Reviewerly/exHarmony huggingface dataset:
Self-Organizing Migrating Algorithm Team To Team Adaptive – SOMA T3A

Swarm intelligence algorithm and its variants are constantly evolving over the years, the SOMA algorithm is also not out of that trend. In this paper, we propose a novel strategy of SOMA, called SOMA T3A. The proposed algorithm is divided into three main processes, namely Organization, Migration, and Update. Migrants are selected from the initial population and migrate towards the selected Leader according to the organization process. The Step and PRT parameters are no longer fixed like in the original version; instead, they are adapted through each migration loop. The performance of the algorithm is proven on the 58 well-known benchmark problems from the CEC2013 as well as CEC2017 benchmark suites. The results are compared with the SOMA family and compared with the state-of-the-art algorithms to show its promising performance.
Automated stitching of microscope images of fluorescence in cells with minimal overlap

The morphology of tumor cells is highly related to their phenotype and activity. To verify the drug response of a brain tumor patient, fluorescence microscope images of drug-treated patient-derived cells in each well are analyzed. Due to the limitation of the field of view (FOV), a large number of small FOVs are acquired to compose one complete microscope well. Here, we propose an automated method for accurately stitching tile-scanned fluorescence microscope images, even with noise and a narrow overlapping region between adjacent fields. The proposed method is based on intensity-based normalized cross-correlation (NCC) and a triangular method-based threshold. The proposed method's quantitative accuracy and the sensitivity of the input was compared to other existing stitching tools, MIST and FijiIS, setting manually stitched images as the ground truth. The test images were 20 samples of 3 × 3 grid images in three versions of the fluorescence channel. The distance between the location of each field and number of cells was determined for different input field overlap ranges (1%, 3%, 5%, and 10%), while the actual value was about 1.15%. The proposed method had a distance error of 1.5 pixels at an input overlap of 1%, showing the lowest minimum error at all channels. Regarding the difference in cell numbers, although the number of overlapping cells was always small because of the narrow overlapping range, the proposed method was able to generate the resultant image with the smallest difference. In addition, to confirm the size limitation of the proposed algorithm, the accuracy of stitching images of grid structures 3 × 3, 5 × 5, 10 × 10–20 × 20 was tested, showing consistent results. In conclusion, quantitative evaluation of the performance of the method proved its improved accuracy compared to other current state-of-art techniques, and it showed robust performance even with noise and a narrow overlapping region between adjacent fields.
Actively Improving Robot Navigation On Different Terrains Using Gaussian Process Mixture Models

Robot navigation in outdoor environments is exposed to detrimental factors such as vibrations or power consumption due to the different terrains on which the robot navigates. In this paper, we address the problem of actively improving navigation by planning paths that aim at reducing over time phenomena such as vibrations during traversal. Our approach uses a Gaussian Process (GP) mixture model and an aerial image of the environment to learn and improve continuously a place-dependent model of such phenomena from the experiences of the robot. We use this model to plan paths that trade-off the exploration of unknown promising regions and the exploitation of known areas where the impact of the detrimental factors on navigation is low, leading to an improved navigation over time. We implemented our approach and thoroughly tested it using real-world data. Our experiments suggest that our approach with no initial information leads the robot, after few runs, to follow paths along which it experiences similar vibrations or energy consumption as if it was following the optimal path computed given the ground truth information.
Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks

Space object recognition is the basis of space attack and defense confrontation. High-quality space object images are very important for space object recognition. Because of the large number of cosmic rays in the space environment and the inadequacy of optical lenses and detectors on satellites to support high-resolution imaging, most of the images obtained are blurred and contain a lot of cosmic-ray noise. So, denoising methods and super-resolution methods are two effective ways to reconstruct high-quality space object images. However, most super-resolution methods could only reconstruct the lost details of low spatial resolution images, but could not remove noise. On the other hand, most denoising methods especially cosmic-ray denoising methods could not reconstruct high-resolution details. So in this paper, a deep convolutional neural network (CNN)-based single space object image denoising and super-resolution reconstruction method is presented. The noise is removed and the lost details of the low spatial resolution image are well reconstructed based on one very deep CNN-based network, which combines global residual learning and local residual learning. Based on a dataset of satellite images, experimental results demonstrate the feasibility of our proposed method in enhancing the spatial resolution and removing the noise of the space objects images.
Multiscale spatial gradient features for 18F-FDG PET image-guided diagnosis of Alzheimer’s disease

18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) is one of the imaging biomarkers to diagnose Alzheimer's Disease (AD). In 18F-FDG PET images, the changes of voxels' intensities reflect the differences of glucose rates, therefore voxel intensity is usually used as a feature to distinguish AD from Normal Control (NC), or at earlier stage to distinguish between progressive and stable Mild Cognitive Impairment (pMCI and sMCI). In this paper, 18F-FDG PET images are characterized in an alternative way-the spatial gradient, which is motivated by the observation that the changes of 18F-FDG rates also cause gradient changes.We improve Histogram of Oriented Gradient (HOG) descriptor to quantify spatial gradients, thereby achieving the goal of diagnosing AD. First, the spatial gradient of 18F-FDG PET image is computed, and then each subject is segmented into different regions by using an anatomical atlas. Second, two types of improved HOG features are extracted from each region, namely Small Scale HOG and Large Scale HOG, then some relevant regions are selected based on a classifier fed with spatial gradient features. Last, an ensemble classification framework is designed to make a decision, which considers the performance of both individual and concatenated selected regions.the evaluation is done on ADNI dataset. The proposed method outperforms other state-of-the-art 18F-FDG PET-based algorithms for AD vs. NC with an accuracy, a sensitivity and a specificity values of 93.65%, 91.22% and 96.25%, respectively. For the case of pMCI vs. sMCI, the three metrics are 75.38%, 74.84% and 77.11%, which is significantly better than most existing methods. Besides, promising results are also achieved for multiple classifications under 18F-FDG PET modality.18F-FDG PET images can be characterized by spatial gradient features for diagnosing AD and its early stage, and the proposed ensemble framework can enhance the classification performance.
Effect of Full Body Avatar in Augmented Reality Remote Collaboration

In this paper, we compared three different types of avatar design (“Body”, “Hand + Arm”, and “Hand only”) for the augmented reality remote instruction system in terms of usability. The result showed that the usability of the remote instruction system with full body avatar has a higher usability. In addition, participants felt more easily to track the full body avatar than the avatar with hand only. However, concerning the understandability of the instruction, there was no difference between three designs.
Modified L-SHADE for Single Objective Real-Parameter Optimization

In this paper we address single objective real parameter optimization by using differential evolution (DE). L-SHADE is a well-known DE with success history-based adaptation and linear population size reduction. We propose a modified L-SHADE (mL-SHADE), in which three modifications are made: (1) removal of the terminal value, (2) addition of polynomial mutation, and (3) proposal of a memory perturbation mechanism. Performance of the proposed mL-SHADE is verified by using ten benchmark functions in the CEC2019 100-Digit Challenge. The results show that mL-SHADE achieves a higher score than seven state-of-the-art adaptive evolutionary algorithms.
Constrained Generative Adversarial Networks for Interactive Image Generation

Generative Adversarial Networks (GANs) have received a great deal of attention due in part to recent success in generating original, high-quality samples from visual domains. However, most current methods only allow for users to guide this image generation process through limited interactions. In this work we develop a novel GAN framework that allows humans to be “in-the-loop” of the image generation process. Our technique iteratively accepts relative constraints of the form “Generate an image more like image A than image B”. After each constraint is given, the user is presented with new outputs from the GAN, informing the next round of feedback. This feedback is used to constrain the output of the GAN with respect to an underlying semantic space that can be designed to model a variety of different notions of similarity (e.g. classes, attributes, object relationships, color, etc.). In our experiments, we show that our GAN framework is able to generate images that are of comparable quality to equivalent unsupervised GANs while satisfying a large number of the constraints provided by users, effectively changing a GAN into one that allows users interactive control over image generation without sacrificing image quality.
Graphless: Toward Serverless Graph Processing

Our society is increasingly solving complex problems through the use of graph processing.Existing graph processing systems focus on performance, which allows addressing ever-larger and more complex problems.They also require uncommon expertise to properly deploy and utilize.To make graph processing generally accessible-to small and medium enterprises and institutions, to common research groups, to individuals-, in this work we design and implement the Graphless graph-processing system.Graphless is based on the serverless paradigm, which proposes to simplify computing by letting developers only focus on small, stateless functions, which are deployed and managed automatically.We address with Graphless the key challenge of combining the stateless functions assumed by serverless computing with the (opposite) data-intensive nature of graph processing.Graphless tackles this challenge through an architectural approach that allows it to deploy with push or with pull operation, and a collection of backend services, such as an orchestrator and a memory-as-a-service component.We implement Graphless and conduct with it real-world experiments using Amazon Lambda for cloud-based serverless resources.Using the LDBC Graphalytics benchmark, we analyze Graphless, and compare its performance and operational cost with the graph-processing systems Apache Giraph (big data domain) and GraphMat (HPC).Overall, we show evidence Graphless provides performance and cost-efficiency similar to Giraph, for algorithms that can benefit from fine-grained elasticity, and lower than GraphMat, but is architecturally easier to deploy, and provides both push and pull operation.
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images

Accurate pose estimation is often a requirement for robust robotic grasping and manipulation of objects placed in cluttered, tight environments, such as a shelf with multiple objects. When deep learning approaches are employed to perform this task, they typically require a large amount of training data. However, obtaining precise 6 degrees of freedom for ground-truth can be prohibitively expensive. This work therefore proposes an architecture and a training process to solve this issue. More precisely, we present a weak object detector that enables localizing objects and estimating their 6D poses in cluttered and occluded scenes. To minimize the human labor required for annotations, the proposed detector is trained with a combination of synthetic and a few weakly annotated real images (as little as 10 images per object), for which a human provides only a list of objects present in each image (no time-consuming annotations, such as bounding boxes, segmentation masks and object poses). To close the gap between real and synthetic images, we use multiple domain classifiers trained adversarially. During the inference phase, the resulting class-specific heatmaps of the weak detector are used to guide the search of 6D poses of objects. Our proposed approach is evaluated on several publicly available datasets for pose estimation. We also evaluated our model on classification and localization in unsupervised and semi-supervised settings. The results clearly indicate that this approach could provide an efficient way toward fully automating the training process of computer vision models used in robotics.
A Benchmark of Population-Based Metaheuristic Algorithms for High-Dimensional Multi-Level Image Thresholding

Multi-level image thresholding is a popular approach for image segmentation where the image is divided into several non-overlapping regions based on the image histogram. Conventional algorithms for multi-level image thresholding are time-consuming. This is in particular so when the number of thresholds increases due to the curse of dimensionality where the search space expands exponentially as the number of parameters (thresholds) increases. One approach to address this problem is to employ population-based metaheuristic algorithms. Since various such optimisation algorithms have been presented in the literature, in this paper, we benchmark the performance of 13 population-based algorithms in the high-dimensional search spaces of the multi-level image thresholding problem. The algorithms we assess include the whale optimisation algorithm (WOA), grey wolf optimiser (GWO), cuckoo optimisation algorithm (COA), biogeography-based optimisation (BBO), teaching-learning-based optimisation (TLBO), gravitational search algorithm (GSA), imperialist competitive algorithm (ICA), cuckoo search (CS), firefly algorithm (FA), bat algorithm (BA), differential evolution (DE), particle swarm optimisation (PSO), and genetic algorithm (GA). We evaluate these on different images with regards to objective function value as well as peak signal-to-noise ratio (PSNR) and also employ a non-parametric statistical test, the Wilcoxon signed rank test, to compare the algorithms and to draw conclusions about their performance for multi-level image thresholding.
A Capacitive-sensing Physical Keyboard for VR Text Entry

In the context of immersive VR Head-Mounted Displays, physical keyboards have been proven to be an efficient typing interface. However, text entry using physical keyboards typically requires external camera-based tracking systems. Touch-sensitive physical keyboards allow for on-surface interaction, with sensing integrated into the keyboard itself, but have not been utilized for VR. We propose to utilize touch-sensitive physical keyboards for text entry as an alternative sensing mechanism for tracking user's fingertips and present a first prototype for VR.
Teaching Robots To Draw

In this paper, we introduce an approach which enables manipulator robots to write handwritten characters or line drawings. Given an image of just-drawn handwritten characters, the robot infers a plan to replicate the image with a writing utensil, and then reproduces the image. Our approach draws each target stroke in one continuous drawing motion and does not rely on handcrafted rules or on predefined paths of characters. Instead, it learns to write from a dataset of demonstrations. We evaluate our approach in both simulation and on two real robots. Our model can draw handwritten characters in a variety of languages which are disjoint from the training set, such as Greek, Tamil, or Hindi, and also reproduce any stroke-based drawing from an image of the drawing.
Social Networks and Graph Theory in the Search for Distant Knowledge

This chapter presents how the analysis of social networks supported in graph theory contributes to the search for “distant knowledge” in the field of industrial engineering, discipline of engineering that in its current form began in the early 20th century when the first engineers began to apply scientific theory to manufacturing. In particular, the case of Chilean documented scientific production in this area of engineering is analyzed as a category of the web of science distinguishing its degree of connection with the great knowledge, generating organizations worldwide, determining its high dissociation with the great contemporary theoretical referents, and recommending the way to reduce these problems in the future.
Deep residual infrared action recognition by integrating local and global spatio-temporal cues

Human action recognition (HAR) is an important area of research in the field of computer vision. Though a lot of efforts have been made in the past for HAR in visible spectrum, yet progress in infrared domain is still very limited. This is due to the fact that very few infrared action recognition datasets are publicly available, and that too with limited number of classes and training samples. To address this issue, we first construct a new dataset called IITR Infrared Action Recognition (IITR-IAR) dataset with 21 classes, each consisting of 70 samples (total 1470 videos). Then two types of modalities are extracted from each video: Stacked Dense Flow Difference Image (SDFDI) and our newly proposed Stacked Saliency Difference Image (SSDI). Second, we propose a novel four-stream deep framework built upon convolutional neural network (CNN) and recurrent neural network (RNN) models. Our CNN stream is based on deep residual architecture called ResNet, while RNN stream is based on bidirectional long short-term memory (BiLSTM) model. Third, to capture spatio-temporal information at global level, a single SDFDI and a single SSDI are generated using entire video, and then two CNN streams are trained. Similarly to capture spatio-temporal information at local level, a video is divided into eight equal segments, and eight SDFDIs and eight SSDIs are generated. These multiple SDFDIs and SSDIs are then used to train two CNN-BiLSTM streams. Finally, the output of all four streams are combined by late fusion to predict the actual class label. With this four-stream architecture in hand, we achieve state-of-the-art results (83.5%) on InfAR dataset. We also present the baseline result of 75.17% on our proposed IITR-IAR dataset, leaving an ample scope of research for remaining computer vision community to develop and apply more advanced deep learning techniques for infrared HAR.
TCP incast solutions in data center networks: A classification and survey

In recent years, Data Centers Networks (DCNs) have been deployed to serve as the backbone to support the extensive variety of services offered through the Internet like social networking, web hosting, and e-commerce. The Transmission Control Protocol (TCP) is at present, the most widely used transmission protocol in DCNs and the Internet. When several senders simultaneously send data to a single receiver in a DCN, congestion occurs at the switches' buffer at the receiver's end. This phenomenon of throughput collapse, particularly in data centers, is termed as the TCP incast problem. In this work, we survey and develop a classification scheme of TCP incast solutions in DCNs. We classify the TCP incast solutions on a multi-level criteria based on the TCP/IP protocol stack either TCP or non-TCP approach from our comprehensive survey. Each level includes sublevel classification such as delay-based congestion control schemes, active queue management based solutions, or probabilistic schemes. We also discuss the performance of each existing solutions in terms of its ability to alleviate the network during incast conditions. Furthermore, we present the strengths and weaknesses of each TCP incast techniques. Finally, we outline the open challenges and issues in mitigating the TCP incast in DCNs. We believe that the result of our survey can serve as a design guide to researchers and engineers in designing future DCN protocols.
Active Sampling based Safe Identification of Dynamical Systems using Extreme Learning Machines and Barrier Certificates

Learning the dynamical system (DS) model from data that preserves dynamical system properties is an important problem in many robot learning applications. Typically, the joint data coming from cyber-physical systems, such as robots have some underlying DS properties associated with it, e.g., convergence, invariance to a set, etc. In this paper, a model learning method is developed such that the trajectories of the DS are invariant in a given compact set. Such invariant DS models can be used to generate trajectories of the robot that will always remain in a prescribed set. In order to achieve invariance to a set, Barrier certificates are employed. The DS is approximated using Extreme Learning Machine (ELM), and a parameter learning problem subject to Barrier certificates enforced at all the points in the prescribed set is solved. To solve an infinite constraint problem for enforcing Barrier Certificates at every point in a given compact set, a modified constraint is developed that is sufficient to hold the Barrier certificates in the entire set. An active sampling strategy is formulated to minimize the number of constraints in learning. Simulation results of ELM learning with and without Barrier certificates are presented which show the invariance property being preserved in the ELM learning when learning procedure involves Barrier constraints. The method is validated using experiments conducted on a robot arm recreating invariant trajectories inside a prescribed set.