Reviewerly/exHarmony huggingface dataset:
Exploring Augmented Reality in Multiplayer Game Design to Promote Empathy and Prosocial Behavior

Empathy, a fundamental human characteristic that influences both prosocial and antisocial behavior, has gained popularity in recent educational programs. This article proposes the design of a multiplayer cyber-physical gaming environment with Augmented Reality for the execution of group activities following a circular empathy model. This model is transformed into a gamified framework of tasks and steps with main emphasis on the execution of scenarios that generate affective, cognitive, reflective and social experiences to promote empathy and prosocial behavior. Our proposal was evaluated by a group of 34 high school students and the results revealed increased empathy and prosocial behavior in the participants. The preliminary results are promising and are a motive for the future construction of gamified environments to promote empathy in terms of prosocial behavior in children and teenagers.
Logical Methodology and the Structure of Logic Syllabi

This paper starts from a dissatisfaction with the way logic is currently taught in philosophy departments. In such a context, we would want logic to be developed from first principles, in such a way that it can then be applied to any other domain of study. But standard "rigorous" logic courses make it depend on a substantial amount of set theory, which itself seems to require notions from logic—confusing students, if not striking them as circular. Instead, this paper proposes to start from a rejuvenated version of the Aristotelian notion of an argument schema, and to use this idea to develop a limited collection of concepts from logic that is sufficient for the introduction of the axioms of set theory. Once set theory is in place, mathematical logic can be taught as usual.
Butterfly: Multiple Reference Frames Feature Propagation Mechanism for Neural Video Compression

Using more reference frames can significantly improve the compression efficiency in neural video compression. However, in low-latency scenarios, most existing neural video compression frameworks usually use the previous one frame as reference. Or a few frameworks which use the previous multiple frames as reference only adopt a simple multi-reference frames propagation mechanism. In this paper, we present a more reasonable multi-reference frames propagation mechanism for neural video compression, called butterfly multi-reference frame propagation mechanism (Butterfly), which allows a more effective feature fusion of multireference frames. By this, we can generate more accurate temporal context conditional prior for Contextual Coding Module. Besides, when the number of decoded frames does not meet the required number of reference frames, we duplicate the nearest reference frame to achieve the requirement, which is better than duplicating the furthest one. Experiment results show that our method can significantly outperform the previous state-of-the-art (SOTA), and our neural codec can achieve -7.6% bitrate save on HEVC Class D dataset when compares with our base single-reference frame model with the same compression configuration.
Augmented Thresholds for MONI

MONI (Rossi et al., 2022) can store a pangenomic dataset T in small space and later, given a pattern P, quickly find the maximal exact matches (MEMs) of P with respect to T. In this paper we consider its one-pass version (Boucher et al., 2021), whose query times are dominated in our experiments by longest common extension (LCE) queries. We show how a small modification lets us avoid most of these queries which significantly speeds up MONI in practice while only slightly increasing its size.
Differentiable programming in machine learning

This paper explains automatic differentiation, discussing two primary modes - forward and backward - and their respective implementation methods. In the context of issues encountered in machine learning and deep learning, the forward mode is deemed more suitable as it efficiently differentiates functions with numerous inputs compared to outputs. Given Python's pivotal role in the ML landscape, the paper elaborates on two widely used deep learning libraries-PyTorch and TensorFlow. While both these libraries support automatic differentiation, they adopt distinct approaches, each carrying its unique strengths and weaknesses.
Post-colonial development in Africa – Samir Amin’s lens

The paper is an attempt at applying Samir Amin’s lens in the analysis of socio-economic development in Africa. Social and economic development in Africa has been substandard, largely because of the economic system followed and because effective structural transformation has not taken place – Samir Amin’s works explained what needed to be done to transform Africa (and the broader global south). It is in this context that the paper posits that post-colonial Africa has had to contend with disruptive socio-economic and political realities instituted by European colonialism, slave trade and inappropriate integration of Africa to the so-called global economy. The fundamental explanation for the poor socio-economic development in Africa is global capitalism, and one of the possible solutions is in Samir Amin’s delinking proposal as well as the restructuring of the African economies.
Quadratic fitting model in no-reference image quality assessment

The perceptual quality of image is affected by distortions during compression, delivery and storage. Distortions also impact automatic image quality assessment (IQA) that needs to be highly correlated with subjective scores. In the absence of reference, which is a typical scenario in practice, no-reference (NR) metrics are necessary for quality measurements. Recently such methods are proposed, and they employ natural scene statistics (NSS). The experimental analysis performed in this paper takes into consideration two fitting or regression models of several NR-IQA metrics relying on different distortion types. The results show quadratic model as promising for making relations in terms of difference mean opinion score and Shannon entropy.
Scale Variant Vehicle Object Recognition by CNN Module of Multi-Pooling-PCA Process

The moving vehicles present different scales in the image due to the perspective effect of different viewpoint distances. The premise of advanced driver assistance system (ADAS) system for safety surveillance and safe driving is early identification of vehicle targets in front of the ego vehicle. The recognition of the same vehicle at different scales requires feature learning with scale invariance. Unlike existing feature vector methods, the normalized PCA eigenvalues calculated from feature maps are used to extract scale-invariant features. This study proposed a convolutional neural network (CNN) structure embedded with the module of multi-pooling-PCA for scale variant object recognition. The validation of the proposed network structure is verified by scale variant vehicle image dataset. Compared with scale invariant network algorithms of Scale-invariant feature transform (SIFT) and FSAF as well as miscellaneous networks, the proposed network can achieve the best recognition accuracy tested by the vehicle scale variant dataset. To testify the practicality of this modified network, the testing of public dataset ImageNet is done and the comparable results proved its effectiveness in general purpose of applications.
Decentralized Conflict Resolution for Multi-Agent Reinforcement Learning Through Shared Scheduling Protocols

Decentralized multi-agent reinforcement learning (MARL) is an inherently difficult problem because agents can have individual, unique objectives and no direct incentive to cooperate. Conflicts often arise over bottlenecks in the environment, such as a shared key or an intersection, where multiple agents need to access a single resource. To resolve these conflicts, we propose the use of a shared scheduling protocol. A scheduling protocol coordinates agent behavior such that one agent is allowed to greedily use the resource while the others are required to wait. In particular, we are interested in decentralized scheduling protocols that can be implemented independently by each agent without a centralized controller. We present three protocols and prove that they resolve conflicts when obeyed by all agents. In training, agents learn to obey the protocol as violations incur a penalty. Experimental results show that scheduling protocols increase the performance of multi-agent training fivefold compared to baseline decentralized MARL.
A General Iterative Extended Kalman Filter Framework for State Estimation on Matrix Lie Groups

In this paper, we focus on state estimation problem for nonlinear systems on joint matrix Lie group and Euclidean space We propose a general iterative Kalman filter, aiming to integrate the prediction step into the iteration scheme, which is not considered in the conventional iterative extended Kalman filter framework. Such an extra iteration scheme in the prediction step helps improving the accuracy of probability density function propagation through nonlinearities, which can further lead to more accurate estimations of the system states. In addition, the proposed framework unifies the Kalman filter based estimation schemes on studied manifold by adopting the perspective from Gaussian Bayesian inference. The improvement of the proposed framework is illustrated by the ES-GIKF algorithm that is instantiated from the proposed framework in a numerical simulation.
Adaptive Headway Motion Control and Motion Prediction for Safe Unicycle Motion Design

Differential drive robots that can be modeled as a kinematic unicycle are a standard mobile base platform for many service and logistics applications. Safe and smooth autonomous motion around obstacles is a crucial skill for unicycle robots to perform diverse tasks in complex environments. A classical control approach for unicycle control is feedback linearization using a headway point at a fixed headway distance in front of the unicycle. The unicycle headway control brings the headway point to a desired goal location by embedding a linear headway reference dynamics, which often results in an undesired offset for the actual unicycle position. In this paper, we introduce a new unicycle headway control approach with an adaptive headway distance that overcomes this limitation, i.e., when the headway point reaches the goal the unicycle position is also at the goal. By systematically analyzing the closed-loop unicycle motion under the adaptive headway controller, we design analytical feedback motion prediction methods that bound the closed-loop unicycle position trajectory and so can be effectively used for safety assessment and safe unicycle motion design around obstacles. We present an application of adaptive headway motion control and motion prediction for safe unicycle path following around obstacles in numerical simulations.
Star Based Reachability Analysis of Interval Neural Networks

The paper explores the computation of output reachable sets for Interval Neural Networks (INNs) with ReLU activation functions. An INN is a generalization of a Neural Network (NN), where the weights and biases are intervals rather than numbers. We propose a novel algorithm for computing precise over-approximations of the output reachable sets for INNs by introducing a novel data structure called interval star set which is a generalized version of the star set. Specifically, when the INN is a traditional NN, our method reduces to the standard star-based verification of NNs. We present experimental results that demonstrate that our method outperforms the existing method based on mixed integer linear programming (MILP) for the problem of INN output reachable set computation.
Switch and Conquer: Efficient Algorithms By Switching Stochastic Gradient Oracles For Decentralized Saddle Point Problems

We consider a class of non-smooth strongly convex-strongly concave saddle point problems in a decentralized setting without a central server. To solve a consensus formulation of problems in this class, we develop an inexact primal dual hybrid gradient (inexact PDHG) procedure that allows generic gradient computation oracles to update the primal and dual variables. We first investigate the performance of inexact PDHG with stochastic variance reduction gradient (SVRG) oracle. Our numerical study uncovers a significant phenomenon of initial conservative progress of iterates of IPDHG with SVRG oracle. To tackle this, we develop a simple and effective switching idea, where a generalized stochastic gradient (GSG) computation oracle is employed to hasten the iterates' progress to a saddle point solution during the initial phase of updates, followed by a switch to the SVRG oracle at an appropriate juncture. The proposed algorithm is named Decentralized Proximal Switching Stochastic Gradient method with Compression (C-DPSSG), and is proven to converge to an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> -accurate saddle point solution with linear rate. Apart from delivering highly accurate solutions, our study reveals that utilizing the best convergence phases of GSG and SVRG oracles makes C-DPSSG well suited for obtaining solutions of low/medium accuracy faster, useful for certain applications. Numerical experiments on two benchmark machine learning applications show C-DPSSG's competitive performance which validate our theoretical findings. The codes used in the experiments can be found here.
Equivariant Filter for Feature-Based Homography Estimation for General Camera Motion

Recent advances in nonlinear observer design for homography estimation have exploited the Lie group structure of SL(3). Existing work requires the group (homography) velocity input, while the available measurements are typically only the camera velocity. Consequently, prior contributions exploiting the SL(3) geometry either reconstruct the group velocity or restrict the camera motion to allow adaptive estimation of the group velocity online. This paper presents a novel symmetry-based approach to observer design for the more general problem of estimating both the homography and the structure parameters of a planar scene, allowing homography estimation for arbitrary trajectories using only camera velocity measurements and direct point-feature corre-spondences between images. A new Lie group is introduced for the homography and structure parameters, whose symmetry structure is exploited to establish the system and output equivariance properties. We show that the system kinematics admit an equivariant lift, and the proposed observer is then designed based on the recently developed Equivariant Filter framework. Simulation results demonstrate the performance and consistency of the proposed approach.
On First-Order Meta-Reinforcement Learning with Moreau Envelopes

Meta-Reinforcement Learning (MRL) is a promising framework for training agents that can quickly adapt to new environments and tasks. In this work, we study the MRL problem under the policy gradient formulation, where we propose a novel algorithm that uses Moreau envelope surrogate regularizers to jointly learn a meta-policy that is adjustable to the environment of each individual task. Our algorithm, called Moreau Envelope Meta-Reinforcement Learning (MEMRL), learns a meta-policy that can adapt to a distribution of tasks by efficiently updating the policy parameters using a combination of gradient-based optimization and Moreau Envelope regularization. Moreau Envelopes provide a smooth approximation of the policy optimization problem, which enables us to apply standard optimization techniques and converge to an appropriate stationary point. We provide a detailed analysis of the MEMRL algorithm, where we show a sublinear convergence rate to a first-order stationary point for non-convex policy gradient optimization. We finally show the effectiveness of MEMRL on a multi-task 2D-navigation problem.
Localized Privacy Preservation by Innovation Perturbation in a Cooperative LQG Control System

We consider a cooperative Linear Quadratic Gaussian (LQG) control system, in which an individual user owns a local plant whose control inputs are provided by a server. In the cooperation, the user takes the plant states as private information and desires to maximize the privacy preservation while ensuring that the server still provides a certain level of control performance. Moreover, the user requires a privacy scheme that is used locally and is unknown to the server, so that it can create a deviation in the server's knowledge of the states from the true value. To achieve this, we propose two privacy schemes localized at the user side, which inject perturbations in the innovation data sent to the server. For both schemes, firstly, we analyze the privacy preservation quality provided by the scheme and the performance loss in the LQG control caused by it. Secondly, based on the trade-off between them, we propose an optimization problem. Thirdly, we propose a recovery procedure by which the control performance is recovered to the optimal one, i.e., the privacy preservation is achieved without any performance loss in control. Finally, simulations are provided, and we give discussions on the two schemes based on the simulation results.
Scalable Forward Reachability Analysis of Multi-Agent Systems with Neural Network Controllers

Neural networks (NNs) have been shown to learn complex control laws successfully, often with performance advantages or decreased computational cost compared to alternative methods. Neural network controllers (NNCs) are, however, highly sensitive to disturbances and uncertainty, meaning that it can be challenging to make satisfactory robustness guarantees for systems with these controllers. This problem is exacerbated when considering multi-agent NN-controlled systems, as existing reachability methods often scale poorly for large systems. This paper addresses the problem of finding overapproximations of forward reachable sets for discretetime uncertain multi-agent systems with distributed NNC architectures. We first reformulate the dynamics, making the system more amenable to reachablility analysis. Next, we take advantage of the distributed architecture to split the overall reach ability problem into smaller problems, significantly reducing computation time. We use quadratic constraints, along with a convex representation of uncertainty in each agent's model, to form semidefinite programs, the solutions of which give overapproximations of forward reachable sets for each agent. Finally, the methodology is tested on two realistic examples: a platoon of vehicles and a power network system.
A Distributed Stochastic First-Order Method for Strongly Concave-Convex Saddle Point Problems

In this paper, we propose a distributed stochastic first-order method for saddle point problems over strongly connected graphs. Existing methods generally suffer from a steady-state error that arises due to the heterogeneous nature of data distribution (captured by the local versus global cost gaps) and the variance of the stochastic gradients. We propose GT-SGDA, a distributed stochastic gradient descent ascent method that uses network-level gradient tracking to eliminate the steady-state error component due to the local versus global cost gap. We show that GT-SGDA converges linearly to an error ball around the unique saddle point for sufficiently small constant step-sizes when the global cost is strongly concave-convex (a necessary condition for the existence of a unique saddle point). Moreover, we show that the size of this error ball depends on the variance of the stochastic gradients. We provide numerical experiments to illustrate the convergence properties of GT-SGDA for different applications and highlight the significance of gradient tracking. We also show the performance of GT-SGDA for training modern applications like distributed generative adversarial networks (GANs).
Learning Personalized Models with Clustered System Identification

We address the problem of learning linear system models by observing multiple trajectories from systems with differing dynamics. This framework encompasses a collaborative scenario where several systems seeking to estimate their dynamics are partitioned into clusters according to system similarity. Thus, the systems within the same cluster can benefit from the observations made by the others. Considering this framework, we present an algorithm where each system alternately estimates its cluster identity and performs an estimation of its dynamics. This is then aggregated to update the model of each cluster. We show that under mild assumptions, our algorithm correctly estimates the cluster identities and achieves an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> -approximate solution with a sample complexity that scales inversely with the number of systems in the cluster, thus facilitating a more efficient and personalized system identification.
Risk-Sensitive RL Using Sampling-Based Expectation-Maximization

There is a need for robust Reinforcement Learning (RL) algorithms that can cope with model misspecification, parameter uncertainty, disturbances, etc. Risk-sensitive methods offer an approach to developing robust RL algorithms by hedging against undesirable outcomes in a probabilistic manner. The Probabilistic Graphical Model (PGM) framework offers systematic exploration for risk-sensitive RL. In this paper, we bridge the Markov Decision Process (MDP) and the PGM frameworks. We exploit the equivalence of optimizing a certain risk-sensitive criterion in the MDP formalism with optimizing a log-likelihood objective in the PGM formalism. By utilizing this equivalence, we offer an approach for developing risk-sensitive algorithms by leveraging the PGM framework. We explore the Expectation-Maximization (EM) algorithm under the PGM formalism. We show that risk-sensitive policy gradient methods can be obtained by applying sampling-based approaches to the EM algorithm, e.g., Monte-Carlo EM, with the log-likelihood. We show that Monte-Carlo EM leads to a risk-sensitive Monte-Carlo policy gradient algorithm. Our simulations illustrate the risk-sensitive nature of the resulting algorithm.
A Policy Gradient Approach for Finite Horizon Constrained Markov Decision Processes

The infinite horizon setting is widely adopted for problems of reinforcement learning (RL). These invariably result in stationary policies that are optimal. In many situations, finite horizon control problems are of interest and for such problems, the optimal policies are time-varying in general. Another setting that has become popular in recent times is of Constrained Reinforcement Learning, where the agent maximizes its rewards while it also aims to satisfy some given constraint criteria. However, this setting has only been studied in the context of infinite horizon MDPs where stationary policies are optimal. We present an algorithm for constrained RL in the Finite Horizon Setting where the horizon terminates after a fixed (finite) time. We use function approximation in our algorithm which is essential when the state and action spaces are large or continuous and use the policy gradient method to find the optimal policy. The optimal policy that we obtain depends on the stage and so is non-stationary in general. To the best of our knowledge, our paper presents the first policy gradient algorithm for the finite horizon setting with constraints. We show the convergence of our algorithm to a constrained optimal policy. We also compare and analyze the performance of our algorithm through experiments and show that our algorithm performs better than some other well known algorithms.