Reviewerly/exHarmony huggingface dataset:
Data Integration Based Human Activity Recognition using Deep Learning Models

Regular monitoring of physical activities such as walking, jogging, sitting, and standing will help reduce the risk of many diseases like cardiovascular complications, obesity, and diabetes.Recently, much research showed that the effective development of Human Activity Recognition (HAR) will help in monitoring the physical activities of people and aid in human healthcare.In this concern, deep learning models with a novel automated hyperparameter generator are proposed and implemented to predict human activities such as walking, jogging, walking upstairs, walking downstairs, sitting, and standing more precisely and robustly.Conventional HAR systems are unable to manage real-time changes in the surrounding infrastructure.Improved HAR approaches overcome this constraint by integrating multiple sensing modalities.These multiple sensors can produce accurate information, leading to a better perception of activity recognition.The proposed approach uses sensor-level fusion to integrate gyroscope and accelerometer sensors.The analysis is carried out using the widely accepted benchmark UCI-HAR dataset.Based on several performance evaluation experiments, the classification accuracy of long shortterm memory (LSTM), convolutional neural network (CNN), and deep neural network (DNN) classifiers is reported to be 96%, 92%, and 93%, respectively.Compared to state-of-the-art deep learning models, the proposed method gives better results.
A Second Life for Educators: A Hybrid Extended Reality Education Between Zuckerberg’s Vision and Educational Researchers’ Imaginary

While Extended Reality (XR) education is a contested future that consists of diverse discourses, Meta’s monopoly of XR technology leads to the misconception that Mark Zuckerberg’s vision will be the only future of Metaverse education. To address this discursive power imbalance, this paper draws on the Sociology of Knowledge Approach to Discourse to analyze the interests and significance behind different discourses on XR education. In particular, through an exhaustive comparison between Zuckerberg’s vision for Metaverse education and educational researchers’ imaginary of XR education, I identify that the primary difference between these discourses is the extent of users’ digital autonomy. This difference unveils the two parties’ grasping motivations: whereas Zuckerberg uses his utopian vision to cover Meta’s recent economic crisis and to secure Meta’s power in the future education market, educational researchers employ their decentralized imaginary to maximize their degree of freedom to create in the future learning environment. Lastly, I argue that the two radical dreams should learn from one another about digital governance. Thus, a hybrid XR education is proposed.
Quantum phase transition in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:mi>X</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:math> central spin model

We investigate the quantum phase transition (QPT) in the XXZ central spin model, which can be described as a spin-1/2 particle coupled to N bath spins. In general, the QPT is supposed to occur only in the thermodynamical limit. In contrast, we present that the central spin model exhibits a normal-to-superradiant phase transition in the limit where the ratio of the transition frequency of the central spin to that of the bath spins and the number of the bath spins tend to infinity. We give the low-energy effective Hamiltonian analytically in the normal phase and the superradiant phase, and we find that the longitudinal interaction can significantly influence the excitation number and the coherence of the ground state. These two quantities are remarkably enhanced for the negative longitudinal interaction while suppressed for the positive longitudinal interaction. We also use the quantum Fisher information (QFI) to characterize the QPT and illustrate a measurement scheme that can be applied in practice. This work builds a novel connection between the qubit-spin systems and the qubit-field systems, which provides a possibility for the realization of criticality-enhanced quantum sensing in central spin systems.
Editorial values for news recommenders

This chapter proposes concrete methods for specifying the editorial behavior of news recommendation systems through collaboration between journalists and technologists to create metrics, data sets, feedback methods, and evaluation protocols. So far, the desired behavior of news recommenders has mostly been specified in terms of principles or guidelines. I argue that natural language specifications are inadequate because the translation to software must subsequently be undertaken by technical specialists, a process which requires consequential values-related decisions. Instead, I propose the specification of recommender editorial values through the collaborative creation of specific value-laden technical artifacts already used in contemporary engineering. These artifacts are much more precise than principles, yet do not require the technical understanding necessary to create novel algorithms.
UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT

This paper presents a novel approach for autonomous cooperating UAVs in search and rescue operations in subterranean domains with complex topology. The proposed system was ranked second in the Virtual Track of the DARPA SubT Finals as part of the team CTU-CRAS-NORLAB. In contrast to the winning solution that was developed specifically for the Virtual Track, the proposed solution also proved to be a robust system for deployment onboard physical UAVs flying in the extremely harsh and confined environment of the real-world competition. The proposed approach enables fully autonomous and decentralized deployment of a UAV team with seamless simulation-toworld transfer, and proves its advantage over less mobile UGV teams in the flyable space of diverse environments. The main contributions of the paper are present in the mapping and navigation pipelines. The mapping approach employs novel map representations—SphereMap for efficient risk-aware long-distance planning, FacetMap for surface coverage, and the compressed topologicalvolumetric LTVMap for allowing multirobot cooperation under low-bandwidth communication. These representations are used in navigation together with novel methods for visibility-constrained informed search in a general 3D environment with no assumptions about the environment structure, while balancing deep exploration with sensor-coverage exploitation. The proposed solution also includes a visual-perception pipeline for on-board detection and localization of objects of interest in four RGB stream at 5 Hz each without a dedicated GPU. Apart from participation in the DARPA SubT, the performance of the UAV system is supported by extensive experimental verification in diverse environments with both qualitative and quantitative evaluation.
Using MOOC to Learn the Python Programming Language

The article carries out a quantitative analysis of online courses on learning the Python programming language as of October 2022. Such online platforms as Codecademy, Alison, FutureLearn, Udemy, Edx were considered. The main requirements for software engineers training, as well as features of online courses on learning programming languages were highlighted. The authors analyzed samples of online courses, which were a) generated with an automatic search by the keyword “Python” and b) found manually by relevant thematic sections. The analysis was carried out separately for each sample according to various criteria: a number of courses, an approximate level of student training, the cost of taking an online course, and a possibility of obtaining a certificate/diploma. The largest number of courses on learning Python is presented on Udemy platform. Alison platform offers the fewest number of courses for learning the Python language. All platforms are aimed at different level of student training (beginner, intermediate, advanced). On Alison and Edx platforms, 100% of the courses are free, while on FutureLearn platform, all courses are payable. After completing online courses, students can receive a certificate (all MOOC) and/or a diploma (Alison, Edx). As Python popularity is growing, further research is planned to analyze online courses which are represented on various MOOC and are related to the use of the Python programming language in different areas.
Improved rate of approximation by modification of Baskakov operator

The optimal order of approximation, |L n f (x)f (x)| of a linear positive operator L n f (x) is 1/n and can not be improved however smooth the function may be.We remove the positivity of the Baskakov operator V n ( f ;x) and introduce its three variants V M,i n ( f ;x) , i = 1,2,3.We prove that the rates of approximation by these operators are improved from the linear order 1/n to quadratic order 1/n 2 and then to cubic order 1/n 3 for sufficiently smooth functions.
Hybrid improved capuchin search algorithm for plant image thresholding

With the development and wider application of meta-heuristic optimization algorithms, researchers increasingly apply them to threshold optimization of multi-level image segmentation. This paper explores the performance and effects of Capuchin Search Algorithm (CAPSA) in threshold optimization. To solve problems of uneven distribution in the initial population of Capuchin Search Algorithm, low levels of global search performance and premature falling into local optima, this paper proposes an improved Capuchin Search Algorithm (ICAPSA) through a multi-strategy approach. ICAPSA uses chaotic opposite-based learning strategy to initialize the positions of individual capuchins, and improve the quality of the initial population. In the iterative position updating process, Levy Flight disturbance strategy is introduced to balance the global optimization and local exploitation of the algorithm. Finally, taking Kapur as the objective function, this paper applies ICAPSA to multi-level thresholding in the plant images, and compares its segmentation effects with the original CAPSA, the Fuzzy Artificial Bee Colony algorithm (FABC), the Differential Coyote Optimization Algorithm (DCOA), the Modified Whale Optimization Algorithm (MWOA) and Improved Satin Bowerbird Optimization Algorithm (ISBO). Through comparison, it is found that ICAPSA demonstrates superior segmentation effect, both in the visual effects of image segmentation and in data comparison.
Decohering tensor network quantum machine learning models

Abstract Tensor network quantum machine learning (QML) models are promising applications on near-term quantum hardware. While decoherence of qubits is expected to decrease the performance of QML models, it is unclear to what extent the diminished performance can be compensated for by adding ancillas to the models and accordingly increasing the virtual bond dimension of the models. We investigate here the competition between decoherence and adding ancillas on the classification performance of two models, with an analysis of the decoherence effect from the perspective of regression. We present numerical evidence that the fully decohered unitary tree tensor network (TTN) with two ancillas performs at least as well as the non-decohered unitary TTN, suggesting that it is beneficial to add at least two ancillas to the unitary TTN regardless of the amount of decoherence may be consequently introduced.
Breast Cancer Detection from Histopathology Images Based on YOLOv5

In biomedicine, cells nuclei detection is a leading topic research and one of the most challenging. Histopathological analysis represents the cells of the tissue sample that provide a set of essential information for detection and characterization of the cancer. Deep learning (DL) has recently demonstrated promising performance in breast cancer diagnosis, thus in this paper, we present a cells detection algorithm, based on the well-knownYOLOv5 DL model as the backbone network. The proposed algorithm is applied on a public dataset of hematoxylin and eosin (H&E) stained tissue images for evaluation.
Modelling L1 and the artificial language during artificial language learning

Artificial language learning research has become a popular tool to investigate universal mechanisms in language learning. However, often it is unclear whether the found effects are due to learning, or due to artefacts of the native language or the artificial language, and whether findings in only one language will generalise to speakers of other languages. The present study offers a new approach to model the influence of both the L1 and the target artificial language on language learning. The idea is to control for linguistic factors of the artificial and the native language by incorporating measures of wordlikeness into the statistical analysis as covariates. To demonstrate the approach, we extend Linzen and Gallagher (2017)’s study on consonant identity pattern to evaluate whether speakers of German and Mandarin rapidly learn the pattern when influences of L1 and the artificial language are accounted for by incorporating measures assessed by analogical and discriminative learning models over the L1 and artificial lexicon. Results show that nonwords are more likely to be accepted as grammatical if they are more similar to the trained artificial lexicon and more different from the L1 and, crucially, the identity effect is still present. The proposed approach is helpful for designing cross-linguistic studies.
Tailored cluster states with high threshold under biased noise

Abstract Fault-tolerant cluster states form the basis for scalable measurement-based quantum computation. Recently, new stabilizer codes for scalable circuit-based quantum computation have been introduced that have very high thresholds under biased noise where the qubit predominantly suffers from one type of error, e.g. dephasing. However, extending these advances in stabilizer codes to generate high-threshold cluster states for biased noise has been a challenge, as the standard method for foliating stabilizer codes to generate fault-tolerant cluster states does not preserve the noise bias. In this work, we overcome this barrier by introducing a generalization of the cluster state that allows us to foliate stabilizer codes in a bias-preserving way. As an example of our approach, we construct a foliated version of the XZZX code which we call the XZZX cluster state. We demonstrate that under a circuit-level-noise model, our XZZX cluster state has a threshold more than double the usual cluster state when dephasing errors are more likely than errors that cause bit flips by a factor of order ~100 or more.
MQT QMAP

Quantum computing is an emerging technology that has the potential to revolutionize fields such as cryptography, machine learning, optimization, and quantum simulation. However, a major challenge in the realization of quantum algorithms on actual machines is ensuring that the gates in a quantum circuit (i.e., corresponding operations) match the topology of a targeted architecture so that the circuit can be executed while, at the same time, the resulting costs (e.g., in terms of the number of additionally introduced gates, fidelity, etc.) are kept low. This is known as the quantum circuit mapping problem. This summary paper provides an overview of QMAP, an open-source tool that is part of the Munich Quantum Toolkit (MQT) and offers efficient, automated, and accessible methods for tackling this problem. To this end, the paper first briefly reviews the problem. Afterwards, it shows how QMAP can be used to efficiently map quantum circuits to quantum computing architectures from both a user's and a developer's perspective. QMAP is publicly available as open-source at https://github.com/cda-tum/qmap.
Analysis on Intelligent Agent based Approach for Software Engineering

A broad area of research, known as "Agent-Based Computing", focuses on developing "agent-based" intelligent software using agent-based techniques. However, there is a scarcity of research focusing on providing enough evidence of the superiority of agent-based techniques in creating complex software systems. This article has attempted to provide evidence for why agent-based techniques are superior to traditional methods for creating complex software systems, such as control systems. A case of a distinct agent-based control system (the power transportation management system used by Iber-26 drola) is used to illustrate these broader principles. This line of reasoning allows advocates of complex software engineering paradigms to accurately assert that their method can replicate the essential ideas behind agent-based computing. When broken down to their most basic components, agent-based systems are just computer programmes, and every programme has the same set of computable functionalities. The value of a paradigm lies in the way of thinking and the tools it teaches to programmers. As such, agent-based ideas and approaches are not just an extension of those now accessible within existing paradigms, but also well suited to the development of large, networked systems.
Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotations

Bilateral postural symmetry plays a key role as a potential risk marker for autism spectrum disorder (ASD) and as a symptom of congenital muscular torticollis (CMT) in infants, but current methods of assessing symmetry require laborious clinical expert assessments. In this paper, we develop a computer vision based infant symmetry assessment system, leveraging 3D human pose estimation for infants. Evaluation and calibration of our system against ground truth assessments is complicated by our findings from a survey of human ratings of angle and symmetry, that such ratings exhibit low inter-rater reliability. To rectify this, we develop a Bayesian estimator of the ground truth derived from a probabilistic graphical model of fallible human raters. We show that the 3D infant pose estimation model can achieve 68% area under the receiver operating characteristic curve performance in predicting the Bayesian aggregate labels, compared to only 61% from a 2D infant pose estimation model and 60% from a 3D adult pose estimation model, highlighting the importance of 3D poses and infant domain knowledge in assessing infant body symmetry. Our survey analysis also suggests that human ratings are susceptible to higher levels of bias and inconsistency, and hence our final 3D pose-based symmetry assessment system is calibrated but not directly supervised by Bayesian aggregate human ratings, yielding higher levels of consistency and lower levels of inter-limb assessment bias <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Real Country Experiences: On-line Teaching in Wartime after Pandemic in Ukraine

The manuscript points on the new challenges in higher education in terms of wartime in Ukraine after the world pandemic. The real country experience in English on-line teaching and learning is defined as it was important during the lockdown and it is suitable now in wartime in Ukraine. The aim of the research is to show helpful ideas, experience, to stress the advantages for English on-line teaching and learning. Some general theoretical methods as analysis and synthesis, and the empirical one as questionnaires discovered the advantages of the issue under study. The research has pointed out to the results: on-line teaching and learning prove the development of all the English language skills; off-line teaching and learning are basically useful for the students with high motivation. The Moodle platform (version 3.7), the University platform “Educational Electronic Information Complex” (EEIC), Zoom, Meet, Skype, Microsoft Teams are used for on-line teaching and learning. The most common advantages are the prospect to watch learning material on a laptop or mobile phone screen, to start any type the recordings as audio as video in the best quality. The conclusion is it is necessary to find a balance between on-line and off-line learning, communication, and interaction.
Internet of Thing Based Confidential Healthcare Data Storage, Access Control and Monitoring Using Blockchain Technique

Internet of Things plays a significant role in multiple sectors like agriculture, manufacturing and healthcare for collecting information to automation. The collected information is in different diversity and consists of confidential and non-confidential information. Secure handling of confidential data is a crucial task in cloud computing like storage, access control and monitoring. The blockchain based storage technique provides immutable data storage, efficient access control and dynamic monitoring to confidential data. Thus, the secure internet of things data storage, access control and monitoring using blockchain technique is proposed in this work. The patients health information that are in different formats are pruned by a decision tree algorithm and it classifies the confidential data and non-confidential data by the fuzzy rule classification technique. Depending on data owner's willing, the fuzzy rule is framed and the confidential and non-confidential data collected by internet of things sensors are classified. To provide confidentiality to confidential data, Attribute Based Encryption is applied to confidential data and stored in an off-chain mode of blockchain instead of entire data encryption and storage. The non-confidential data is stored in a plaintext form in cloud storage. When compared to support vector machine, K-nearest neighbor and Naive Bayes classification techniques, the proposed fuzzy rule based confidential data identification produces greater than 96 % of accuracy based on data owner willing and confidential data storage takes lesser than 20 % of storage space and processing time in an entire data storage. Additionally, the blockchain performances like throughput, network scalability and latency is optimized through minimal block size and transactions. Thus, our experimental results show that the proposed blockchain based internet of things data storage, access control and monitoring technique provides better confidentiality and access control to confidential data than the conventional cloud storage technique with lesser processing time.
The Effect of Structural Equation Modeling on Chatbot Usage: An Investigation of Dialogflow

This study aims to understand users' perceptions of using the Dialogflow framework and verify the relationships among service awareness, task-technology fit, output quality, and TAM variables. Generalized Structured Component Analysis was employed to experiment with six hypotheses. Two hundred twenty-seven participants were recruited through the purposive non-random sampling technique. Google Forms was utilized as a medium to develop and distribute survey questionnaires to subjects of interest. The experimental results indicated that perceived ease of use and usefulness had a statistically significant and positive influence on behavioral intention. Awareness of service and output quality was considered reliable predictors of perceived usefulness. Also, perceived task-technology fit positively affected perceived ease of use. The model specification accounted for 50.04% of the total variation. The findings can be leveraged to reinforce TAM in future research in a comparative academic context to validate the hypothesis. Several practitioner recommendations and the study's limitations have been presented.