sets demonstrate that our approach significantly improves the capability of defending the proximity privacy breaches, the scalability and the time-efficiency of local-recoding anonymization over existing approaches.

Big sensor data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity it is difficult to process using on-hand database management tools or traditional data processing applications. Cloud computing provides a promising platform to support the addressing of this challenge as it provides a flexible stack of massive computing, storage, and software services in a scalable manner at low cost. Some techniques have been developed in recent years for processing sensor data on cloud, such as sensor-cloud. However, these techniques do not provide efficient support on fast detection and locating of errors in big sensor data sets. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature of WSN. Firstly, a set of sensor data error types are classified and defined. Based on that classification, the network feature of a clustered WSN is introduced and analyzed to support fast error detection and location. Specifically, in our proposed approach, the error detection is based on the scale-free network topology and most of detection operations can be conducted in limited temporal or spatial data blocks instead of a whole big data set. Hence the detection and location process can be dramatically accelerated. Furthermore, the detection and location tasks can be distributed to cloud platform to fully exploit the computation power and massive storage. Through the experiment on our cloud computing platform of U-Cloud, it is demonstrated that our proposed approach can significantly reduce the time for error detection and location in big data sets generated by large scale sensor network systems with acceptable error detecting accuracy.

Cloud computing opens a new era in IT as it can provide various elastic and scalable IT services in a pay-as-you-go fashion, where its users can reduce the huge capital investments in their own IT infrastructure. In this philosophy, users of cloud storage services no longer physically maintain direct control over their data, which makes data security one of the major concerns of using cloud. Existing research work already allows data integrity to be verified without possession of the actual data file. When the verification is done by a trusted third party, this verification process is also called data auditing, and this third party is called an auditor. However, such schemes in existence suffer from several common drawbacks. First, a necessary authorization/authentication process is missing between the auditor and cloud service provider, i.e., anyone can challenge the cloud service provider for a proof of integrity of certain file, which potentially puts the quality of the so-called ‘auditing-as-a-service’ at risk; Second, although some of the recent work based on BLS signature can already support fully dynamic data updates over fixed-size data blocks, they only support updates with fixed-sized blocks as basic unit, which we call coarse-grained updates. As a result, every small update will cause re-computation and updating of the authenticator for an entire file block, which in turn causes higher storage and communication overheads. In this paper, we provide a formal analysis for possible types of fine-grained data updates and propose a scheme that can fully support authorized auditing and fine-grained update requests. Based on our scheme, we also propose an enhancement that can dramatically reduce communication overheads for verifying small updates. Theoretical analysis and experimental results demonstrate that our scheme can offer not only enhanced security and flexibility, but also significantly lower overhead for big data applications with a large number of frequent small updates, such as applications in social media and business transactions.

SUMMARY Big data and cloud computing are two disruptive trends nowadays, provisioning numerous opportunities to the current information technology industry and research communities while posing significant challenges on them as well. Cloud computing provides powerful and economical infrastructural resources for cloud users to handle ever increasing data sets in big data applications. However, processing or sharing privacy‐sensitive data sets on cloud probably engenders severe privacy concerns because of multi‐tenancy. Data encryption and anonymization are two widely‐adopted ways to combat privacy breach. However, encryption is not suitable for data that are processed and shared frequently, and anonymizing big data and manage numerous anonymized data sets are still challenges for traditional anonymization approaches. As such, we propose a scalable and cost‐effective framework for privacy preservation over big data on cloud in this paper. The key idea of the framework is that it leverages cloud‐based MapReduce to conduct data anonymization and manage anonymous data sets, before releasing data to others. The framework provides a holistic conceptual foundation for privacy preservation over big data. Further, a corresponding proof‐of‐concept prototype system is implemented. Empirical evaluations demonstrate that scalable and cost‐effective framework for privacy preservation can anonymize large‐scale data sets and mange anonymous data sets in a highly flexible, scalable, efficient, and cost‐effective fashion. Copyright © 2013 John Wiley & Sons, Ltd.

Nowadays, wireless sensor networks are widely used to monitor real time events and answer the ad hoc queries from a certain member node. However, computing and maintaining the information of aggregate queries in event monitoring wireless sensor networks incurs high spatial and temporal overhead for storage and transmission where potentially high volumes of unnecessary data may run through with changing time. Failure of processing that data can lead to unsuccessful event detection which can be very dangerous and costly in real world application. In order to reduce the overhead caused by unnecessary data for aggregate, suppression techniques such as data fusion, data sharing, data prediction, lossless data compression and base station side query rewriting are widely discussed in the WSN research community. In this paper, a technique which makes use of the spatial data relationship of local sensor nodes collaboratively is proposed to rein the detection of composite events with data aggregate. An empirical study is carried out to show the efficiency of the new technique. In addition, the new algorithm is compared to the previous event detection algorithms without spatial data suppression technique to demonstrate the significant performance gains.

Instead of purchasing and maintaining their own computing infrastructure, scientists can now run data-intensive scientific applications in cloud computing environment by facilitating its vast storage and computation capabilities. During the scheduling of such scientific applications for execution, various computation data flows will happen between the controller and computing server instances. Amongst various quality-of-service (QoS) metrics, data security is one of the greatest concerns to scientists because their data may be intercepted or stolen by malicious parties during those data flows. An existing typical method for addressing this issue is to apply Internet Key Exchange (IKE) scheme to generate and exchange session keys, and then to apply these keys for performing symmetric-key encryption which will encrypt those data flows. However, the IKE scheme suffers from low efficiency due to its low performance of asymmetric-key crypto logical operations over a large amount of data and high-density operations which are exactly the characteristics of scientific applications. In this paper, we propose Cloud Computing Background Key Exchange (CCBKE), a novel authenticated key exchange scheme that aims at efficient security-aware scheduling of scientific applications. Our scheme is designed based on randomness-reuse strategy and Internet Key Exchange (IKE) scheme. Theoretical analyses and simulation results demonstrate that, compared with the IKE scheme, our CCBKE scheme can significantly improve the efficiency by dramatically reducing time consumption and computation load without sacrificing the level of security.

In this paper, the existing trust models and firewall technology are studied. Then we develop an approach based on Cloud Computing to estimate dynamic context and present the definition of risk signal. A collaborative trust model of firewall-through based on Cloud Environment is proposed. The model has three advantages: Firstly, there are different security policies for different domains. Secondly, the model considers the transaction context, the historical data of entity influences and the measurement of trust value dynamically. Thirdly, the trust model is compatible with the firewall and does not break the firewall's local control policies. To verify the reliability and effectiveness of the coordinated trust model, a simulation is carried out. The simulation result shows that this trust model is robust and out-performances traditional trust models based on domains because it successfully blocks malicious access under Cloud Computing environment.

A critical issue in service composition area is how to achieve an optimized overall end-to-end quality of service(QoS) requirements by effectively coordinating QoS constraints for individual service. However, this issue has not yet been well addressed. In this paper, we propose a novel method by employing a recursive bargaining Strategy to gradually remove QoS constraint violations for Optimizing service composition execution Path. Our method mainly exploits the hidden market competitive relationships which widely exist in real business world for developing a novel bargaining strategy. Based on this strategy, concessions can be made by service providers to offer better QoS values. By recursively using bargaining strategy, an initial execution path built by a local optimization policy for service composition, can be continually updated to be close to the optimal one by reselecting better service providers for meeting overall end-to-end QoS requirements. An experiment and evaluation have been made to demonstrate the feasibility and effectiveness of our proposed method.

Current methods for querying XML data streams are mostly based on events filtering techniques. It is unavoidable for the query systems to adopt an event filter to process the incoming XML tags. It is well known that during the filtering, some data items have to be buffered before the filter can make the proper decision for adopting strategies to deal with them. Furthermore, for a single filter system, the buffer size often increases exponentially in the real application. Lots of work both theoretically and empirically, has been done to dissolve or depress that exponential explosion, but it still remains an active research topic. Considering the fast development of computer supported collaboration, we propose a new multi-filters collaborative querying technique to process XML data streams. We show that the multi-filters collaboration can effectively share and reduce the filtering space consumption if some XML document and query constraints can be satisfied. Deploying our multi-filters collaboration technique, the querying systems together can break the limitation of the concurrency lower bound, an important theoretic lower bound within XML data streams research field. The empirical study shown in this paper demonstrates that our multi-filters collaboration outperforms the limitation of the concurrency lower bound.

In research of heterogeneous information processing and integration method in enterprise management information systems, XML-based Global-as-View that is XGAV for short will be put forward in this passage. XGAV adopts such technologies as XML Schema, XSLT to describe Global-as-View formally, integration of heterogeneous information will be implemented effectively by this way. Based on XGAV, using available storage information integration platform designed and implemented by Shanhua Carpet Group Company, the testing results illustrates XGAV of dynamic flexibility and being reusable, meeting the needs of heterogeneous information processing and integrating.