Reviewerly/exHarmony huggingface dataset:
Evaluation of Defogging: A Real-World Benchmark Dataset, A New Criterion and Baselines

Modern defogging methods are able to achieve very comparable results whose differences are too subtle for people to qualitatively judge. On the other hand, existing quantitative evaluation methods are also not convincing due to a lack of proper datasets. In this work, we attempt to address these issues and establish a long-term lacking benchmark dataset, namely BeDDE (BEnchmark Dataset for Defogging Evaluation), for evaluating the performance of defogging algorithms. To our knowledge, BeDDE is the first real-world dataset comprising foggy images with their registered clear counterparts. Using BeDDE, we set up a new criterion for evaluating defogging methods where VSI, a full reference image quality assessment metric, is calculated and averaged on registered ROIs of all image pairs. The evaluation results of the proposed criterion correlate well with human judgements. 10 state-of-the-art defogging methods are evaluated as baselines on BeDDE. BeDDE is available online.
New Improved SALSHADE-cnEpSin Algorithm with Adaptive Parameters

Differential Evolution algorithm is very challenging algorithm and has been found to put forth the basis of evolutionary computation. This algorithm because of its simple structure and linear nature, has been applied to a large number of optimization problems from various diversified fields. In this paper, we propose a new variant of DE by modifying the original LSHADE-cnEpSin algorithm. Two new modifications are proposed, keeping all the modifications of LSHADE-cnEpSin intact. The modifications proposed include the introduction of adaptive parameters by using Weibull distribution based scaling factor and exponentially decreasing crossover rate. Apart from that linearly decreasing population size is also used. The main reason for these adaptations is to make an adaptive algorithm so that no parameter needs to be changed from the end user perspective. The proposed algorithm has been applied to solve CEC2017 and CEC2019 benchmark problems. The numerical results prove that the newly proposed SALSHADE-cnEpSin algorithm performs better than SaDE, JADE, SHADE, LSHADE, CV1.0, CVnew, MVMO and other algorithms.
Predicting particle-size distribution using thermal infrared spectroscopy from reclaimed mine land in the semi-arid grassland of North China

Particle-size distribution is one of the vital properties of semi-arid and reclaimed soils that closely correlates with desertification. The thermal infrared (TIR) spectrum between 8 and 14 μm has the potential for particle-size monitoring in remote sensing. This study aims to investigate the characteristic TIR spectra of reclaimed mine soils and establish a particle-size prediction model. The characteristics of the reststrahlen band (8–9.5 μm) could be used to distinguish the coarse soil content that occurs in various land use and reclamation areas. A significant negative correlation was noted between the coarse sand content and the TIR spectrum (8–14 μm). The support vector machine calibration model exhibited a higher prediction accuracy for estimating the coarse sand content, with a cross-validated R2 of 0.95 and root mean square error (RMSE) of 3.01%, than the partial least squares regression model. These outcomes provide a theoretical basis and technical support for particle-size distribution estimations using TIR spectroscopy in semi-arid and reclamation areas. Hence, this study proposes that the spectral characteristics and model undergo further testing and optimization before wider application for the observation of semi-arid and reclaimed mine land ecosystems.
Automated patch porting across forked projects

Forking projects provides a straightforward method for developers to reuse existing source code and tailor it to their own application scenarios, which can significantly reduce developers' burden. However, this process makes forked projects (upstream projects and their forks) share the same defects on reused code as well. With the independent development of forked projects, some defects can only be repaired in one of them, where the patches need to be ported to others as well. Manually tracking all such activities among them is hard. Previous studies reveal that porting patches across forked projects is imperative and call research in this direction. Targeting at this problem, we conducted an empirical study to analyze the characteristics of patches in forked projects. We found that 20.5% patches need to be ported among all analyzed patches, which is a non-negligible portion. Among all those patches that need to be ported, 73.2% can be easily ported by simple syntactic code transformations. However, it is still challenging for other 26.8% patches since the corresponding code has experienced different modifications in the forked projects. As a result, according to the insights from the study, we proposed a new approach, which aims to automatically identify and port patches across forked projects.
Multi-Level Encoder-Decoder Architectures for Image Restoration

Many real-world solutions for image restoration are learning-free and based on handcrafted image priors such as self-similarity. Recently, deep-learning methods that use training data have achieved state-of-the-art results in various image restoration tasks (e.g., super-resolution and inpainting). Ulyanov et al. bridge the gap between these two families of methods (CVPR 18). They have shown that learning-free methods perform close to the state-of-the-art learning-based methods (approximately 1 PSNR). Their approach benefits from the encoder-decoder network. In this paper, we propose a framework based on the multi-level extensions of the encoder-decoder network, to investigate interesting aspects of the relationship between image restoration and network construction independent of learning. Our framework allows various network structures by modifying the following network components: skip links, cascading of the network input into intermediate layers, a composition of the encoder-decoder subnetworks, and network depth. These handcrafted network structures illustrate how the construction of untrained networks influence the following image restoration tasks: denoising, super-resolution, and inpainting. We also demonstrate image reconstruction using flash and no-flash image pairs. We provide performance comparisons with the state-of-the-art methods for all the restoration tasks above.
Underwater Image Enhancement using Colour Restoration based on YCbCr Colour Model

Abstract In this study, improvement of underwater images via colour re-correction was performed by designing a new method that uses colour restoration based on the integrated colour model with Rayleigh distribution (CRBICMRD). The proposed method was utilised for colour restoration in an image in the RGB model and for colour transformation in YCbCr space, where it processed the Y component. The integrated colour model with Rayleigh distribution was applied. Quantitative analyses indicated that the proposed method outperformed the multi-scale retinex algorithm with colour restoration, unsupervised colour correction method and Rayleigh stretching and averaging image planes by calculating no-reference-image quality assessments. Results indicated that CRBICMRD performed considerably better than the other methods in enhancing underwater images.
Training Cascade Compact CNN With Region-IoU for Accurate Pedestrian Detection

Recently, pedestrian detection has made significant advances benefiting from the region-based convolutional neural networks (R-CNN). However, training R-CNN with a holistic intersection over union (IoU) always brings many flawed positive samples. This paper introduces a strict matching metric, which is beneficial to selecting well-aligned positive samples. Specifically, this matching metric is defined on a set of region-IoUs instead of a holistic IoU, which considers the alignments of different part regions in a whole bounding box simultaneously. A positive sample matches a ground truth only if all its region-IoUs are bigger than a threshold. Secondly, an improved negative example selection strategy using both the classification and localization information is proposed to mine hard negative examples, which can further suppress the false positive detections near the pedestrians. Based on the proposed sample selection strategy, a cascade compact convolutional neural network (CC-CNN) is proposed for accurate pedestrian detection. Each stage of the CC-CNN is constructed with a compact network that only consists of a small number of parameters, thus making the detector suitable to be implemented on onboard embedded systems. Experimental results on two widely used pedestrian datasets demonstrate that the proposed training strategy and the CC-CNN based detector can effectively improve the detection rate and the localization accuracy using fewer parameters.
Defending Against Adversarial Attacks Using Random Forest

As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and physical world. Unfortunately, some recent studies show that adversarial examples, which are hard to be distinguished from real examples, can easily fool DNNs and manipulate their predictions. Upon observing that adversarial examples are mostly generated by gradient-based methods, in this paper, we first propose to use a simple yet very effective non-differentiable hybrid model that combines DNNs and random forests, rather than hide gradients from attackers, to defend against the attacks. Our experiments show that our model can successfully and completely defend the white-box attacks, has a lower transferability, and is quite resistant to three representative types of black-box attacks; while at the same time, our model achieves similar classification accuracy as the original DNNs. Finally, we investigate and suggest a criterion to define where to grow random forests in DNNs.
MDI: A Multi-channel Dynamic Immersion Headset for Seamless Switching between Virtual and Real World Activities

We present a study of the usability and induced workload of a new head-mounted display (HMD) which allows the wearer to more easily switch between tasks in the virtual and real world using multiple optical channels dynamically at the press of a button. Most HMDs provide full immersion by occluding the real world from the view of the user, and replacing it with a computer-generated virtual environment. One trade-off, however, is that such HMDs make it difficult to interact with real-world objects and people, or to react to events while being immersed. Tasks that are simple in the unmediated real world, such as taking a drink or responding to a text, become nearly impossible while wearing an occlusive HMD, and normally require the user to take off the HMD. To address this limitation, we propose an HMD which provides optical channels to view the near-field real world in the periphery of the HMD while still wearing the HMD by pressing a button. We call our approach Multi-channel Dynamic Immersion (MDI). We embedded transparency controllable LCD panels around the periphery of an HMD and installed a fish-eye lens in front of its forward-facing camera. We then compared MDI to three other methods of switching from working in VR to real world activities: (a) a front camera video-see-through method, (b) using dynamic LCDs only, and (c) taking off the HMD. Participants had to interrupt their work in a VR office to carry out typical real world daily activities, such as picking up a mug, responding to a phone call, or replying to a text message. Our results show no significant differences in task execution time between the four conditions. However, we observed a tendency favouring MDI as the easiest to use among the HMDs. In addition, we found a significantly higher rated ease of use for the two HMDs with controllable LCDs compared to the two occlusive HMDs. Our data also underscores that a substantial amount of time is lost when switching between work contexts in occlusive HMDs and the real world.
Complementary Attributes: A New Clue to Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint seen objects via sharing attributes. The generalization performance of ZSL is governed by the attributes, which transfer semantic information from seen classes to unseen classes. To take full advantage of the knowledge transferred by attributes, in this paper, we introduce the notion of the complementary attributes (CAs), as a supplement to the original attributes, to enhance the semantic representation ability. Theoretical analyses demonstrate that CAs can improve the PAC-style generalization bound of the original ZSL model. Since the proposed CA focuses on enhancing the semantic representation, CA can be easily applied to any existing attribute-based ZSL methods, including the label-embedding strategy-based ZSL (LEZSL) and the probability-prediction strategy-based ZSL (PPZSL). In PPZSL, there is a strong assumption that all attributes are independent of each other, which is arguably unrealistic in practice. To solve this problem, a novel rank aggregation (RA) framework is proposed to circumvent the assumption. Extensive experiments on five ZSL benchmark datasets and the large-scale ImageNet dataset demonstrate that the proposed CA and RA can significantly and robustly improve the existing ZSL methods and achieve state-of-the-art performance.
A Reinforcement Learning Approach for Control of a Nature-Inspired Aerial Vehicle

In this work, reinforcement learning is used to develop a position controller for an underactuated nature-inspired Unmanned Aerial Vehicle (UAV). This particular configuration of UAVs achieves lift by spinning its entire body contrary to standard multi-rotors or fixed-wing aircraft. Deep Deterministic Policy Gradients (DDPG) with Ape-X Distributed Prioritized Experience Replay was used to train neural network function approximators that were implemented as the final control policy. The reinforcement learning agent was trained in simulations and directly ported over to real-life hardware. Position control tests were performed on the learned control policy and compared to a baseline PID controller. The learned controller was found to exhibit better control over the inherent oscillations that arise from the non-linear dynamics of the platform.
Interledger for the Industrial Internet of Things

The upsurge of Industrial Internet of Things is forcing industrial information systems to enable less hierarchical information flow. The connections between humans, devices, and their digital twins are growing in numbers, creating a need for new kind of security and trust solutions. To address these needs, industries are applying distributed ledger technologies, aka blockchains. A significant number of use cases have been studied in the sectors of logistics, energy markets, smart grid security, and food safety, with frequently reported benefits in transparency, reduced costs, and disintermediation. However, distributed ledger technologies have challenges with transaction throughput, latency, and resource requirements, which render the technology unusable in many cases, particularly with constrained Internet of Things devices.To overcome these challenges within the Industrial Internet of Things, we suggest a set of interledger approaches that enable trusted information exchange across different ledgers and constrained devices. With these approaches, the technically most suitable ledger technology can be selected for each use case while simultaneously enjoying the benefits of the most widespread ledger implementations. We present state of the art for distributed ledger technologies to support the use of interledger approaches in industrial settings.
Primer on machine learning

Pain researchers and clinicians increasingly encounter machine learning algorithms in both research methods and clinical practice. This review provides a summary of key machine learning principles, as well as applications to both structured and unstructured datasets.Aside from increasing use in the analysis of electronic health record data, machine and deep learning algorithms are now key tools in the analyses of neuroimaging and facial expression recognition data used in pain research.In the coming years, machine learning is likely to become a key component of evidence-based medicine, yet will require additional skills and perspectives for its successful and ethical use in research and clinical settings.
An Integrated Task and Motion Planning Technique for Multi-Robot-Systems

In this paper, we propose an integrated motion and task planning methodology for multiple robot arm systems with high DOF applications performing different tasks simultaneously in a shared workspace. In the proposed methodology, we introduce a high-level task planning layer and a low level motion planning layer. We develop an algorithm to integrate these two layers in order to achieve reliable and collision free tasks execution. At the low level motion planning layer, we extend heuristics based path planning algorithms for systems of multiple high DOF robotic arms. We introduce concept of shared space graph (SSG) to check whether two arms sharing certain parts of the workspace and quantify cooperation of such arm pairs, which is essential to the selection of arm sequences and scheduling of each arm in the sequence to perform a task or a sub-task.
Semantic relation based expansion of abbreviations

Identifiers account for 70% of source code in terms of characters, and thus the quality of such identifiers is critical for program comprehension and software maintenance. For various reasons, however, many identifiers contain abbreviations, which reduces the readability and maintainability of source code. To this end, a number of approaches have been proposed to expand abbreviations in identifiers. However, such approaches are either inaccurate or confined to specific identifiers. To this end, in this paper we propose a generic and accurate approach to expand identifier abbreviations. The key insight of the approach is that abbreviations in the name of software entity e have great chance to find their full terms in names of software entities that are semantically related to e. Consequently, the proposed approach builds a knowledge graph to represent such entities and their relationships with e, and searches the graph for full terms. The optimal searching strategy for the graph could be learned automatically from a corpus of manually expanded abbreviations. We evaluate the proposed approach on nine well known open-source projects. Results of our k-fold evaluation suggest that the proposed approach improves the state of the art. It improves precision significantly from 29% to 85%, and recall from 29% to 77%. Evaluation results also suggest that the proposed generic approach is even better than the state-of-the-art parameter-specific approach in expanding parameter abbreviations, improving F1 score significantly from 75% to 87%.
Memetic Multi-agent Optimization in High Dimensions using Random Embeddings

In this paper, we propose a memetic multi-agent optimization (MeMAO) paradigm to enhance the search efficacy of classical EAs (i.e., Differential Evolution (DE)) in solving the complex optimization problems. The essential backbone of MeMAO is a recently proposed memetic multi-agent learning system wherein agents acquire increasing learning capabilities by interacting with the environment mainly in a reinforcement learning manner. Differing from MeMAS, the particular interest of MeMAO is placed on addressing the specific challenges when applying classical EAs to optimize the high dimensional optimization problems with a "low effective dimensionality". To achieve this, the target optimization problem is firstly re-formulated into multiple low dimensional tasks via random embedding methods. Further, MeMAO employs DE as the fundamental population based evolutionary solver for multiple agents to optimize multiple low dimensional tasks in a multi-agent scenario. Importantly, MeMAO constructs the social interaction mechanisms among multiple agents, hence improves their convergence speed for solving the target optimization problem by sharing the beneficial information across multiple agents. Lastly, to testify the efficacy of the proposed MeMAO, comprehensive empirical studies on 8 synthetic optimization problems with a dimensionality of 2,000 are provided.
EvoDNN - An Evolutionary Deep Neural Network with Heterogeneous Activation Functions

Many problems in Computational Biology and Bioinformatics involve classification, such as the classification of cell samples into malignant (cancer) or benign (normal). For such tasks, we propose EvoDNN, an evolutionary deep neural network that employs an evolutionary algorithm to evolve deep heterogeneous feed-forward neural networks. While the majority of current feed-forward neural networks employ user defined homogeneous activation functions, EvoDNN creates heterogeneous multi-layer networks where each neuron's activation function is not statically defined by the user, but dynamically optimized during evolution. The main advantage offered by EvoDNN lies in that the activation functions do not need to be differentiable. This feature gives users a great degree of flexibility over which activation functions EvoDNN can utilize. This paper demonstrates how EvoDNN can simultaneously optimize each neuron's weight, bias, and activation function, and empirically shows a superior performance compared to a backpropagation-trained feed-forward neural network at the cost of additional training time. In addition, advantages of the deep architecture of EvoDNN over our earlier approach, EvoNN, which employed a single hidden layer are discussed.
Network representation learning: models, methods and applications

With the rise of large-scale social networks, network mining has become an important sub-domain of data mining. Generating an efficient network representation is one important challenge in applying machine learning to network data. Recently, representation learning methods are widely used in various domains to generate low dimensional latent features from complex high dimensional data. A significant amount of research effort is made in the past few years to generate node representations from graph-structured data using representation learning methods. Here, we provide a detailed study of the latest advancements in the field of network representation learning (also called network embedding). We first discuss the basic concepts and models of network embedding. Further, we build a taxonomy of network embedding methods based on the type of networks and review the major research works that come under each category. We then cover the major datasets used in network embedding research and describe the major applications of network embedding with respect to various network mining tasks. Finally, we provide various directions for future work which enhance further research.