Reviewerly/exHarmony huggingface dataset:
Digital Solutions in Educators’ Training: Concept for Implementing a Virtual Reality Simulator

Over the past decade virtual reality technologies have been actively introduced into all spheres of life, affecting, among other things, the education sector. The purpose of this study is to develop a concept introducing a virtual reality simulator into educators’ training. The underlying principle of the study is to design a virtual reality environment that, firstly, facilitates the development of conflict resolution competence for the students of pedagogical faculties, and secondly, facilitates its performance evaluation. In this article, approaches to design such a virtual reality simulator are discussed. The purpose of the simulator is to develop conflict resolution competences that are indispensable to future teachers. Besides the current study presents a set of situational cases, that are possible to resolve in a virtual reality environment. The resolution of those cases will help future teachers to prevent conflicts as well as to determine their behaviour in pre-conflict and conflict situations. The components of conflict resolution competence that are possible to develop with the help of the designed virtual reality simulator have been defined. The conducted study has also identified approaches to evaluate the effectiveness of conflict resolution competence development.
Temporal action detection based on two-stream You Only Look Once network for elderly care service robot

Human action segmentation and recognition from the continuous untrimmed sensor data stream is a challenging issue known as temporal action detection. This article provides a two-stream You Only Look Once-based network method, which fuses video and skeleton streams captured by a Kinect sensor, and our data encoding method is used to turn the spatiotemporal temporal action detection into a one-dimensional object detection problem in constantly augmented feature space. The proposed approach extracts spatial–temporal three-dimensional convolutional neural network features from video stream and view-invariant features from skeleton stream, respectively. Furthermore, these two streams are encoded into three-dimensional feature spaces, which are represented as red, green, and blue images for subsequent network input. We proposed the two-stream You Only Look Once-based networks which are capable of fusing video and skeleton information by using the processing pipeline to provide two fusion strategies, boxes-fusion or layers-fusion. We test the temporal action detection performance of two-stream You Only Look Once network based on our data set High-Speed Interplanetary Tug/Cocoon Vehicles-v1, which contains seven activities in the home environment and achieve a particularly high mean average precision. We also test our model on the public data set PKU-MMD that contains 51 activities, and our method also has a good performance on this data set. To prove that our method can work efficiently on robots, we transplanted it to the robotic platform and an online fall down detection experiment.
Information and Communications Technology in the Professional Training of Future Professionals in the Field of Culture and Art

The article deals with the self-education of future specialists in the field of culture and art within the context of philosophical, psychological, and pedagogical studies of the postmodern era. This substantiates the need to use e-learning in professional training. The use of cloud computing technologies is one of the educational process’ innovations. As shown by our research and personal experience implementing cloud computing technologies into the educational process proves to be feasible for training future professionals in the field of culture and art, including their self-education. The article shows that the aim of self-education of the future professionals in the field of culture and art is personal and professional perfection, and also achievement, at the maximum possible level, of the creative ideal. Today their professional training is focused on self-perfection, ability to self-educate, skill to foresee the consequences of their professional activity and search for the new ways to solve their professional tasks using ICT. The article justifies that cloud computing technologies allow spreading content in the Internet, they facilitate posting the text messages, photographs, pictures and musical files. Accessibility, openness, interactivity of the cloud computing technologies cause their wide use for both on-line and off-line communication, data sharing of text, multimedia and audio resources for all participants of the educational process that enriches the self-education of future professionals in the field of culture and art and makes it rich in content, practically-oriented and enhances professional competence. The article concludes that e-learning best meets methodological requirements of the postmodern society.
Satellite Orbit Prediction Using Big Data and Soft Computing Techniques to Avoid Space Collisions

The number of satellites and debris in space is dangerously increasing through the years. For that reason, it is mandatory to design techniques to approach the position of a given object at a given time. In this paper, we present a system to do so based on a database of satellite positions according to their coordinates (x,y,z) for one month. We have paid special emphasis on the preliminary stage of data arrangement, since if we do not have consistent data, the results we will obtain will be useless, so the first stage of this work is a full study of the information gathered locating the missing gaps of data and covering them with a prediction. With that information, we are able to calculate an orbit error which will estimate the position of a satellite in time, even when the information is not accurate, by means of prediction of the satellite’s position. The comparison of two satellites over 26 days will serve to highlight the importance of the accuracy in the data, provoking in some cases an estimated error of 4% if the data are not well measured.
Secure and Authenticated Data Access and Sharing Model for Smart Wearable Systems

Contrary to the public cloud storage services that impose users to accept the security restrictions delivered by the service provider, users in the private cloud benefit from self-managed, authenticated data access services. However, this may lead to security issues. A critical challenge is the provision of secure and authenticated data storage for the data owner. Moreover, the data owner should be able to access the stored data and share it with others in a controlled manner. In this article, a secure and authenticated data storage, access, and sharing model is proposed for private cloud storage, which has three components. The data storage component provides the user with secure storage of information. The data-sharing component enables sharing the stored data under the control of the data owner. The data access component enables authenticated access to the cloud storage. The security analysis demonstrates that the model is secure against various attacks. The scheme is validated to be secure via the Scyther tool, BAN Logic, and in Random Oracle Model. The performance analysis regarding the computation and communication cost via simulation in OMNeT++ show that it obtains the required security goals and efficiency of computation and communication, compared to the related methods.
Quantum Secure Lightweight Cryptography with Quantum Permutation Pad

Quantum logic gates represent certain quantum operations to perform quantum computations.Of those quantum gates, there is a category of classical behavior gates called quantum permutation gates.As a quantum algorithm, quantum permutation pad or QPP consists of multiple quantum permutation gates to be implemented both in a quantum computing system as a quantum circuit operating on n-qubits' states for transformations and in a classical computing system represented by a pad of n-bit permutation matrices.Since first time proposed in 2020, QPP has been recently applied to create a quantum safe lightweight block cipher by replacing SubBytes and AddRoundKey with QPP in AES called AES-QPP.In AES-QPP, QPP consists of 16 selected 8-bit permutation matrices based on the shared classical key materials.For quantum safe, the key length can be any size from 256 bits to 4 KB.That means, this QPP holds up to 4 KB of Shannon information entropy.Its code size is less than 2 KB with 4 KB of RAM memory.In this paper, we propose to apply QPP for a streaming cipher and carry out its encryption performance and the randomness analysis of this streaming cipher.The proposed QPP streaming cipher demonstrates not only good randomness in its ciphertexts but also huge performance improvement: 13x faster than AES-256, with an overall runtime space (6.8 KB).
GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation

Practical dialogue systems require robust methods of detecting out-of-scope (OOS) utterances to avoid conversational breakdowns and related failure modes.Directly training a model with labeled OOS examples yields reasonable performance, but obtaining such data is a resource-intensive process.To tackle this limited-data problem, previous methods focus on better modeling the distribution of in-scope (INS) examples.We introduce GOLD as an orthogonal technique that augments existing data to train better OOS detectors operating in low-data regimes.GOLD generates pseudo-labeled candidates using samples from an auxiliary dataset and keeps only the most beneficial candidates for training through a novel filtering mechanism.In experiments across three target benchmarks, the top GOLD model outperforms all existing methods on all key metrics, achieving relative gains of 52.4%, 48.9% and 50.3% against median baseline performance.We also analyze the unique properties of OOS data to identify key factors for optimally applying our proposed method. 1
A Survey on Simulation for Weight Perception in Virtual Reality

Virtual reality (VR) technology can provide users with an immersive experience as if they are in the real world, which can be applied in the fields of entertainment, education and scientific research, etc.In order to improve the sense of presence and immersion in VR, the design of multimodal feedback is an important component.In particular, the simulation of weight of virtual objects poses many challenges due to the limitations of hardware and software.Many researchers focused on this issue in various ways.These methods are mainly divided into two categories: device-based simulation and software-based simulation.This paper investigates the focus of software-based simulation, particularly for the virtual feedback methods proposed by researchers in recent years.We introduce the background of these proposed methods, technical implementation principles, application scenarios, the advantages and disadvantages of these simulation methods, and the evaluation criteria.We also propose the future challenges and the development of simulation methods for weight perception of virtual objects in VR.
The Breakdown of Anti-Racist Norms: A Natural Experiment on Normative Uncertainty after Terrorist Attacks

Terrorist attacks can have profound consequences for the erosion of social norms, yet the causes of this erosion are not well understood. We argue that these attacks create substantial uncertainty about whether norms of civic conversation still hold. Observing breaches of these norms then leads people to express their own anti-immigrant attitudes more readily, as compared to a context where these norms are unambiguous. To test our theory, we examine (i) the impact of terrorist attacks on the level of hate speech against refugees in online discussions, and (ii) how the effect of terrorist attacks depends on the uncertainty about social norms of prejudice expression. To this end, we report on the results of a unique combination of a natural and a lab-in-the-field experiment. We exploit the occurrence of two consecutive Islamist terrorist attacks in Germany, the Wurzburg and Ansbach attacks, in July 2016. Hateful comments towards refugees in an experimental online forum, but not towards other minority groups (i.e., gender rights), increased as a result of the attacks. The experiment compares the effect of the terrorist attacks in contexts where a descriptive norm against the use of hate speech is emphasized, i.e., participants observe only neutral or positive comments towards a minority group, to contexts in which the norm is ambiguous because participants observe anti-minority comments. Observing anti-immigrant comments had a considerable impact on our participants’ own comments after the attacks, while observing anti-gender-rights comments did not. We end by discussing implications of the findings for the literature on social norms, sociological methods and policy.
State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing

In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.
Moving Object Detection for Event-based Vision using k-means Clustering

Moving object detection is a crucial task in computer vision.Event-based cameras are bio-inspired cameras that mimic the working of the human eye.Unlike conventional frame-based cameras, these cameras pose multiple advantages, like reduced latency, HDR, reduced motion blur during high motion, low power consumption, etc.However, these advantages come at a high cost, as event-based cameras are sensitive to noise and have low resolution.Moreover, for the lack of useful visual features like texture and colour, moving object detection in these cameras becomes more challenging.Our proposed method uses k-Means clustering for detecting moving objects in event-based data.We further compare the proposed method against state-of-the-art algorithms and show performance improvement over them.
VOLTA: adVanced mOLecular neTwork Analysis

Abstract Motivation Network analysis is a powerful approach to investigate biological systems. It is often applied to study gene co-expression patterns derived from transcriptomics experiments. Even though co-expression analysis is widely used, there is still a lack of tools that are open and customizable on the basis of different network types and analysis scenarios (e.g. through function accessibility), but are also suitable for novice users by providing complete analysis pipelines. Results We developed VOLTA, a Python package suited for complex co-expression network analysis. VOLTA is designed to allow users direct access to the individual functions, while they are also provided with complete analysis pipelines. Moreover, VOLTA offers when possible multiple algorithms applicable to each analytical step (e.g. multiple community detection or clustering algorithms are provided), hence providing the user with the possibility to perform analysis tailored to their needs. This makes VOLTA highly suitable for experienced users who wish to build their own analysis pipelines for a wide range of networks as well as for novice users for which a ‘plug and play’ system is provided. Availability and implementation The package and used data are available at GitHub: https://github.com/fhaive/VOLTA and 10.5281/zenodo.5171719. Supplementary information Supplementary data are available at Bioinformatics online.
Better Short than Greedy: Interpretable Models through Optimal Rule Boosting

Rule ensembles are designed to provide a useful trade-off between predictive accuracy and model interpretability. However, the myopic and random search components of current rule ensemble methods can compromise this goal: they often need more rules than necessary to reach a certain accuracy level or can even outright fail to accurately model a distribution that can actually be described well with a few rules. Here, we present a novel approach aiming to fit rule ensembles of maximal predictive power for a given ensemble size (and thus model comprehensibility). In particular, we present an efficient branch-and-bound algorithm that optimally solves the per-rule objective function of the popular second-order gradient boosting framework. Our main insight is that the boosting objective can be tightly bounded in linear time of the number of covered data points. Along with an additional novel pruning technique related to rule redundancy, this leads to a computationally feasible approach for boosting optimal rules that, as we demonstrate on a wide range of common benchmark problems, consistently outperforms the predictive performance of boosting greedy rules.
Real-time intelligent image processing for security applications

The advent of machine learning techniques and image processing techniques has led to new research opportunities in this area. Machine learning has enabled automatic extraction and analysis of information from images. The convergence of machine learning with image processing is useful in a variety of security applications. Image processing plays a significant role in physical as well as digital security. Physical security applications include homeland security, surveillance applications, identity authentication, and so on. Digital security implies protecting digital data. Techniques like digital watermarking, network security, and steganography enable digital security.
The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Results

This report summarizes the second International Verification of Neural Networks Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for ML-Enabled Autonomous Systems that was collocated with the 33rd International Conference on Computer-Aided Verification (CAV). Twelve teams participated in this competition. The goal of the competition is to provide an objective comparison of the state-of-the-art methods in neural network verification, in terms of scalability and speed. Along this line, we used standard formats (ONNX for neural networks and VNNLIB for specifications), standard hardware (all tools are run by the organizers on AWS), and tool parameters provided by the tool authors. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this competition.
Investigating the Use of an Adaptive Neuro-Fuzzy Inference System in Software Development Effort Estimation

Modeling software development effort estimation models has been a hot research topic over the last three decades. Numerous models were proposed in these decades to predict the effort. The key challenges for future software development is providing accurate software estimation. Failure to acknowledge the accuracy of effort estimation can cause inaccurate estimation, customer disappointment, and poor software development or project failure. This research presents a novel computational technique, named adaptive neuro-fuzzy inference system (ANFIS), for the modeling of software effort estimation. It was developed utilizing the Constructive Cost Model (COCOMO) dataset. The data were randomly divided into two sets: 83% for training and 17% for testing. The mean magnitude relative-error (MMRE) and the coefficient of correlation (R) were used as assessment indices. Results showed that the accuracy of the proposed model is quite satisfactory in comparison with actual values. Moreover, a comparison study was conducted with another approach. The results showed that ANFIS produced better results in comparison with Function Point Analysis, Software Lifecycle Management, and COCOMO methods. ANFIS was found to be a potential predictive model for software development effort estimation.
Relationship between Perceived UX Design Attributes and Persuasive Features: A Case Study of Fitness App

Research shows that a well-designed user interface is more likely to be persuasive than a poorly designed one. However, there is a limited understanding of the relationship between user-experience (UX) design attributes and users’ receptiveness to the persuasive features of a persuasive technology aimed at motivating behavior change. To bridge this gap, we carried out an online case study among 228 participants from Canada and the United States to investigate the relationship between perceived UX design attributes and users’ receptiveness to persuasive features. The study serves as exploratory work by focusing on a single prototype (homepage of a fitness app); four commonly researched UX design attributes (perceived aesthetics, perceived usability, perceived credibility, and perceived usefulness); and six commonly employed persuasive features (Goal-Setting/Self-Monitoring, Reward, Cooperation, Competition, Social Comparison, and Social Learning) illustrated on storyboards. The results of the Partial Least Square Path Modeling show that perceived usefulness, followed by perceived aesthetics, has the strongest relationship with users’ receptiveness to the persuasive features of a fitness app. Specifically, perceived usefulness and perceived aesthetics have a significant relationship with users’ receptiveness to all but two of the six persuasive features, respectively, as well as with the overall perceived persuasiveness of the fitness app. These findings are supported by participants’ comments on the perceived UX design attributes of the fitness app and the persuasive features illustrated on the storyboards. However, perceived usability and perceived credibility have weak or non-significant relationships with users’ receptiveness to the six persuasive features. The findings suggest that designers should prioritize utilitarian benefits (perceived usefulness) and hedonic benefits (perceived aesthetics) over perceived usability and perceived credibility when designing fitness apps to support behavior change.
The Role of Bounded Fields-of-View and Negative Information in Finite Set Statistics (FISST)

The role of negative information is particularly important to search-detect-track problems in which the number of objects is unknown a priori, and the size of the sensor field-of-view is far smaller than that of the region of interest. This paper presents an approach for systematically incorporating knowledge of the field-of-view geometry and position and object inclusion/exclusion evidence into object state densities and random finite set multi-object cardinality distributions. The approach is derived for a representative set of multi-object distributions and demonstrated through a sensor planning problem involving a multi-Bernoulli process with up to one-hundred potential targets.