This volume contains the proceedings of the Logic at Harvard conference in honor of W. Hugh Woodin's 60th birthday, held March 27-29, 2015, at Harvard University. It presents a collection of papers ...related to the work of Woodin, who has been one of the leading figures in set theory since the early 1980s.The topics cover many of the areas central to Woodin's work, including large cardinals, determinacy, descriptive set theory and the continuum problem, as well as connections between set theory and Banach spaces, recursion theory, and philosophy, each reflecting a period of Woodin's career. Other topics covered are forcing axioms, inner model theory, the partition calculus, and the theory of ultrafilters.This volume should make a suitable introduction to Woodin's work and the concerns which motivate it. The papers should be of interest to graduate students and researchers in both mathematics and philosophy of mathematics, particularly in set theory, foundations and related areas.
The selection of the mutation strategy for differential evolution (DE) algorithm plays an important role in the optimization performance, such as exploration ability, convergence accuracy and ...convergence speed. To improve these performances, an improved differential evolution algorithm with neighborhood mutation operators and opposition-based learning, namely NBOLDE, is developed in this paper. In the proposed NBOLDE, the new evaluation parameters and weight factors are introduced into the neighborhood model to propose a new neighborhood strategy. On this basis, a new neighborhood mutation strategy based on DE/current-to-best/1, namely DE/neighbor-to-neighbor/1, is designed in order to replace large-scale global mutation by local neighborhood mutation with high search efficiency. Then, a generalized opposition-based learning is employed to optimize the initial population and select the better solution between the current solution and reverse solution in order to approximate global optimal solution, which can amend the convergence direction, accelerate convergence, improve efficiency, enhance the stability and avoid premature convergence. Finally, the proposed NBOLDE is compared with four state-of-the-art DE variants by 12 benchmark functions with low-dimension and high-dimension. The experiment results indicate that the proposed NBOLDE has a faster convergence speed, higher convergence accuracy, and better optimization capabilities in solving high-dimensional complex functions.
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This paper describes the TPTP problem library and associated infrastructure, from its use of Clause Normal Form (CNF), via the First-Order Form (FOF) and Typed First-order Form (TFF), through to the ...monomorphic Typed Higher-order Form (TH0). TPTP v6.4.0 was the last release prior to the introduction of the polymorphic Typed Higher-order Form, and thus serves as the exemplar. This paper summarizes the aims and history of the TPTP, documents its growth up to v6.4.0, reviews the structure and contents of TPTP problems, and gives an overview of TPTP-related infrastructure.
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This comprehensive and accurate survey of state-of-art research on intuitionistic fuzzy sets theory updates the author's work over the past 12 years, and describes the latest general ideas and open ...problems in this expanding field.
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Federated learning (
FL
) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data.
FL
is reshaping existing industry ...paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of
FL
have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the
FL
security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and
FL
as the Blockchain-based federated learning (
BCFL
) framework. This paper introduces an in-depth survey of
BCFL
and discusses the insights of such a new paradigm. In particular, we first briefly introduce the
FL
technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of
BCFL
. Furthermore, we present the attempts ins improving
FL
performance with Blockchain and several combined applications of incentive mechanisms in
FL
. Finally, we summarize the industrial application scenarios of
BCFL
.
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Real-world problems are complex as they are multidimensional and multimodal in nature that encourages computer scientists to develop better and efficient problem-solving methods. Nature-inspired ...metaheuristics have shown better performances than that of traditional approaches. Till date, researchers have presented and experimented with various nature-inspired metaheuristic algorithms to handle various search problems. This paper introduces a new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems. The framework is mainly based on the foraging strategy of butterflies, which utilize their sense of smell to determine the location of nectar or mating partner. In this paper, the proposed algorithm is tested and validated on a set of 30 benchmark test functions and its performance is compared with other metaheuristic algorithms. BOA is also employed to solve three classical engineering problems (spring design, welded beam design, and gear train design). Results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.
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The fundamental criteria for industrial manipulator applications are vibration free and smooth motion with minimum time. This paper investigates the trajectory tracking and vibration control of ...rotary flexible joint manipulator with parametric uncertainties. Firstly, the dynamic modeling via Euler Lagrange equation for a single link flexible joint manipulator is discussed. Secondly, for the execution of smooth motion between two points, bounded and continuous jerk trajectory is developed and implemented. In addition, the prospective strategy uses the concatenation of fifth-order polynomials to provide a smooth trajectory between two-way points. In the planned algorithm, user can independently define the position, velocity, acceleration and jerk values at both initial and final positions. The feature of user-defined parameters gives the versatility to the suggested algorithm for generating trajectories for diverse applications of robotic manipulators. Moreover, the planned scheme is easy to implement and computationally efficient. In the last, the performance of the presented scheme is examined by comparison with cubic splines and a linear segment with parabolic blends (LSPB) techniques. Generated trajectories were evaluated successfully by carrying multiple experiments on QUANSER’s flexible joint manipulator.
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This paper proposes a new population-based optimization algorithm called hunter–prey optimizer (HPO). This algorithm is inspired by the behavior of predator animals such as lions, leopards and ...wolves, and preys such as stag and gazelle. There are many scenarios of animal hunting behavior, and some of them have transformed into optimization algorithms. The scenario used in this paper is different from the scenario of the previous algorithms. In the proposed approach, a prey and predator population, and a predator attacks a prey that moves away from the prey population. The hunter adjusts his position toward this far prey, and the prey adjusts his position toward a safe place. The search agent’s position that was the best value of the fitness function considered a safe place. The HPO algorithm implemented on several test functions to evaluate its performance. Also, to performance verification, the proposed algorithm is applied to several engineering problems. The results showed that the proposed algorithm performed effective in solving test functions and engineering problems.
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Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different ...approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a
deep learning
-based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called “Deep Packet,” can handle both
traffic characterization
in which the network traffic is categorized into major classes (e.g., FTP and P2P) and
application identification
in which identifying end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. The Deep Packet framework employs two deep neural network structures, namely stacked autoencoder (SAE) and convolution neural network (CNN) in order to classify network traffic. Our experiments show that the best result is achieved when Deep Packet uses CNN as its classification model where it achieves recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.
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