Reviewerly/exHarmony huggingface dataset:
Towards the Simplest Model of Quantum Supremacy: Atomic Boson Sampling in a Box Trap

We describe boson sampling of interacting atoms from the noncondensed fraction of Bose-Einstein-condensed (BEC) gas confined in a box trap as a new platform for studying computational ♯P-hardness and quantum supremacy of many-body systems. We calculate the characteristic function and statistics of atom numbers via the newly found Hafnian master theorem. Using Bloch-Messiah reduction, we find that interatomic interactions give rise to two equally important entities-eigen-squeeze modes and eigen-energy quasiparticles-whose interplay with sampling atom states determines the behavior of the BEC gas. We infer that two necessary ingredients of ♯P-hardness, squeezing and interference, are self-generated in the gas and, contrary to Gaussian boson sampling in linear interferometers, external sources of squeezed bosons are not required.
Modern schoolchildren choose artificial intelligence as a direction for future professions

Artificial intelligence currently remains a topical issue for the development of content in school education in informatics and information and communication technologies. Artificial intelligence technologies are included in the school curriculum back in 2021 and are already being studied in almost half of secondary schools in Russia, and by the end of 2024 it is planned that artificial intelligence issues will be studied in most Russian schools. When studying these issues, schoolchildren often demonstrate increased motivation for the material. In this regard, the purpose of this article is to share with the pedagogical community the results of a survey of participants in the final stage of the All-Russian Olympiad in Artificial Intelligence 2022 regarding artificial intelligence trends. Some results in the article are presented in comparison with the responses of schoolchildren to a similar survey in 2021. The expressed opinions of schoolchildren make it possible both to identify gaps in the modern school informatics course on this issue, and to identify new directions for the development of educational content that will be most in demand, according to modern schoolchildren, in their future professions in the context of the digital transformation of society.
Formation of digital literacy of schoolchildren in the aspect of application of augmented reality technology

In modern school education, the problem of teaching schoolchildren the technology of augmented reality and developing the corresponding components of digital literacy is becoming more urgent. The elective course "The real world with elements of unreality", aimed at developing digital literacy of schoolchildren in the aspect of using augmented reality technology, includes the following modules: "Theoretical basis of augmented reality", "Software and hardware of augmented reality", "Augmented reality browsers", "Development of an augmented reality product". Practical work with AR applications is organized based on the use of blended learning technology "Changing work areas". While going through the route, students get acquainted with the functionality of augmented reality applications in the following subject areas: foreign language, biology, history, geometry, fine arts. The creation of an augmented reality product (stereometric figure) is organized through project activities. Testing of the elective course showed the availability of educational material and a high level of cognitive interest of students in studying augmented reality technology.
Problems on the theme "Maximum non-decreasing subsequence"

In the article, using the example of solving few problems, the methodology for studying the theme "Maximum non-decreasing subsequence" is illustrated in preparing schoolchildren for Olympiads in informatics. The study is based on the sequential solution of increasingly complex problems. For each problem, the following materials are given: the formulation of the problem, the idea of a solution, the solution in the Pascal programming language. Distance learning system (http://dl.gsu.by) is the technical base for teaching. The system allows to offer for a student a formulation of the problem; to submit the solution for review; to get a verdict from the system — a correct or incorrect solution; for incorrect solution, the number of the test on which the solution did not pass is indicated. A student can take a test (input and output data), on which his solution did not pass, figure out what the error is in his program, correct it and send the solution again. In addition, for each problem there is a link on it to the topic in the forum at site, where you can ask a question on solving this problem and / or read the answer if the questions have already been asked before.
Studying 3d modeling and prototyping in pre-professional classes with the help of the T-FLEX CAD program. Implementation of the project result in the form of an educational startup

The article continues the consideration of using the Russian computer-aided design program T-FLEX CAD to study 3D modeling and prototyping in pre-professional engineering classes, which began in the journal "Informatics at School" No 5-2022. The use of this program when performing an individual project is presented. Practical works are offered that are aimed at creating a model and prototype of a respirator mask. The result of the works is the technical part of an individual project, which can be realized as an educational startup. When realizing the project as an educational startup, legal regulations, financing opportunities and other features of implementing such a project for continuing education are considered. The material will be useful for teachers of informatics and technology working in engineering or information and technology classes, when teaching an advanced course in informatics or a special course in 3D modeling and prototyping.
Organization of various types of sign-symbolic activity when teaching schoolchildren programming

Individual actions of students when solving problems on the theme "Programming" can be referred to sign-symbolic activity. When teaching programming, the teacher should pay special attention to combining various plans: actions with specific data, sign-symbolic description of this data and the algorithm structure, writing program code. The following types of sign-symbolic activity are distinguished: modeling, schematization, coding, substitution. It is advisable to include tasks in the preparation process at the first stages of training, in which these types of activity are independent units, and not one of the steps to solve the programming problem. It is advisable to implement modeling activity using trace table completion tasks. To organize schematization, tasks are used to describe the algorithm verbally and make flow charts. Distinguishing of the substitute and substituent is facilitated by tasks for matchmaking between code lines and their purpose. The article provides examples of tasks that contribute to sign-symbolic activity organization of various types in programming training.
Improving Trust in AI with Mitigating Confirmation Bias: Effects of Explanation Type and Debiasing Strategy for Decision-Making with Explainable AI

AbstractWith advancements in artificial intelligence (AI), explainable AI (XAI) has emerged as a promising tool for enhancing the explainability of complex machine learning models. However, the explanations generated by an XAI may lead to cognitive biases among human users. To address this problem, this study aims to investigate how to mitigate users’ cognitive biases based on their individual characteristics. In the literature review, we found two factors that can be helpful in remedying biases: 1) debiasing strategies that have been reported to potentially reduce biases in users’ decision-making via additional information or change in information delivery, and 2) explanation modality types. To examine these factors’ effects, we conducted an experiment with a 4 (debiasing strategy) × 3 (explanation type) between-subject design. In the experiment, participants were exposed to an explainable interface that provides an AI’s outcomes with explanatory information, and their behavioral and attitudinal responses were collected. Specifically, we statistically examined the effects of textual and visual explanations on users’ trust and confirmation bias toward AI systems, considering the moderating effects of debiasing methods and watching time. The results demonstrated that textual explanations lead to higher trust in XAI systems compared to visual explanations. Moreover, we found that textual explanations are particularly beneficial for quick decision-makers to evaluate the outputs of AI systems. Next, the results indicated that the cognitive bias can be effectively mitigated by providing users with a priori information. These findings have theoretical and practical implications for designing AI-based decision support systems that can generate more trustworthy and equitable explanations.Keywords: Artificial intelligenceexplanationtrustsatisfactioncognitive bias Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the faculty research fund of Sejong University in 2023.Notes on contributorsTaehyun HaTaehyun Ha is an Assistant Professor in the Department of Data Science at Sejong University. His research focuses on Online User Behavior, Human-AI Interaction, and Trust Formation.Sangyeon KimSangyeon Kim is a research professor at the Institute of Engineering Research at Korea University. He received a PhD from the Department of Interaction Science at Sungkyunkwan University in 2022. His research interests include human-computer interaction, gestural interaction, accessible computing, and human-centered AI.
Modified state activation functions of deep learning-based SC-FDMA channel equalization system

Abstract The most important function of the deep learning (DL) channel equalization and symbol detection systems is the ability to predict the user’s original transmitted data. Generally, the behavior and performance of the deep artificial neural networks (DANNs) rely on three main aspects: the network structure, the learning algorithms, and the activation functions (AFs) used in each node in the network. Long short-term memory (LSTM) recurrent neural networks have shown some success in channel equalization and symbol detection. The AFs used in the DANN play a significant role in how the learning algorithms converge. Our article shows how modifying the AFs used in the tan h units (block input and output) of the LSTM units can significantly boost the DL equalizer's performance. Additionally, the learning process of the DL model was optimized with the help of two distinct error-measuring functions: default ( cross-entropy ) and sum of squared error ( SSE ). The DL model's performance with different AFs is compared. This comparison is conducted using three distinct learning algorithms: Adam, RMSProp, and SGdm. The findings clearly demonstrate that the most frequently used AFs (sigmoid and hyperbolic tangent functions) do not really make a significant contribution to perfect network behaviors in channel equalization. On the other hand, there are a lot of non-common AFs that can outperform the frequently employed ones. Furthermore, the outcomes demonstrate that the recommended loss functions ( SSE ) exhibit superior performance in addressing the channel equalization challenge compared to the default loss functions ( cross-entropy ).
Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds

The recent impact of COVID-19, as a contagious disease, led researchers to focus on designing and fabricating personal healthcare devices and systems. With the help of wearable sensors, sensing and communication technologies, and recommendation modules, personal healthcare systems were designed for ease of use. More specifically, personal healthcare systems were designed to provide recommendations for maintaining a safe distance and avoiding contagious disease spread after the COVID-19 pandemic. The personal recommendations are analyzed based on the wearable sensor signals and their consistency in sensing. This consistency varies with human movements or other activities that hike/cease the sensor values abruptly for a short period. Therefore, a consistency-focused recommendation system (CRS) for personal healthcare (PH) was designed in this research. The hardware sensing intervals for the system are calibrated per the conventional specifications from which abrupt changes can be observed. The changes are analyzed for their saturation and fluctuations observed from neighbors within the threshold distance. The saturation and fluctuation classifications are performed using random forest learning to differentiate the above data from the previously sensed healthy data. In this process, the saturated data and consistency data provide safety recommendations for the moving user. The consistency is verified for a series of intervals for the fluctuating sensed data. This alerts the user if the threshold distance for a contagious disease is violated. The proposed system was validated using a prototype model and experimental analysis through false rates, data analysis rates, and fluctuations.
Review of Meredith, Giles &amp; Stommel (2022): Analysing Digital Interaction

Preview this online first article: Review of Meredith, Giles & Stommel (2022): Analysing Digital Interaction, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/10.1075/resla.22051.jod/resla.22051.jod-1.gif
MuSelect Chain: trusted decentralized mutual selection through blockchain

Abstract Mutual selection (the process of two types of objects choosing each other) often occurs in practical applications, such as those concerning financial credit. Considering the increasing demands for credibility, traditional artificial methods often cannot satisfy the corresponding requirements for security and transparency. Blockchain technology has the characteristics of decentralization, traceability, transparency, and being tamper-resistant, making it a potential method for solving the abovementioned problems. However, the existing consensus algorithms have some shortcomings in terms of efficiency, fault tolerance, security, and other relevant aspects, rendering them unsuitable for direct implementation in a mutual selection scenario. In this study, a system for mutual selection operations, denoted as “MuSelect Chain," is established. First, the institution information initialization method on blockchain is developed via a smart contract, ensuring the authenticity of information stored on the chain. Second, a mutual selection relationship confirmation algorithm is designed to ensure a reliable automated mutual selection process. Next, considering the characteristics of nodes participating in the network, a consensus algorithm called “Proof-of-Leadership” is proposed to ensure consistency of information stored by different nodes on the chain. Subsequently, an incentive mechanism is established with the focus on improving MuSelect Chain efficiency. Finally, a MuSelect Chain prototype is built. Simulation results prove that the proposed MuSelect Chain is secure with strong fault tolerance.
Physical education of students in the framework of inclusive education

The article touches upon the issues of physical education in higher education within the framework of inclusive education. Its purpose is to describe the options for such organizational and substantive forms. Methods of generalization and systematization of practical experience, logical modeling are used. An algorithm for formation of an inclusive culture among medical students through volunteer activities with children with disabilities is given. Possible forms of physical education classes for students with disabilities through recreational, individual, group forms are described. The authors give an approximate algorithm for organizing a collective inclusive physical education class.
Metaheuristic Algorithms in UAV Path-Planning Optimization: A Systematic Review (2018–2022)

Unmanned Aerial Vehicles (UAVs), a subset of aerial robots, play crucial roles in various domains, such as disaster management, agriculture, and healthcare. Their application proves invaluable in situations where human intervention poses risks or involves high costs. However, traditional approaches to UAV path planning struggle in efficiently navigating complex and dynamic environments, often resulting in suboptimal routes and extended mission durations. This study seeks to investigate and improve the utilization of meta-heuristic algorithms for optimizing UAV path planning. Toward this aim, we carried out a systematic review of five major databases focusing on the period from 2018 to 2022. Following a rigorous two-stage screening process and a thorough quality appraisal, we selected 68 papers out of the initial 1500 to answer our research questions. Our findings reveal that hybrid algorithms are the dominant choice, surpassing evolutionary, physics-based, and swarm-based algorithms, indicating their superior performance and adaptability. Notably, time optimization takes precedence in mathematical models, reflecting the emphasis on CPU time efficiency. The prevalence of dynamic environmental types underscores the importance of real-time considerations in UAV path planning, with three-dimensional (3D) models receiving the most attention for accuracy in complex trajectories. Additionally, we highlight the trends and focuses of the UAV path planning optimization research community and several challenges in using meta-heuristic algorithms for the optimization of UAV path planning. Finally, our analysis further highlights a dual focus in UAV research, with a significant interest in optimizing single-UAV operations and a growing recognition of the challenges and potential synergies in multi-UAV systems, alongside a prevalent emphasis on single-target mission scenarios, but with a notable subset exploring the complexities of multi-target missions.
The evolution of physical education of students in the system of higher education in Russia

On the basis of a retrospective analysis, the description of formation stages of physical education of students of higher educational institutions is given. It is shown that the system of physical education of students of higher educational institutions began to form in the nineteenth century. It is pointed out that initially its content was of a militaryphysical orientation. The period of centralized regulation of physical education in higher educational institutions has been established. It began with the introduction of the first program in 1930 and ended with the end of the program in 2000. A total of ten programs have been implemented in the country. Each program was correlated with the programs of social development of the society and the targets in the field of physical culture and sport. With the introduction of the Federal State Educational Standards, the right to choose the content and forms of physical education of students in order to form their first general cultural, and now universal competencies, was granted to educational organizations that implement the standards of training of bachelors and specialists in higher education programs. As a result, numerous models of physical education of students have appeared.
Performance metrics for the continuous distribution of entanglement in multiuser quantum networks

Entangled states shared among distant nodes are frequently used in quantum network applications. When quantum resources are abundant, entangled states can be continuously distributed across the network, allowing nodes to consume them whenever necessary. This continuous distribution of entanglement enables quantum network applications to operate continuously while being regularly supplied with entangled states. Here, we focus on the steady-state performance analysis of protocols for continuous distribution of entanglement. We propose the virtual neighborhood size and the virtual node degree as performance metrics. We utilize the concept of Pareto optimality to formulate a multiobjective optimization problem to maximize the performance. As an example, we solve the problem for a quantum network with a tree topology. One of the main conclusions from our analysis is that the entanglement consumption rate has a greater impact on the protocol performance than the fidelity requirements. The metrics that we establish in this paper can be utilized to assess the feasibility of entanglement distribution protocols for large-scale quantum networks.
Event-Triggered Hierarchical Planner for Autonomous Navigation in Unknown Environment

End-to-end deep neural network (DNN)-based motion planners have shown great potential in high-speed autonomous UAV flight. Yet, most existing methods only employ a single high-capacity DNN, which typically lacks generalization ability and suffers from high sample complexity. We propose a novel event-triggered hierarchical planner (ETHP), which exploits the bi-level optimization nature of the navigation task to achieve both efficient training and improved optimality. Specifically, we learn a depth-image-based end-to-end motion planner in a hierarchical reinforcement learning framework, where the high-level DNN is a reactive collision avoidance rerouter triggered by the clearance distance, and the low-level DNN is a goal-chaser that generates the heading and velocity references in real time. Our training considers the field-of-view constraint and explores the bi-level structural flexibility to promote the spatio–temporal optimality of planning. Moreover, we design simple yet effective rules to collect hindsight experience replay buffers, yielding more high-quality samples and faster convergence. The experiments show that, compared with a single-DNN baseline planner, ETHP significantly improves the success rate and generalizes better to the unseen environment.
Parameter-Agnostic Deep Graph Clustering

Deep graph clustering, efficiently dividing nodes into multiple disjoint clusters in an unsupervised manner, has become a crucial tool for analyzing ubiquitous graph data. Existing methods have acquired impressive clustering effects by optimizing the clustering network under the parametric condition – predefining the true number of clusters ( K tr ). However, K tr is inaccessible in pure unsupervised scenarios, in which existing methods are incapable of inferring the number of clusters ( K ), causing limited feasibility. This paper proposes the first Parameter-Agnostic Deep Graph Clustering method (PADGC), which consists of two core modules: K -guidence clustering and topological-hierarchical inference, to infer K efficiently and gain impressive clustering predictions. Specifically, K -guidence clustering is employed to optimize the cluster assignments and discriminative embeddings in a mutual promotion manner under the latest updated K , even though K may deviate from K tr . In turn, such optimized cluster assignments are utilized to explore more accurate K in the topological-hierarchical inference, which can split the dispersive clusters and merge the coupled ones. In this way, these two modules are complementarily optimized until generating the final convergent K and discriminative cluster assignments. Extensive experiments on several benchmarks, including graphs and images, can demonstrate the superiority of our method. The mean values of our inferred K , in 11 out of 12 datasets, deviates from K tr by less than 1. Our method can also achieve competitive clustering effects with existing parametric deep graph clustering.
Synthetic Landau levels and robust chiral edge states for dark-state polaritons in a static and scalable continuum media

The mechanism of electromagnetically induced transparency to generate a synthetic magnetic field for neutral particles termed dark-state polaritons (DSP) is invoked. The ability to create Landau levels and robust edge states in DSP is demonstrated, thereby simulating phenomena observed in condensed matter physics.