Reviewerly/exHarmony huggingface dataset:
Pedagogical Design Considerations for Mobile Augmented Reality Serious Games (MARSGs): A Literature Review

As technology advances, conceptualizations of effective strategies for teaching and learning shift. Due in part to their facilitation of unique affordances for learning, mobile devices, augmented reality, and games are all becoming more prominent elements in learning environments. In this work, we examine mobile augmented reality serious games (MARSGs) as the intersection of these technology-based experiences and to what effect their combination can yield even greater learning outcomes. We present a PRISMA review of 23 papers (from 610) spanning the entire literature timeline from 2002–2023. Among these works, there is wide variability in the realized application of game elements and pedagogical theories underpinning the game experience. For an educational tool to be effective, it must be designed to facilitate learning while anchored by pedagogical theory. Given that most MARSG developers are not pedagogical experts, this review further provides design considerations regarding which game elements might proffer the best of three major pedagogical theories for modern learning (cognitive constructivism, social constructivism, and behaviorism) based on existing applications. We will also briefly touch on radical constructivism and the instructional elements embedded within MARSGs. Lastly, this work offers a synthesis of current MARSG findings and extended future directions for MARSG development.
Post-disaster building damage detection using multi-source variational domain adaptation

Timely detection of building damage after a disaster can provide support and help in saving lives and reducing losses. The emergence of transfer learning can solve the problem of difficulty in obtaining several labeled samples to train deep models. However, some degree of differences exists among different scenarios, which may affect the transfer performance. Furthermore, in reality, data can be collected from multiple historical scenarios but cannot be directly combined using single-source domain adaptation methods. Therefore, this study proposes a multi-source variational domain adaptation (MVDA) method to complete the task of post-disaster building assessment. The MVDA method consists of two stages: first, the distributions of each pair of source and target domains in specific feature spaces are aligned separately; second, the outputs of the pre-trained classifiers are aligned using domain-specific decision boundaries. This method maximizes the relevant information in the historical scene, solves the problem of inconsistent image classification in the current scene, and improves the migration efficiency from the history to the current disaster scene. The proposed approach is validated by two challenging multi-source transfer tasks using the post-disaster hurricane datasets. The average accuracy rate of 83.3% for the two tasks is achieved, obtaining an improvement of 0.9% compared with the state-of-the-art methods.
PS-Merge operator in the classification of gait biomarkers: A preliminary approach to eXplainable Artificial Intelligence

Background: Many neurodegenerative diseases affect human gait. Gait analysis is an example of a non-invasive manner to diagnose these diseases. Nevertheless, gait analysis is difficult to do because patients with different neurodegenerative diseases may have similar human gaits. Machine learning algorithms may improve the correct identification of these pathologies. However, the problem with many classification algorithms is a lack of transparency and interpretability for the final user. Methods: In this study, we implemented the PS-Merge operator for the classification, employing gait biomarkers of a public dataset. Results: The highest classification percentage was 83.77%, which means an acceptable degree of reliability. Conclusions: Our results show that PS-Merge has the ability to explain how the algorithm chooses an option, i.e., the operator can be seen as a first step to obtaining an eXplainable Artificial Intelligence (XAI).
Comparison of the Effectiveness of Various Classifiers for Breast Cancer Detection Using Data Mining Methods

Countless women and men worldwide have lost their lives to breast cancer (BC). Although researchers from around the world have proposed various diagnostic methods for detecting this disease, there is still room for improvement in the accuracy and efficiency with which they can be used. A novel approach has been proposed for the early detection of BC by applying data mining techniques to the levels of prolactin (P), testosterone (T), cortisol (C), and human chorionic gonadotropin (HCG) in the blood and saliva of 20 women with histologically confirmed BC, 20 benign subjects, and 20 age-matched control women. In the proposed method, blood and saliva were used to categorize the severity of the BC into normal, benign, and malignant cases. Ten statistical features were collected to identify the severity of the BC using three different classification schemes—a decision tree (DT), a support vector machine (SVM), and k-nearest neighbors (KNN) were evaluated. Moreover, dimensionality reduction techniques using factor analysis (FA) and t-stochastic neighbor embedding (t-SNE) have been computed to obtain the best hyperparameters. The model has been validated using the k-fold cross-validation method in the proposed approach. Metrics for gauging a model’s effectiveness were applied. Dimensionality reduction approaches for salivary biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 66.67% to 93.3% and 90%, respectively, by utilizing t-SNE and FA. Furthermore, dimensionality reduction strategies for blood biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 60% to 80% and 93.3%, respectively, by utilizing FA and t-SNE. These findings point to t-SNE as a potentially useful feature selection for aiding in the identification of patients with BC, as it consistently improves the discrimination of benign, malignant, and control healthy subjects, thereby promising to aid in the improvement of breast tumour early detection.
Evaluation and Analysis of Heuristic Intelligent Optimization Algorithms for PSO, WDO, GWO and OOBO

In order to comprehensively evaluate and analyze the effectiveness of various heuristic intelligent optimization algorithms, this research employed particle swarm optimization, wind driven optimization, grey wolf optimization, and one-to-one-based optimizer as the basis. It applied 22 benchmark test functions to conduct a comparison and analysis of performance for these algorithms, considering descriptive statistics such as convergence speed, accuracy, and stability. Additionally, time and space complexity calculations were employed, alongside the nonparametric Friedman test, to further assess the algorithms. Furthermore, an investigation into the impact of control parameters on the algorithms’ output was conducted to compare and analyze the test results under different algorithms. The experimental findings demonstrate the efficacy of the aforementioned approaches in comprehensively analyzing and comparing the performance on different types of intelligent optimization algorithms. These results illustrate that algorithm performance can vary across different test functions. The one-to-one-based optimizer algorithm exhibited superior accuracy, stability, and relatively lower complexity.
XAutn: Blockchain-based cross domain authentication for digital certificates in the education sector

Traditional testimony and electronic endorsements are extremely challenging to uphold and defend, and there is a problem with challenging authentication. The identity of the student is typically not recognized when it comes to requirements for access to a student’s academic credentials that are scattered over numerous sites. This is an issue with cross-domain authentication methods. On the one hand, whenever the volume of cross-domain authentication requests increases dramatically, the response time can become intolerable because of the slow throughput associated with blockchain mechanisms. These systems still do not give enough thought to the cross-domain scenario’s anonymity problem. This research proposes an effective cross-domain authentication mechanism called XAutn that protects anonymity and integrates seamlessly through the present Certificate Transparency (CT) schemes. XAutn protects privacy and develops a fast response correctness evaluation method that is based on the RSA (Rivest, Shamir, and Adleman) cryptographic accumulator, Zero Knowledge Proof Algorithm, and Proof of Continuous work consensus Algorithm (POCW). We also provide a privacy-aware computation authentication approach to strengthen the integrity of the authentication messages more securely and counteract the discriminatory analysis of malevolent requests. This research is primarily used to validate identities in a blockchain network, which makes it possible to guarantee their authenticity and integrity while also increasing security and privacy. The proposed technique greatly outperformed the current methods in terms of authentication time, period required for storage, space for storage, and overall processing cost. The proposed method exhibits a speed gain of authentication of roughly 9% when compared to traditional blockchain systems. The security investigation and results from experiments demonstrate how the proposed approach is more reliable and trustworthy.
Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control Paradigm

In general, optimal motion planning can be performed both locally and globally. In such a planning, the choice in favor of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning or local planning alongside global planning. When designing optimal motion planning, both local and global, the key metrics to bear in mind are execution time, asymptotic optimality, and quick reaction to dynamic obstacles. Such planning approaches can address the aforementioned target metrics more efficiently compared to other approaches, such as path planning followed by smoothing. Thus, the foremost objective of this study is to analyze related literature in order to understand how the motion planning problem, especially the trajectory planning problem, is formulated when being applied for generating optimal trajectories in real-time for multirotor aerial vehicles, as well as how it impacts the listed metrics. As a result of this research, the trajectory planning problem was broken down into a set of subproblems, and the lists of methods for addressing each of the problems were identified and described in detail. Subsequently, the most prominent results from 2010 to 2022 were summarized and presented in the form of a timeline.
Utilizing User Preferences in Designing the AGILE (Accelerating Access to Gender-Based Violence Information and Services Leveraging on Technology Enhanced) Chatbot

Technology advancements have enhanced artificial intelligence, leading to a user shift towards virtual assistants, but a human-centered approach is needed to assess for acceptability and effectiveness. The AGILE chatbot is designed in Kenya with features to redefine the response towards gender-based violence (GBV) among vulnerable populations, including adolescents, young women and men, and sexual and gender minorities, to offer accurate and reliable information among users.We conducted an exploratory qualitative study through focus group discussions (FGDs) targeting 150 participants sampled from vulnerable categories; adolescent girls and boys, young women, young men, and sexual and gender minorities. The FGDs included multiple inquiries to assess knowledge and prior interaction with intelligent conversational assistants to inform the user-centric development of a decision-supportive chatbot and a pilot of the chatbot prototype. Each focus group comprised 9-10 members, and the discussions lasted about two hours to gain qualitative user insights and experiences. We used thematic analysis and drew on grounded theory to analyze the data.The analysis resulted in 14 salient themes composed of sexual violence, physical violence, emotional violence, intimate partner violence, female genital mutilation, sexual reproductive health, mental health, help-seeking behaviors/where to seek support, who to talk to, and what information they would like, features of the chatbot, access of chatbot, abuse and HIV, family and community conflicts, and information for self-care.Adopting a human-centered approach in designing an effective chatbot with as many human features as possible is crucial in increasing utilization, addressing the gaps presented by marginalized/vulnerable populations, and reducing the current GBV epidemic by moving prevention and response services closer to people in need.
Quantum communication protocols: From theory to implementation in the quantum computer

In recent years, notable progress has been achieved in the theoretical investigation of quantum systems as computational tools. This has given rise to the development of quantum computing and quantum information, fields that delve into the feasibility of employing quantum systems for information processing objectives. Essential to the manipulation of qubits and the facilitation of quantum computations are quantum gates. Comparable to classical gates, these quantum counterparts are actions designed to alter the state of qubits. Among them are the Hadamard gate, CNOT gate, and Toffoli gate, each imbued with distinct functionalities that collectively enrich the repertoire of quantum computation tools. As we progress through this chapter, we embark on a journey that unveils the complexities of quantum communication. From the foundational concepts of quantum mechanics to the advanced realms of quantum teleportation, we have witnessed the potency of quantum entanglement to teleport quantum states. Furthermore, we have delved into the practical implementation of circuits using Qiskit, gaining a grasp of the art of orchestrating qubit operations, measurements, and corrections. Standing at the convergence of the quantum and classical realms, this chapter aims to provide a comprehensive perspective, exposing the intricate web of quantum communication and computing, while paving the way for a future in which quantum technologies redefine the boundaries of the achievable.
Practical Applications of Quantum Computing

With the rapid advancement of quantum computing technology, there is a strong motivation to explore suitable practical applications for quantum algorithms and quantum computers. This chapter focuses on reviewing recent research on practical applications of quantum computing, specifically dimension reduction, pattern recognition, quantum sorting, and quantum communications for which optimized/modified versions of the quantum wavelet transform (QWT) and Grover’s algorithm are presented. For practical implementations of the presented algorithms, this chapter also includes methodologies for classical-to-quantum (C2Q) data encoding and quantum-to-classical (Q2C) data decoding. Additionally, the chapter presents an efficient quantum sorting technique by combining perfect-shuffle and bitonic networks. In the domain of quantum communications, the chapter reviews a technique that combines chaotic communications with quantum key distribution (QKD) to enhance both security and communication range. The effectiveness of these techniques is validated through practical results obtained from experiments conducted on IBM-Q simulators and hardware, as well as on high-performance-reconfigurable-computers (HPRCs). This chapter aims to provide readers with a comprehensive understanding of these applications, covering the necessary prerequisites by showcasing the potential of quantum computing in various domains for innovative problem-solving.
Perspective Chapter: Higher Education Challenges

One of the main challenges that current higher education faces is aspiring to a coherent educational formation in a society that is in permanent change. This has direct implications for how a proposal consistent with the demands and needs of the world of work is built and how it innovates as it progresses. Flexibility and contextualization are two fundamental axes when facing the challenge of current higher education. This allows students to present themselves in a dynamic world that is in permanent change. This means that, in turn, the academic offer must, necessarily, diversify, attentive to the rapid changes in the environment. Therefore, offering a wide variety of options gives students a more realistic and achievable possibility of connecting with the world of work. In this sense, it is important to promote the empowerment of students in the face of their professional development within their university educational path.
Highly-enhanced active beam-wander-correction for free-space quantum communications

In practical applications to free-space quantum communications, the utilization of active beam coupling and stabilization techniques offers notable advantages, particularly when dealing with limited detecting areas or coupling into single-mode fibers(SMFs) to mitigate background noise. In this work, we introduce highly-enhanced active beam-wander-correction technique, specifically tailored to efficiently couple and stabilize beams into SMFs, particularly in scenarios where initial optical alignment with the SMF is misaligned. To achieve this objective, we implement a SMF auto-coupling algorithm and a decoupled stabilization method, effectively and reliably correcting beam wander caused by atmospheric turbulence effects. The performance of the proposed technique is thoroughly validated through quantitative measurements of the temporal variation in coupling efficiency(coincidence counts) of a laser beam(entangled photons). The results show significant improvements in both mean values and standard deviations of the coupling efficiency, even in the presence of 2.6 km atmospheric turbulence effects. When utilizing a laser source, the coupling efficiency demonstrates a remarkable mean value increase of over 50 %, accompanied by a substantial 4.4-fold improvement in the standard deviation. For the entangled photon source, a fine mean value increase of 14 % and an approximate 2-fold improvement in the standard deviation are observed. Furthermore,the proposed technique successfully restores the fidelity of the polarization-entangled state, which has been compromised by atmospheric effects in the free-space channel, to a level close to the fidelity measured directly from the source. Our work will be helpful in designing spatial light-fiber coupling system not only for free-space quantum communications but also for high-speed laser communications.
The cultural-social nucleus of an open community: A multi-level community knowledge graph and NASA application

The challenges faced by science, engineering, and society are increasingly complex, requiring broad, cross-disciplinary teams to contribute to collective knowledge, cooperation, and sensemaking efforts. However, existing approaches to collaboration and knowledge sharing are largely manual, inadequate to meet the needs of teams that are not closely connected through personal ties or which lack the time to respond to dynamic requests for contextual information sharing. Nonetheless, in the current remote-first, complexity-driven, time-constrained workplace, such teams are both more common and more necessary. For example, the NASA Center for HelioAnalytics (CfHA) is a growing and cross-disciplinary community that is dedicated to aiding the application of emerging data science techniques and technologies, including AI/ML, to increase the speed, rigor, and depth of space physics scientific discovery. The members of that community possess innumerable skills and competencies and are involved in hundreds of projects, including proposals, committees, papers, presentations, conferences, groups, and missions. Traditional structures for information and knowledge representation do not permit the community to search and discover activities that are ongoing across the Center, nor to understand where skills and knowledge exist. The approaches that do exist are burdensome and result in inefficient use of resources, reinvention of solutions, and missed important connections. The challenge faced by the CfHA is a common one across modern groups and one that must be solved if we are to respond to the grand challenges that face our society, such as complex scientific phenomena, global pandemics and climate change. We present a solution to the problem: a community knowledge graph (KG) that aids an organization to better understand the resources (people, capabilities, affiliations, assets, content, data, models) available across its membership base, and thus supports a more cohesive community and more capable teams, enables robust and responsible application of new technologies, and provides the foundation for all members of the community to co-evolve the shared information space. We call this the Community Action and Understanding via Semantic Enrichment (CAUSE) ontology. We demonstrate the efficacy of KGs that can be instantiated from the ontology together with data from a given community (shown here for the CfHA). Finally, we discuss the implications, including the importance of the community KG for open science.
Past tense reference to future eventualities

Abstract The simple past in Dutch, as in many other European languages, is not necessarily used to refer to a past eventuality. A Dutch example of a verb in simple past that does not refer to a past event, taken from the Spoken Dutch Corpus (CGN), is: Pa die ging morgen golfen ‘Dad is going to play golf tomorrow.’ Here, the past tense verb ging ‘went’ can be called a ‘fake past’, since it refers to a future eventuality, as can be seen from the adverb tomorrow . We argue that this use of the past tense is not modal, because it does not involve reference to a counterfactual, hypothetical, or unlikely eventuality. We present a Reichenbachian (1947) analysis of this use of past tense, in which we argue that while the eventuality takes place in the future, past tense is used to indicate that the point of perspective (R) is situated in the past (i.e. R-S-E).
Everything is not equal in adult and child Dutch

Abstract An investigation into the production of universal quantifiers with negation in the CHILDES database of Dutch shows several scopal properties that have not been discussed before. First, it shows a crucial distinction between child and adult Dutch. A universal quantifier with scope over negation has an isomorphic interpretation in adult Dutch, but an inverse scope interpretation in child Dutch. This raises the question why children do not adopt the surface scope interpretation. Second, it indicates a possible answer to the puzzle why languages often avoid a universal quantifier under the scope of negation. I will discuss the idea that the explanation may lie in the type of reading of a quantifier, collective/distributive and specific/non-specific. It might also explain why no language has a lexicalized negated universal pronoun * neverything .