Reviewerly/exHarmony huggingface dataset:
Terms of Digital Communication in the Media Education Process

The article investigates the process of formation of the conceptual and terminological apparatus of the new segment, whose activities are based on the use of digital information and communication technologies -Internet media, websites, forums, multifunctional web portals, blog platforms, wiki projects, search engines, social networks, audio and video hosts, messengers, service applications, and much more.The research focuses on the problem of digital media terminology in the paradigm of the media education process.It is important to identify the genesis, typology and functioning of the system of terminological usage, and to analyze it in the framework of terminology studies.Considerable attention in this article is paid to the sphere of knowledge of media educators.The concept of "special vocabulary" is considered as a scientific definition.The most important and relevant concepts, representing methodological terminology, are identified.The article reveals the content of the requirements for the terms of new media.The pragmatic requirements, conditioned by the term specific functioning, are singled out; they include the following ones: adaptability, modernity, internationality and euphony of the term.With regard to the problem in question, the aim of text-based professional vocabulary teaching is to develop students' language competence in the context of media education.The study showed that students can successfully complete professional media thesaurus tasks in the classroom.They gain experience by studying professional media language in the Journalism for Schoolchildren unit and by independently producing vocabulary articles on new digital media communication terms.
A toolkit for wide-screen dynamic area of interest measurements using the Pupil Labs Core Eye Tracker

Abstract Eye tracking measurements taken while watching a wide field screen are challenging to perform. Commercially available remote eye trackers typically do not measure more than 35 degrees in eccentricity. Analysis software was developed using the Pupil Core Eye Tracking data to analyze viewing behavior under circumstances as natural as possible, on a 1.55-m-wide screen allowing free head movements. Additionally, dynamic area of interest (AOI) analyses were performed on data of participants viewing traffic scenes. A toolkit was created including software for simple allocation of dynamic AOIs (semi-automatically and manually), measurement of parameters such as dwell times and time to first entry, and overlaying gaze and AOIs on video. Participants ( n =11) were asked to look at 13 dynamic AOIs in traffic scenes from appearance to disappearance in order to validate the setup and software. Different AOI margins were explored for the included objects. The median ratio between total appearance time and dwell time was about 90% for most objects when appropriate margins were chosen. This validated open-source toolkit is readily available for researchers who want to perform dynamic AOI analyses with the Pupil Core eye tracker, especially when measurements are desired on a wide screen, in various fields such as psychology, transportation, and low vision research.
LEAP4FNSSA lexicon: Towards a new dataset of keywords dealing with food security

The main objective of the project LEAP4FNSSA (Long-term EU-AU Research and Innovation Partnership for Food and Nutrition Security and Sustainable Agriculture) is to provide a tool for European and African institutions to engage in a sustainable partnership platform for research and innovation on Food and Nutrition Security, and Sustainable Agriculture (FNSSA). The FNSSA roadmap facilitates the involvement of stakeholders for addressing and linking research to innovation dealing with food security issues. In this context, the LEAP4FNSSA project supports the driving of the roadmap. Research and innovation activities were captured in different data, i.e. LEAP4FNSSA database and heterogeneous textual data including project reports, websites, scientific publications, workshop reports and student theses. The Knowledge Extractor Pipeline System (KEOPS) was implemented to support the processing and analysis of textual data associated with FNSSA activities. KEOPS is based on the LEAP4FNSSA lexicon presented in this data paper. The LEAP4FNSSA lexicon composed of 331 keywords associated with 12 concepts of the food security domain is the result of 3 steps of work and brainstorming. The lexicon enables the capturing of research and innovation topics dealing with food security and conducted by African and European partners. This data paper presents the obtained lexicon and a summary of the method to build it.
Performing the virtual double

In what follows the idea of the online double is historically contextualised and analysed. Beginningwith the contemporary idea of the ‘selfie’ as online self-induced double, I proceed to discuss theDoppelgänger’s (double) mythical and literary roots, in order to expand the discussion of the selfieto include the online double. Two instances of the online double are unpacked, namely the doubleas shadow and the double as a stand-in or alter-ego, which correspond significantly with MarshallMcLuhan’s analysis of the Narcissus myth and technological use. McLuhan reveals the doublednature of our technological engagement that leads to either self-amputation or self-amplification.In my analysis, the double as shadow is correlated with self-amputation and the double as alteregowith self-amplification. It is argued that the double as shadow is evoked online through themining of data regarding the self that is captured consciously and unconsciously to create whatis known as the Data Doppelgänger. The figure of the Doppelgänger is further vividly conjuredthrough virtual stand-ins or alter-egos that act on behalf of the self to create a tele-presencethrough examples such as Project Lifelike and rep.licants.org.
Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine Learning

Multiple methods of finding the vertices belonging to a planted dense subgraph in a random dense $G(n, p)$ graph have been proposed, with an emphasis on planted cliques. Such methods can identify the planted subgraph in polynomial time, but are all limited to several subgraph structures. Here, we present PYGON, a graph neural network-based algorithm, which is insensitive to the structure of the planted subgraph. This is the first algorithm that uses advanced learning tools for recovering dense subgraphs. We show that PYGON can recover cliques of sizes $\Theta\left(\sqrt{n}\right)$, where $n$ is the size of the background graph, comparable with the state of the art. We also show that the same algorithm can recover multiple other planted subgraphs of size $\Theta\left(\sqrt{n}\right)$, in both directed and undirected graphs. We suggest a conjecture that no polynomial time PAC-learning algorithm can detect planted dense subgraphs with size smaller than $O\left(\sqrt{n}\right)$, even if in principle one could find dense subgraphs of logarithmic size.
Coherent strong observers for a class of linear quantum stochastic systems with unknown inputs

In this paper, we extend our previous research on design of coherent observers for open quantum systems (as quantum plants) to the case where there exist unknown inputs in the quantum plants. The unknown inputs represent unmodeled dynamics or disturbances which are prevalent in reality in quantum systems. Since no prior knowledge for the unknown inputs is assumed, the quantum version of Luenberger observers or Kalman filters are not adequate in such a scenario. We thus employ linear quantum stochastic systems and an inner model to tackle this problem. In addition, in order to preserve the coherence in the information flow due to quantum mechanics, any measurement of the quantum system is ruled out, which brings in new challenges compared to the classical counterpart. To be more specific, we develop coherent strong observers for open quantum harmonic oscillators with unknown inputs involved, showing a detailed procedure regarding synthesis of coherent strong observers for such systems that accomplish the task of simultaneously estimating system variables together with the unknown inputs, which is also illustrated by a simulation example. The results in this paper enrich our exploration on observer design for quantum systems under various circumstances, and thus pave the way towards more systematic and comprehensive analysis concerning coherent estimation.
The Relation Dimension in the Identification and Classification of Lexically Restricted Word Co-Occurrences in Text Corpora

The speech of native speakers is full of idiosyncrasies. Especially prominent are lexically restricted binary word co-occurrences of the type high esteem, strong tea, run [an] experiment, war break(s) out, etc. In lexicography, such co-occurrences are referred to as collocations. Due to their semi-decompositional nature, collocations are of high relevance to a large number of natural language processing applications as well as to second language learning. A substantial body of work exists on the automatic recognition of collocations in textual material and, increasingly also on their semantic classification, even if not yet in the mainstream research. Especially classification with respect to the lexical function (LF) taxonomy, which is the most detailed semantically oriented taxonomy of collocations available to date, proved to be of real use to human speakers and machines alike. The most recent approaches in the field are based on multilingual neural graph transformer models that use explicit syntactic dependencies. Our goal is to explore whether the extension of such a model by a semantic relation extraction network improves its classification performance or whether it already learns the corresponding semantic relations from the dependencies and the sentential contexts, such that an additional relation extraction network will not improve the overall performance. The experiments show that the semantic relation extraction layer indeed improves the overall performance of a graph transformer. However, this improvement is not very significant, such that we can conclude that graph transformers already learn to a certain extent the semantics of the dependencies between the collocation elements.
Construction of Virtual Interaction Location Prediction Model Based on Distance Cognition

Due to the difference in distance cognition between virtual and real symmetric space, it is difficult for users to accurately interact with the target in the Digital Twin system. In order to study the cross-effects of interaction task, target size and target location on the accuracy of egocentric peripersonal distance cognition, a 2 × 5 × 9 × 5 asymmetric experiment was designed and carried out. There were two kinds of interaction tasks, five kinds of interaction target widths and nine kinds of spatial locations set to estimate the five egocentric peripersonal distances. Based on the experimental data, with interaction task, target width and the actual spatial location as independent variables and virtual interaction location as a dependent variable, the mapping model between the actual physical location and virtual interaction location of different interaction targets was constructed and evaluated by multiple linear regression method. The results showed that the prediction model constructed by stepwise regression method was simple and less computationally intensive, but it had better stability and prediction ability. The correlation coefficients R2 on xp, yp and zp were 0.994, 0.999 and 0.998, RMSE values were 2.583 cm, 1.0774 cm and 1.3155 cm, rRMSE values were 26.57%, 12.60% and 1.15%, respectively. The research of relevant experiments and the construction of models are helpful to solve the layout optimization problem of virtual interactive space in the Digital Twin system.
World Models with an Entity-Based Representation

Reinforcement learning (RL) is a powerful way to solve sequential decision-making tasks. However training an RL agent in a complex environment requires a large amount of interactions, which is non-ideal when acting in an environment is costly or dangerous. One alternative is to learn an approximation of the real environment, referred to as a world model. This simulator can be used to train an agent and transfer the learned policy to the real environment. Unfortunately, training world models have traditionally required a significant number of interactions in the real environment. This brings us to the same problem when it is costly or dangerous to act in the real environment. To address this problem, we present an entity-based representation and corresponding architecture, which allows for greater data efficiency in world model training. Our approach outperforms other world model baselines in an initial application to the game Pong.
EM-Glue: A Platform for Decoupling Experience Managers and Environments

Experience Management uses AI technologies to improve people's experiences within an interactive application by changing the environment while the experience is underway. Game-related research in this field has a trend where each experience manager is built in a way that is tightly integrated with the environment that it can change. One consequence of this integration is that it becomes difficult to compare one manager to another in a single environment, or a single manager to itself across multiple environments. With this paper, we propose a solution for decoupling experience managers from the environments that they can change, through the use of an intermediate software platform. We describe the structure of the platform, a protocol that facilitates communication between a manager and an environment, and how normal communication happens. Moreover, we introduce the Camelot Wrapper, software built to extend the interactive visualization engine Camelot and connect it to our platform.
Never a Dull Moment: Believable Dynamic Character Beat Generation between Game World Events

This work presents a heterogeneous system using ABL, CiF, and a virtual game world to demonstrate how dynamic character beats can be used to preserve believability across game world events. We describe the individual components used in our system and detail how each component contributes to the alignment of physical and mental contexts of NPC agents. Finally, we provide a brief account of the demo experience showcasing our system’s capabilities.
When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias

An increased awareness concerning risks of algorithmic bias has driven a surge of efforts around bias mitigation strategies. A vast majority of the proposed approaches fall under one of two categories: (1) imposing algorithmic fairness constraints on predictive models, and (2) collecting additional training samples. Most recently and at the intersection of these two categories, methods that propose active learning under fairness constraints have been developed. However, proposed bias mitigation strategies typically overlook the bias presented in the observed labels. In this work, we study fairness considerations of active data collection strategies in the presence of label bias. We first present an overview of different types of label bias in the context of supervised learning systems. We then empirically show that, when overlooking label bias, collecting more data can aggravate bias, and imposing fairness constraints that rely on the observed labels in the data collection process may not address the problem. Our results illustrate the unintended consequences of deploying a model that attempts to mitigate a single type of bias while neglecting others, emphasizing the importance of explicitly differentiating between the types of bias that fairness-aware algorithms aim to address, and highlighting the risks of neglecting label bias during data collection.
The large key space image encryption algorithm based on modulus synchronization between real and complex fractional-order dynamical systems

This paper constructs and analyzes the dynamical properties of a new fractional-order real hyper-chaotic system and its corresponding complex variable system. A thorough analysis was done by employing stability of equilibrium points, phase plots, Lyapunov spectrum, and bifurcation analysis for the consequences of varying fractional-order derivative and parameter values on the system. For the first time, a modulus synchronization scheme is proposed to synchronize real and complex fractional-order dynamical systems. Based on Lyapunov stability theory, non-linear controllers are designed to achieve the proposed modulus synchronization scheme. A new modulus synchronization encryption algorithm with a large key space size for digital images is introduced for the application. The experimental results and analysis validate the desired algorithm. Also, we compare our result of the new encryption algorithm with the previously published literature and verify the efficacy of the considered scheme. Numerical simulations are given to validate the theoretical analysis.
CHIME: Causal Human-in-the-Loop Model Explanations

Explaining the behaviour of Artificial Intelligence models has become a necessity. Their opaqueness and fragility are not tolerable in high-stakes domains especially. Although considerable progress is being made in the field of Explainable Artificial Intelligence, scholars have demonstrated limits and flaws of existing approaches: explanations requiring further interpretation, non-standardised explanatory format, and overall fragility. In light of this fragmentation, we turn to the field of philosophy of science to understand what constitutes a good explanation, that is, a generalisation that covers both the actual outcome and, possibly multiple, counterfactual outcomes. Inspired by this, we propose CHIME: a human-in-the-loop, post-hoc approach focused on creating such explanations by establishing the causal features in the input. We first elicit people's cognitive abilities to understand what parts of the input the model might be attending to. Then, through Causal Discovery we uncover the underlying causal graph relating the different concepts. Finally, with such a structure, we compute the causal effects different concepts have towards a model's outcome. We evaluate the Fidelity, Coherence, and Accuracy of the explanations obtained with CHIME with respect to two state-of-the-art Computer Vision models trained on real-world image data sets. We found evidence that the explanations reflect the causal concepts tied to a model's prediction, both in terms of causal strength and accuracy.
Development of a hybrid method for calculation of software complexity

The use of code metrics allows software developers and project managers to evaluate various features of the software (to be built or already in existence), predict workload, determine software complexity and reliability, and quantify the quality of software systems being developed. Articles written in recent years have proposed various methods for solving this problem. However, there is still no very effective approach to measuring software complexity. This article provides a brief overview of existing software complexity metrics and proposes a new hybrid method for computing software complexity. The proposed hybrid method for evaluating software complexity combines the key features of the Halsted, Maccabe, and SLOC metrics and also allows for a more efficient assessment of complexity.
BBTv2: Towards a Gradient-Free Future with Large Language Models

Most downstream adaptation methods tune all or part of the parameters of pre-trained models (PTMs) through gradient descent, where the tuning cost increases linearly with the growth of the model size. By contrast, gradient-free methods only require the forward computation of the PTM to tune the prompt, retaining the benefits of efficient tuning and deployment. Though, past work on gradient-free tuning often introduces gradient descent to seek a good initialization of prompt and lacks versatility across tasks and PTMs. In this paper, we present BBTv2, an improved version of Black-Box Tuning, to drive PTMs for few-shot learning. We prepend continuous prompts to every layer of the PTM and propose a divide-and-conquer gradient-free algorithm to optimize the prompts at different layers alternately. Extensive experiments across various tasks and PTMs show that BBTv2 can achieve comparable performance to full model tuning and state-of-the-art parameter-efficient methods (e.g., Adapter, LoRA, BitFit, etc.) under few-shot settings while maintaining much fewer tunable parameters.
Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter Aircrafts

The advent of deep learning (DL) gave rise to significant breakthroughs in Reinforcement Learning (RL) research. Deep Reinforcement Learning (DRL) algorithms have reached super-human level skills when applied to vision-based control problems as such in Atari 2600 games where environment states were extracted from pixel information. Unfortunately, these environments are far from being applicable to highly dynamic and complex real-world tasks as in autonomous control of a fighter aircraft since these environments only involve 2D representation of a visual world. Here, we present a semi-realistic flight simulation environment Harfang3D Dog-Fight Sandbox for fighter aircrafts. It is aimed to be a flexible toolbox for the investigation of main challenges in aviation studies using Reinforcement Learning. The program provides easy access to flight dynamics model, environment states, and aerodynamics of the plane enabling user to customize any specific task in order to build intelligent decision making (control) systems via RL. The software also allows deployment of bot aircrafts and development of multi-agent tasks. This way, multiple groups of aircrafts can be configured to be competitive or cooperative agents to perform complicated tasks including Dog Fight. During the experiments, we carried out training for two different scenarios: navigating to a designated location and within visual range (WVR) combat, shortly Dog Fight. Using Deep Reinforcement Learning techniques for both scenarios, we were able to train competent agents that exhibit human-like behaviours. Based on this results, it is confirmed that Harfang3D Dog-Fight Sandbox can be utilized as a 3D realistic RL research platform.
MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation

Industrial recommender systems usually employ multi-source data to improve the recommendation quality, while effectively sharing information between different data sources remain a challenge. In this paper, we introduce a novel Multi-View Approach with Hybrid Attentive Networks (MV-HAN) for contents retrieval at the matching stage of recommender systems. The proposed model enables high-order feature interaction from various input features while effectively transferring knowledge between different types. By employing a well-placed parameters sharing strategy, the MV-HAN substantially improves the retrieval performance in sparse types. The designed MV-HAN inherits the efficiency advantages in the online service from the two-tower model, by mapping users and contents of different types into the same features space. This enables fast retrieval of similar contents with an approximate nearest neighbor algorithm. We conduct offline experiments on several industrial datasets, demonstrating that the proposed MV-HAN significantly outperforms baselines on the content retrieval tasks. Importantly, the MV-HAN is deployed in a real-world matching system. Online A/B test results show that the proposed method can significantly improve the quality of recommendations.