Blockchain(BC), as an emerging distributed database technology with advanced security and reliability, has attracted much attention from experts who devoted to e-finance, intellectual property protection, the internet of things (IoT) and so forth. However, the inefficient transaction processing speed, which hinders the BC’s widespread, has not been well tackled yet. In this paper, we propose a novel architecture, called Dual-Channel Parallel Broadcast model (DCPB), which could address such a problem to a greater extent by using three methods which are dual communication channels, parallel pipeline processing and block broadcast strategy. In the dual-channel model, one channel processes transactions, and the other engages in the execution of BFT. The parallel pipeline processing allows the system to operate asynchronously. The block generation strategy improves the efficiency and speed of processing. Extensive experiments have been applied to BeihangChain, a simplified prototype for BC system, illustrates that its transaction processing speed could be improved to 16K transaction per second which could well supportmany real-world scenarios such as BC-based energy trading system andMicro-film copyright trading system in CCTV.
Business processes often involve operational processes, contracts, and regulations. The modeling of such processes must address regulation monitoring and enforcement and maintain a reliable history of data for evidence. This study proposes modeling business processes as chaincode (CC) on permissioned blockchains (BCs). The challenges encountered by the proposed approach are state synchronizations among distributed nodes (called authnodes)and realtime requirements. This study separates CC executions from the state management of multiple BCs and demonstrates the validity of the proposed approach with a payment authorization system at a Chinese bank.
The blockchain is a radical innovation that has a considerable effect on payments, stock exchanges, cybersecurity, and computational law. However, its limitations in terms of the uncertainty involved in transaction confirmation are significant. In this paper, we describe the design of a decentralized voting protocol for the election of a block generator in a consortium blockchain and propose a new system framework that allows fast and exact confirmation of all transactions. In addition, to replace a transaction’s owner signature, a new interactive incontestable signature between the dealer and owner is used to confirm a transaction. By means of this signature, the dealer can assure the owner that a transaction will be permanently included in the blockchain in a non-repudiation manner. Moreover, the signatures of all transactions in a block share only one witness that provides membership proof between the block and these transactions. Finally, a security and performance analysis shows that the proposed schemes are provably secure and highly efficient.
The number of IoT (internet of things) connected devices increases rapidly. These devices have different operation systems and therefore cannot communicate with each other. As a result, the data they collected is limited within their own platform. Besides, IoT devices have very constrained resources like weak MCU (micro control unit) and limited storage. Therefore, they need direct communication method to cooperate with each other, or with the help of nearby devices with rich resources. In this paper, we propose a secure method to exchange resources (SMER) between heterogeneous IoT devices. In order to exchange resources among devices, SMER adopts a compensable mechanism for resource exchange and a series of security mechanisms to ensure the security of resource exchanges. Besides, SMER uses a smart contract based scheme to supervise resource exchange, which guarantees the safety and benefits of IoT devices. We also introduce a prototype system and make a comprehensive discussion.
Android applications (APPS) are in widespread use and have enriched our life. To ensure the quality and security of the apps, many approaches have been proposed in recent years for detecting bugs and defects in the apps, of which program analysis is a major one. This paper mainly makes an investigation of existing works on the analysis of Android apps. We summarize the purposes and proposed techniques of existing approaches, and make a taxonomy of these works, based on which we point out the trends and challenges of research in this field. From our survey, we sum up four main findings: (1) program analysis in Android security field has gained particular attention in the past years, the fields of functionality and performance should also gain proper attention; the infrastructure that supports detection of various defects should be enriched to meet the industry’s need; (2) many kinds of defects result from developers’ misunderstanding or misuse of the characteristics and mechanisms in Android system, thus the works that can systematically collect and formalize Android recommendations are in demand; (3) various program analysis approaches with techniques in other fields are applied in analyzing Android apps; however, they can be improved with more precise techniques to be more applicable; (4) The fragmentation and evolution of Android system blocks the usability of existing tools, which should be taken into consideration when developing new approaches.
Exascale computing is one of the major challenges of this decade, and several studies have shown that communications are becoming one of the bottlenecks for scaling parallel applications. The analysis on the characteristics of communications can effectively aid to improve the performance of scientific applications. In this paper, we focus on the statistical regularity in time-dimension communication characteristics for representative scientific applications on supercomputer systems, and then prove that the distribution of communication-event intervals has a power-law decay, which is common in scientific interests and human activities. We verify the distribution of communication-event intervals has really a power-lawdecay on the Tianhe-2 supercomputer, and also on the other six parallel systems with three different network topologies and two routing policies. In order to do a quantitative study on the power-law distribution, we exploit two groups of statistics: bursty vs. memory and periodicity vs. dispersion. Our results indicate that the communication events show a “strong-bursty and weak-memory” characteristic and the communication event intervals show the periodicity and the dispersion. Finally, our research provides an insight into the relationship between communication optimizations and time-dimension communication characteristics.
Multi-label learning is more complicated than single-label learning since the semantics of the instances are usually overlapped and not identical. The effectiveness of many algorithms often fails when the correlations in the feature and label space are not fully exploited. To this end, we propose a novel non-negative matrix factorization (NMF) based modeling and training algorithm that learns from both the adjacencies of the instances and the labels of the training set. In the modeling process, a set of generators are constructed, and the associations among generators, instances, and labels are set up, with which the label prediction is conducted. In the training process, the parameters involved in the process of modeling are determined. Specifically, an NMF based algorithm is proposed to determine the associations between generators and instances, and a non-negative least square optimization algorithm is applied to determine the associations between generators and labels. The proposed algorithm fully takes the advantage of smoothness assumption, so that the labels are properly propagated. The experimentswere carried out on six set of benchmarks. The results demonstrate the effectiveness of the proposed algorithms.
Most traditional collaborative filtering (CF) methods only use the user-item rating matrix to make recommendations, which usually suffer from cold-start and sparsity problems. To address these problems, on the one hand, some CF methods are proposed to incorporate auxiliary information such as user/item profiles; on the other hand, deep neural networks, which have powerful ability in learning effective representations, have achieved great success in recommender systems. However, these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the weights. Therefore, they maybe lack of calibrated probabilistic predictions and make overly confident decisions. To this end, we propose a new Bayesian dual neural network framework, named BDNet, to incorporate auxiliary information for recommendation. Specifically, we design two neural networks, one is to learn a common low dimensional space for users and items from the rating matrix, and another one is to project the attributes of users and items into another shared latent space. After that, the outputs of these two neural networks are combined to produce the final prediction. Furthermore, we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic predictions. Extensive experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors.
We study implicit discourse relation detection, which is one of the most challenging tasks in the field of discourse analysis. We specialize in ambiguous implicit discourse relation, which is an imperceptible linguistic phenomenon and therefore difficult to identify and eliminate. In this paper, we first create a novel task named implicit discourse relation disambiguation (IDRD). Second, we propose a focus-sensitive relation disambiguation model that affirms a truly-correct relation when it is triggered by focal sentence constituents. In addition, we specifically develop a topicdriven focus identification method and a relation search system (RSS) to support the relation disambiguation. Finally, we improve current relation detection systems by using the disambiguation model. Experiments on the penn discourse treebank (PDTB) show promising improvements.
Serializable snapshot isolation (SSI) is a promising technique to exploit parallelism for multi-core databases. However, SSI suffers from excessive transaction aborts. Existing remedies have three drawbacks: 1) tracking prohibitively transitive dependencies; 2) aborting on every writewrite conflict; and 3) requiring manual annotation on transaction programs.
In this paper, we propose to suppress transaction aborts by reassigning timestamps. We combine static analysis with augmented query plan. In this way, we save both aborts caused by read-write and write-write conflicts, without tracking transitive dependency and annotating transaction programs. As such, our approach does not exhibit drawbacks of existing methods. Extensive experiments demonstrate the effectiveness and practicality of our approach.
The propagation of information in online social networks plays a critical role in modern life, and thus has been studied broadly. Researchers have proposed a series of propagation models, generally, which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his neighbors. However, the research on the mechanism how social ties between users play roles in propagation process is still limited. Specifically, comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works, so that the existing models failed to clearly describe the process a user be activated by a neighbor. To this end, in this paper, we analyze the close correspondence between social tie in propagation process and communication channel, thus we propose to exploit the communication channel to describe the information propagation process between users, and design a social tie channel (STC) model. The model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference, and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social tie. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.
N-hop neighborhoods information is very useful in analytic tasks on large-scale graphs, like finding clique in a social network, recommending friends or advertising links according to one’s interests, predicting links among websites and etc. To get the N-hop neighborhoods information on a large graph, such as a web graph, a twitter social graph, the most straightforward method is to conduct a breadth first search (BFS) on a parallel distributed graph processing framework, such as Pregel and GraphLab. However, due to the massive volume of message transfer, the BFS method results in high communication cost and has low efficiency.
In this work, we propose a key/value based method, namely KVB, which perfectly fits into the prevailing parallel graph processing framework and computes N-hop neighborhoods on a large scale graph efficiently. Unlike the BFS method, our method need not transfer large amount of neighborhoods information, thus, significantly reduces the overhead on both the communication and intermediate results in the distributed framework.We formalize the N-hop neighborhoods query processing as an optimization problem based on a set of quantitative cost metrics of parallel graph processing. Moreover, we propose a solution to efficiently load only the relevant neighborhoods for computation. Specially, we prove the optimal partial neighborhoods load problem is NP-hard and carefully design a heuristic strategy. We have implemented our algorithm on a distributed graph framework- Spark GraphX and validated our solution with extensive experiments over a number of real world and synthetic large graphs on a modest indoor cluster. Experiments show that our solution generally gains an order of magnitude speedup comparing to the state-of-art BFS implementation.
This paper aims at a semi-dense visual odometry system that is accurate, robust, and able to run realtime on mobile devices, such as smartphones, AR glasses and small drones. The key contributions of our system include: 1) the modified pyramidal Lucas-Kanade algorithm which incorporates spatial and depth constraints for fast and accurate camera pose estimation; 2) adaptive image resizing based on inertial sensors for greatly accelerating tracking speed with little accuracy degradation; and 3) an ultrafast binary feature description based directly on intensities of a resized and smoothed image patch around each pixel that is sufficiently effective for relocalization. A quantitative evaluation on public datasets demonstrates that our system achieves better tracking accuracy and up to about 2X faster tracking speed comparing to the state-of-the-art monocular SLAM system: LSD-SLAM. For the relocalization task, our system is 2.0X∼4.6X faster than DBoW2 and achieves a similar accuracy.
Typical stereo algorithms treat disparity estimation and view synthesis as two sequential procedures. In this paper, we consider stereo matching and view synthesis as two complementary components, and present a novel iterative refinement model for joint view synthesis and disparity refinement. To achieve the mutual promotion between view synthesis and disparity refinement, we apply two key strategies, disparity maps fusion and disparity-assisted plane sweep-based rendering (DAPSR). On the one hand, the disparity maps fusion strategy is applied to generate disparity map from synthesized view and input views. This strategy is able to detect and counteract disparity errors caused by potential artifacts from synthesized view. On the other hand, the DAPSR is used for view synthesis and updating, and is able to weaken the interpolation errors caused by outliers in the disparity maps. Experiments onMiddlebury benchmarks demonstrate that by introducing the synthesized view, disparity errors due to large occluded region and large baseline are eliminated effectively and the synthesis quality is greatly improved.