Reviewerly/exHarmony huggingface dataset:
Sampled Softmax with Random Fourier Features

The computational cost of training with softmax cross entropy loss grows linearly with the number of classes. For the settings where a large number of classes are involved, a common method to speed up training is to sample a subset of classes and utilize an estimate of the loss gradient based on these classes, known as the sampled softmax method. However, the sampled softmax provides a biased estimate of the gradient unless the samples are drawn from the exact softmax distribution, which is again expensive to compute. Therefore, a widely employed practical approach involves sampling from a simpler distribution in the hope of approximating the exact softmax distribution. In this paper, we develop the first theoretical understanding of the role that different sampling distributions play in determining the quality of sampled softmax. Motivated by our analysis and the work on kernel-based sampling, we propose the Random Fourier Softmax (RF-softmax) method that utilizes the powerful Random Fourier Features to enable more efficient and accurate sampling from an approximate softmax distribution. We show that RF-softmax leads to low bias in estimation in terms of both the full softmax distribution and the full softmax gradient. Furthermore, the cost of RF-softmax scales only logarithmically with the number of classes.
Learning Deep Bilinear Transformation for Fine-grained Image Representation

Bilinear feature transformation has shown the state-of-the-art performance in learning fine-grained image representations. However, the computational cost to learn pairwise interactions between deep feature channels is prohibitively expensive, which restricts this powerful transformation to be used in deep neural networks. In this paper, we propose a deep bilinear transformation (DBT) block, which can be deeply stacked in convolutional neural networks to learn fine-grained image representations. The DBT block can uniformly divide input channels into several semantic groups. As bilinear transformation can be represented by calculating pairwise interactions within each group, the computational cost can be heavily relieved. The output of each block is further obtained by aggregating intra-group bilinear features, with residuals from the entire input features. We found that the proposed network achieves new state-of-the-art in several fine-grained image recognition benchmarks, including CUB-Bird, Stanford-Car, and FGVC-Aircraft.
A New Training Method based on Black Hole Algorithm for Convolutional Neural Network

One of the commonly used neural networks currently is the Convolutional Neural Networks (CNN). They have different applications but have recently been proven useful in deep learning. With its continued growth in the more convoluted domains, the difficulty of its training process is equally increasing. As such, several hybrid algorithms have been developed and implemented to solve this problem. This paper proposes the use of the Black Hole (BH) algorithm to train CNN in a bid to improve its performance by avoiding local minima entrapment. The performance of the new training algorithm was compared to the currently used training algorithms in terms of the convergence analysis, computational accuracy and cost using a benchmark problem specific to Optical Character Recognition (OCR) applications.
Modeling dangerous driving events based on in-vehicle data using Random Forest and Recurrent Neural Network

Modern vehicles produce big data with a wide variety of formats due to missing open standards. Thus, abstractions of such data in the form of descriptive labels are desired to facilitate the development of applications in the automotive domain. We propose an approach to reduce vehicle sensor data into semantic outcomes of dangerous driving events based on aggressive maneuvers. The supervised time-series classification is implemented with Random Forest and Recurrent Neural Network separately. Our approach works with signals of a real vehicle obtained through a back-end solution, with the challenge of low and variable sampling rates. We introduce the idea of having a dangerous driving classifier as the first discriminant of relevant instances for further enrichment (e.g., type of maneuver). Additionally, we suggest a method to increment the number of driving samples for training machine learning models by weighting the window instances based on the portion of the labeled event they include. We show that a dangerous driving classifier can be used as a first discriminant to enable data integration and that transitions in driving events are relevant to consider when the dataset is limited, and sensor data has a low and unreliable frequency.
Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing

We propose a method to improve named entity recognition (NER) for chemical compounds using multi-task learning by jointly training a chemical NER model and a chemical compound paraphrase model.Our method enables the long short-term memory (LSTM) of the NER model to capture chemical compound paraphrases by sharing the parameters of the LSTM and character embeddings between the two models.The experimental results on the BioCreative IV's CHEMDNER task show that our method improves chemical NER and achieves state-of-the-art performance (+1.43 F-score).
Expression Conditional Gan for Facial Expression-to-Expression Translation

In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to another one based on an additional expression attribute. The proposed ECGAN is a generic framework and is applicable to different expression generation tasks where specific facial expression can be easily controlled by the conditional attribute label. Besides, we introduce a novel face mask loss to reduce the influence of background changing. Moreover, we propose an entire framework for facial expression generation and recognition in the wild, which consists of two modules, i.e., generation and recognition. Finally, we evaluate our framework on several public face datasets in which the subjects have different races, illumination, occlusion, pose, color, content and background conditions. Even though these datasets are very diverse, both the qualitative and quantitative results demonstrate that our approach is able to generate facial expressions accurately and robustly.
Semantic graph parsing with recurrent neural network DAG grammars

Semantic parses are directed acyclic graphs (DAGs), so semantic parsing should be modeled as graph prediction.But predicting graphs presents difficult technical challenges, so it is simpler and more common to predict the linearized graphs found in semantic parsing datasets using well-understood sequence models.The cost of this simplicity is that the predicted strings may not be wellformed graphs.We present recurrent neural network DAG grammars, a graph-aware sequence model that ensures only well-formed graphs while sidestepping many difficulties in graph prediction.We test our model on the Parallel Meaning Bank-a multilingual semantic graphbank.Our approach yields competitive results in English and establishes the first results for German, Italian and Dutch.
What’s Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering

Multi-hop textual question answering requires combining information from multiple sentences.We focus on a natural setting where, unlike typical reading comprehension, only partial information is provided with each question.The model must retrieve and use additional knowledge to correctly answer the question.To tackle this challenge, we develop a novel approach that explicitly identifies the knowledge gap between a key span in the provided knowledge and the answer choices.The model, GapQA, learns to fill this gap by determining the relationship between the span and an answer choice, based on retrieved knowledge targeting this gap.We propose jointly training a model to simultaneously fill this knowledge gap and compose it with the provided partial knowledge.On the Open-BookQA dataset, given partial knowledge, explicitly identifying what's missing substantially outperforms previous approaches.
Detecting Causal Language Use in Science Findings

Causal interpretation of correlational findings from observational studies has been a major type of misinformation in science communication.Prior studies on identifying inappropriate use of causal language relied on manual content analysis, which is not scalable for examining a large volume of science publications.In this study, we first annotated a corpus of over 3,000 PubMed research conclusion sentences, then developed a BERT-based prediction model that classifies conclusion sentences into "no relationship", "correlational", "conditional causal", and "direct causal" categories, achieving an accuracy of 0.90 and a macro-F1 of 0.88.We then applied the prediction model to measure the causal language use in the research conclusions of about 38,000 observational studies in PubMed.The prediction result shows that 21.7% studies used direct causal language exclusively in their conclusions, and 32.4% used some direct causal language.We also found that the ratio of causal language use differs among authors from different countries, challenging the notion of a shared consensus on causal language use in the global science community.Our prediction model could also be used to help identify the inappropriate use of causal language in science publications.
A novel dual and triple shifted RSVP paradigm for P300 speller

A speller system enables disabled people, specifically those with spinal cord injuries, to visually select and spell characters. A problem of primary speller systems is that they are gaze shift dependent. To overcome this problem, a single Rapid Serial Visual Presentation (RSVP) paradigm was initially introduced in which characters are displayed one-by-one at the center of a screen. Two new protocols, Dual and Triple shifted RSVP paradigms, are introduced and compared against the single paradigm. In the Dual and Triple paradigms, two and three characters are displayed at the center of the screen simultaneously, holding the advantage of displaying the target character twice and three times respectively, compared to the one-time appearance in the single paradigm. To compare the named paradigms, three subjects participated in experiments using all three paradigms. Offline results demonstrate an average character detection accuracy of 97% for the single and double protocols, and 80% for the Triple paradigm. In addition, average ITR is calculated to be 5.45, 7.62 and 7.90 bit/min for the single, Dual and Triple paradigms respectively. Results identify the Dual RSVP paradigm as the most suitable approach that provides the best balance between ITR and character detection accuracy. The novel speller system (the Dual paradigm) suggested in this paper demonstrates improved performance compared to existing methods, and overcomes the gaze dependency issue. Overall, our novel method is a reliable alternative that both removes limitations for users suffering from impaired oculomotor control and improves performance.
Cooperative Unmanned Aerial System Reconnaissance in a Complex Urban Environment and Uneven Terrain

Using unmanned robotic systems in military operations such as reconnaissance or surveillance, as well as in many civil applications, is common practice. In this article, the problem of monitoring the specified area of interest by a fleet of unmanned aerial systems is examined. The monitoring is planned via the Cooperative Aerial Model, which deploys a number of waypoints in the area; these waypoints are visited successively by unmanned systems. The original model proposed in the past assumed that the area to be explored is perfectly flat. A new formulation of this model is introduced in this article so that the model can be used in a complex environment with uneven terrain and/or with many obstacles, which may occlude some parts of the area of interest. The optimization algorithm based on the simulated annealing principles is proposed for positioning of waypoints to cover as large an area as possible. A set of scenarios has been designed to verify and evaluate the proposed approach. The key experiments are aimed at finding the minimum number of waypoints needed to explore at least the minimum requested portion of the area. Furthermore, the results are compared to the algorithm based on the lawnmower pattern.
Consistent Embedded GAN for Image-to-Image Translation

Generative Adversarial Networks (GANs) have achieved remarkable progress in image-toimage translation tasks.However, these methods have the common problem that lacking the ability to generate both perceptually realistic and diverse images in the target domain.To tackle the problem, in this paper, we propose a novel model named Consistent Embedded Generative Adversarial Networks (CEGAN) for the image-to-image translation task.It aims to learn conditional generation models for generating perceptually realistic outputs and capture the full distribution of potential multiple modes of results by enforcing tight connections in both the real image space and latent space.To achieve realism, unlike existing GANs models that their discriminators attempt to differentiate between real images from the dataset and fake samples produced by the generator, the discriminator in our model distinguishes the real images and fake images in the latent space to alleviate the impact of the redundancy and noise in generated images.On the other hand, we learn a low-dimensional latent code that is distilled from the possible multiple distribution in the latent space to achieve diversity.By this way, our model avoids the problem of mode collapse and produces more diverse and realistic results.Extensive experimental results demonstrate the superiority of the proposed method.
A Blockchain Smart Contract Based on Light- Weighted Quantum Blind Signature

To improve the security performance of blockchain smart contracts against quantum attacks, a smart contract based on light-weighted quantum blind signature is proposed here. Firstly, a smart contract architecture is built after the discussion of related researches, and information processing and information transmitting for quantum blind signature are analyzed. Secondly, the life cycle and signature rules of quantum blind signature for smart contracts are presented. Thirdly, a quantum blind signature protocol for single signer in light-weighted smart contract is put forward, where its algorithm design and business flow are introduced in detail before the security performance of the proposed algorithm is analyzed. Fourthly, based on the former single-person signature algorithm, a more complex multi-person quantum blind signature algorithm is proposed, and its security is also analyzed. Fifthly, related references are compared to verify the proposed method. The proposed quantum signature schemes based on quantum entanglement features can be used in single signer case or more, and will improve the security of blockchain smart contracts against quantum attacks, but with light-weighted structure and no need of any trusted third party or arbitrary institute. Finally, some suggestions for engineering applications and the research results are summarized and the future research is prospected.
LB-LSD: A length-based line segment detector for real-time applications

Line segments play an important role in the perception and representation of images by providing the geometry information about the scene. These segments can also be used as low-level features to analyze and detect more elaborated shapes. A length-based line segment detector is proposed in this paper. Based on the edge proportion statistics, an adaptive, robust and effective edge detection method is presented to extract edge segments from the image. Each of the segments is a clean, contiguous, 1-pixel wide chain of pixels. The line segment detector then approximates all the edge segments in a way that the curve satisfies a criterion of length condition. The detection result is a series of piecewise-linear segments with some dominant corners. Experimental results indicate that the proposed detector has a good detection accuracy and outperforms the state-of-the-art methods in terms of execution time.
Gamified Augmented Reality Training for An Assembly Task: A Study About User Engagement

Augmented Reality and Gamification are displaying beneficial effects to enhance user experience and performance in many domains.They are widespread across many areas like education, industrial training, marketing, and services.However, the idea of combining the two approaches for an innovative training instrument is fairly new, especially in assembly training.Moreover, learning about the effects of gamification on human, user engagement, in particular, is a complicated subject.There have been several efforts toward this direction, yet the overall situation is still nascent.In this work, we present a gamified augmented reality training for an industrial task and investigate user engagement effect while training with the gamified and the nongamified system.The result shows that people perform better and engage to a greater degree in the gamified design.
Variational quantum simulation of ultrastrong light-matter coupling

We propose the simulation of quantum-optical systems in the ultrastrong-coupling regime using a variational quantum algorithm. More precisely, we introduce a short-depth variational form to prepare the groundstate of the multimode Dicke model on a quantum processor and present proof-of-principle results obtained via cloud access to an IBM device. We moreover provide an algorithm for characterizing the groundstate by Wigner state tomography. Our work is a first step towards digital quantum simulation of quantum-optical systems with potential applications to the spin-boson, Kondo and Jahn-Teller models.
Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration

Word sense disambiguation (WSD) is a fundamental but significant task in natural language processing, which directly affects the performance of upper applications. However, WSD is very challenging due to the problem of knowledge bottleneck, i.e., it is hard to acquire abundant disambiguation knowledge, especially in Chinese. To solve this problem, this paper proposes a graph-based Chinese WSD method with multi-knowledge integration. Particularly, a graph model combining various Chinese and English knowledge resources by word sense mapping is designed. Firstly, the content words in a Chinese ambiguous sentence are extracted and mapped to English words with BabelNet. Then, English word similarity is computed based on English word embeddings and knowledge base. Chinese word similarity is evaluated with Chinese word embedding and HowNet, respectively. The weights of the three kinds of word similarity are optimized with simulated annealing algorithm so as to obtain their overall similarities, which are utilized to construct a disambiguation graph. The graph scoring algorithm evaluates the importance of each word sense node and judge the right senses of the ambiguous words. Extensive experimental results on SemEval dataset show that our proposed WSD method significantly outperforms the baselines.
Automated Playtesting of Matching Tile Games

Matching tile games are an extremely popular game genre. Arguably the most popular iteration, Match-3 games, are simple to understand puzzle games, making them great benchmarks for research. In this paper, we propose developing different procedural personas for Match-3 games in order to approximate different human playstyles to create an automated playtesting system. The procedural personas are realized through evolving the utility function for the Monte Carlo Tree Search agent. We compare the performance and results of the evolution agents with the standard Vanilla Monte Carlo Tree Search implementation as well as to a random move-selection agent. We then observe the impacts on both the game's design and the game design process. Lastly, a user study is performed to compare the agents to human play traces.
Skill-based curiosity for intrinsically motivated reinforcement learning

Abstract Reinforcement learning methods rely on rewards provided by the environment that are extrinsic to the agent. However, many real-world scenarios involve sparse or delayed rewards. In such cases, the agent can develop its own intrinsic reward function called curiosity to enable the agent to explore its environment in the quest of new skills. We propose a novel end-to-end curiosity mechanism for deep reinforcement learning methods, that allows an agent to gradually acquire new skills. Our method scales to high-dimensional problems, avoids the need of directly predicting the future, and, can perform in sequential decision scenarios. We formulate the curiosity as the ability of the agent to predict its own knowledge about the task. We base the prediction on the idea of skill learning to incentivize the discovery of new skills, and guide exploration towards promising solutions. To further improve data efficiency and generalization of the agent, we propose to learn a latent representation of the skills. We present a variety of sparse reward tasks in MiniGrid, MuJoCo, and Atari games. We compare the performance of an augmented agent that uses our curiosity reward to state-of-the-art learners. Experimental evaluation exhibits higher performance compared to reinforcement learning models that only learn by maximizing extrinsic rewards.
Gaze Direction Visualization Techniques for Collaborative Wide-Area Model-Free Augmented Reality

In collaborative tasks, it is often important for users to understand their collaborator's gaze direction or gaze target. Using an augmented reality (AR) display, a ray representing the collaborator's gaze can be used to convey such information. In wide-area AR, however, a simplistic virtual ray may be ambiguous at large distances, due to the lack of occlusion cues when a model of the environment is unavailable. We describe two novel visualization techniques designed to improve gaze ray effectiveness by facilitating visual matching between rays and targets (Double Ray technique), and by providing spatial cues to help users understand ray orientation (Parallel Bars technique). In a controlled experiment performed in a simulated AR environment, we evaluated these gaze ray techniques on target identification tasks with varying levels of difficulty. The experiment found that, assuming reliable tracking and an accurate collaborator, the Double Ray technique is highly effective at reducing visual ambiguity, but that users found it difficult to use the spatial information provided by the Parallel Bars technique. We discuss the implications of these findings for the design of collaborative mobile AR systems for use in large outdoor areas.
A new Transparent Ensemble Method based on Deep learning

Rather than making one model and hoping this model is the best/most accurate predictor we can make, ensemble methods which improve machine learning results by combining different models. However, one of the major criticisms is their being inexplicable, since they do not provide results explanation and do not allow prior knowledge integration. With the development of the machine learning the explanation of classification results and the ability to introduce domain knowledge inside the learned model have become a necessity. In this paper, we present a novel deep ensemble method based on argumentation that combines machine learning algorithms with multi-agent system to improve classification. The idea is to extract arguments from classifiers and to combine them using argumentation in order to exploit the internal knowledge of each classifiers and provide explanation behind decisions and to allow injecting prior knowledge. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance.