Reviewerly/exHarmony huggingface dataset:
Free-Space QKD with Modulating Retroreflectors Based on the B92 Protocol

Free-space quantum key distribution (QKD) has attracted considerable attention due to its lower channel loss and link flexibility. It allows two participants share theoretical unconditional secure keys, and can potentially be applied to air-to-ground quantum communication to establish a global quantum network. Free-space QKD using modulating retro-reflectors (MRR-QKD) significantly reduces the pointing requirement and simplifies the structure of the mobile terminal, therefore making it suitable for lightweight aircraft such as unmanned aerial vehicle and Cubesat, etc. Based on intensity modulation of two non-orthogonal states and the B92 protocol, we proposed a scheme to improve the previous work (Optics Express 2018, 26, 11331). Our scheme simplifies the optical structure and shows more robustness in equipment imperfection. The analysis and simulation show that the number of multiple quantum well modulators needed in our scheme decreases from eight to three with similar performance. Additionally, while the previous scheme cannot work due to low modulator extinction ratio or high optical misalignment, our scheme can still operate.
Increasing teacher creativity through strengthening transformational leadership, teamwork, and work engagement

Teacher creativity is needed to encourage the improvement of the quality of school graduates, so that in the end it will accelerate the achievement of national education goals in Indonesia.This study aims to find strategies and ways to increase teacher creativity to be used as input and recommendations for related parties, namely teachers, school principals, and the office of the ministry of religion.This is done by testing the strength of the relationship between teacher creativity (Y) and transformational leadership (X1), teamwork (X2, and work engagement (X3).This research uses correlational research methods, and is carried out using quantitative methods and data collection through surveys.then explained by statistical analysis, analyzed by correlational analysis and SITOREM analysis.The population of this study were 163 permanent teachers of madrasah tsanawiayah in Madiun City.The research sample of 116 respondents was determined by the Taro Yamane formula, selected using the proportional random sampling method.The results of quantitative research found that transformational leadership that emphasizes idealized influence, intellectual simulation, inspirational motivation, instructional support, individual consideration, and the influence of ethical values can increase teacher creativity.Teamwork that emphasizes group goals, active participation of members, prioritizing togetherness, communicating with each other, complementing each other, and sharing can improve the quality of teacher services.Work engagement that emphasizes active participation in work, competence, importance of work, development opportunities, level of performance, and interest in organizational goals can improve the quality of teacher services.
AI Derivatives: the Application to the Derivative Work Right to Literary and Artistic Productions of AI Machines

This Article predicts that there will be attempts to use courts to try to broaden the derivative work right in litigation either to prevent the use of, or claim protection for, literary and artistic productions made by Artificial Intelligence (AI) machines. The Article considers the normative valence and the (significant) doctrinal pitfalls associated with such attempts. It also considers a possible legislative alternative, namely attempts to introduce a new sui generis right in AI productions. Finally, the Article explains how, whether such attempts succeed or not, the debate on rights (if any) in productions made by AI machines is distinct from the debate on text and data mining exceptions.
THE USE OF ROSETTA STONE LANGUAGE LEARNING SOFTWARE IN TEACHING LISTENING: DOCUMENTARY RESEARCH

This study focused on analyzing the use of Rosetta Stone Language Learning Software in teaching listening comprehension. Based on three previous journal from different universities in Indonesia the writer found that selected institutional repository website in Indonesia discusses about using Rosetta Stone Language Learning Software in teaching listening comprehension. The data described the facts on the use of Rosetta Stone based on the results of research by three researchers which have been published in three scholarly publications in Indonesia. This research is to find out the effectiveness of Rosetta Stone Software in teaching listening comprehension, this study had two data, the primary data (research journals) and secondary data (books). The conclusion that can be taken based on these research reports is the use of Rosetta Stone Software in improving students’ listening skills is effective. There are significant differences in learning listening comprehension between those who had been taught by using Rosetta Stone media and those who had not been taught by using Rosetta Stone media in terms of the scores the students got in the pre-test and post-test.
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation

Development of distributed Multi-Agent Reinforcement Learning (MARL) algorithms has attracted an increasing surge of interest lately. Generally speaking, conventional Model-Based (MB) or Model-Free (MF) RL algorithms are not directly applicable to the MARL problems due to utilization of a fixed reward model for learning the underlying value function. While Deep Neural Network (DNN)-based solutions perform well, they are still prone to overfitting, high sensitivity to parameter selection, and sample inefficiency. In this paper, an adaptive Kalman Filter (KF)-based framework is introduced as an efficient alternative to address the aforementioned problems by capitalizing on unique characteristics of KF such as uncertainty modeling and online second order learning. More specifically, the paper proposes the Multi-Agent Adaptive Kalman Temporal Difference (MAK-TD) framework and its Successor Representation-based variant, referred to as the MAK-SR. The proposed MAK-TD/SR frameworks consider the continuous nature of the action-space that is associated with high dimensional multi-agent environments and exploit Kalman Temporal Difference (KTD) to address the parameter uncertainty. The proposed MAK-TD/SR frameworks are evaluated via several experiments, which are implemented through the OpenAI Gym MARL benchmarks. In these experiments, different number of agents in cooperative, competitive, and mixed (cooperative-competitive) scenarios are utilized. The experimental results illustrate superior performance of the proposed MAK-TD/SR frameworks compared to their state-of-the-art counterparts.
Precision Poverty Alleviation Methods in the Agricultural Field Based upon Wireless Communication Networks and Blockchain

Poverty in the agricultural sector has always existed and needs to be urgently solved. How to achieve precise poverty alleviation and poverty alleviation in the agricultural sector has become a top priority now. In this regard, this study combines blockchain technology and wireless network technology to build a blockchain platform. The platform is responsible for assisting the development of agricultural poverty alleviation projects, integrating project data into a public area according to the characteristics of the blockchain. The data cannot be tampered with during storage and transmission, ensuring the authenticity of the data. As the core of blockchain technology, the algorithm of the consensus mechanism plays a crucial role in alleviating poverty in agriculture, using the RBFT consensus mechanism to achieve “precise identification” of the poor. The pilot has shown that the RBFT algorithm will achieve a throughput of 2,980 strokes/second. In addition, the time from node failure to recovery is diminished by 60% compared to the time consumed by the PBFT algorithm. It can be clearly seen that the blockchain platform using the RBFT algorithm is more robust.
Cryptocurrency market reactions to regulatory news 1

Cryptocurrencies are often thought to operate out of the reach of national regulation, but in fact their valuations, transaction volumes and user bases react substantially to news about regulatory actions. The impact depends on the specific regulatory category to which the news relates: events related to general bans on cryptocurrencies or to their treatment under securities law have the greatest adverse effect, followed by news on combating money laundering and the financing of terrorism, and on restricting the interoperability of cryptocurrencies with regulated markets. News pointing to the establishment of specific legal frameworks tailored to cryptocurrencies and initial coin offerings coincides with strong market gains. These results suggest that cryptocurrency markets rely on regulated financial institutions to operate and that these markets are segmented across jurisdictions.
Sovereign Digital Consent through Privacy Impact Quantification and Dynamic Consent

Digitization is becoming more and more important in the medical sector. Through electronic health records and the growing amount of digital data of patients available, big data research finds an increasing amount of use cases. The rising amount of data and the imposing privacy risks can be overwhelming for patients, so they can have the feeling of being out of control of their data. Several previous studies on digital consent have tried to solve this problem and empower the patient. However, there are no complete solution for the arising questions yet. This paper presents the concept of Sovereign Digital Consent by the combination of a consent privacy impact quantification and a technology for proactive sovereign consent. The privacy impact quantification supports the patient to comprehend the potential risk when sharing the data and considers the personal preferences regarding acceptance for a research project. The proactive dynamic consent implementation provides an implementation for fine granular digital consent, using medical data categorization terminology. This gives patients the ability to control their consent decisions dynamically and is research friendly through the automatic enforcement of the patients’ consent decision. Both technologies are evaluated and implemented in a prototypical application. With the combination of those technologies, a promising step towards patient empowerment through Sovereign Digital Consent can be made.
Dynamic Activity Index for Feature Engineering of Geodynamic Data for Safe Underground Isolation of High-Level Radioactive Waste

In this study, we developed a new approach for feature engineering in geosciences. The main focus of this study was feature engineering based on the implementation of the dynamic activity index (MDAI) as a function of the anomaly of the spatial distribution of data, using systems and discrete mathematical analysis. The methodology for calculating MDAI by groups, geomorphological variability, the density of tectonic faults, stress-strain state, and magnetic field anomalies, is presented herein for a specific area. A detailed analysis of the correlation matrix of MDAI revealed weak correlations between the development features. This showed that the considered properties of the geological environment are independent sets and can be used in the analysis of its geodynamic stability. As a result, it was found that most of the territory where high-level radioactive waste (HLRW) disposal is currently planned is in a relatively stable zone.
A New <i>L</i>₁ Multi-Kernel Learning Support Vector Regression Ensemble Algorithm With AdaBoost

This paper proposes a new multi-kernel learning ensemble algorithm, called Ada-<inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula>MKL-WSVR, which can be regarded as an extension of multi-kernel learning (MKL) and weighted support vector regression (WSVR). The first novelty is to add the <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> norm of the weights of the combined kernel function to the objective function of WSVR, which is used to adaptively select the optimal base models and their parameters. In addition, an accelerated method based on fast iterative shrinkage thresholding algorithm (FISTA) is developed to solve the weights of the combined kernel function. The second novelty is to propose an integrated learning framework based on AdaBoost, named Ada-<inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula>MKL-WSVR. In this framework, we integrate FISTA into AdaBoost. At each iteration, we optimize the weights of the combined kernel function and update the weights of the training samples at the same time. Then an ensemble regression function of a set of regression functions is output. Finally, two groups of the experiments are designed to verify the performance of our algorithm. On the first group of the experiments including eight datasets from UCI machine learning repository, the MAEs and RMSEs of Ada-<inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula>MKL-WSVR are reduced by 11.14&#x0025; and 9.08&#x0025; on average, respectively. Furthermore, on the second group of the experiments including the COVID-19 epidemic datasets from eight countries, the MAEs and RMSEs of Ada-<inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula>MKL-WSVR are reduced by 31.19&#x0025; and 29.98&#x0025; on average, respectively.
Adaptive Center-of-Mass Relocation for Aerial Manipulator Fault Tolerance

This work proposes a fault tolerant aerial manipulator based on a standard hexarotor vehicle, which is able to maintain independent control of torque and vertical thrust in case of motor failure by adequately changing the manipulator configuration and therefore modifying the position of the system’s center of mass. By analyzing the disturbance torque that the manipulator exerts on the vehicle, conditions to achieve stable flight and operation limits are established. To provide an experimental proof of concept, several flights are performed, both in indoor and outdoor environments, for different size and weight of manipulators.
Implications for COVID-19 vaccination following the European Court of Human Right’s decision in Vavřička and oths v Czech

Mass vaccination in the fight against the global pandemic of COVID-19 brings new questions to the ‘old’ issue of mandatory vaccination. The intention to restrict access to some spheres of life on the basis of an individual’s vaccination status provides fertile soil for potential violations of fundamental subjective rights, such as the right to bodily integrity and the interest not to be subject to medical intervention without consent, the right to a private life, and the right to engage fully within society without unjust discrimination. The potential implications of mandated or necessary vaccination for individual rights have not been fully explored, but the recent decision from the European Court of Human Rights in the case of Vavřička and others v. The Czech Republic (2021) gives us a sense of the Court’s approach to the question, given that the hearing (and subsequent decision) took place at the height of the pandemic. In this commentary, the judgement and the dissenting opinion will be analysed and compared from the perspective of the different understanding of the doctrine of proportionality. It will be argued that the approach, wherein the nature of the considered right is becoming the central question, corresponds more adequately with the aims of the Convention. Consequently, it can be suggested that in considering how to regulate vaccination against coronavirus, it is necessary to reframe the analysis in order to ensure that States remain true to the underlying ethos of the Convention to protect individual rights against State interference.
Detecting Face-Touch Hand Moves Using Smartwatch Inertial Sensors and Convolutional Neural Networks

As per World Health Organization (WHO), avoiding touching the face when people are in public or crowded places is an effective way to prevent respiratory viral infections.This recommendation has become more crucial with the current health crisis and the worldwide spread of COVID-19 pandemic.However, most face touches are done unconsciously, that is why it is difficult for people to monitor their hand moves and try to avoid touching the face all the time.Hand-worn wearable devices like smartwatches are equipped with multiple sensors that can be utilized to track hand moves automatically.This work proposes a smartwatch application that uses small, efficient, and end-to-end Convolutional Neural Networks (CNN) models to classify hand motion and identify Face-Touch moves.To train the models, a large dataset is collected for both left and right hands with over 28k training samples that represents multiple hand motion types, body positions, and hand orientations.The app provides real-time feedback and alerts the user with vibration and sound whenever attempting to touch the face.Achieved results show state of the art face-touch accuracy with average recall, precision, and F1-Score of 96.75%, 95.1%, 95.85% respectively, with low False Positives Rate (FPR) as 0.04%.By using efficient configurations and small models, the app achieves high efficiency and can run for long hours without significant impact on battery which makes it applicable on most off-the-shelf smartwatches.
Socialization in language learning: Topic modeling and bibliometric analysis

This study identifies research status, trends, and topics within the field of socialization in language learning using a topic-based bibliometric methodology combining structural topic modeling, the Mann-Kendall trend test, and keyword analysis with bibliometric indicators. Based on relevant literature on socialization in language learning from the Web of Science database, we focused on answering questions such as “what technologies have been adopted to facilitate socialization in language learning?” and “what research topics have been investigated by scholars?” to understand where the research field has been and where it is going. Because traditional manual coding and systematic reviews are prone to error and coding inconsistency when evaluating limited literature data, we integrated rigorous machine learning algorithms and statistical tests, using a topic-based bibliometric methodology. Such a methodology is smarter because it allows time-honored bibliometrics to mine large volumes of literature data enabling a comprehensive understanding of diverse aspects of socialization in language learning. These include the top contributors (e.g., journals, subjects, countries/regions, and institutions), research topics and the dynamics of interest in topics. Results derived from our smart analysis provide insights into effective ways to advance smart computer-assisted language learning from a socialization perspective through scientific collaborations and the effective adoption of innovative technologies and analytical techniques.
Cross-View Panorama Image Synthesis

In this paper, we tackle the problem of ground-view panorama image generation conditioning on top-view aerial image, which is a challenging problem due to large gap between image domains associated with different view-points. Instead of learning the underlying cross-view mapping by a feedforward network as previous methods, we propose a novel adversarial feedback GAN framework named PanoGAN consisting of two key components: an adversarial feedback module and a dual branch discrimination strategy. First, aerial image is fed into the generator to produce panorama image and segmentation map, which facilitates the use of semantic layout information for model training. Second, the discriminator's feature responses of model outputs are encoded by the adversarial feedback module and then fed back to the generator for the next round of generation. Continual improvement of generated image quality is achieved through an iterative generation process. Third, to pursue high-fidelity and semantic consistency of the generated panorama image, we propose a pixel-segmentation alignment mechanism under the dual branch discrimiantion strategy that promotes the cooperation between generator and discriminator. Extensive experimental results on two challenging cross-view image datasets show that the proposed PanoGAN generates high-quality panorama images with more convincing details than state-of-the-art methods. The source code and trained models are available at https://github.com/sswuai/PanoGAN.
Image Real-Time Detection Using LSE-Yolo Neural Network in Artificial Intelligence-Based Internet of Things for Smart Cities and Smart Homes

In this paper, a novel visual image real-time detection LSE-Yolo neural network is presented, which is in artificial intelligence-based Internet of Things for smart cites and smart homes. Despite the great achievements that have been acquired in image detection, the issue of visual image real-time detection combined with privacy data protection to serve for smart cities and smart homes has been overlooked. The technique we applied in our study is referred to as visual object detection, which can contribute to more healthy and comfortable life. When several studies have been carried out to test the validity, it is suggested that our proposed LSE-Yolo neural network has better performance in image real-time detection based on AIoT for smart cities and smart homes. And it is similar to state-of-the-art. The fruitful work has made great contributions to our present understanding of the visual image detection serving for smart cities and smart homes.
Zonotopic Kalman Observer-based Sensor Fault Estimation for Discrete-Time Takagi-Sugeno Fuzzy Systems

This paper focuses on sensor fault estimation for discrete-time Takagi-Sugeno fuzzy systems with unknown process and measurement noises. First, the original T-S system is transformed into an augmented one to estimate the fault and system state simultaneously. A Zonotopic Kalman Observer is then designed by combing the structure of Kalman observer and zonotope theory. In addition, we propose a gain optimality method by minimizing the Frobenius norm of zonotopes. Finally, A truck-trailer example is given to highlight the effectiveness of the proposed sensor fault estimation method.