Reviewerly/exHarmony huggingface dataset:
Novel Techniques for Anticipating the Focus of Visual Attention across Different Mining Landscapes

Surface mining activities support socioeconomic development but also cause significant landscape alteration and degradation. By definition, the concept of landscape requires observers; thus, the way mining landscapes are actually observed needs to be taken into consideration for mitigating visual nuisance from open pit mines. This paper utilizes eye tracking techniques for recording and rendering the actual attention patterns of observers, along with saliency models that &lsquo;predict&rsquo; the focus of attention in mining landscape photographs. As it turns out, saliency models can aid in reliably anticipating the attention focus across a range of different mining landscapes.
EAGAN: Efficient Two-Stage Evolutionary Architecture Search for GANs

Generative adversarial networks (GANs) have proven successful in image generation tasks. However, GAN training is inherently unstable. Although many works try to stabilize it by manually modifying GAN architecture, it requires much expertise. Neural architecture search (NAS) has become an attractive solution to search GANs automatically. The early NAS-GANs search only generators to reduce search complexity but lead to a sub-optimal GAN. Some recent works try to search both generator (G) and discriminator (D), but they suffer from the instability of GAN training. To alleviate the instability, we propose an efficient two-stage evolutionary algorithm-based NAS framework to search GANs, namely EAGAN. We decouple the search of G and D into two stages, where stage-1 searches G with a fixed D and adopts the many-to-one training strategy, and stage-2 searches D with the optimal G found in stage-1 and adopts the one-to-one training and weight-resetting strategies to enhance the stability of GAN training. Both stages use the non-dominated sorting method to produce Pareto-front architectures under multiple objectives (e.g., model size, Inception Score (IS), and Fréchet Inception Distance (FID)). EAGAN is applied to the unconditional image generation task and can efficiently finish the search on the CIFAR-10 dataset in 1.2 GPU days. Our searched GANs achieve competitive results (IS = 8.81 ± 0.10, FID = 9.91) on the CIFAR-10 dataset and surpass prior NAS-GANs on the STL-10 dataset (IS = 10.44 ± 0.087, FID = 22.18). Source code: https://github.com/marsggbo/EAGAN .
Efficient Stride 2 Winograd Convolution Method Using Unified Transformation Matrices on FPGA

Winograd algorithm can effectively reduce the computational complexity of convolution operation. Effectively using the parallelism of Winograd convolution algorithm can effectively improve the performance of accelerator architectures on FPGA. The stride represents the number of elements that the window slides when filter is scanned on the input feature map. The Winograd algorithm with the stride of 2 implemented in previous studies divided the input feature maps into multiple groups of Winograd algorithms to complete the operations, resulting in additional precomputation and hardware resource overhead. In this paper, we propose a new Winograd convolution algorithm with the stride of 2. This method uses the unified Winograd transformation matrices instead of the grouping method to complete the calculation. Therefore, the method proposed in this paper can realize 2D Winograd convolution and 3D Winograd convolution by nested 1D Winograd convolution, just like the Winograd convolution algorithm with the stride of 1. In this paper, Winograd transformation matrices with kernel size of 3, 5, and 7 are provided. In particular, for convolution with the kernel of 3, this method reduces the addition operations of Winograd algorithm by 30.0%-31.5% and removes unnecessary shift operations completely. In addition, we implement Winograd convolution algorithm with the stride of 2 through template design, and realize pipeline and data reuse. Compared to the state-of-the-art implementation, the proposed method results in a speedup of 1.24 and reduces resource usage.
Resolution based Feature Distillation for Cross Resolution Person Re-Identification

Person re-identification (re-id) aims to retrieve images of same identities across different camera views. Resolution mismatch occurs due to varying distances between person of interest and cameras, this significantly degrades the performance of re-id in real world scenarios. Most of the existing approaches resolve the re-id task as low resolution problem in which a low resolution query image is searched in a high resolution images gallery. Several approaches apply image super resolution techniques to produce high resolution images but ignore the multiple resolutions of gallery images which is a better realistic scenario. In this paper, we introduce channel correlations to improve the learning of features from the degraded data. In addition, to overcome the problem of multiple resolutions we propose a Resolution based Feature Distillation (RFD) approach. Such an approach learns resolution invariant features by filtering the resolution related features from the final feature vectors that are used to compute the distance matrix. We tested the proposed approach on two synthetically created datasets and on one original multi resolution dataset with real degradation. Our approach improves the performance when multiple resolutions occur in the gallery and have comparable results in case of single resolution (low resolution re-id).
An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

Most current studies on multi-agent evolution based on deep learning take a cooperative equilibrium strategy, while interactive self-learning is not always considered. An interactive self-learning game and evolution method based on non-cooperative equilibrium (ISGE-NCE) is proposed to take the benefits of both game theory and interactive learning for multi-agent confrontation evolution. A generative adversarial network (GAN) is designed combining with multi-agent interactive self-learning, and the non-cooperative equilibrium strategy is well adopted within the framework of interactive self-learning, aiming for high evolution efficiency and interest. For assessment, three typical multi-agent confrontation experiments are designed and conducted. The results show that, first, in terms of training speed, the ISGE-NCE produces a training convergence rate of at least 46.3% higher than that of the method without considering interactive self-learning. Second, the evolution rate of the interference and detection agents reaches 60% and 80%, respectively, after training by using our method. In the three different experiment scenarios, compared with the DDPG, our ISGE-NCE method improves the multi-agent evolution effectiveness by 43.4%, 50%, and 20%, respectively, with low training costs. The performances demonstrate the significant superiority of our ISGE-NCE method in swarm intelligence.
DiLBERT: Cheap Embeddings for Disease Related Medical NLP

Electronic Health Records include health-related information, among which there is text mentioning health conditions and diagnoses. Usually, text is also coded using appropriate terminologies and classifications. The act of coding is time consuming and prone to mistakes. Consequently, there is increasing demand for clinical text mining tools to help coding. In last few years Natural Language Processing (NLP) models has been shown to be effective in sentence-level tasks. Taking advantage from the transfer learning capabilities of those models, a number of biomedicine and health specific models have been also developed. However, also biomedical models can be seen as too general for some specific area like diagnostic expressions. In this paper, we describe a BERT model specialized on tasks related to diagnoses and health conditions. To obtain a disease-related language model, we created a pre-training corpora starting from ICD-11 entities, and enriched them with documents selected by querying PubMed and Wikipedia with entity names. Fine-tuning has been carried out towards three downstream tasks on two different datasets. Results show that our model, besides being trained on a much smaller corpora than state-of-the-art algorithms, leads to comparable or higher accuracy scores on all the considered tasks, in particular 97.53% accuracy on death certificate coding, and 81.32% on clinical document coding, which are both slightly higher than other models. To summarize the practical implications of our work, we pre-trained and fine-tuned a domain specific BERT model on a small corpora, with comparable or better performance than state-of-the-art models. This approach may also simplify the development of models for languages different from English, due to the minor quantity of data needed for training.
Research on Thread Optimization and Opening Library Based on Parallel PVS Algorithm in Amazon Chess

This paper introduces the main algorithms used in Amazon game system, and mainly studies the number of threads in parallel PVS algorithm based on OpenMp. In view of the low utilization of computer hardware by the current algorithm, this paper improves the efficiency of the algorithm by changing the number of parallel algorithm threads to be equal to the number of CPU cores, and draws a conclusion that the multi-core resources can be maximized when the number of parallel algorithm threads is equal to the number of CPU cores. This paper studies the design of the opening Library of Amazon chess, puts forward the design idea of the opening Library of Amazon chess and the necessity of the opening library system.
Towards utility-aware privacy-preserving sensor data anonymization in distributed IoT

With the increased deployment of Internet of Things (IoT) systems, there come multiple privacy concerns regarding how the sensor data is stored and processed. Our research aims to protect user privacy by developing distributed data anonymization techniques that obscure sensitive information in the raw sensor data before it is used by third-party applications. Since users may have different privacy requirements, we propose to leverage meta-learning to allow our model to adapt to various privacy-utility trade-offs. We will assess the efficacy of the proposed techniques on real-world datasets and design defense mechanisms against various adversaries.
Optical Network Architecture Supporting Dynamic and End-to-End Quantum Secure Networking

This paper proposes an optical network architecture including physical layer and control plane supporting dynamic networking and co-existence of quantum and classical channels over the same fibre infrastructure. It will discuss technological challenges for realising the proposed architecture and potential solutions for addressing them.
A Scientometric Visualization Analysis of Image Captioning Research from 2010 to 2020

Image captioning has gradually gained attention in the field of artificial intelligence and become an interesting and challenging task for image understanding. It needs to identify important objects in images, extract attributes, tell relationships, and help the machine generate human-like descriptions. Recent works in deep neural networks have greatly improved the performance of image caption models. However, machines are still unable to imitate the way humans think, talk and communicate, so image captioning remains an ongoing task. It is thus very important to keep up with the latest research and results in the field of image captioning whereas publications on this topic are numerous. Our work aims to help researchers to have a macro-level understanding of image captioning from four aspects: spatial-temporal distribution characteristics, collaborative networks, trends in subject research, and historical evolutionary path. We employ scientometric visualization methods to achieve this goal. The results show that China has published the largest amount of publications in image captioning, but the United States has the greatest impact on research in this area. Besides, thirteen academic groups are identified in the field of image description, with institutions such as Microsoft, Google, Australian National University, and Georgia Institute of Technology being the most prominent research institutions. Meanwhile, we find that evaluation methods, datasets, novel image captioning models based on generative adversarial networks, reinforcement learning, and Transformer, as well as remote sensing image captioning, are the new research trends. Lastly, we conclude that image captioning research has gone through three major development stages from 2010 to 2020, and on this basis, we propose a more comprehensive taxonomy of image captioning.
Is Gender “In-the-Wild” Inference Really a Solved Problem?

Soft biometrics analysis is seen as an important research topic, given its relevance to various applications. However, even though it is frequently seen as a solved task, it can still be very hard to perform in wild conditions, under varying image conditions, uncooperative poses, and occlusions. Considering the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gender</i> trait as our topic of study, we report an extensive analysis of the feasibility of its inference regarding image (resolution, luminosity, and blurriness) and subject-based features (face and body keypoints confidence). Using three state-of-the-art datasets (PETA, PA-100K, RAP) and five Person Attribute Recognition models, we correlate feature analysis with gender inference accuracy using the Shapley value, enabling us to perceive the importance of each image/subject-based feature. Furthermore, we analyze face-based gender inference and assess the pose effect on it. Our results suggest that: 1) image-based features are more influential for low-quality data; 2) an increase in image quality translates into higher subject-based feature importance; 3) face-based gender inference accuracy correlates with image quality increase; and 4) subjects’ frontal pose promotes an implicit attention towards the face. The reported results are seen as a basis for subsequent developments of inference approaches in uncontrolled outdoor environments, which typically correspond to visual surveillance conditions.
Engineering non-classical correlation and teleportation with Robust fidelity using Jaynes–Cummings model

In this paper, we propose a model to describe the geometry of quantum correlations and entanglement through their distinct physical significance in quantum information processing and modern communications. However, geometric discord, using trace, Hilbert–Schmidt distances, and entanglement of formation, is engineered to be a well-defined non-classical correlation measure of an atomic field system. It consists of employing Jaynes–Cummings model to study the interaction between an excited atom at two levels and a single electromagnetic field mode inside an electrodynamic cavity in two cases, namely resonance and non-resonance. In fact, the dynamics of these measures depends decisively on the atom-field initial parameters where, importantly, the field parameters can be specified as control settings to implement an optimal teleportation protocol. The obtained results reveal that the behaviors of teleported geometric quantum discord and entanglement are similar to those displayed for maximum fidelity in terms of fully entanglement fraction. Therefore, since fidelity always exceeds the classical limit, one can design a quantum teleportation scheme with robust fidelity superior to any conventional communication protocol.
Clarifying Ambiguous Keywords with Personal Word Embeddings for Personalized Search

Personalized search tailors document ranking lists for each individual user based on her interests and query intent to better satisfy the user’s information need. Many personalized search models have been proposed. They first build a user interest profile from the user’s search history, and then re-rank the documents based on the personalized matching scores between the created profile and candidate documents. In this article, we attempt to solve the personalized search problem from an alternative perspective of clarifying the user’s intention of the current query. We know that there are many ambiguous words in natural language such as “Apple.” People with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, we propose a personalized search model with personal word embeddings for each individual user that mainly contain the word meanings that the user already knows and can reflect the user interests. To learn great personal word embeddings, we design a pre-training model that captures both the textual information of the query log and the information about user interests contained in the click-through data represented as a graph structure. With personal word embeddings, we obtain the personalized word and context-aware representations of the query and documents. Furthermore, we also employ the current session as the short-term search context to dynamically disambiguate the current query. Finally, we use a matching model to calculate the matching score between the personalized query and document representations for ranking. Experimental results on two large-scale query logs show that our designed model significantly outperforms state-of-the-art personalization models.
A coral-reef approach to extract information from HTML tables

This article presents Coraline, which is a new table-understanding proposal. Its novelty lies in a coral-reef optimisation algorithm that addresses the problem of feature selection in synchrony with a clustering technique and some custom heuristics that help extract information in a totally unsupervised manner. Our experimental analysis was performed on a large collection of tables with a variety of layouts, encoding problems, and formatting alternatives. Coraline could achieve an F1 score as high as 0.90 and took 7.07 CPU seconds per table, which improves on the best supervised proposal by 6.67% regarding effectiveness and 40.54% regarding efficiency; it also improves on the best unsupervised proposal by 11.11% regarding effectiveness while it remains very competitive regarding efficiency.
Learning 2D Gabor filters by infinite kernel learning regression

Gabor functions have wide-spread applications both in analyzing the visual cortex of mammalians and in designing machine vision algorithms. It is known that the receptive field of neurons of V1 layer in the visual cortex can be accurately modeled by Gabor functions. In addition, Gabor functions are extensively used for feature extraction in machine vision tasks. In this paper, we prove that Gabor functions are translation-invariant positive-definite kernels and show that the problem of image representation with Gabor functions can be formulated as infinite kernel learning regression. Specifically, we use the stabilized infinite kernel learning regression algorithm that has already been introduced for learning translation-invariant positive-definite kernels and has enough flexibility and generality to embrace the class of Gabor kernels. The algorithm yields a representation of the image as a support vector expansion with a compound kernel that is a finite mixture of Gabor functions. The problem with this representation is that all Gabor functions are present at all support vector pixels. Using LASSO, we propose a method for sparse representation of an image with Gabor functions in which each Gabor function is positioned at a very sparse set of pixels. As a practical application, we introduce a novel method for learning a dataset-specific set of Gabor filters that can be used subsequently for feature extraction. Our experiments on CMU-PIE and Extended Yale B datasets show that use of the learned Gabor filters significantly improves the recognition accuracy of a recently introduced face recognition algorithm.
Generating Explainable Abstractions for Wikidata Entities

The large coverage and quality of the Wikidata knowledge graph make it suitable for usage in downstream applications, such as entity summarization, entity linking, and question answering. Yet, most retrieval and similarity-based methods for Wikidata make limited use of its semantics, and lose the link between the rich structure in Wikidata and the decision-making algorithm. In this paper, we investigate how to define abstractive representations (profiles) of Wikidata entities. We propose a scalable method that can produce profiles for Wikidata entities based on salient labels associated with their types. We represent the resulting profiles as a graph, and compute profile embeddings. Our empirical analysis shows that the profiles can capture similarity competitively to baselines, but excel in terms of explainability. On the task of neural entity linking in tables, the profiles outperform all baselines in terms of accuracy, whereas their human-readable representation clearly explains the source of improvement. We make our code and data available to facilitate novel use cases based on the Wikidata profiles.
The Improved CenterNet for Ship Detection in Scale-Varying Images

Recently, deep learning for object detection has been widely used in face recognition, traffic detection, and other fields. However, due to the dataset limitation and the target scale variation issues, ship detection is not as perfect as in other fields. To address such issues, in this paper, an anchor-free detection method is proposed in framework of CenterNet. By improving the network structure and using the unique activation function, the detection issues are successfully solved. Experimental results show that the improved CenterNet method is 5.6% higher in mAP in contrast to the state-of-the-art, and the FPS is increased by 4, respectively.
Validate on Sim, Detect on Real - Model Selection for Domain Randomization

A practical approach to learning robot skills, often termed sim2real, is to train control policies in simulation and then deploy them on a real robot. Popular sim2real techniques build on domain randomization (DR) - training the policy on diverse randomly generated domains for better generalization to the real world. Due to the large number of hyper-parameters in both the policy learning and DR algorithms, one often ends up with a large number of trained policies, where choosing the best policy among them demands costly evaluation on the real robot. In this work we ask - can we rank the policies without running them in the real world? Our main idea is that a predefined set of real world data can be used to evaluate all policies, using out-of-distribution detection (OOD) techniques. In a sense, this approach can be seen as a ‘unit test’ to evaluate policies before any real world execution. However, we find that by itself, the OOD score can be inaccurate and very sensitive to the particular OOD method. Our main contribution is a simple-yet-effective policy score that combines OOD with an evaluation in simulation. We show that our score - VSDR - can significantly improve the accuracy of policy ranking without requiring additional real world data. We evaluate the effectiveness of VSDR on sim2real transfer in a robotic grasping task with image inputs. We extensively evaluate different DR parameters and OOD methods, and show that VSDR improves policy selection across the board. More importantly, our method achieves significantly better ranking, and uses significantly less data compared to baselines. Project website is at https://sites.google.com/view/vsdr/home