Blind Image Quality Assessment by Pairwise Ranking Image Series

Image quality assessment(IQA)is constantly innovating,but there are still three types of stickers that have not been resolved:the“content sticker”-limitation of training set,the“annotation sticker”-subjective instability in opinion scores and the“distortion sticker”-disordered distortion settings.In this paper,a No-Reference Image Quality Assessment(NR IQA)approach is proposed to deal with the problems.For“content sticker”,we introduce the idea of pairwise comparison and generate a largescale ranking set to pre-train the network;For“annotation sticker”,the absolute noise-containing subjective scores are transformed into ranking comparison results,and we design an indirect unsupervised regression based on EigenValue Decomposition(EVD);For“distortion sticker”,we propose a perception-based distortion classification method,which makes the distortion types clear and refined.Experiments have proved that our NR IQA approach Experiments show that the algorithm performs well and has good generalization ability.Furthermore,the proposed perception based distortion classification method would be able to provide insights on how the visual related studies may be developed and to broaden our understanding of human visual system.

Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning

Deep learning-based approaches are applied successfully in manyfields such as deepFake identification,big data analysis,voice recognition,and image recognition.Deepfake is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artificial intelligence for political abuse,spreading false information,and pornography.The artificial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and ethics.This paper has analyzed the features related to the computer vision of digital content to determine its integrity.This method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction method.By the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning approach.This proposed approach has improved the accuracy of the detection rate by 98%on various datasets.

基于Pairwise策略优化的双塔召回算法模型研究

在推荐或广告系统中,召回阶段扮演着快速从大量的候选集中挑选出较为合适的物品Item集合,作为下一阶段精排的准入,从而起到在较为贴合用户兴趣的基础上,缓解精排的计算压力的作用,故召回阶段面临的候选集数量通常非常大,一般在几百万至上亿规模。此时,在训练召回模型时,采样便成了很重要的问题,尤其是负采样,一般情况下,训练召回模型时,会做随机负采样,由于召回模型负样本的不置信问题,采到的负样本有可能并不是真负样本,导致影响了模型的训练效果。针对此问题,本文提出了一个基于双塔模型的Pairwise训练策略,利用度量学习的思想规避召回模型负采样过程中负样本不置信的问题,不直接针对负样本判别真假,而是学习样本对之间的差距,提升模型学习精度与效率。

Pairwise马尔科夫模型下的势均衡多目标多伯努利滤波器

由于在实际应用中目标模型不一定满足隐马尔科夫模型(Hidden Markov model,HMM)隐含的马尔科夫假设和独立性假设条件,一种更为一般化的Pairwise马尔科夫模型(Pairwise Markov model,PMM)被提出.它放宽了HMM的结构性限制,可以有效地处理更为复杂的目标跟踪场景.本文针对杂波环境下的多目标跟踪问题,提出一种在PMM框架下的势均衡多目标多伯努利(Cardinality balanced multi-target multi-Bernoulli,CBMe MBer)滤波器,并给出它在线性高斯PMM条件下的高斯混合(Gaussian mixture,GM)实现.最后,采用一种满足HMM局部物理特性的线性高斯PMM,将本文所提算法与概率假设密度(Probability hypothesis density,PHD)滤波器进行比较.实验结果表明本文所提算法的跟踪性能优于PHD滤波器.

基于PairWise排序学习算法研究综述

Learning to Rank(L2R)技术是对搜索结果进行排序,是近几年的研究热点。现关于L2R中的PairWise方法进行研究分析,PairWise方法将排序问题转化为二元分类问题,其缺点是只考虑两篇文档的相对顺序,而不考虑文档出现在搜索结果列表中的位置。另外,不同的查询拥有的文档对数目不同,结果会向拥有文档对较多的查询偏移。对常用的PairWise算法的损失函数及其求解方法、基本思想、算法框架、效用评价以及算法应用进行概括分析。

基于pairwise的改进ranking算法

传统基于pairwise的ranking算法,学习后得到的模型在用NDCG这样的ranking标准评价时效果并不好,对此提出了一种新型ranking算法。该算法也是使用样本对作为训练数据,但定义了一个面向NDCG评估标准的目标函数。针对此目标函数非平滑、难以直接优化的特点,提出使用割平面算法进行学习,不仅解决了上述问题,而且使算法迭代的次数不再依赖于训练样本对数。最后基于基准数据集的实验证明了算法的有效性。

基于Pairwise算法的人车事故中人伤影响因素分析

根据影响骑车人头部损伤的可能因素(汽车车速、电动车车速、碰撞角、骑车人面朝方向、地面附着系数、前置汽车发动机罩离地高度等9个因素)建立模型进行试验。经研究分析发现:在正向撞击过程中,骑车人头部HIC值受肢体作用汽车表面的影响,此作用随车速的提升迅速减缓;汽车处于低速行驶撞击时(速度小于30 km/h),骑车人HIC值随汽车发动机罩离地高度逐次增加而减小;汽车处于高速行驶撞击时(速度大于等于30 km/h),骑车人HIC值随汽车发动机罩离地高度逐次增加而增大;当相对速度(即汽车前进方向上,电动车速度与汽车分速度之和)超过40 km/h时,骑车人头部有很大可能受到伤害或严重损伤。碰撞角为0°时骑车人头部伤害最重,碰撞角为90°时骑车人头部伤害最轻;骑车人头部与接触面作用时间越短,头部HIC值越大。

基于Pairwise算法的组合测试应用研究

本文对基于Pairwise算法的组合测试常用实现方法进行了综述和比较分析。利用Pairwise算法思想对电商平台系统测试进行了需求分析及测试设计,使用PICT工具对电商平台进行组合测试实验,根据电商平台运行需求利用贪心算法对约束关系进行了优化,进一步提高组合测试效能。为验证PICT在电商平台组合测试的有效性,对电商平台相关关联关系进行了仿真实验和实际测试,其在测试用例覆盖、测试生成速度和测试成本等方面具有一定优势。

基于Pairwise排序学习的因子分解推荐算法

针对基于内存的协同过滤推荐算法存在推荐列表排序效果不佳的问题,提出基于Pairwise排序学习的因子分解推荐算法(简称Pairwise-SVD推荐算法)。新算法将因子分解的预测结果作为排序学习算法的输入,把排序问题转化成分类问题使用排序学习理论进行排序产生推荐列表。实验结果表明相比基于内存的协同过滤推荐算法,Pairwise-SVD推荐算法的排序效果更佳。其在指标Kendall-tau上提高了近一倍,在指标MRR上提高了近30%,且在指标MAP上也有小幅提高。

Pairwise Shared Genealogical Ancestry in Structured Populations

Simulation was used to investigate the effects of population structure and migration on metrics of pairwise shared ancestry. Random and hierarchical structures, or migration geometries, were examined. Compared to panmictic populations, progress to all qualitative metrics of pairwise ancestry is delayed in structured populations. However, unless migration is very low, the time required is generally less than triple and often less than twice that required in a panmictic population of the same total size. Population structure also increases, to a similar degree, the time required for a population-wide most recent common ancestor (MRCA). As a result, the relationships between various qualitative metrics of pairwise shared ancestry and MRCA time are relatively unaffected by population structure. For example, the mean time to most recent shared ancestor (MRSA) with global sampling of pairs is 40% - 50% of the MRCA time for almost all simulated structures and migration levels. Quantitative pairwise genealogical overlap is strongly affected by population structure. With global sampling, pairwise quantitative overlap never approaches 1.0, as it does in panmictic populations;and instead eventually becomes stationary at much lower values. Possible implications of the present results for human pairwise shared ancestry are discussed. For globally sampled pairs, the longest time to most recent shared ancestor (MRSA) for humans is suggested to be approximately 2100 years before the present. If generation time is 30 years, then all humans are 69th, or closer, cousins. For people with recent European ancestry, the MRSA time may be only half as long, about 1000 years.

Pairwise Shared Ancestry in Random-Mating Constant-Size Populations

In a panmictic population of constant size N, random pairs of individuals will have a most recent shared ancestor who lived slightly more than 0.5 log2N generations previously, on average. The probability that a random pair of individuals will share at least one ancestor who lived 0.5 log2N generations ago, or more recently, is about 50%. Those individuals, if they do share an ancestor from that generation, would be cousins of degree (0.5 log2N) - 1. Shared ancestry from progressively earlier generations increases rapidly until there is universal pairwise shared ancestry. At that point, every individual has one or more ancestors in common with every other individual in the population, although different pairs may share different ancestors. Those ancestors lived approximately 0.7 log2N generations in the past, or more recently. Qualitatively, the ancestries of random pairs have about 50% similarity for ancestors who lived about 0.9 log2N generations before the present. That is, about half of the ancestors from that generation belonging to one member of the pair are present also in the genealogy of the other member. Qualitative pairwise similarity increases to more than 99% for ancestors who lived about 1.4 log2N generations in the past. Similar results apply to a metric of quantitative pairwise genealogical overlap.

Impurity in Pairwise Entanglement of Heisenberg XX Open Chain

We calculate the concurrence of all pairwise entanglement of Heisenberg XX open chain with single systemimpurity in three-qubit and four-qubit cases,and find that the impurity parameter J_i has great effect on pairwiseentanglement.Choosing the proper parameter J_i,we can obtain the maximal pairwise entanglement of the nearestqubits and make the non-nearest qubits entangle.

The Construction of Pairwise Additive Minimal BIB Designs with Asymptotic Results

An asymptotic existence of balanced incomplete block (BIB) designs and pairwise balanced designs (PBD) has been discussed in [1]-[3]. On the other hand, the existence of additive BIB designs and pairwise additive BIB designs with k = 2?and?λ = 1?has been discussed with direct and recursive constructions in [4]-[8]. In this paper, an asymptotic existence of pairwise additive BIB designs is proved by use of Wilson’s theorem on PBD, and?also for some l?and k the exact existence of l?pairwise additive BIB designs with block size k and?λ = 1?is discussed.

Pairwise comparisons in the analysis of carcinogenicity data

Analysis of carcinogenicity data generally involves a trend test across all dose groups and a pairwise comparison of the high dose group with the control. The most commonly used test for a positive trend is the Cochran-Armitage test. This test is asymptotically normal. For the pairwise comparison of the high dose group with the control group, we propose two modifications: the first modification is to apply the test on the data from high dose and control groups after dropping the data from the low and the medium dose groups;the second modification is to adjust the test conditional on data from all dose groups. We compare the power performance of these two modifications for the pairwise comparisons.

Hierarchical Hypercube Based Pairwise Key Establishment Scheme for Sensor Networks

Security schemes of pairwise key establishment, which enable sensors to communicate with each other se-curely, play a fundamental role in research on security issue in wireless sensor networks. A general frame-work for key predistribution is presented, based on the idea of KDC (Key Distribution Center) and polyno-mial pool schemes. By utilizing nice properties of H2 (Hierarchical Hypercube) model, a new security mechanism for key predistribution based on such model is also proposed. Furthermore, the working per-formance of tolerance resistance is seriously inspected in this paper. Theoretic analysis and experimental fig-ures show that the algorithm addressed in this paper has better performance and provides higher possibilities for sensor to establish pairwise key, compared with previous related works.

The Probability of Pairwise Shared Ancestry and the Expected Number of Pairs of k-th Cousins in a Population Sample

An analytical solution is derived for the probability that a random pair of individuals from a panmictic population of size N will share ancestors who lived G generations previously. The analysis is extended to obtain 1) the probability that a sample of size s will contain at least one pair of (G - 1)th cousins;and 2) the expected number of pairs of (G - 1)th cousins in that sample. Solutions are given for both monogamous and promiscuous (non-monogamous) cases. Simulation results for a population size of N = 20,000 closely approximate the analytical expectations. Simulation results also agree very well with previously derived expectations for the proportion of unrelated individuals in a sample. The analysis is broadly consistent with genetic estimates of relatedness among a sample of 406 Danish school children, but suggests that a different genetic study of a heterogenous sample of Europeans overestimates the frequency of cousin pairs by as much as one order of magnitude.

Estimation of Decision Alternatives on the Basis of Interval Pairwise Comparison Matrices

This paper deals with the calculation of a vector of reliable weights of decision alternatives on the basis of interval pairwise comparison judgments of experts. These weights are used to construct the ranking of decision alternatives and to solve selection problems, problems of ratings construction, resources allocation problems, scenarios evaluation problems, and other decision making problems. A comparative analysis of several popular models, which calculate interval weights on the basis of interval pairwise comparison matrices (IPCMs), was performed. The features of these models when they are applied to IPCMs with different inconsistency levels were identified. An algorithm is proposed which contains the stages for analyzing and increasing the IPCM inconsistency, calculating normalized interval weights, and calculating the ranking of decision alternatives on the basis of the resulting interval weights. It was found that the property of weak order preservation usually allowed identifying order-related intransitive expert pairwise comparison judgments. The correction of these elements leads to the removal of contradictions in resulting weights and increases the accuracy and reliability of results.

Pairwise tagging framework for end-to-end emotion-cause pair extraction

Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a document.It generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and causes.Existing works adopt pipelined approaches or multi-task learning to address the ECPE task.However,the pipelined approaches easily suffer from error propagation in real-world scenarios.Typical multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction results.To address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging task.Unlike prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging task.Through this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause pairs.To validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark dataset.The experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.

Pairwise entanglement and local polarization of Heisenberg model

The characteristics of pairwise entanglement and local polarization (LP) are dis-cussed by studying the ground state (states) of the Heisenberg XX model. The re-sults show that: the ground state (states) is (are) composed of the micro states with the minimal polarization (0 for even qubit and 1/2 for odd qubit); LP and the prob-ability of the micro state have an intimate relation, i.e. the stronger the LP, the smaller the probability, and the same LP corresponds to the same probability; the pairwise entanglement of the ground state is the biggest in all eigenvectors. It is found that the pairwise entanglement is decreased by the state degeneracy and the system size. The concurrence approaches a fixed value of about 0.3412 (for odd-qubit chain) or 0.3491 (for even-qubit chain) if the qubit number is large enough.

Pairwise distance-based heteroscedasticity test for regressions

In this study,we propose nonparametric testing for heteroscedasticity in nonlinear regression models based on pairwise distances between points in a sample.The test statistic can be formulated such that Ustatistic theory can be applied to it.Although the limiting null distribution of the statistic is complicated,we can derive a computationally feasible bootstrap approximation for such a distribution;the validity of the introduced bootstrap algorithm is proven.The test can detect any local alternatives that are different from the null at a nearly optimal rate in hypothesis testing.The convergence rate of this test statistic does not depend on the dimension of the covariates,which significantly alleviates the impact of dimensionality.We provide three simulation studies and a real-data example to evaluate the performance of the test and demonstrate its applications.