Excellent text recognition results have been obtained by training recognition models with synthetic images. However, recognizing text from real-world images still faces challenges due to the domain shift between synthetic and real-world text images. One strategy to eliminate this domain difference without manual annotation is unsupervised domain adaptation (UDA). Due to the characteristics of sequential labeling tasks, most popular UDA methods cannot be directly applied to text recognition. To tackle this problem, we proposed a UDA method that minimizes latent entropy on sequence-to-sequence attention-based models with class-balanced self-paced learning. Experimental results show that our proposed framework achieves better recognition results than the existing methods on most UDA text recognition benchmarks. All codes are publicly available1.

BACKGROUND: Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD). RESULTS: In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power. CONCLUSIONS: The results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future.

In agriculture, the experiences of farmers are very valuable but difficult to replace and passing on. The lack of working power is also a serious problem for many agriculture countries. For planting organic crops, irrigation is one of the most critical steps but also a very labor intensive work. This paper provides a machine learning-based precise and smart irrigation system with LoRa P2P networks to automatically and seamlessly learn the irrigation experiences from expert farmers for greenhouse organic crops. The proposed system will firstly calculate the amount of water for each irrigation based on the trained irrigation model combined with the environment data, such as air temperature/humidity, soil temperate/humidity, light intensity, etc., and then irrigate the crops automatically via the long-distance and low-power wireless LoRa P2P network. The MAC protocol of standard LoRaWAN is Aloha based (random access) and may not be suitable for real-time automatically control. We implement the automatic irrigation system with LoRa P2P network which is a master-slave and TDM-based MAC protocol. Experimental results show that the proposed smart and precise irrigation system is very suitable for modern green house-based agriculture.

Perceptual similarity measurement allows mobile applications to eliminate unnecessary computations without compromising visual experience. Existing pixel-wise measures incur significant overhead with increasing display resolutions and frame rates. This paper presents an ultra lightweight similarity measure called LSIM, which assesses the similarity between frames based on the transformation matrices of graphics objects. To evaluate its efficacy, we integrate LSIM into the Open Graphics Library and conduct experiments on an Android smartphone with various mobile 3D games. The results show that LSIM is highly correlated with the most widely used pixel-wise measure SSIM, yet three to five orders of magnitude faster. We also apply LSIM to a CPU-GPU governor to suppress the rendering of similar frames, thereby further reducing computation energy consumption by up to 27.3% while maintaining satisfactory visual quality.

An ultralow power (ULP), multirate frequency shift keying (FSK) demodulator applied for high-speed biomedical zero-IF receivers is presented. A digital-assisted calibrated delay-line (DL) based phase shifter is used for realizing multirate and low jitter demodulation under reported highest data rate. All circuits are operated in subthreshold region for achieving ULP consumption. Moreover, the power consumption of the demodulator is relative to the data rate. Therefore, energy consumption per received bit can be optimized according to the applications. Implemented in 0.18 μm CMOS process, a minimum energy consumption of 11 pJ per received bit and demodulated peak-to-peak jitter of 1.89 ns are achieved under the maximum data rate of 40 Mb/s.

This paper presents a new method for multiple fuzzy rules interpolation with weighted antecedent variables in sparse fuzzy rule-based systems based on polygonal membership functions. First, the proposed method calculates the normalized weighting vector of each closest fuzzy rule. Then, it calculates the composite weight of each closest fuzzy rule. Then, it calculates the left normal point [Formula: see text] and the right normal point [Formula: see text] of the fuzzy interpolative reasoning result [Formula: see text], respectively. Finally, it calculates the characteristic points [Formula: see text] and [Formula: see text] of the fuzzy interpolative reasoning result B*, respectively. The experimental results show that the proposed method can generate more reasonable fuzzy interpolative reasoning results than the existing methods for sparse fuzzy rule-based systems. The proposed method can overcome the drawbacks of Chang etal.'s method (IEEE Trans. Fuzzy Syst.16(5) (2008) 1285–1301), Chen and Ko's method (IEEE Trans. Fuzzy Syst.16(6) (2008) 1626–1648) and Huang and Shen's method (IEEE Trans. Fuzzy Syst.14(2) (2006) 340–359) for multiple fuzzy rules interpolation. It provides us with a useful way for dealing with multiple fuzzy rules interpolation in sparse fuzzy rule-based systems.

In this paper, we present a new method for fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 Gaussian fuzzy sets and genetic algorithms. First, we present a method to deal with the interpolation of fuzzy rules based on interval type-2 Gaussian fuzzy sets. We also prove that the proposed method guarantees to produce normal interval type-2 Gaussian fuzzy sets. Then, we present a method to learn optimal interval type-2 Gaussian fuzzy sets for sparse fuzzy rule-based systems based on genetic algorithms. We also apply the proposed fuzzy rules interpolation method and the proposed learning method to deal with multivariate regression problems and time series prediction problems. The experimental results show that the proposed fuzzy rules interpolation method using the optimally learned interval type-2 Gaussian fuzzy sets gets higher average accuracy rates than the existing methods.

A feature-based digital image watermarking scheme is proposed to resist geometrical transformations. Such interest point extraction as scale-invariant feature transform is applied to form the invariant regions for signal embedding. Along with the watermark, the pilot signal used for synchronized detection will also be embedded to help to determine larger areas for improving the payload of watermarking and/or increasing the confidence of watermark detection. Both embedded signals are weighted by the just noticeable difference of Watson's perceptual model to meet the requirement of invisibility. In the detection phase, after the same interest point extraction, synchronization can be achieved via adjusting the related affine parameters of grids on the investigated image to detect the possible hidden pilot signal so that the watermark can be successfully retrieved afterward. Experimental results show the feasibility of the proposed method.

In this paper, we present a weights-learning algorithm based on the CHC algorithm, which is a specialization of traditional genetic algorithms, to automatically learn the optimal weights of the antecedent variables of the fuzzy rules for the proposed weighted fuzzy interpolative reasoning method based on bell-shaped membership functions. We also apply the proposed method to deal with the truck backer-upper control problem. The experimental results show that the proposed method using the optimally learned weights gets better accuracy rates than the existing methods for dealing with the truck backer upper control problem.

In this paper, we present a new method for fuzzy rule interpolation with interval type-2 Gaussian fuzzy sets for sparse fuzzy rule-based systems based on genetic algorithms. The proposed fuzzy rule interpolation method deals with the interpolation of fuzzy rules based on the multiple fuzzy rules interpolation scheme. We also present a new learning method to learn optimal interval type-2 Gaussian fuzzy sets for sparse fuzzy rule-based systems based on genetic algorithms. We apply the proposed fuzzy rule interpolation method and the proposed learning method to deal with the Mackey-Glass chaotic time series prediction problem. The experimental result shows that the proposed fuzzy rule interpolation method using the optimally learned interval type-2 Gaussian fuzzy sets obtained by the proposed learning method gets higher average accuracy rates than the existing methods to deal with the Mackey-Glass chaotic time series prediction problem.

In this paper, we propose a weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. It is based on a genetic algorithm (GA)-based weight-learning technique. The proposed method can deal with fuzzy rule interpolation with weighted antecedent variables. It also can deal with fuzzy rule interpolation based on polygonal membership functions and bell-shaped membership functions. We also propose a GA-based weight-learning algorithm to automatically learn the optimal weights of the antecedent variables of the fuzzy rules. Furthermore, we apply the proposed weighted fuzzy interpolative reasoning method and the proposed GA-based weight-learning algorithm to deal with the truck backer-upper control problem, the computer activity prediction problem, multivariate regression problems, and time series prediction problems. Based on statistical analysis techniques, the experimental results show that the proposed weighted fuzzy interpolative reasoning method by the use of the optimally learned weights that were obtained by the proposed GA-based weight-learning algorithm has statistically significantly smaller error rates than the existing methods.

This paper presents a weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems which allows the antecedent variables appearing in the fuzzy rules to have different weights. We also present a weights-learning algorithm to automatically learn the optimal weights of the antecedent variables of the fuzzy rules for the proposed weighted fuzzy interpolative reasoning method. We apply the proposed weighted fuzzy interpolative reasoning method and the proposed weights-learning algorithm to deal with the truck backer-upper control problem. The experimental results show that the proposed fuzzy interpolative reasoning method using the optimally learned weights by the proposed weights-learning algorithm gets better truck backer-upper control results than the existing methods.

Some fuzzy rule interpolation methods have been presented for sparse fuzzy rule-based systems based on interval type-2 fuzzy sets. However, they have the drawbacks that they can not guarantee the convexity of the fuzzy interpolated result and may generate the same fuzzy interpolated result with respect to different observations. In this paper, we present a new method for fuzzy rule interpolation for sparse fuzzy rule-based systems based on the ratio of fuzziness of interval type-2 fuzzy sets. It can overcome the drawbacks of the existing methods. We also use some examples to compare the fuzzy interpolated results of the proposed method with the results by the existing methods. The proposed fuzzy rule interpolation method gets more reasonable results than the existing methods.

In this paper, we present a new method to deal with temperature prediction based on fuzzy clustering and fuzzy rules interpolation techniques. First, the proposed method constructs fuzzy rules from training samples based on the fuzzy C-Means clustering algorithm, where each fuzzy rule corresponds to a cluster and the linguistic terms appearing in the fuzzy rules are represented by triangular fuzzy sets. Then, it performs fuzzy inference based on the multiple fuzzy rules interpolation scheme, where it calculates the weight of each fuzzy rule with respect to the input observation based on the defuzzified values of triangular fuzzy sets. Finally, it uses the weight of each fuzzy rule to calculate the forecasted output. We also apply the proposed method to handle the temperature prediction problem. The experimental result shows that the proposed method gets higher average forecasting accuracy rates than Chen and Hwang's method.

In this paper, we present a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. The proposed method uses weighted increment transformation and weighted ratio transformation techniques to handle weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems. It allows each variable that appears in the antecedent parts of fuzzy rules to associate with a weight between zero and one. Moreover, we also propose an algorithm that automatically tunes the optimal weights of the antecedent variables appearing in the antecedent parts of fuzzy rules. We also apply the proposed weighted fuzzy interpolative reasoning method to handle the truck backer-upper control problem. The proposed weighted fuzzy interpolative reasoning method performs better than the ones obtained by the traditional fuzzy inference system (2000), Huang and Shen's method (2008), and Chen and Ko's method (2008). The proposed method provides us with a useful way to deal with weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems.

Fuzzy rule interpolation techniques have been used to handle the problems of sparse fuzzy rule bases in sparse fuzzy rule-based systems. In the existing fuzzy rule interpolation methods, there are many variables in the antecedents of fuzzy rules, where the variables in the antecedents of fuzzy rules have the same weight. If we can handle fuzzy rule interpolation with weighted antecedent variables, then there is room for more flexibility. In this paper, we present a new method for multiple fuzzy rules interpolation with weighted antecedent variables. The proposed method not only can handle fuzzy rule interpolation with polygonal membership functions, but also can preserve the convexity of fuzzy interpolative reasoning results. The fuzzy interpolative reasoning results of the proposed method also satisfy the logically consistency with respect to the ratios of fuzziness. The experimental result shows that the proposed method can generate reasonable fuzzy interpolative reasoning results for sparse fuzzy rule-based systems with weighted antecedent variables. The proposed method provides us a useful way for fuzzy rule interpolation in sparse fuzzy rule-based systems with weighted antecedent variables.

Fuzzy rule interpolation plays an important role in sparse fuzzy rule-based systems. In this paper, we present a new method for handling fuzzy rule interpolation in sparse fuzzy rule-based systems based on interval type-2 fuzzy sets. The proposed method handles fuzzy rule interpolation based on the principle membership functions and the uncertainty grade functions of interval type-2 fuzzy sets. The proposed method can handle fuzzy rule interpolation with polygonal interval type-2 fuzzy sets. It also can handle fuzzy rule interpolation with multiple antecedent variables and can generate reasonable fuzzy interpolative reasoning results in sparse fuzzy rule-based systems based on interval type-2 fuzzy sets.

Fuzzy interpolative reasoning is an inference technique for dealing with the sparse rules problem in sparse fuzzy-rule-based systems. In this paper, we present a new fuzzy interpolative reasoning method for sparse fuzzy-rule-based systems based on the areas of fuzzy sets. The proposed method uses the weighted average method to infer the fuzzy interpolative reasoning results and has the following advantages: 1) it holds the normality and the convexity of the fuzzy interpolative reasoning result, 2) it can deal with fuzzy interpolative reasoning with complicated membership functions, 3) it can deal with fuzzy interpolative reasoning when the fuzzy sets of the antecedents and the consequents of the fuzzy rules have different kinds of membership functions, 4) it can handle fuzzy interpolative reasoning with multiple antecedent variables, 5) it can handle fuzzy interpolative reasoning with multiple fuzzy rules, and 6) it can handle fuzzy interpolative reasoning with logically consistent properties with respect to the ratios of fuzziness. We use some examples to compare the fuzzy interpolative reasoning results of the proposed method with those of the existing fuzzy interpolative reasoning methods. In terms of the six evaluation indices, the experimental results show that the proposed method performs more reasonably than the existing methods. The proposed method provides us a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy-rule-based systems.

Fuzzy interpolative reasoning is an important inference technique for sparse fuzzy rule-based systems. In this paper, we present a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on the areas of fuzzy sets. The proposed method can guarantee the normality and the convexity of the conclusion and can deal with fuzzy interpolative reasoning with complicated membership functions, such as hexagons, polygons and Gaussians. Moreover, the proposed method can deal with the situation when the antecedents and the consequents of fuzzy rules belong to different kinds of membership functions. We use several examples to compare the fuzzy interpolative reasoning results of the proposed method with the ones of the existing methods. The experimental results show that the proposed method is more suitable to deal with fuzzy interpolative reasoning than the existing methods. The proposed method provides a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy rule-based systems.

In this paper, we present a new method for query reweighting to deal with document retrieval. The proposed method uses genetic algorithms to reweight a user's query vector, based on the user's relevance feedback, to improve the performance of document retrieval systems. It encodes a user's query vector into chromosomes and searches for the optimal weights of query terms for retrieving documents by genetic algorithms. After the best chromosome is found, the proposed method decodes the chromosome into the user's query vector for dealing with document retrieval. The proposed query reweighting method can find the best weights of query terms in the user's query vector, based on the user's relevance feedback. It can increase the precision rate and the recall rate of the document retrieval system for dealing with document retrieval.

In this paper, we extend the work of Kraft et al. to present a new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. First, we present a fuzzy agglomerative hierarchical clustering algorithm for clustering documents and to get the document cluster centers of document clusters. Then, we present a method to construct fuzzy logic rules based on the document clusters and their document cluster centers. Finally, we apply the constructed fuzzy logic rules to modify the user's query for query expansion and to guide the information retrieval system to retrieve documents relevant to the user's request. The fuzzy logic rules can represent three kinds of fuzzy relationships (i.e., fuzzy positive association relationship, fuzzy specialization relationship and fuzzy generalization relationship) between index terms. The proposed fuzzy information retrieval method is more flexible and more intelligent than the existing methods due to the fact that it can expand users' queries for fuzzy information retrieval in a more effective manner.