Reviewerly/exHarmony huggingface dataset:
Multi-Task Particle Swarm Optimization With Dynamic Neighbor and Level-Based Inter-Task Learning

Existing multifactorial particle swarm optimization algorithms treat all particles equally with a consistent inter-task exemplar selection and generation strategy. This may lead to poor performance when the algorithm searches partial optimal areas belonging to different tasks at the later stage. In pedagogy, teachers teach students in different levels distinctively under their cognitive and learning abilities. Inspired by this idea, in this work, we devise a novel level-based inter-task learning strategy upon a dynamic local topology of inter-task particles. The proposed method separates particles into several levels and assigns particles to different levels with distinct inter-task learning methods. Specifically, we propose a level-based inter-task learning strategy to transfer sharing information among the cross-task neighborhood. By assigning the particles with diverse search preferences, the algorithm is able to explore the search space by using the cross-task knowledge, while reserving an ability to refine the search area. In addition, to address the issue of inter-task neighbor selection, we reform dynamically the local topology structure across the inter-task particles by methodical sampling, evaluating and selecting processes. Experimental results on the benchmark problems demonstrate that the proposed method enables the efficient cross-domain information transfer via the level-based inter-task learning.
Classification of breast cancer histopathological image with deep residual learning

Abstract Breast cancer has high incidences and mortality rates in women worldwide. Malignancy could be detected manually by experienced pathologists based on Hematoxylin and Eosin (H&amp;E) stained images. However, it is time‐consuming and experience‐dependent, making early diagnosis a big challenge. In this paper, a methodology for breast cancer classification based on histopathological images with deep learning was described. A residual learning‐based convolutional neural network named myResNet‐34 was designed for malignancy‐and‐benign classification. In addition, an algorithm automatically generating the target image for stain normalization was proposed, which eliminated the bias caused by manual selection of the reference image. Elastic distortion was introduced and combined with affine transformation for data augmentation considering the characteristics of the H&amp;E images. Experiments were conducted on BreakHis dataset with the proposed framework. Promising results were achieved with an average classification accuracy of around 91% on image‐level classification. Results indicated that both our data augmentation and stain normalization effectively improved the classification accuracy by 2‐3%.
Human Face Sketch to RGB Image with Edge Optimization and Generative Adversarial Networks

Generating an RGB image from a sketch is a challenging and interesting topic. This paper proposes a method to transform a face sketch into a color image based on generation confrontation network and edge optimization. A neura... | Find, read and cite all the research you need on Tech Science Press
Robust multiple color images encryption using discrete Fourier transforms and chaotic map

Based on discrete Fourier transforms and logistic-exponent-sine map, this paper investigates an encryption algorithm for multiple color images. In the encryption process, each color image represented in trinion matrix is performed by block-wise discrete trinion Fourier transforms. Then the first real matrix is constructed by splicing real and imaginary parts of transformed results. Followed by two-dimensional discrete Fourier transform, the second real matrix is synthesized only using half of the spectrum on the basis of the conjugate symmetry property. In order to further enhance the randomness of interim result, the random multi-resolution singular value decomposition is exploited. Afterwards, a sharing process is utilized to get final cipherimages. Numerical simulations performed on 300 color images have shown that quality of correctly decrypted images is much better, where the PSNR value is up to 305.03 dB. The number of changing pixel rate and unified average changing intensity are respectively greater than 99.99% and 33.33%, indicating good sensitivity. The comparison with other methods under noise and cropping attacks validates the reliability of the proposed algorithm.
The Matter of Tools

Tools, as extensions of hand and mind, prescribe defining properties for a practice. We anchor our tools research within a case study of electronic textiles (eTextiles), combining textile materials and electronic and computational functionality. While the field of eTextiles is expanding into new personal and ubiquitous applications, its tools as productive means, however, are rarely investigated. We fill this gap by both proposing and exploring new tools, aiming at an integrated eTextile craft practice across disciplinary boundaries. Results from a research through design process have been developed into research products and proposed to a wider community of novices and practitioners. Research insights from making, using and reflecting on our new tools show they not only guide habits of making, but also are formative to the understanding of eTextiles as a practice and a field. Their form and function matter for the skills, processes and users, ultimately prescribing the technologies that surround us.
Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study

Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. This is also the case in other learning-based metaheuristics such as evolutionary algorithms and particle swarm optimization. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this work we present and study an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. Moreover, we compare our proposal to some well-known existing negative learning approaches from the related literature. Our study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases we are able to show that our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.
Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging

In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces.
The #bookstagram: distributed reading in the social media age

Social media platforms have given rise to diverse ‘reading formations’ (Bennett and Woollacott, 1987) from bespoke reading communities with strong affective bonds to the ‘ambient affiliation’ (Zappavigna, 2014) more typical of followers of reading-related hashtags (#amreading). What makes all of these formations distinctive is that they ‘bring into visibility an entirely new social dimension to reading’ (Pinder, 2012:68), and a sociality that is often reliant as much on the sharing of images as on words. This article will focus specifically on shared acts of reading on Instagram, a social networking service for sharing photographs and videos. In particular, I will explore the phenomenon of the #bookstagram where readers share images of the book they are currently reading aestheticised and personalised through the use of evocative backdrops and objects. Such activities are all too often dismissed as empty displays by narcissistic millennials keen to make their mark in the ‘attention economy’. However, I will argue that the #bookstagram offers a way for readers to share acts of reading with others in a way that evokes the sensory and the sensual, and also provides a form of ‘embodied reenactment’ (Tolins and Samermit, 2016) that can generate discussion and empathy between users. My analysis will focus on the ways in which body parts, especially hands, feature in the images to evoke a sense of an embodied connection between reader and book. I will also examine how the images create a strong sense of reading as a situated activity that is associated with calmness, serenity and being close to nature. My discussion of emerging reading practices is informed by theories which move away from an exclusive focus on reading as a series of mental processes or the decoding of signs, to consider reading as a distributed, embodied activity that involves interacting with others and one's environment. The analysis of the #bookstagram will examine how the activity can be located in relation to culturally and historically grounded practices relating to the book as object of display and to representations of reading bodies from visual art. However, I will also be arguing that the specific affordances of social media help produce readerly interactions that are dialogic and reliant on an ethos of participation where displays of intimate and private acts are routinely commented on and repurposed by others.
Online Extrinsic Calibration Based on Per-Sensor Ego-Motion Using Dual Quaternions

In this work, we propose an approach for extrinsic sensor calibration from per-sensor ego-motion estimates. Our problem formulation is based on dual quaternions, enabling two different online capable solving approaches. We provide a certifiable globally optimal and a fast local approach along with a method to verify the globality of the local approach. Additionally, means for integrating previous knowledge, for example, a common ground plane for planar sensor motion, are described. Our algorithms are evaluated on simulated data and on a publicly available dataset containing RGB-D camera images. Further, our online calibration approach is tested on the KITTI odometry dataset, which provides data of a lidar and two stereo camera systems mounted on a vehicle. Our evaluation confirms the short run time, state-of-the-art accuracy, as well as online capability of our approach while retaining the global optimality of the solution at any time.
Robot Learning With Crash Constraints

In the past decade, numerous machine learning algorithms have been shown to successfully learn optimal policies to control real robotic systems. However, it is common to encounter failing behaviors as the learning loop progresses. Specifically, in robot applications where failing is undesired but not catastrophic, many algorithms struggle with leveraging data obtained from failures. This is usually caused by (i) the failed experiment ending prematurely, or (ii) the acquired data being scarce or corrupted. Both complicate the design of proper reward functions to penalize failures. In this paper, we propose a framework that addresses those issues. We consider failing behaviors as those that violate a constraint and address the problem of learning with crash constraints, where no data is obtained upon constraint violation. The no-data case is addressed by a novel GP model (GPCR) for the constraint that combines discrete events (failure/success) with continuous observations (only obtained upon success). We demonstrate the effectiveness of our framework on simulated benchmarks and on a real jumping quadruped, where the constraint threshold is unknown a priori. Experimental data is collected, by means of constrained Bayesian optimization, directly on the real robot. Our results outperform manual tuning and GPCR proves useful on estimating the constraint threshold.
Asymptotic optimality in stochastic optimization

We study local complexity measures for stochastic convex optimization problems, providing a local minimax theory analogous to that of Hájek and Le Cam for classical statistical problems. We give complementary optimality results, developing fully online methods that adaptively achieve optimal convergence guarantees. Our results provide function-specific lower bounds and convergence results that make precise a correspondence between statistical difficulty and the geometric notion of tilt-stability from optimization. As part of this development, we show how variants of Nesterov's dual averaging—a stochastic gradient-based procedure—guarantee finite time identification of constraints in optimization problems, while stochastic gradient procedures fail. Additionally, we highlight a gap between problems with linear and nonlinear constraints: standard stochastic-gradient-based procedures are suboptimal even for the simplest nonlinear constraints, necessitating the development of asymptotically optimal Riemannian stochastic gradient methods.
Time-weighted Fuzzy Support Vector Machines for classification in changing environments

The predictive performance of classification methods relies heavily on the nature of the environment, as in the joint distribution of inputs and outputs may evolve over time. This issue is known as dataset shift. Given that most statistical and machine learning techniques assume that the training sample is drawn from the same distribution as the test data used for evaluation, an appreciable amount of researchers and practitioners tend to ignore this issue at the model construction stage. In this paper, we propose a novel Fuzzy Support Vector Machine strategy, in which the traditional hinge loss function is redefined to account for dataset shift. Additionally, we propose a general version of this loss function applying aggregation operators in order to improve performance by dealing with dataset shift via fuzzy logic. Originally developed as linear approaches, our proposals are extended to kernel-based classification for non-linear machine learning. Our methods are able to perform best compared to traditional classifiers in terms of out-of-time prediction using simulated and real-world dataset for credit scoring, confirming the theoretical virtues of our approach.
Red Blood Cell Classification Using Image Processing and CNN

In the medical field, the analysis of the blood sample of the patient is a critical task. Abnormalities in blood cells are accountable for various health issues. Red blood cells (RBCs) are one of the major components of blood. Classifying the RBC can allow us to diagnose different diseases. The traditional, time-consuming technique of visualizing RBC manually under the microscope, is a tedious task and may lead to wrong interpretation because of the human error. The various health conditions can change the shape, texture, and size of normal RBCs. The proposed method has involved the use of image processing to classify the RBCs with the help of convolution neural networks. The algorithm can extract the feature of each segmented cell image and classify it into 9 various types. Images of blood slides were collected from the hospital. The overall accuracy was 98.5%. The system has been developed to provide accurate and fast results that can save patients’ lives.
Mineral prospectivity mapping using a VNet convolutional neural network

Major mineral discoveries have declined in recent decades, and the natural resource industry is in the process of adapting and incorporating novel technologies such as machine learning and artificial intelligence to help guide the next generation of exploration. One such development is an artificial intelligence architecture called VNet that uses deep learning and convolutional neural networks. This method is designed specifically for use with geoscience data and is suitable for a multitude of exploration applications. One such application is mineral prospectivity in which the machine is tasked with identifying the complex pattern between many layers of geoscience data and a particular commodity of interest, such as gold. The VNet algorithm is designed to recognize patterns at different spatial scales, which lends itself well to the mineral prospectivity problem of there often being local and regional trends that affect where mineralization occurs. We test this approach on an orogenic gold greenstone belt setting in the Canadian Arctic where the algorithm uses gold values from sparse drill holes for training purposes to predict gold mineralization elsewhere in the region. The prospectivity results highlight new target areas, and one such target was followed up with a direct-current induced polarization survey. A chargeability anomaly was discovered wherein the VNet had predicted gold mineralization, and subsequent drilling encountered a 6 g/t Au intercept within 10 m of drilling that averaged more than 1.0 g/t Au. Although most of the prospectivity targets generated from VNet were not drill tested, this first intercept helps validate the approach. We believe this method can help maximize the use of existing geoscience data for successful and efficient exploration programs in the future.
A Model for Verification and Validation of Law Compliance of Smart Contracts in IoT Environment

The interest of Industry 4.0 in Smart Contracts and blockchain technologies is growing up day by day.Smart Contracts have enabled new kinds of interactions whereby contractors can even fully automate processes they agree on.This technology is really appealing in Internet of Things (IoT) domain because smart devices generate events for software agents involved in a smart contract execution, making full automation possible.However, smart contracts have to comply with national and international laws and accountability of participant's actions.Soundness of a smart contract has to be verified in terms of law compliance.Here we propose a model for verification and validation of law compliance of smart-contracts in IoT environments.The main goal of this work is to propose a formal model (based on multi-agent logic and ontological description of contracts) for validating law compliance of smart contracts and to determine potential responsibilities of failures.
Appearance Matters, So Does Audio: Revealing the Hidden Face via Cross-Modality Transfer

Recently, there has been an exponential increase in the security concerns raised by faking face ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , deepfake), which automatically changes the identity with a specifically learned deep generative model. With numerous approaches proposed to identify the fake content, much less work has been dedicated to automatically revealing the authentic one that is originally acquired. Here, we propose a new paradigm that seeks to reveal the authentic face hidden behind the fake one by leveraging the joint information of face and audio. More specifically, given the fake face as well as the audio segment, the cross-modality transferable capability is exploited by learning to generate the feature of the authentic face, based on the underlying clues from the audio as well as the fake face appearance. The effectiveness of the proposed scheme is validated through a series of evaluations, and experimental results show that the proposed model achieves promising face reconstruction performance in revealing the hidden faces, in terms of reconstruction quality, as well as identity and face attribute inference accuracy.
“We Dissect Stupidity and Respond to It”: Response Videos and Networked Harassment on YouTube

Over the past decade YouTube “response videos” in which a user offers counterarguments to a video uploaded by another user have become popular among political creators. While creators often frame response videos as debates, those targeted assert that they function as vehicles for harassment from the creator and their networked audience. Platform policies, which base moderation decisions on individual pieces of content rather than the relationship between videos and audience behavior, may therefore fail to address networked harassment. We analyze the relationship between amplification and harassment through qualitative content analysis of 15 response videos. We argue that response videos often provide a blueprint for harassment that shows both why the target is wrong and why harassment would be justified. Creators use argumentative tactics to portray themselves as defenders of enlightened public discourse and their targets as irrational and immoral. This positioning is misleading, given that creators interpellate the viewer as part of a networked audience with shared moral values that the target violates. Our analysis also finds that networked audiences act on that blueprint through the social affordances of YouTube, which we frame as harassment affordances. We argue that YouTube’s current policies are insufficient for addressing harassment that relies on amplification and networked audiences.
Online 3-Dimensional Path Planning with Kinematic Constraints in Unknown Environments Using Hybrid A* with Tree Pruning

In this paper we present an extension to the hybrid A* (HA*) path planner. This extension allows autonomous underwater vehicle (AUVs) to plan paths in 3-dimensional (3D) environments. The proposed approach enables the robot to operate in a safe manner by accounting for the vehicle’s motion constraints, thus avoiding collisions and ensuring that the calculated paths are feasible. Secondly, we propose an improvement for operations in unexplored or partially known environments by endowing the planner with a tree pruning procedure, which maintains a valid and feasible search-tree during operation. When the robot senses new obstacles in the environment that invalidate its current path, the planner prunes the tree of branches which collides with the environment. The path planning algorithm is then initialised with the pruned tree, enabling it to find a solution in a lower time than replanning from scratch. We present results obtained through simulation which show that HA* performs better in known underwater environments than compared algorithms in regards to planning time, path length and success rate. For unknown environments, we show that the tree pruning procedure reduces the total planning time needed in a variety of environments compared to running the full planning algorithm during replanning.