scholarly journals Improved Local Search with Momentum for Bayesian Networks Structure Learning

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 750
Author(s):  
Xiaohan Liu ◽  
Xiaoguang Gao ◽  
Zidong Wang ◽  
Xinxin Ru
Keyword(s):  
Local Search ◽  
Complex Networks ◽  
Bayesian Networks ◽  
Structure Learning ◽  
Hybrid Methods ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Local Optimum ◽  
Networks Structure

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.

scholarly journals Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 6722-6729 ◽  
Author(s):  
Ziming Li ◽  
Julia Kiseleva ◽  
Maarten De Rijke
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Imitation Learning ◽  
Local Optimum ◽  
Inverse Reinforcement Learning ◽  
High Quality ◽  
Overall Performance

The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator. The reward signal from a poor discriminator can be very sparse and unstable, which may lead the generator to fall into a local optimum or to produce nonsense replies. To alleviate the first problem, we first extend a recently proposed adversarial dialogue generation method to an adversarial imitation learning solution. Then, in the framework of adversarial inverse reinforcement learning, we propose a new reward model for dialogue generation that can provide a more accurate and precise reward signal for generator training. We evaluate the performance of the resulting model with automatic metrics and human evaluations in two annotation settings. Our experimental results demonstrate that our model can generate more high-quality responses and achieve higher overall performance than the state-of-the-art.

scholarly journals Spreading predictability in complex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Na Zhao ◽  
Jian Wang ◽  
Yong Yu ◽  
Jun-Yan Zhao ◽  
Duan-Bing Chen
Keyword(s):  
Infectious Diseases ◽  
Prediction Model ◽  
Complex Networks ◽  
State Of The Art ◽  
Experimental Results ◽  
Vaccination Strategies ◽  
Macro Scale ◽  
Good Consistency ◽  
The Future ◽  
Rumor Control

AbstractMany state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infection probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this report, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future, not just the macro scale of infection. Experimental results on synthetic and real networks demonstrate that the infected individuals predicted by the model have good consistency with the actual infected ones based on simulations.

scholarly journals A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

2021 ◽  
Vol 11 (23) ◽  
pp. 11344
Author(s):  
Wei Ke ◽  
Ka-Hou Chan
Keyword(s):  
Probability Distribution ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Final Analysis ◽  
The State ◽  
Experimental Results ◽  
Content Adaptive

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.

scholarly journals RDFuzz: Accelerating Directed Fuzzing with Intertwined Schedule and Optimized Mutation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jiaxi Ye ◽  
Ruilin Li ◽  
Bin Zhang
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Exploration And Exploitation ◽  
Balance Problem ◽  
Evaluation Strategy ◽  
Testing Schedule ◽  
Available Resources

Directed fuzzing is a practical technique, which concentrates its testing energy on the process toward the target code areas, while costing little on other unconcerned components. It is a promising way to make better use of available resources, especially in testing large-scale programs. However, by observing the state-of-the-art-directed fuzzing engine (AFLGo), we argue that there are two universal limitations, the balance problem between the exploration and the exploitation and the blindness in mutation toward the target code areas. In this paper, we present a new prototype RDFuzz to address these two limitations. In RDFuzz, we first introduce the frequency-guided strategy in the exploration and improve its accuracy by adopting the branch-level instead of the path-level frequency. Then, we introduce the input-distance-based evaluation strategy in the exploitation stage and present an optimized mutation to distinguish and protect the distance sensitive input content. Moreover, an intertwined testing schedule is leveraged to perform the exploration and exploitation in turn. We test RDFuzz on 7 benchmarks, and the experimental results demonstrate that RDFuzz is skilled at driving the program toward the target code areas, and it is not easily stuck by the balance problem of the exploration and the exploitation.

scholarly journals FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning

2020 ◽  
Vol 10 (8) ◽  
pp. 2864 ◽  
Author(s):  
Muhammad Asad ◽  
Ahmed Moustafa ◽  
Takayuki Ito
Keyword(s):  
Privacy Preservation ◽  
State Of The Art ◽  
Homomorphic Encryption ◽  
Evaluation Criteria ◽  
The State ◽  
Compression Algorithm ◽  
Experimental Results ◽  
Local Learning ◽  
Learning Models ◽  
Communication Efficiency

Artificial Intelligence (AI) has been applied to solve various challenges of real-world problems in recent years. However, the emergence of new AI technologies has brought several problems, especially with regard to communication efficiency, security threats and privacy violations. Towards this end, Federated Learning (FL) has received widespread attention due to its ability to facilitate the collaborative training of local learning models without compromising the privacy of data. However, recent studies have shown that FL still consumes considerable amounts of communication resources. These communication resources are vital for updating the learning models. In addition, the privacy of data could still be compromised once sharing the parameters of the local learning models in order to update the global model. Towards this end, we propose a new approach, namely, Federated Optimisation (FedOpt) in order to promote communication efficiency and privacy preservation in FL. In order to implement FedOpt, we design a novel compression algorithm, namely, Sparse Compression Algorithm (SCA) for efficient communication, and then integrate the additively homomorphic encryption with differential privacy to prevent data from being leaked. Thus, the proposed FedOpt smoothly trade-offs communication efficiency and privacy preservation in order to adopt the learning task. The experimental results demonstrate that FedOpt outperforms the state-of-the-art FL approaches. In particular, we consider three different evaluation criteria; model accuracy, communication efficiency and computation overhead. Then, we compare the proposed FedOpt with the baseline configurations and the state-of-the-art approaches, i.e., Federated Averaging (FedAvg) and the paillier-encryption based privacy-preserving deep learning (PPDL) on all these three evaluation criteria. The experimental results show that FedOpt is able to converge within fewer training epochs and a smaller privacy budget.

scholarly journals Saga of Superfluid Solids

Physics ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 49-66 ◽  
Author(s):  
Vyacheslav I. Yukalov
Keyword(s):  
Numerical Simulations ◽  
State Of The Art ◽  
Theoretical Models ◽  
The State ◽  
Experimental Results ◽  
Standing Problem

The article presents the state of the art and reviews the literature on the long-standing problem of the possibility for a sample to be at the same time solid and superfluid. Theoretical models, numerical simulations, and experimental results are discussed.

scholarly journals Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis

2019 ◽  
Vol 33 ◽  
pp. 4295-4303 ◽  
Author(s):  
Rung-Tzuo Liaw ◽  
Chuan-Kang Ting
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Benchmark Problems ◽  
Good Estimator ◽  
Multiple Tasks ◽  
Effectiveness And Efficiency ◽  
Evolutionary Multitasking

Evolutionary multitasking is a significant emerging search paradigm that utilizes evolutionary algorithms to concurrently optimize multiple tasks. The multi-factorial evolutionary algorithm renders an effectual realization of evolutionary multitasking on two or three tasks. However, there remains room for improvement on the performance and capability of evolutionary multitasking. Beyond three tasks, this paper proposes a novel framework, called the symbiosis in biocoenosis optimization (SBO), to address evolutionary many-tasking optimization. The SBO leverages the notion of symbiosis in biocoenosis for transferring information and knowledge among different tasks through three major components: 1) transferring information through inter-task individual replacement, 2) measuring symbiosis through intertask paired evaluations, and 3) coordinating the frequency and quantity of transfer based on symbiosis in biocoenosis. The inter-task individual replacement with paired evaluations caters for estimation of symbiosis, while the symbiosis in biocoenosis provides a good estimator of transfer. This study examines the effectiveness and efficiency of the SBO on a suite of many-tasking benchmark problems, designed to deal with 30 tasks simultaneously. The experimental results show that SBO leads to better solutions and faster convergence than the state-of-the-art evolutionary multitasking algorithms. Moreover, the results indicate that SBO is highly capable of identifying the similarity between problems and transferring information appropriately.

scholarly journals Input-Aware Implication Selection Scheme Utilizing ATPG for Efficient Concurrent Error Detection

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 258 ◽  
Author(s):  
Abdus Hassan ◽  
Umar Afzaal ◽  
Tooba Arifeen ◽  
Jeong Lee
Keyword(s):  
Error Detection ◽  
High Probability ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Concurrent Error Detection ◽  
Selection Algorithm ◽  
Probability Of Error ◽  
Selection Scheme ◽  
Selection Strategies

Recently, concurrent error detection enabled through invariant relationships between different wires in a circuit has been proposed. Because there are many such implications in a circuit, selection strategies have been developed to select the most valuable implications for inclusion in the checker hardware such that a sufficiently high probability of error detection ( P d e t e c t i o n ) is achieved. These algorithms, however, due to their heuristic nature cannot guarantee a lossless P d e t e c t i o n . In this paper, we develop a new input-aware implication selection algorithm with the help of ATPG which minimizes loss on P d e t e c t i o n . In our algorithm, the detectability of errors for each candidate implication is carefully evaluated using error prone vectors. The evaluation results are then utilized to select the most efficient candidates for achieving optimal P d e t e c t i o n . The experimental results on 15 representative combinatorial benchmark circuits from the MCNC benchmarks suite show that the implications selected from our algorithm achieve better P d e t e c t i o n in comparison to the state of the art. The proposed method also offers better performance, up to 41.10%, in terms of the proposed impact-level metric, which is the ratio of achieved P d e t e c t i o n to the implication count.

Language-Independent Type Inference of the Instances from Multilingual Wikipedia

2019 ◽  
Vol 15 (2) ◽  
pp. 22-46 ◽  
Author(s):  
Tianxing Wu ◽  
Guilin Qi ◽  
Bin Luo ◽  
Lei Zhang ◽  
Haofen Wang
Keyword(s):  
Knowledge Base ◽  
State Of The Art ◽  
The State ◽  
Type Inference ◽  
Experimental Results ◽  
High Quality ◽  
New Approach ◽  
Semantic Associations ◽  
Quality Type ◽  
Type Information

Extracting knowledge from Wikipedia has attracted much attention in recent ten years. One of the most valuable kinds of knowledge is type information, which refers to the axioms stating that an instance is of a certain type. Current approaches for inferring the types of instances from Wikipedia mainly rely on some language-specific rules. Since these rules cannot catch the semantic associations between instances and classes (i.e. candidate types), it may lead to mistakes and omissions in the process of type inference. The authors propose a new approach leveraging attributes to perform language-independent type inference of the instances from Wikipedia. The proposed approach is applied to the whole English and Chinese Wikipedia, which results in the first version of MulType (Multilingual Type Information), a knowledge base describing the types of instances from multilingual Wikipedia. Experimental results show that not only the proposed approach outperforms the state-of-the-art comparison methods, but also MulType contains lots of new and high-quality type information.

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