With the rapid advancement of 3D visualization, 3D Gaussian Splatting (3DGS) has emerged as a leading technique for real-time, high-fidelity rendering. While prior research has emphasized algorithmic performance and visual fidelity, the perceptual quality of 3DGS-rendered content, especially under varying reconstruction conditions, remains largely underexplored. In practice, factors such as viewpoint sparsity, limited training iterations, point downsampling, noise, and color distortions can significantly degrade visual quality, yet their perceptual impact has not been systematically studied. To bridge this gap, we present 3DGS-QA, the first subjective quality assessment dataset for 3DGS. It comprises 225 degraded reconstructions across 15 object types, enabling a controlled investigation of common distortion factors. Based on this dataset, we introduce a no-reference quality prediction model that directly operates on native 3D Gaussian primitives, without requiring rendered images or ground-truth references. Our model extracts spatial and photometric cues from the Gaussian representation to estimate perceived quality in a structure-aware manner. We further benchmark existing quality assessment methods, spanning both traditional and learning-based approaches. Experimental results show that our method consistently achieves superior performance, highlighting its robustness and effectiveness for 3DGS content evaluation. The dataset and code are made publicly available to facilitate future research in 3DGS quality assessment.

Omnidirectional videos (ODVs) present distinct challenges for accurate audio-visual saliency prediction due to their immersive nature, which combines spatial audio with panoramic visuals to enhance the user experience. While auditory cues are crucial for guiding visual attention across the panoramic scene, the interaction between audio and visual stimuli in ODVs remains underexplored. Existing models primarily focus on spatiotemporal visual cues and treat audio signals separately from their spatial and temporal contexts, often leading to misalignments between audio and visual content and undermining temporal consistency across frames. To bridge these gaps, we propose a novel audio-induced saliency prediction model for ODVs that holistically integrates audio and visual inputs through a multi-modal encoder, an audio-visual interaction module, and an audio-visual transformer. Unlike conventional methods that isolate audio cue locations and attributes, our model employs a query-based framework, where learnable audio queries capture comprehensive audio-visual dependencies, thus enhancing saliency prediction by dynamically aligning with audio cues. Besides, we introduce a novel consistency loss to enforce temporal coherence in saliency regions across frames. Extensive experiments demonstrate that our model outperforms state-of-the-art methods in predicting audio-visual salient regions in ODVs, establishing its robustness and superior performance.

Omnidirectional images, offering immersive 360° views, have gained significant attention, but assessing their perceptual quality, especially for stereoscopic content, remains a complex challenge. A major limitation lies in the fact that head-mounted devices restrict the viewer’s experience to a single viewport at a time, necessitating a comprehensive understanding of how multiple viewport images interact and aggregate during the viewing process. Moreover, the depth dimension inherent in stereoscopic content further complicates the 360° visual experience, a factor often oversimplified by existing methods, limiting their ability to accurately differentiate perceptual quality across viewports. To address these challenges, we propose a novel no-reference quality assessment model for stereoscopic omnidirectional images. Our approach integrates binocular vision principles within a viewport hypergraph convolutional network framework. First, guided by the unique viewing patterns of stereoscopic omnidirectional images, our model selects panoramic viewports that align with human visual preferences. Next, we devise an image feature extraction network that simulates the binocular fusion and rivalry mechanisms within the human visual system, leveraging a twin encoder-decoder network and tensor decomposition to capture key features. Finally, to assess overall image quality, we introduce a hypergraph structure module that captures complex positional and content-based interactions among sampled viewports through the Graph Influence Network. Extensive experiments on the NBU-SOID, SOLID, and LIVE 3D VR databases demonstrate the superior accuracy and robustness of our model compared to state-of-the-art methods.

This study investigates scalable uplink cell-free massive multiple-input multiple-output networks, comprising user equipments (UEs), radio units (RUs), data routers, and decentralized processing units (DUs). In our model, UEs are served by dynamically allocated user-centric clusters of RUs. The corresponding cluster processors, implementing the physical layer for each user, are hosted as software-defined virtual network functions by the DUs. In our paradigm, RUs, data routers, and DUs are not fully connected, necessitating a holistic approach to address the joint challenges of cluster processor placement (at one of the DUs) and the allocation of fronthaul data links among RUs, routers, and DUs. We simultaneously consider the fronthaul topology, the limited fronthaul communication capacity, and computation constraints at the DUs. Specifically, we formulate the joint optimization of fronthaul load balancing and cluster processor placement as a mixed-integer linear problem. Furthermore, we present numerical results that shed light on the interplay between these elements under finite resolution of the A/D quantization at the RUs.

The emergence of virtual reality technology has made stereoscopic omnidirectional images (SOI) easily accessible and prompted the need to evaluate their perceptual quality. At present, most stereoscopic omnidirectional image quality assessment (SOIQA) methods rely on one of the projection formats, i.e., Equirectangular Projection (ERP) or CubeMap Projection (CMP). However, while ERP provides global information and the less distorted CMP complements it by providing local structural guidance, research on leveraging both ERP and CMP in SOIQA remains limited, hindering a comprehensive understanding of both global and local visual cues. Motivated by this gap, our study introduces a novel dual-stream perception-driven network for blind quality assessment of stereoscopic omnidirectional images. By integrating both ERP and CMP, our method effectively captures both global and local information, marking the first attempt to bridge this gap in SOIQA, particularly through deep learning methodologies. We employ an inter-intra feature fusion module, which considers both the inter-complementarity between ERP and CMP and the intra-relationships within CMP images. This module dynamically and complementarily adjusts the contributions of features from both projections and effectively integrates them to achieve a more comprehensive perception. Besides, deformable convolution is employed to extract the local region of interest, simulating the orientation selectivity of the primary visual cortex. Finally, with the features of left and right views of SOI, a stereo cross attention module that simulates the binocular fusion mechanism is proposed to predict the quality score. Extensive experiments are conducted to evaluate our model and the state-of-the-art competitors, demonstrating that our model has achieved the best performance on the databases of LIVE 3D VR, SOLID, and NBU.

Approximate Nearest Neighbor(ANN) search is the core problem in many large-scale machine learning and computer vision applications such as multimodal retrieval. Hashing is becoming increasingly popular, since it can provide efficient similarity search and compact data representations suitable for handling such large-scale ANN search problems. Most hashing algorithms concentrate on learning more effective projection functions. However, the accuracy loss in the quantization step has been ignored and barely studied. In this paper, we analyse the importance of various projected dimensions, distribute them into several groups and quantize them with two types of values which can both better preserve the neighborhood structure among data. One is Variable Integer-based Quantization (VIQ) that quantizes each projected dimension with integer values. The other is Variable Codebook-based Quantization (VCQ) that quantizes each projected dimension with corresponding codebook values. We conduct experiments on five common public data sets containing up to one million vectors. The results show that the proposed VCQ and VIQ algorithms can both achieve much higher accuracy than state-of-the-art quantization methods. Furthermore, although VCQ performs better than VIQ, ANN search with VIQ provides much higher search efficiency.

Abstract Uncertain data mining has attracted so much interest in many emerging applications over the past decade. An issue of particular interest is to discover the frequent itemsets in uncertain databases. As an item would not appear in a transaction of such database for certain, several probability models are presented to measure the frequency of an itemset, and the frequent itemset over probabilistic data generally has two different definitions: the expected support‐based frequent itemset and probabilistic frequent itemset. Meanwhile, it is noted that the frequency itself cannot identify useful or meaningful patterns in some scenarios. Other measures such as the importance of items should be also taken into account. To this end, some studies recently have been done on weighted (importance) frequent itemset mining in uncertain databases. However, they are only designed for the expected support‐based frequent itemset, and suffer from low efficiency due to generating too many frequent itemset candidates. To address this issue, we propose a novel weighted probabilistic frequent itemsets (w‐PFIs) algorithm. Moreover, we derive a probability model for the support of a w‐PFI candidate in our method and present three pruning techniques to narrow the search space and remove the unpromising candidates immediately. Extensive experiments have been conducted on both real and synthetic datasets, to evaluate the performance of our w‐PFI algorithm in terms of runtime, accuracy and scalability. Results show that our algorithm yields the best performance among the existing algorithms.

As a large number of mobile terminals are connected to the IoT, the security problem of IoT is a challenge to the IoT technology. Blockchain technology has the characteristics of decentralization, data encryption, smart contract, and so on, especially suitable in the complex heterogeneous network. However, sequential access based on block files in the blockchain hinders efficient query processing. The problem is due to current blockchain solutions do not support temporal data processing. In this paper, we propose two index building methods (TISD and TIF) to address this issue in Hyperledger Fabric System. TISD (temporal index based on state databases) segments the historical data by time interval in the time dimension and indexes events at the same time interval. TIF (temporal index based on files) builds the index of files by the block transaction data, which is arranged in chronological order and is stored at a certain time interval. In the experimental part, we compare the query time on two datasets and analyse the query performance. Experiments demonstrated that our two methods are relatively stable in overall time performance on different datasets in the Hyperledger Fabric System.

Recently, with the proliferation of communication devices, Internet of Things (IoT) has become an emerging technology which facilitates massive devices to be enabled with connectivity by heterogeneous networks. However, it is usually a technical challenge for traditional networks to handle such a huge number of devices in an efficient manner. Recently, the software-defined network (SDN) technique with its agility and elasticity has been incorporated into IoT to meet the potential scale and flexibility requirements and form a novel IoT architecture also known as SDN-IoT. As the size of SDN-IoT increases, efficient routing protocols with low latency and high security are required, while the default routing protocols of SDN are still vulnerable to dynamic change of flow control rules especially when the network is under attack. To address the above issues, a deep-reinforcement-learning-based quality-of-service (QoS)-aware secure routing protocol (DQSP) is proposed in this article. While guaranteeing the QoS, our method can extract knowledge from history traffic demands by interacting with the underlying network environment, and dynamically optimize the routing policy. Extensive simulation experiments have been conducted with respect to several network performance metrics, demonstrating that our DQSP has good convergence and high effectiveness. Moreover, DQSP outperforms the traditional OSPF routing protocol, at least 10% relative performance gains in most cases.

One of the most important problems for categorized radio-frequency identification systems is to find large categories and small categories. In this article, we propose a protocol that can find the large categories and the small categories quickly. The protocol uses multiple hash pairs to allocate as many tag categories as possible to distinct slots to estimate the sizes of the tag categories. Specifically, we optimize the parameters by minimizing the execution time of the protocol. Extensive simulation results demonstrate that the proposed protocol is superior to the state-of-the-art protocols. Particularly, when the length of single-one-geometric (SOG) string is 32 b, it reduces nearly 24% of the required execution time compared with the Top-k protocol and reduces 77% of the required execution time compared with the ART protocol.

Summary The segmentation of a shape into a series of meaningful parts is a fundamental problem in shape analysis and part‐based object representation. However, it is difficult to make the result of shape segmentation accord with the expectations of humans performing the same task. There is still a need for an effective way to segment the shape although a variety of methods have been proposed. In this paper, we present a novel shape decomposition algorithm, which is implemented in a coarse‐to‐fine manner, taking into account the critical points on the silhouette. First, a part‐cut hypotheses candidate set is generated and classified into 2 categories. Then, the hypotheses with adjacent endpoints are determined first, and later, the other kinds of hypotheses are finely determined by our presented measures such as chord arc ratio and inner angle. We note that the proposed coarse‐to‐fine decomposition conforms to the mechanism of human vision. The extensive experimental results on a large set of shapes show that our algorithm can generate shape decomposition results that better accord with human intuition compared to competing algorithms.

Organizing shapes by convex parts is a fundamental procedure for many shape-related applications. However, convexity is sensitive to noise and shape variations. Recent publications in the field concentrated on decomposing shapes into near-convex parts. Although a variety of methods have been presented, there is still a need for a robust and versatile method, especially when a shape possesses long curved branches such as a lizard with a long curved tail. It is difficult to capture the tail as a whole part because its concavity is too high based on classic measures. To address this issue, we propose a `Visibility Range', novel shape signature in this paper. Visibility range reaches low values for points in concave regions and high values in convex regions. Moreover, a novel concavity measure based on visibility range is presented. Compared to previous measures, the novel measure describes long curved branches better. With these, a simple but effective shape decomposition algorithm is designed. The decomposition is formulated as a problem of detecting points with extreme visibility range in a visibility matrix. Extensive experiments have been done on shapes with various kinds of near-convex parts, demonstrating that the proposed method is more robust and effective than the state-of-art methods based on other concave-convex features.

As a novel architecture, software-defined networking (SDN) is viewed as the key technology of future networking. The core idea of SDN is to decouple the control plane and the data plane, enabling centralized, flexible, and programmable network control. Although local area networks like data center networks have benefited from SDN, it is still a problem to deploy SDN in wide area networks (WANs) or large-scale networks. Existing works show that multiple controllers are required in WANs with each covering one small SDN domain. However, the problems of SDN domain partition and controller placement should be further addressed. Therefore, we propose the spectral clustering based partition and placement algorithms, by which we can partition a large network into several small SDN domains efficiently and effectively. In our algorithms, the matrix perturbation theory and eigengap are used to discover the stability of SDN domains and decide the optimal number of SDN domains automatically. To evaluate our algorithms, we develop a new experimental framework with the Internet2 topology and other available WAN topologies. The results show the effectiveness of our algorithm for the SDN domain partition and controller placement problems.

Subspace skyline, as an important variant of skyline, has been widely applied for multiple-criteria decisions, business planning. With the development of mobile internet, subspace skyline query in mobile distributed environments has recently attracted considerable attention. However, efficiently obtaining the meaningful subset of skyline points in any subspace remains a challenging task in the current mobile internet. For more and more mobile applications, subspace skyline query on mobile units is usually limited by big data and wireless bandwidth. To address this issue, in this paper, we propose a system model that can support subspace skyline query in mobile distributed environment. An efficient algorithm for processing the Subspace Skyline Query using MapReduce (SSQ) is also presented which can obtain the meaningful subset of points from the full set of skyline points in any subspace. The SSQ algorithm divides a subspace skyline query into two processing phases: the preprocess phase and the query phase. The preprocess phase includes the pruning process and constructing index process which is designed to reduce network delay and response time . Additionally, the query phase provides two filtering methods, SQM-filtering and ε-filtering, to filter the skyline points according to user preference and reduce network cost. Extensive experiments on real and synthetic data are conducted and the experimental results indicate that our algorithm is much efficient, meanwhile, the pruning strategy can further improve the efficiency of the algorithm.

Summary Reverse nearest neighbor (RNN) queries are the complimentary problem and particular interest in the past few years, such as location‐based services, profile‐based marketing, resource allocation, and traffic monitoring system. The one major drawback for the existing RNN is that it has inherent sequential nature and uses in‐memory algorithm, which limits its applicability to large‐scale spatial data queries. This paper proposes scalable algorithms for RNN queries in a distributed environment. Firstly, we investigate the Basic‐scalable reverse nearest neighbor (SRNN) initialization query method based on the inverted grid index. Secondly, two optimization methods Lazy‐SRNN and Eager‐SRNN are proposed to effectively process scalable multi‐dimensional RNN queries. Among them, Lazy‐SRNN prunes the search space when all RNN objects are discovered in one pass; Eager‐SRNN attempts to prune spatial objects incrementally as soon as they are visited. In addition, the SRNN algorithm is proved to be the first attempt for the exact scalable RNN algorithms in a distributed environment on multi‐dimensional data sets. We show in an extensive experimental evaluation on real‐world and synthetic data the scalability and the performance of our novel approach. Copyright © 2015 John Wiley & Sons, Ltd.

As applications of computing systems have permeated in all aspects of daily life, the power of computing system has become increasingly critical, which offers many challenging problems on the area of efficiency, performance, reliability, security, and interoperability. New programming paradigms, interconnection networks, and storage systems have joined the traditional workflow and parallel computing technologies for the highest-performance systems. This special issue presents the recent advances in parallel computing and distributed network, which were selected out of the significantly extended versions of accepted papers in the 2014 World Ubiquitous Science Congress (U-Science 2014) 1, the 14th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2014) 2, and a large number of open submissions. The selection has been very rigorous, and only the best papers were selected. Tang et al. 3 note that there exists the correlation of volunteer or desktop failures in Desktop Grid and Volunteer Computing Systems. To achieve long-term and sustained high throughput, they present a hybrid MapReduce (HybridMR) computing environment, in which the cluster nodes and the volunteer computing nodes are integrated. HybridMR includes two innovative solutions. The first one is a hybrid distributed file system to alleviate the volatility of desktop PCs. The second innovation is a new node priority-based fair scheduling algorithm to achieve both data storage balance and job assignment balance. They also provide performance evaluation on the I/O, fault-tolerance and cost-saving of HybridMR, denoting that new model is not only able to achieve a higher throughput and efficiency, but also able to achieve the “green computing” goal. Ji et al. 4 study the efficient and scalable RNN algorithms in the distributed environment. Noted that the major downside of the existing RNN is its inherent sequential nature and using in-memory algorithm, they firstly use the inverted grid, not the R-tree or Voronoi to index the data. It is proved that the grid increases opportunities for parallelism. Furthermore, two pruning ways Lazy-Scalable Reverse Nearest Neighbor (SRNN) and Eager-SRNN are proposed to improve the performance. Finally, they perform extensive experiments on both real and synthetic datasets, demonstrating that their methods outperform the state-of-the-art algorithms in scalable RNN queries. As in future extreme-scale systems, one compute node will have multiple accelerators. Dong et al. 5 make an attempt on this kind of programming clusters that have multiple Xeon Phi coprocessors in each compute node. To increase the efficiency, they present an offload programming approach that allows each coprocessor to run an independent sub-program, while bi-directional and asynchronous coprocessor–coprocessor data transfers are directly enabled by Intel's low-level APIs of COI and SCIF. They also present a hybrid programming strategy combining techniques such as Message Passing Interface (MPI), Open Multi-Processing (OpenMP), Coprocessor Offload Infrastructure (COI) and Symmetric Communication Interface (SCIF), thus extending their work to cover clusters with multi-coprocessor nodes. They also provide performance results of the proposed COI-SCIF approach running on Tianhe-2, in terms of both bandwidth benchmark measurements and time usages of a real-world 3D application. Topology structure is usually viewed as a big issue for interconnection networks. Zhang et al. 6 detailed studied one of the typical topology structures, hyper-star graph HS(2n, n), and found some interesting and attractive properties. More specifically, they show that the surface area of HS(2n; n) is . Furthermore, they prove that HS(2n, n) is isomorphic to the well-known middle cube, thus linking the Hamiltonicity of HS(2n, n) to that of the middle cube. Finally, they study the embedding properties of HS(2n, n) by showing that full binary trees can be embedded into the network with dilation 1, and an optimal algorithm is found for performing neighborhood broadcasting on HS(2n, n). Noting that the balance of performance and hardware costs becomes quite challenging in traditional hypercube-based topology structures, Qi et al. 7 build a new interconnection topology structure named EFH. Different from EH, some complementary edges are added to link a node with its farthest node of the Hypercube. Owe to these complementary edges, the authors investigate that the network diameter of EFH is about half of the diameter of EH. Furthermore, they design a more efficient routing algorithm and a load balancing algorithm used in their EFH structure. They also give a lot of strict proofs on the properties of EFH and analyze its fault tolerance capabilities such as fault diameter and cost effectiveness factor. Aiming at handling distance fraud attacks and relay attacks in anonymous RFID applications, Yang et al. 8 presents an improved Distance-Bounding Trust Protocol (DBTP). With the proposed DBTP, a tag can defend distance fraud from the malicious reader by the output of trust values. In addition, they deploy trusted third party architecture to provide anonymity for tags in anonymous RFID systems without requiring tag identifiers. This enabled DBTP to further defend relay attacks in anonymous RFID systems. Finally, they build a prototype of DBTP using commercial RFID readers to track off-the-shelf RFID tags and evaluate the performance of DBTP not only by theoretical analysis but also by a large set of extensive simulations. Cheng et al. 9 noted that in hybrid storage systems, the two-level cache DRAM and SSD typically use independent cache replacement policies, which makes cache resource management inefficient and lower the system performance. They propose a novel AMC replacement algorithm to deal with this problem. Compared with classic multi-level exclusive caching techniques, they introduce combined selective Promote and Demote operations to dynamically determine the level and keep “hot” data blocks in DRAM and SSD caches. Furthermore, they design an online method using probabilistic Promote and Demote values that are adjusted via the usage of blocks already cached. In experiments, they show the proposed AMC algorithm reduces average response time and increases SSD lifetime, compared with the traditional multi-level cache algorithms 2C-Least Recently Used (LRU), ind-LRU, and exc-LRU. We hope that you will enjoy reading these papers in this special issue. We would like to thank the authors for contributing their papers to this issue, and thank all the reviewers for their time and constructive reviews. Finally, we would like to thank the editors of Concurrency and Computation: Practice and Experience for providing this opportunity to publish this special issue.

This special issue presents the recent advances in cloud computing and software-defined network, which \were selected out of the significantly extended versions of accepted papers in the Fifth IEEE International Conference on Big Data and Cloud Computing (BDCloud 2015) 1, the Ninth International Conference on Frontier of Computer Science and Technology 2, and a large number of open submissions. The selection has been very rigorous, and only the best papers were selected. Wei et al. 3 observe that most existing message forwarding algorithms in delay-tolerant networks prefer to deliver messages to the nodes with a higher popularity or centrality. This forwarding scheme can achieve high delivery ratio and low end-to-end delay but is prone to cause unfair load distribution and further lead to network congestion. To tackle this, they first track the evolution of communities through a novel distributed community detection approach. The second one is to develop a congestion avoidance mechanism to divert load away from congested areas and further present a congestion-aware message forwarding algorithm where messages can avoid being transmitted to the congested nodes Since the rapid growth of large-scale online services, massive amounts of the generated traffic have been seen in the data center network. Li et al. 4 study the emerging congestion problem in the software-defined data center network. Note that existing approaches are either hard to be implemented in hardware or unable to obtain the optimal solutions. The authors first propose a heuristic algorithm for efficiently compute a timeslot allocation for the coming packets. Further, they model the path selection as a bin-packing problem. By seamlessly combining the timeslot allocation and path selection, each data packet will not suffer queuing and waiting in the data center network. Anomaly detection is an effective approach to enhance availability and reliability of cloud infrastructures. Hong et al. 5 study the anomaly detection problem in cloud computing systems without the need for prior knowledge about normal or anomalous behaviors. They propose an unsupervised online anomaly detection scheme based on hidden Markov model. In order to achieve high scalability, their proposed algorithm runs in a distribution manner among multiple computing machines in the cloud. They also perform extensive experiments based on real data sets to validate the high detection accuracy for their proposed algorithm. Because of the benefits of reducing the communication overhead, distributed data-centric storage in wireless sensor networks have received considerable attention. Xu et al. 6 focus on the big data storage problem in wireless sensor network with the nonuniform node distribution. Note that most existing distribution methods can significantly consume more energy and are unable to deal with the case of nonuniform sensor nodes distribution. To address this issue, they propose an efficient storage retrieval algorithm to estimate the real distribution of the sensor nodes and the real addresses of these nodes. Based on this algorithm, they further take the data redundancy among sensor nodes into account and exploit an efficient routing mechanism. Incorporating cloud computing into vehicular networks is a promising solution to the collection, storage, and analysis of big traffic-related data but can lead to new challenges to the allocation and management for cloud resources in road-side cloudlet. Yao et al. 7 study a VM migration problem with the goal of minimizing the total network cost, by making the decisions on which VM should be migrated and where the VM shall be migrated. They further formulate an optimization for the static off-line VM placement problem and then propose a heuristic algorithm with polynomial time to solve the optimization. NoSQL systems, replicating and partitioning data over many servers for improving the performance, are widely used for storing big data. Conventional radon virtual nodes and manual configuration methods for consistent hashing can significantly lead to imbalanced data partition. Huang et al. 8 study the performance degradation problem caused by the imbalanced data partition. They first propose a novel imbalance coefficient of data distribution. They further propose a dynamic programming algorithm to compute the position of the new coming node in the consistent ring. Finally, they conduct comprehensive simulations based on a benchmark Yahoo Cloud Serving Benchmark (YCSB) to show the benefit of their proposed algorithm. Data centers are increasingly deploying the NUMA architecture. Zhu et al. 9 focus on the performance degradation problem when running multi-threaded programs on such NUMA systems. Note that the existing works mainly use the single-threaded multi-programming workloads to study the performance of NUMA on the resource contention and data locality. To solve the performance lagging problem, they propose a novel scheduler—symmetric scheduler, which can balance the number of costly remote shared data accesses for threads on NUMA systems. Finally, they perform extensive simulations on the PARSEC benchmark, and their proposed schedulers can significantly outperform Linux kernel scheduling mechanism. As the number of functionally equivalent services in the cloud grows, collaborative service QoS prediction has recently garnered increasing attention. Tang et al. 10 propose a collaborative QoS prediction method with location-based data smoothing, for addressing the data sparsity issue and improving the QoS prediction accuracy. Note that existing solutions, simply exploring the historical QoS information generated by interactions between users and services, however, can significantly suffer from the data sparsity issue. To address this issue, the authors firstly compute neighborhoods of users and services based on their locations, which provide a basis for data smoothing. They further combine user-based and service-based collaborative filtering techniques to make QoS predictions. Finally, they conduct comprehensive experiments on real service invocation dataset to validate the performance of their proposed QoS prediction method. We hope that you will enjoy reading these papers in this special issue. We would like to thank the authors for contributing their papers to this issue and thank all the reviewers for their time and constructive reviews. Finally, we would like to thank the editors of Concurrency and Computation: Practice and Experience for providing this opportunity to publish this special issue.

Summary Cloud computing came into being and is currently an essential infrastructure of many commerce facilities. To achieve the promising potentials of cloud computing, effective and efficient scheduling algorithms are fundamentally important. However, conventional scheduling methodology encounters a number of challenges. During the tasks scheduling in cloud systems, how to make full use of resources and how to effectively select resources are also important factors. At the same time, communication delay also plays an important role in cloud scheduling, which not only leads to waiting between tasks but also results in much idle interval time between processing units. In this paper, a fuzzy clustering method is used to effectively preprocess the cloud resources. Combining the list scheduling with the task duplication scheduling scheme, a new directed acyclic graph based scheduling algorithm called earliest finish time duplication algorithm for heterogeneous cloud systems is presented. Earliest finish time duplication attempts to insert suitable immediate parent nodes of the current selected node in order to reduce its waiting time on the processor. The case study and experimental results illustrate that the algorithm proposed in this paper is better than the popular heterogeneous earliest finish time algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

The host cardinality refers to the number of different peers that an Internet host communicates with. Stealthy superpoint is a host that its cardinality is between two thresholds during a measurement period. Detecting stealthy superpoints helps intrusion systems identify potential attackers. However, stealthy superpoints may perform scanning deliberately at a low rate, and they can easily evade the detection. The existing algorithm can not directly be used to detect them. This paper proposes an algorithm based on Bitmap which can detect stealthy superpoints. The algorithm includes online module and offline module. The online module consists of two submodules. One uses a bloom filter to filter the duplicate packets and store the source addresses. The other uses two-dimensional bit arrays to process packets. The offline estimates the cardinality. The theoretical analysis and experimental results show that our algorithm can precisely detect stealthy superpoints and estimate their cardinalities.

Skyline queries are useful in decision making applications. Skyline queries in highly mobile distributed environments have attracted many attentions recently due to the development of mobile internet device. The properties of distributed computing make skyline queries more complicated especially in any subspace. Conventional skyline algorithms do not support subspace skyline queries in distributed environment. In this paper, we focus on how to perform distributed skyline queries in any subspace according to user preference. So we propose a system model in a mobile and distributed environment. An efficient parallel algorithm for processing the Subspace Skyline Query (SSQ) using MapReduce is applied to the system model. This algorithm can report skyline points in any subspace. Meanwhile, a pruning strategy is also proposed in order to reduce the network communication and minimize the response time. We conduct experiments on real and synthetic data. Experimental results indicate that our SSQ algorithm is much more efficient. Furthermore, the pruning strategy can further improve the performance of the algorithm.

Collaborative Filtering (CF) algorithm is the common solution to Recommender System (RS). With the development of network and storage technology, the amount of users and items in RS system is exclusively growing. How to increase the scalability and recommendation accuracy of CF are the main concerns in the related research. In this paper, an efficient implementation for user-based CF algorithm on MapReduce is presented. We exploit Bag of Word (BoW) method and design a hierarchical inverted index to further increase the scalability of our method. Meanwhile, a soft-assignment mechanism for the hierarchical inverted index is proposed to make up the recommendation accuracy decrease caused by the index. The Mapreduce implementations of our methods are detailed discussed and analyzed on both simulated data and real data, demonstrating that our implementation has the ability to scale to huge numbers of users and items, eanwhile ensures recommendation accuracy.

This paper proposes a color image retrieval method based on visual weighted blocks in order to further enhance the efficiency of image retrieval. The color image histogram is such an effective method that it is used widely in various image retrieval systems. But the color image histogram has many limitations because it does not contain spatial information in its retrieval scheme, so the retrieval results are not satisfactory. In this paper, the color images are divided into blocks in order to make the color image histogram more distinguished. And human visual characteristics and local features are also utilized in this paper. Firstly, images are divided into blocks and the sub-block color histograms are calculated. Second, each pixel is arranged a saliency score which could reflect the local variation of each pixel, and then the visual weighted value for sub-blocks are also calculated. Finally, all the histograms of sub-blocks are combined into one in order to compute the similarity between color images. To prove the capability of this paper, we evaluate our framework on the standard dataset and the experimental results have shown that the proposed method is more accurate and efficient.

How to reduce the transmission latency is a main concern in the context of Mobile Visual Search (MVS). Transmitting extremely compacted visual descriptor in a progressive manner is the start-of-art solution. In this paper, we present a novel MVS system following the client-server architecture. To reduce the transmission latency, the inquiry image is represented by a set of hash bits, which are then progressively transmitted. In the server side, all images are indexed by their hash bits, similar as the classic Bag-of-Word (BoW) model. Owe to the merit of the proposed system, the IDF weight of the hash bits are encoded into a sparse vector which retained in the mobile client, and provides a transmission order of the inquiry hash bits. The hash bit with lower IDF weight will be more discriminative, which should have higher priority during the transmission. As far as we know, this work is the first one attempting to transmit the hash bits in a proper progressive manner in MVS. Extensive experiments have been done on the public Stanford MVS database, demonstrating that the proposed progressive transmission strategy achieves higher recognition rate compared to other strategies, when delivering the same amount of data.

As a kind of interesting mobile application, Mobile Visual Search (MVS) has attracted extensive research efforts from both academy and industry. Most of the MVS systems adopt the client-server framework, in which transmission latency caused by the limited bandwidth in wireless network is a big problem. To address this problem, the state-of-the-art work focuses on designing low bit-rate descriptors for MVS. However, few work focuses on reducing the number of descriptors. To further reduce the latency, we propose a novel framework of MVS based on the weighted matching of dominant descriptor. Firstly, we present an affinity propagation based algorithm for dominant descriptor selection. Secondly, we propose a weighted feature matching method to consider the differences of dominant descriptors in feature matching. By the proposed framework, we not only reduce the network latency in MVS, but also avoid transmitting useless descriptors to improve the retrieval accuracy of MVS. The experimental results on Stanford MVS data set show that when using CHoG descriptors, the proposed framework outperforms the existing framework by reducing more than 40% of the amount of data transmission and increasing 5% of the average retrieval accuracy.

Due to the bandwidth limitation in wireless networks, transmission overhead is a big problem in Mobile Visual Search (MVS). Existing work proposes transmitting the compressed local feature descriptors instead of the query image to reduce the transmission overhead. Although many kinds of compressed descriptors are proposed, designing a suitable lossless compressed descriptor has proven elusive. In this paper, we propose a novel framework for MVS with low transmission overhead rather than focusing on compressed descriptors. The key point of the proposed framework is to migrate the vector quantization in the bag of visual words model from the server to the client. In this framework, no matter what descriptors are used, the client only transmits the ID numbers of the visual words to the server, thereby reaching the minimal possible transmission overhead. To achieve this goal, we present vocabulary decomposition by which we can decompose the large vocabulary into several small ones satisfying storage constraints on mobile devices. In this paper, we first formulate vocabulary decomposition as an optimization problem. We then present Joint Product Quantization (JPQ) and Joint Optimized Product Quantization (JOPQ) to address the proposed optimization problem. Finally , we conduct a large number of simulation experiments and real experiments. The experimental results show that the proposed framework outperforms the existing framework by reducing more than 95% of the transmission overhead.

k-means is undoubtedly one of the most popular clustering algorithms owing to its simplicity and efficiency. However, this algorithm is highly sensitive to the chosen initial centers and thus a proper initialization is crucial for obtaining an ideal solution. To address this problem, k-means++ is proposed to sequentially choose the centers so as to achieve a solution that is provably close to the optimal one. However, due to its weak scalability, k-means++ becomes inefficient as the size of data increases. To improve its scalability and efficiency, this paper presents Map Reduce k-means++ method which can drastically reduce the number of Map Reduce jobs by using only one MapReduce job to obtain k centers. The k-means++ initialization algorithm is executed in the Mapper phase and the weighted k-means++ initialization algorithm is run in the Reducer phase. As this new Map Reduce k-means++ method replaces the iterations among multiple machines with a single machine, it can reduce the communication and I/O costs significantly. We also prove that the proposed Map Reduce k-means++ method obtains O(α2)approximation to the optimal solution of k-means. To reduce the expensive distance computation of the proposed method, we further propose a pruning strategy that can greatly avoid a large number of redundant distance computations. Extensive experiments on real and synthetic data are conducted and the performance results indicate that the proposed Map Reduce k-means++ method is much more efficient and can achieve a good approximation.

A shape signature is defined as any 1-D function on a shape, which is a compact and concise representation for some essence of the shape. Although a variety of shape signatures are proposed and utilized in shape retrieval and recognition tasks, the existing signatures cannot yet provide entirely satisfactory solutions to describe the shape variations well, especially when significant noise or articulation occurs. Motivated by the fact that electrical charge distributions are almost the same for similar shapes but not vice versa when shapes reach their electrical equilibrium condition, we propose a novel shape signature based on the electrical charge distribution on the shape (ECDS). Compared to other shape descriptors, ECDS is more intuitively and robust, which is computed in a global manner. Furthermore, as well as being invariant to translation, scale and rotation, ECDS is articulation insensitive and therefore exhibits better performance by the introduction of generalized coulomb potentials. This allows it to better match shapes whose parts can move independently, such as scissors. Finally, numerous experiments have done on public databases, demonstrating that ECDS has the above properties and compares well with other shape descriptors in many kinds of shape retrieval and recognition tasks.

To eliminate the influence of subjective factors when evaluating the quality of website, this paper proposes a novel system using link analysis. In the proposed system, the indexes are classified into two categories: research index and contrast index. Principal component analysis (PCA) and fuzzy comprehensive evaluation method are used to analyze them, respectively. After analyzing, we can find the subjective factors by computing the consistency of indexes. In the evaluation of websites, we ignore these subjective factors to improve the authority of evaluation results. We conduct a large number of experiments on a group of websites. The experimental results show that the proposed system is effective.

We propose a novel curvature-aware simplification technique for point-sampled geometry based on the locally optimal projection (LOP) operator. Our algorithm includes two new developments. First, a weight term related to surface variation at each point is introduced to the classic LOP operator. It produces output points with a spatially adaptive distribution. Second, for speeding up the convergence of our method, an initialization process is proposed based on geometry-aware stochastic sampling. Owing to the initialization, the relaxation process achieves a faster convergence rate than those initialized by uniform sampling. Our simplification method possesses a number of distinguishing features. In particular, it provides resilience to noise and outliers, and an intuitively controllable distribution of simplification. Finally, we show the results of our approach with publicly available point cloud data, and compare the results with those obtained using previous methods. Our method outperforms these methods on raw scanned data.

We present a novel algorithm to reconstruct curves with self-intersections and multiple parts from unorganized strip-shaped points, which may have different local shape scales and sampling densities. We first extract an initial curve, a graph composed of polylines, to model the different structures of the points. Then a least-squares optimization is used to improve the geometric approximation. The initial curve is extracted in three steps: anisotropic farthest point sampling with an adaptable sphere, graph construction followed by non-linear region identification, and edge refinement. Our algorithm produces faithful results for points sampled from non-simple curves without pre-segmenting them. Experiments on many simulated and real data demonstrate the efficiency of our method, and more faithful curves are reconstructed compared to other existing methods.