Reviewerly/exHarmony huggingface dataset:
The look at the various uses of VR

Abstract Virtual, augmented and mixed reality (VR, AR and MR) infiltrated not only gaming, industry, engineering, live events, entertainment, real estate, retail, military, etc., but as surveys indicate, also healthcare and education. In all these areas there is a lack of software development experts for VR, AR and MR to meet the needs of practice. Therefore, our intention at the Department of Computer Science, Faculty of Natural Sciences, Matej Bel University in Banská Bystrica, Slovakia, is to focus on the education and enlightenment of these areas. The aim of this article is to show the role of interactivity in different VR applications and its impact on users in three different areas: gaming, healthcare and education. In the case of one application of Arachnophobia, we also present the results of the research using a questionnaire.
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning

Deep neural network-based systems are now state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness. Small perturbations to sensor inputs (from noise or adversarial examples) are often enough to change network-based decisions, which was recently shown to cause an autonomous vehicle to swerve into another lane. In light of these dangers, numerous algorithms have been developed as defensive mechanisms from these adversarial inputs, some of which provide formal robustness guarantees or certificates. This work leverages research on certified adversarial robustness to develop an online certifiably robust for deep reinforcement learning algorithms. The proposed defense computes guaranteed lower bounds on state-action values during execution to identify and choose a robust action under a worst case deviation in input space due to possible adversaries or noise. Moreover, the resulting policy comes with a certificate of solution quality, even though the true state and optimal action are unknown to the certifier due to the perturbations. The approach is demonstrated on a deep Q-network (DQN) policy and is shown to increase robustness to noise and adversaries in pedestrian collision avoidance scenarios, a classic control task, and Atari Pong. This article extends our prior work with new performance guarantees, extensions to other reinforcement learning algorithms, expanded results aggregated across more scenarios, an extension into scenarios with adversarial behavior, comparisons with a more computationally expensive method, and visualizations that provide intuition about the robustness algorithm.
On the concept of RNPLS&amp;T’s Open Archive

The authors examine the principles and technologies of RNPLS&amp;T’s Open Archive development. Special attention is given to the urgency of the task under the circumstances of digitalization. The main goal is seen as building the Single Open Archive of Information (SOAI) of the Russian National Public Library for Science and Technology that is to accumulate every digital full-text resources designed or acquired legally by the Library. The key principles of SOAI as an integrated information system are specified. The functionalities and protocol table structure for RNPLS&amp;T’s SOAI soft- and hardware are discussed. The projected functional structure of the archive is described and explained. Possible software and hardware solutions for SOAI application and metadata server are discussed along with the interaction with large related information systems like the National Electronic Library (NEL). The system implementation will become the next important stage in building the typical model of e-library as a player in the open archive system. This is to facilitate the technology of data exchange in library information systems, units of science information within research organizations, universities, general educational institutions, and to support information retrieval via the single access point at web-portals. The article is prepared within the framework of the State Order № 075-01300-20-00 “Design and development of RNPLS&amp;T’s Open Archive of Integrated Information Library Resources as a modern system of knowledge management within the digital environment: On the way to Open Science” for the years 2020–2022.
Improved Learning of Robot Manipulation Tasks Via Tactile Intrinsic Motivation

In this letter we address the challenge of exploration in deep reinforcement learning for robotic manipulation tasks. In sparse goal settings, an agent does not receive any positive feedback until randomly achieving the goal, which becomes infeasible for longer control sequences. Inspired by touch-based exploration observed in children, we formulate an intrinsic reward based on the sum of forces between a robot's force sensors and manipulation objects that encourages physical interaction. Furthermore, we introduce contact-prioritized experience replay, a sampling scheme that prioritizes contact rich episodes and transitions. We show that our solution accelerates the exploration and outperforms state-of-the-art methods on three fundamental robot manipulation benchmarks.
Investigating the importance of shape features, color constancy, color spaces, and similarity measures in open-ended 3D object recognition

Abstract Despite the recent success of state-of-the-art 3D object recognition approaches, service robots still frequently fail to recognize many objects in real human-centric environments. For these robots, object recognition is a challenging task due to the high demand for accurate and real-time response under changing and unpredictable environmental conditions. Most of the recent approaches use either the shape information only and ignore the role of color information or vice versa. Furthermore, they mainly utilize the $$L_n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>L</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:math> Minkowski family functions to measure the similarity of two object views, while there are various distance measures that are applicable to compare two object views. In this paper, we explore the importance of shape information, color constancy, color spaces, and various similarity measures in open-ended 3D object recognition. Toward this goal, we extensively evaluate the performance of object recognition approaches in three different configurations, including color-only , shape-only , and combinations of color and shape , in both offline and online settings. Experimental results concerning scalability, memory usage, and object recognition performance show that all of the combinations of color and shape yield significant improvements over the shape-only and color-only approaches. The underlying reason is that color information is an important feature to distinguish objects that have very similar geometric properties with different colors and vice versa. Moreover, by combining color and shape information, we demonstrate that the robot can learn new object categories from very few training examples in a real-world setting.
A CRF-Based Framework for Tracklet Inactivation in Online Multi-Object Tracking

Online multi-object tracking (MOT) is an active research topic in the domain of computer vision. Although many previously proposed algorithms have exhibited decent results, the issue of tracklet inactivation has not been sufficiently studied. Simple strategies such as using a fixed threshold on classification scores are adopted, yielding undesirable tracking mistakes and limiting the overall performance. In this paper, a conditional random field (CRF) based framework is put forward to tackle the tracklet inactivation issue in online MOT problems. A discrete CRF which exploits the intra-frame relationship between tracking hypotheses is developed to improve the robustness of tracklet inactivation. Separate sets of feature functions are designed for the unary and binary terms in the CRF, which take into account various tracking challenges in practical scenarios. To handle the problem of varying CRF nodes in the MOT context, two strategies named as hypothesis filtering and dummy nodes are employed. In the proposed framework, the inference stage is conducted by using the loopy belief propagation algorithm, and the CRF parameters are determined by utilizing the maximum likelihood estimation method followed by slight manual adjustment. Experimental results show that the tracker combined with the CRF-based framework outperforms the baseline on the MOT16 and MOT17 benchmarks. The extensibility of the proposed framework is further validated by an extensive experiment.
Decentralized Video Input Authentication as an Edge Service for Smart Cities

Situational awareness is essential for a safe and sustainable urban environment. While the wide deployment of Internet of Video Things (IoVT) enables efficient monitoring of the footage of the Smart Cities, it also attracts attackers and abusers. Disinformation injected into the IoVT can mislead the city administrations, policy makers, and emergency responders, and lead to disastrous consequences. It is very challenging to timely authenticate each video stream from pervasively deployed IoVT devices. This article presents a Blockchain Enhanced Video input Authentication (BEVA) scheme as an edge service to fight against the visual layer attacks on smart IoVT systems such as online false video injection and offline video streams tampering attacks. The BEVA scheme leverages the electrical network frequency (ENF) embedded in video recordings as an environmental fingerprint for online false frame detection. Blockchain is integrated to enable a decentralized security networking infrastructure, and it empowers an immutable, traceable, and auditable distributed ledger for ENF-based authentication scheme without relying on a third-party trust authority. This secure-by-design solution efficiently provides trust and secure services in IoVT in modern Smart Cities.
VR based Power Wheelchair Simulator: Usability Evaluation through a Clinically Validated Task with Regular Users

Power wheelchairs are one of the main solutions for people with reduced mobility to maintain or regain autonomy and a comfortable and fulfilling life. However, driving a power wheelchair in a safe way is a difficult task that often requires training methods based on real-life situations. Although these methods are widely used in occupational therapy, they are often too complex to implement and unsuitable for some people with major difficulties. In this context, we collaborated with clinicians to develop a Virtual Reality based power wheelchair simulator. This simulator is an innovative training tool adapted to any type of situations and impairments. In this paper, we present a clinical study in which 29 power wheelchair regular users were asked to complete a clinically validated task designed by clinicians within two conditions: driving in a virtual environment with our simulator and driving in real conditions with a real power wheelchair. The objective of this study is to compare performances between the two conditions and to evaluate the Quality of Experience provided by our simulator in terms of Sense of Presence and Cybersickness. Results show that participants complete the tasks in a similar amount of time for both real and virtual conditions, using respectively a real power wheelchair and our simulator. Results also show that our simulator provides a high level of Sense of Presence and provokes only slight to moderate Cybersickness discomforts resulting in a valuable Quality of Experience.
A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.
Multi-Party Quantum Private Comparison Based on Entanglement Swapping of Bell Entangled States within d-Level Quantum System

In this paper, a multi-party quantum private comparison (MQPC) scheme is suggested based on entanglement swapping of Bell entangled states within d-level quantum system, which can accomplish the equality comparison of secret binary sequences from n users via one execution of scheme. Detailed security analysis shows that both the outside attack and the participant attack are ineffective. The suggested scheme needn't establish a private key among n users beforehand through the quantum key distribution (QKD) method to encrypt the secret binary sequences. Compared with previous MQPC scheme based on d-level Cat states and d-level Bell entangled states, the suggested scheme has distinct advantages on quantum resource, quantum measurement of third party (TP) and qubit efficiency.
Hebbian semi-supervised learning in a sample efficiency setting

We propose to address the issue of sample efficiency, in Deep Convolutional Neural Networks (DCNN), with a semi-supervised training strategy that combines Hebbian learning with gradient descent: all internal layers (both convolutional and fully connected) are pre-trained using an unsupervised approach based on Hebbian learning, and the last fully connected layer (the classification layer) is trained using Stochastic Gradient Descent (SGD). In fact, as Hebbian learning is an unsupervised learning method, its potential lies in the possibility of training the internal layers of a DCNN without labels. Only the final fully connected layer has to be trained with labeled examples. We performed experiments on various object recognition datasets, in different regimes of sample efficiency, comparing our semi-supervised (Hebbian for internal layers + SGD for the final fully connected layer) approach with end-to-end supervised backprop training, and with semi-supervised learning based on Variational Auto-Encoder (VAE). The results show that, in regimes where the number of available labeled samples is low, our semi-supervised approach outperforms the other approaches in almost all the cases.
Securing Pharmaceutical Supply Chain using Blockchain Technology

The production and distribution of counterfeit drugs is an urgent and increasingly critical worldwide issue, especially in pandemics. The imperfect supply chain system in the pharmaceutical industry is one of the reasons for drug counterfeiting. Drugs ownership changes from manufacturers to wholesaler, distributor, and then pharmacist before it reaches the customer thus making it difficult to keep track of it. In this paper, we have compared the existing proposed architectures of blockchain and IoT based supply chain management systems. The system implemented using hyper ledger fabric ensures sharing, storing, transparency, and traceability of data in each link of the supply chain. On the other hand, Ethereum architecture utilized the features of smart contracts to manage the interactions between sender and receiver. Finally, the study mainly focuses on increasing the safety of pharmaceutical products and reducing the manual operation of the supply chain with the most efficient architecture.
New Concept of Engineering Education for Sustainable Development of Society

The need to modernize the engineering education in the context of the transition to a digital economy has been substantiated. The drawbacks in the existing system of training modern engineers have been updated. The contradiction between the quality of training of engineers and the requirements of employers is revealed. Requirements for a modern engineer have been formulated. It is shown that a new model of engineering training is required to train an engineer of a new type that meets the modern conditions of sustainable development. Innovative engineering activity means the creation of technical facilities for a world free of hunger and epidemics, with open access to fresh water and information, to education for all, and to equality and sustainable development. It should be based on two guiding principles: competence to implement a solution and responsibility, to be aware of the consequences of this solution for the problem itself, as well as for the social, ecological and economic environment. On the basis of the revealed differences between the natural cognitive process and the structure of the existing engineering training, the concept of the natural structure of engineering training is formulated. Its main idea is that the educational process is an activity to develop the initial level of knowledge, skills and abilities of the student to the level necessary and sufficient for the implementation of a certain type of activity in technology. The structure of the educational process as an activity should coincide with the generalized structure of activity. Successful approbation of this concept on the example of continuous geometric-graphic training of students of a technical university allows us to hope that its implementation will help to form a new type of engineer.
Automated issue assignment: results and insights from an industrial case

We automate the process of assigning issue reports to development teams by using data mining approaches and share our experience gained by deploying the resulting system, called IssueTAG, at Softtech. Being a subsidiary of the largest private bank in Turkey, Softtech on average receives 350 issue reports daily from the field, which need to be handled with utmost importance and urgency. IssueTAG has been making all the issue assignments at Softtech since its deployment on Jan 12, 2018. Deploying IssueTAG presented us not only with an unprecedented opportunity to observe the practical effects of automated issue assignment, but also with an opportunity to carry out user studies, both of which (to the best of our knowledge) have not been done before in this context. We first empirically determine the data mining approach to be used in IssueTAG. We then deploy IssueTAG and make a number of valuable observations. First, it is not just about deploying a system for automated issue assignment, but also about designing/changing the assignment process around the system. Second, the accuracy of the assignments does not have to be higher than that of manual assignments in order for the system to be useful. Third, deploying such a system requires the development of additional functionalities, such as creating human-readable explanations for the assignments and detecting deteriorations in assignment accuracies, for both of which we have developed and empirically evaluated different approaches. Last but not least, stakeholders do not necessarily resist change and gradual transition helps build confidence.
Feedback-Based Quantum Optimization

It is hoped that quantum computers will offer advantages over classical computers for combinatorial optimization. Here, we introduce a feedback-based strategy for quantum optimization, where the results of qubit measurements are used to constructively assign values to quantum circuit parameters. We show that this procedure results in an estimate of the combinatorial optimization problem solution that improves monotonically with the depth of the quantum circuit. Importantly, the measurement-based feedback enables approximate solutions to the combinatorial optimization problem without the need for any classical optimization effort, as would be required for the quantum approximate optimization algorithm (QAOA). We experimentally demonstrate this feedback-based protocol on a superconducting quantum processor for the graph-partitioning problem MaxCut, and present a series of numerical analyses that further investigate the protocol's performance.
Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation

We present Success weighted by Completion Time (SCT), a new metric for evaluating navigation performance for mobile robots. Several related works on navigation have used Success weighted by Path Length (SPL) as the primary method of evaluating the path an agent makes to a goal location, but SPL is limited in its ability to properly evaluate agents with complex dynamics. In contrast, SCT explicitly takes the agent’s dynamics model into consideration, and aims to accurately capture how well the agent has approximated the fastest navigation behavior afforded by its dynamics. While several embodied navigation works use point-turn dynamics, we focus on unicycle-cart dynamics for our agent, which better exempli-fies the dynamics model of popular mobile robotics platforms (e.g., LoCoBot, TurtleBot, Fetch, etc.). We also present RRT*-Unicycle, an algorithm for unicycle dynamics that estimates the fastest collision-free path and completion time from a starting pose to a goal location in an environment containing obstacles. We experiment with deep reinforcement learning and reward shaping to train and compare the navigation performance of agents with different dynamics models. In evaluating these agents, we show that in contrast to SPL, SCT is able to capture the advantages in navigation speed a unicycle model has over a simpler point-turn model of dynamics. Lastly, we show that we can successfully deploy our trained models and algorithms outside of simulation in the real world. We embody our agents in a real robot to navigate an apartment, and show that they can generalize in a zero-shot manner. A video summary is available here: https://youtu.be/QOQ56XVIYVE
The Development and Construction of Bilingual Machine Translation Auxiliary Tool between Chinese and Kazakh Languages

Abstract This paper introduces the design and construction process of a bilingual machine translation auxiliary tool between Chinese and Kazakh languages. The tool uses the Jieba word segmentation tool to segment the input sentence, and then translates it according to the two language correspondence and rule-based dictionary method. This paper introduces the development and implementation process and overall framework of said auxiliary tool, and the role of some functional modules of it.