Reviewerly/exHarmony huggingface dataset:
Variational learning for quantum artificial neural networks

In the last few years, quantum computing and machine learning fostered rapid developments in their respective areas of application, introducing new perspectives on how information processing systems can be realized and programmed. The rapidly growing field of Quantum Machine Learning aims at bringing together these two ongoing revolutions. Here we first review a series of recent works describing the implementation of artificial neurons and feed-forward neural networks on quantum processors. We then present an original realization of efficient individual quantum nodes based on variational unsampling protocols. While keeping full compatibility with the overall memory-efficient feed-forward architecture, such a construction effectively reduces the quantum circuit depth required to determine the activation probability of single neurons upon input of the relevant data-encoding quantum states. This suggests a viable approach towards the use of quantum neural networks for pattern classification on near-term quantum hardware.
CLA‐GAN: A Context and Lightness Aware Generative Adversarial Network for Shadow Removal

Abstract In this paper, we propose a novel context and lightness aware Generative Adversarial Network (CLA‐GAN) framework for shadow removal, which refines a coarse result to a final shadow removal result in a coarse‐to‐fine fashion. At the refinement stage, we first obtain a lightness map using an encoder‐decoder structure. With the lightness map and the coarse result as the inputs, the following encoder‐decoder tries to refine the final result. Specifically, different from current methods restricted pixel‐based features from shadow images, we embed a context‐aware module into the refinement stage, which exploits patch‐based features. The embedded module transfers features from non‐shadow regions to shadow regions to ensure the consistency in appearance in the recovered shadow‐free images. Since we consider pathces, the module can additionally enhance the spatial association and continuity around neighboring pixels. To make the model pay more attention to shadow regions during training, we use dynamic weights in the loss function. Moreover, we augment the inputs of the discriminator by rotating images in different degrees and use rotation adversarial loss during training, which can make the discriminator more stable and robust. Extensive experiments demonstrate the validity of the components in our CLA‐GAN framework. Quantitative evaluation on different shadow datasets clearly shows the advantages of our CLA‐GAN over the state‐of‐the‐art methods.
Robust Adaptive Semi-supervised Classification Method based on Dynamic Graph and Self-paced Learning

Despite the computers have developed rapidly in recent years, there are still many difficulties to obtain a large number of labelled data in many practical problems, for example, medical image diagnosis, internet fraud, and pedestrian detection. To deal with learning problems with only a few labeled data, a novel semi-supervised learning method combined with dynamic graph learning with self-paced learning mechanism is present in this work, namely SS-GSELM. Firstly, according to the loss value of labeled samples in each training, the algorithm selects the sample with the smaller loss value for learning, and then gradually adds the sample with the larger loss value during the training process until all labeled samples are trained. In particular, different weights are given to samples through a regularization function to adjust the importance of different samples on the model results. Secondly, the algorithm uses local consistency property as supplementary information to enhance the performance of the learning machine, so an adaptive graph matrix is constructed to retain data similarity information. To do this, an alternative strategy is proposed to update graph matrices and self-paced weights to adapt to the classifier. Experimental results on real data sets exhibit that the proposed method superior to the classic methods in classification tasks.
Dynamic facial asset and rig generation from a single scan

The creation of high-fidelity computer-generated (CG) characters for films and games is tied with intensive manual labor, which involves the creation of comprehensive facial assets that are often captured using complex hardware. To simplify and accelerate this digitization process, we propose a framework for the automatic generation of high-quality dynamic facial models, including rigs which can be readily deployed for artists to polish. Our framework takes a single scan as input to generate a set of personalized blendshapes, dynamic textures, as well as secondary facial components ( e.g. , teeth and eyeballs). Based on a facial database with over 4, 000 scans with pore-level details, varying expressions and identities, we adopt a self-supervised neural network to learn personalized blendshapes from a set of template expressions. We also model the joint distribution between identities and expressions, enabling the inference of a full set of personalized blendshapes with dynamic appearances from a single neutral input scan. Our generated personalized face rig assets are seamlessly compatible with professional production pipelines for facial animation and rendering. We demonstrate a highly robust and effective framework on a wide range of subjects, and showcase high-fidelity facial animations with automatically generated personalized dynamic textures.
Polyadic Quantum Classifier

We introduce here a supervised quantum machine learning algorithm for multi-class classification on NISQ architectures. A parametric quantum circuit is trained to output a specific bit string corresponding to the class of the input datapoint. We train and test it on an IBMq 5-qubit quantum computer and the algorithm shows good accuracy - compared to a classical machine learning model - for ternary classification of the Iris dataset and an extension of the XOR problem. Furthermore, we evaluate with simulations how the algorithm fares for a binary and a quaternary classification on resp. a known binary dataset and a synthetic dataset.
Starfish

Cameras are key enablers for a wide range of IoT use cases including smart cities, intelligent transportation, AI-enabled farms, and more. These IoT applications require cloud software (including models) to act on the images. However, traditional task oblivious compression techniques are a poor fit for delivering images over low power IoT networks that are lossy and limited in capacity. The key challenge is their brittleness against packet loss; they are highly sensitive to small amounts of packet loss requiring retransmission for transport, which further reduces the available capacity of the network. We propose Starfish, a design that achieves better compression ratios and is graceful with packet loss. In addition to that, Starfish features content-awareness and task-awareness, meaning that we can build specialized codecs for each application scenario and optimized for task objectives, including objective/perceptual quality as well as AI tasks directly. We carefully design the DNN architecture and use an AutoML method to search for TinyML models that work on extremely low power/cost AIoT accelerators. Starfish is not only the first image compress framework that works on a $3 AIoT accelerators but also outperforms JPEG, a well-established baseline, by up to 3x, in terms of bandwidth efficiency and up to 2.5x as efficient in energy consumption. It also features graceful and gradual performance degradation in the presence of packet loss. The application-level simulation indicates that Starfish could deliver 3.7x images while providing better image quality.
Disentangling Multiple Features in Video Sequences Using Gaussian Processes in Variational Autoencoders

We introduce MGP-VAE (Multi-disentangled-features Gaussian Processes Variational AutoEncoder), a variational autoencoder which uses Gaussian processes (GP) to model the latent space for the unsupervised learning of disentangled representations in video sequences. We improve upon previous work by establishing a framework by which multiple features, static or dynamic, can be disentangled. Specifically we use fractional Brownian motions (fBM) and Brownian bridges (BB) to enforce an inter-frame correlation structure in each independent channel, and show that varying this structure enables one to capture different factors of variation in the data. We demonstrate the quality of our representations with experiments on three publicly available datasets, and also quantify the improvement using a video prediction task. Moreover, we introduce a novel geodesic loss function which takes into account the curvature of the data manifold to improve learning. Our experiments show that the combination of the improved representations with the novel loss function enable MGP-VAE to outperform the baselines in video prediction.
OBA: An Ontology-Based Framework for Creating REST APIs for Knowledge Graphs

In recent years, Semantic Web technologies have been increasingly adopted by researchers, industry and public institutions to describe and link data on the Web, create web annotations and consume large knowledge graphs like Wikidata and DBpedia. However, there is still a knowledge gap between ontology engineers, who design, populate and create knowledge graphs; and web developers, who need to understand, access and query these knowledge graphs but are not familiar with ontologies, RDF or SPARQL. In this paper we describe the Ontology-Based APIs framework (OBA), our approach to automatically create REST APIs from ontologies while following RESTful API best practices. Given an ontology (or ontology network) OBA uses standard technologies familiar to web developers (OpenAPI Specification, JSON) and combines them with W3C standards (OWL, JSON-LD frames and SPARQL) to create maintainable APIs with documentation, units tests, automated validation of resources and clients (in Python, Javascript, etc.) for non Semantic Web experts to access the contents of a target knowledge graph. We showcase OBA with three examples that illustrate the capabilities of the framework for different ontologies.
HCE: Hierarchical Context Embedding for Region-Based Object Detection

State-of-the-art two-stage object detectors apply a classifier to a sparse set of object proposals, relying on region-wise features extracted by RoIPool or RoIAlign as inputs. The region-wise features, in spite of aligning well with the proposal locations, may still lack the crucial context information which is necessary for filtering out noisy background detections, as well as recognizing objects possessing no distinctive appearances. To address this issue, we present a simple but effective Hierarchical Context Embedding (HCE) framework, which can be applied as a plug-and-play component, to facilitate the classification ability of a series of region-based detectors by mining contextual cues. Specifically, to advance the recognition of context-dependent object categories, we propose an image-level categorical embedding module which leverages the holistic image-level context to learn object-level concepts. Then, novel RoI features are generated by exploiting hierarchically embedded context information beneath both whole images and interested regions, which are also complementary to conventional RoI features. Moreover, to make full use of our hierarchical contextual RoI features, we propose the early-and-late fusion strategies (i.e., feature fusion and confidence fusion), which can be combined to boost the classification accuracy of region-based detectors. Comprehensive experiments demonstrate that our HCE framework is flexible and generalizable, leading to significant and consistent improvements upon various region-based detectors, including FPN, Cascade R-CNN and Mask R-CNN.
Implementation of Automatic Text Summarization with TextRank Method in the Development of Al-Qur’an Vocabulary Encyclopedia

Studying the Qur’an by understanding the Qur’an’s vocabulary so that understanding its meaning is not easy. Then we need a Qur’anic vocabulary encyclopedia that focuses on explaining the meaning of the words in it. The development of encyclopedias used automatic text summarization with the TextRank method because from one query that is searched there are many meanings of words that must be summarized. This method starts by selecting documents that are relevant to the query, then summarizes the selected documents using the TextRank method so that they get a summary based on all the word meanings, finally testing the summary results by the system by comparing them with summary targets that are constructed manually by humans. The application of the TextRank method for automatic text summarization has an average value of F-Score 0.6173. The results of automatic text summarization using the TextRank method are no duplicates, and for some queries, it is almost the same as the summary results created manually by humans.
Causal artificial neural network and its applications in engineering design

To reduce the computational cost in engineering design, expensive high-fidelity simulation models are approximated by mathematical models, named as metamodels. Typical metamodeling methods assume that expensive simulation models are black-box functions. In this paper, in order to improve the accuracy of metamodels and reduce the cost of building metamodels, knowledge about engineering design problems is employed to help develop a novel metamodel, named as causal artificial neural network (causal-ANN). Cause–effect relations intrinsic to the design problem are employed to decompose an ANN into sub-networks and values of intermediate variables are utilized to train these sub-networks. By involving knowledge of the design problem, the accuracy of causal-ANN is higher than the traditional metamodeling methods that assume black-box functions. Additionally, one can identify attractive subspaces from the causal-ANN by leveraging the structure of the causal-ANN and the theory of Bayesian Networks. The impacts of fidelity of causal graphs and design variable correlations are also discussed in the paper. The engineering case studies demonstrate that the causal-ANN can be accurately constructed with a small number of expensive simulations, and attractive design subspaces can be identified directly from the causal-ANN.
Salient object detection on hyperspectral images in wireless network using CNN and saliency optimization

Salient object detection on hyperspectral images has made some progress in recent years, benefiting from the development of wireless network and hyperspectral imaging technology. However, most object detection methods on hyperspectral images focus more on the spectrum and do not fully mine the spatial information, especially high-level spatial–spectral information. In this paper, we propose a salient object detection model on hyperspectral images in wireless network by applying saliency optimization to convolutional neural network (CNN) features. In the model, we firstly use CNN with two channels to extract spatial and spectral features of the same dimension respectively and conduct feature fusion at the end. Then, we generate the final saliency maps by optimizing the saliency values of the foreground and background cues, computing from the CNN features. The experimental results confirm that the proposed method is effective and has better performance on hyperspectral images.
SDABS: A Flexible and Efficient Multi-Authority Hybrid Attribute-Based Signature Scheme in Edge Environment

The explosive growth of the Internet of Things and modern networking technologies lay the foundation for the development of intelligent transportation systems and smart cities. To analyzing massive data under the required time for transportation issues, the edge computing paradigm is applied, which pre-processing large amounts of data at the network edge to save bandwidth and improve response time. However, data reliability and security are still facing many challenges in the edge environment. In this article, we propose a multi-authority hybrid attribute-based signature scheme (SDABS). It is composed of four phases: system initialization, signature generation, signature verification, and attribute revocation phases. To better describe frequently changing features in the transportation systems like location, the dynamic attribute is introduced in building the signature. The multi-layer policy tree is applied to support flexible and various access policies, which also naturally form user groups and help data searching. Besides, the multi-authority structure is more suitable for the distributed edge environment. We evaluate SDABS from both theoretical analysis and practical analysis. Compared with two classical signature schemes (MABS and ODMA-ABS), experimental results demonstrate that the proposed SDABS can achieve better performance at an acceptable cost in the terms of attributes and attribute authorities.
Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks

The multi-robot coverage problem is an essential building block for systems that perform tasks like inspection, exploration, or search and rescue. We discretize the coverage problem to induce a spatial graph of locations and represent robots as nodes in the graph. Then, we train a Graph Neural Network controller that leverages the spatial equivariance of the task to imitate an expert open-loop routing solution. This approach generalizes well to much larger maps and larger teams that are intractable for the expert. In particular, the model generalizes effectively to a simulation of ten quadrotors and dozens of buildings in an urban setting. We also demonstrate the GNN controller can surpass planning-based approaches in an exploration task.
Collaborative Storytelling with Large-scale Neural Language Models

Storytelling plays a central role in human socializing and entertainment. However, much of the research on automatic storytelling generation assumes that stories will be generated by an agent without any human interaction. In this paper, we introduce the task of collaborative storytelling, where an artificial intelligence agent and a person collaborate to create a unique story by taking turns adding to it. We present a collaborative storytelling system which works with a human storyteller to create a story by generating new utterances based on the story so far. We constructed the storytelling system by tuning a publicly-available large scale language model on a dataset of writing prompts and their accompanying fictional works. We identify generating sufficiently human-like utterances to be an important technical issue and propose a sample-and-rank approach to improve utterance quality. Quantitative evaluation shows that our approach outperforms a baseline, and we present qualitative evaluation of our system’s capabilities.
Generating photo-realistic training data to improve face recognition accuracy

Face recognition has become a widely adopted biometric in forensics, security and law enforcement thanks to the high accuracy achieved by systems based on convolutional neural networks (CNNs). However, to achieve good performance, CNNs need to be trained with very large datasets which are not always available. In this paper we investigate the feasibility of using synthetic data to augment face datasets. In particular, we propose a novel generative adversarial network (GAN) that can disentangle identity-related attributes from non-identity-related attributes. This is done by training an embedding network that maps discrete identity labels to an identity latent space that follows a simple prior distribution, and training a GAN conditioned on samples from that distribution. A main novelty of our approach is the ability to generate both synthetic images of subjects in the training set and synthetic images of new subjects not in the training set, both of which we use to augment face datasets. By using recent advances in GAN training, we show that the synthetic images generated by our model are photo-realistic, and that training with datasets augmented with those images can lead to increased recognition accuracy. Experimental results show that our method is more effective when augmenting small datasets. In particular, an absolute accuracy improvement of 8.42% was achieved when augmenting a dataset of less than 60k facial images.
Human Pose Transfer by Adaptive Hierarchical Deformation

Abstract Human pose transfer, as a misaligned image generation task, is very challenging. Existing methods cannot effectively utilize the input information, which often fail to preserve the style and shape of hair and clothes. In this paper, we propose an adaptive human pose transfer network with two hierarchical deformation levels. The first level generates human semantic parsing aligned with the target pose, and the second level generates the final textured person image in the target pose with the semantic guidance. To avoid the drawback of vanilla convolution that treats all the pixels as valid information, we use gated convolution in both two levels to dynamically select the important features and adaptively deform the image layer by layer. Our model has very few parameters and is fast to converge. Experimental results demonstrate that our model achieves better performance with more consistent hair, face and clothes with fewer parameters than state‐of‐the‐art methods. Furthermore, our method can be applied to clothing texture transfer. The code is available for research purposes at https://github.com/Zhangjinso/PINet_PG .
Scalable quantum processor noise characterization

Measurement fidelity matrices (MFMs) (also called error kernels) are a natural way to characterize state preparation and measurement errors in near-term quantum hardware. They can be employed in post processing to mitigate errors and substantially increase the effective accuracy of quantum hardware. However, the feasibility of using MFMs is currently limited as the experimental cost of determining the MFM for a device grows exponentially with the number of qubits. In this work we present a scalable way to construct approximate MFMs for many-qubit devices based on cumulant expansions. Our method can also be used to characterize various types of correlation error.
Moving Object Detection Based on Background Compensation and Deep Learning

Detecting moving objects in a video sequence is an important problem in many vision-based applications. In particular, detecting moving objects when the camera is moving is a difficult problem. In this study, we propose a symmetric method for detecting moving objects in the presence of a dynamic background. First, a background compensation method is used to detect the proposed region of motion. Next, in order to accurately locate the moving objects, we propose a convolutional neural network-based method called YOLOv3-SOD for detecting all objects in the image, which is lightweight and specifically designed for small objects. Finally, the moving objects are determined by fusing the results obtained by motion detection and object detection. Missed detections are recalled according to the temporal and spatial information in adjacent frames. A dataset is not currently available specifically for moving object detection and recognition, and thus, we have released the MDR105 dataset comprising three classes with 105 videos. Our experiments demonstrated that the proposed algorithm can accurately detect moving objects in various scenarios with good overall performance.