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Multitarget tracking technology is the core topic in the field of intelligent driving. Multi-target complex manoeuvring, measurement outliers and unknown environmental prior parameters strongly affect the tracking accuracy of the target state. To address the accurate tracking of multitarget under the above complex working conditions, we propose a new multitarget tracking algorithm, named the Multiconstrained Generalized Probabilistic Data Association Filtering (MCGPDAF) algorithm. In this algorithm, we use the target position and heading information to construct constraint parameters to calculate the association probability between each effective measurement combination and the target track. This algorithm can effectively suppress the measurement association anomalies and aprior information errors, as well as enable the robust association of single-sensor multitarget measurements and accurate tracking of target states under complex working conditions. On this basis, a multitarget tracking method based on composite perception fusion is further constructed, and the correlation sequential track association algorithm and covariance cross fusion algorithm are used to enable the track association and the estimation and fusion of target states among multiple sensors, which further enhances the tracking accuracy of the multitarget state. The simulation and real vehicle experiment results reveal that, compared to current advanced algorithms, the RMSE and MAPE of the MCGPDAF algorithm for multitarget tracking are enhanced by an average of 23.97% and 24.35%, respectively. Additionally, the MOTA and MOTP of the MCGPDAF algorithm improve by an average of 14.68% and 15.71%. Moreover, compared to single-sensor multitarget tracking, the RMSE and MAPE of composite perception fusion results based on the MCGPDAF algorithm are further enhanced by 26.43% and 27.15% on average, which reflects the practicality of the tracking method showcased in this research.

The method of building and deploying applications through the combination of container virtualization technology and a microservices framework has been widely used in Internet-of-Things clouds. However, there are gaps and a lack of coordination mechanisms between the Internet-of-Things and cloud computing. This study constructs a resource management platform, which is based on application container virtualization technology and combined with the microservices framework. The platform provide a support environment for the construction and deployment of Internet-of-Things cloud applications. However, there is no unified specification for the microservices templates. Therefore, a new service model called tool service was designed. The invocation relationship between services is studied, and developers can combine services through the invocation relationship between services to form a service function chain. However, container-based service deployment remains an unresolved issue. The deployment method of a container involves the quality of service of end users and the profit of cloud providers. To balance the profits of both parties, it is necessary to minimize the service response time and improve the resource utilization of the cloud data center. To address this problem, an accelerated particle swarm optimization strategy is proposed to realize service deployment. Through the invocation relationship between services, the execution containers are aggregated, so as to reduce the service transmission overhead and improve resource utilization. Compared with the experimental results of existing deployment strategies, the proposed optimization strategy has significantly improved performance parameters such as service transmission overhead, container aggregation, and resource utilization.

Container virtualization methods based on application deployment levels have been widely adopted in cloud-computing environments to implement application construction, deployment, and migration. However, most application containers focus on the interface between the applications and hosts and lack collaboration between application containers. This study proposes a new application container model that contains users, application services, documents, and messages, called Band-area Application Container. A salient feature of the Band-area is that it can express a variety of things in reality, such as organizations or individuals. End users can build a complex and changeable application system through cooperation between the Band-areas. However, the resource allocation of non Internet-of-Thing and Internet-of-Thing tasks from the application container is an open issue. The resource allocation method of tasks should not only improve the quality of the user experience, but also reduce energy consumption by improving the resource utilization of the server. To solve this problem, an artificial fish swarm algorithm is proposed to optimize container-based task scheduling. The algorithm considers not only the reliability, processing time overhead, and energy consumption of the task, but also the resource utilization of the servers. Experimental evaluation shows that, compared with the existing three algorithms, the algorithm obtains a better improvement rate in task processing time overhead, energy consumption, reliability, and cluster load balancing.

The automatic generation of Chinese enterprise abbreviations is a task that extracts enterprise abbreviations to represent the enterprise full name. Traditional methods do not divide abbreviations in detail, which leads to a poor generation effect of irregular Chinese enterprise abbreviations generation, and the best selection method of candidate abbreviations among traditional methods is still coarse-grained relationship modeling. To solve the problem of irregular abbreviation generation and abbreviation screening, this paper proposes a two-channel Chinese enterprise automatic abbreviation generation method. First, in the two-channel method, the enterprise component channel outputs regular candidate abbreviations, and the single-character channel outputs irregular candidate abbreviations to improve the processing effect of the method on irregular abbreviations. Then we design a Bayesian filtering model based on the position relationship of abbreviations in enterprise components to improve the final effect of the automatic generation of Chinese enterprise abbreviations. The results show that our effect is the best in the data performance of Chinese enterprises.

Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling.

Testing has been a major factor that limits our response to the COVID-19 pandemic. The method of sample pooling and group test has recently been introduced and adopted. However, it is still not clearly known how to determine the appropriate group size. In this paper, we treat asymptomatic COVID-19 screening acceleration as an optimization problem, and solve the problem using an analytical approach and an algorithmic procedure. We develop a two-level hierarchical pooling strategy for accelerating asymptomatic COVID-19 screening. In the first level, a population is divided into groups, which results in inter-group acceleration. In the second level, a group is divided into subgroups, which results in intra-group and inter-subgroup acceleration. By using our analytical methods and numerical algorithms, we determine the optimal group size and the optimal subgroup size, which minimize the total number of tests, maximize the speedup of the hierarchical pooling strategy, and minimize both time and cost of testing. It is discovered that the optimal group size and the optimal subgroup size are determined by the fraction of infected people. Furthermore, the optimal group size, the optimal subgroup size, and the achieved speedup grow sublinearly with the reciprocal of the fraction of infected people. Our research has important social implications and financial impacts. For example, if the fraction of infected people is 0.01, by using group size of 25 and subgroup size of 5, we can achieve speedup of at least 11, which means that months of testing time can be reduced to days, and over 91% of the testing cost can be saved. Such results have not been available in the known literature. The paper makes significant progress and great advance in pooling strategy optimization for accelerating asymptomatic COVID-19 screening, and represents the contribution of computer science to the global pandemic.

With the large-scale deployment of cloud datacenters, high energy consumption and serious service level agreement (SLA) violations in datacenters have become an increasingly urgent problem to be addressed. Implementing an effective virtual machine (VM) consolidation methods is of great significance to reduce energy consumption and SLA violations. The VM consolidation problem is a well-known NP-hard problem. Meanwhile, efficient VM consolidation should consider multiple factors synthetically, including quality of service, energy consumption, and migration overhead, which is a multi-objective optimization problem. To solve the problem above, we propose a new multi-objective VM consolidation approach based on double thresholds and ant colony system (ACS). The proposed approach leverages double thresholds of CPU utilization to identify the host load status, VM consolidation is triggered when the host is overloaded or underloaded. During consolidation, the approach selects migration VMs and destination hosts simultaneously based on ACS, utilizing diverse selection policies according to the host load status. The extensive experiment is conducted to compare our proposed approach with the state-of-art VM consolidation approaches. The experimental results demonstrate that the proposed approach remarkably reduces energy consumption and optimizes SLA violation rates thus achieving better comprehensive performance.

Reconstructing precise dynamic points with multiple camera systems (MCSs) is a pivotal work in many computer vision applications, such as motion capture. However, the deviation of 2-D position leads to frequent mismatch when searching for correspondence from multi-view. This paper puts forward a two-stage framework based on passive optical motion capture system to reconstruct precise dynamic points with MCSs. Our proposed method improves the performance of calibration and matching simultaneously. In the calibration stage, the extrinsic parameters of numerous cameras are calibrated synchronously via an L-shaped frame, where the position of four reference points is optimized with multiple geometric constraints. Bundle adjustment occurs after calibration. In the reconstruction stage, we propose a novel sparse multi-view matching method called cyclical voting, which includes multiple pairs of global voting and in-group voting. Point residual method is proposed to exclude outliers in matching groups further. The experiments show that our proposed method can decrease mismatching significantly and achieve commendable reconstruction results compared with Cortex (one of the most successful commercial motion analysis software).

The booms of big data and graphic processing unit technologies have allowed us to explore more appropriate data structures and algorithms with smaller time complexity. However, the application of machine learning as a potential alternative for the traditional data structures, especially using deep learning, is a relatively new and largely uncharted territory. In this paper, we propose a novel recurrent neural network-based learned inverted index, called Pavo, to efficiently and flexibly organize inverted data. The basic hash function in the traditional inverted index is replaced by a hierarchical neural network, which makes Pavo be able to well adapt for various data distributions while showing lower collision rate as well as higher space utilization rate. A particular feature of our approach is that a novel unsupervised learning strategy to construct the hash function is proposed. To the best of our knowledge, there are no similar results, in which the unsupervised learning strategy is employed to design hash functions, in the existing literature. Extensive experimental results show that the unsupervised model owns some advantages than the supervised one. Our approaches not only demonstrate the feasibility of deep learning-based data structures for index purpose but also provide benefits for developers to make more accurate decisions on both the design and the configuration of data organization, operation, and parameters tuning of neural network so as to improve the performance of information searching.

Searchable encryption provides an effective mechanism that achieves secure search over encrypted data. A popular application model of searchable encryption is that a data owner stores encrypted data to a server and the server can effectively perform keyword-based search over encrypted data according to a query trapdoor submitted by a data user, where the owner’s data and the user’s queries are kept secret in the server. Recently, many searchable encryptions have been proposed to achieve better security and performance, provide secure data updatable feature (dynamics), and search results verifiable capability (verifiability). However, most of the existing works endow the data user an unlimited search capacities and do not consider a data user’s search permissions. In practical application, granting search privileges for data users is a very important measure to enforce data access control. In this paper, we propose an attribute-based searchable encryption scheme by leveraging the ciphertext-policy attribute-based encryption technique. Our scheme allows the data owner to conduct a fine-grained search authorization for a data user. The main idea is that a data owner encrypts an index keyword under a specified access policy, if and only if, a data user’s attributes satisfy the access policy, the data user can perform search over the encrypted index keyword. We provide the detailed correctness analyses, performance analyses, and security proofs for our scheme. The extensive experiments demonstrate that our proposed scheme outperforms the similar work CP-ABKS proposed by Zheng on many aspects.

Low-power and short-latency memory access is critical to the performance of chip multiprocessor (CMP) system devices, especially to bridge the performance gap between memory and CPU. Together with increased demand for low-energy consumption and high-speed memory, scratch-pad memory (SPM) has been widely adopted in multiprocessor systems. In this paper, we employ a hybrid SPM, composed of a static random-access memory and a nonvolatile memory (NVM), to replace the cache in CMP. However, there are several challenges related to the CMP that need to be addressed, including how to dynamically assign processors to application tasks and dynamically allocate data to memories. To solve these problems based on this architecture, we propose a novel dynamic data allocation and task scheduling algorithm, i.e., dynamic greedy data allocation and task scheduling (DGDATS). Experiments on DSP benchmarks demonstrate the effectiveness and efficiency of our proposed algorithms; namely, our proposed algorithm can generate a highly efficient dynamic data allocation and task scheduling approach to minimize the total execution cost and produce the least amount of write operations on NVMs. Our extensive simulation study demonstrates that our proposed algorithm exhibits an excellent performance compared with the heuristic allocation (HA) and adaptive genetic algorithm for data allocation (AGADA) algorithms. Based on the CMP systems with hybrid SPMs, DGDATS reduces the total execution cost by 22.18% and 51.37% compared with those of the HA and AGADA algorithms, respectively. Additionally, the average number of write operations on NVM is 19.82% lower than that of HA.

We investigate optimal power and performance management for heterogeneous and arbitrary cloud servers in a data center. In particular, we study the problems of power-constrained performance optimization and performance-constrained power optimization in a data center with multiple heterogeneous and arbitrary servers. These problems are essential to find optimal server speeds, such that: 1) the average task response time is minimized, and that the total power consumption does not exceed certain power constraint or 2) the total power consumption is minimized, and that the average task response time does not exceed certain performance constraint. Each server is treated as a G/G/1 queuing system, whose task interarrival times and task execution requirements can have arbitrary probability distributions. Furthermore, these servers are entirely heterogeneous in terms of task interarrival time, task execution requirement, and power consumption models. The main contributions of this paper are summarized as follows: 1) we formulate the average task response time as well as the total power consumption in a data center with multiple heterogeneous and arbitrary servers as the functions of server speeds; 2) we define our optimization problems by finding optimal server speeds, since the server speeds determine both the average task response time and total power consumption; 3) we develop algorithms to find the optimal solutions and demonstrate numerical data; and 4) we also develop several closed-form heuristic solutions and compare their quality with that of the optimal solution. Our approach provides an analytical way of studying the power-performance tradeoff at the data center level.

Over these years, object tracking algorithms combined with correlation filters and convolutional features have achieved excellent performance in accuracy and real-time speed. However, tracking failures in some challenging sequences are caused by the insensitivity of deeper convolutional features to target appearance changes and the unreasonable updating of correlation filters. In this paper, we propose dual model learning combined with multiple feature selection for accurate visual tracking. First, we fuse the handcrafted features with the multi-layer features extracted from the convolutional neural network to construct a correlation filter learning model, which can precisely localize the target. Second, we propose an index named hierarchical peak to sidelobe ratio (HPSR). The fluctuation of HPSR determines the activation of an online classifier learning model to redetect the target. Finally, the target locations predicted by the dual learning models mentioned above are combined to obtain the final target position. With the help of dual learning models, the accuracy and performance of tracking have been greatly improved. The results on the OTB-2013 and OTB-2015 datasets show that the proposed algorithm achieves the highest success rate and precision compared with the 12 state-of-the-art tracking algorithms. The proposed method is better adaptive to various challenges in visual object tracking.

This research revisits the fundamental problem of processing privacy-preserving Boolean queries over outsourced databases on untrusted public clouds. Much current searchable encryption (SE) schemes try to seek an appropriate trade-off between security and efficiency, yet most of them suffer from an unacceptable query leakage due to their conjunctive/disjunctive terms that are processed individually. We show, however, this trade-off still can be deeply optimized for more security. We consider a Boolean formula as a set of deterministic finite automatons (DFAs) and propose a novel approach to running an encrypted DFA, which can be effectively and efficiently processed by the cloud. We give three constructions for conjunctive, disjunctive, and Boolean queries, respectively. Their notable advantages are single-round, highly-efficient, adaptively-secure, and leakage-minimized. A lot of experiments are made to evaluate overall efficiency. Testing results show that the schemes achieve enhanced security almost without sacrificing anything of search efficiency.

Virtualization technology, as a key technology in cloud computing, makes the virtual machine placement (VMP) play an important role in improving the energy efficiency of data centers. In this paper, an energy-aware algorithm named GATA is proposed for the VMP problem. It combines the genetic algorithm with the tabu search algorithm. The goal is to obtain an optimal VMP scheme to achieve energy efficiency while maximizing load balance among various resources. The algorithm is compared with two meta-heuristic algorithms and a newly proposed algorithm that is based on ant colony algorithm. The execution time of these algorithms is also discussed. The results show that the proposed algorithm is superior to those methods mentioned above.

The proliferation of cloud computing has resulted in the establishment of large-scale data centers containing thousands of computing nodes and consuming enormous amounts of electrical energy. However, the low-cost and high-efficiency slogans are getting louder and louder, and the IT industry is also striving for this pursuit. Therefore, it is vital to minimizing the energy consumption for cloud providers while ensuring the quality of service for cloud users. In this paper, we propose several heuristic strategies to optimize these two metrics based on a two-level management model under a heterogeneous cloud environment. First, to detect whether a physical node is continuously overloaded, we propose an empirical forecast algorithm, which predicts the future state of the host by statistically analyzing the historical utilization data of the host. Second, we propose a weighted priority virtual machine (VM) selection algorithm. For each VM on the overloaded host, we weight several utilization factors and calculate its migration priority. Then, we simulate the proposed approach and compare it with the existing overloaded hosts detection algorithms with different VM selection policies under different workloads.

With the enhancement of people's health awareness, more and more users are willing to wear portable micro-health monitoring equipment and communicate with remote medicine center for real-time diagnosis. Although, under normal circumstances, users' health status can be detected at any time, in extreme circumstances, such as earthquakes, how to make the medical center monitor user data for a long time for rescue will be of great significance. In this paper, we will study the networking of portable wearable devices based on wireless sensor networks. We mainly use minimal connected dominating sets (MCDSs) to organize nodes in extreme environments effectively, form virtual backbone networks, send data to the rescue or medical personnel, and maximize network lifetime. Specifically, we propose an adverse dominator selection procedure (ADSP), where the dominators are selected by their children-independent nodes. The ADSP has two versions, which are Independent node degree-based Adverse Dominator Selection Procedure (IADSP) and residual Energy-based Adverse Dominator Selection Procedure (EADSP). Based on IADSP and EADSP, two approximation MCDS construction algorithms named Independent node degree based MCDS (IMCDS) and Energy-efficient Independent neighbor-based MCDS (EIMCDS) are proposed, respectively. Both of them have the message complexity as O($N\Delta $ ). The performance ratio of IMCDS has an upper bound as O($\sqrt {N}$ ). The simulation results show that IMCDS and EIMCDS perform well in terms of CDS size, and the routing algorithm based on EIMCDS has better energy efficiency performance than that of IMCDS and classical routing protocol.

Software-defined networks (SDNs) are a trend of research in networks. Rule placement, a common SDN operation, becomes a challenging problem due to the capacity limitation of devices in which a large number of rules need to be deployed. Prior works mostly consider rule placement in a single device. However, the position relationships between neighbor devices also have influences on rule placement and should be considered. Our basic idea is to classify the devices position relationships into two categories: the serial relationship and the parallel relationship, and we present novel strategies for rule placement based on the two different position relationships. There are two challenges of implementing our strategies: to check whether a rule is contained by a rule set or not and to check whether a rule can be merged with other rules or not. To handle the challenges, we propose a novel data structure called OPTree to represent the rules, which is convenient to check whether a rule is covered by other rules. We design an insertion algorithm and a search algorithm for OPTree. Extensive experiments show that our approach can effectively reduce the number of rules while ensuring placed rules work. On the other hand, the experimental results also demonstrate that it is necessary to consider the position relationships between neighbor devices when placing rules.

Clustering, which explores the visualization and distribution of data, has recently been widely studied. Although current clustering algorithms such as DBSCAN, can detect the arbitrary-shape clusters and work well, the parameters involved in these methods are often difficult to determine. Clustering using a fast search of density peaks is a promising technique for solving this problem. However, the current methods suffer from the problem of uneven distribution within local clusters. To solve this problem, we propose a new density peak based clustering algorithm employing a hierarchical strategy, namely, HCFS, which consists mainly of two stages. In the first stage, the HCFS estimates the density and distance of each point. The points with higher density and distance are selected as candidate centers, and then subclusters centered on them are further obtained. In the second stage, considering that adjacent subclusters based on certain candidate centers are highly similar and connected within the same cluster, we propose a new mechanism for measuring dissimilarity and connectivity between the subclusters. Those highly similar and connected subclusters are merged to increase the dissimilarity between different clusters and to obtain the final clustering results. The experiments conducted on a large number of datasets show that our method can effectively identify unevenly distributed clusters and yield better or comparable performance for different datasets.

There are two fundamental purposes in mobile edge computing, i.e., performance enhancement and cost reduction. By offloading computation tasks to a mobile edge cloud (MEC), a user equipment (UE), also called mobile user, mobile subscriber, or mobile device, can possibly reduce its average response time, which is the main performance measure, and can possibly reduce its average power consumption. Optimizing both performance and cost may be conflicting requirements. In this paper, we optimize the cost-performance ratio (CPR), i.e., the power-time product, which combines performance (average response time) and cost (average power consumption) into one quantity. A unique feature in mobile edge computing is the competitiveness of mobile users, who are selfish in competing for resources in a mobile edge cloud. We take a game theoretic approach to the stabilization of a competitive mobile edge computing environment. The main contributions of the paper are summarized as follows. 1) We consider a mobile edge computing environment with multiple UEs and a single MEC. We establish an M/G/1 queueing model for the UEs and an M/G/m queueing model for the MEC. The UEs are entirely heterogeneous in terms of task characteristics, computation and communication speeds, and power consumption models for both computation and communication. 2) We analytically derive the average response time and the average power consumption of each UE and the MEC, so that cost-performance ratio optimization can be studied mathematically and rigorously. 3) We establish a non-cooperative game framework to systematically study the stabilization of a competitive mobile edge computing environment. Our framework includes a set of seven non-cooperative games among the UEs and the MEC, each attempts to minimize its payoff function, i.e., its cost-performance ratio. These games are different in terms of the number of variables to play and which variables to play. 4) We develop efficient algorithms for each player to find the best response in each game. All these algorithms are the poly-log time in the length of an initial search interval and the accuracy requirement. We also develop an iterative algorithm to find the Nash equilibrium of the games. 5) We demonstrate the numerical examples of our algorithms and performance data of our games for the idle-speed model and the constant-speed model respectively.

To improve the control performance of the semi-controlled open-end winding permanent magnetic synchronous motor (SOEW-PMSM) control system, a novel prediction control method is presented in this paper. First, the current-voltage dual delay-compensation algorithm is presented to overcome the negative influence on the steady-state performance of the whole control system. Then, in order to implement the simultaneous control of the d-axis current, the q-axis current, and the zero-sequence current, the current-error-based cost function is designed. Additionally, to further improve the control performance and decrease the calculation complexity of the prediction algorithm, controllable converter vector optimization-based prediction control method is presented. The effectiveness of the proposed prediction methods is tested by the simulation and experimental results.

Visual Question Answering (VQA) is a challenging multi-modal learning task since it requires an understanding of both visual and textual modalities simultaneously. Therefore, the approaches used to represent the images and questions in a fine-grained manner play key roles in the performance. In order to obtain the fine-grained image and question representations, we develop a co-attention mechanism using an end-to-end deep network architecture to jointly learn both the image and the question features. Specifically, textual attention implemented by a self-attention model will reduce unrelated information and extract more discriminative features for question-level representations, which is in turn used to guide visual attention. We also note that a lot of finished works use complex models to extract feature representations but neglect to use high-level information summary such as question types in learning. Hence, we introduce the question type in our work by directly concatenating it with the multi-modal joint representation to narrow down the candidate answer space. A new network architecture combining the proposed co-attention mechanism and question type provides a unified model for VQA. The extensive experiments on two public datasets demonstrate the effectiveness of our model as compared with several state-of-the-art approaches.

Urban hotspots refer to regions where flourishing shopping centers are located, the travel volume is very large, and there is high traffic. The formation of hotspots is strongly correlated with many features, i.e., time, space, and the distribution of points of interest; however, most studies have used qualitative analyses to describe the relevance of these features, and there is a lack of quantitative analyses. Therefore, we propose the concept and a model of the attractiveness index of a hotspot that is used to quantify the spatio-temporal distribution of the hotspots and determine the degree of attractiveness to the residents. In addition, a novel algorithm of hotspot similarity is designed and implemented to improve the efficiency of summarizing the hotspot data, which is usually performed manually. We mine the data and determine different hotspots using a one-week GPS trajectory dataset collected from 6599 taxis in Kunming. Furthermore, we calculate the attractiveness index of hotspots and visualize their characteristics. The research results provide a scientific basis and reference for public infrastructure planning, land-value evaluation, store location planning, consumption recommendations, and other applications.

The advanced productivity of the modern society has created a wide range of similar commodities. However, the descriptions of commodities are always incomplete. Therefore, it is difficult for consumers to make choices. In the face of this problem, skyline query is a useful tool. However, the existing algorithms are unable to address incomplete probabilistic databases. In addition, it is necessary to wait for query completion to obtain even partial results. Furthermore, traditional skyline algorithms are usually serial. Thus, they cannot utilize multi-core processors effectively. Therefore, a parallel progressive skyline query algorithm for incomplete databases is imperative, which provides answers gradually and much faster. To address these problems, we design a new algorithm that uses multi-level grouping, pruning strategies, and pruning tuple transferring, which significantly decreases the computational costs. Experimental results demonstrate that the skyline results can be obtained in a short time. The parallel efficiency for an Octa-core processor reaches 90% on high-dimensional, large databases.

In this paper, we address the problem of accurately modeling the cloud data center energy consumption. As minimizing energy consumption has become a crucial issue for the efficient operation and management of cloud data centers, an energy consumption model plays an important role in cloud datacenter energy management and control. Moreover, such model is essential for guiding energy-aware algorithms, such as resource provisioning policies and virtual machine migration policies. To this end, we propose a holistic cloud data center energy consumption model that is based on the principal component analysis and regression methods. Unlike the exiting approaches that focus on single system component in the datacenter, the proposed approach takes into account the energy consumption of the processing unit, memory, disk, and network interface card as well as the application characteristics. The proposed approach is validated through extensive experiments with the SPECpower benchmark. The experimental results show that the proposed energy consumption model achieves more than 95% prediction accuracy.

This paper gives an overview of the stability analysis of systems with delay-dependent coefficients. Such systems are frequently encountered in various scientific and engineering applications. Most such analyses are generalization of those on systems with delay-independent coefficients. Therefore an introduction on systems with delay-independent coefficients is also given, with an emphasis on the τ-decomposition approach. Methods for two key ingredients of this approach are discussed, namely the identification of imaginary characteristic roots with the corresponding delays, and local behavior analysis of these roots as the delay increases through these critical values. For systems with delay-dependent coefficients, we review the methods of analysis for systems with a single delay and commensurate delays, their application to output feedback control and a geometric perspective that establishes a link between systems with and without delay-dependent coefficients. We provide the main ideas of various stability analysis methods and their advantages and limitations. We also present our perspectives on future directions of research on this interesting topic.

With the considerable growth of cybersecurity risks in modern automobiles, cybersecurity issues in the in-vehicle network environment have attracted significant attention from security researchers in recent years. Enhancing the cybersecurity ability of in-vehicle networks while considering the computing resource and cost constraints become an urgent issue. To address this problem, a novel information entropy-based method is proposed in this paper, which uses a fixed number of messages as sliding windows. By improving the sliding window strategy and optimizing the decision conditions, the detection accuracy is increased and the false positive rate is reduced. Experimental results demonstrate that the proposed method can provide real-time response to attacks with a considerably improved detection precision for intrusion detection in the in-vehicle network environment.

Cybersecurity is increasingly important for the safety and reliability of autonomous vehicles. The controller area network (CAN) is the most widely used in-vehicle network for automotive safety-critical applications. Enhancing the cybersecurity ability of CAN while considering the real-time, schedulability, and cost constraints becomes an urgent issue. To address this problem, a real-time, and schedulability analysis-guaranteed security mechanism [identification hopping CAN (IDH-CAN)] is proposed in this paper, which aims to improve the security performance of CAN under the constraints of automotive real-time applications. In order to support the operation of the IDH-CAN mechanism, an IDH-CAN controller is also designed and implemented on a field-programmable gate array, which can work as a hardware firewall in the data link layer of CAN to isolate cyberattacks from the physical layer. Meanwhile, to maximize the information entropy of the CAN message ID on the physical layer, the ID hopping table generation and optimization algorithms for IDH-CAN are also proposed. Then, information security evaluation experiments based on information entropy comparison are deployed. The simulation and practical evaluations demonstrate the effectiveness of the proposed mechanism in defending reverse engineering, targeted denial of service, and replay attacks without violating real-time and schedulability constraints.

The problem of optimal temporal partitioning of a multicore server processor for virtual machine allocation in cloud computing is addressed as multivariable optimization problems and solved algorithmically and numerically. Analytical models for virtual machines are developed, i.e., partially available multi-server systems. The problem of optimal temporal partitioning of a multicore server processor for virtual machine allocation is formulated, where the overall performance (i.e., the average task response time) of a group of virtual machines is optimized. An algorithm is developed to solve the problem numerically. The problem of optimal temporal partitioning of a multicore server processor with power consumption constraint is also formulated and solved, where the overall performance of a group of virtual machines is optimized and the total power consumption of the virtual machines does not exceed certain available power. A virtual machine is treated as a queuing system with multiple servers, i.e., an M/M/m queuing system. The system performance measures are the average task response time and the average power consumption. Two core speed and power consumption models are considered, namely, the idle-speed model and the constant-speed model. Numerical examples are presented to demonstrate our methods.