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Multicasting does not work well for video applications such as video on demand, where individual users request video content at discrete and unpredictable moments. In this paper, we study a concurrent video multicast orchestration solution for the caching-assisted and centrally controlled mobile networks. The proposed solution strives to obtain rapid video prefetch and effective traffic reduction. By rapid video prefetch, user devices can cache required contents in advance, which can effectively improve the streaming fluency in unsteady wireless environments, especially the highly mobile vehicular environment. We introduce a maximum-rate concurrent multicast (MRCM) problem whose solution is intended to maximize the total video delivery rate by a concurrent multicast manner, and prove that the MRCM problem is NP-hard. Then, we propose a dynamic concurrent multicast tree construction algorithm and a global concurrent multicast tree optimization algorithm to approximately solve the MRCM problem in polynomial time. We also introduce a cache competition mechanism that strives to maximize the total cache hit rate when the cache space is insufficient. Performance evaluations show that compared with the existing cache-assisted multicast delivery solutions, the proposed solution significantly reduces the video delivery duration and the traffic stress of backhaul links.

Human–computer interaction (HCI) plays a vital role in modern intelligent systems, such as brain-machine integration, human action recognition, telemedicine, and somatosensory game. A decision support system is a combination of the information system and decision-making technology. Visual human–computer interaction decision-making is a key technique in the decision support system. This paper proposes a new visual decision-making system applicable for industrial applications, i.e. data mining topics. To assist the performance of the proposed visual decision-making system, it is designed for data mining technique applications. Furthermore, the architecture of the decision support system is analyzed based on practical data mining case study. The comprehensive experiment shows that the proposed method is effective and robust in comparison to other methods.

Background Medical image data, like most patient information, have a strong requirement for privacy and confidentiality. This makes transmitting medical image data, within an open network, problematic, due to the aforementioned issues, along with the dangers of data/information leakage. Possible solutions in the past have included the utilization of information-hiding and image-encryption technologies; however, these methods can cause difficulties when attempting to recover the original images. Methods In this work, we developed an algorithm for protecting medical image key regions. Coefficient of variation is first employed to identify key regions, a.k.a. image lesion areas; then additional areas are processed as blocks and texture complexity is analyzed. Next, our novel reversible data-hiding algorithm embeds lesion area contents into a high-texture area, after which an Arnold transformation is utilized to protect the original lesion information. After this, we use image basic information ciphertext and decryption parameters to generate a quick response (QR) code used in place of original key regions. Results The approach presented here allows for the storage (and sending) of medical image data within open network environments, while ensuring only authorized personnel are able to recover sensitive patient information (both image and meta-data) without information loss. Discussion Peak signal to noise ratio and the Structural Similarity Index measures show that the algorithm presented in this work can encrypt and restore original images without information loss. Moreover, by adjusting the threshold and the Mean Squared Error, we can control the overall quality of the image: the higher the threshold, the better the quality and vice versa. This allows the encryptor to control the amount of degradation as, at appropriate amounts, degradation aids in the protection of the image. Conclusions As shown in the experimental results, the proposed method allows for (a) the safe transmission and storage of medical image data, (b) the full recovery (no information loss) of sensitive regions within the medical image following encryption, and (c) meta-data about the patient and image to be stored within and recovered from the public image.

With the rapid development of virtualization technology, OpenStack and Kubernetes as virtualization service have gradually become the core content of cloud computing. Virtualization technology is transforming to the direction of containerization. Many enterprise users are gradually increasing their support for container images while using virtual machine images. The number and scale of virtualized resource images have increased significantly. For example, there are currently more than 7 million container images stored in the Docker hub alone. In addition, the increase in private registries has also increased the performance and capacity requirements of the image storage system. Because there are a large number of redundant data and redundant files in the image file, using its high redundancy to reduce duplicate data can greatly reduce the ever-increasing registry storage requirements. Based on the characteristics of virtual machine images and container images, this paper proposes and designs a high-efficiency storage system for virtualized resource images given the large volume of virtualized resource images and high storage occupation. Through this system, the utilization rate of image storage can be improved and reduced the cost of store images.

For the Social Internet of Things (SIoT), the interaction among ever increasing number of smart devices results in an exponential increase of services, which leads to an extreme difficulty for users to find suitable services. To address this issue, most existing recommendation algorithms are based on the data stored on the centralized server and distributed schemes are ignored. Meanwhile, distributed recommendation algorithms face the problems of privacy leakage and efficiency, which decrease the quality of experience (QoE). Therefore, we propose a novel SIoT service recommendation scheme called SIoT- SR, which adopts LSH Forest while combining with collaborative filtering algorithm to predict the Quality of Service (QoS) data of users. The LSH forest implements binary search by sorting and also has the ability to self-correct parameters. It can achieve a good tradeoff among memory, accuracy, efficiency, and privacy. Finally, we validate the effectiveness of the scheme based on the dataset WS-DREAM. The experimental results show that SIoT-SR has high prediction accuracy and efficiency while saving computing resources and are suitable for service recommendation of SIoT with resource-constrained devices.

This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details.

Background The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis. Methods This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time. Results Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. Discussion Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising. Conclusions Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

At present, there is no unified network resource scheduling and business optimization system in cloud-edge collaboration services with supercomputing as the core, especially network transmission and data management. In this poster, we focus on how to optimize the quality of user experience while meeting huge computing power requirements, and design a novel network resource scheduling and traffic optimization platform centered on supercomputing system, which is called PANGU. PANGU is a multi-dimensional network resource scheduling solution for supercomputing that can guarantee collaborative network services. PANGU can make up for the lack of computing power of edge computing while solving the adaptability of supercomputing nodes to pervasive cloud edge computing. Through effective network resource management, PANGU can provide higher productivity.

The cloud-edge integration service model combines the advantages of computing capabilities both from cloud and edge. Therefore, the data centers with cloud-edge integrated are an irreversible trend for the evolution of future data center. Software-Defined Network (SDN), emerging as a novel network model, separates content forwarding and control, and that makes resource management across data center network more efficient. This article focuses on the network data transmission and management for future data centers. First, it reviews measurement, analysis, and monitoring methods meant for new features of global SDN network. Then it focuses on unified management of SDN resources like traffic scheduling theory for cross-domain data centers based on cloud. Specifically, we proposed a novel fault response mechanism across the network with a more precise location and less response time. With dynamic changes of cloud computing and edge computing services combined, global QoS control and QoE optimization methods are proposed correspondingly. Finally, a set of SDN control platforms supporting the functions mentioned above are formulated. We hope that our work will shed some new light and provide new theoretical support for cloud-edge-combined cross-domain data center network architecture.

For reliable multicasting, packet retransmission is an important mechanism in ensuring lossless data delivery. Existing packet retransmission methods cannot effectively address the recovery redundancy problem. This paper presents an OpenFlow-enabled elastic loss recovery solution, called ECast, for reliable multicasting. It employs a novel communication method, called elastic area multicast (EAM), to better retransmit the multicast recovery packets in OpenFlow-enabled networks. With the flexible flow-matching ability of OpenFlow switches, any subtree of a multicast tree can be used as an EAM tree. EAM approximately implements an elastic local multicast. Therefore, it can effectively alleviate the recovery redundancy with good scalability. We introduce an optimal EAM-based packet retransmission orchestration problem, named outdegree-constrained minimum-cost loss recovery (OCMCLR), which finds the EAM-based packet retransmission scheme with the minimum recovery cost. ECast uses an optimized tree-based feedback method to obtain the status of packet losses, and uses a fast cluster-based algorithm to approximately solve the OCMCLR problem based on the collected packet loss status.

Polar harmonic transforms (PHTs) are a kind of continuous orthogonal moments (COMs) that have excellent image description capability. To improve the anti-noise and reconstruction performance of PHTs, integer-order PHTs are generalized to order by a set of novel decimal-order polar harmonic transforms (DPHTs). On this basis, in combination with trinion theory, the DPHTs are extended to trinion decimal-order polar harmonic transforms (TDPHTs) applicable to color images. The color image is processed as a whole, and the internal relations among three components of color images are fully reserved and utilized. Compared with quaternion color image processing, trinion color image processing effectively avoids information redundancy and improves computational efficiency. Color image reconstruction and zero-watermarking algorithm experiments show that TDPHTs have excellent color image description ability and robustness.

The transmission of medical image data in an open network environment is subject to privacy issues including patient privacy and data leakage. In the past, image encryption and information-hiding technology have been used to solve such security problems. But these methodologies, in general, suffered from difficulties in retrieving original images. We present in this paper an algorithm to protect key regions in medical images. First, coefficient of variation is used to locate the key regions, a.k.a. the lesion areas, of an image; other areas are then processed in blocks and analyzed for texture complexity. Next, our reversible data-hiding algorithm is used to embed the contents from the lesion areas into a high-texture area, and the Arnold transformation is performed to protect the original lesion information. In addition to this, we use the ciphertext of the basic information about the image and the decryption parameter to generate the Quick Response (QR) Code to replace the original key regions. Consequently, only authorized customers can obtain the encryption key to extract information from encrypted images. Experimental results show that our algorithm can not only restore the original image without information loss, but also safely transfer the medical image copyright and patient-sensitive information.

Obstructive sleep apnea syndrome (OSAS) is a breathing disorder presenting during sleep. Although polysomnography (PSG) is the gold standard to diagnose OSAS, it is an expensive method that is quite complicated to use. Worse, it takes a long time between testing and getting a diagnosis from PSG. Thus, we have designed an algorithm aimed at diagnosing OSAS in a more efficient manner. First, blood oxygen saturation (SpO2) data are processed to obtain statistical features, which are then trained to establish a classification model based on a support vector machine (SVM) strategy; the resulting SVM model performs the diagnosis of OSAS. Furthermore, in order to allow remote diagnosis, we combine our algorithm with a monitoring system. To achieve this, physiological data are collected from a smart phone and then uploaded to the SVM model in the cloud. Once processed, a diagnosis report is returned to the smart phone. A preliminary evaluation of our algorithm based on real-world data is extremely promising as we find its accuracy, sensitivity, and specificity to be 90.2%, 87.6%, and 94.1%, respectively.

The unique input/output (UIO) sequence is an important state identification technique in the FSM-based protocol conformance testing. However, some states of a FSM might have no UIO sequence. To address the above problem, this paper introduces an extended UIO sequence, called a GUIO sequence, which distinguishes a designated state from the remaining states by a group-by-group means. The problem of searching the optimal GUIO sequence is NP-hard. In this paper, we present a GUIO search method based on a greedy heuristics and a @f-hop search approach. The proposed method can obtain desirable GUIO sequences with relatively low computational complexity.

This paper presents a two-tiered reliable application layer multicast (ALM) solution, called HRALM, to provide lossless ALM services. HRALM builds a domain-based multicast tree, and divides the members into two transport planes (i.e., Plane 1 and Plane 2) in terms of the tree. In any domain, the distance between each member and the domain header is below a given threshold, which improves the capability of topology-awareness. According to the loss detected by members in different planes, HRALM adopts different but correlated recovery solutions. In HRALM, a member duplicates and forwards the received recovery packet to each of its children if it has not received the data unit carried by the recovery packet before receiving the recovery packet, which can actively recover the loss at the downstream nodes. The simulation experiments show that HRALM has desirable transport and recovery performance.

Model checking is an important technique for ensuring the correctness of investigated system. However, the model checking tools subject to the state-space explosion problem, which is an ignored hurdle to the practical application of the technique. This paper presents a case study of model checking the business flow of retail banking System, through an example of verifying automatic teller machine (ATM) with SPIN. We present the specific approach to effectively abstract the related part of ATM system, and give our experiment results. The verification results show that model checking is feasible technique for verifying the ATM system.

This paper proposes a service-oriented domain architecture designing model to solve numerous legacy systems and complex business requirements in the IT field. This model is centered by the architecture and varied with demands. The bank field is taken as a case to discuss emphasisly how to design domain architecture to exhibit the service and application characteristics and to adapt to their variabilities. It provides an architecture design reference for flexible construction of applications and reuse of existing resources.

Requirement traceability matrix (RTM) is an important static requirement tracing method which is still applied widely in software projects. But traditional RTM mainly support vertical traceability which is started from functional requirements to the other software products, such as design documents, source code and test cases, and usually leads to lower efficiency of defects modification and respondence of requirement changes for neglect of lateral traceability between functional requirements. We put forward an improved RTM based on ontology by which we could trace not only vertical but also lateral functional requirements. First, an ontology of RTM was constructed in which we defined the classes and their relations of RTM. Then, instances or individuals of this ontology needed to be built to support some relevant ontology queries for lateral traceability. Finally, we applied this improved RTM in banking business system.

In the protocol conformance testing, many existing test methods can effectively detect the possible faults of the implementation under test. However, it is difficult to diagnose the found faults in terms of the test results. This paper presents a diagnosable input/output (DIO) sequence, to differentiate a state from other states under a given condition. We further propose a two-tier protocol conformance testing and diagnosing method based on DIO sequences. The proposed method can effectively detect and diagnose the possible faults of the implementation of a protocol.