A SYSTEMATIC REVIEW OF STRUCTURAL EQUATION MODEL (SEM)

Structural Equation Model (SEM) is a multivariate statistical technique that has been explored to test relationships between variables. The use of SEM to analyze relationship between variables is premised on the weak assumption of path analysis, regression analysis and so on; that variables are measured without error. This review thus sheds light on the meaning of SEM, its assumptions, steps and some of the terms used in SEM. The importance of item parcelling to SEM and its methods were briefly examined. It also dealt on the stages involved in SEM, similarities and differences between SEM and conventional statistical methods, software packages that can be used for SEM. This article employed systematic literature review method because it critically synthesized research studies and findings on structural equation modeling (SEM). It could be concluded that SEM is useful in analyzing a set of relationships between variables using diagrams. SEM can also be useful in minimizing measurement errors and in enhancing reliability of constructs. Based on this, it is recommended that SEM should be employed to test relationship between variables since it can explore complex relationships among variables such as direct, indirect, spurious, hierarchical and non-hierarchical.

A Smoothing Newton Method with a Mixed Line Search for Monotone Weighted Complementarity Problems

In this paper, we present a smoothing Newton method for solving the monotone weighted complementarity problem (WCP). In each iteration of our method, the iterative direction is achieved by solving a system of linear equations and the iterative step length is achieved by adopting a line search. A feature of the line search criteria used in this paper is that monotone and nonmonotone line search are mixed used. The proposed method is new even when the WCP reduces to the standard complementarity problem. Particularly, the proposed method is proved to possess the global convergence under a weak assumption. The preliminary experimental results show the effectiveness and robustness of the proposed method for solving the concerned WCP.

Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis

Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA) aims to leverage useful information in multiple source domains to help do SA in an unlabeled target domain that has no supervised information. Existing algorithms of MS-UDA either only exploit the shared features, i.e., the domain-invariant information, or based on some weak assumption in NLP, e.g., smoothness assumption. To avoid these problems, we propose two transfer learning frameworks based on the multi-source domain adaptation methodology for SA by combining the source hypotheses to derive a good target hypothesis. The key feature of the first framework is a novel Weighting Scheme based Unsupervised Domain Adaptation framework ((WS-UDA), which combine the source classifiers to acquire pseudo labels for target instances directly. While the second framework is a Two-Stage Training based Unsupervised Domain Adaptation framework (2ST-UDA), which further exploits these pseudo labels to train a target private extractor. Importantly, the weights assigned to each source classifier are based on the relations between target instances and source domains, which measured by a discriminator through the adversarial training. Furthermore, through the same discriminator, we also fulfill the separation of shared features and private features.Experimental results on two SA datasets demonstrate the promising performance of our frameworks, which outperforms unsupervised state-of-the-art competitors.

Revisit Division Property Based Cube Attacks: Key-Recovery or Distinguishing Attacks?

Cube attacks are an important type of key recovery attacks against stream ciphers. In particular, they are shown to be powerful against Trivium-like ciphers. Traditional cube attacks are experimental attacks which could only exploit cubes of size less than 40. At CRYPTO 2017, division property based cube attacks were proposed by Todo et al., and an advantage of introducing the division property to cube attacks is that large cube sizes which are beyond the experimental range could be explored, and so powerful theoretical attacks were mounted on many lightweight stream ciphers.In this paper, we revisit the division property based cube attacks. There is an important assumption, called Weak Assumption, proposed in division property based cube attacks to support the effectiveness of key recovery. Todo et al. in CRYPTO 2017 said that the Weak Assumption was expected to hold for theoretically recovered superpolies of Trivium according to some experimental results on small cubes. In this paper, it is shown that the Weak Assumption often fails in cube attacks against Trivium, and moreover a new method to recover the exact superpoly of a given cube is developed based on the bit-based division property. With our method, for the cube I proposed by Todo et al. at CRYPTO 2017 to attack the 832-round Trivium, we recover its superpoly pI(x, v) = v68v78 · (x58⊕v70) · (x59x60⊕x34⊕x61). Furthermore, we prove that some best key recovery results given at CRYPTO 2018 on Trivium are actually distinguishing attacks. Hopefully this paper gives some new insights on accurately recovering the superpolies with the bit-based division property and also attract some attention on the validity of division property based cube attacks against stream ciphers.

Intrinsic Dispositional Properties and Immanent Realism

Tugby (2013a) and Yates (2016) have recently argued that immanent realism is incompatible with the existence of intrinsic but (at least partially) relationally constituted genuine dispositional properties. The success of Tugby’s and Yates’ arguments depends either on a strong or on a weak assumption about the interworld identity of dispositional properties. In this paper, the author evaluates the strength of the arguments in question under those two assumptions. He also offers an alternative metaphysical picture for the fundamental dispositional properties which rejects these assumptions and, consequently, undermines the arguments themselves.

ITERATED LOGARITHM SPEED OF RETURN TIMES

In a general setting of an ergodic dynamical system, we give a more accurate calculation of the speed of the recurrence of a point to itself (or to a fixed point). Precisely, we show that for a certain $\unicode[STIX]{x1D709}$ depending on the dimension of the space, $\liminf _{n\rightarrow +\infty }(n\log \log n)^{\unicode[STIX]{x1D709}}d(T^{n}x,x)=0$ almost everywhere and $\liminf _{n\rightarrow +\infty }(n\log \log n)^{\unicode[STIX]{x1D709}}d(T^{n}x,y)=0$ for almost all $x$ and $y$. This is done by assuming the exponential decay of correlations and making a weak assumption on the invariant measure.

Optimality Conditions for Nondifferentiable Multiobjective Semi-Infinite Programming Problems

We have considered a multiobjective semi-infinite programming problem with a feasible set defined by inequality constraints. First we studied a Fritz-John type necessary condition. Then, we introduced two constraint qualifications and derive the weak and strong Karush-Kuhn-Tucker (KKT in brief) types necessary conditions for an efficient solution of the considered problem. Finally an extension of a Caristi-Ferrara-Stefanescu result for the (Φ,ρ)-invexity is proved, and some sufficient conditions are presented under this weak assumption. All results are given in terms of Clark subdifferential.