Reviewerly/exHarmony huggingface dataset:
Research on the Application of Artificial Intelligence in Education

In recent years, breakthroughs have been made in artificial intelligence technology, which has brought about changes in people's lives. New technologies are changing rapidly in all aspects of life. Artificial intelligence + education came into being, and the form of education has been constantly reshaped. This article introduces the connotation, characteristics and key technologies of artificial intelligence education applications, based on the consideration of the current situation of artificial intelligence application in education in China, so as to sort out and reflect on the advancement strategies of artificial intelligence education applications in the intelligent era, to provide development ideas for the application of artificial intelligence education.
Learning to Count Grave Sites for Cemetery Observation Models With Satellite Imagery

Understanding how people occupy open spaces is important for research in support of population modeling, policy, national security, emergency response, and sustainability. For the past decade, there has been an increase in research toward capturing and reporting population dynamics and patterns of life at the building level and in some open public spaces such as cemeteries and parks. This is done through observation models developed from local sociocultural information acquired at various spatiotemporal scales to inform night, day, and episodic population occupancy estimates (people/1000 sq ft). Sociocultural information for cemeteries and parks is scarcely available and often collected manually. The process is not only marred by inconsistencies but is laborious and time consuming. In this study, we leverage convolutional neural networks (CNNs) and satellite imagery to derive grave site counts as proxy variables to support scalable and accurate sociocultural data required in a population observation model. Through a hybrid workflow (weak localization plus regression model), we characterize a large scale automation process to counting of grave sites. We evaluate and demonstrate the efficacy of proposed workflow using out-of-data set large satellite imagery and establish its broader impact on cemetery observation models.
Serving at the Edge: A Redactable Blockchain with Fixed Storage

As a promising approach to extend cloud resource and service on the Internet-of-Things (IoT), edge computing has attracted significant attention. However, edge computing faces challenges in its decentralized management and data reliability. To meet this gap, many approaches propose to use blockchain technology to enable distributed storage and computation at the edge nodes, thus guaranteeing reliable access and control of the network. However, the resource-constraint nature of the edge node makes it difficult to store the entire chain as the data volume increases. To address this issue, we propose Re-chain, a re-writable blockchain with fixed storage. Re-chain supports re-writing of the onchain historical transactions in chronological order without changing the block hash, as a result, the total size of the blockchain will not increase. Our protocol is consensus-based and uses the proposed threshold trapdoor chameleon hash (TTCH) to constraint re-write operations. With this regards, Re-chain achieves both decentralized re-writing design and fault-tolerance at the same time. We provide security analysis and evaluation experiments to demonstrate the feasibility of Re-chain, the results show that the performance of Re-chain is acceptable when it is executed at a medium scale.
On the Round Complexity of the Shuffle Model

The shuffle model of differential privacy [Bittau et al. SOSP 2017; Erlingsson et al. SODA 2019; Cheu et al. EUROCRYPT 2019] was proposed as a viable model for performing distributed differentially private computations. Informally, the model consists of an untrusted analyzer that receives messages sent by participating parties via a shuffle functionality, the latter potentially disassociates messages from their senders. Prior work focused on one-round differentially private shuffle model protocols, demonstrating that functionalities such as addition and histograms can be performed in this model with accuracy levels similar to that of the curator model of differential privacy, where the computation is performed by a fully trusted party. A model closely related to the shuffle model was presented in the seminal work of Ishai et al. on establishing cryptography from anonymous communication [FOCS 2006]. Focusing on the round complexity of the shuffle model, we ask in this work what can be computed in the shuffle model of differential privacy with two rounds. Ishai et al. showed how to use one round of the shuffle to establish secret keys between every two parties. Using this primitive to simulate a general secure multi-party protocol increases its round complexity by one. We show how two parties can use one round of the shuffle to send secret messages without having to first establish a secret key, hence retaining round complexity. Combining this primitive with the two-round semi-honest protocol of Applebaum, Brakerski, and Tsabary [TCC 2018], we obtain that every randomized functionality can be computed in the shuffle model with an honest majority, in merely two rounds. This includes any differentially private computation. We hence move to examine differentially private computations in the shuffle model that (i) do not require the assumption of an honest majority, or (ii) do not admit one-round protocols, even with an honest majority. For that, we introduce two computational tasks: common element, and nested common element with parameter $$\alpha $$ . For the common element problem we show that for large enough input domains, no one-round differentially private shuffle protocol exists with constant message complexity and negligible $$\delta $$ , whereas a two-round protocol exists where every party sends a single message in every round. For the nested common element we show that no one-round differentially private protocol exists for this problem with adversarial coalition size $$\alpha n$$ . However, we show that it can be privately computed in two rounds against coalitions of size cn for every $$c<1$$ . This yields a separation between one-round and two-round protocols. We further show a one-round protocol for the nested common element problem that is differentially private with coalitions of size smaller than cn for all $$0<c<\alpha <1/2$$ .
Dynamic polarization control for free-space continuous-variable quantum key distribution

Free-space quantum key distribution (QKD) is attractive for the establishment of future global-scale quantum networks. However, it can be quite difficult for dynamic polarization control required in continuous-variable QKD systems to work properly in the presence of channel fading. Here we propose a dynamic polarization control scheme and verify its validity via simulations and an experiment performed over a 150 m free-space channel. The results indicate the capability of the scheme to effectively control the states of polarization for free-space continuous-variable quantum communication.
High-fidelity entangling gates in a three-dimensional ion crystal under micromotion

An ion trap is one of the most promising candidates for quantum computing. Current schemes mainly focus on a linear chain of up to about 100 ions in a Paul trap. To further scale up the qubit number, one possible direction is to use 2D or 3D ion crystals (Wigner crystals). In these systems, ions are generally subjected to large micromotion due to the strong fast-oscillating electric field, which can significantly influence the performance of entangling gates. In this paper, we develop an efficient numerical method to design high-fidelity entangling gates in a general 3D ion crystal. We present numerical algorithms to solve the equilibrium configuration of the ions and their collective normal modes. We then give a mathematical description of the micromotion and use it to generalize the gate scheme for linear ion chains into a general 3D crystal. The involved time integral of highly oscillatory functions is expanded into a fast-converging series for accurate and efficient evaluation and optimization. As a numerical example, we show a high-fidelity entangling gate design between two ions in a 100-ion crystal, with a theoretical fidelity above 99.9%.
Cloud Computing Based on Computational Characteristics for Disaster Monitoring

Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics; explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications; and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance.
Pengaruh Game-Based Learning Terhadap Motivasi dan Prestasi Belajar

The curriculum requirement requires the lecturer to be creative in developing the learning style. However, in interpersonal skill subject, the lecturer only provides things that available on the textbook conventionally and focusing on the material accomplishment of the curriculum without considering the learning expected output. To resolve that problem, the lecturer needs to modify the learning style by providing a learning innovation. One of the alternatives is implementing game-based learning model by playing media card. Therefore, the research objective was to describe positive effect of game-based learning model towards collegian’s learning motivation and achievement in Interpersonal Skill subject in STMIK STIKOM Indonesia. The research method used was Quasi-Experiment with non-equivalent control group design. The research instrument used was a motivation questionnaire and learning achievement test. The research result showed that there was a positive effect of game-based learning model towards Collegian’s motivation and learning achievement in Interpersonal Skill subject.
Warm-Start AlphaZero Self-play Search Enhancements

Recently, AlphaZero has achieved landmark results in deep reinforcement learning, by providing a single self-play architecture that learned three different games at super human level. AlphaZero is a large and complicated system with many parameters, and success requires much compute power and fine-tuning. Reproducing results in other games is a challenge, and many researchers are looking for ways to improve results while reducing computational demands. AlphaZero’s design is purely based on self-play and makes no use of labeled expert data or domain specific enhancements; it is designed to learn from scratch. We propose a novel approach to deal with this cold-start problem by employing simple search enhancements at the beginning phase of self-play training, namely Rollout, Rapid Action Value Estimate (RAVE) and dynamically weighted combinations of these with the neural network, and Rolling Horizon Evolutionary Algorithms (RHEA). Our experiments indicate that most of these enhancements improve the performance of their baseline player in three different (small) board games, with especially RAVE based variants playing strongly.
A GNC Architecture for Planetary Rovers with Autonomous Navigation

This paper proposes a Guidance, Navigation, and Control (GNC) architecture for planetary rovers targeting the conditions of upcoming Mars exploration missions such as Mars 2020 and the Sample Fetching Rover (SFR). The navigation requirements of these missions demand a control architecture featuring autonomous capabilities to achieve a fast and long traverse. The proposed solution presents a two-level architecture where the efficient navigation (lower) level is always active and the full navigation (upper) level is enabled according to the difficulty of the terrain. The first level is an efficient implementation of the basic functionalities for autonomous navigation based on hazard detection, local path replanning, and trajectory control with visual odometry. The second level implements an adaptive SLAM algorithm that improves the relative localization, evaluates the traversability of the terrain ahead for a more optimal path planning, and performs global (absolute) localization that corrects the pose drift during longer traverses. The architecture provides a solution for long-range, low supervision, and fast planetary exploration. Both navigation levels have been validated on planetary analog field test campaigns.
Dynamic Background Subtraction Using Least Square Adversarial Learning

Dynamic Background Subtraction (BS) is a fundamental problem in many vision-based applications. BS in real complex environments has several challenging conditions like illumination variations, shadows, camera jitters, and bad weather. In this study, we aim to address the challenges of BS in complex scenes by exploiting conditional least squares adversarial networks. During training, a scene-specific conditional least squares adversarial network with two additional regularizations including L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -Loss and Perceptual-Loss is employed to learn the dynamic background variations. The given input to the model is video frames conditioned on corresponding ground truth to learn the dynamic changes in complex scenes. Afterwards, testing is performed on unseen test video frames so that the generator would conduct dynamic background subtraction. The proposed method consisting of three loss-terms including least squares adversarial loss, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -Loss and Perceptual-Loss is evaluated on two benchmark datasets CDnet2014 and BMC. The results of our proposed method show improved performance on both datasets compared with 10 existing state-of-the-art methods.
Cloning of Quantum Entanglement

Quantum no-cloning, the impossibility of perfectly cloning an arbitrary unknown quantum state, is one of the most fundamental limitations due to the laws of quantum mechanics, which underpin the physical security of quantum key distribution. Quantum physics does allow, however, approximate cloning with either imperfect state fidelity and/or probabilistic success. Whereas approximate quantum cloning of single-particle states has been tested previously, experimental cloning of quantum entanglement---a highly nonclassical correlation---remained unexplored. Based on a multiphoton linear optics platform, we demonstrate quantum cloning of two-photon entangled states for the first time. Remarkably our results show that one maximally entangled photon pair can be broadcast into two entangled pairs, both with state fidelities above 50%. Our results are a key step towards cloning of complex quantum systems, and are likely to provide new insights into quantum entanglement.
REMIAN

Missing value (MV) imputation is a critical preprocessing means for data mining. Nevertheless, existing MV imputation methods are mostly designed for batch processing, and thus are not applicable to streaming data, especially those with poor quality. In this article, we propose a framework, called Real-time and Error-tolerant Missing vAlue ImputatioN (REMAIN), to impute MVs in poor-quality streaming data. Instead of imputing MVs based on all the observed data, REMAIN first initializes the MV imputation model based on a-RANSAC which is capable of detecting and rejecting anomalies in an efficient manner, and then incrementally updates the model parameters upon the arrival of new data to support real-time MV imputation. As the correlations among attributes of the data may change over time in unforseenable ways, we devise a deterioration detection mechanism to capture the deterioration of the imputation model to further improve the imputation accuracy. Finally, we conduct an extensive evaluation on the proposed algorithms using real-world and synthetic datasets. Experimental results demonstrate that REMAIN achieves significantly higher imputation accuracy over existing solutions. Meanwhile, REMAIN improves up to one order of magnitude in time cost compared with existing approaches.
Exploiting dependency information to improve biomedical event detection via gated polar attention mechanism

This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset.
Scalable Learned Image Compression With A Recurrent Neural Networks-Based Hyperprior

Recently learned image compression has achieved many great progresses, such as representative hyperprior and its variants based on convolutional neural networks (CNNs). However, CNNs are not fit for scalable coding and multiple models need to be trained separately to achieve variable rates. In this paper, we incorporate differentiable quantization and accurate entropy models into recurrent neural networks (RNNs) architectures to achieve a scalable learned image compression. First, we present an RNN architecture with quantization and entropy coding. To realize the scalable coding, we allocate the bits to multiple layers, by adjusting the layer-wise lambda values in Lagrangian multiplier-based rate-distortion optimization function. Second, we add an RNN-based hyperprior to improve the accuracy of entropy models for multiple-layer residual representations. Experimental results demonstrate that our performance can be comparable with recent CNN-based hyperprior methods on Kodak dataset. Besides, our method is a scalable and flexible coding approach, to achieve multiple rates using one single model, which is very appealing.
Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic Models

We discuss Bayesian model uncertainty analysis and forecasting in sequential dynamic modeling of multivariate time series. The perspective is that of a decision-maker with a specific forecasting objective that guides thinking about relevant models. Based on formal Bayesian decision-theoretic reasoning, we develop a time-adaptive approach to exploring, weighting, combining and selecting models that differ in terms of predictive variables included. The adaptivity allows for changes in the sets of favored models over time, and is guided by the specific forecasting goals. A synthetic example illustrates how decision-guided variable selection differs from traditional Bayesian model uncertainty analysis and standard model averaging. An applied study in one motivating application of long-term macroeconomic forecasting highlights the utility of the new approach in terms of improving predictions as well as its ability to identify and interpret different sets of relevant models over time with respect to specific, defined forecasting goals.
Co-designing Gamified Intelligent Tutoring Systems with Teachers

Researchers are increasingly interested in Gamified Intelligent Tutoring Systems (ITSs) to provide adaptive instruction and to enhance engagement of students. However, although teachers are demanding to be active users of gamified ITS, they have been not considered as first-class citizens in the design of these kinds of systems. In order to contribute to the active and customized use of gamified ITS by teachers, three technical problems should be considered. First, designing ITS is very complex (i.e., considering different theories, components, and stakeholders) and including gamification may significantly increase such complexity and variability. Second, gamified ITS features can be used depending on several elements (e.g., educational level, knowledge domain, gamification and ITS theories, etc). Thus, it is imperative to take advantage of theories and practices from both topics to reduce the design space of these systems. Third, in order to effectively aid teachers to actively use such systems, it is needed to provide a simple and usable solution for them. To target these problems, in this paper, we present a solution for authoring gamified ITS by teachers that makes use of an ontology-based feature model (OntoSPL) to deal with the variability at runtime and takes advantage of an ontology (GaTO) that connects gamified ITS theories and design practices to constrain the variability space for designing these systems. Our main results indicate that teachers have a high acceptance level (i.e., ease of use, usability, and low complexity) in the design of gamified ITS using the authoring solution, customizing their own gamified tutors in less than five minutes. These results indicate a promising way to explore the use of authoring tools, ontologies, and software engineering to take advantage of both artificial intelligence techniques (mainly for aiding adaptation for students) as well as on the human intelligence of teachers to co-design gamified intelligent tutoring systems.