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Chronic actinic dermatitis (CAD) is a common debilitating photodermatosis. Patients often have to completely avoid outdoor activities, which severely impacts their quality of life. Phototherapy is effective for CAD and seems to increase patients' tolerance towards sunlight and consequently decrease the extent of disease. Unfortunately, the slower onset and time-consuming nature of phototherapy limits the clinical application. Considering the effectiveness and time-saving nature of ultraviolet (UV)-A rush hardening in solar urticaria, we performed a pilot study to determine whether UV-A rush hardening is effective in CAD. Six patients with CAD were exposed to multiple sessions of UV-A for 4-5 days at 1-h intervals/day. Subsequently, maintenance UV-A exposure was performed at 1-2-week intervals. Phototesting at baseline showed that three patients were sensitive to both UV-A and -B, and the other three patients only showed UV-A sensitivity. All of the patients responded well to UV-A rush hardening and four (67%) maintained a good remission status after 1 year. The results of this pilot study suggest that UV-A rush hardening phototherapy is effective and well tolerated in the treatment of CAD, while future larger prospective studies using objective scores of disease activity and quality of life are needed.

While much research has been devoted to algorithm improvement of the machine learning model for multimedia applications, relatively little research has focused on the acquisition of massive multimedia datasets with strict data demands for model training. In this paper, we propose a Quality-of-Information (QoI) aware incentive mechanism in multimedia crowdsensing, with the objective of promoting the growth of an initial training model. We begin with a reverse auction incentive model to maximize social welfare while meeting the requirements in quality, timeliness, correlation, and coverage. Then, we discuss how to achieve the optimal social welfare in the presence of an NP-hard winner determination problem. Lastly, we design an effective incentive mechanism to solve the auction problem, which is shown to be truthful, individually rational and computationally efficient. Our evaluation study is carried out using a real multimedia dataset. Extensive simulation results demonstrate that the proposed incentive mechanism produces close-to-optimal social welfare noticeably, while accompanied by accelerating the growth of the machine learning model with a high-QoI dataset.

Background Boxing programs designed specifically for people with Parkinson's disease (PD) can be beneficial in improving the physical, mental, and functional health of persons with PD. Purpose This study examined the effect of a boxing program, Rock Steady Boxing (RSB), on the health of people with PD from both the boxers' and their caregivers' perspectives as well as evaluated balance, quality of life (QoL), and depressive symptoms after 12 weeks of RSB. Methods A mixed methods, one-group experimental design with focus groups were embedded within an intervention study. Six boxers completed baseline and 12-week postassessments. Results The majority of boxers maintained or improved scores for balance, QoL, and reduced depressive symptoms. Findings indicate physical and mental benefits of RSB among persons with PD and this was further validated by qualitative data from boxers and caregivers, along with quantitative data of boxers. Caregivers and boxers reported that boxers' ability to perform activities of daily living (ADLs) improved and is supported by quantitative improvements in the Parkinson's disease Questionnaire-39 ADL subscale. All boxers reported reduced stigma, and caregivers and boxers valued the opportunity to work out in an environment free of stigma, a finding not previously reported. Conclusions It is important for nurse practitioners to recognize exercise benefits for people with PD and recommend the program to patients and include caregivers in their overall assessment of health and wellness. Implications for practice Nurse practitioners and other health practitioners may consider recommending a boxing program, such as RSB, for their PD patients as a sole or supplemental exercise program.

To improve the scalability and elasticity of application deployment and operation in cloud computing environments, new architectures and techniques are developed and studied, e.g., microservice architecture, and Docker container. Especially, Docker container enables the sharing on operation system and supporting libraries, which is more lightweight, prompt and scalable than Hypervisor based virtualization. These features make it ideally suit for applications deployed in microservice architecture. However, existing models and schemes, which are mostly designed for Hypervisor based virtualization techniques, fall short to be efficiently used for Docker container based application deployment. To take the benefits of microservice architecture and Docker containers, we explore the optimization of application deployment in cloud data centers using microservice and Docker containers. Our goal is to minimize the application deployment cost as well as the operation cost while preserving service delay requirements for applications. In this paper, we first formulate the application deployment problem by examining the features of Docker, the requirements of microservice-based applications, and available resources in cloud data centers. We further propose a communication efficient framework and a suboptimal algorithm to determine the container placement and task assignment. The proposed algorithm works in a distributed and incremental manner, which makes it scalable to massive physical resources and diverse applications under the framework. We validate the efficiency of our solution through comparisons with three existing strategies in Docker Swarm using real traces from Google Cluster. The evaluation results show that the proposed framework and algorithm provide more flexibility and save more cost than existing strategies.

As an extension of remote cloud data centers, cloudlets process the workloads from mobile users at the network edge, thereby satisfying the requirements of resource-intensive and latency-sensitive applications. One of the fundamental yet important issues for cloudlet infrastructure providers (ISP) is how to plan the placement and capacities of cloudlets so that minimize their long-term cost with a guarantee on service delay. However, existing work mostly focuses on resource provision or resource management for mobile services on existing cloudlets, while very little attention has been paid to the cloudlet placement and capacity planning problem. In contrast to those studies, we aim to optimize the long-term total cost of cloudlets&rsquo, ISPs through intelligently planning the location and capacities of cloudlets under constraints on the service delay experienced by mobile users. This problem is then decomposed into two sub-problems and algorithms are devised to solve it. Evaluations on randomly generated traces and real traces exhibit the superior performance of the proposed solution on saving ISP&rsquo, s long-term cost.

Incorporating link correlation awareness into wireless network protocols to facilitate data transmission is an important research issue. In this paper, we focus on link correlation based security threat and countermeasure in wireless networks. By taking advantage of the vulnerability of beacon-based link correlation measurement and the blind spot of malicious node detection mechanisms, we design a new type of link correlation spoofing attack (LCSA) to decrease protocol performance by distorting link correlation information while escaping the tracking of any watchdog and trust systems. Typical cases are analyzed to quantify how the LCSA covertly weakens protocol performance. We also propose beacon-trap (BT), a countermeasure embedded in the beacon-based link condition measurement protocol. Using link diversity as a cover, BT sets traps in the beacon sending sequence to ambush malicious nodes that launch LCSAs without extra control overhead. The performance of BT is not affected by changes in the size of a network or the distribution of nodes. Numerical results demonstrate the superiority and effectiveness of BT against LCSAs in terms of malicious node detection success rate and speed under different parameter settings.

Wireless cognitive sensor networks arranged in smart community intelligently use the idle channels that alleviates spectrum scarcity. To enhance the connectivity of network, the existing spectrum-aware clustering algorithms group sensors into clusters due to the sensed idle channels of each sensor. However, a clustering metric should depend on various evaluation criteria. This poster proposes a novel spectrum-aware clustering algorithm to obtain more reasonable clustering, where a new weighted clustering metric is used to elect clusterheads. After clustering, the clusterheads sense spectrum instead of all nodes that greatly reduces the energy consumption and efficiently increase the network throughout.

The grid-based target localization for internet of vehicle has the property of sparsity, which can be transformed to a sparse recovery problem due to compressive sensing. The accurate target localization relies on the sufficient measurements, but we cannot determine the number of measurements without knowing the number of targets. In this poster, we propose a novel target localization algorithm based on sequential compressed sensing to select the optimal number of measurements and estimate target locations via l p -norm (0 0 -norm or l 1 -norm minimization.

Aiming to minimize the operators' cost while preserving user experience, we propose a resource scheduling mechanism for cooperative cloudlets in edge computing with a centralized controller. The interactions between cloudlets and the controller are formulated as a two-stage Stackelberg game to determine the amount of physical resources assigned to each cloudlet during deployment phase and the price of resources shared among cooperated cloudlets during operation phase.

In traditional mobile crowdsensing (MCS) applications, the crowdsensing server (CS-server) need mobile users’ precise locations for optimal task allocation, which raises privacy concerns. This work proposes a framework P2TA to optimize task acceptance rate while protecting users’ privacy. Specifically, edge nodes are introduced as an anonymous server and a task allocation agent to prevent CS-server from directly obtaining user data and dispersing privacy risks. On this basis, a genetic algorithm that performed on edge nodes is designed to choose an initial obfuscation strategy. Furthermore, a privacy game model is used to optimize user/adversary objectives against each other to obtain a final obfuscation strategy which can be immune to posterior inference. Finally, edge nodes take user acceptance rate and task allocation rate into account comprehensively, focusing on maximizing the expected accepted task number under the constraint of differential privacy and distortion privacy. The effectiveness and superiority of P2TA to the exiting MCS task allocation schemes are validated via extensive simulations on the synthetic data, as well as the measured data collected by ourselves.

To address restrictions on data collection, incentivizing multimedia data acquisition for machine learning system is proposed. This paper presents an effective QoI (Quality-of-Information)-aware incentive mechanism in multimedia crowdsensing, with the objective of promoting the growth of an initial training model. Firstly, an incentive model is constructed in the form of reverse auction to maximize the social welfare while meeting the requirements in quality, timeliness, correlation and coverage. Then, we discuss how to achieve the optimal social welfare in the presence of an NP-hard winner determination problem. Lastly, a practical incentive mechanism to solve the auction problem is designed, which is shown to be truthful, individually rational and computationally efficient. Extensive simulation results demonstrate the proposed incentive mechanism produces close-to-optimal social welfare noticeably and high-QoI dataset is obtained. In particular, a significant performance improvement for machine learning model growth is achieved with lower complexity.

In this paper, a directional antenna based opportunistic routing (DAOR) scheme is proposed for cognitive radio sensor networks (CRSNs) with QoS assurances and energy efficient design. Specifically, based on the investigation and understanding about how directional antenna operation affects spectrum access and route selection, a joint optimization problem is formulated. After dividing angular domain into multiple antenna sectors with fixed beamwidth and direction, an approximate strategy is presented to determine the antenna sector and transmission channel. With obtained antenna and channel parameters, a heuristic algorithm is further designed to construct prioritized permutation of forwarding candidates, aimed at reducing computational complexity while approaching the optimal solution. Simulation results demonstrate that DAOR outperforms existing QoS routing schemes in terms of QoS provisioning and energy efficiency.

With the further development of oilfield, liquid-gas separation has become an essential problem. Cylindrical cyclone separators are popular in the industrial process due to the advantage that they are simple, compact, and inexpensive to manufacture. In this paper, a three-dimensional turbulence model including Reynolds stress model was established to describe the mixture flow field in the separator. Through the numerical simulation, the separation efficiency was investigated under different parameter cases such as separator length, gas phase outlet diameter, and inlet shape. It can be indicated from the simulation results that the separation efficiency decreases with the increase of the separator length, and the separation efficiency increases firstly and then decreases with the increase of the gas phase outlet diameter as well as the liquid phase outlet. Furthermore, the rectangular inlet is more suitable than the circular inlet with the separation efficiency changing from 66.45% to 79.04%. In the end, the optimal geometrical structure was presented with separation efficiency of 86.15%.

As the information era is coming, more and more sensitive information will be encrypted into databases for secure accessing. This paper focuses on encrypting String data in databases and provides a method to build index for sensitive attributes. The index is composed of partition value, characteristic value and character information value. So the index can be used to filter most of unmatched records, and reduce the number of records need decode. Two-phase query is used in the proposed method. The first phase executes query on the index to filter most unneeded records; the second phase decodes the result set and executes exact query on it.

Compressed sensing is a new concept in signal processing where one seeks to minimize the number of measurements to be taken from signals while still retaining the information necessary to approximate them well. Nonlinear algorithms, such as l1 norm optimization problem, are used to reconstruct the signal from the measured data. This paper proposes a maximum entropy function method which intimately relates to homotopy method as a computational approach to solve the l1 optimization problem. Maximum entropy function method makes it possible to design random measurements which contain the information necessary to reconstruct signal with accuracy. Both the theoretical evidences and the extensive experiments show that it is an effective technique for signal reconstruction. This approach offers several advantages over other methods, including scalability and robustness.