Reviewerly/exHarmony huggingface dataset:
Rethinking Graph Transformers with Spectral Attention

In recent years, the Transformer architecture has proven to be very successful in sequence processing, but its application to other data structures, such as graphs, has remained limited due to the difficulty of properly defining positions. Here, we present the $\textit{Spectral Attention Network}$ (SAN), which uses a learned positional encoding (LPE) that can take advantage of the full Laplacian spectrum to learn the position of each node in a given graph. This LPE is then added to the node features of the graph and passed to a fully-connected Transformer. By leveraging the full spectrum of the Laplacian, our model is theoretically powerful in distinguishing graphs, and can better detect similar sub-structures from their resonance. Further, by fully connecting the graph, the Transformer does not suffer from over-squashing, an information bottleneck of most GNNs, and enables better modeling of physical phenomenons such as heat transfer and electric interaction. When tested empirically on a set of 4 standard datasets, our model performs on par or better than state-of-the-art GNNs, and outperforms any attention-based model by a wide margin, becoming the first fully-connected architecture to perform well on graph benchmarks.
Real-time gesture control UAV with a low resource framework

This study showcases a low-resource framework that enables people with no technical know-how to interact with drones, it also explores the capabilities of 2D- computer vision and deep learning techniques for gesture based interface systems on a low-cost micro drone with an onboard RGB camera. This Human-Robot Interaction system processes the real-time human pose to allow a user to command the drone, i.e., by providing direction to move and execute actions. A linear PD controller and image processing techniques are implemented to track humans whilst maintaining a safe distance from the user by perceiving depth information through pose estimation. We incorporated the gesture recognition results into a drone using the Robot Operating System (ROS) and evaluated system performance indoor and outdoor. This low computation framework can be applied further to control robotic arms or mobile robots.
Offline Reinforcement Learning as One Big Sequence Modeling Problem

Reinforcement learning (RL) is typically concerned with estimating stationary policies or single-step models, leveraging the Markov property to factorize problems in time. However, we can also view RL as a generic sequence modeling problem, with the goal being to produce a sequence of actions that leads to a sequence of high rewards. Viewed in this way, it is tempting to consider whether high-capacity sequence prediction models that work well in other domains, such as natural-language processing, can also provide effective solutions to the RL problem. To this end, we explore how RL can be tackled with the tools of sequence modeling, using a Transformer architecture to model distributions over trajectories and repurposing beam search as a planning algorithm. Framing RL as sequence modeling problem simplifies a range of design decisions, allowing us to dispense with many of the components common in offline RL algorithms. We demonstrate the flexibility of this approach across long-horizon dynamics prediction, imitation learning, goal-conditioned RL, and offline RL. Further, we show that this approach can be combined with existing model-free algorithms to yield a state-of-the-art planner in sparse-reward, long-horizon tasks.
An Analysis on Novel Corona Virus by a Plithogenic Approach to Fuzzy Cognitive Map

In this paper a Plithogenic approach to Fuzzy cognitive map has been proposed to analyse the impact of novel corona virus.Contradiction degree is an advantageous feature of Plithogenic sets which highly deals with the uncertainty and it substantially increases the accuracy of results. In this research, a new approach is proposed to accumulate the opinions of experts.Using contradiction degree between opinions of experts and plithogenicoperator,the connection matrices obtained from distinct experts are aggregated to improvise a degree of uncertainty. An Analysis on covid-19 (causes, spread and precaution) is done based on the proposed methodology.
Offline RL Without Off-Policy Evaluation

Most prior approaches to offline reinforcement learning (RL) have taken an iterative actor-critic approach involving off-policy evaluation. In this paper we show that simply doing one step of constrained/regularized policy improvement using an on-policy Q estimate of the behavior policy performs surprisingly well. This one-step algorithm beats the previously reported results of iterative algorithms on a large portion of the D4RL benchmark. The simple one-step baseline achieves this strong performance without many of the tricks used by previously proposed iterative algorithms and is more robust to hyperparameters. We argue that the relatively poor performance of iterative approaches is a result of the high variance inherent in doing off-policy evaluation and magnified by the repeated optimization of policies against those high-variance estimates. In addition, we hypothesize that the strong performance of the one-step algorithm is due to a combination of favorable structure in the environment and behavior policy.
Chapter 8. Data-driven learning

This chapter investigates the effect of data-driven learning (DDL) by comparing the use of a new corpus tool, #LancsBox, with the use of a corpus-based collocations dictionary for academic collocation learning. Learners in the study improved their collocational knowledge by using #LancsBox, although not significantly. No improvement was found through consulting the collocations dictionary. The majority of learners believed that direct corpus consultation using concordance lines and collocation graphs facilitated their collocation learning, whereas learners in the dictionary group considered the use of a dictionary to be more beneficial for writing than learning collocations. Interestingly, learners from both groups reported that they would continue using the assigned tools in future language learning and teaching.
Automatic Data Augmentation for Generalization in Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize beyond their training environments. To alleviate this problem, recent work has proposed the use of data augmentation. However, different tasks tend to benefit from different types of augmentations and selecting the right one typically requires expert knowledge. In this paper, we introduce three approaches for automatically finding an effective augmentation for any RL task. These are combined with two novel regularization terms for the policy and value function, required to make the use of data augmentation theoretically sound for actor-critic algorithms. We evaluate our method on the Procgen benchmark which consists of 16 procedurally generated environments and show that it improves test performance by 40% relative to standard RL algorithms. Our approach also outperforms methods specifically designed to improve generalization in RL, thus setting a new state-of-the-art on Procgen. In addition, our agent learns policies and representations which are more robust to changes in the environment that are irrelevant for solving the task, such as the background.
Camera calibration method using synthetic speckle pattern with an improved gray wolf optimizer algorithm

Camera calibration is essential for various vision-based 3D metrological techniques. In this paper, a novel camera calibration method, to the best of our knowledge, combining synthetic speckle pattern and an improved gray wolf optimizer algorithm is presented. The synthetic speckle pattern serves as the calibration target. The particle swarm algorithm-based digital image correlation is employed to achieve matches among 3D control points and 2D image points; then the improved gray wolf optimizer algorithm is used to calculate the camera parameters. For verification, simulated and real tests are conducted. Through the analysis of calibration results, the proposed method performs better and is more stable than other calibration targets. Research on the influence of camera pose and optimization algorithm is conducted, showing that the improved gray wolf optimizer algorithm performs better than other benchmark algorithms. The camera parameters can be obtained through one captured image when the speckle patterns are added in the portion of the camera sensor.
Early-stopped neural networks are consistent

This work studies the behavior of neural networks trained with the logisticloss via gradient descent on binary classification data where the underlyingdata distribution is general, and the (optimal) Bayes risk is not necessarilyzero. In this setting, it is shown that gradient descent with early stoppingachieves population risk arbitrarily close to optimal in terms of not justlogistic and misclassification losses, but also in terms of calibration,meaning the sigmoid mapping of its outputs approximates the true underlyingconditional distribution arbitrarily finely. Moreover, the necessary iteration,sample, and architectural complexities of this analysis all scale naturallywith a certain complexity measure of the true conditional model. Lastly, whileit is not shown that early stopping is necessary, it is shown that anyunivariate classifier satisfying a local interpolation property is necessarilyinconsistent.
Ghost Engineering: Technologies for Designing Ourselves via Embodied Interaction

Recent embodied cognition studies revealed that our mind can be influenced by the state and recognition of our bodies. Meanwhile, recent developments in virtual reality and augmented human technologies enable us to flexibly modify and extend our body itself and recognition of our bodily states. Considering these together, our mind would be designed by utilizing these technologies which augment our body or body perception. In other words, we can design ourselves through the design of the virtual body and embodied interaction we use in cyber-physical world to become the person we want to be. Here, in reference to the fact that the relationship between the body and the mind has been discussed in many works including M. Shirow's "GHOST IN THE SHELL" since the presentation of G. Ryle's concept of "Ghost in the machine," the function of the human mind that arises from the body is defined as "ghost", and the technology to create the desired change in our mind by changing our virtual body is called as "Ghost Engineering". There are several patterns in the way our body is made possible by technology for the first time, and what kind of mind function changes depends on these patterns. For example, "Transformation", the experience of becoming someone else, or being able to demonstrate abilities beyond one's own, can change one's self-image and influence behavior, abilities, and even thinking. The "Body multiplexing" experience of manipulating multiple bodies not only expands our physical abilities, but also affects our ability to balance our thoughts and changes the way we communicate with multiple people. Merged body, in which multiple people manipulate a single body, not only enables a new kind of cooperative work, but also enables the transfer of motor intentions and physical skills from human to human. I would like to introduce recent achievements and future implications of Ghost Engineering technology, and discuss what kind of possibilities for the self and society that such technology enables.
Multi-camera object tracking system

The article proposes a multi-camera tracking system for an object, implemented using computer vision technologies and allowing the video surveillance operator in real time to select an object that will be monitored by the system in future. It will be ready to give out the location of the object at any time. The solution to this problem is divided into three main stages: the detection stage, the tracking stage and the stage of interaction of several cameras. Methods of detection, tracking of objects and the interaction of several cameras have been investigated. To solve the problem of detection, the method of optical flow and the method of removing the background were investigated, to solve the problem of tracking — the method of matching key points and the correlation method, to solve the problem of interaction between several surveillance cameras — the method of the topological graph of a network of cameras. An approach is proposed for constructing a system that uses a combination of the background removal method, the correlation method and the method of the topological graph of a network of cameras. The stages of detection and tracking have been experimentally implemented, that is, the task of tracking an object within the coverage area of one video camera has been solved. The implemented system showed good results: a sufficiently high speed and accuracy with rare losses of the tracked object and with a slight decrease in the frame rate.
Information access and preventive measures during COVID-19 among migrants and general population

Abstract Background Access to information and adherence to preventive measures are crucial for containing the COVID-19 outbreak. This study examines access to information and adherence to preventive measures during the COVID-19 pandemic among migrant origin persons and the general population. Methods Data from the Finnish MigCOVID Survey conducted 10/2020 to 2/2021 among persons aged 20 to 66 years was used (n = 3 668). Participants in Health 2017 follow-up survey were the reference group (n = 3 490). We examined age and sex-adjusted access to information by language skills and adherence to preventive measures (staying home if fluish; keeping a safety distance; wearing a face mask; good hand and coughing hygiene; avoiding traveling abroad; avoiding shaking hands with others; downloading the Koronavilkku contact tracing app). Results Persons with excellent Finnish/Swedish language skills (96.8%; 95% CI 95.0-97.9) reported receiving sufficient information more often those with intermediate (92.3; 95% CI 88.8-94.8) or basic (91.0%; 95% CI 88.0-93.3) language skills. Overall high adherence to preventive measures was observed both among the migrant origin and the general population. However, significant differences in the migrant origin population by region of origin were observed. More pronounced differences between migrant origin persons and general population persons were observed for avoiding traveling abroad (83.7%; 95% CI 81.1-86.0 vs. 96.6%; 95% CI 95.6-97.3) and downloading the Koronavilkku app (42.8%; 95% CI 39.8-45.9 vs. 64.3%; 95% CI 61.7-66.8). Conclusions Poorer perceived access to information by language skills points to the continuous need for use of simple language and multilingual communication materials. Self-reported adherence to key preventive measures campaigned by health authorities was generally high both among persons of migrant origin and the general population, although concerns were noted in adherence to these recommendation in some regional groups. Key messages Multilingual and multichannel communication is central in crisis situations to ensure adequate access to information among different population groups. Higher incidence of COVID-19 among migrant origin persons is likely related not only to information access and adherence to preventive measures, but also to various social and structural factors.
Towards Robust and Reliable Algorithmic Recourse

As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan approvals), there has been growing interest in post hoc techniques which provide recourse to affected individuals. These techniques generate recourses under the assumption that the underlying predictive model does not change. However, in practice, models are often regularly updated for a variety of reasons (e.g., dataset shifts), thereby rendering previously prescribed recourses ineffective. To address this problem, we propose a novel framework, RObust Algorithmic Recourse (ROAR), that leverages adversarial training for finding recourses that are robust to model shifts. To the best of our knowledge, this work proposes the first solution to this critical problem. We also carry out detailed theoretical analysis which underscores the importance of constructing recourses that are robust to model shifts: 1) we derive a lower bound on the probability of invalidation of recourses generated by existing approaches which are not robust to model shifts. 2) we prove that the additional cost incurred due to the robust recourses output by our framework is bounded. Experimental evaluation on multiple synthetic and real-world datasets demonstrates the efficacy of the proposed framework and supports our theoretical findings.
Controllable and Compositional Generation with Latent-Space Energy-Based Models

Controllable generation is one of the key requirements for successful adoption of deep generative models in real-world applications, but it still remains as a great challenge. In particular, the compositional ability to generate novel concept combinations is out of reach for most current models. In this work, we use energy-based models (EBMs) to handle compositional generation over a set of attributes. To make them scalable to high-resolution image generation, we introduce an EBM in the latent space of a pre-trained generative model such as StyleGAN. We propose a novel EBM formulation representing the joint distribution of data and attributes together, and we show how sampling from it is formulated as solving an ordinary differential equation (ODE). Given a pre-trained generator, all we need for controllable generation is to train an attribute classifier. Sampling with ODEs is done efficiently in the latent space and is robust to hyperparameters. Thus, our method is simple, fast to train, and efficient to sample. Experimental results show that our method outperforms the state-of-the-art in both conditional sampling and sequential editing. In compositional generation, our method excels at zero-shot generation of unseen attribute combinations. Also, by composing energy functions with logical operators, this work is the first to achieve such compositionality in generating photo-realistic images of resolution 1024x1024.
Optimal Path Search for Robot Manipulator using Deep Reinforcement Learning

Much research using deep reinforcement learning has been conducted to make robot manipulators perform certain tasks with little prior knowledge of the work environment. However, due to the high-dimension continuous action-state spaces that a robot manipulator must work in, it is difficult for deep reinforcement learning agents to find or even learn optimal policies. In this paper, we present a method for generating optimal paths for the end effector of a robot manipulator to move through. This is achieved by using images of the workspace that allow us to avoid the problems of high dimensionality while the system learns pick-and-place tasks using deep reinforcement learning. The robot simulator Webots was used to implement the robot model in a virtual work environment. Simulations confirmed that the robot manipulator is able to successfully move target objects from their starting points to their destinations while avoiding moving obstacles in real time, even if the size, position, and number of obstacles are varied.
A High Performance Deep Learning Load Classification Method for the Massive Class Imbalanced Load Data

High performance analysis and processing technology of load data is an important basic of situation awareness technology in the context of power Internet of things. Under this background, a high performance deep learning load classification method for the massive class imbalanced load data is presented. Firstly, the mogrifier long short-term memory (Mogrifier-LSTM) network is presented as the basic classification algorithm to solve the deficiency of information interaction between input layer and hidden layer in traditional LSTM. In addition, attention mechanism and bidirectional feature fusion mechanism are introduced to further improve the ability of learning the association characteristics of time series data. Secondly, the Spark distributed computing framework is employed to realize the parallel processing of massive load data, which can improve the efficiency of classification task. And then, ensemble learning is introduced to improve the classification accuracy of the parallel processing. At last, the ADASYN method is employed to balance the load data, which can solve the potential class imbalance issue. The experiments show that the presented load classification method has advantages in terms of classification accuracy and efficiency.
Vernam Encryption and Steganography of a Number of Images in the Digital Video

Due to the need to protect the information sent over the Internet from being violated by unauthorized persons, many security solutions have appeared to overcome the problems of data leakage. From these solutions is encryption, but once the data is decoded, it will be easy to violate, so to increase the strength of protection, many have appeared. Masking algorithms and methods that extend to digital media copyright protection. In this research, we propose to encrypt and hide a group of images, where four images containing a biography were proposed by securely transferring them within a video of the receiving party. Where the protection process goes through two phases: the encryption phase using the development of the Vernam encryption algorithm, where each image is encrypted using a cut-out image from the video, which is the carrier of confidential data, and the encryption process produces three gray images, as this data is hidden using the least important bit method by masking The data is in random clips inside the video, depending on a secret key between the sender and the receiver, then the same secret key is used in the process of encrypting the video as well, which was used as a cover not to hide the four encrypted images of the CV.
Reward is enough for convex MDPs

Maximising a cumulative reward function that is Markov and stationary, i.e., defined over state-action pairs and independent of time, is sufficient to capture many kinds of goals in a Markov Decision Process (MDP) based on the Reinforcement Learning (RL) problem formulation. However, not all goals can be captured in this manner. Specifically, it is easy to see that Convex MDPs in which goals are expressed as convex functions of stationary distributions cannot, in general, be formulated in this manner. In this paper, we reformulate the convex MDP problem as a min-max game between the policy and cost (negative reward) players using Fenchel duality and propose a meta-algorithm for solving it. We show that the average of the policies produced by an RL agent that maximizes the non-stationary reward produced by the cost player converges to an optimal solution to the convex MDP. Finally, we show that the meta-algorithm unifies several disparate branches of reinforcement learning algorithms in the literature, such as apprenticeship learning, variational intrinsic control, constrained MDPs, and pure exploration into a single framework.
Inverse-Sybil Attacks in Automated Contact Tracing

Automated contract tracing aims at supporting manual contact tracing during pandemics by alerting users of encounters with infected people. There are currently many proposals for protocols (like the “decentralized” DP-3T and PACT or the “centralized” ROBERT and DESIRE) to be run on mobile phones, where the basic idea is to regularly broadcast (using low energy Bluetooth) some values, and at the same time store (a function of) incoming messages broadcasted by users in their proximity. In the existing proposals one can trigger false positives on a massive scale by an “inverse-Sybil” attack, where a large number of devices (malicious users or hacked phones) pretend to be the same user, such that later, just a single person needs to be diagnosed (and allowed to upload) to trigger an alert for all users who were in proximity to any of this large group of devices.