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Mobile Edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider's perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This study aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) -- an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable.

Edge data caching has attracted tremendous attention in recent years. Service providers can consider caching data on nearby locations to provide service for their app users with relatively low latency. The key to enhance the user experience is appropriately choose to cache data on the suitable edge servers to achieve the service providers' objective, e.g., minimizing data retrieval latency and minimizing data caching cost, etc. However, Quality of Experience (QoE), which impacts service providers' caching benefit significantly, has not been adequately considered in existing studies of edge data caching. This is not a trivial issue because QoE and Quality-of-Service (QoS) are not correlated linearly. It significantly complicates the formulation of cost-effective edge data caching strategies under the caching budget, limiting the number of cache spaces to hire on edge servers. We consider this problem of QoE-aware edge data caching in this paper, intending to optimize users' overall QoE under the caching budget. We first build the optimization model and prove the NP-completeness about this problem. We propose a heuristic approach and prove its approximation ratio theoretically to solve the problem of large-scale scenarios efficiently. We have done extensive experiments to demonstrate that the MPSG algorithm we propose outperforms state-of-the-art approaches by at least 68.77%.

Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs. For this, the participants were provided with a novel large-scale dataset consisting of noisy-clean image pairs captured in the wild. The runtime of all models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali GPU capable of accelerating floating-point and quantized neural networks. The proposed solutions are fully compatible with any mobile GPU and are capable of processing 480p resolution images under 40-80 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper., Mobile AI 2021 Workshop and Challenges: https://ai-benchmark.com/workshops/mai/2021/. arXiv admin note: substantial text overlap with arXiv:2105.07809, arXiv:2105.07825

As many applications and services are moving towards a more human-centered design, app vendors are taking the quality of experience (QoE) increasingly seriously. End-to-end latency is a key factor that determines the QoE experienced by users, especially for latency-sensitive applications such as online gaming, health care, critical warning systems and so on. Recently, edge computing has emerged as a promising solution to the high latency problem. In an edge computing environment, edge servers are deployed at cellular base stations, offering processing power and low network latency to users within their geographic proximity. In this paper, we tackle the user allocation problem in edge computing from an app vendor's perspective, where the vendor needs to decide which edge servers to serve which users in a specific area. Also, the vendor must consider the various levels of quality of service (QoS) for its users. Each QoS level results in a different QoE level; thus, the app vendor needs to decide the QoS level for each user so that the overall user experience is maximized. To tackle the NP-hardness of this problem, we formulate it as a potential game then propose QoEGame, an effective and efficient game-theoretic approach that admits a Nash equilibrium as a solution to the user allocation problem. Being a distributed algorithm, QoEGame is able to fully utilize the distributed nature of edge computing. Finally, we theoretically and empirically evaluate the performance of QoEGame, which is illustrated to be significantly better than the state of the art and other baseline approaches.

An ultrathin and low-profile meta-lens antenna based on nonlocal Huygens' metasurface is proposed for 28-GHz band applications. The lens consist of a series of modified dipoles etched on a single-layer dielectric substrate, and there is a half-unit dislocation along y-axis between the dipoles on top and bottom surfaces. The artificial magnetism is induced between these dislocated dipoles. Due to the half-unit dislocation, the Huygens' resonance essentially is nonlocal effect. As a result, the discontinuity of metasurface is half the period (p/2, ~0.215λ 0 for 28GHz), not the period (p, ~0.43λ 0 for 28GHz). Nonlocal effect provides subwavelength scale tunable abilities for Huygens' metasurface. Then the Huygens' resonance is simulated which provides the feasibility of full transmission phase coverage. The size of lens is about 15.9λ ο ×15.9λ ο and the F/D ratio just is 0.3. Full-wave simulation demonstrates its excellent antenna radiated performance.

In mobile edge computing (MEC), edge servers are deployed at base stations to provide highly accessible computational resources and storage capacities to nearby mobile devices. Caching data on edge servers can ensure the service quality and network latency for those mobile devices. However, an app vendor needs to ensure that the data caching cost does not exceed its data caching budget. In this paper, we present the budgeted edge data caching (BEDC) problem as a constrained optimization problem to maximize the overall reduction in data retrieval for all its app users within the budget, and prove that it is NP-hard. Then, we provide an approach named IP-BEDC for solving the BEDC problem optimally based on Integer Programming. We also provide an O(k) -approximation algorithm, namely α-BEDC, to find near-optimal solutions to the BEDC problems efficiently. Our proposed approaches are evaluated on a real-world data set and a synthesized data set. The results demonstrate that our approaches can solve the BEDC problem effectively and efficiently while significantly outperforming five representative approaches.

Most existing researches on cloud workflow systems have focused on resource scheduling with the aims to minimize system delay under budget constraints or optimize system cost under deadline constraints. However, cloud providers cannot guarantee a failure-free cloud environment, a compact scheduling plan is prone to failure, thus, workflow system reliability has been identified as a critical and challenging issue in the volatile cloud environment. With the ability of cloud, it is easy for users to implement the active fault tolerance schemes, e.g., Scale-Out. However, it will lead to issues like security problem and extra management cost. In this paper, we first investigate Scale-Up and Scale-Hybrid schemes to fully explore the possibilities offered by the ability of cloud. We formally model the problem of optimizing the reliability of a cloud workflow system under budget constraints with these three fault-tolerance schemes. These optimization problems are discrete and non-convex. Thus, we propose a genetic algorithm based method for workflow fault tolerance (GA4WFT). Finally, we evaluate the effectiveness and efficiency of proposed GA4WFT with three different fault-tolerance schemes through experiments conducted on Amazon EC2 data.

Mobile edge computing has emerged as a new distributed computing paradigm that overcomes the limitations of traditional cloud computing. In an edge computing environment, an app vendor can hire computing and storage resources on edge servers for deploying their applications to deliver lower-latency services to their app users. Under a budget constraint, an optimal edge application deployment strategy allows an app vendor to deploy application instances on edge servers in a specific area and provide services to the most app users in the area. In this paper, we make the first attempt to tackle this edge application deployment (EAD) problem. Specifically, we formulate the EAD problem as a constrained optimization problem and prove its $\mathcal{NP}$ -hardness. Then, we propose an optimal approach named EAD- $opt$ to find the optimal solution of EAD based on integer programming, and an approximation approach named EAD- $apx$ to find approximate solutions in large-scale EAD scenarios efficiently. We evaluate our approaches by conducting experiments on a widely used real-world data set and a synthetic data set with comparison against two baseline approaches. The experimental results demonstrate that our approaches can solve the EAD problem effectively and efficiently.

Mobile data traffic is exploding in recent years with the exponential growth of mobile users. In the edge computing environment where edge servers are deployed around mobile users, caching data on edge servers can ensure mobile users' fast access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data cache and replacement problem with the consideration of the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we attack proactive data caching and replacement problem in the edge computing environment from the service providers' perspective, who would like to maximize their venues of caching their data. This problem is complicated because data caching produces benefits at a cost and there usually is a trade-off in-between. In this paper, we formulate the data caching and replacement problem as an integer programming problem, and maximizes the revenue of the service provider while satisfying a constraint for data access latency. We also propose an online algorithm to solve problems in large-scale scenarios. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users.

Microgrid's effective energy management is the foundation to ensure its normal operation. In this article, we considered the cost of microgrid power generation, pollution costs, and user preferences.We constructed a Stackelberg game model for multiple power companies and multiple microgrids with multiple users.Then systematically analyzes the equilibrium strategy, prove the existence and uniqueness of the game.

Nowadays, more and more EVs are connected to the power grid, bringing some load to the grid that cannot be ignored. At the same time, the charging and discharging optimization of EVs becomes extremely complicated due to the random and uncertain behavior of EV users. In past optimization, the benefits of smart grids have always been a priority, with little regard for customer satisfaction. In fact, the interests and satisfaction of users are the basis for the realization of strategy optimization. In this paper, the price and charge and discharge interaction decision between smart grid and EV is constructed into a Generalized Stackelberg model to improve the satisfaction of EV users under the condition that the grid benefit is maximized.

Mobile Edge Computing (MEC) is an emerging and prospective computing paradigm that supports low-latency content delivery. In a MEC environment, edge servers are attached to base stations or access points in closer proximity to end-users to reduce the end-to-end latency in their access to online content. From an edge infrastructure provider’s perspective, a cost-effective k edge server placement (kESP) places k edge servers within a particular geographic area to maximize their coverage. However, in the distributed MEC environment, edge servers are often subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. End-users connected to a failed edge server have to access online content from the remote cloud if they are not covered by any other edge servers. This significantly jeopardizes end-users’ quality of experience. Thus, the robustness of an edge server network must be considered in edge server placement. In this paper, we formally model this Robustness-oriented k Edge Server Placement (RkESP) problem, and prove that finding the optimal solution to this problem is $\mathcal{N}\mathcal{P}$-hard. Thus, we firstly propose an integer programming based optimal approach, namely Opt, to find optimal solutions to small-scale RkESP problems. Then, we propose an approximate approach, namely Approx, for solving large-scale RkESP problems efficiently with an O(k)-approximation ratio. Finally, the performance of the two approaches is experimentally evaluated against five state-of-the-art approaches on a real-world dataset and a large-scale synthesized dataset.

The public toilet is an important symbol to measure a country's civilization. Improving the condition of the public toilet is directly related to people's health and environment. This paper analyzes the current operation mode of public toilets in China. The background of the current reform of public toilets in China is analyzed through logical analysis, functional analysis and other research methods. We summarize the inadequacy of the current hardware equipment, management system, operation mode and other aspects of public toilets, and put forward a new concept of "netred intelligent environmental protection public toilets" based on the deep flow operation thinking. It aims to provide a new business model of "keeping toilets by toilets" for the reform and operation of public toilets in China and a theoretical basis for the improvement of public toilets.

© 2019 IEEE. Multiple multi-dimensional data streams are ubiquitous in the modern world, such as IoT applications, GIS applications and social networks. Detecting anomalies in such data streams in real-time is an important and challenging task. It is able to provide valuable information from data and then assists decision-making. However, exiting approaches for anomaly detection in multi-dimensional data streams have not properly considered the correlations among multiple multi-dimensional streams. Moreover, for multi-dimensional streaming data, online detection speed is often an important concern. In this paper, we propose a fast yet effective anomaly detection approach in multiple multi-dimensional data streams. This is based on a combination of ideas, i.e., stream pre-processing, locality sensitive hashing and dynamic isolation forest. Experiments on real datasets demonstrate that our approach achieves a magnitude increase in its efficiency compared with state-of-the-art approaches while maintaining competitive detection accuracy.

Shape morphing is at the core of future research, which shows promise for wide applications ranging from reconfigurable electronics to soft material robots. In this paper, we present a novel buckling-induced mechanism for shape morphing using dielectric elastomer actuators (DEAs), by bonding the planar precursor structure’s “feet” with the DEA. With spatially-varying electric fields applied, the inplane deformation of the DEA generates compressive loads to trigger buckling of planar precursors into desired three-dimensional configurations. To enlarge the achievable motion range at the “feet”, we develop a design optimization approach to the electrode arrangement which is concisely described by cosine functions. By numerically optimizing the cosine function coefficients, we obtain the optimal spatially-varying electrodes for various precursors with different patterns of bonding sites. The experimental results demonstrate the remarkable shape-morphing from two dimensions to three dimensional configurations. Our work paves the way to novel actuation mechanisms for shape-morphing structures, with advantages of rapid response and reversible controllability.

When the holistic containerized edge cloud data center is overloaded, VM consolidation and Dynamic Voltage Frequency Scaling cannot function well. Brownout has shown to be a promising technique to handle overloads through dynamically deactivating optimal containers. However, it cannot guarantee the efficiency of handling data center overload and the profit of edge service provider. Therefore, we propose a container selection policy based on penalty and resource utilization equilibrium PU-CSP. Considering the profit of edge service provider, it also can minimize the overload time of data center. The results show that our approach reduces about 26.14% penalty and 33.33% overload time than the approach with lowest utilization container first selection policy LUCF.

In the past decade, a large number and variety of services have been published on the Internet, e.g., public services, web services, cloud services, etc., offering customers a wide range of choices for fulfilling their demands. This has made it a challenge for customers to select from many mutually-substitutable competing services, especially when they are offered with differentiated quality. Thus, service recommendation that helps customers find the right services has become of paramount research and practical importance. The correlations in customers' preferences for different quality dimensions make service recommendation an even more complicated problem. It has not been properly addressed by existing approaches. This paper proposes DQCSR (diversified quality centric service recommendation), an approach that finds diversified services that are representative in different quality dimensions with respect to customer's quality preferences. The results of experiments conducted on a real-world dataset demonstrate the representativeness and diversity in the recommendation results produced by DQCSR.

With the rapid increase in the use of mobile devices in people's daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed in close proximity to mobile users, caching popular data on edge servers can ensure mobile users' low-latency access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data caching problem on the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we tackle this data caching problem in the edge computing environment from a service provider's perspective to maximize its data caching revenue. This problem is challenging because there is a trade-off between the benefit produced and the cost incurred by caching data on edge servers. In the meantime, the constraint for data access latency must also be fulfilled. We formulate the data caching problem in the edge computing environment as an integer programming (IP) problem and prove its NP-completeness. To solve this problem effectively and efficiently in large-scale scenarios, we propose an approximation approach to find near-optimal solutions. Experiments are conducted on a real-world dataset to evaluate our approaches.

Soft robots have recently received much interest because of their advantages over traditional rigid robots in terms of withstanding large deformations and interacting well with environments. Soft actuators, as a critical part of a soft robot, provide actuation force to generate desired motion. Dielectric elastomers have shown great promise because of their considerable voltage-induced deformation. Particularly, air-filled dielectric elastomer actuators have been well investigated, where the air inside provides prestretches to improve the actuation capability. This paper proposes a networked design of inflated dielectric elastomer actuators, capable of spanning a large actuation space and a corresponding large control space. Through comparing the performance of modulated operation mode and that of independent operation mode, we theoretically demonstrate that the networked design is able to, to a large extent, postpone the occurrence of material failures of the actuators, resulting in a large and continuous actuation range for their control.

The photoacoustic (PA) effect combing with the piezoelectric effect has been designed for trace gas detection, which has been expected to be operational over a wide temperature range. In this paper, the lead-free piezoelectric crystals LiNbO3 (LN), NdCa 4 O(BO 3 ) 3 (NdCOB) and $\alpha-$BiB 3 O 6 (BIBO) are studied and compared for potential PA trace gas detection. The piezoelectric voltage coefficients of LN $(g21)$, NdCOB $(g12)$ and BIBO $(g21)$ were characterized and found to be on the order of 0.026 Vm/N, 0.047 Vm/N and 0.225 Vm/N at room temperature, respectively. Among the three kinds of piezoelectric crystals, the BIBO crystal possesses the highest longitudinal piezoelectric coefficient $d_{22}$ and the largest piezoelectric voltage coefficient $g_{22}$, being on the order of 40 pC/N and 0.538 Vm$\bullet$/N, respectively. The piezoelectric voltage coefficients for NdCOB and BIBO crystal were obtained to show good temperature stability and maintained the similar values over the temperature range, while the value of $g_{21}$ for LN crystal showed a decrease with increasing temperature. Taking advantage of the strong piezoelectric activity and high mechanical quality factor Q of BIBO crystal, piezoelectric transducer with central resonance frequency of $\sim$443 kHz was fabricated for PA trace gas detection. The final detection limit for trace sulfur hexafluoride (SF 6 ) gas was extended to the parts-per-quadrillion level.

Accurate measure of hacking skills level of a person allows to develop a response plan, whether it is for a cyber security learners or facing an adversary. Numerous attempts have been made to rank hackers, qualitatively. We introduce a quantitative approach that calculates the probability of performing certain cyber security actions and match them to well-known hackers ranks. The calculated probabilities are used to design a special-purpose system that measures the hackers' skills level and rank them. Our system uses a machine-learning approach - it starts with statistically calculated or observed initial values, then it can be trained using data generated either by calculating a weighted sum using a neural networks or by further statistics-based estimation and observation. The initial values were gathered and calculated against the participants of a number of hacking competitions. The probabilistic system is controlled in a manner that it allows the admin/owner to design tasks and challenges with a specific difficulty to examine the systems users skills. Alternatively, it can be designed in an open-end manner which can rank the user's skill based on the level reached. To demonstrate our system, we analysed the hacking skills for 8 university students based on their training results.

A 24-GHz high linearity down-conversion mixer in 90-nm CMOS is presented in this paper. The mixer utilizes folded architecture, LC tank, distributed derivative superposition (DS) linearization technique to achieve high linearity with relatively low power. The mixer achieves 0 dBm $\boldsymbol{IP}_{1\mathbf{dB}}$ . The mixer provides −3 dB conversion gain and the IIP 3 is 21 dBm with only 10-mW dc consumption.

Trajectory based operation (TBO) is a future operational concept for the air traffic management. This paper focuses on the trajectory optimization for the arrival phase in the TBO environment. The trajectory management process for future operation of arrival phase is described firstly. Then, an optimization schedule model is proposed to assign conflict-free controlled times of arrival (CTAs) at the key waypoints for aircraft. The average arrival delay, flight distance in terminal and balance of runway traffic flow are considered in the proposed model. The speed profile adjustment algorithm based on the assigned CTA is also presented. Finally, the simulation results at Beijing capital international airport is shown to valid the effectiveness of the developed trajectory optimization method.

Soft robots have recently evoked extensive attention due to their abilities to work effectively in unstructured environments. As an actuation technology of soft robots, dielectric elastomers exhibit many intriguing attributes such as large strain and high energy density. This work presents a novel dielectric elastomer based soft crawling robot inspired by inchworms. To fill the need of control of the soft robot, a model describing the interaction between the dielectric elastomer actuator and the environment is proposed, which takes inertia, viscoelasticity and friction into consideration. The model can well describe the robot's dynamic performances and the modelling approach used here can be extended to other dielectric elastomer actuators with complicated geometries for control purposes. The obtained model allows us to design a feedforward plus feedback control scheme for the robot to achieve desired motion. Simulation shows fast response and good tracking performances which are further confirmed by the experiments.

Piezoelectric materials functional at elevated temperatures without failure are desirable for various piezoelectric applications. In this paper, the lithium niobate (LiNbO 3 , LN) crystal and the radial extensional (RE) properties were studied. Based on the crystal symmetry, the equations to evaluate the k p value with Z- and Y-oriented discs were deduced. Using the deduced equations, the k p values of the Z-oriented and Y-oriented LN crystal discs were calculated and obtained to be 3.83% and 24.7%, respectively. In addition, the radial extension mode was studied experimentally by impedance method, where the k p values were found to be on the order of 3.80% and 24.5%, respectively. Moreover, the temperature dependence of RE electromechanical properties were investigated from room temperature to 500oC, where the variation of the k p was found to be p value and minimal temperature dependent behavior of Y-oriented crystal cuts indicating the potential uses for high temperature piezoelectric application.

Soft robots, primarily made out of intrinsically soft materials, have flourished greatly in the past decade due to their advantages such as flexibility and adaptability over rigid-bodied robots. A rich repertoire of soft robots designed from intuitive or biomimetic approaches have been developed to provide new solutions for robots. However, these design approaches are limited by the designers' experience and inspiration, and a systematic design methodology for soft robots is still missing. We tackle this issue by mathematically recasting the design problem under the framework of topology optimization problem. To demonstrate the effectiveness of the proposed methodology, in this paper, we develop a pneumatically actuated soft gripper consisting of three fingers, each finger is able to undergo a free travel bending and deliver a grasping force. Hence, each gripper finger is designed as a continuum compliant mechanism to achieve its maximal bending deformation. The proposed soft gripper with complex shape is directly fabricated through 3D printing technology. Experimental results show that the deflected soft finger is able to achieve a 41° free travel bending and generate 0.68N blocked force upon 0.11MPa actuation pressure. This work represents an important step towards the goal of designing soft robots automatically.

A 4.6-GHz Class-F−1 CMOS power amplifier implemented in 0.18-μm CMOS process for wireless applications with high output power and high efficiency is proposed. By using harmonics tuning technique and cascode devices, the proposed power amplifier achieves an output power of 27.8-dBm with power-add-efficiency (PAE) of 32% and quiescent 158-mW dc power consumption.

Soft actuators play an important role in producing motions in soft robots, and dielectric elastomers have shown great promise because of their considerable voltage-induced deformation. In particular, air-filled dielectric elastomer actuators have been well studied, where the air inside provides prestretches to improve the actuation range. This paper proposes a network of inflated dielectric elastomer actuators, interconnected via a chamber, with the advantages to be highly deformable and continuously controllable. Theoretical analyses show that the networked design is able to largely postpone the occurrence of material failures of the actuators, resulting in a large and continuous actuation range for their control. We further carried out experiments for validation, and the results were largely in line with the theoretical predictions. These findings essentially provide insight into developing networked soft actuators, for achieving large actuation capability.

High temperature piezoelectric materials serving at harsh environment without failure are desired for structural health monitoring and/or non destructive evaluation of the modern aerospace structures at elevated temperatures (above 600°C). In this report, different high temperature piezoelectric crystals, including the langasite (LGS) type crystals and GaPO4 in trigonal system, melilite type and fresnoite type crystals in tetragonal system, and oxyborate crystals in monoclinic system are surveyed, and their properties are compared. Among the LGS type piezoelectric crystals, the structural ordered trigonal Ca3TaGa3Si2O14 (CTGS) crystals as well as the Al substituted CTGS crystals shew relative low dielectric loss (

This paper describes the design of 5-GHz fully integrated CMOS class-F−1 power amplifier (PA) for wireless transmitter applications in 0.18-μm CMOS process. The proposed class-F−1 PA employs the cascode topology with 350-nm common gate device for stability consideration, and the conventional output matching network of class-F−1 PA is replaced by a transformer and a shunt capacitor. The proposed PA exhibits 26.2-dBm saturation power (Psat) at 4.8 GHz, and 41% peak power added efficiency (PAE). The output power at 1-dB compression point (OP1dB) is 25.1 dBm with 4-V supply voltage.

Dielectric elastomers show great promise for soft actuators. As the allowable deformation range of dielectric elastomers is strictly limited by the material failures, the actuation ability of a dielectric elastomer actuator is accordingly bounded. Although the material failures have been well studied, little literature has been released regarding the maximum actuation ability of a dielectric elastomer actuator. Taking a dielectric elastomer balloon as an example, this paper proposes a novel concept of the actuation capability of a dielectric elastomer balloon actuator concerning not only quasi-static states but also dynamic states, analyzes the actuation capability in terms of various operating conditions of the balloon actuator, and thus suggests how to operate the balloon actuator in order to obtain the largest actuation range. This new concept offers a significant guidance for applications of the balloon actuator.

This paper develops a method to learn very few discriminative part detectors from training videos directly, for action recognition. We hold the opinion that being discriminative to action classification is of primary importance in selecting part detectors, not just intuitive. For this purpose, part selection based on feature selection is proposed, employing SVM method. Firstly, large number of candidate detectors are trained using k-means and Exemplar-LDA techniques in whitened feature space. Secondly, each candidate part detector is regarded as a visual feature, so that detector selection can be achieved by feature selection. Detectors with larger weight, indicating more discriminative, will be selected. Meanwhile, to keep space-volume structure information, we use the novel method saliency-driven pooling to form feature primitives which are concatenated into mid-level feature vector. Finally, we conduct experiments on three challenging action datasets (KTH, Olympic Sports, HMDB51) and the results outperform the state-of-the-art.

In this report, the dielectric and hydrostatic piezoelectric properties of the monoclinic bismuth triborate crystals (BiB3O6, BIBO) were investigated at ambient temperature for potential hydrophone application. The pressure dependence of the hydrostatic charge and voltage coefficients dielectric properties (dh and gh), and hydrostatic figure of merit (FOM) dh×gh, were also studied. Results indicated that the BIBO crystals possess low dielectric loss being on the order of

Multi-tenant service-based systems (SBSs) have become a major paradigm in software engineering in the cloud environment. Instead of serving a single end-user, a multitenant SBS provides multiple tenants with similar and yet customised functionalities with potentially different quality-of service (QoS) values. Thus, existing approaches to service selection for single-tenant SBSs are no longer suitable. Furthermore, the target multi-tenancy maturity level also needs to be considered in the service selection approach for an SBS. In this paper, we propose three novel QoS-aware service selection approaches for composing multi-tenant SBSs that achieve three different multi-tenancy maturity levels. Extensive and comprehensive experiments are conducted and the experimental results show that our approaches outperform the existing approach in both effectiveness and efficiency.

In cloud environments, IT solutions are delivered to users via shared infrastructure, enabling cloud service providers to deploy applications as services according to user QoS (Quality of Service) requirements. One consequence of this cloud model is the huge amount of energy consumption and significant carbon footprints caused by large cloud infrastructures. A key and common objective of cloud service providers is thus to develop cloud application deployment and management solutions with minimum energy consumption while guaranteeing performance and other QoS specified in Service Level Agreements (SLAs). However, finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance is an extremely challenging task, which requires the evaluation of system performance and energy consumption under various workloads and deployment configurations. In order to simplify this process we have developed Stress Cloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. Stress Cloud supports the modelling of realistic cloud application workloads, the automatic generation of load tests, and the profiling of system performance and energy consumption. We demonstrate the utility of Stress Cloud by analysing the performance and energy consumption of a cloud application under a broad range of different deployment configurations.

With the development of comprehensive transportation system, the competition among various harbors becomes more intense. The paper proposes the evaluation method of container harbor's competition based on the discrete mutation — topsis matter element and establishes a model against the actual harbor's presentation and gives a solution. By using this method to build the port competitiveness evaluation method which serves as reference for the objective a proper understanding and evaluation of port competitiveness, the port can find out the weak links and promote its improvement correspondingly.

Runtime anomalies occurring to service-based systems (SBSs) must be located and fixed in a timely manner in order to guarantee successful delivery of outcomes in response to user requests. Monitoring all component services constantly is impractical due to excessive resource consumption. Inspecting all component services upon anomalies is time-consuming and thus also impractical. In this work, we propose a novel approach that employs spectrum-based fault localisation techniques to locate runtime anomalies in SBSs. Large-scale experiments are conducted and experimental results are presented to demonstrate the effectiveness and efficiency of the proposed approach.