Reviewerly/exHarmony huggingface dataset:
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset

Large-scale public datasets have been shown to benefit research in multiple areas of modern artificial intelligence. For decision-making research that requires human data, high-quality datasets serve as important benchmarks to facilitate the development of new methods by providing a common reproducible standard. Many human decision-making tasks require visual attention to obtain high levels of performance. Therefore, measuring eye movements can provide a rich source of information about the strategies that humans use to solve decision-making tasks. Here, we provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. We demonstrate the usefulness of the dataset through two simple applications: predicting human gaze and imitating human demonstrated actions. The quality of the data leads to promising results in both tasks. Moreover, using a learned human gaze model to inform imitation learning leads to an 115% increase in game performance. We interpret these results as highlighting the importance of incorporating human visual attention in models of decision making and demonstrating the value of the current dataset to the research community. We hope that the scale and quality of this dataset can provide more opportunities to researchers in the areas of visual attention, imitation learning, and reinforcement learning.
Story Realization: Expanding Plot Events into Sentences

Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries. Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence). However, typical neural language generation approaches to event-to-sentence can ignore the event details and produce grammatically-correct but semantically-unrelated sentences. We present an ensemble-based model that generates natural language guided by events.We provide results---including a human subjects study---for a full end-to-end automated story generation system showing that our method generates more coherent and plausible stories than baseline approaches.
GTNet: Generative Transfer Network for Zero-Shot Object Detection

We propose a Generative Transfer Network (GTNet) for zero-shot object detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge Transfer Module. The Object Detection Module can learn large-scale seen domain knowledge. The Knowledge Transfer Module leverages a feature synthesizer to generate unseen class features, which are applied to train a new classification layer for the Object Detection Module. In order to synthesize features for each unseen class with both the intra-class variance and the IoU variance, we design an IoU-Aware Generative Adversarial Network (IoUGAN) as the feature synthesizer, which can be easily integrated into GTNet. Specifically, IoUGAN consists of three unit models: Class Feature Generating Unit (CFU), Foreground Feature Generating Unit (FFU), and Background Feature Generating Unit (BFU). CFU generates unseen features with the intra-class variance conditioned on the class semantic embeddings. FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively. We evaluate our method on three public datasets and the results demonstrate that our method performs favorably against the state-of-the-art ZSD approaches.
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy

While maximizing expected return is the goal in most reinforcement learning approaches, risk-sensitive objectives such as conditional value at risk (CVaR) are more suitable for many high-stakes applications. However, relatively little is known about how to explore to quickly learn policies with good CVaR. In this paper, we present the first algorithm for sample-efficient learning of CVaR-optimal policies in Markov decision processes based on the optimism in the face of uncertainty principle. This method relies on a novel optimistic version of the distributional Bellman operator that moves probability mass from the lower to the upper tail of the return distribution. We prove asymptotic convergence and optimism of this operator for the tabular policy evaluation case. We further demonstrate that our algorithm finds CVaR-optimal policies substantially faster than existing baselines in several simulated environments with discrete and continuous state spaces.
A Variational Autoencoder with Deep Embedding Model for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) is a challenging task that aims to recognize not only unseen classes unavailable during training, but also seen classes used at training stage. It is achieved by transferring knowledge from seen classes to unseen classes via a shared semantic space (e.g. attribute space). Most existing GZSL methods usually learn a cross-modal mapping between the visual feature space and the semantic space. However, the mapping model learned only from the seen classes will produce an inherent bias when used in the unseen classes. In order to tackle such a problem, this paper integrates a deep embedding network (DE) and a modified variational autoencoder (VAE) into a novel model (DE-VAE) to learn a latent space shared by both image features and class embeddings. Specifically, the proposed model firstly employs DE to learn the mapping from the semantic space to the visual feature space, and then utilizes VAE to transform both original visual features and the features obtained by the mapping into latent features. Finally, the latent features are used to train a softmax classifier. Extensive experiments on four GZSL benchmark datasets show that the proposed model significantly outperforms the state of the arts.
Multi-Channel Reverse Dictionary Model

A reverse dictionary takes the description of a target word as input and outputs the target word together with other words that match the description. Existing reverse dictionary methods cannot deal with highly variable input queries and low-frequency target words successfully. Inspired by the description-to-word inference process of humans, we propose the multi-channel reverse dictionary model, which can mitigate the two problems simultaneously. Our model comprises a sentence encoder and multiple predictors. The predictors are expected to identify different characteristics of the target word from the input query. We evaluate our model on English and Chinese datasets including both dictionary definitions and human-written descriptions. Experimental results show that our model achieves the state-of-the-art performance, and even outperforms the most popular commercial reverse dictionary system on the human-written description dataset. We also conduct quantitative analyses and a case study to demonstrate the effectiveness and robustness of our model. All the code and data of this work can be obtained on https://github.com/thunlp/MultiRD.
Zero-Resource Cross-Lingual Named Entity Recognition

Recently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features. However, these models still require manually annotated training data, which is not available for many languages. In this paper, we propose an unsupervised cross-lingual NER model that can transfer NER knowledge from one language to another in a completely unsupervised way without relying on any bilingual dictionary or parallel data. Our model achieves this through word-level adversarial learning and augmented fine-tuning with parameter sharing and feature augmentation. Experiments on five different languages demonstrate the effectiveness of our approach, outperforming existing models by a good margin and setting a new SOTA for each language pair.
A New Approach to Plan-Space Explanation: Analyzing Plan-Property Dependencies in Oversubscription Planning

In many usage scenarios of AI Planning technology, users will want not just a plan π but an explanation of the space of possible plans, justifying π. In particular, in oversubscription planning where not all goals can be achieved, users may ask why a conjunction A of goals is not achieved by π. We propose to answer this kind of question with the goal conjunctions B excluded by A, i. e., that could not be achieved if A were to be enforced. We formalize this approach in terms of plan-property dependencies, where plan properties are propositional formulas over the goals achieved by a plan, and dependencies are entailment relations in plan space. We focus on entailment relations of the form ∧g∈A g ⇒ ⌝ ∧g∈B g, and devise analysis techniques globally identifying all such relations, or locally identifying the implications of a single given plan property (user question) ∧g∈A g. We show how, via compilation, one can analyze dependencies between a richer form of plan properties, specifying formulas over action subsets touched by the plan. We run comprehensive experiments on adapted IPC benchmarks, and find that the suggested analyses are reasonably feasible at the global level, and become significantly more effective at the local level.
Scientific Text Sentiment Analysis using Machine Learning Techniques

Over time, textual information on the World Wide Web (WWW) has increased exponentially, leading to potential research in the field of machine learning (ML) and natural language processing (NLP).Sentiment analysis of scientific domain articles is a very trendy and interesting topic nowadays.The main purpose of this research is to facilitate researchers to identify quality research papers based on their sentiment analysis.In this research, sentiment analysis of scientific articles using citation sentences is carried out using an existing constructed annotated corpus.This corpus is consisted of 8736 citation sentences.The noise was removed from data using different data normalization rules in order to clean the data corpus.To perform classification on this data set we developed a system in which six different machine learning algorithms including Naïve-Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN) and Random Forest (RF) are implemented.Then the accuracy of the system is evaluated using different evaluation metrics e.g.F-score and Accuracy score.To improve the system' accuracy additional features selection techniques, such as lemmatization, n-graming, tokenization, and stop word removal are applied and found that our system provided significant performance in every case compared to the base system.Our method achieved a maximum of about 9% improved results as compared to the base system.
Learning the Ising model with generative neural networks

This paper studies the representational properties of restricted Boltzmann machines and variational autoencoders in terms of their ability to capture physical features of the Ising model. The authors provide a detailed analysis of different network architectures and training algorithms, and identify significant differences in the learning performance of both probabilistic models
Gigahertz-Clocked Teleportation of Time-Bin Qubits with a Quantum Dot in the Telecommunication <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>C</mml:mi></mml:math> Band

Teleportation is a fundamental concept of quantum mechanics with an important application in extending the range of quantum communication channels via quantum relay nodes. To be compatible with real-world technology such as secure quantum key distribution over fibre networks, such a relay node must operate at GHz clock rates and accept time-bin encoded qubits in the low-loss telecom band around 1550 nm. Here, we show that InAs/InP droplet epitaxy quantum dots with their sub-Poissonian emission near 1550 nm are ideally suited for the realisation of this technology. To create the necessary on-demand photon emission at GHz clock rates, we develop a flexible pulsed optical excitation scheme, and demonstrate that the fast driving conditions are compatible with a low multiphoton emission rate. We show further that, even under these driving conditions, photon pairs obtained from the biexciton cascade show an entanglement fidelity close to 90\%, comparable to the value obtained under cw excitation. Using asymetric Mach Zehnder interferometers and our photon source, we finally construct a time-bin qubit quantum relay able to receive and send time-bin encoded photons, and demonstrate mean teleportation fidelities of $0.82\pm0.01$, exceeding the classical limit by nearly 10 standard deviations.
Web-Based Heart Disease Diagnosis System With Forward Chaining Method (Case Study Of Ibnu Sina Islamic Hospital)

Expert systems are computer-based applications that are used to solve problems thought by experts. The benefits of expert systems are to find solutions to problems that occur in various fields including medicine and medical. The problem that often occurs is the problem of public knowledge of the initial problems of heart disease and the lack of public attention to heart health. From this problem, the writer wants to raise the title "Expert System of Web-Based Heart Disease Diagnosis with Forward Chaining Method (Case Study of Ibnu Sina Islamic Hospital)". The purpose of this system is so that people can understand, understand the facts of heart disease. The method used in making this system is forward chaining because this method is a punishment starting from the fact that it contains sentences that are received. The output of this system is a diagnosis of heart disease, as well as providing recommendations for how to treat the disease.
Optical image encryption based on chaotic fingerprint phase mask and pattern-illuminated Fourier ptychography

An optical image encryption scheme based on chaotic fingerprint phase masks (CFPMs) and pattern-illuminated Fourier ptychography (FP) is proposed. During the encryption process, the primary image that is under the non-uniform intensity patterns’ illumination can be encrypted into multiple low-resolution intensity images with the CFPMs placed respectively at the input plane and the Fourier plane. And during the decryption process, the primary image can be recovered from these encrypted images with a Fourier ptychographic recovery algorithm and CFPMs. In this proposed image encryption scheme, the fingerprint image and the chaotic parameters are served as secret keys directly, and the CFPMs are just used as interim variables and functions. If the fingerprint image is shared by the sender and the authorized receiver, the security of the proposed image encryption scheme will be enhanced greatly, and the management and transmission of the secret keys will also become convenient to some extent. Additionally, the illumination pattern can also act as an additional secret key. Numerical simulations have been performed to verify the feasibility, security and robustness of the proposed image encryption scheme.
RiWalk: Fast Structural Node Embedding via Role Identification

Nodes performing different functions in a network have different roles, and these roles can be gleaned from the structure of the network. Learning latent representations for the roles of nodes helps to understand the network and to transfer knowledge across networks. However, most existing structural embedding approaches suffer from high computation and space cost or rely on heuristic feature engineering. Here we propose RiWalk, a flexible paradigm for learning structural node representations as it decouples the structural embedding problem into a role identification procedure and a network embedding procedure. Through role identification, rooted kernels with structural dependencies kept are built to better integrate network embedding methods. To demonstrate the effectiveness of RiWalk, we develop two different role identification methods named RiWalk-SP and RiWalk-WL respectively and employ random walk based network embedding methods. Experiments on within-network classification tasks show that our proposed algorithms achieve comparable performance with other baselines while being an order of magnitude more efficient. Besides, we also conduct across-network role classification tasks. The results show potential of structural embeddings in transfer learning. RiWalk is also scalable, making it capable of capturing structural roles in massive networks.
Feature selection with missing labels based on label compression and local feature correlation

Abstract Feature selection can efficiently alleviate the issue of curse of dimensionality, especially for multi-label data with multiple features to embody diverse semantics. Although many supervised feature selection methods have been proposed, they generally assume the labels of training data are complete, whilst we only have data with incomplete labels in many real applications. Some methods try to select features with missing labels of training data, they still can not handle feature selection with a large and sparse label space. In addition, these approaches focus on global feature correlations, but some feature correlations are local and shared by a subset of data. In this paper, we introduce an approach called Feature Selection with missing labels based on Label Compression and Local feature Correlation (FSLCLC for short). FSLCLC adopts the low-rank matrix factorization on the sparse sample-label association matrix to compress labels and recover the missing labels in the compressed label space. In addition, it utilizes sparsity regularization and local feature correlation induced manifold regularizations to select the discriminative features. To solve the joint optimization objective for label compression, recovering missing labels and feature selection, we develop an iterative algorithm with guaranteed convergence. Experimental results on benchmark datasets show that the proposed FSLCLC outperforms the state-of-the-art multi-label feature selection algorithms.
Nondegenerate Parametric Amplifiers Based on Dispersion-Engineered Josephson-Junction Arrays

Determining the state of a qubit on a time scale much shorter than its relaxation time is an essential requirement for quantum information processing. With the aid of a nondegenerate parametric amplifier, we demonstrate the continuous detection of quantum jumps of a transmon qubit with $90\mathrm{%}$ fidelity of state discrimination. Entirely fabricated by standard two-step optical-lithography techniques, this type of parametric amplifier consists of a dispersion-engineered Josephson-junction (JJ) array. By using long arrays, containing ${10}^{3}\phantom{\rule{0.2em}{0ex}}\mathrm{JJs}$, we can obtain amplification in multiple eigenmodes with frequencies below $10\phantom{\rule{0.2em}{0ex}}\mathrm{GHz}$, which is the typical range for qubit readout. Moreover, if a moderate flux tunability of each mode is introduced, employing superconducting-quantum-interference-device junctions, a single amplifier device could potentially cover the entire frequency band between 1 and 10 GHz.
Exploring the Learning Motivation and Effectiveness of Applying Virtual Reality to High School Mathematics

Nowadays, it is an era of knowledge explosion.All countries in the world have already planned education in the direction of integrating information technology into teaching.Therefore, the application of information technology in education has become a global teaching trend.The purpose of this study is to integrate the virtual reality (VR) of information technology into the teaching of system of linear equations in three unknowns in high school mathematics and to explore the learning motivation and effectiveness of students after digital teaching.In this study, a quasi-experimental research design and questionnaire survey method was used to design a set of high school mathematics VR tutorial material.The system of linear equations in three unknowns was used as the learning content, and only the experimental group students were used to adopt the VR and the traditional teacher's teaching method.Before and after the experiment, the subjects were filled out with the ARCS Model and the Bloom's taxonomy which analyzed and discussed in a quantitative way, and conducted simple interviews with the subjects and the teachers to collect and analyze the data in a qualitative manner.It is found from the experimental results that the VR has the effect of improving students' learning motivation and learning effectiveness in the digital teaching of mathematics.
Aircraft atypical approach detection using functional principal component analysis

In this paper, a post-operational detection method based on functional principal component analysis and clustering is presented and compared with regard to designed operational criteria. The methodology computes an atypical scoring on a sliding window. It enables not only to detect but also to localize where trajectories deviate statistically from the others. The algorithm is applied to the total energy management, estimated from ground-based data, during approach and landing. The detected atypical flights show non-nominal energy behaviors such as glide interceptions from above or high speed approaches. This promising methodology could help to enhance flight data analysis and safety, highlighting non-monitored behaviors.
A Bean Optimization-Based Cooperation Method for Target Searching by Swarm UAVs in Unknown Environments

This paper studies the target searching problem using swarms of unmanned aerial vehicles (UAVs) in unknown environments which information is unknown to the UAVs, other than features they detect through their sensors.Effective decision and control methods are required for UAVs that consider their limitations and characteristics when confronted with target searching problems.A cooperative target searching method is proposed for swarm UAVs based on an improved bean optimization algorithm (BOA) called Robot Bean Optimization Algorithm (RBOA).Compared with conventional BOAs used for optimal computation, RBOA has two main modifications for the cooperative control of swarm robots: 1) it accounts for the free motion space of individual UAVs using a Thiessen polygon; and 2) it adds a free space search mechanism to improve the efficiency of target searching.Based on the above improvements, and by integrating a multi-phase search mechanism and scheduling control strategy, a swarm UAV collaborative search simulation platform is built for experimental purposes.The results obtained from search simulations show that the RBOA can outperform adaptive robotic particle swarm optimization (A-RPSO) in target searches in complex and unknown environments, especially with fewer evolutionary generations and smaller numbers of robots.The RBOA, which is inspired by plant population evolutionary patterns, has fast and effective search capabilities, distributed collaborative interaction, and emergent swarm intelligence.It provides new ideas and support for research into the control of swarm UAVs and swarm robots.
Optimal approximate quantum error correction for quantum metrology

The authors study the estimation of a Hamiltonian parameter under Markovian noise with the help of fast quantum controls. When the sensing time is long, the paper shows that for a generic set of Markovian evolutions, there exists an approximate quantum error correction strategy which is the best among all types of quantum controls and minimizes the estimation error asymptotically.