Reviewerly/exHarmony huggingface dataset:
UCTGAN: Diverse Image Inpainting Based on Unsupervised Cross-Space Translation

Although existing image inpainting approaches have been able to produce visually realistic and semantically correct results, they produce only one result for each masked input. In order to produce multiple and diverse reasonable solutions, we present Unsupervised Cross-space Translation Generative Adversarial Network (called UCTGAN) which mainly consists of three network modules: conditional encoder module, manifold projection module and generation module. The manifold projection module and the generation module are combined to learn one-to-one image mapping between two spaces in an unsupervised way by projecting instance image space and conditional completion image space into common low-dimensional manifold space, which can greatly improve the diversity of the repaired samples. For understanding of global information, we also introduce a new cross semantic attention layer that exploits the long-range dependencies between the known parts and the completed parts, which can improve realism and appearance consistency of repaired samples. Extensive experiments on various datasets such as CelebA-HQ, Places2, Paris Street View and ImageNet clearly demonstrate that our method not only generates diverse inpainting solutions from the same image to be repaired, but also has high image quality.
The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

This paper studies the problem of predicting the distribution over multiple possible future paths of people as they move through various visual scenes. We make two main contributions. The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals. This provides the first benchmark for quantitative evaluation of the models to predict multi-future trajectories. The second contribution is a new model to generate multiple plausible future trajectories, which contains novel designs of using multi-scale location encodings and convolutional RNNs over graphs. We refer to our model as Multiverse. We show that our model achieves the best results on our dataset, as well as on the real-world VIRAT/ActEV dataset (which just contains one possible future).
Graph Neural Networks in TensorFlow and Keras with Spektral [Application Notes]

Graph neural networks have -enabled the application of deep learning to problems that can be described by graphs, which are found throughout the different fields of sci-ence, from physics to biology, natural language processing, telecommunications or medicine. In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processing graphs and loading popular benchmark datasets. The purpose of this library is to provide the essential building blocks for creating graph neural networks, focusing on the guiding principles of user-friendliness and quick prototyping on which Keras is based. Spektral is, therefore, suitable for absolute beginners and expert deep learning practitioners alike. In this work, we present an overview of Spektral's features and report the performance of the methods implemented by the library in scenarios of node classification, graph classification, and graph regression.
DSLR: Deep Stacked Laplacian Restorer for Low-Light Image Enhancement

Various images captured in complicated lighting conditions often suffer from deterioration of the image quality. Such poor quality not only dissatisfies the user expectation but also may lead to a significant performance drop in many applications. In this paper, anovel method for low-light image enhancement is proposed by leveraging useful propertiesof the Laplacian pyramid both in image and feature spaces. Specifically, the proposed method, so-called a deep stacked Laplacian restorer (DSLR), is capable of separately recovering the global illumination and local details from the original input, and progressively combining them in the image space. Moreover, the Laplacian pyramid defined in the feature space makes such recovering processes more efficient based on abundant connectionsof higher-order residuals in a multiscale structure. This decomposition-based scheme is fairly desirable for learning the highly nonlinear relation between degraded images and their enhanced results. Experimental results on various datasets demonstrate that the proposed DSLR outperforms state-of-the-art methods. The code and model are publicly available at: https://github.com/SeokjaeLIM/DSLR-release .
Experimental High-Dimensional Quantum Teleportation

Quantum teleportation provides a way to transmit unknown quantum states from one location to another. In the quantum world, multilevel systems which enable high-dimensional systems are more prevalent. Therefore, to completely rebuild the quantum states of a single particle remotely, one needs to teleport multilevel (high-dimensional) states. Here, we demonstrate the teleportation of high-dimensional states in a three-dimensional six-photon system. We exploit the spatial mode of a single photon as the high-dimensional system, use two auxiliary entangled photons to realize a deterministic three-dimensional Bell state measurement. The fidelity of teleportation process matrix is $F=0.596\ifmmode\pm\else\textpm\fi{}0.037$. Through this process matrix, we can prove that our teleportation is both nonclassical and genuine three dimensional. Our work paves the way to rebuild complex quantum systems remotely and to construct complex quantum networks.
Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods

This paper presents a brief review on major accidents and conducts bibliometric analysis of risk assessment methods for excavation system in recent year. The summarization of potential risks during excavation provides an important index for establishing an early warning system. The applications of fuzzy set theory and machine learning methods in risk assessment during excavation are presented. A case study of excavation in Guangzhou metro station is used to demonstrate the application of a machine learning method for risk evaluation. The large amount of data collected by 3S techniques (RS, GIS and GPS) and sensors increases accuracy of risk assessment levels in excavation. These procedures, integrated into building information modelling (BIM) management platform, can manipulate dynamic safety risk monitoring, control, and management. Finally, the processing and analysis of big data obtained from 3S techniques and sensors provide promising perspectives for establishing integrated technology system for excavation.
ECFFNet: Effective and Consistent Feature Fusion Network for RGB-T Salient Object Detection

Under ideal environmental conditions, RGB-based deep convolutional neural networks can achieve high performance for salient object detection (SOD). In scenes with cluttered backgrounds and many objects, depth maps have been combined with RGB images to better distinguish spatial positions and structures during SOD, achieving high accuracy. However, under low-light and uneven lighting conditions, RGB and depth information may be insufficient for detection. Thermal images are insensitive to lighting and weather conditions, being able to capture important objects even during nighttime. By combining thermal images and RGB images, we propose an effective and consistent feature fusion network (ECFFNet) for RGB-T SOD. In ECFFNet, an effective cross-modality fusion module fully fuses features of corresponding sizes from the RGB and thermal modalities. Then, a bilateral reversal fusion module performs bilateral fusion of foreground and background information, enabling the full extraction of salient object boundaries. Finally, a multilevel consistent fusion module combines features across different levels to obtain complementary information. Comprehensive experiments on three RGB-T SOD datasets show that the proposed ECFFNet outperforms 12 state-of-the-art methods under different evaluation indicators.
Pose Recognition with Cascade Transformers

In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them into 1). heatmap-based and 2). regression-based. In general, heatmap-based methods achieve higher accuracy but are subject to various heuristic designs (not end-to-end mostly), whereas regression-based approaches attain relatively lower accuracy but they have less intermediate non-differentiable steps. Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches. We demonstrate the keypoint hypothesis (query) refinement process across different self-attention layers to reveal the recursive self-attention mechanism in Transformers. In the experiments, we report competitive results for pose recognition when compared with the competing regression-based methods.
JKT: A joint graph convolutional network based Deep Knowledge Tracing

Knowledge Tracing (KT) aims to trace the student’s state of evolutionary mastery for a particular knowledge or concept based on the student’s historical learning interactions with the corresponding exercises. Taking the “exercise-to-concept” relationships as input, several existing methods have been developed to trace and model students’ mastery states. However, these studies face two major shortcomings in KT: 1) they only consider “exercise-to-concept” relationships; 2) the multi-hot embeddings lack interpretability. In order to address the above issues, we propose a Joint graph convolutional network based deep Knowledge Tracing (JKT) framework to model the multi-dimensional relationships of “exercise-to-exercise”, and “concept-to-concept” into graph and fuse them with “exercise-to-concept” relationships. In JKT, it is not only possible to establish connections between exercises under cross-concepts, but also to help capture high-level semantic information and increase the model’s interpretability. In addition, sufficient experiments conducted on four real-world datasets have demonstrated that JKT performs better than the other baseline models. We further illustrate a case study to demonstrate its interpretability for learning analysis
Blockchain technology applications in healthcare: An overview

Blockchain is an emerging technology being applied for creating innovative solutions in various sectors, including healthcare. A Blockchain network is used in the healthcare system to preserve and exchange patient data through hospitals, diagnostic laboratories, pharmacy firms, and physicians. Blockchain applications can accurately identify severe mistakes and even dangerous ones in the medical field. Thus, it can improve the performance, security, and transparency of sharing medical data in the health care system. This technology is helpful to medical institutions to gain insight and enhance the analysis of medical records. In this paper, we studied Blockchain technology and its significant benefits in healthcare. Various Capabilities, Enablers, and Unified Work-Flow Process of Blockchain Technology to support healthcare globally are discussed diagrammatically. Finally, the paper identifies and debates fourteen significant applications of Blockchain for healthcare. Blockchain plays a decisive part in handling deception in clinical trials; here, the potential of this technology offer is to improve data efficiency for healthcare. It can help avoid the fear of data manipulation in healthcare and supports a unique data storage pattern at the highest level of security. It provides versatility, interconnection, accountability, and authentication for data access. For different purposes, health records must be kept safe and confidential. Blockchain helps for the decentralised protection of data in healthcare and avoids specific threats.
The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study

Deep learning (DL)-based methods have advanced the field of Prognostics and Health Management (PHM) in recent years, because of their powerful feature representation ability. The data in PHM are typically regular data represented in the Euclidean space. Nevertheless, there are an increasing number of applications that consider the relationships and interdependencies of data and represent the data in the form of graphs. Such kind of irregular data in non-Euclidean space pose a huge challenge to the existing DL-based methods, making some important operations (e.g., convolutions) easily applied to Euclidean space but difficult to model graph data in non-Euclidean space. Recently, graph neural networks (GNNs), as the emerging neural networks, have been utilized to model and analyze the graph data. However, there still lacks a guideline on leveraging GNNs for realizing intelligent fault diagnostics and prognostics. To fill this research gap, a practical guideline is proposed in this paper, and a novel intelligent fault diagnostics and prognostics framework based on GNN is established to illustrate how the proposed guideline works. In this framework, three types of graph construction methods are provided, and seven kinds of graph convolutional networks (GCNs) with four different graph pooling methods are investigated. To afford benchmark results for helping further study, a comprehensive evaluation of these models is performed on eight datasets, including six fault diagnosis datasets and two prognosis datasets. Finally, four issues related to the performance of GCNs are discussed and potential research directions are provided. The code library is available at: https://github.com/HazeDT/PHMGNNBenchmark.
Robust Aggregation for Federated Learning

Federated learning is the centralized training of statistical models from decentralized data on mobile devices while preserving the privacy of each device. We present a robust aggregation approach to make federated learning robust to settings when a fraction of the devices may be sending corrupted updates to the server. The approach relies on a robust aggregation oracle based on the geometric median, which returns a robust aggregate using a constant number of iterations of a regular non-robust averaging oracle. The robust aggregation oracle is privacy-preserving, similar to the non-robust secure average oracle it builds upon. We establish its convergence for least squares estimation of additive models. We provide experimental results with linear models and deep networks for three tasks in computer vision and natural language processing. The robust aggregation approach is agnostic to the level of corruption; it outperforms the classical aggregation approach in terms of robustness when the level of corruption is high, while being competitive in the regime of low corruption. Two variants, a faster one with one-step robust aggregation and another one with on-device personalization, round off the paper.
MIMO Channel Information Feedback Using Deep Recurrent Network

In a multiple-input multiple-output (MIMO) system, the availability of channel state information (CSI) at the transmitter is essential for performance improvement. Recent convolutional neural network (NN)-based techniques show competitive ability in realizing CSI compression and feedback. By introducing a new NN architecture, we enhance the accuracy of quantized CSI feedback in MIMO communications. The proposed NN architecture invokes a module named long short-term memory that admits the NN to benefit from exploiting temporal and frequency correlations of wireless channels. Compromising performance with complexity, we further modify the NN architecture with a significantly reduced number of parameters to be trained. Finally, experiments show that the proposed NN architectures achieve better performance in terms of both CSI compression and recovery accuracy.
A hyper-heuristic for improving the initial population of whale optimization algorithm

This paper improves the performance of the recently-proposed Whale Optimization Algorithm (WOA). WOA is a meta-heuristic that simulates the foraging behavior of humpback whales. There are several improvements in the literature for this algorithm of which chaotic maps and Opposition-Based Learning (OBL) are proved to be the most effective. In the former method, however, there are many chaotic maps that make it difficult to choose the best one for a given optimization algorithm. In the latter method, OBL should be applied to a portion of solutions in the population, which is normally obtained manually, which is time-consuming. This work proposed a hyper-heuristic to alleviate these drawbacks by automatically choosing a chaotic map and a portion of the population using the Differential Evolution (DE) algorithm. The proposed algorithm, which called DEWCO, has high ability to improve the exploration and local optima avoidance of WOA. In order to investigate the performance of the proposed DEWCO algorithm, several experiments are conducted on 35 standard CEC2005 functions and using seven algorithms. The experimental results show the superior performance of the proposed DEWCO algorithm to determine the optimal solutions of the test function problems.
Spatiotemporal Representation Learning for Translation-Based POI Recommendation

The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are the two most important contextual factors in the user’s decision-making for choosing a POI to visit. In this article, we focus on the spatiotemporal context-aware POI recommendation, which considers the joint effect of time and location for POI recommendation. Inspired by the recent advances in knowledge graph embedding, we propose a spatiotemporal context-aware and translation-based recommender framework (STA) to model the third-order relationship among users, POIs, and spatiotemporal contexts for large-scale POI recommendation. Specifically, we embed both users and POIs into a “transition space” where spatiotemporal contexts (i.e., a &lt; time, location &gt; pair) are modeled as translation vectors operating on users and POIs. We further develop a series of strategies to exploit various correlation information to address the data sparsity and cold-start issues for new spatiotemporal contexts, new users, and new POIs. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate that our STA framework achieves the superior performance in terms of high recommendation accuracy, robustness to data sparsity, and effectiveness in handling the cold-start problem.
Transfer Learning with ResNet-50 for Malaria Cell-Image Classification

Malaria is an infectious disease caused by single-celled parasite of plasmodium group. The disease is more often spread by an Infected Female Anopheles mosquito. In 2017 alone 219 million cases and nearly 435,000 deaths were reported, with more than 40% of global population at risk. In spite of many advanced evaluation techniques for identifying the infection, microscopists at resource constrained regions face challenge in improving the diagnostic accuracy. Deep learning based classification of cell images prevent the wrong diagnostic decisions. This paper focuses on the implementation of Transfer learning based classification of malarial infected cells to improve the diagnostic accuracy. The experimental results show that transfer learning performs well on microscopic cell-images.
Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction

Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR) and genetic data. Our study cohort included 109, 490 individuals. In the first experiment, we extracted aggregated and longitudinal features from EHR. We applied logistic regression, random forests, gradient boosting trees, convolutional neural networks (CNN) and recurrent neural networks with long short-term memory (LSTM) units. In the second experiment, we applied a late-fusion approach to incorporate genetic features. We compared the performance with approaches currently utilized in routine clinical practice - American College of Cardiology and the American Heart Association (ACC/AHA) Pooled Cohort Risk Equation. Our results indicated that incorporating longitudinal feature lead to better event prediction. Combining genetic features through a late-fusion approach can further improve CVD prediction, underscoring the importance of integrating relevant genetic data whenever available.
cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks

Swarm intelligence algorithms have been widely adopted in solving many highly nonlinear, multimodal problems and have achieved tremendous successes. However, their application on deep neural networks is largely unexplored. On the other hand, deep neural networks, especially convolutional neural network (CNN), have recently achieved breakthroughs in tackling many intractable problems; nevertheless their performance depends heavily on the chosen values of their hyper-parameters, whose fine-tuning is both labor-intensive and time-consuming. In this paper, we propose a novel particle swarm optimization (PSO) variant cPSO-CNN for optimizing the hyper-parameter configuration of architecture-determined CNNs. cPSO-CNN utilizes a confidence function defined by a compound normal distribution to model experts' knowledge on CNN hyper-parameter fine-tunings so as to enhance the canonical PSO's exploration capability. cPSO-CNN also redefines the scalar acceleration coefficients of PSO as vectors to better adapt for the variant ranges of CNN hyper-parameters. Besides, a linear prediction model is adopted for fast ranking the PSO particles to reduce the cost of fitness function evaluation. The experimental results demonstrate that cPSO-CNN performs competitively when compared with several reported algorithms in terms of both CNN hyper-parameter superiority and overall computation cost.
Fiat-Shamir: from practice to theory

We give new instantiations of the Fiat-Shamir transform using explicit, efficiently computable hash functions. We improve over prior work by reducing the security of these protocols to qualitatively simpler and weaker computational hardness assumptions. As a consequence of our framework, we obtain the following concrete results.