Reviewerly/exHarmony huggingface dataset:
Confirmnet: Convolutional Firmnet and Application to Image Denoising and Inpainting

We address the problem of efficient convolutional sparse coding (CSC) and develop a non-convex-penalty-regularized CSC formulation, namely, minimax-concave CSC (MC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SC). MC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SC leads to an optimal sparse representation than the standard ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -penalty based approach. In addition, suitable convergence guarantees can also be provided for MC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SC. We propose a convolutional iterative firm-thresholding algorithm (CIFTA) building on our previously proposed IFTA, and its deep-unfolded version, namely, convolutional-FirmNet (ConFirmNet). As an application, we develop the ConFirmNet based sparse autoencoder (ConFirmNet-SAE) for learning an application-specific convolutional dictionary, the applications being image denoising and inpainting. Further, we also show that training ConFirmNet-SAE with the Huber loss imparts robustness to outliers. It also turns out that ConFirmNet-SAE is robust to mismatch between training and test noise conditions than convolutional learned iterative soft-thresholding algorithm (LISTA).
On Divergence Approximations for Unsupervised Training of Deep Denoisers Based on Stein’s Unbiased Risk Estimator

Recently, there have been several works on unsupervised learning for training deep learning based denoisers without clean images. Approaches based on Stein's unbiased risk estimator (SURE) have shown promising results for training Gaussian deep denoisers. However, their performance is sensitive to hyper-parameter selection in approximating the divergence term in the SURE expression. In this work, we briefly study the computational efficiency of Monte-Carlo (MC) divergence approximation over recently available exact divergence computation using backpropagation. Then, we investigate the relationship between smoothness of nonlinear activation functions in deep denoisers and robust divergence term approximations. Lastly, we propose a new divergence term that does not contain hyper-parameters. Both unsupervised training methods yield comparable performance to supervised training methods with ground truth for denoising on various datasets. While the former method still requires roughly tuned hyper parameter selection, the latter method removes the necessity of choosing one.
Quantum correlations and coherence in a driven two-qubit system under non-Markovian dissipative effect

By considering an exactly solvable model for a driven two non-interacting qubits, each coupled to a bosonic environment with zero temperature, under the non-Markovian dissipative process, we study the variation of coherence and correlations in terms of different physical parameters. We show the influence of the external classical driving field as well as the initial quantum states. Moreover, we highlight the relationship between the coherence, single-qubit population, and correlations according to the physical parameters of the whole system. We reveal that the preservation and enhancement of coherence and correlations may occur by adjusting the strength of the classical driving field, initial states, and non-Markovian effects.
Synthetic Crowd and Pedestrian Generator for Deep Learning Problems

Deep Neural networks (DNN) dominate the state of art results in computer vision (CV) and other fields. One of the primary reasons why DNN outperform existing algorithms is that these produce superior results when more labelled data are used, unlike classic CV techniques. Nonetheless, it is well known that DNN requires a very large amount of data to generalise well. Collecting and labelling these datasets are expensive, time-consuming and sometimes impossible. Therefore, researchers tried to use alternative techniques, such as graphics simulators to automatically generate labelled datasets. However, these techniques are still expensive and require domain knowledge to produce good datasets. In this paper, therefore, a graphics simulator is presented which automatically generates multi-model datasets in real-time providing the corresponding ground truth and annotation. The tool concentrates on pedestrian and crowd analysis including 3D human pose estimation, pedestrian detection as well as crowd density and flow estimation.
A General Difficulty Control Algorithm for Proof-of-Work Based Blockchains

Designing an efficient difficulty control algorithm is an essential problem in Proof-of-Work (PoW) based blockchains because the network hash rate is randomly changing. This paper proposes a general difficulty control algorithm and provides insights for difficulty adjustment rules for PoW based blockchains. The proposed algorithm consists a two-layer neural network. It has low memory cost, meanwhile satisfying the fast-updating and low volatility requirements for difficulty adjustment. Real data from Ethereum are used in the simulations to prove that the proposed algorithm has better performance for the control of the block difficulty.
Semi-Supervised Learning for Text Classification by Layer Partitioning

Most recent neural semi-supervised learning (SSL) algorithms rely on adding small perturbation to either the input vectors or their representations. These methods have been successful on computer vision tasks as the images form a continuous manifold, but are not appropriate for discrete input such as sentence. To adapt these methods to text input, we propose to decompose a neural network M into two components F and U so that M = U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sub> F . The layers in F are then frozen and only the layers in U will be updated during most time of the training. In this way, F serves as a feature extractor that maps the input to high-level representation and adds systematical noise using dropout. We can then train U using any state-of-the-art SSL algorithms such as Π-model, temporal ensembling, mean teacher, etc. Furthermore, this gradually unfreezing schedule also prevents a pretrained model from catastrophic forgetting. The experimental results demonstrate that our approach provides improvements when compared to state of the art methods especially on short texts.
Lie Group State Estimation via Optimal Transport

Many applications in science and engineering involve tracking the state of a stochastic differential equation (SDE) evolving in a Lie group. This has been tackled by particle filtering although some existing schemes fail to satisfy geometric constraints. Further the conventional particle filter suffers from particle deprivation. Here we overcome these problems by managing the geometry with the Cayley transform and particle depletion with optimal transport. Simulations show the superiority over 'regular' geometry aware particle filtering.
An efficient approximation of the Kalman filter for multiple systems coupled via low-dimensional stochastic input

We formulate a recursive estimation problem for multiple dynamical systems coupled through a low dimensional stochastic input, and we propose an efficient sub-optimal solution. The suggested approach is an approximation of the Kalman filter that discards the off diagonal entries of the correlation matrix in its “update” step. The time complexity associated with propagating this approximate block-diagonal covariance is linear in the number of systems, compared to the cubic complexity of the full Kalman filter. The stability of the proposed block-diagonal filter and its behavior for a large number of systems are analyzed in some simple cases. It is then examined in the context of electric field estimation in a high-contrast space coronagraph, for which it was designed. The numerical simulations provide encouraging results for the cost-efficiency of the newly suggested filter.
A Bidirectional Context Propagation Network for Urine Sediment Particle Detection in Microscopic Images

The microscopic urine sediment examination is a crucial part in the evaluation of renal and urinary tract diseases. Recently, there are emerging CNNs-based detectors to detect the urine sediment particles in an end-to-end manner. However, it is not very compatible to transfer CNNs-based detector directly from natural images application to microscopic images, especially in which small objects are in majority. This paper proposes a bidirectional context propagation network called BCPNet for urine sediment particle detection. In BCPNet, spatial details encoded by shallow convolutional layers are propagated upward to improve the localisation ability of deep features. On the contrary, high semantic information encoded by deep convolutional layers is propagated downward to enhance the distinctiveness of shallow features. With the refinement by convolutional block attention modules, the enriched features are more powerful to both localisation and classification. Experimental results on urine sediment particle dataset USE demonstrate effectiveness of the proposed BCPNet.
SDTCN: Similarity Driven Transmission Computing Network for Image Dehazing

Transmission similarity is an important feature which can greatly increase the capability of convolutional neural network (CNN) to fit transmission map. However, it is not sufficiently utilized in existing algorithms. In this paper, we propose a novel light-weight similarity driven transmission computing network called SDTCN that is guided by the attributes of transmission similarity. First, we adopt a non-data-driven image segmentation method to acquire the transmission similarity. Compared with CNN based segmentation approaches, our method can not only greatly save computing resources, but also separate the objects and background precisely. Second, a full convolutional network is introduced to reduce blocky effects in SDTCN. Finally, unlike previous "first airlight then transmission" mode, a dependable airlight estimation approach is designed drawing on the transmission map generated by SDTCN, which can improve the accuracy of airlight effectively. Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art methods on synthetic and real-world images.
Neural Query-Biased Abstractive Summarization Using Copying Mechanism

This paper deals with the query-biased summarization task. Conventional non-neural network-based approaches have achieved better performance by primarily including the words overlapping between the source and the query in the summary. However, recurrent neural network (RNN)-based approaches do not explicitly model this phenomenon. Therefore, we model an RNN-based query-biased summarizer to primarily include the overlapping words in the summary, using a copying mechanism. Experimental results, in terms of both automatic evaluation with ROUGE and manual evaluation, show that the strategy to include the overlapping words also works well for neural query-biased summarizers.
Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization

Abstractive text summarization is a challenging task, and one need to design a mechanism to effectively extract salient information from the source text and then generate a summary. A parsing process of the source text contains critical syntactic or semantic structures, which is useful to generate more accurate summary. However, modeling a parsing tree for text summarization is not trivial due to its non-linear structure and it is harder to deal with a document that includes multiple sentences and their parsing trees. In this paper, we propose to use a graph to connect the parsing trees from the sentences in a document and utilize the stacked graph convolutional networks (GCNs) to learn the syntactic representation for a document. The selective attention mechanism is used to extract salient information in semantic and structural aspect and generate an abstractive summary. We evaluate our approach on the CNN/Daily Mail text summarization dataset. The experimental results show that the proposed GCNs based selective attention approach outperforms the baselines and achieves the state-of-the-art performance on the dataset.
RNN Based Language Generation Models for a Hindi Dialogue System

Natural Language Generation (NLG) is a crucial component of a Spoken Dialogue System. Its task is to generate utterances with intended attributes like fluency, variation, readability, scalability and adequacy. As the handcrafted models are rigid and tedious to build, people have proposed many statistical and deep-learning based models to bring about more suitable options for generating utterance on a given Dialogue-Act (DA). This paper presents some Recurrent Neural Network Language Generation (RNNLG) framework based models along with their analysis of how they extract intended meaning in terms of content planning (modelling semantic input) and surface realization (final sentence generation) on a proposed unaligned Hindi dataset. The models have shown consistent performance on our natively developed dataset where the Modified-Semantically-Controlled LSTM (MSC-LSTM) performs better than all in terms of total slot-error (T-Error).
AI-Powered Text Generation for Harmonious Human-Machine Interaction: Current State and Future Directions

In the last two decades, the landscape of text generation has undergone tremendous changes and is being reshaped by the success of deep learning. New technologies for text generation ranging from template-based methods to neural network-based methods emerged. Meanwhile, the research objectives have also changed from generating smooth and coherent sentences to infusing personalized traits to enrich the diversification of newly generated content. With the rapid development of text generation solutions, one comprehensive survey is urgent to summarize the achievements and track the state of the arts. In this survey paper, we present the general systematical framework, illustrate the widely utilized models and summarize the classic applications of text generation.
Obstacle Avoidance for Unmanned Vehicle based on a 2D LIDAR

In recent years, international trade ports have become increasingly busy. As the most important form of transportation in international trade, container shipping plays an important role in reducing the labor cost of terminals, improving port capacity and reducing the energy consumption of loading and unloading operations. AGV is gradually becoming the main tool for container handling in large international ports. It can work continuously and continuously, improving the efficiency of container operation. Due to the complex port environment, the technical requirements for unmanned vehicles are highly demanded. Unmanned vehicle obstacle avoidance technology is an important part of many related technologies. The detection and avoidance of surrounding obstacles is to be achieved by unmanned vehicle in safety condition. How to get environmental information and plan the route is very important. In this paper, we design and develop a laser-based avoidance system for autonomous guided vehicle materialized in this work by two-wheeled robot. This system can quickly collect obstacle distance information and effectively avoid the obstacles and determine the new direction and a new path after information processing on the level of computation platform. The local processing on the edge platform expressed by a Raspberry Pi is followed by cloud processing. Experimental results are presented that validate the capability of the system. on navigation based on 2D LIDAR.
Audio-Assisted Image Inpainting for Talking Faces

The goal of our work is to complete missing areas of images of talking faces, exploiting information from both the visual and audio modalities. Existing image inpainting methods rely solely on visual content that doesn't always provide sufficient information for the task. To counter this, we propose a neural network that employs an encoder-decoder architecture with a bimodal fusion mechanism, thus taking into account both visual and audio content. Our proposed method demonstrates consistently superior performance over a baseline visual-only model, reaching for example up to 17% relative improvement in mean absolute error. The presented model is applicable to practical video editing tasks, such as object and overlay-text removal from talking faces, where existing lip and face generation works are not applicable as they require clean input.
State-Space Gaussian Process for Drift Estimation in Stochastic Differential Equations

This paper is concerned with the estimation of unknown drift functions of stochastic differential equations (SDEs) from observations of their sample paths. We propose to formulate this as a non-parametric Gaussian process regression problem and use an Ito-Taylor expansion for approximating the SDE. To address the computational complexity problem of Gaussian process regression, we cast the model in an equivalent state-space representation, such that (non-linear) Kalman filters and smoothers can be used. The benefit of these methods is that computational complexity scales linearly with respect to the number of measurements and hence the method remains tractable also with large amounts of data. The overall complexity of the proposed method is O(N log N), where N is the number of measurements, due to the requirement of sorting the input data. We evaluate the performance of the proposed method using simulated data as well as with real-data applications to sunspot activity and electromyography.
Continual Learning Through One-Class Classification Using VAE

Artificial neural networks (ANNs) suffer from catastrophic forgetting, a sharp decrease in performance on previously learned tasks, when trained on a new task without constant rehearsal. In this paper, we propose a new method for overcoming this phenomenon based on one-class classification. It is not only able to incrementally learn new but also detect unknown classes. This is a desirable property, since it enables the detection of new and unknown classes in a stream of data and adaption to a changing environment. Experiments on commonly used continual learning setups show competitive results and verify the concept.
Investigations on adaptive connectivity and shape prior based fuzzy graph‐cut colour image segmentation

Abstract Image segmentation is a challenging problem in computer vision with wide application. It is a process which considers the similarity criterion required to separate an image into different homogenous connected regions. First, an Optimized Adaptive Connectivity and Shape Prior in Modified Graph Cut Segmentation method has been applied to handle the structural irregularities in images. Second, an Optimized Adaptive Connectivity and Shape Prior in Modified Fuzzy Graph Cut Segmentation (Opac‐MFGseg) is proposed to partition the images based on feature values. In this method, a fuzzy rule based system is used with optimization algorithm to provide the information on how much a specific feature is involved in image boundaries. The graph obtained from this fuzzy approach is further used in adaptive shape prior in modified graph cuts framework. Moreover, this method supports moving images (videos). In such a situation, a fully dynamic method called Optimized Adaptive Connectivity and Shape Prior in Dynamic Fuzzy Graph Cut Segmentation (Opac‐DFGseg) method is proposed for the image segmentation. The effectiveness of the Opac‐MFGseg and Opac‐DFGseg methods is tested in terms of average sensitivity, precision, area overlap measure, relative error, and accuracy and computation time.
Flexibly-tunable bitcube-based perceptual encryption within jpeg compression 

We propose a perceptual encryption within JPEG compression (EWJ). Although some of the conventional EWJs have the `tun-ability,' which is a property of how many perceptual degradation levels can be provided with the single encryption technique, it is insufficient because either strong level of security or low bitrate overhead is regarded as important. The proposed EWJ detects the specific subspaces of the bits in the quantized discrete cosine transform (QDCT) coefficient blocks (`bitcubes'), such that the non-zero bits crowd, and then the bits are permuted within each bitcube, i.e., not only within each bitplane like the conventional EWJs but also between adjacent bitplanes. The experiments show that the bitcube-based EWJ actually provides more flexible tunability than the conventional EWJs, while compromising the relation between the bitrate overhead and the attack robustness.
A Fifo Based Accelerator for Convolutional Neural Networks

In recent years, Deep Neural Networks (DNNs) have achieved state-of-the-art results in various fields like Computer Vision, Natural Language Processing and Speech Recognition. Of all the DNN architectures, Convolutional Neural Networks (CNNs) have been most effective in tasks like image classification and object detection. The high performance of the CNNs comes at the cost of computational complexity. Currently Graphics Processing Units (GPUs) are used to accelerate CNN training and inference on workstations and data servers. Though popular, GPUs are not suitable for embedded applications because they are not energy efficient. ASIC and FPGA accelerators have the potential to run CNNs that are optimized for energy and performance.In this paper we present an architecture which takes a novel approach to compute convolution results using row-wise inputs as opposed to traditional tile-based processing. We are able to exceed the results of state of the art architectures when implemented on an inexpensive PYNQ Z1 board running at 100Mhz. The total latency to run the convolution layers in the VGG16 benchmark is nearly 1.5x lower for our architecture than state of the art architectures.
Self-Attention and Retrieval Enhanced Neural Networks for Essay Generation

In this paper, we focus on essay generation, which aims at generating an essay (a paragraph) according to a set of topic words. Automatic essay generation can be applied to many scenarios to reduce human workload. Recently the recurrent neural networks (RNN) based methods are proposed to solve this task. However, the RNN-based methods suffer from incoherence problem and duplication problem. To overcome these shortcomings, we propose a self-attention and retrieval enhanced neural network for essay generation. We retrieve sentences relevant to topic words from corpus as material to assist in generation to alleviate the duplication problem. To improve the coherence of essays, the self-attention based encoders are applied to encode topic and material, and the self-attention based decoder are used to generate essay respectively. The final essay is generated under the guidance of topic and material. Experimental results on a real essay dataset show that our model outperforms state-of-the-art baselines according to automatic evaluation and human evaluation.