Reviewerly/exHarmony huggingface dataset:
Multi-Agent Reinforcement Learning for Thermalling in Updrafts

Maximizing the energy intake by thermalling updrafts while avoiding collisions between two gliders or gliders with obstacles exceeds existing guidance strategies. Existing thermalling algorithms cannot independently incorporate multiple other gliders with an arbitrary number of updrafts and obstacles. Therefore, they would need to incorporate heuristics or be combined with other systems for this purpose. This leaves them vulnerable to unforeseen scenarios. In this research, we show how the outlined scenario can be formulated as an optimization problem and then propose to solve it with multi-agent reinforcement learning. We show the efficacy of this solution in different simulations using only the location of other gliders and a map of updrafts for the control strategy. This approach only requires the perception of other gliders, which is easier than also tracking heading or speed and is, therefore, applicable for future flight tests. Our approach has been successfully tested in simulations for up to a dozen gliders and also works on previously unseen problems like new environments and different sizes of the agent population.
Using Graph Embedding to Monitor Communities of Learners

How to keep track of the learning process of a community of learners is a problem whose resolution requires accurate assessment tools and appropriate teaching and learning strategies. Peer Assessment is a standard didactic strategy which requires students in a course to correct their peers’ assignments. Since the representation of a community, even a large one, of students, is based on directed graphs, it is difficult to follow its whole dynamics. In this paper, we investigate the possibility of using two machine learning techniques: Graph Embeddings, and Principal Component Analysis, to represent a students’ communities by points in a 2D space, in order to have valuable and understandable information on the dynamics of the group. For this purpose we present a case study based on three real Peer Assessment sessions. The first results are encouraging.
Foveated streaming of real-time graphics

Remote rendering systems comprise powerful servers that render graphics on behalf of low-end client devices and stream the graphics as compressed video, enabling high end gaming and Virtual Reality on those devices. One key challenge with them is the amount of bandwidth required for streaming high quality video. Humans have spatially non-uniform visual acuity: We have sharp central vision but our ability to discern details rapidly decreases with angular distance from the point of gaze. This phenomenon called foveation can be taken advantage of to reduce the need for bandwidth. In this paper, we study three different methods to produce a foveated video stream of real-time rendered graphics in a remote rendered system: 1) foveated shading as part of the rendering pipeline, 2) foveation as post processing step after rendering and before video encoding, 3) foveated video encoding. We report results from a number of experiments with these methods. They suggest that foveated rendering alone does not help save bandwidth. Instead, the two other methods decrease the resulting video bitrate significantly but they also have different quality per bit and latency profiles, which makes them desirable solutions in slightly different situations.
A New 16 Bit Symmetric Key Quantum Encryption Algorithm Based On Dynamic Pauli Gates

Quantum cryptography appropriates the features of photons to create a secured algorithm in real-time. In this study, a new algorithm is illustrated using the symmetric key based on the basics of quantum and classical computation. In this algorithm, the message contains blocks, the space of each block is 16 bits. First, the classical encryption process depends on the key that makes rotation and permutation in the bits of block message. Second, the bits of encrypted block messages are converted to qubits. Third, Pauli gates are used, and applies the output of the second stage to them depends on a key to achieving the non-orthogonality of ciphertext states. Because of the basis of quantum computing, the eavesdroppers cannot detect the encrypted message states that changed to non-orthogonal states. The proposed algorithm is proven to increase the message space, the various algorithm analysis proofed that our proposed algorithm has an improved strength against various attacks, including the brute force attack.
A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?

Abstract Deep learning has recently experienced explosive growth in use, largely due to advances in neural networks and the availability of large corpora of domain data. Project management activities generate and handle large volumes of data. Software engineering closely relates to project management, so software engineering projects must be prone to the use of neural networks. We seek to obtain an accurate vision of how neural networks are being used in software engineering projects through a systematic mapping study. We confirm that neural networks have already made their way into these projects; however, we show that their current uses are limited to certain repetitive and legacy tasks. Given uncovered ample room for expansion, we point out a few directions the industry and academy can lean toward to in the next years for taking better advantage of neural networks in software engineering projects and immediately advancing the field. We investigate if, how, and to what extent have neural networks been employed to the advancement of software engineering projects. As such, a systematic mapping study was conducted, which led to the conclusion that even though these algorithms have indeed been employed on several software engineering tasks, this employment so far has been shy, mostly relying on legacy types of neural networks. More modern variants, namely, deep learning algorithms, are slowly gaining momentum and should be the trend going forward.
Intelligent route planning model of industrial robot based on inertia moment parameter optimization

In order to improve the intelligent planning ability of monocular vision dynamic underactuated industrial robot's action route, an intelligent planning method of monocular vision dynamic underactuated industrial robot's action route based on inertia moment parameter optimization is proposed. Establishing a spatial grid area planning model of monocular vision dynamic underactuated industrial robot action route, adopting a cell moment parameter optimization method to carry out grid regionalization matching in the process of monocular vision dynamic underactuated industrial robot action route optimization planning, establishing optimization constraint parameters of monocular vision dynamic underactuated industrial robot action route distribution. Based on kinematic feature analysis method, the optimization planning and moment parameter optimization analysis of monocular vision dynamic underactuated industrial robot are carried out. Through automatic condition tracking feedback and moment parameter optimization analysis, the path optimization of monocular vision dynamic underactuated industrial robot is realized. Combined with unsupervised learning algorithm of fuzzy PID network, the intelligent planning model of action route is constructed to improve control stability. Tests show that this method has good adaptability and strong obstacle avoidance ability for monocular vision dynamic underactuated industrial robot.
Single-Loss Multi-task Learning For Improving Semantic Segmentation Using Super-Resolution

We propose a novel means to improve the accuracy of semantic segmentation based on multi-task learning. More specifically, in our Multi-Task Semantic Segmentation and Super-Resolution (MT-SSSR) framework, we jointly train a super-resolution and semantic segmentation model in an end-to-end manner using the same task loss for both models. This allows us to optimize the super-resolution model towards producing images that are optimal for the segmentation task, rather than ones that are of high-fidelity. Simultaneously we adapt the segmentation model to better utilize the improved images and thereby improve the segmentation accuracy. We evaluate our approach on multiple public benchmark datasets, and our extensive experimental results show that our novel MT-SSSR framework outperforms other state-of-the-art approaches.
Methods for Automatic Sensitive Data Detection in Large Datasets: a Review

In recent years, the need for detection and de-identification of sensitive data in both structured and unstructured forms has increased. The methods used for these tasks have evolved accordingly and currently there are many solutions in different areas of interest. This paper describes the need for the detection of sensitive data in large datasets and describes the challenges associated with automating the detection process. It gives a brief overview of the rule-based and machine learning methods used in this area and examples of their application. The advantages and disadvantages of the described methods are also discussed. We show that the most recent detection solutions are based on the latest and most advanced models proposed in the field of natural language processing, but that there are still some rule-based methods used for certain types of sensitive data. In recent years, the need for detection and de-identification of sensitive data in both structured and unstructured forms has increased. The methods used for these tasks have evolved accordingly and currently there are many solutions in different areas of interest. This paper describes the need for the detection of sensitive data in large datasets and describes the challenges associated with automating the detection process. It gives a brief overview of the rule-based and machine learning methods used in this area and examples of their application. The advantages and disadvantages of the described methods are also discussed. We show that the most recent detection solutions are based on the latest and most advanced models proposed in the field of natural language processing, but that there are still some rule-based methods used for certain types of sensitive data.
Characterization of Modeling Errors Affecting Performances of a Robotics Deep Reinforcement Learning Controller in a Sim-to-Real Transfer

Simulation is a powerful tool used to train Reinforcement Learning (RL) agents involved in robotic tasks. It allows to collect large amount of data in comparatively faster and safer way than on the real robot. However, a simulator is only an approximation of the physical system to be controlled. Due to modeling errors, a controller learned on the simulator dynamics may behave differently once applied to the real robot. In the worst case, the controller, although being successful when applied on the simulator, may fail when applied on the real platform. In this paper, we deal with the sim-to-real transfer of a RL controller for a Poppy Ergo-Jr robotic arm involved in a positioning task: i.e., moving the servo joints in order to reach a desired target position with the end-effector. In particular, we want to investigate the differences between the real robot and its simulator and how they affect the controller performance after its transfer from the simulator to the real platform.
eyemR-Talk: Using Speech to Visualise Shared MR Gaze Cues

In this poster we present eyemR-Talk, a Mixed Reality (MR) collaboration system that uses speech input to trigger shared gaze visualisations between remote users. The system uses 360° panoramic video to support collaboration between a local user in the real world in an Augmented Reality (AR) view and a remote collaborator in Virtual Reality (VR). Using specific speech phrases to turn on virtual gaze visualisations, the system enables contextual speech-gaze interaction between collaborators. The overall benefit is to achieve more natural gaze awareness, leading to better communication and more effective collaboration.
The (non-)existence of perfect codes in Lucas cubes

The Fibonacci cube of dimension n, denoted as Γn, is the subgraph of the n-cube Qn induced by vertices with no consecutive 1’s. Ashrafi and his co-authors proved the non-existence of perfect codes in Γn for n ≥ 4. As an open problem the authors suggest to consider the existence of perfect codes in generalizations of Fibonacci cubes. The most direct generalization is the family Γn(1s) of subgraphs induced by strings without 1s as a substring where s ≥ 2 is a given integer. In a precedent work we proved the existence of a perfect code in Γn(1s) for n = 2p − 1 and s ≥ 3.2p − 2 for any integer p ≥ 2. The Lucas cube Λn is obtained from Γn by removing vertices that start and end with 1. Very often the same problems are studied on Fibonacci cubes and Lucas cube. In this note we prove the non-existence of perfect codes in Λn for n ≥ 4 and prove the existence of perfect codes in some generalized Lucas cube Λn(1s).
Are Nonimage Data Really Necessary for Disease Prediction With Graph Convolutional Networks?

Graph convolutional networks (GCNs) have been widely applied to automatic disease diagnosis based on neuroimaging data, and nonimage data are used to determine local connections in the GCN mode. However, previous studies reveal the GCN model may perform even worse than the linear model if nonimage data are inappropriate or unavailable. Considering that manually identifying disease-related nonimage data among numerous alternatives is time-consuming and nonimage data are usually not available in medical data sets for privacy reasons, this shortage limits the application of the GCN model. Besides, whether nonimage data are really necessary is also worth discussing, since much literature have revealed that neuroimaging data can well characterize nonimage data. To overcome the limitation, we apply a nonlocal operation (a special form of attention mechanism) to the GCN model (nonlocal GCN), which automatically determines local connection based on image data and no more relies on the nonimage data. The experiments on two public data sets show that the proposed model can perform better or achieve almost the same performance without using any nonimage data. The results on simulation data sets reveal that our model can perform better in feature-driven data sets if image data contain nonimage data, in which the nonimage data are not necessary.
Collaborative Learning with Augmented Reality Tornado Simulator

Research has shown that AR can improve learning with better content understanding. In addition, collaborative learning has shown to increase motivation and improve learning performance. Using the subject of tornado, the paper describes the design of a multi-user augmented reality tornado simulator using Unity to facilitate collaborative learning. Students working in pairs first set up a village and configure the funnel width, pressure difference, and rotation speed of a tornado and then watch the tornado whipping through the village to destroy properties on Apple iPads. To evaluate the performance of collaborative learning, 22 sixth-grade students were recruited into control and experimental groups, whose learning performance was measured with pre and post tornado knowledge test questionnaires. The results demonstrate a significant performance improvement with collaborative learning.
Tightly Coupled Multimodal Sensor Data Fusion for Robust State Observation With Online Delay Estimation and Compensation

Ego-vehicle state estimation can be achieved by utilizing classic vehicle and environmental sensors. However, each type of sensor has specific strengths and limitations regarding accuracy and robustness. Using existing sensor concept cannot fulfill the requirements for both accuracy and robustness against changing vehicle parameters and environments while driving. Therefore, we propose a framework to exploit the advantages of each individual sensor type seeking high accuracy and robustness. The uncertainty of state variables is estimated by the fusion of an inertial measurement unit, a global navigation satellite system, a radar sensor, and a lidar sensor. The multimodal sensor data are processed in a distributed manner in each sensor model at a low level, and the physical quantities from the sensor models are fused centrally by a probabilistic method, namely, the synchronized error-state extended Kalman filter, at a high level. We propose an event-based approach to estimate sensor system delay and compensate lagged signals using forward prediction fusion. The concept was implemented in a test vehicle and evaluated in field tests for dynamic driving and on public roads. The algorithm represents real-time estimation with high accuracy for different driving maneuvers and robustness against different disturbances caused by the environment and changing chassis parameters.
An efficient operator-splitting radial basis function-generated finite difference (RBF-FD) scheme for image noise removal based on nonlinear total variation models

Image noise removal is one of the most important parts of image processing that can dramatically improve other parts of image processing performance by enhancing the quality of images in databases. Total variation models are second-order partial differential equations for image denoising. These models have some complexities, such as being multidimensional problems, non-linearity, and having large spatial and temporal domains making them challenging problems to be solved numerically. Thus, in this work, we propose the radial basis function generated finite differences (RBF-FD) method in conjunction with a suitable operator splitting technique to overcome these difficulties. This approach has some significant advantages, such as high accuracy, low computational complexity, and the sparsity of the coefficients matrices derived from it. The peak signal-to-noise ratio, structure similarity index measure, and mean-square error metrics are considered to evaluate the proposed approach’s effectiveness and accuracy compared to the other common denoising approaches. • An algorithm based on the OS and RBF-FD approaches is developed for image noise removal. • Dividing multidimensional PDEs into simpler sub-problems whose coefficient matrices are truly sparse. • The obtained results are evaluated by different criteria such as PSNR, MSE, and SSIM. • The visual results of TV and MTV models are compared with other well-known models.
Universal Decoding of Quantum Stabilizer Codes via Classical Guesswork

A universal decoding scheme is conceived for quantum stabilizer codes (QSCs) by appropriately adapting the ‘guessing random additive noise decoding’ (GRAND) philosophy of classical domain codes. We demonstrate that the generalized quantum decoder conceived is eminently suitable for different QSC decoding paradigms, namely for both stabilizer-measurement-based as well as the inverse-encoder-based decoding. We then harness the resultant decoder for both quantum Bose-Chaudhuri-Hocquenghem (BCH) codes and quantum polar codes and quantify both their quantum block error rate (QBLER), and QBLER per logical qubits as well as their decoding complexity. Furthermore, we provide a parametric study of the associated design trade-offs and offer design guideline for the implementation of GRAND-based QSC decoders.
Toward Cooperative Federated Learning Over Heterogeneous Edge/Fog Networks

Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation, treating edge/fog devices and other infrastructure participating in ML as separate processing elements. Consequently, FL has been vulnerable to several dimensions of network heterogeneity, such as varying computation capabilities, communication resources, data qualities, and privacy demands. We advocate for cooperative federated learning (CFL), a cooperative edge/fog ML paradigm built on device-to-device (D2D) and device-toserver (D2S) interactions. Through D2D and D2S cooperation, CFL counteracts network heterogeneity in edge/fog networks through enabling a model/data/resource pooling mechanism, which will yield substantial improvements in ML model training quality and network resource consumption. We propose a set of core methodologies that form the foundation of D2D and D2S cooperation and present preliminary experiments that demonstrate their benefits. We also discuss new FL functionalities enabled by this cooperative framework such as the integration of unlabeled data and heterogeneous device privacy into ML model training. Finally, we describe some open research directions at the intersection of cooperative edge/fog and FL.
Identifying exceptional (dis)agreement between groups

Under the term behavioral data, we consider any type of data featuring individuals performing observable actions on entities. For instance, voting data depict parliamentarians who express their votes w.r.t. legislative procedures. In this work, we address the problem of discovering exceptional (dis)agreement patterns in such data, i.e., groups of individuals that exhibit an unexpected (dis)agreement under specific contexts compared to what is observed in overall terms. To tackle this problem, we design a generic approach, rooted in the Subgroup Discovery/Exceptional Model Mining framework, which enables the discovery of such patterns in two different ways. A branch-and-bound algorithm ensures an efficient exhaustive search of the underlying search space by leveraging closure operators and optimistic estimates on the interestingness measures. A second algorithm abandons the completeness by using a sampling paradigm which provides an alternative when an exhaustive search approach becomes unfeasible. To illustrate the usefulness of discovering exceptional (dis)agreement patterns, we report a comprehensive experimental study on four real-world datasets relevant to three different application domains: political analysis, rating data analysis and healthcare surveillance.
Blockchain-grade privacy protection in surveillance systems

We propose a solution to increase the privacy of people recorded with security cameras without decreasing the details stored in the videos. We strongly believe that CCTV recordings are a necessary and precious source of information to be analyzed when a crime or other unfortunate events happen; for this reason, we would like to have powerful surveillance systems that are able to hide the identity of the recorded people while allowing subsequent recovery of the data.
Optimised multi‐hypothesis tracking algorithm based on the two‐dimensional constraints and manoeuvre detection

With the increase of target mobility and environment complexity, highly reliable single-period statistical judgments become more and more difficult. The multiple hypothesis tracking (MHT) algorithm is a method based on delay logic, and can effectively solve the problem of data association in the course of tracking. However, the number of hypotheses generated by the MHT algorithm is exponentially related to the false alarm rate and the number of targets. Therefore, the hypotheses reduction techniques are required for the implementation of the algorithm. The N -scan-back method and K -best method are often used. On the basis of understanding the K -Best method and the N -Scan-back method, this study proposes the two-dimensional constraints by the K -Best method and the N -Scan-back method, and besides adds the target manoeuvre detection method to jointly manage the hypotheses based on the mean of the filter residual. According to the manoeuvre detection results, the optimised MHT algorithm can adjust the likelihood probability calculation model of the plot-track association and the posterior probability calculation model of the hypothetical branch track, and shorten the decision depth N in the N -Scan-back method. Through simulation, it is proved that the optimised algorithm can reduce the calculation amount and improve the tracking accuracy.