Reviewerly/exHarmony huggingface dataset:
A Multimodal Multiobjective Genetic Algorithm for Feature Selection

When performing feature selection on most data sets, there is a general situation that some different feature subsets have the same number of selected features and classification error rate. This indicates that feature selection in some data sets is a multimodal multiobjective optimization (MMO) problem. Most of the current studies on feature selection ignore the MMO problems. Therefore, this paper proposes a feature selection method based on a multimodal multiobjective genetic algorithm (MMOGA) to solve the problem. This algorithm is mainly improved in three aspects. First, a special initialization strategy based on symmetric uncertainty is designed to improve the fitness of the initial population. Second, this paper adds a niche strategy to the genetic algorithm to search for multimodal solutions. Unlike traditional niche methods that has a central individual, this algorithm also considers the distances between individuals in the niche. Third, to effectively utilize excellent individuals for evolution, this algorithm uses a method based on the Pareto set of the niche to generate offspring. Finally, by comparing with other algorithms, the effectiveness of the MMOGA in feature selection is verified. This algorithm can successfully find equivalent feature subsets on different datasets.
A Versatile Detection Method for Various Contrast Enhancement Manipulations

Contrast enhancement manipulation is a common method to improve the visual effect of an image. Meanwhile, it can also be considered a type of global image forgery because it changes the image’s visual appearance without alerting its semantics. Moreover, for local image forgery, a tampered image may be composited by images with different contrast enhancement manipulations or post-processed by a contrast enhancement manipulation to conceal the trails of tampering. Therefore, contrast enhancement manipulation detection is critical to global image forgery detection. The existing methods can only detect a particular type of contrast enhancement manipulation, such as gamma correction or histogram equalization. To break this limitation, we propose the zero-gap spans (ZGS) as the fingerprint to explore the traces of contrast enhancement manipulations. Based on ZGS, various contrast enhancement manipulations can be distinguished by a simple classification method at image-level and patch-level; different gamma corrections can be identified, and their gamma value can be estimated. Experimental results indicate that the proposed ZGS-based classification method can achieve and maintain good classification performance under different cases (gamma correction, simple histogram equalization, modified histogram equalization techniques). Meanwhile, ZGS can estimate the gamma value with the mean squared error (MSE) below 0.1156. For the local forgery images, the proposed ZGS also can be utilized to locate the regions with different contrast enhancement manipulations.
Comparing Cross-Subject Performance on Human Activities Recognition Using Learning Models

Human activities recognition (HAR) plays a vital role in fields like ambient assisted living and health monitoring, in which cross-subject recognition is one of the main challenges coming from the diversity of various users.Although recent studies have achieved satisfactory results in a noncross-subject condition, the recognition performance has significant degradation under the cross-subject criterion.In this paper, we evaluate three traditional machine learning methods and five deep neural network architectures under the same metrics on three popular HAR datasets: mHealth, PAMAP2, and UCIDSADS.The experimental results show that traditional machine learning approaches are generally more robust to the new subject scenarios under strict leave-one-subject-out cross-validation.Extra analysis indicates that hand-crafted features are one major reason for the better performance of traditional machine learning on cross-subject HAR, while deep learning is more prone to learning subject-dependent features under an endto-end training process.A novel training strategy for decision-tree-based methods is also proposed in this paper, resulting in an improvement on the random forest model which achieves competitive performance at an average F1-score (accuracy) of 94.49% (95.09%), 91.64% (92.21%), and 92.70% (93.29%) on the three datasets, compared with state-of-the-art solutions for cross-subject HAR.
Asynchronous Evolutionary Algorithm for Finding Backdoors in Boolean Satisfiability

In this work we propose an asynchronous parallel evolutionary algorithm that is efficient for a specific type of gray-box optimization problems, in which the calculation of the fitness function may be split into a set of several independent calculations. An example of such an optimization problem is the search for backdoors (hidden structures) in the Boolean satisfiability problem: subsets of variables that allow an efficient splitting of the problem into a set of independent subproblems. Our experiments show that the proposed asynchronous approach allows speeding up the algorithm considerably, while also effi-ciently utilizing comnuting cluster time.
Rule Induction Using Set-Based Particle Swarm Optimisation

This paper presents a new approach to induce a list of rules from a dataset by using a set-based particle swarm optimisation algorithm. Many contemporary rule induction algorithms tend to use similar information gain based approaches to fit a training dataset. The proposed novel algorithm is a meta-heuristic approach which finds an optimal rule list while providing the flexibility to overcome traditional drawbacks such as overfitting and rigidity to the datatypes that can be used. This paper shows that the proposed algorithm performs comparatively well when compared to existing rule induction algorithms and it has the potential to be expanded further by adding rule pruning techniques.
Restructuring Particle Swarm Optimization algorithm based on linear system theory

The original Particle Swarm Optimization (PSO) used two formulas to describe updating of particle's position and velocity, respectively, based on simulating the foraging behavior of bird swarm. The general improving methods on PSO are to adjust and optimize its parameters or combine new learning strategy to update velocity formula for the better performance. But these methods lack of theoretical analysis and make the algorithm more complex. This paper proposes a new formulation to restructure the particles' position updating behaviors based on linear system theory, and obtain a Restructuring PSO algorithm (RPSO). Compared with the conventional PSO algorithm, RPSO only uses one particle position updating formula, without velocity updating formula, and takes fewer parameters. In order to verify the effectiveness of RPSO, experiments on the CEC 2013 benchmark functions have been conducted to compare with four algorithms, and the final results show that proposed algorithm has a certain degree of competition.
Continual Learning for anomaly detection on turbomachinery prototypes - A real application

We apply a Recurrent Neural Network (RNN), Kullback-Leibler (KL) divergence and a continual learning approach to check the status of several hundreds of sensors during turbo-machinery prototype testing. Turbo-machinery prototypes can be instrumented with up to thousands of sensors. Therefore, checking the health of each sensor is a time consuming activity. Prototypes are also tested on several different and a-priori unknown operating conditions, so we cannot apply a purely supervised model to detect potential anomalies of sensors and, moreover, we have to take into account a covariate shift because measurements drift continuously day by day. We continuously train a RNN (daily) to build a virtual sensor from other sensors and we compare the predicted signal vs the real signal to raise (in case) an anomaly. Furthermore, KL is used to estimate the overlap between the input distributions available at training time and the ones seen at test time, and thus the confidence level of the prediction. Finally we implement an end-to-end system to automatically train and evaluate the models. The paper presents the system and reports the application to a test campaign of about five hundred sensors.
Multiple Topology SHADE with Tolerance-based Composite Framework for CEC2022 Single Objective Bound Constrained Numerical Optimization

To further enhance the convergence performance and accuracy of SHADE, a SHADE with tolerance-based multiple topology selection framework (MTT_SHADE) is proposed in this paper. In MTT_SHADE, three population topologies are established employing the k-nearest neighbor network, small-world network, and random network, respectively, and the evolution of individuals depends on the neighborhoods derived from different topologies. The tolerance-based composite framework is proposed to select the appropriate topology for an individual at the same time. Specifically, local tolerance and global tolerance are predetermined, corresponding to the tolerance for individuals and the population, respectively. The topology involved in the evolution of the individual is replaced when the individual does not progress after successive iterations. The population that does not improve in effect after successive iterations are considered to have exceeded the global tolerance and the three population topologies are reconstructed. The CEC2022 competition on single objective bound-constrained numerical optimization and four state-of-the-art DE variants are employed to investigate the effectiveness of the proposed algorithm. Experimental results show that MTT_SHADE is competitive in terms of accuracy and convergence.
Adaptive Multi-subpopulation based Differential Evolution for Global Optimization

Properly configuring mutation strategies and their associated parameters in DE is inherently a difficult issue. In this paper, an adaptive multi-subpopulation based differential evolution has been proposed and employed for global optimization. In the proposed method, the entire population is firstly adaptively divided at each generation according to a devised population division strategy, which try to partition the population into multiple subpopulations according to the potential of individuals. Then, a suitable mutation strategy along with an appropriate parameter control scheme is introduced and assigned to each subpopulation for evolution, with the purpose of delivering a balanced evolution. The performance of proposed algorithm has been evaluated on CEC'2015 benchmark functions and compared with related methods. The results show that our method can outperform related methods to be compared.
Effects of the Training Set Size: A Comparison of Standard and Down-Sampled Lexicase Selection in Program Synthesis

From a practitioners perspective, the number of in-put/output examples used during the training process in program synthesis studies is too large, as in practice, these examples must be labeled by hand. Therefore, this paper analyzes the influence of different training set sizes on the performance, generalization ability, as well as the structure of the programs generated by grammar-guided genetic programming. We compare down-sampled lexicase selection with standard lexicase selection on three common problems from the general program synthesis benchmark suite. First, we find that both lexicase variants are robust against reducing the amount of training data. We find that standard lexicase has a tendency to overfit the training data on some problems. With down-sampled lexicase, in contrast, overfitting on training data is reduced and evolved programs generalize better on held-out test cases. Consequently, we suggest to use grammar-guided genetic programming with down-sampled lexicase selection in the program synthesis domain.
Spatial template-based geometric complexity reduction method for photo-realistic modeling of large-scale indoor spaces

Recent progresses in image-based rendering (IBR) have demonstrated the feasibility of photo-realistic modeling in room-scale indoor spaces. However, it is difficult to extend the method to large-scale indoor spaces, because the computational complexity increases exponentially as the geometric complexity increases. In this study, we propose a framework that automatically generates photo-realistic model of large-scale indoor spaces. We first define primary factors that increase geometrical complexity as geometrically excluded objects (GEOs). The proposed framework removes GEOs in images and point clouds to efficiently represent large-scale indoor spaces. To this end, we introduce a segmentation method to segment GEOs from every image coherently. In addition, we also introduce an image inpainting method to fill in the segmented images for photo-realistic indoor modeling. Experiments are conducted in three small-scale spaces and two large-scale indoor spaces. In the experiments, the proposed modules are validated thoroughly. In addition, the experimental results show that the proposed method enables to generate photo-realistic indoor models automatically and efficiently.
Learning unsupervised disentangled skill latents to adapt unseen task and morphological modifications

Learning adaptable policies in the absence of explicit reward signals is a challenging problem in reinforcement learning. We propose an algorithm that disentangles the morphology-aware variables from skill latent space for adapting quickly to unseen morphological changes. Through several task learning experiments using MuJoCo Ant environments, we demonstrate that the agent can perform zero-shot inference and adapt to mild modification in morphology within an expected performance range. Furthermore, in the case of severe unseen morphological damage to the agent’s body, with the help of add-on training steps, we can subjoin additional value incorporated into the disentangled latent space without catastrophically destroying the pre-trained network. We observe that the proposed separable-skill based method outperforms prior evolutionary meta-learning-based approaches, and the presented approach opens up research direction toward reinforcement learning for open-world novelty. Our source code is available at:https://github.com/boratw/sd4m.
Interval Weight-Based Abstraction for Neural Network Verification

In recent years, neural networks (NNs) have gained much maturity and efficiency, and their applications have spread to various domains, including some modules of safety-critical systems. On the other hand, recent studies have demonstrated that NNs are vulnerable to adversarial attacks, thus a neural network model must be verified and certified before its deployment. Despite the number of existing formal verification methods of neural networks, verifying a large network remains a major challenge for these methods. This is mostly due to the scalability limitations of these approaches and the non-linearity introduced by the activation functions in the NNs. To help tackle this issue, we propose a novel abstraction method that allows the reduction of the NN size while preserving its behavioural features. The main idea of the approach is to reduce the size of the original neural network by merging neurons belonging to the same layer, and defining the new weights as intervals and sums of absolute values of those of the merged neurons. The approach allows for producing an abstract (i.e., reduced) model that is smaller and simpler to verify, while guaranteeing that this abstract model is an over-approximation of the original one. Our early experiments show that the approach enhances the scalability when performing verification operations, such as output range computation, on the abstract model.
Robust Logo Detection Across Large Style Variations

Style variation of logo refers to changes in the logo's visual characteristics during the evolution of the logo, which is a common yet easily overlooked phenomenon. However, conventional logo detection methods suffer from severe performance degradation once the visual characteristics of the logo change, because they fail to establish a relation between different styles due to their lack-of-interaction learning procedure. In this paper, we attend to address this detection failure by learning a transferable and flexible cross-style relation under the meta-learning policy. Our proposed method contains one more sibling branch except for the vanilla Faster-RCNN pipeline, which creates a pair-wise comparing environment. Meanwhile, the classification head of the detector is remodeled into a matching module which meta-learns how to classify regions through pair-wise matching. This pair-wise matching mechanism gives matching module the ability to establish deep transferable relations across styles. Additionally, two logo detection datasets are proposed to support research on logo detection across style variations. Experiments revealed the superior performance of our proposed method.
PSP-MVSNet: Deep Patch-Based Similarity Perceptual for Multi-view Stereo Depth Inference

This paper proposes PSP-MVSNet for depth inference problem in multi-view stereo (MVS). We first introduce a novel patch-based similarity perceptual (PSP) module for effectively constructing 3D cost volume. Unlike previous methods that leverage variance-based operators to fuse feature volumes of different views, our method leverages a cosine similarity measure to calculate matching scores for pairs of deep feature vectors and then treats these scores as weights for constructing the 3D cost volume. This is based on an important observation that many performance degradation factors, e.g., illumination changes or occlusions, will lead to pixel differences between multi-view images. We demonstrate that a patch-based cosine similarity can be used as explicit supervision for feature learning and can help speed up convergence. Furthermore, To adaptively set different depth ranges for different pixels, we extend an existing dynamic depth range searching method with a simple yet effective improvement. We can use this improved searching method to train our model in an end-to-end manner and further improve the performance of our method. Experimental results show that our method achieves state-of-the-art performance on the DTU dataset and comparative results on the intermediate set of Tanks and Temples dataset.
Wide aspect ratio matching for robust face detection

Recently, anchor-based methods have achieved great progress in face detection. They adopt standard anchor matching strategy to sample positive anchors according to predefined IoU threshold. However, the max IoUs of extreme aspect ratio faces are still lower than fixed positive threshold, leading to the sampling failure from these faces. To construct a more robust detection model, more positive anchors from extreme aspect ratio faces need to be sampled and participate in the training phase. The goal of the present research is to improve the detection performance by reasonably extending sampling range of face aspect ratio. In this paper, we firstly explore the factors that affect the max IoU of each face in theory. Then, anchor matching simulation is performed to evaluate the sampling range of face aspect ratio. Finally, we propose a Wide Aspect Ratio Matching (WARM) strategy to collect more representative positive anchors from ground-truth faces across a wide range of aspect ratios. Besides, we present a novel feature enhancement module, named Receptive Field Diversity (RFD) module, to provide diverse receptive field corresponding to different aspect ratios. Extensive experiments have been conducted on popular benchmarks to show the effectiveness of our method, which can help detectors better capture extreme aspect ratio faces. Our method achieves promising APs on WIDER FACE validation dataset (easy: 0.965, medium: 0.955, hard: 0.904) and impressive generalization capability on FDDB dataset.
Law enforcement training using simulation for locally customized encounters

Law enforcement professionals require up to date training for interacting with individuals on the autism spectrum in a manner that facilitates positive citizen response. Although these officers interact with the public regularly, they may only have sporadic interactions with citizens who are not neurotypical. The timing of these interactions is not easy to predict; therefore, it is important to provide regular opportunities to practice contacts with special needs communities. However, in much the same way that it can be difficult to provide regular sessions with other protected groups of people, it is not practical to pull individuals on the autism spectrum to participate in law enforcement training. Role play with neurotypical individuals and classroom training presenting facts about autism do little to prepare these officers for their real-world encounters. Virtual interactions with people on the autism spectrum allow officers to practice techniques without compromising the health and safety of the communities they serve. This paper presents results of a study comparing police training through experiences in virtual reality (VR) with video training regarding police interactions with individuals on the autism spectrum. Police officers in a municipal police department who participated in the study were divided into three groups for continuing training purposes. One group received video training, one group received practice in VR, and one group received training through both video and VR. The differences in training method did not result in significant differences in training effectiveness. However, subjective data did support the efficacy of practice in a virtual setting. This project addressed three important challenges with training in VR. First, the team needed to define the specifics of behavior and language that the simulated individuals would exhibit. Second, the VR had to be tailored to be relevant to the officers participating. Third and finally, the schedule for training delivery had to minimize the time that officers were away from their assigned duties. Officer feedback on their training experiences indicated the approach to these challenges was well-received. The primary research question is whether training in VR is any more effective that watching a training video.
A Typed Iteration Approach for Spoken Language Understanding

A spoken language understanding (SLU) system usually involves two subtasks: intent detection (ID) and slot filling (SF). Recently, joint modeling of ID and SF has been empirically demonstrated to lead to improved performance. However, the existing joint models cannot explicitly use the encoded information of the two subtasks to realize mutual interaction, nor can they achieve the bidirectional connection between them. In this paper, we propose a typed abstraction mechanism to enhance the performance of intent detection by utilizing the encoded information of SF tasks. In addition, we design a typed iteration approach, which can achieve the bidirectional connection of the encoded information and mitigate the negative effects of error propagation. The experimental results on two public datasets ATIS and SNIPS present the superiority of our proposed approach over other baseline methods, indicating the effectiveness of the typed iteration approach.