Reviewerly/exHarmony huggingface dataset:
Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation

In recent years unmanned aerial vehicles (UAVs) have become smaller, cheaper, and more efficient, enabling the use of multiple autonomous drones where previously a single, human-operated drone would have been used. This likely includes crisis response and search and rescue missions. These systems will need a method of navigating unknown and dynamic environments. Typically, this would require an incremental heuristic search algorithm, however, these algorithms become increasingly computationally and memory intensive as the environment size increases. This paper used two different Swarm Intelligence (SI) algorithms: Particle Swarm Optimisation and Reynolds flocking to propose an overall system for controlling and navigating groups of autonomous drones through unknown and dynamic environments. This paper proposes Particle Swarm Optimisation Pathfinding (PSOP): a dynamic, cooperative algorithm; and, Drone Flock Control (DFC): a modular model for controlling systems of agents, in 3D environments, such that collisions are minimised. Using the Unity game engine, a real-time application, simulation environment, and data collection apparatus were developed and the performances of DFC-controlled drones—navigating with either the PSOP algorithm or a D* Lite implementation—were compared. The simulations do not consider UAV dynamics. The drones were tasked with navigating to a given target position in environments of varying size and quantitative data on pathfinding performance, computational and memory performance, and usability were collected. Using this data, the advantages of PSO-based pathfinding were demonstrated. PSOP was shown to be more memory efficient, more successful in the creation of high quality, accurate paths, more usable and as computationally efficient as a typical incremental heuristic search algorithm when used as part of a SI-based drone control model. This study demonstrated the capabilities of SI approaches as a means of controlling multi-agent UAV systems in a simple simulation environment. Future research may look to apply the DFC model, with the PSOP algorithm, to more advanced simulations which considered environment factors like atmospheric pressure and turbulence, or to real-world UAVs in a controlled environment.
Classification of Communication and Collaboration Apps via Advanced Deep-Learning Approaches

The lockdowns and lifestyle changes during the COVID-19 pandemic have caused a measurable impact on Internet traffic in terms of volumes and application mix, with a sudden increase of usage of communication and collaboration apps. In this work, we focus on five such apps, whose traffic we collect, reliably label at fine granularity (per-activity), and analyze from the viewpoint of traffic classification. To this aim, we employ state-of-art deep learning approaches to assess to which degree the apps, their different use cases (activities), and the pairs app-activity can be told apart from each other. We investigate the early behavior of the biflows composing the traffic and the effect of tuning the dimension of the input, via a sensitivity analysis. The experimental analysis highlights the figures of the different architectures, in terms of both traffic-classification performance and complexity w.r.t. different classification tasks, and the related trade-off. The outcome of this analysis is informative for a number of network management tasks, including monitoring, planning, resource provisioning, and (security) policy enforcement.
System-Cluster Technology of e-Learning Improvement under the Conditions of COVID-19

The paper defines the scientific provisions on the feasibility and effectiveness of using e-learning under conditions of quarantine restrictions related to the COVID-19 pandemic. It was proved that within the use of e-learning there has been a convergence between the scientific and methodological bases and the learning process itself, and new approaches to the general didactic and information content of e-learning courses within the education process have been formed. Modern platforms of e-learning are examined and a scheme involving the synchronous technology of e-learning was formed. Within the framework of the suggested learning platform, effective learning tools under conditions of quarantine restrictions were identified, the use of which allows the most successful solution of didactic tasks facing e-learning to be ensured on the basis of the everyday use of digital media and electronic devices for learning purposes. The fractal-cluster technology of an e-learning organization was suggested for an introduction. The functional dependencies on the forms and technologies of the educational process for the efficiency of higher education were obtained. The expediency of introducing fractal-cluster structures into the organizational component of the educational process was determined. Based on the tools of the fractal-cluster approach, a model of organization of the educational process in the information-entropy format was formulated.
A Method to Improve Exact Matching Results in Compressed Text using Parallel Wavelet Tree

The process of searching on the World Wide Web (WWW) is increasing regularly, and users around the world also use it regularly. In WWW the size of the text corpus is constantly increasing at an exponential rate, so we need an efficient indexing algorithm that reduces both space and time during the search process. This paper proposes a new technique that utilizes Word-Based Tagging Coding compression which is implemented using Parallel Wavelet Tree, called WBTC_PWT. WBTC_PWT uses the word-based tagging coding encoding technique to reduce the space complexity of the index and uses a parallel wavelet tree which reduces the time it takes to construct indexes. This technique utilizes the features of compressed pattern matching to minimize search time complexity. In this technique, all the unique words present in the text corpus are divided into different levels according to the word frequency table and a different wavelet tree is made for each level in parallel. Compared to other existing search algorithms based on compressed text, the proposed WBTC_PWT search method is significantly faster and it reduces the chances of getting the false matching result.
The Effects of Virtual and Physical Elevation on Physiological Stress During Virtual Reality Height Exposure

Advances in virtual reality technology have greatly benefited the acrophobia research field. Virtual reality height exposure is a reliable method of inducing stress with low variance across ages and demographics. When creating a virtual height exposure environment, researchers have often used haptic feedback elements to improve the sense of realism of a virtual environment. While the quality of the rendered for the virtual environment increases over time, the physical environment is often simplified to a conservative passive haptic feedback platform. The impact of the increasing disparity between the virtual and physical environment on the induced stress levels is unclear. This article presents an experiment that explored the effect of combining an elevated physical platform with different levels of virtual heights to induce stress. Eighteen participants experienced four different conditions of varying physical and virtual heights. The measurements included gait parameters, heart rate, heart rate variability, and electrodermal activity. The results show that the added physical elevation at a low virtual height shifts the participant’s walking behaviour and increases the perception of danger. However, the virtual environment still plays an essential role in manipulating height exposure and inducing physiological stress. Another finding is that a person’s behaviour always corresponds to the more significant perceived threat, whether from the physical or virtual environment.
Rural Population and COVID-19: A Model for Assessing the Economic Effects of Drop-Out in Higher Education

Higher education is one of the ways to overcome social inequalities in rural areas in developing countries. This has led states to develop public policies aimed at access, retention and timely graduation of students in those sectors, yet the high drop-out rates among the rural student population, which were catalysed by COVID-19, prevent the intrinsic and extrinsic benefits of obtaining a higher education degree from materialising. Thus, the study of the phenomenon of dropout before and after the pandemic has not sufficiently addressed the economic issues raised by this phenomenon for the different actors at the educational level. The purpose of this paper is to model the economic effects of rural student dropout at the higher education level for students and families, Higher Education Institutions (HEIs) and the State, based on public policies for access to higher education, in the pandemic and post-pandemic scenario. In order to delimit the operationalisation of the proposed model, a set of undergraduate training programmes in Colombia was taken as a reference. System dynamics was used as the main modelling technique. The model was based on data from the 20 training programmes with the highest number of students enrolled in rural areas for the year 2019, by running three computational simulations. The results showed the description of the dynamic model and the financial effects of dropout for the actors of the educational level with the current policies of access to higher education, the scenario in which COVID-19 would not have occurred and the consolidation of the public policy of tuition fee exemption in public HEIs as a result of the pandemic. It was concluded that the model developed is very useful for the valuation of these economic effects and for decision-making on policies to be implemented, given that the costs of dropout are characterised by high costs for students and their families as well as for HEIs, and where it was determined that current policies are inefficient in preventing and mitigating dropout.
Children’s cognitive function and mental health based on finite element nonlinear mathematical model

Abstract This article uses a finite element nonlinear mathematical model to analyse the psychological conditions of children with certain physical defects. The thesis uses exercise and psychological intervention to intervene in children with material defects and scores the children’s mental state and cognitive status after the intervention. After our training and psychological intervention, the study found that the psychological evaluation scores of children with physical defects increased significantly. Before and after the intervention, there are apparent differences in children’s cognition in vocabulary and arithmetic. There is a nonlinear negative correlation between children’s cognitive abilities with physical defects and the scores of mental health status and neuropsychological evaluation. For this reason, the study concluded that movement and psychological intervention play a significant role in improving the cognitive function of children with physical defects.
A cross‐project defect prediction method based on multi‐adaptation and nuclear norm

Cross-project defect prediction (CPDP) is an important research direction in software defect prediction. Traditional CPDP methods based on hand-crafted features ignore the semantic information in the source code. Existing CPDP methods based on the deep learning model may not fully consider the differences among projects. Additionally, these methods may not accurately classify the samples near the classification boundary. To solve these problems, the authors propose a model based on multi-adaptation and nuclear norm (MANN) to deal with samples in projects. The feature of samples were embedded into the multi-core Hilbert space for distribution and the multi-kernel maximum mean discrepancy method was utilised to reduce differences among projects. More importantly, the nuclear norm module was constructed, which improved the discriminability and diversity of the target sample by calculating and maximizing the nuclear norm of the target sample in the process of domain adaptation, thus improving the performance of MANN. Finally, extensive experiments were conducted on 11 sizeable open-source projects. The results indicate that the proposed method exceeds the state of the art under the widely used metrics.
Cautious classification based on belief functions theory and imprecise relabelling

The performances of standard classifiers, i.e., any method of point prediction for classification, decline in case of imperfect data. In some sensitive domains where these imperfections are present, these classifiers need to be adapted in order to avoid any misclassification that has serious consequences. Recent works proposed to deal with this problem by using cautious classification techniques. This paper is in line with these works, especially with imprecise classifiers, i.e., the output of the classifier for an input sample that is subject to considerable imperfections is a subset of classes. The distinctive feature of our imprecise classification proposition is that it considers that in some applications, data imperfection is not limited to new samples to be classified but can also be present in training data. We therefore propose a relabelling procedure which allows us to identify imperfect samples in the training data and relabel them with an appropriate subset of candidate classes. This approach to imprecise classification is close, in some aspects, to hierarchical classification where a “parent” can be considered as a subset of classes that are the “children” in the leaves. Furthermore, the belief functions framework is considered to represent the uncertainty and imprecision about the class of a new sample where the focal elements are contained in the set of new labels of the training data. A criterion based on a generalised Fβ score and the obtained mass function is established to decide which subset of classes should be associated to the new sample. Several options are presented to build our classifier for the relabelling procedure and for the reasoning step. Thus, the performances of each option are presented before comparison with state-of-the-art imprecise classifiers' performances. The comparisons are conducted first on randomly generated data and then on 11 UCI datasets based on five measures of imprecise classification performances. They show that our classifier achieves performances close to, sometimes better than, the best on the five measures.
Combining collective and artificial intelligence for global health diseases diagnosis using crowdsourced annotated medical images

Visual inspection of microscopic samples is still the gold standard diagnostic methodology for many global health diseases. Soil-transmitted helminth infection affects 1.5 billion people worldwide, and is the most prevalent disease among the Neglected Tropical Diseases. It is diagnosed by manual examination of stool samples by microscopy, which is a time-consuming task and requires trained personnel and high specialization. Artificial intelligence could automate this task making the diagnosis more accessible. Still, it needs a large amount of annotated training data coming from experts.In this work, we proposed the use of crowdsourced annotated medical images to train AI models (neural networks) for the detection of soil-transmitted helminthiasis in microscopy images from stool samples leveraging non-expert knowledge collected through playing a video game. We collected annotations made by both school-age children and adults, and we showed that, although the quality of crowdsourced annotations made by school-age children are sightly inferior than the ones made by adults, AI models trained on these crowdsourced annotations perform similarly (AUC of 0.928 and 0.939 respectively), and reach similar performance to the AI model trained on expert annotations (AUC of 0.932). We also showed the impact of the training sample size and continuous training on the performance of the AI models.In conclusion, the workflow proposed in this work combined collective and artificial intelligence for detecting soil-transmitted helminthiasis. Embedded within a digital health platform can be applied to any other medical image analysis task and contribute to reduce the burden of disease.
Mathematical Modeling and Simulation of Table Tennis Trajectory Based on Digital Video Image Processing

Digital sports training based on digital video image processing promises to reduce the reliance on the experience of coaches in the table tennis training process and to achieve a more general physical education base. Based on this approach, this paper describes the specific forms of exercise content, movement characteristics, and skill levels in the table tennis framework and specifies the calculation methods of motion capture and movement characteristics suitable for table tennis. Meanwhile, to further improve the accuracy of the inertial motion capture system in restoring the position posture of the trainees, this paper improves the original inertial motion capture system from two aspects: contact judgment of both feet and correction of the position posture based on the contact position constraint. The simulation results show that the corrected human posture has good action smoothness. This paper first proposes a knowledge-based generic sports-assisted training framework based on generalizing the traditional sports training model. The framework contains four main modules: domain knowledge, trainees, sport evaluation, and controller. The domain knowledge module is a digital representation of the knowledge of the exercise content, improvement instructions, and skill indicators of the sport; the trainee module is the active response of the trainee to the exercise content and improvement instructions; the motion evaluation module uses motion capture technology to obtain the raw motion data of the trainee and further calculates the motion characteristics; the controller module proposes improvement instructions to the trainee or makes him/her practice new content based on the results of the motion evaluation. Based on the results of the motion evaluation, the controller module proposes improvement instructions or makes the trainee practice new content until the trainee achieves the desired goal.
An Ideological Analysis of the Former President Donald Trump’s Tweets During COVID-19

This study aimed to explore the former US President Donald Trump’s representation of “us” and “others” in his tweets related to COVID-19 and how he portrayed the representation of “us” and “others”. In this study, the sample related to COVID-19 which ranged from January to May 2020, consisted of the tweets posted by the former US President Donald Trump. To analyze the ideological discourse of the former President Donald Trump’s tweets during COVID-19, the ideological framework proposed by Van Dijk (Approaches to Media Discourse. Blackwell Publishing, 1997) was adopted to reveal the underlying intentions and ideologies of us-representation and others-representation. The results of this research indicate that Donald Trump portrayed a positive us-group and a negative others-group. Moreover, argumentation/authority, comparison, repetition, capitalization, and metaphors are the typical strategies that former President Trump used to portray both groups. It was also found that the positive opinions about the us-group and the negative ones about the others-group were not always consistent. For example, there were also changes when the same descriptions were given to both groups, which may well be coherent concerning the basic interests of the groups (Van Dijk, in: Approaches to Media Discourse. Blackwell Publishing, 1997). The findings of this study may contribute to an analysis of the president’s discourse on Twitter, which is a relatively recent channel for political information (Kerbleski, in: Trump, Twitter, and the trees: a critical discourse analysis of Donald Trump's tweets surrounding the november 2018 California Wildfires. Doctoral dissertation, Northern Arizona University, 2019).
Toward a linguistically grounded dialog model for chatbot design

The increasing interest in various types of conversational interfaces has been supported by a progressive standardization of the technological frameworks used to build them.However, the landscape of available methodological frameworks for designing conversations is much more fragmented.We propose a highly generalizable methodology for designing conversational flows rooted in a functionalist-pragmatics perspective, with an explicit adherence to a conversationalist approach.In parallel, we elaborate a practical-procedural workflow for undertaking chatbots projects in which we situate the theoretical starting point.At last, we elaborate a general casestudy on which we transpose the identified approach in Italian language and using one of the most authoritative NLU platforms.
Quantum anonymous collision detection for quantum networks

Abstract Quantum mechanics offers new opportunities for diverse information processing tasks in communication and computational networks. In the last two decades, the notion of quantum anonymity has been introduced in several networking tasks that provide an unconditional secrecy of identity for the communicating parties. In this article, we propose a quantum anonymous collision detection (QACD) protocol which detects not only the collision but also guarantees the anonymity in the case of multiple senders. We show that the QACD protocol serves as an important primitive for a quantum anonymous network that features tracelessness and resource efficiency. Furthermore, the security analysis shows that this protocol is robust against the adversary and malicious participants.
Election Prediction Based on Sentiment Analysis using Twitter Data

Election prediction has always been of interest to many people. In the last decade, an increasing influence of social networks and the possibility of sharing opinions and ideas has rendered election prediction based on social network data analysis. This paper, drawing on Twitter data and sentiment analysis, uses the proportion of positive messages rate to negative messages rate as an effective indicator for predicting elections. Then, using the aging estimation method, it predicts the values of this indicator in future time windows. The experiments conducted on Twitter data related to the 2020 United States presidential election in a four-month time window indicate that the indicator values and eventually the election results can be predicted with high accuracy.