An adaptive epidemic information dissemination model for wireless sensor networks

We propose an adaptive bio-inspired information dissemination model that exploits the specific characteristics of the sampled/generated data stream (DS) in a wireless sensor network. Our model extends the basic epidemic algorithm by adapting key operational parameters (i.e., the forwarding probability and validity period) of the data dissemination process. The main idea is that the forwarding probability is tuned according to the variability of the involved DS. Our findings from the introduction of this adaptive epidemic are quite promising. Our scheme supersedes conventional probabilistic information dissemination algorithms in terms of efficiency and reliability.     

P2P网络中的局域搜索免疫

针对P2P网络的特性,本文提出了一种局域搜索免疫。实验使用SIS病毒传播模型征BA无标度网络和实际网络中测试了局域搜索免疫策略,结果表明与其它免疫策略相比,该策略可以通过免疫相等或较少的节点来消灭病毒。     

加权无标度网络病毒传播和局部免疫策略研究

为了进一步描述现实生活中复杂网络的病毒传播问题,改进加权无标度网络模型的传统构造方法,考虑流量带宽和个体抵抗力两个重要因子,利用平均场理论模拟仿真病毒传播过程,对实验数据进行分析,验证该模型的有效性.现实生活中往往只能了解复杂网络的局部拓扑信息,传统病毒免疫策略大多基于全局拓扑信息,在仅了解局部信息的前提下,提出加权无标度网络中基于局部最优的病毒免疫策略,通过动态模拟病毒传播的免疫仿真实验,与随机免疫策略和目标免疫策略对病毒传播影响进行比较,验证局部最优免疫策略的有效性.     

Accelerating dissipative particle dynamics simulations on GPUs: Algorithms,numerics and applications

We present a scalable dissipative particle dynamics simulation code, fully implemented on the Graphics Processing Units (GPUs) using a hybrid CUDA/MPI programming model, which achieves 10–30 times speedup on a single GPU over 16 CPU cores and almost linear weak scaling across a thousand nodes. A unified framework is developed within which the efficient generation of the neighbor list and maintaining particle data locality are addressed. Our algorithm generates strictly ordered neighbor lists in parallel, while the construction is deterministic and makes no use of atomic operations or sorting. Such neighbor list leads to optimal data loading efficiency when combined with a two-level particle reordering scheme. A faster in situ generation scheme for Gaussian random numbers is proposed using precomputed binary signatures. We designed custom transcendental functions that are fast and accurate for evaluating the pairwise interaction. The correctness and accuracy of the code is verified through a set of test cases simulating Poiseuille flow and spontaneous vesicle formation. Computer benchmarks demonstrate the speedup of our implementation over the CPU implementation as well as strong and weak scalability. A large-scale simulation of spontaneous vesicle formation consisting of 128 million particles was conducted to further illustrate the practicality of our code in real-world applications.     

Algorithm substitution attacks against receivers

This work describes a class of Algorithm Substitution Attack (ASA) generically targeting the receiver of a communication between two parties. Our work provides a unified framework that applies to any scheme where a secret key is held by the receiver; in particular, message authentication schemes (MACs), authenticated encryption (AEAD) and public key encryption (PKE). Our unified framework brings together prior work targeting MAC schemes (FSE’19) and AEAD schemes (IMACC’19); we extend prior work by showing that public key encryption may also be targeted. ASAs were initially introduced by Bellare, Paterson and Rogaway in light of revelations concerning mass surveillance, as a novel attack class against the confidentiality of encryption schemes. Such an attack replaces one or more of the regular scheme algorithms with a subverted version that aims to reveal information to an adversary (engaged in mass surveillance), while remaining undetected by users. Previous work looking at ASAs against encryption schemes can be divided into two groups. ASAs against PKE schemes target key generation by creating subverted public keys that allow an adversary to recover the secret key. ASAs against symmetric encryption target the encryption algorithm and leak information through a subliminal channel in the ciphertexts. We present a new class of attack that targets the decryption algorithm of an encryption scheme for symmetric encryption and public key encryption, or the verification algorithm for an authentication scheme. We present a generic framework for subverting a cryptographic scheme between a sender and receiver, and show how a decryption oracle allows a subverter to create a subliminal channel which can be used to leak secret keys. We then show that the generic framework can be applied to authenticated encryption with associated data, message authentication schemes, public key encryption and KEM/DEM constructions. We consider practical considerations and specific conditions that apply for particular schemes, strengthening the generic approach. Furthermore, we show how the hybrid subversion of key generation and decryption algorithms can be used to amplify the effectiveness of our decryption attack. We argue that this attack represents an attractive opportunity for a mass surveillance adversary. Our work serves to refine the ASA model and contributes to a series of papers that raises awareness and understanding about what is possible with ASAs.

     

内存计算框架局部数据优先拉取策略

内存计算框架的低延迟特性大幅提高了集群的计算效率,但Shuffle过程的性能瓶颈仍不可规避.宽依赖的同步操作导致大多数工作节点等待慢节点的计算结果,同步过程不仅浪费计算资源,更增加了作业延时,这一现象在异构集群环境下尤为突出.针对内存计算框架Shuffle操作的同步问题,建立了资源需求模型、执行效率模型和任务分配及调度模型.给出了分配效能熵(allocation efficiency entropy, AEE)和节点贡献度(worker contribution degree, WCD)的定义,提出了算法的优化目标.根据模型的相关定义求解,设计了局部数据优先拉取算法(partial data shuffled first algorithm, PDSF),通过高效节点优先调度,提高流水线与宽依赖任务的时间重合度,减少宽依赖Shuffle过程的同步延时,优化集群资源利用率;通过适度倾斜的任务分配,在保障慢节点计算连续性的前提下,提高分配任务量与节点计算能力的适应度,优化作业执行效率;通过分析算法的相关优化原则,证明了算法的帕累托最优性.实验表明：PDSF算法提高了内存计算框架的作业执行效率,并使集群资源得到有效利用.     

GoDisco++: A gossip algorithm for information dissemination in multi-dimensional community networks

In this paper we propose GoDisco++, a gossip based approach for information dissemination in online social community networks. GoDiscoo++ uses local information available to nodes—that is information associated with a node and its neighbors. The algorithm exploits multiple relations which may exist between nodes, and applies social principles and behavior inspired decentralized mechanisms for targeted dissemination. The dissemination process works with the dual aims of (i) maximizing the spread among relevant nodes (high recall) and (ii) minimizing spamming among non-relevant nodes (high precision). Such a designed dissemination scheme can have interesting applications like probabilistic publish/subscribe, decentralized recommendation and contextual advertisement systems, to name a few. We validate the proposed approach with simulation experiments performed using real and synthetic datasets.     

Relevance feature mapping for content-based multimedia information retrieval

This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the original features. Furthermore, additional performance gain is achieved by incorporating our new ranking scheme which modifies instance rankings based on the weighted average of relevance feature values. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework.     

Evaluation and comparison of various indexing schemes in single-channel broadcast communication environment

Wireless Data Broadcasting is a newly developed data dissemination method for spreading public information to a tremendous number of mobile subscribers. Access Latency and Tuning Time are two main criteria to evaluate the performance of such system. With the help of indexing technology, clients can reduce tuning time significantly by searching indices first and turning to doze mode during waiting period. Different indexing schemes perform differently, so we can hardly compare the efficiency of different indexing schemes. In this paper, we redesigned several most popular indexing schemes for data broadcasting systems, i.e., distributed index, exponential index, hash table, and Huffman tree index. We created a unified communication model and constructed a novel evaluation strategy by using the probability theory to formulate the performance of each scheme theoretically and then conducted simulations to compare their performance by numerical experiments. This is the first work to provide scalable communication environment and accurate evaluation strategies. Our communication model can easily be modified to meet specific requirements. Our comparison model can be used by the service providers to evaluate other indexing schemes to choose the best one for their systems.     

Effective opportunistic dissemination of spatio-temporal contents in mobile environments

Dissemination of spatio-temporally valid content from content providers to consumers is critical in certain application contexts as data items could lose their validity across time and space. Content sharing in challenged opportunistic environments remains a research challenge as existing solutions fail to exploit dissemination patterns across spatio-temporal limits. In this paper, we propose spatio-temporal reachability graphs to depict reachability of time- and space-sensitive content in opportunistic mobile environments. Furthermore, we develop an analytical framework to estimate content distribution in such environments and validate its feasibility over long-term datasets. We perform extensive trace-driven simulation studies to determine content dissemination properties of environments with known mobility patterns. The analytical framework estimates dissemination ratio, optimizes parameter setting, and tests transmission capacities of opportunistic environments. Proposed scheme is useful to content providers as well as receivers.     

面向P2P特定信息的传播动力学模型研究

鉴于P2P特定信息传播与传染病传播的相似性,传播动力学是P2P特定信息传播的最新研究方向。针对现有传播动力学模型都不能准确模拟P2P特定信息传播过程的问题,对现有的SEIR传播动力学模型进行改进,建立了SEInR模型。该模型的主要特点包括:将传统的感染者(I)分为n个子类,每个子类赋予不同的模型参数;建立潜伏者(E)与移除者(R)之间的转换关系等。应用现代数学中的矩阵理论,得到SEInR模型的基本再生数计算公式,并对其进行分析。仿真结果表明,所提出的SEInR模型比传统SEIR模型能够更准确地模拟P2P特定信息传播过程,得到的基本再生数计算公式能够准确反映P2P特定信息的传播阂值。     

Understanding Dynamic Collaboration in Teleconsultation

Information and communication technology has been widely deployed in the provision of healthcare for decades. Teleconsultation, one of the new means of providing healthcare solutions, has been prevalently implemented in numerous countries. In principle, it is expected with great potential to improve the effectiveness and efficiency of healthcare service through wide accessibility and cost control. However, many teleconsultation systems have been installed but abandoned rapidly or used at a disappointing low level. This paper explores the antecedents of low usage in post-adoption of teleconsultation service in clinical practice. We identify specific theoretical attributes targeted on the research problem and extend the Technology–Organization–Environment (TOE) framework into a multi-dimensional analytical framework. We design a comparative case study and conduct deductive analysis to test our propositions using data from multiple sources. The proposed analytical framework and empirical findings not only provide theoretical contribution by articulating the TOE framework to reflect the specific and distinguished characteristics in teleconsultation services, but also provide implications for practitioners to develop better strategies for teleconsultation collaboration.     

基于兴趣匹配的机会社会网络消息分发机制

机会社会网络(opportunistic social networks)能够利用节点移动创造的相遇机会,在缺乏持续端到端连接的网络中,为用户提供稳定的消息分发途径,但在消息分发效率以及用户体验方面存在不足.为提高消息分发系统的性能、改善网络用户体验,提出一种基于节点兴趣匹配的机会社会网络分发机制.通过引入混合结构的机会社会网络分发系统解决网络拓扑信息获取不全与节点计算能力不足的问题;从节点行为规律与兴趣爱好2方面对网络进行分析,并提出一种用于复杂关系数据分析的联合聚类方法;针对用户需求,设计消息属性与节点兴趣匹配优先的消息分发策略.仿真结果表明,该机制能够在投递率、投递时延、缓存占用率等方面提升网络性能,且具有较高的分发效率、覆盖率与兴趣匹配度.     

无标度网络中基于最短路径免疫策略的病毒传播研究

传统病毒免疫策略大多基于网络的全局拓扑信息。然而现实生活中的大部分复杂网络仅仅只能了解其局部 拓扑信息。鉴于许多实际复杂网络具有无标度特性,研究了在无标度复杂演化网络中基于网络局部拓扑信息最短路 径免疫策略的病毒传播现象。利用平均场理论建立含个体抵杭力重要因素的无标度网络病毒传播模型,并引入基于 最短路径的免疫策略。比较了随机免疫、目标免疫和最短路径免疫3种策略对无标度复杂网络病毒传播的影响,结果 表明了基于最短路径免疫策略的有效性。     

Guide Me in Analysis: A Framework for Guidance Designers

Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk-through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues.     

Creating a model of the dynamics of socio-technical groups

Individuals participating in technologically mediated forms of organization often have difficulty recognizing when groups emerge, and how the groups they take part in evolve. This paper contributes an analytical framework that improves awareness of these virtual group dynamics through analysis of electronic trace data from tasks and interactions carried out by individuals in systems not explicitly designed for context adaptivity, user modeling or user personalization. We discuss two distinct cases to which we have applied our analytical framework. These two cases provide a useful contrast of two prevalent ways for analyzing social relations starting from electronic trace data: either artifact-mediated or direct person-to-person interactions. Our case study integrates electronic trace data analysis with analysis of other, triangulating data specific to each application. We show how our techniques fit in a general model of group informatics, which can serve to construct group context, and be leveraged by future tool development aimed at augmenting context adaptivity with group context and a social dimension. We describe our methods, data management strategies and technical architecture to support the analysis of individual user task context, increased awareness of group membership, and an integrated view of social, information and coordination contexts.     

利用区域变形和背景更新实现运动对象跟踪

从时域统计的角度出发,提出了一种结合自适应混合背景更新模型的区域变形跟踪算法．该算法以模型更新得到的前景／背景二值分割掩膜作为区域特征,将跟踪问题抽象为一个水平集(Level Set)偏微分方程的数值求解问题,并分析了算法的自适应性．为了进一步提高算法的实现效率,引入了窄带跟踪方案．实验表明,该算法可以对视频序列中的指定运动对象进行快速精确的跟踪。     

Lattice-based volumetric global illumination

We describe a novel volumetric global illumination framework based on the Face-Centered Cubic (FCC) lattice. An FCC lattice has important advantages over a Cartesian lattice. It has higher packing density in the frequency domain, which translates to better sampling efficiency. Furthermore, it has the maximal possible kissing number (equivalent to the number of nearest neighbors of each site), which provides optimal 3D angular discretization among all lattices. We employ a new two-pass (illumination and rendering) global illumination scheme on an FCC lattice. This scheme exploits the angular discretization to greatly simplify the computation in multiple scattering and to minimize illumination information storage. The GPU has been utilized to further accelerate the rendering stage. We demonstrate our new framework with participating media and volume rendering with multiple scattering, where both are significantly faster than traditional techniques with comparable quality.     

Disseminating streaming data in a dynamic environment: an adaptive and cost-based approach

In a distributed stream processing system, streaming data are continuously disseminated from the sources to the distributed processing servers. To enhance the dissemination efficiency, these servers are typically organized into one or more dissemination trees. In this paper, we focus on the problem of constructing dissemination trees to minimize the average loss of fidelity of the system. We observe that existing heuristic-based approaches can only explore a limited solution space and hence may lead to sub-optimal solutions. On the contrary, we propose an adaptive and cost-based approach. Our cost model takes into account both the processing cost and the communication cost. Furthermore, as a distributed stream processing system is vulnerable to inaccurate statistics, runtime fluctuations of data characteristics, server workloads, and network conditions, we have designed our scheme to be adaptive to these situations: an operational dissemination tree may be incrementally transformed to a more cost-effective one. Our adaptive strategy employs distributed decisions made by the distributed servers independently based on localized statistics collected by each server at runtime. For a relatively static environment, we also propose two static tree construction algorithms relying on apriori system statistics. These static trees can also be used as initial trees in a dynamic environment. We apply our schemes to both single- and multi-object dissemination. Our extensive performance study shows that the adaptive mechanisms are effective in a dynamic context and the proposed static tree construction algorithms perform close to optimal in a static environment.