Reviewerly/exHarmony huggingface dataset:
Spider Monkey Optimization Algorithm Based Collision-Free Navigation and Path Optimization for a Mobile Robot in the Static Environment

This paper concentrates on the recognition of a navigational algorithm, which can resolve path optimization challenges in minimum time. This navigational algorithm helps the mobile robot to avoid collision with obstacles. Implementation of spider monkey optimization (SMO) algorithm is carried out to explain the objectives of this paper. SMO algorithm is established upon the splitting-combination nature of spider monkeys. The proposed algorithm is examined in various terrains to determine the robustness and effectuality of the proposed algorithm. Simulation results confirmed that spider monkey optimization algorithm selects a path, which guides the robot to reach the target in minimum possible time. Subsequently, it decreases the computational cost, as the time for computation is minimized. In extension, we replicated the previously used environment, and the proposed algorithm is compared with the corresponding implemented algorithm. Our results demonstrate that Spider monkey optimization (SMO) provides better results on the basis of time by selecting a path with lesser complexity. To determine the adequacy of the prescribed standard route-outlining control design, the common element of this paper is the numerical simulation.
Universal intermediate gradient method for convex problems with inexact oracle

In this paper, we propose new first-order methods for minimization of a convex function on a simple convex set. We assume that the objective function is a composite function given as a sum of a simple convex function and a convex function with inexact Hölder-continuous subgradient. We propose Universal Intermediate Gradient Method. Our method enjoys both the universality and intermediateness properties. Following the ideas of Nesterov (Math. Program. 152 (2015), pp. 381–404) on Universal Gradient Methods, our method does not require any information about the Hölder parameter and constant and adjusts itself automatically to the local level of smoothness. On the other hand, in the spirit of the Intermediate Gradient Method proposed by Devolder et al. (CORE Discussion Paper 2013/17, 2013), our method is intermediate in the sense that it interpolates between Universal Gradient Method and Universal Fast Gradient Method. This allows to balance the rate of convergence of the method and rate of the oracle error accumulation. Under the additional assumption of strong convexity of the objective, we show how the restart technique can be used to obtain an algorithm with faster rate of convergence.
Blind image deblurring via hybrid deep priors modeling

Blind image deblurring is a challenging low-level vision problem which aims to restore a sharp image only from the blurry observation. Few known information makes this problem fundamentally ill-posed. Most recent works focus on designing various priors on both latent image and blur kernel based on the maximum a posteriori (MAP) model to restrict the solution space. However, their performance is highly related to these hand-crafted explicit priors. In fact, the pre-designed explicit priors may have less flexibility to fit different image structures in real-world scenarios. To overcome these difficulties, we propose a novel framework, named Hybrid Deep Priors Model (HDPM), to simulate the propagation of sharp latent image used in kernel estimation and final deconvolution. Specifically, we introduce the learnable implicit deep prior and hand-crafted explicit prior as regularizations into the MAP inference process to extract the detailed texture and sharp structures of latent image, respectively. In HDPM, we can successfully take the advantages of explicit cues based on task information and implicit deep priors from training data to facilitate the propagation of sharp latent image which is beneficial for the kernel estimation. Extensive experiments demonstrate that the proposed method performs favorably against the state-of-the-art deblurring methods on benchmarks, challenging scenarios and non-uniform images.
Enhancing semantic segmentation with detection priors and iterated graph cuts for robotics

To foster human–robot interaction, autonomous robots need to understand the environment in which they operate. In this context, one of the main challenges is semantic segmentation, together with the recognition of important objects, which can aid robots during exploration, as well as when planning new actions and interacting with the environment. In this study, we extend a multi-view semantic segmentation system based on 3D Entangled Forests (3DEF) by integrating and refining two object detectors, Mask R-CNN and You Only Look Once (YOLO), with Bayesian fusion and iterated graph cuts. The new system takes the best of its components, successfully exploiting both 2D and 3D data. Our experiments show that our approach is competitive with the state-of-the-art and leads to accurate semantic segmentations.
Understanding Automatically-Generated Patches Through Symbolic Invariant Differences

Developer trust is a major barrier to the deployment of automatically-generated patches. Understanding the effect of a patch is a key element of that trust. We find that differences in sets of formal invariants characterize patch differences and that implication-based distances in invariant space characterize patch similarities. When one patch is similar to another it often contains the same changes as well as additional behavior; this pattern is well-captured by logical implication. We can measure differences using a theorem prover to verify implications between invariants implied by separate programs. Although effective, theorem provers are computationally intensive; we find that string distance is an efficient heuristic for implication-based distance measurements. We propose to use distances between patches to construct a hierarchy highlighting patch similarities. We evaluated this approach on over 300 patches and found that it correctly categorizes programs into semantically similar clusters. Clustering programs reduces human effort by reducing the number of semantically distinct patches that must be considered by over 50%, thus reducing the time required to establish trust in automatically generated repairs.
Reducing distance computations for distance-based outliers

The mining task of outlier detection is essential in many expert and intelligent systems exploited in a wide range of applications, from intrusion detection to molecular biology. In some of such applications the ability to process large amounts of data in a very short time can be critical, for instance in intrusion and fraud detection. This paper explores a solution for the optimisation of an exact, unsupervised outlier detection method by avoiding unnecessary computations, and therefore reducing the running time and making the method usable also in settings where response times are crucial. In particular, we enhance the SolvingSet-based approach by using a mechanism that exploits the knowledge learned during the algorithm execution and avoids a large amount of distance computations. We demonstrate the strength of the proposed solution, named FastSolvingSet, through both theoretical and experimental analysis.
Classification of plantar foot alterations by fuzzy cognitive maps against multi-layer perceptron neural network

Load distribution analysis on foot surface allows knowing human mechanical behavior and aids the doctor in the detection of gait disorders like, the risk of foot ulcerations, leg discrepancy, and footprint alterations. Plantar pressure data combined with techniques that use integral reasoning produce easy understanding medical tools for assisting in treatment, early detection, and the development of preventive strategies. The present research compares the classification of human plantar foot alterations using Fuzzy Cognitive Maps (FCM) trained by Genetic Algorithm (GA) against a Multi-Layer Perceptron Neural Network (MLPNN). One hundred and fifty-one subject volunteers (aged 7–77) were classified previously with the flat foot (n = 70) and cavus foot (n = 81) by specialized physicians of the Piédica diagnostic center. The trial walking was conducted using plantar pressure platforms FreeMed®. The foot surface was divided into 14 areas that included toe 1 st to 5th, metatarsal joint 1st to 5th, lateral midfoot, medial midfoot, lateral heel, and medial heel. Pressure data were normalized for each area. Better performance in the classification using small amounts of data were found by using Fuzzy rather than non-Fuzzy approach.
Graph deconvolutional networks

Graphs and networks are very common data structure for modelling complex systems that are composed of a number of nodes and topologies, such as social networks, citation networks, biological protein-protein interactions networks, etc. In recent years, machine learning has become an efficient technique to obtain representation of graph for downstream graph analysis tasks, including node classification, link prediction, and community detection. Different with traditional graph analytical models, the representation learning on graph tries to learn low dimensional embeddings by means of machine learning models that could be trained in supervised, unsupervised or semi-supervised manners. Compared with traditional approaches that directly use input node attributes, these embeddings are much more informative and helpful for graph analysis. There are a number of developed models in this respect, that are different in the ways of measuring similarity of vertexes in both original space and feature space. In order to learn more efficient node representation with better generalization property, we propose a task-independent graph representation model, called as graph deconvolutional network (GDN), and corresponding unsupervised learning algorithm in this paper. Different with graph convolution network (GCN) from the scratch, which produces embeddings by convolving input attribute vectors with learned filters, the embeddings of the proposed GDN model are desired to be convolved with filters so that reconstruct the input node attribute vectors as far as possible. The embeddings and filters are alternatively optimized in the learning procedure. The correctness of the proposed GDN model is verified by multiple tasks over several datasets. The experimental results show that the GDN model outperforms existing alternatives with a big margin.
Reliable correlation tracking via dual-memory selection model

Correlation-filter-based trackers have shown favorable accuracy and efficiency in visual tracking. However, most of these trackers are prone to drift in cases of heavy occlusions and temporal tracking failures because they only maintain the short-term memory of target appearance via a highly adaptive update mode. In this paper, we propose a reliable visual tracking method based on a dual-memory selection (DMS) model to alleviate tracking drift. Considering that long-term memory is robust to heavy occlusions while short-term memory performs well in rapid appearance changes, the proposed DMS model combines these two memory patterns of the target appearance and adaptively selects a reliable memory pattern to handle the current tracking challenges via a memory selector. For each memory pattern, a memory tracker is established based on discriminative correlation filters. The short-term tracker aggressively updates the target model to capture recent appearance changes via a linear interpolation update model, while the long-term tracker conservatively updates the target model to maintain historical appearance characteristics with a memory-improved update model and a dynamic learning rate. Furthermore, a novel memory evaluation criterion (MEC) is developed to evaluate the reliability of each tracker for memory selection. From credibility and discriminability measurements considering the temporal context, the memory tracker with the highest reliability score is selected to determine the target location in each frame. Extensive experiments on public benchmark datasets demonstrate that the proposed tracking method performs favorably compared to multiple recent state-of-the-art methods.
Zero-shot learning by mutual information estimation and maximization

The key of zero-shot learning is to use the visual-semantic embedding to transfer the knowledge from seen classes to unseen classes. In this paper, we propose to build the visual-semantic embedding by maximizing the mutual information between visual features and corresponding attributes. Then, the mutual information between visual and semantic features can be utilized to guide the knowledge transfer from seen domain to unseen domain. Since we are primarily interested in maximizing mutual information, we introduce the noise-contrastive estimation to calculate lower-bound value of mutual information. Through the noise-contrastive estimation, we reformulate zero-shot learning as a binary classification problem, i.e., classifying the matching visual-semantic pairs (positive samples) and mismatching visual-semantic pairs (negative/noise samples). Experiments conducted on five datasets demonstrate that the proposed mutual information estimators outperforms current state-of-the-art methods both in conventional and generalized zero-shot learning settings.
WLIB-SIFT: A Distinctive Local Image Feature Descriptor

SIFT descriptor plays a great role in image mosaic, image retrieval and target recognition for good invariance of translation, rotation and zoom. However, the disadvantages of SIFT descriptor are its high dimensionality and complex computation. Besides, SIFT descriptor has poor performance when massive similar local features and complex background exist in the matching image. In this paper, a distinctive and robust weighted local intensity binary SIFT descriptor(WLIB-SIFT) is proposed. A WLIB-SIFT descriptor consists of a weighted binary SIFT descriptor(B-SIFT) and a weighted local intensity binary descriptor. The experimental results show that the WLIB-SIFT descriptor has better accuracy and faster speed than the SIFT descriptor. Compared with the B-SIFT descriptor, the WLIB-SIFT descriptor has better robustness and distinctiveness at almost the same speed.
Development of a laboratory medical algorithm for simultaneous detection and counting of erythrocytes and leukocytes in digital images of a blood smear

Quantitative and qualitative evaluation of blood cells is an important parameter used to detect and suspect pathologies, whether hematological or infectious. Considering that laboratory medical exams often present costs that are inaccessible to populations of underdeveloped and developing countries, it is of great importance to create tools that facilitate the obtaining of medical reports with low cost and high reliability. The present study aims to develop an algorithm that is able to perform the quantification of red blood cells (RBCs) and leukocytes through personal computers, making the methodology more accessible to diverse populations. For this, an algorithm based on the Hough transform and object detection by coloration (HT-DC) was developed in Matlab software version 8.3 of 64 bits (2014a). Thus, 10 images in digital format of blood smear fields involving erythrocytes and leukocytes in a nonpathological state were submitted to the HT-DC algorithm. As a result, accuracy, sensitivity, and specificity of 85%, 70%, and 99% respectively, were obtained, demonstrating the high reliability of the methodology. The processing time of the algorithm was evaluated through the CPU time analyses, showing a mean time of 5.20–7.46 s depending on the processor used (Intel Dual Core, Intel Quad, Intel i3, or Intel i5). In this way, the HT-DC methodology can be seen as the initial step for the development of a blood count from simple usual everyday tools, such as a computer.
Exploring virtual reality experiences of slum tourism

The popularity of slum tourism has been growing and is a topic of substantial discussion. Proponents suggest tours bring awareness and economic opportunity, whereas others critique their voyeuristic nature and claims of community benefits. Virtual reality head mounted displays (VR HMDs) have become relatively accessible in recent years creating a visually immersive experience of a different environment. VR technology is being used by tourism promoters as well as in education and training fields to acquaint people with foreign environments. This exploratory study draws from interviews with 16 participants who declared an interest in slum tourism. Participants discussed their experiences with, perceptions of, and motivations for visiting slum communities, and then watched a VR HMD tour of a slum in Manilla. Immediately following the video participants were asked for reactions and reflections, as well as at a subsequent meeting one to two weeks later. Findings show that the VR HMD was generally positively received, and many participants expressed a sense of trust in the representation of the community and experience because of the media’s immersive nature. Participants reported having their understandings of slum communities both reinforced and challenged, leading to more confidence that their awareness of issues in general, and slum tourism specifically, were realistic. Most participants felt more inspired, confident, and comfortable to actually take part in a tour, however some expressed concerns and described feeling less motivated to visit. Discussion includes critique of the video, as well as implications for research and practice.
A New Aggregation of DNN Sparse and Dense Labeling for Saliency Detection

As a fundamental requirement to many computer vision systems, saliency detection has experienced substantial progress in recent years based on deep neural networks (DNNs). Most DNN-based methods rely on either sparse or dense labeling, and thus they are subject to the inherent limitations of the chosen labeling schemes. DNN dense labeling captures salient objects mainly from global features, which are often hampered by other visually distinctive regions. On the other hand, DNN sparse labeling is usually impeded by inaccurate presegmentation of the images that it depends on. To address these limitations, we propose a new framework consisting of two pathways and an Aggregator to progressively integrate the DNN sparse and DNN dense labeling schemes to derive the final saliency map. In our "zipper" type aggregation, we propose a multiscale kernels approach to extract optimal criteria for saliency detection where we suppress nonsalient regions in the sparse labeling while guiding the dense labeling to recognize more complete extent of the saliency. We demonstrate that our method outperforms in saliency detection compared to other 11 state-of-the-art methods across six well-recognized benchmarking datasets.
Age Estimation From Facial Parts Using Compact Multi-Stream Convolutional Neural Networks

Age is a very useful property in the characterization of individuals, since it is an inherent biological attribute and plays a key role in many real-world applications such as preventing purchase of alcohol and tobacco by minors, human-computer interaction, soft biometrics, electronic customer relationship and as age synthesis in Forensic Art to find lost people. The aging process is influenced by external (health, lifestyle, smoking) and internal (genetics, gender) factors, which makes its estimation difficult for humans, and even more difficult for machines. In this work, we present and evaluate an age estimation approach in unconstrained images using facial parts (eyebrows, eyes, nose and mouth), cropped from the input images using landmarks, to feed a compact multi-stream convolutional neural network (CNN) architecture. Experimental results obtained in the challenging Adience benchmark with real-world images labeled with their respective age groups show that our method is competitive with the literature, even with a significantly smaller CNN and lower computational cost.
Multi-Label Image Classification with Attention Mechanism and Graph Convolutional Networks

The task of multi-label image classification is to predict a set of proper labels for an input image. To this end, it is necessary to strengthen the association between the labels and the image regions, and utilize the relationship between the labels. In this paper, we propose a novel framework for multi-label image classification, which uses attention mechanism and Graph Convolutional Network (GCN) simultaneously. The attention mechanism can focus on specific target regions while ignoring other useless information around, thereby enhancing the association of the labels with the image regions. By constructing a directed graph over the labels, GCN can learn the relationship between the labels from a global perspective and map this label graph to a set of inter-dependent object classifiers. The framework first uses ResNet to extract features while using attention mechanism to generate attention maps for all labels and obtain weighted features. GCN uses weighted fusion features from the output of the resnet and attention mechanism to achieve classification. Experimental results show that both the attention mechanism and GCN can effectively improve the classification performance, and the proposed framework is competitive with the state-of-the-art methods.
Shadow detection in very high resolution RGB images using a special thresholding on a new spectral–spatial index

Shadow detection plays an important role in remote sensing applications. Shadow should be detected with damage assessment algorithms, and it should be removed from the ground surface with semantic labeling applications. The procedure of a typical shadow detection method includes defining a shadow index and thresholding it, either automatic or manually. An automatic shadow detection method is proposed to facilitate the process of automatic applications. Specifications of shadow and nonshadow areas are analyzed to construct a new spectral–spatial shadow index. Spectral elements of the index can handle the dark shadow extraction. The spatial element of the index provides a high separability between light shadow and dark nonshadow areas such as water bodies. Index definition follows a segmentation algorithm to provide a segment-based analysis. Thresholding is a nonseparable part of shadow detection methods as it divides the region into shadow and nonshadow areas. To avoid high false positive or negative results, the proposed thresholding method is dependent on the enhanced bimodality test. Bimodal distribution can easily be thresholded by typical techniques such as the Otsu thresholding, whereas a special clustering-based thresholding is proposed in the unimodal distribution. The evaluations show a great improvement in shadow detection of very high-resolution RGB images.
Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow

The authors propose an alternative route to circumvent the limitation of neutron flux using the recent deep learning super-resolution technique. The feasibility of accelerating data collection has been demonstrated by using small-angle neutron scattering (SANS) data collected from the EQ-SANS instrument at Spallation Neutron Source (SNS). Data collection time can be reduced by increasing the size of binning of the detector pixels at the sacrifice of resolution. High-resolution scattering data is then reconstructed by using a deep learning-based super-resolution method. This will allow users to make critical decisions at a much earlier stage of data collection, which can accelerate the overall experimental workflow.