Reviewerly/exHarmony huggingface dataset:
Regulating disinformation with artificial intelligence: Effects of disinformation initiatives on freedom of expression and media pluralism

This study examines the consequences of the increasingly prevalent use of artificial intelligence (AI) disinformation initiatives upon freedom of expression, pluralism and the functioning of a democratic polity. The study examines the trade-offs in using automated technology to limit the spread of disinformation online. It presents options (from self-regulatory to legislative) to regulate automated content recognition (ACR) technologies in this context. Special attention is paid to the opportunities for the European Union as a whole to take the lead in setting the framework for designing these technologies in a way that enhances accountability and transparency and respects free speech. The present project reviews some of the key academic and policy ideas on technology and disinformation and highlights their relevance to European policy.Chapter 1 introduces the background to the study and presents the definitions used. Chapter 2 scopes the policy boundaries of disinformation from economic, societal and technological perspectives, focusing on the media context, behavioural economics and technological regulation. Chapter 3 maps and evaluates existing regulatory and technological responses to disinformation. In Chapter 4, policy options are presented, paying particular attention to interactions between technological solutions, freedom of expression and media pluralism.
DeepAlerts: Deep Learning Based Multi-Horizon Alerts for Clinical Deterioration on Oncology Hospital Wards

Machine learning and data mining techniques are increasingly being applied to electronic health record (EHR) data to discover underlying patterns and make predictions for clinical use. For instance, these data may be evaluated to predict clinical deterioration events such as cardiopulmonary arrest or escalation of care to the intensive care unit (ICU). In clinical practice, early warning systems with multiple time horizons could indicate different levels of urgency, allowing clinicians to make decisions regarding triage, testing, and interventions for patients at risk of poor outcomes. These different horizon alerts are related and have intrinsic dependencies, which elicit multi-task learning. In this paper, we investigate approaches to properly train deep multi-task models for predicting clinical deterioration events via generating multi-horizon alerts for hospitalized patients outside the ICU, with particular application to oncology patients. Prior knowledge is used as a regularization to exploit the positive effects from the task relatedness. Simultaneously, we propose task-specific loss balancing to reduce the negative effects when optimizing the joint loss function of deep multi-task models. In addition, we demonstrate the effectiveness of the feature-generating techniques from prediction outcome interpretation. To evaluate the model performance of predicting multi-horizon deterioration alerts in a real world scenario, we apply our approaches to the EHR data from 20,700 hospitalizations of adult oncology patients. These patients' baseline high-risk status provides a unique opportunity: the application of an accurate model to an enriched population could produce improved positive predictive value and reduce false positive alerts. With our dataset, the model applying all proposed learning techniques achieves the best performance compared with common models previously developed for clinical deterioration warning.
Visual Perception for Multiple Human–Robot Interaction From Motion Behavior

Visual perception is an important component for human-robot interaction processes in robotic systems. Interaction between humans and robots depends on the reliability of the robotic vision systems. The variation of camera sensors and the capability of these sensors to detect many types of sensory inputs improve the visual perception. The analysis of activities, motions, skills, and behaviors of humans and robots have been addressed by utilizing the heat signatures of the human body. The human motion behavior is analyzed by body movement kinematics, and the trajectory of the target is used to identify the objects and the human target in the omnidirectional (O-D) thermal images. The process of human target identification and gesture recognition by traditional sensors have problem for multitarget scenarios since these sensors may not keep all targets in their narrow field of view (FOV) at the same time. O-D thermal view increases the robots' line-of-sights and ability to obtain better perception in the absence of light. The human target is informed of its position, surrounding objects and any other human targets in its proximity so that humans with limited vision or vision disability can be assisted to improve their ability in their environment. The proposed method helps to identify the human targets in a wide FOV and light independent conditions to assist the human target and improve the human-robot and robot-robot interactions. The experimental results show that the identification of the human targets is achieved with a high accuracy.
Agile Software Development Using Cloud Computing: A Case Study

Agile software development is successful due to self-organizing teams, adaptive planning, a cooperative environment with respect to communication with clients and team members, small development cycles, continuous design improvements, continuous delivery and feedback of clients.Cloud computing helps to reduce cost, enables scalability and enhances communication through its services.A generic framework with the conjunction of Agile Development and Cloud Computing (ADCC) proposed in an earlier study is evaluated in this study.The Malaysia Research and education network (MyRen) cloud is utilized to implement the framework.A case study is conducted to evaluate the framework.Before conducting the case study, the participants are educated on the ADCC framework.The results of the case study show that the performance of agile methods is improved with the usage of the ADCC framework.The improvement is measured in terms of local and distributed agile development environments.
Large Universe Ciphertext-Policy Attribute-Based Encryption with Attribute Level User Revocation in Cloud Storage

Ciphertext-Policy Attribute-Based Encryption (CP-ABE), especially large universe CP-ABE that is not bounded with the attribute set, is getting more and more extensive application in the cloud storage. However, there exists an important challenge in original large universe CP-ABE, namely dynamic user and attribute revocation. In this paper, we propose a large universe CP-ABE with efficient attribute level user revocation, namely the revocation to an attribute of some user cannot influence the common access of other legitimate attributes. To achieve the revocation, we divide the master key into two parts: delegation key and secret key, which are sent to the cloud provider and user separately. Note that, our scheme is proved selectively secure in the standard model under "q-type" assumption. Finally, the performance analysis and experimental verification have been carried out in this paper, and the experimental results show that, compared with the existing revocation schemes, although our scheme increases the computational load of storage Service Provider (CSP) in order to achieve the attribute revocation, it does not need the participation of Attribute Authority (AA), which reduces the computational load of AA. Moreover, the user does not need any additional parameters to achieve the attribute revocation except of the private key, thus saving the storage space greatly
Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

Solar thermal systems (STS) are efficient and environmentally safe devices to meet the rapid increasing energy demand now a days.But it is very important to optimize their performance under required operating condition for efficient usage.Hence intelligent system-based techniques like artificial neural network (ANN) play an important role for system performance prediction in accurate and speedy way.In present paper, it is attempted to scrutinize the approach of ANN as an intelligent system based method to optimize the performance prediction of different solar thermal systems accurately.Here, 25 research works related to various solar thermal systems have been reviewed and summarized to understand the impact of different ANN models and learning algorithms on performance prediction of STS.Using ANN, a brief stepwise summary of research work on various STS like solar air heaters, solar stills, solar cookers, solar dryers and solar hybrid systems, their predictions (results) and architectures (network and learning algorithms) in the literature till now, are also discussed here.This paper will genuinely help the future researchers to overview the work concisely related to solar thermal system performance prediction using various types of ANN models and learning algorithm and compare it with other global methods of machine learning.
Particle filtering methods for stochastic optimization with application to large-scale empirical risk minimization

This paper is concerned with sequential filtering based stochastic optimization (FSO) approaches that leverage a probabilistic perspective to implement the incremental proximity method (IPM). The present FSO methods are derived based on the Kalman filter (KF) and the extended KF (EKF). In contrast with typical methods such as stochastic gradient descent (SGD) and IPMs, they do not need to pre-schedule the learning rate for convergence. Nevertheless, they have limitations that inherit from the KF mechanism. As the particle filtering (PF) method outperforms KF and its variants remarkably for nonlinear non-Gaussian sequential filtering problems, it is natural to ask if FSO methods can benefit from PF to get around of their limitations. We provide an affirmative answer to this question by developing two PF based stochastic optimizers (PFSOs). For performance evaluation, we apply them to address nonlinear least-square fitting with simulated data, and empirical risk minimization for binary classification of real datasets. Experimental results demonstrate that PFSOs outperform remarkably a benchmark SGD algorithm, the vanilla IPM, and KF-type FSO methods in terms of numerical stability, convergence speed, and flexibility in handling diverse types of loss functions.
Efficient hierarchical graph partitioning for image segmentation by optimum oriented cuts

In this work, a hierarchical graph partitioning based on optimum cuts in graphs is proposed for unsupervised image segmentation, that can be tailored to the target group of objects, according to their boundary polarity, by extending Oriented Image Foresting Transform (OIFT). The proposed method, named UOIFT, theoretically encompasses as a particular case the single-linkage algorithm by minimum spanning tree (MST) and gives superior segmentation results compared to other approaches commonly used in the literature, usually requiring a lower number of image partitions to accurately isolate the desired regions of interest with known polarity. The method is supported by new theoretical results involving the usage of non-monotonic-incremental cost functions in directed graphs and exploits the local contrast of image regions, being robust in relation to illumination variations and inhomogeneity effects. UOIFT is demonstrated using a region adjacency graph of superpixels in medical and natural images.
PoseConvGRU: A Monocular Approach for Visual Ego-motion Estimation by Learning

Visual ego-motion estimation is one of the longstanding problems which estimates the movement of cameras from images. Learning based ego-motion estimation methods have seen an increasing attention since its desirable properties of robustness to image noise and camera calibration independence. In this work, we propose a data-driven approach of learning based visual ego-motion estimation for a monocular camera. We use an end-to-end learning approach in allowing the model to learn a map from input image pairs to the corresponding ego-motion, which is parameterized as 6-DoF transformation matrix. We introduce a two-module Long-term Recurrent Convolutional Neural Networks called PoseConvGRU. The feature-encoding module encodes the short-term motion feature in an image pair, while the memory-propagating module captures the long-term motion feature in the consecutive image pairs. The visual memory is implemented with convolutional gated recurrent units, which allows propagating information over time. At each time step, two consecutive RGB images are stacked together to form a 6-channel tensor for feature-encoding module to learn how to extract motion information and estimate poses. The sequence of output maps is then passed through the memory-propagating module to generate the relative transformation pose of each image pair. In addition, we have designed a series of data augmentation methods to avoid the overfitting problem and improve the performance of the model when facing challengeable scenarios such as high-speed or reverse driving. We evaluate the performance of our proposed approach on the KITTI Visual Odometry benchmark and Malaga 2013 Dataset. The experiments show a competitive performance of the proposed method to the state-of-the-art monocular geometric and learning methods and encourage further exploration of learning-based methods for the purpose of estimating camera ego-motion even though geometrical methods demonstrate promising results.
A Method of Hierarchical Image Retrieval for Real-Time Photogrammetry Based on Multiple Features

In near real-time photogrammetry, the first step in processing each new added image is determining the most relevant image in pre-sequence unordered images quickly and exactly, which is pivotal for accurate image matching and 3D reconstruction. This paper presents a hierarchical image retrieval algorithm based on multiple features and details the choice for representation of multiple features which is critical to the improvement of accuracy of this algorithm. First, we represent global features using AlexNet-FC7(fully connected layers) or ResNet101-Pool5(pooling layers) and local features using SIFT (scale-invariant feature transform) in two parallel threads with support of GPU (Graphics Processing Unit). Next, we obtain candidates based on cosine similarities between global features of each pre-sequence image and new added image. Finally, we determine the most relevant image from those candidates according to feature matching results for each candidate and new added image. The experimental results confirm that the second step is rather fast and the third step is necessary to tackle the problem that global features cannot distinguish objects from the same class. The total time our algorithm takes is about 83.6ms for determining the most relevant image in 5063 pre-sequence unordered images of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024\times 768$ </tex-math></inline-formula> , which outperforms exhaustive pairwise matching, Bag of Words and multi-vocabulary trees. Accuracy of our algorithm also perform better than the state-of-the-art methods on three benchmark datasets. SIFT matching results obtained in the third step after eliminating mismatches with RANSAC (Random Sample Consensus) can also be used for high-precision incremental SFM (Structure from Motion) reconstruction.
A robust surface recover algorithm based on random phase noise correction for white light interferometry

White light scanning interferometry is a very important technique for nanometrology. In this paper, a robust white light interference signal processing algorithm using random phase noise correction method is proposed. By analyzing the phase response of the correlgram mathematically, the phase noise is modelized by the combination of random noise and systematic deviation. The random noise can be corrected by assembling the least square result of phase information. As a result, the relationship between phase distribution and surface height is established in a more accurate way. The simulations show that the novel algorithm is capable of achieving robust measurements with standard deviation one tenth comparing to the scanning step length errors, and has an extremely high noise tolerance 14.25 nm with scanning step length 20 nm. In the experiments, a step height standard (VLSI, 182.7 ± 2.0 nm), a protected aluminum mirror and a drive shaft cover of aircraft engine were tested, where the repeatability error for the step height standard is 0.5 nm, the roughness repeatability of the mirror is only 0.03 nm, and the roughness repeatability of the cover is 5.14 nm, which proves the accuracy and robust of the algorithm.
Noise2Inverse: Self-supervised deep convolutional denoising for linear inverse problems in imaging

Recovering a high-quality image from noisy indirect measurement is animportant problem with many applications. For such inverse problems, superviseddeep convolutional neural network (CNN)-based denoising methods have shownstrong results, but their success critically depends on the availability of ahigh-quality training dataset of similar measurements. For image denoising,methods are available that enable training without a separate training datasetby assuming that the noise in two different pixels is uncorrelated. However,this assumption does not hold for inverse problems, resulting in artifacts inthe output of existing methods. Here, we propose Noise2Inverse, a deepCNN-based denoising method for linear inverse problems in imaging that does notrequire any additional clean or noisy data. Training a CNN-based denoiser isenabled by exploiting the noise model to compute multiple statisticallyindependent reconstructions. We develop a theoretical framework which showsthat such training indeed obtains a denoising CNN, assuming the measured noiseis element-wise independent and zero-mean. On simulated CT datasets,Noise2Inverse demonstrates a substantial improvement in peak signal-to-noiseratio (> 2dB) and structural similarity index (> 30%) compared to imagedenoising methods and conventional reconstruction methods, such asTotal-Variation Minimization. We also demonstrate that the method is able tosignificantly reduce noise in challenging real-world experimental datasets.
A Relation Network Based Approach to Curved Text Detection

In this paper, a new relation network based approach to curved text detection is proposed by formulating it as a visual relationship detection problem. The key idea is to decompose curved text detection into two subproblems, namely detection of text primitives and prediction of link relationship for each nearby text primitive pair. Specifically, an anchor-free region proposal network based text detector is first used to detect text primitives of different scales from different feature maps of a feature pyramid network, from which a manageable number of text primitive pairs are selected. Then, a relation network is used to predict whether each text primitive pair belongs to a same text instance. Finally, isolated text primitives are grouped into curved text instances based on link relationships of text primitive pairs. Because pairwise link prediction has used features extracted from the bounding boxes of each text primitive and their union, the relation network can effectively leverage wider context information to improve link prediction accuracy. Furthermore, since the link relationships of relatively distant text primitives can be predicted robustly, our relation network based text detector is capable of detecting text instances with large inter-character spaces. Consequently, our proposed approach achieves superior performance on not only two public curved text detection datasets, namely Total-Text and SCUT-CTW1500, but also a multi-oriented text detection dataset, namely MSRA-TD500.
Attentively Conditioned Generative Adversarial Network for Semantic Segmentation

Generative Adversarial Network has proven to produce state-of-the-art results by framing a generative modeling task into a supervised learning problem.In this paper, we propose Attentively Conditioned Generative Adversarial Network (ACGAN) for semantic segmentation by designing a segmentor model that generates probability maps from images and a discriminator model which discriminates the segmentor's output from the ground truth labels.Additionally, we conditioned the discriminator's dual inputs with extra information as a conditional adversarial model such that, an attention obtained probability distribution of the segmentor's feature maps is incorporated, and the ground truth is also accompanied by a vector of the class label.We demonstrate that our proposed model can provide better semantic segmentation results while stabilizing the discriminator to model long-range dependencies as a result of the supplementary inputs to the network.The attention network particularly provides more insights by extracting cues from the feature locations, and alongside the class label vector, gives the model an advantage to inform better spectral sensitivity.Experiments on the PASCAL VOC 2012 and the CamVid datasets show that our adversarial training technique yields improved accuracy.
Optimal Robot Path Planning using Enhanced Particle Swarm Optimization algorithm

The aim of robot path planning is to search for a safe path for the mobile robot. Even though there exist various path planning algorithms for mobile robots, yet only a few are optimized. The optimized algorithms include the Particle Swarm Optimization (PSO) that finds the optimal path with respect to avoiding the obstacles while ensuring safety. In PSO, the sub-optimal solution takes place frequently while finding a solution to the optimal path problem. This paper proposes an enhanced PSO algorithm that contains an improved particle velocity. Experimental results show that the proposed Enhanced PSO performs better than the standard PSO in terms of solutionâ€™s quality. Hence, a mobile robot implementing the proposed algorithm operates better and is more secure.
Research on image classification based on Capsnet

Capsnet is a very innovative and innovative deep learning network. This paper uses a capsule network to classify and test the MNIST and CIFAR10 datasets, respectively. And in order to compare with the traditional convolutional neural network, two architectures are designed. Then the experiment is carried out and the results are compared. By comparing and analyzing the experimental results, Capsnet has obvious advantages over the CNN method regardless of the MNIST dataset or the CIFAR10 dataset. Also, Capsnet will greatly reduce the number of parameters, which has the certain guiding significance in the classification of the image.
An unknown input extended Kalman filter for nonlinear stochastic systems

This paper proposes an Unknown Input Extended Kalman Filter (UIEKF) for stochastic non linear systems affected by Gaussian noises and Unknown Inputs (UI) in both state and measurement equations. The proposed approach is based on a total decoupling of the UI, in spite of the presence of nonlinearities in the measurement equation. The UI is decoupled under some structural constraints, and a state estimator is provided. Besides an UI estimator is also proposed. Finally, the proposed filter is applied on a classical navigation example, illustrating its advantages.