Reviewerly/exHarmony huggingface dataset:
Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest

Medical research shows that eye movement disorder is related to many kinds of neurological diseases. Eye movement characteristics can be used as biomarkers of Parkinson's disease, Alzheimer's disease, schizophrenia and other diseases. However, due to the unknown medical mechanism of some diseases, it is difficult to establish an intuitive correspondence between eye movement characteristics and diseases. In this paper, we propose a classification method of diseases based on decision tree and random forest. Firstly, a variety of experimental schemes are designed to obtain eye movement images, and information such as pupil position and area are extracted as original features. Secondly, with the original features as training samples, the LSTM network is used to build classifiers, and the classification results of the samples are regarded as the evolutionary features. After that, multiple decision trees are constructed according to C4.5 rules based on the evolutionary features. Finally, a random forest is constructed with these decision trees, and the results of disease classification are determined by voting. Experiments show that the classification accuracy of random forest is obviously better than the performance of the previous classifiers, and has good robustness. This study shows that the application of advanced AI technology in the pathological analysis of eye movement has obvious advantages and good prospects.
Lessons learned from data stream classification applied to credit scoring

The financial credibility of a person is a factor used to determine whether a loan should be approved or not, and this is quantified by a ‘credit score,’ which is calculated using a variety of factors, including past performance on debt obligations, profiling, amongst others. Machine learning has been widely applied to automate the development of effective credit scoring models over the years. Yet, studies show that the development of robust credit scoring models may take longer than a year, and thus, if the behavior of customers changes over time, the model will be outdated even before its deployment. In this paper, we made 3 anonymized real-world credit scoring datasets available alongside the results obtained. In each of these datasets, we verify whether the credit scoring task should be thought as an ephemeral scenario since many of the variables may drift over time, and thus, data stream mining techniques should be used since they were tailored for incremental learning and to detect and adapt to changes in the data distribution. Therefore, we compare both traditional batch machine learning algorithms with data stream algorithms in different validation schemes using both Kolmogorov–Smirnov and Population Stability Index metrics. Furthermore, we also provide insights on the importance of features according to their Information Value, Mean Decrease Impurity, and Mean Positional Gain metrics, such that the last depicts changes in the importance of features over time. For 2 of the 3 tested datasets, the results obtained by data stream learners are comparable to predictive models currently in use, thus showing the efficiency of data stream classification for the credit scoring task.
Are Future Professionals Willing to Implement Service Robots? Attitudes of Hospitality and Tourism Students towards Service Robotization

This paper aims to examine attitudes of hospitality and tourism students, as future professionals, towards willingness to implement service robots. The study proposes a new theoretical conceptual model that includes new constructs and items, differentiating it from the others. The model was formed based on the extensive literature review and the interview with an eight-member focus group (hotel managers and academic researchers). Data collection was performed in two stages, pilot research based on 82 respondents and the main study, with the final number of respondents being 236. The initial results of the exploratory factor analysis were further tested using the confirmatory factor analysis. After the exclusion of several items due to low factor loadings and in order to improve model validity, analyses further suggested a nine-dimensional solution with 45 items. The study findings reveal a positive relationship between seven constructs and students’ willingness to implement service robots, with the expected business outcome being the most influencing one. On the other hand, positive relation was not found for empathy and social influence constructs. Theoretical contributions and practical implications are discussed in the paper. In conclusion, study limitations and future research suggestions are provided.
Artificial Synapse with Mnemonic Functionality using GSST-based Photonic Integrated Memory

Here we present a multi-level discrete-state nonvolatile photonic memory based on an ultra-compact ( ) hybrid phase change material GSST-silicon Mach Zehnder modulator, with low insertion losses (3dB), to serve as node in a photonic neural network. Emulating an opportunely trained 100×100 fully connected multilayered perceptron neural network with this weighting functionality embedded as photonic memory, shows up to 92% inference accuracy and robustness towards noise when performing predictions of unseen data.
The Effects of Augmented Reality to Motivation and Performance in EFL Vocabulary Learning

The main purpose of this study was to examine the differences in students' English vocabulary learning performance as well as the instructional materials motivation, comparing the traditional lecturing method and the Augmented Reality method.This study adopted an unequal pre-test and post-test experimental design.Overall, there were a total of 42 students in two fifth grade classes in an elementary school in Central Taiwan who served as the participants of this study.Data were collected by English vocabulary tests, an instructional materials motivation questionnaire, and structured interviews.Data analyses were mixed with the quantitative and qualitative methods.The results showed that the instructional materials motivation and performance of the students taught using AR were superior to those of students taught using the traditional lecturing method.In addition, the qualitative findings of this study identified the opportunities and obstacles when adopting AR and using the traditional lecturing methods to learn English vocabulary.These findings might provide useful insights toward the successful application of English instruction in the educational realm for elementary school EFL students and teachers in Taiwan.
Compact Dilithium Implementations on Cortex-M3 and Cortex-M4

We present implementations of the lattice-based digital signature scheme Dilithium for ARM Cortex-M3 and ARM Cortex-M4. Dilithium is one of the three signature finalists of the NIST post-quantum cryptography competition. As our Cortex-M4 target, we use the popular STM32F407-DISCOVERY development board. Compared to the previous speed records on the Cortex-M4 by Ravi, Gupta, Chattopadhyay, and Bhasin we speed up the key operations NTT and NTT−1 by 20% which together with other optimizations results in speedups of 7%, 15%, and 9% for Dilithium3 key generation, signing, and verification respectively. We also present the first constant-time Dilithium implementation on the Cortex-M3 and use the Arduino Due for benchmarks. For Dilithium3, we achieve on average 2 562 kilocycles for key generation, 10 667 kilocycles for signing, and 2 321 kilocycles for verification.Additionally, we present stack consumption optimizations applying to both our Cortex- M3 and Cortex-M4 implementation. Due to the iterative nature of the Dilithium signing algorithm, there is no optimal way to achieve the best speed and lowest stack consumption at the same time. We present three different strategies for the signing procedure which allow trading more stack and flash memory for faster speed or viceversa. Our implementation of Dilithium3 with the smallest memory footprint uses less than 12kB. As an additional output of this work, we present the first Cortex-M3 implementations of the key-encapsulation schemes NewHope and Kyber.
Compressed Monte Carlo with application in particle filtering

Bayesian models have become very popular over the last years in several fields such as signal processing, statistics, and machine learning. Bayesian inference requires the approximation of complicated integrals involving posterior distributions. For this purpose, Monte Carlo (MC) methods, such as Markov Chain Monte Carlo and importance sampling algorithms, are often employed. In this work, we introduce the theory and practice of a Compressed MC (C-MC) scheme to compress the statistical information contained in a set of random samples. In its basic version, C-MC is strictly related to the stratification technique, a well-known method used for variance reduction purposes. Deterministic C-MC schemes are also presented, which provide very good performance. The compression problem is strictly related to the moment matching approach applied in different filtering techniques, usually called as Gaussian quadrature rules or sigma-point methods. C-MC can be employed in a distributed Bayesian inference framework when cheap and fast communications with a central processor are required. Furthermore, C-MC is useful within particle filtering and adaptive IS algorithms, as shown by three novel schemes introduced in this work. Six numerical results confirm the benefits of the introduced schemes, outperforming the corresponding benchmark methods. A related code is also provided.
Patient Consent Management by a Purpose-Based Consent Model for Electronic Health Record Based on Blockchain Technology

Objectives Currently, patientsâ consent is essential to use their medical records for various purposes; however, most people give their consent using paper forms and have no control over it. Healthcare organizations also have difficulties in dealing with patient consent. The objective of this research is to develop a system for patients to manage their consent flexibly and for healthcare organizations to obtain patient consent efficiently for a variety of purposes. Methods We introduce a new e-consent model, which uses a purpose-based access control scheme; it is implemented by a blockchain system using Hyperledger Fabric. All metadata of patient records, consents, and data access are written immutably on the blockchain and shared among participant organizations. We also created a blockchain chaincode that performs business logic managing patient consent. Results We developed a prototype and checked business logics with the chaincode by validating doctorsâ data access with purpose-based consent of patients stored in the blockchain. The results demonstrate that our system provides a fine-grained way of handling medical staff âs access requests with diverse intended purposes for accessing data. In addition, patients can create, update, and withdraw their consents in the blockchain. Conclusions Our consent model is a solution for consent management both for patients and healthcare organizations. Our system, as a blockchain-based solution that provides high reliability and availability with transparency and traceability, is expected to be used not only for patient data sharing in hospitals, but also for data donation for biobank research purposes. Keywords: Health Information Exchange, Electronic Health Records, Blockchain, Consent Forms, Access to Information
Intrinsic Probing through Dimension Selection

Most modern NLP systems make use of pretrained contextual representations that attain astonishingly high performance on a variety of tasks.Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it.In this paper, we draw a distinction between intrinsic probing, which examines how linguistic information is structured within a representation, and the extrinsic probing popular in prior work, which only argues for the presence of such information by showing that it can be successfully extracted.To enable intrinsic probing, we propose a novel framework based on a decomposable multivariate Gaussian probe that allows us to determine whether the linguistic information in word embeddings is dispersed or focal.We then probe fastText and BERT for various morphosyntactic attributes across 36 languages.We find that most attributes are reliably encoded by only a few neurons, with fastText concentrating its linguistic structure more than BERT. 1
Experimental adaptive Bayesian estimation of multiple phases with limited data

Abstract Achieving ultimate bounds in estimation processes is the main objective of quantum metrology. In this context, several problems require measurement of multiple parameters by employing only a limited amount of resources. To this end, adaptive protocols, exploiting additional control parameters, provide a tool to optimize the performance of a quantum sensor to work in such limited data regime. Finding the optimal strategies to tune the control parameters during the estimation process is a non-trivial problem, and machine learning techniques are a natural solution to address such task. Here, we investigate and implement experimentally an adaptive Bayesian multiparameter estimation technique tailored to reach optimal performances with very limited data. We employ a compact and flexible integrated photonic circuit, fabricated by femtosecond laser writing, which allows to implement different strategies with high degree of control. The obtained results show that adaptive strategies can become a viable approach for realistic sensors working with a limited amount of resources.
Nonreciprocal coherent coupling of nanomagnets by exchange spin waves

Non-Hermitian physics has recently attracted much attention in optics and photonics. Less explored is non-Hermitian magnonics that provides opportunities to take advantage of the inevitable dissipation of magnons or spin waves in magnetic systems. Here we demonstrate non-Hermitian coherent coupling of two distant nanomagnets by fast spin waves with sub-50 nm wavelengths. Magnons in two nanomagnets are unidirectionally phase-locked with phase shifts controlled by magnon spin torque and spin-wave propagation. Our results are attractive for analog neuromorphic computing that requires unidirectional information transmission.
What effect did the introduction of Bitcoin futures have on the Bitcoin spot market?

Bitcoin futures were introduced in December 2017 and this was seen by some as a sign of the most popular cryptocurrency finally being accepted by the financial community. In this paper, we examine the impact of the introduction of Bitcoin futures on the Bitcoin spot market in terms of five characteristics – returns, volatility, skewness, kurtosis and liquidity, using a Bayesian diffusion-regression (state-space) structural time-series model. Our results indicate that the introduction of bitcoin futures potentially exerted a downward impact on the USD bitcoin spot market return and skewness and an upward one on volatility, kurtosis and liquidity, which became higher after futures were introduced. Therefore, our paper offers important insights for investors and regulators, while providing some guidance as to the potential impact of futures markets on other cryptocurrencies.
Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences

In social settings, much of human behavior is governed by unspoken rules of conduct rooted in societal norms.For artificial systems to be fully integrated into social environments, adherence to such norms is a central prerequisite.To investigate whether language generation models can serve as behavioral priors for systems deployed in social settings, we evaluate their ability to generate action descriptions that achieve predefined goals under normative constraints.Moreover, we examine if models can anticipate likely consequences of actions that either observe or violate known norms, or explain why certain actions are preferable by generating relevant norm hypotheses.For this purpose, we introduce Moral Stories, a crowd-sourced dataset of structured, branching narratives for the study of grounded, goaloriented social reasoning.Finally, we propose decoding strategies that combine multiple expert models to significantly improve the quality of generated actions, consequences, and norms compared to strong baselines. 1
Integrated nanophotonics for the development of fully functional quantum circuits based on on-demand single-photon emitters

In recent years, research on integrated quantum circuits has developed rapidly and exciting results have been achieved. The overarching goal of this emerging research direction in the field of modern quantum technology is the scalable integration of quantum functionality on robust chips. Such chips can work independently of one another, but it is even more interesting to develop them modularly for integration into larger quantum networks, thereby linking quantum computation and quantum communication in the same framework. In this context, the ongoing development and further optimization of integrated quantum circuits aim, inter alia, to achieve a quantum advantage in the area of quantum computing and to pave the way for multipartite quantum networks. The functionality of such chips is essentially based on single-photon operations, such as interference at beam splitters in combination with phase shifters in the field of linear optical quantum computing and Bell-state measurements for entanglement swapping in long-distance quantum networks. While individual functionalities such as CNOT gates and more complex quantum computing operations such as boson sampling in a combination of waveguide chips and external photon sources and detectors were successfully demonstrated, the field is currently facing the major challenge of integrating all necessary components monolithically on chip in order to exploit the full potential of integrated quantum nanophotonics. The present Perspective discusses the status and the present challenges of integrated quantum nanophotonics based on on-demand single-photon emitters and gives an outlook on required developments to enter the next level of fully functional quantum circuits for photonic quantum technology.
Blockchain Storage and Computation Offloading for Cooperative Mobile-Edge Computing

To enable more Internet-of-Things (IoT) devices for participating in the Proof-of-Work (PoW) mining process of public blockchains, we propose a cooperative mobile-edge computing (MEC)-aided blockchain network. In the network, devices can offload computation-intensive PoW mining tasks to base stations and store their block data to the cloud service provider. Then, we study the joint computation offloading, block storage, and resource service pricing problem as a three-stage Stackelberg game. We analyze the subgame optimization problem in each stage and propose an iterative algorithm based on backward induction to achieve the Nash equilibrium of the Stackelberg game. Furthermore, we derive the upper bound of the ergodic throughput of the cooperative scheme and the maximum number of devices connected to the network. The analysis shows that the proposed cooperative MEC-aided blockchain network can significantly improve the system throughput, and more devices can access the blockchain network. Analytical results show that the proposed backward induction-based iterative algorithm can efficiently attain the Nash equilibrium of the game. Numerical results show that our proposed backward induction-based iterative algorithm has fast convergence and good stability, and the proposed cooperative scheme can serve more devices in comparison with other noncooperative schemes.
Design and Development of Educational Robot Teaching Resources Using Artificial Intelligence Technology

To further innovate English teaching reform and promote the optimization and development of intelligent and innovative English teaching resources, an intelligent educational robot has been developed based on the current vigorous development of artificial intelligence educational robot technology. In addition, a set of educational robot assisted teaching curriculum resources is designed based on the robot, which is applied in primary school English teaching practice, and has achieved good application effect. The research shows that the robot can complete the functions of English vocabulary teaching, role play and free dialogue, and can further improve students' attention and initiative in classroom practice. The results of the questionnaire survey show that the teaching curriculum based on educational robot has higher scores in the completion of teaching objectives, the design of teaching content, the fluency of teaching links and the innovation of teaching process, which is superior to the traditional English teaching. This exploration provides important case data and theoretical basis for primary school English teaching reform, and it is of great significance to further innovate educational resources and promote teaching reform.
Visualizing Transformers for NLP: A Brief Survey

The introduction of Transformer neural networks has changed the landscape of Natural Language Processing during the last three years. While models inspired by it have managed to lead the boards for a variety of tasks, some of the mechanisms through which these performances were achieved are not necessarily well-understood. Our survey is focused mostly on explaining Transformer architectures through visualizations. Since visualization enables some degree of explainability, we have examined the various Transformer facets that can be explored through visual analytics. The field is still at a nascent stage and is expected to witness dynamic growth in the near future, since the results are already interesting and promising. Currently, some of the visualizations are relatively close to their original models, whereas others are model-agnostic. The visualizations designed to explore the Transformer architectures enable some additional features, like exploration of all neuronal cells or attention maps, therefore providing an advantage for this particular task. We conclude by proposing a set of requirements for future Transformer visualization frameworks.
FE-YOLO: A Feature Enhancement Network for Remote Sensing Target Detection

In the past few decades, target detection from remote sensing images gained from aircraft or satellites has become one of the hottest topics. However, the existing algorithms are still limited by the detection of small remote sensing targets. Benefiting from the great development of computing power, deep learning has also made great breakthroughs. Due to a large number of small targets and complexity of background, the task of remote sensing target detection is still a challenge. In this work, we establish a series of feature enhancement modules for the network based on YOLO (You Only Look Once) -V3 to improve the performance of feature extraction. Therefore, we term our proposed network as FE-YOLO. In addition, to realize fast detection, the original Darknet-53 was simplified. Experimental results on remote sensing datasets show that our proposed FE-YOLO performs better than other state-of-the-art target detection models.