Reviewerly/exHarmony huggingface dataset:
Improved Carnivorous Plant Algorithm based on Hybrid Strategy and its application

An improved Carnivorous plant algorithm (ICPA) based on a hybrid strategy method was proposed. By introducing an elite opposition-based learning strategy, the problem of population quality reduction of the basic carnivorous plant algorithm was solved. The quadratic interpolation method is helpful to improve the convergence rate. The disturbance through the Levy-flight strategy is beneficial to jump out of the local optimum. In each iteration, the adaptive weight factor determines whether to carry out reverse learning and quadratic interpolation, which balances the global and local search capabilities of the carnivorous plant algorithm. A set of benchmark test functions and the simulation results of wireless sensor network coverage optimization show that ICPA has good robustness, optimization accuracy, and optimization speed in solving the optimal value problem.
Improving the Efficiency of Sampling-based Motion Planners via Runtime Predictions for Motion-Planning Problems with Dynamics

While sampling-based approaches have made significant progress, motion planning with dynamics still poses significant challenges as the planner has to generate not only collision-free but also dynamically-feasible trajectories that enable the robot to reach its goal. To improve the efficiency of sampling-based motion planners, this paper develops a framework, termed Motion-Planning Runtime Prediction (MPRP), that relies on machine learning to train models to predict the expected runtime of a planner. When solving a new motion-planning problem, the trained model is then incorporated into the motion planner to more effectively guide the search toward parts of the state space that are associated with low expected runtime predictions. This paper applies the MPRP framework to state-of-the-art sampling-based motion planners to obtain new planners, which are shown to be significantly faster.
Map-Free Visual Relocalization: Metric Pose Relative to a Single Image

Can we relocalize in a scene represented by a single reference image? Standard visual relocalization requires hundreds of images and scale calibration to build a scene-specific 3D map. In contrast, we propose Map-free Relocalization, i.e., using only one photo of a scene to enable instant, metric scaled relocalization. Existing datasets are not suitable to benchmark map-free relocalization, due to their focus on large scenes or their limited variability. Thus, we have constructed a new dataset of 655 small places of interest, such as sculptures, murals and fountains, collected worldwide. Each place comes with a reference image to serve as a relocalization anchor, and dozens of query images with known, metric camera poses. The dataset features changing conditions, stark viewpoint changes, high variability across places, and queries with low to no visual overlap with the reference image. We identify two viable families of existing methods to provide baseline results: relative pose regression, and feature matching combined with single-image depth prediction. While these methods show reasonable performance on some favorable scenes in our dataset, map-free relocalization proves to be a challenge that requires new, innovative solutions.
Unified Multi-Weather Visibility Restoration

Automated surveillance is widely opted for applications such as traffic monitoring, vehicle identification, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . But, various weather degradation factors such as rain and snow streaks, along with atmospheric veil severely affect the perceptual quality of an image, eventually affecting the performance of these applications. There exist weather specific (rain, haze, snow, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> .) methods focusing on respective restoration task. As image restoration is a preprocessing step for high level surveillance applications, it is practically inapplicable to have different architectures for different weather restoration. In this paper, we propose a lightweight unified network, having 1.1M parameters (1 / 40 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{th}$</tex-math></inline-formula> and 1 / 6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{th}$</tex-math></inline-formula> of the existing state-of-the-art rain with veil removal, and snow with veil removal methods respectively) for removal of rain and snow along with the veiling effect present in the images. In this network, we propose two parallel streams to handle the degradations and restoration: First, degradation removal stream (DRS) focuses mainly on removing randomly repeating degradations <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> rain and snow streaks, through the proposed adaptive multi-scale feature sharing block (AMFSB) and stage-wise subtractive block (SSB). Second, feature corrector stream (FCS) mainly focuses on refining the partial outputs of the first stream, reducing the veiling effect and acts supplementary to the first stream. Finally, we leverage contrastive regularization for better convergence of the proposed network. Substantial experiments on synthetic as well as real-world images, along with extensive ablation studies, demonstrate that the proposed method performs competitively with the existing state-of-the-art methods for multi-weather image restoration. The code is available at: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/AshutoshKulkarni4998/UVRNet</uri> </i> .
Reasonable Setting Values for Anonymization Algorithms for Online Educational Data Analysis Support System

We propose a criterion of reasonable parameters for an algorithm that aggregates target data with anonymized data for safe data analysis in online educational systems, called learning management systems (LMSs). We also statistically investigate parameters that can satisfy the proposed criteria using an anonymization algorithm and real large-scale data. We use an open dataset containing one year's worth of product review data due to the difficulty of collecting LMS data large enough for the evaluation. Furthermore, we discuss an approach to address cases where no parameter satisfies the proposed criteria.
Fully Distributed Cartesian Genetic Programming

Cartesian genetic programming is a popular version of genetic programming and has meanwhile proven its performance in many use cases. This paper introduces an algorithmic level decomposition of program evolution that can be solved by a multi-agent system in a fully distributed manner. A heuristic for distributed combinatorial problem solving is adapted to evolve programs. The applicability of the approach and the effectiveness of the multi-agent approach as well as of the evolved genetic programs are demonstrated using symbolic regression, n-parity, and classification problems.
incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection

Novelty detection is a key capability for practical machine learning in the real world, where models operate in non-stationary conditions and are repeatedly exposed to new, unseen data. Yet, most current novelty detection approaches have been developed exclusively for static, offline use. They scale poorly under more realistic, continual learning regimes in which data distribution shifts occur. To address this critical gap, this paper proposes incDFM (incremental Deep Feature Modeling), a self-supervised continual novelty detector. The method builds a statistical model over the space of intermediate features produced by a deep network, and utilizes feature reconstruction errors as uncertainty scores to guide the detection of novel samples. Most importantly, incDFM estimates the statistical model incrementally (via several iterations within a task), instead of a single-shot. Each time it selects only the most confident novel samples which will then guide subsequent recruitment incrementally. For a certain task where the ML model encounters a mixture of old and novel data, the detector flags novel samples to incorporate them to old knowledge. Then the detector is updated with the flagged novel samples, in preparation for a next task. To quantify and benchmark performance, we adapted multiple datasets for continual learning: CIFAR-10, CIFAR-100, SVHN, iNaturalist, and the 8-dataset. Our experiments show that incDFM achieves state of the art continual novelty detection performance. Furthermore, when examined in the greater context of continual learning for classification, our method is successful in minimizing catastrophic forgetting and error propagation.
A Unified Framework for Discrete Multi-kernel k-means with Kernel Diversity Regularization

Multiple kernel clustering seeks to combine several kernels for boosting the clustering performance. However, most existing MKKM methods fail to evaluate kernel correlation adequately, which may inevitably select highly correlated kernels resulting in kernel redundancy. Besides, most existing methods solve the NP-hard cluster labels assignment task in two stages: first learning the relaxed labels with continuous values and then obtaining the discrete labels via other discretization methods like k-means. This two-stage strategy may result in the loss of information owing to the deviation between the genuine solution and the approximated one. In this work, we present a unified framework for Discrete Multi-kernel k-means with Kernel Diversity Regularization (DMK-KDR). It is capable of penalizing highly correlated kernels through a well-designed matrix-induced regularization, thus allowing for improved diversity and reduced redundancy in kernel fusion. Additionally, it learns both discrete and continuous clustering indicator matrices simultaneously, thereby ensuring the integrity of the discrete solution without over-reliance on k-means or the loss of information. The efficacy of our model has been evaluated in a number of experiments using real-world datasets.
Script-Level Word Sample Augmentation for Few-Shot Handwritten Text Recognition

The variety of handwriting styles and the scarcity of training data often result in poor performance of character recognizer. Rather than tedious data collection and annotation, researchers prefer to use low-cost data augmentation to improve the robustness of the recognizer. However, most existing data augmentation methods treat handwritten text as ordinary images and generate new samples through holistic transformation, which greatly limits the diversity of generated samples. To solve the problem, this paper proposes a script-level handwritten text augmentation method, where each component is treated as a Bézier curve. Specifically, we first segment the character into components based on skeleton detection. Then, we move the control points of each component according to the prior knowledge of languages. Finally, we transform the component by Bézier curves and assemble them into new samples. Our method is simple, controllable, and friendly to few-shot handwritten text. Experiments on four datasets in different languages show that the proposed script-level augmentation method performs better than the holistic augmentation methods. Apart from it, we also modify Affine transformation, a commonly used augmentation method, from a holistic to script-level way. Experimental results demonstrate that script-level affine can achieve better performance than holistic affine in the character recognition task. Our code is available at https://github.com/IMU-MachineLearningSXD/script-level_aug_ICFHR2022 .
Energy-efficient Orienteering Problem in the Presence of Ocean Currents

In many environmental monitoring applications robots are often tasked to visit various distinct locations to make observations and/or collect specific measurements. The problem of scheduling and assigning robots to the various tasks and planning feasible paths for the robots can be posed as an Orienteering Problem (OP). In the standard OP, routing and scheduling is achieved by maximizing an objective function by visiting the most rewarding locations while respecting a limited travel budget. However, traditional formulations for such problems usually neglect some environmental features that can greatly impact the tour, e.g., flows, such as wind or ocean currents. This is of particular importance for applications in marine and atmospheric environments where vehicle motions can be significantly impacted by the environmental dynamics and the environment exerts a non-negligible force on the vehicles. In this paper, we tackle the OP in fluid environments where robots must operate in the presence of ocean and/or atmospheric currents. We introduce a novel multi-objective formulation that combines both task and path planning problems, and whose goals are to (i) maximize the collected reward, while (ii) minimizing the energy expenditure by leveraging the environmental dynamics wherever possible. We validate our strategy using simulated ocean model data to show that our approach can generate a diverse set of solutions that have an adequate compromise between both objectives.
Enhanced Accuracy and Robustness via Multi-teacher Adversarial Distillation

Adversarial training is an effective approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, adversarial training (AT) will reduce the performance of identifying clean examples. Meanwhile, Adversarial training can bring more robustness for large models than small models. To improve the robust and clean accuracy of small models, we introduce the Multi-Teacher Adversarial Robustness Distillation (MTARD) to guide the adversarial training process of small models. Specifically, MTARD uses multiple large teacher models, including an adversarial teacher and a clean teacher to guide a small student model in the adversarial training by knowledge distillation. In addition, we design a dynamic training algorithm to balance the influence between the adversarial teacher and clean teacher models. A series of experiments demonstrate that our MTARD can outperform the state-of-the-art adversarial training and distillation methods against various adversarial attacks. Our code is available at https://github.com/zhaoshiji123/MTARD .
Research Hotspots and Trends of Educational Application of Artificial Intelligence at Home and Abroad in the Past Ten Years – Visual Analysis Based on CiteSpace

With the development of “artificial intelligence plus education”, the application of artificial intelligence(AI) in education has become a research hotspot that many researchers pay attention to. In order to explore the research status of education applications of artificial intelligence (EDA-AI) at home and abroad in the past ten years, this paper uses CiteSpace to analyze 346 papers retrieved from China National Knowledge Infrastructure (CNKI) and 235 papers retrieved from Web of Science (WOS). The study found that: (1) The research on EDA-AI at home and abroad is developing, and the number of published papers has surged since 2017 in China and 2019 in foreign respectively; (2) Foreign institutions and scholars have closer cooperative relations and more frequent exchanges than domestic institutions and scholars; (3) The research content focuses on the application of AI technology and AI products in education. Among them, domestic researches were more focused on theoretical exploration and elaboration, while foreign researches were more focused on practical application and AI system development. Finally, in order to provide reference for the application of AI in education, this paper puts forward prospects from four aspects: interdisciplinary research, ethics research, educational application scenarios and forms, and research and development of AI products in education.
Deep Learning Implementation for Snail Trails Detection in Photovoltaic Module

The degradation of fossil fuel reserves and the increasing enthusiasm for the development of renewable resources have led the world to seek and create renewable resources. Among the alternative energy sources available, solar energy is claimed to be the main choice that can replace fossil resources to face the demands of power consumption needs at this time due to the unlimited supply of solar energy. However, the awareness to check the condition of Photovoltaic modules is still low. Detecting the snail trails is necessary to determine the performance of snail trails as an initial step to prevent ongoing damage. This study aims to develop a snail trails detection system on photovoltaic module images using the machine learning method called as deep learning with the YOLO (You Only Look Once) algorithm version 3. There are several important processes required in the YOLOv3 method, which are image annotation, training data, and testing data. The result of detecting snail trail objects on the photovoltaic module obtained 99.7% of accuracy. This value suggests that this method can be proposed to contribute to the implementation of the development of effective damage detection research early on solar panel modules.
Risk-aware Real-time Object Detection

Autonomous systems such as self-driving cars and infrastructure inspection robots must be able to mitigate risk by dependably detecting entities that represent factors of risk in their environment (e.g., humans and obstacles). Nevertheless, current machine learning (ML) techniques for real-time object detection disregard risk factors in their training and verification. As such, they produce ML models that place equal emphasis on the correct detection of all classes of objects of interest—including, for instance, buses and cats in a self-driving scenario. To address this limitation of existing solutions, this short paper introduces a work-in-progress method for the development of risk-aware ML ensembles for real-time object detection. Our new method supports the dependable use of real-time object detection in autonomous systems by (i) identifying the risks that require treatment, (ii) training a set of ML models that mitigate these risks, and (iii) using multi-objective genetic algorithms to combine the ML models into risk-aware ML ensembles. We present preliminary experiments that show the effectiveness of our method at constructing a dependable ML ensemble for realtime object detection in a simulated self-driving case study.
HairNet: Hairstyle Transfer with Pose Changes

We propose a novel algorithm for automatic hairstyle transfer, specifically targeting complicated inputs that do not match in pose. The input to our algorithm are two images, one for the hairstyle and one for the identity (face). We do not require any additional inputs such as segmentation masks. Our algorithm consists of multiple steps and we contribute three novel components. The first contribution is the idea to include baldification into hairstyle editing pipelines to simplify inpainting of background and face regions covered by hair. The second contribution is a novel embedding algorithm that can handle both pose changes and semantic image blending. The third contribution is the hairnet architecture that semantically blends the hairstyle and identity images, performing multiple tasks jointly, such as baldification of the identity image, transformation estimation between the two images, warping, and hairstyle copying. Our results show a clear improvement over current state of the art methods in both quantitative and qualitative results. Code and data will be released.
Lightweight Remote Sensing Image Denoising via Knowledge Distillation

Since multispectral remote sensing images (RSIs) contain the abundant information of the surface environment, they have been widely applied in diverse research areas, such as earth observation, agricultural monitoring, and geological exploration. However, RSIs generally suffer from the interference of random noise during the recording and transmission, which significantly impacts the accuracy and reliability of subsequent tasks. Existing neural network-based denoising methods often rely on the heavy parameters to model the generation process of the clean image from its noisy counterpart. However, it is time-consuming deploying such model in real application scenarios. To address the above issue, we present a lightweight denoising model based on the proposed Simplified Residual Spatial-Spectral Module (SRSSM) to effectively extract the spatial and spectral features of RSIs, while maintaining a low computation complexity. A training strategy based on the knowledge distillation is designed to constrain the feature distribution by referring to a larger teacher model. Extensive experiments demonstrate the effectiveness and efficiency of the proposed method compared against existing models. Moreover, the downstream tasks including object detection and clustering are performed on the denoised images to further validate the necessity of denoising process.
Managing Technical Debt Using Intelligent Techniques - A Systematic Mapping Study

Technical Debt (TD) is a metaphor reflecting technical compromises that can yield short-term benefits but might hurt the long-term health of a software system. With the increasing amount of data generated when performing software development activities, an emergent research field has gained attention: applying Intelligent Techniques to solve Software Engineering problems. Intelligent Techniques were used to explore data for knowledge discovery, reasoning, learning, planning, perception, or supporting decision-making. Although these techniques can be promising, there is no structured understanding related to their application to support Technical Debt Management (TDM) activities. Within this context, this study aims to investigate to what extent the literature has proposed and evaluated solutions based on Intelligent Techniques to support TDM activities. To this end, we performed a Systematic Mapping Study (SMS) to investigate to what extent the literature has proposed and evaluated solutions based on Intelligent Techniques to support TDM activities. In total, 150 primary studies were identified and analyzed, dated from 2012 to 2021. The results indicated a growing interest in applying Intelligent Techniques to support TDM activities, the most used: Machine Learning and Reasoning under uncertainty. Intelligent Techniques aimed to assist mainly TDM activities related to identification, measurement, and monitoring. Design TD, Code TD, and Architectural TD are the TD types in the spotlight. Most studies were categorized at automation levels 1 and 2, meaning that existing approaches still require substantial human intervention. Symbolists and Analogizers are levels of explanation presented by most Intelligent Techniques, implying that these solutions conclude a general truth after considering a sufficient number of particular cases. Moreover, we also cataloged the empirical research types, contributions, and validation strategies described in primary studies. Based on our findings, we argue that there is still room to improve the use of Intelligent Techniques to support TDM activities. The open issues that emerged from this study can represent future opportunities for practitioners and researchers.