Entity resolution for distributed probabilistic data

The problem of entity resolution over probabilistic data (ERPD) arises in many applications that have to deal with probabilistic data. In many of these applications, probabilistic data is distributed among a number of nodes. The simple, centralized approach to the ERPD problem does not scale well as large amounts of data need to be sent to a central node. In this paper, we present FD (Fully Distributed), a decentralized algorithm for dealing with the ERPD problem over distributed data, with the goal of minimizing bandwidth usage and reducing processing time. FD is completely distributed and does not depend on the existence of certain nodes. We validated FD through implementation over a 75-node cluster and simulation using the PeerSim simulator. We used both synthetic and real-world data in our experiments. Our performance evaluation shows that FD can achieve major performance gains in terms of bandwidth usage and response time.     

Modelling biological processes by using a probabilistic P system software

In this paper we present a probabilistic P system simulator that implements the evolution-communication model proposed in (Cavaliere, 2003) enriched with some probabilistic parameters inspired by the cell biology.After describing the software and its working, we compare the mathematical model used with the biological reality of the cell. Then, we present some mathematical and biological applications showing how one can use this software to simulate simple but interesting biological phenomena, related to respiration and photosynthesis processes in some bacteria.     

Parallel simulation of Population Dynamics P systems: updates and roadmap

Population Dynamics P systems are a type of multienvironment P systems that serve as a formal modeling framework for real ecosystems. The accurate simulation of these probabilistic models, e.g. with Direct distribution based on Consistent Blocks Algorithm, entails large run times. Hence, parallel platforms such as GPUs have been employed to speedup the simulation. In 2012, the first GPU simulator of PDP systems was presented. However, it was able to run only randomly generated PDP systems. In this paper, we present current updates made on this simulator, involving an input modu le for binary files and an output module for CSV files. Finally, the simulator has been experimentally validated with a real ecosystem model, and its performance has been tested with two high-end GPUs: Tesla C1060 and K40.     

Probabilistic verification and synthesis of the next generation airborne collision avoidance system

The next generation airborne collision avoidance system, ACAS X, departs from the traditional deterministic model on which the current system, TCAS, is based. To increase robustness, ACAS X relies on probabilistic models to represent the various sources of uncertainty. The work reported in this paper identifies verification challenges for ACAS X, and studies the applicability of probabilistic verification and synthesis techniques in addressing these challenges. Due to shortcomings of off-the-shelf probabilistic analysis tools, we developed a new framework, named VeriCA (Verification for Collision Avoidance). VeriCA is a combined probabilistic synthesis and verification framework that is custom designed for ACAS X and systems with similar characteristics. VeriCA supports Java as a modeling language, is memory efficient, employs parallelization, and provides an interactive simulator that displays aircraft encounters and the corresponding ACAS X behavior. We describe the application of our framework to ACAS X, together with the results and recommendations that our analysis produced.     

粒子群优化算法的概率特性分析及算法改进

群体多样性的丧失是导致粒子群优化（PSO）出现早期收敛的重要原因,鉴于此,对PSO运动方程进行概率特性分析,指出了方程中学习参数的概率分布及参数问的相依性与群体多样性丧失之间的关系,并提出了一种白适应学习的PSO算法．该算法通过调整学习参数的概率特性来保持种群多样性,同时设计了随进化状态白适应变化的学习参数来协调粒子的...     

Strongly simulation-extractable leakage-resilient NIZK

This paper defines strongly simulation-extractable (sSE) leakage resiliency (LR), which is a new notion for non-interactive zero-knowledge (NIZK) proof system. For an sSE-NIZK proof system, there exists a probabilistic polynomial-time extractor that can always extract a correct witness from any valid proof generated by the adversary, who can obtain proofs of true statements previously given by the simulator. The proof generated by the adversary may depend on a statement–tag pair which has already been used by the simulator. Furthermore, if the adversary can also learn leakage on witnesses and randomness which can explain the proofs generated by the simulator, then the sSE-NIZK proof system is said to satisfy the property of LR. In ASIACRYPT 2010, Dodis, Haralambiev, López-Alt, and Wichs proposed the definitions of true simulation-extractable (tSE) NIZK proof system and sSE-NIZK proof system and gave their constructions. The tSE-NIZK proof system is the same as the sSE-NIZK proof system except that the proof generated by the adversary cannot depend on a statement–tag pair which was used by the simulator. As an extension of the tSE-NIZK proof system, Garg, Jain, and Sahai defined a new notion for NIZK proof system called tSE-LR in CRYPTO 2011 and provided the construction of tSE-LR-NIZK proof system. We extend the notion of tSE-LR-NIZK proof system and construct it by improving the construction of tSE-LR-NIZK proof system. An sSE-LR-NIZK proof system is applicable to construct a fully leakage-resilient signature scheme which is strongly existentially unforgeable, while a tSE-LR-NIZK proof system is applicable to construct one which just satisfies the weak existentially unforgeability. Although there has already been a great deal of research proposed for cryptographic primitives in the leakage models, as far as we know, this is the first fully leakage-resilient signature scheme that is strongly existentially unforgeable.     

一种提高推荐多样性的概率选择模型

传统的推荐算法多以优化推荐列表的精确度为目标,而忽略了推荐算法的另一个重要指标——多样性。提出了一种新的提高推荐列表多样性的方法。该方法将列表生成步骤转换为N次概率选择过程,每次概率选择通过两个步骤完成:类型选择与项目选择。在类型选择中,引入项目的类型信息,根据用户对不同项目类型的喜好计算概率矩阵,并依照该概率矩阵选择一个类型;在项目选择中,根据项目的预测评分、项目的历史流行度、项目的推荐流行度3个因素重新计算项目的最终得分,选择得分最高的项目推荐给用户。通过阈值TR来调节多样性与精确度之间的折中。最后,通过对比实验证明了该方法的有效性。     

空间数据关联的多目标粒子群优化算法

提出一种空间联合概率数据关联的多目标粒子群优化(DS-MOPSO)算法。采用正态分布确保初始样本均匀分布,通过采用拥挤距离和先验概率采样确立外部归档中非支配解的拥挤度来保持解的多样性;采用Sigma方法作为选择精英粒子策略寻找全局最优解;利用空间联合概率数据关联动态生成每个粒子的惯性权值,增强粒子的搜索区域,防止算法陷入局部最优。仿真实验结果表明,采用所提出的算法所得到的Pareto解集具有很好的收敛性和多样性。     

A novel approach in parameter adaptation and diversity maintenance for genetic algorithms

In this paper, we propose a probabilistic rule-driven adaptive model (PRAM) for parameter adaptation and a repelling approach for diversity maintenance in genetic algorithms. PRAM uses three parameter values and a set of greedy rules to adapt the value of the control parameters automatically. The repelling algorithm is proposed to maintain the population diversity. It modifies the fitness value to increase the survival opportunity of chromosomes with rare alleles. The computation overheads of repelling are reduced by the lazy repelling algorithm, which decreases the frequency of the diversity fitness evaluations. From experiments with commonly used benchmark functions, it is found that the PRAM and repelling techniques outperform other approaches on both solution quality and efficiency.     

Self-scaling cooperative discovery of service compositions in unstructured P2P networks

We propose an efficient technique for improving the performance of automatic and cooperative compositions in unstructured Peer-to-Peer networks during service discovery. The technique exploits a probabilistic forwarding algorithm that uses different sources of knowledge, such as network density and service grouping, to reduce the amount of messages exchanged in the network. The technique, analysed in several network configurations by using a simulator to observe resolution time, recall and message overhead, presents good performance especially in dense and large-scale service networks.     

Collaboration Through the Exploitation of Local Interactions in Autonomous Collective Robotics: The Stick Pulling Experiment

This article presents an experiment which investigates how collaboration in a group of simple reactive robots can be obtained through the exploitation of local interactions. A test-bed experiment is proposed in which the task of the robots is to pull sticks out of the ground—an action which requires the collaboration of two robots to be successful. The experiment is implemented in a physical setup composed of groups of 2 to 6 Khepera robots, and in Webots, a 3D simulator of Khepera robots.The results using these two implementations are compared with the predictions of a probabilistic modeling methodology (A. Martinoli, A. Ijspeert, and F. Mondada, 1999, Robotics and Autonomous Systems, 29:51–63, 1999; A. Martinoli, A. Ijspeert, and L. Gambardella, 1999, in Proceedings of Fifth European Conference on Artificial Life, ECAL99, Lecture Notes in Computer Science, Springer Verlag: Berlin, pp. 575–584) which is here extended for the characterization and the prediction of a collaborative manipulation experiment. Instead of computing trajectories and sensory information, the probabilistic model represents the collaboration dynamics as a set of stochastic events based on simple geometrical considerations. It is shown that the probabilistic model qualitatively and quantitatively predicts the collaboration dynamics. It is significantly faster than a traditional sensor-based simulator such as Webots, and its minimal set of parameters allows the experimenter to better identify the effect of characteristics of individual robots on the team performance.Using these three implementations (the real robots, Webots and the probabilistic model), we make a quantitative investigation of the influence of the number of workers (i.e., robots) and of the primary parameter of the robots' controller—the gripping time parameter—on the collaboration rate, i.e., the number of sticks successfully taken out of the ground over time. It is found that the experiment presents two significantly different dynamics depending on the ratio between the amount of work (the number of sticks) and the number of robots, and that there is a super-linear increase of the collaboration rate with the number of robots. Furthermore, we investigate the usefulness of heterogeneity in the controllers' parameters and of a simple signalling scheme among the robots. Results show that, compared to homogeneous groups of robots without communication, heterogeneity and signalling can significantly increase the collaboration rate when there are fewer robots than sticks, while presenting a less noticeable or even negative effect otherwise.     

The effects of habituation and adding a rest-frame on experienced simulator sickness in an advanced mobility scooter driving simulator

The aim of this article is to investigate the effect of a physical rest-frame, habituation and age on simulator sickness in an advanced mobility scooter driving simulator. Twenty-six young and 34 older adults completed a total of 12 drives in an advanced mobility scooter driving simulator over two visits. A 2x2 crossover design was used to measure the effect of a rest frame that was added to the driving simulator on either the first or second visit. The Simulator Sickness Questionnaire was used to measure simulator sickness symptoms. A significant decrease in simulator sickness was observed between the first and the second visit. Older adults reported more severe simulator sickness symptoms compared to younger participants. No effect of rest-frame could be found. Habituation appears to be the most effective method to reduce simulator sickness in an advanced mobility scooter driving simulator. More research is needed to investigate simulator sickness in patient groups.

Practitioner summary: Experiencing simulator sickness is a major problem across all types of simulators. The present experiment investigated the effect of a rest-frame, habituation and age on developing simulator sickness symptoms in an advanced mobility scooter driving simulator. Habituation appeared to be the most effective method to reduce simulator sickness.     

A diversity maintaining population-based incremental learning algorithm

In this paper we propose a new probability update rule and sampling procedure for population-based incremental learning. These proposed methods are based on the concept of opposition as a means for controlling the amount of diversity within a given sample population. We prove that under this scheme we are able to asymptotically guarantee a higher diversity, which allows for a greater exploration of the search space. The presented probabilistic algorithm is specifically for applications in the binary domain. The benchmark data used for the experiments are commonly used deceptive and attractor basin functions as well as 10 common travelling salesman problem instances. Our experimental results focus on the effect of parameters and problem size on the accuracy of the algorithm as well as on a comparison to traditional population-based incremental learning. We show that the new algorithm is able to effectively utilize the increased diversity of opposition which leads to significantly improved results over traditional population-based incremental learning.     

噪声环境下基于蒲丰距离的依概率多峰优化算法

针对噪声环境下求解多个极值点的问题, 本文提出了噪声环境下基于蒲丰距离的依概率多峰优化算法(Probabilistic multimodal optimization algorithm based on the Button distance, PMB). 算法依据蒲丰投针原理提出噪声下的蒲丰距离和极值分辨度概念, 理论推导证明了二者与算法峰值检测率符合依概率关系. 在全局范围内依据蒲丰距离划分搜索空间, 可以使PMB算法保持较好的搜索多样性. 在局部范围内利用改进的斐波那契法进行探索, 减少了算法陷入噪声引起的局部最优的概率. 基于34个测试函数, 从依概率特性验证、寻优结果影响因素分析、多极值点寻优和多维函数寻优四个角度进行实验. 证明了蒲丰距离与算法的峰值检测率符合所推导的依概率关系. 对比噪声环境下的改进蝙蝠算法和粒子群算法, PMB算法在噪声环境中可以依定概率更精确地定位多峰函数的更多极值点, 从而证明了PMB算法原理的正确性和噪声条件下全局寻优的依概率性能, 具有理论意义和实用价值.     

A Novel Probabilistic Saturating Counter Design for Secure Branch Predictor

In a modern processor,branch prediction is crucial in effectively exploiting the instruction-level parallelism for high-performance execution.However,recently exposed vulnerabilities reveal the urgency to improve the security of branch predictors.The vital cause of the branch predictor vulnerabilities is that the update strategy of the saturating counter is deterministic.As a fundamental building block in a modern branch predictor,previous studies have paid too much attention to the performance and hardware cost and ignored the security of saturating counter.This leaves attackers with the opportunities to perform side-channel attacks on the branch predictor.This paper focuses on the saturating counter to explore a secure and lightweight design to mitigate branch predictor side-channel attacks.Instead of applying the isolation mechanism to branch predictor resources,we propose a novel probabilistic saturating counter design to confuse the attacker's perception of the victim's behaviour.It changes the conventional deterministic state transition function to a probabilistic state transition function.When a branch is committed,the conventional saturating counter needs to be updated about whether the prediction results are correct or not.While for the probabilistic saturating counter,the branch predictor determines whether the update is performed based on the update probability.The probabilistic saturating counter dramatically reduces the ability of the attacker to spy the saturating counter's state.Our analyses using a cycle-accurate simulator suggest that the proposed mechanism incurs 2.4％ performance overhead and hardware cost while providing strong protection.     

Application-driven,energy-efficient communication in wireless sensor networks

Several sensor network applications based on data diffusion and data management can determine the communication transfer rate between two sensors beforehand. In this framework, we consider the problem of energy efficient communication among nodes of a wireless sensor network and propose an application-driven approach that minimizes radio activity intervals and prolongs network lifetime. On the basis of possible communication delays we estimate packet arrival intervals at any intermediate hop of a fixed-rate data path. We study a generic strategy of radio activity minimization wherein each node maintains the radio switched on just in the expected packet arrival intervals and guarantees low communication latency. We define a probabilistic model that allows the evaluation of the packet loss probability that results from the reduced radio activity. The model can be used to optimally choose the radio activity intervals that achieve a certain probability of successful packet delivery for a specific radio activity strategy. Relying on the probabilistic model we also define a cost model that estimates the energy consumption of the proposed strategies, under specific settings. We propose three specific strategies and numerically evaluate the associated costs. We finally validate our work with a simulation made with TOSSIM (the Berkeley motes’ simulator). The simulation results confirm the validity of the approach and the accuracy of the analytic models.     

Tank monitoring: a pAMN case study

The introduction of probabilistic behaviour into the B-method is a recent development. In addition to allowing probabilistic behaviour to be modelled, the relationship between expected values of the machine state can be expressed and verified. This paper explores the application of probabilistic B to a simple case study: tracking the volume of liquid held in a tank by measuring the flow of liquid into it. The flow can change as time progresses, and sensors are used to measure the flow with some degree of accuracy and reliability, modelled as non-deterministic and probabilistic behaviour respectively. At the specification level, the analysis is concerned with the expectation clause in the probabilistic B machine and its consistency with machine operations. At the refinement level, refinement and equivalence laws on probabilistic GSL are used to establish that a particular design of sensors delivers the required level of reliability.     

A computer simulator for use in an introductory course on computer systems and assembly language programming

This paper describes a simulator for a computer system comprising a simple central processor, memory and a variety of peripheral devices. The simulator is used in the laboratory component of an introductory course on computer systems and assembly language programming. The simulator exploits the limited “cursor graphics” capabilities of commonly available display terminals; the graphics are used to present a view of the internal operations of the simulated computer.     

Dimensional variation propagation analysis in straight-build mechanical assemblies using a probabilistic approach

Product quality in mechanical assemblies is determined by controlling the propagation of manufacturing variations as the structure is built. This paper focuses on straight-build assembly and uses a probabilistic approach to analyse the influence of component variation on the eccentricity of the build. Connective models are used to predict assembly variations arising from individual component variations, and a probabilistic approach is used to calculate the probability density function (pdf) for the eccentricity of the build. The probabilistic approach considers three different straight-build scenarios: (i) direct build; (ii) best build; and (iii) worst build, for two-dimensional “axi-symmetric” assemblies. The probabilistic approach is much more efficient than Monte Carlo simulation. The paper also uses numerical examples to investigate the accuracy of the probabilistic approach in comparison to Monte Carlo simulation.     

An algebra for probabilistic databases

An algebra is presented for a simple probabilistic data model that may be regarded as an extension of the standard relational model. The probabilistic algebra is developed in such a way that (restricted to α-acyclic database schemes) the relational algebra is a homomorphic image of it. Strictly probabilistic results are emphasized. Variations on the basic probabilistic data model are discussed. The algebra is used to explicate a commonly used statistical smoothing procedure and is shown to be potentially very useful for decision support with uncertain information