Reviewerly/exHarmony huggingface dataset:
A novel path planning approach for smart cargo ships based on anisotropic fast marching

Path planning is an essential tool for smart cargo ships that navigate in coastal waters, inland waters or other crowded waters. These ships require expert and intelligent systems to plan safe paths in order to avoid collision with both static and dynamic obstacles. This research proposes a novel path planning approach based on the anisotropic fast marching (FM) method to specifically assist with safe operations in complex marine navigation environments. A repulsive force field is specially produced to describe the safe area distribution surrounding obstacles based on the knowledge of human. In addition, a joint potential field is created to evaluate the travel cost and a gradient descent method is used to search for appropriate paths from the start point to the end point. Meanwhile, the approach can be used to constantly optimize the paths with the help of the expert knowledge in collision avoidance. Particularly, the approach is validated and evaluated through simulations. The obtained results show that it is capable of providing a reasonable and smooth path in a crowded waters. Moreover, the ability of this approach exhibits a significant contribution to the development of expert and intelligent systems in autonomous collision avoidance.
Ancient mural restoration based on a modified generative adversarial network

Abstract How to effectively protect ancient murals has become an urgent and important problem. Digital image processing developments have made it possible to repair damaged murals to a certain extent. This study proposes a consistency-enhanced generative adversarial network (GAN) model to repair missing mural areas. First, the convolutional layer from a fully convolutional network (FCN) is used to extract deep image features; then, through deconvolution, the features are mapped to the size of the original image and the repaired image is output, thereby completing the regenerative network. Next, global and local discriminant networks are applied to determine whether the repaired mural image is “authentic” in terms of both the modified and unmodified areas. In adversarial learning, the generative and discriminant network models are optimized to better complete the mural repair. The network introduces a dilated convolution that increases the convolution kernel’s receptive field. Each network convolutional layer joins in the batch standardization (BN) process to accelerate network convergence and increase the number of network layers and adopts a residual module to avoid the vanishing gradient problem and further optimizing the network. Compared with existing mural restoration algorithms, the proposed algorithm increases the peak signal-to-noise ratio (PSNR) by an average of 6–8 dB and increases the structural similarity (SSIM) index by 0.08–0.12. From a visual perspective, this algorithm successfully complements mural images with complex textures and large missing areas; thus, it may contribute to digital restorations of ancient murals.
Personalized Geographical Influence Modeling for POI Recommendation

Point-of-interest (POI) recommendation has great significance in helping users find favorite places from a large number of candidate venues.One challenging in POI recommendation is to effectively exploit geographical information since users usually care about the physical distance to the recommended POIs.Though spatial relevance has been widely considered in recent recommendation methods, it is modeled only from the POI perspective, failing to capture user personalized preference to spatial distance.Moreover, these methods suffer from a diversity-deficiency problem since they are often based on collaborative filtering which always favors popular POIs.To overcome these problems, we propose in this paper a personalized geographical influence modeling method called PGIM, which jointly learns users' geographical preference and diversity preference for POI recommendation.Specifically, we model geographical preference from three aspects: user global tolerance, user local tolerance, and spatial distance.We also extract user diversity preference from interactions among users for diversity-promoting recommendation.Experimental results on three real-world datasets demonstrate the superiority of PGIM.
Blockchain-Enabled Contextual Online Learning Under Local Differential Privacy for Coronary Heart Disease Diagnosis in Mobile Edge Computing

Due to the increasing medical data for coronary heart disease (CHD) diagnosis, how to assist doctors to make proper clinical diagnosis has attracted considerable attention. However, it faces many challenges, including personalized diagnosis, high dimensional datasets, clinical privacy concerns and insufficient computing resources. To handle these issues, we propose a novel blockchain-enabled contextual online learning model under local differential privacy for CHD diagnosis in mobile edge computing. Various edge nodes in the network can collaborate with each other to achieve information sharing, which guarantees that CHD diagnosis is suitable and reliable. To support the dynamically increasing dataset, we adopt a top-down tree structure to contain medical records which is partitioned adaptively. Furthermore, we consider patients' contexts (e.g., lifestyle, medical history records, and physical features) to provide more accurate diagnosis. Besides, to protect the privacy of patients and medical transactions without any trusted third party, we utilize the local differential privacy with randomised response mechanism and ensure blockchain-enabled information-sharing authentication under multi-party computation. Based on the theoretical analysis, we confirm that we provide real-time and precious CHD diagnosis for patients with sublinear regret, and achieve efficient privacy protection. The experimental results validate that our algorithm {outperforms} other algorithm benchmarks on running time, error rate and diagnosis accuracy.
Image Encryption Scheme Based on a Generalized Arnold Map and RSA Algorithm

This study proposes a new image encryption scheme based on a generalized Arnold map and Rivest–Shamir–Adleman (RSA) algorithm. First, the parameters of the generalized Arnold map are generated by an asymmetric encryption-system RSA algorithm, and the keystream is produced iteratively. To change the distribution of pixel values, the image data are hidden by XOR diffusion. Second, both rows and columns of the image are cyclically confused to hide the image data again. Then, the additive mode diffusion operation is performed to realize third-layer hiding for image content. The overall diffusion and confusion operations are conducted twice to obtain the final cipher image. Test results prove that the encryption scheme proposed in this study is effective and has strong antiattack capabilities and key sensitivity. In addition, because the scheme security relies on the RSA algorithm, it has high security.
EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks

Dropout is a well-known regularization method by sampling a sub-network from a larger deep neural network and training different sub-networks on different subsets of the data. Inspired by the dropout concept, we propose EDropout as an energy-based framework for pruning neural networks in classification tasks. In this approach, a set of binary pruning state vectors (population) represents a set of corresponding sub-networks from an arbitrary provided original neural network. An energy loss function assigns a scalar energy loss value to each pruning state. The energy-based model stochastically evolves the population to find states with lower energy loss. The best pruning state is then selected and applied to the original network. Similar to dropout, the kept weights are updated using backpropagation in a probabilistic model. The energy-based model again searches for better pruning states and the cycle continuous. Indeed, this procedure is in fact switching between the energy model, which manages the pruning states, and the probabilistic model, which updates the temporarily unpruned weights, in each iteration. The population can dynamically converge to a pruning state. This can be interpreted as dropout leading to pruning the network. From an implementation perspective, EDropout can prune typical neural networks without modification of the network architecture. We evaluated the proposed method on different flavours of ResNets, AlexNet, and SqueezeNet on the Kuzushiji, Fashion, CIFAR-10, CIFAR-100, and Flowers datasets, and compared the pruning rate and classification performance of the models. On average the networks trained with EDropout achieved a pruning rate of more than $50\%$ of the trainable parameters with approximately $<5\%$ and $<1\%$ drop of Top-1 and Top-5 classification accuracy, respectively.
Paraphrase Generation by Learning How to Edit from Samples

Neural sequence to sequence text generation has been proved to be a viable approach to paraphrase generation.Despite promising results, paraphrases generated by these models mostly suffer from lack of quality and diversity.To address these problems, we propose a novel retrieval-based method for paraphrase generation.Our model first retrieves a paraphrase pair similar to the input sentence from a pre-defined index.With its novel editor module, the model then paraphrases the input sequence by editing it using the extracted relations between the retrieved pair of sentences.In order to have fine-grained control over the editing process, our model uses the newly introduced concept of Micro Edit Vectors.It both extracts and exploits these vectors using the attention mechanism in the Transformer architecture.Experimental results show the superiority of our paraphrase generation method in terms of both automatic metrics, and human evaluation of relevance, grammaticality, and diversity of generated paraphrases.
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks

Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the generalization gap of adaptive gradient methods an open problem. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". We design a new algorithm, called Partially adaptive momentum estimation method, which unifies the Adam/Amsgrad with SGD by introducing a partial adaptive parameter $p$, to achieve the best from both worlds. We also prove the convergence rate of our proposed algorithm to a stationary point in the stochastic nonconvex optimization setting. Experiments on standard benchmarks show that our proposed algorithm can maintain fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks.
Deep Statistical Model Checking

Neural networks (NN) are taking over ever more decisions thus far taken by humans, even though verifiable system-level guarantees are far out of reach. Neither is the verification technology available, nor is it even understood what a formal, meaningful, extensible, and scalable testbed might look like for such a technology. The present paper is a modest attempt to improve on both the above aspects. We present a family of formal models that contain basic features of automated decision making contexts and which can be extended with further orthogonal features, ultimately encompassing the scope of autonomous driving. Due to the possibility to model random noise in the decision actuation, each model instance induces a Markov decision process (MDP) as verification object. The NN in this context has the duty to actuate (near-optimal) decisions. From the verification perspective, the externally learnt NN serves as a determinizer of the MDP, the result being a Markov chain which as such is amenable to statistical model checking. The combination of a MDP and a NN encoding the action policy is central to what we call “deep statistical model checking” (DSMC). While being a straightforward extension of statistical model checking, it enables to gain deep insight into questions like “how high is the NN-induced safety risk?”, “how good is the NN compared to the optimal policy?” (obtained by model checking the MDP), or “does further training improve the NN?”. We report on an implementation of DSMC inside The Modest Toolset in combination with externally learnt NNs, demonstrating the potential of DSMC on various instances of the model family.
Do Neural Language Models Show Preferences for Syntactic Formalisms?

Recent work on the interpretability of deep neural language models has concluded that many properties of natural language syntax are encoded in their representational spaces.However, such studies often suffer from limited scope by focusing on a single language and a single linguistic formalism.In this study, we aim to investigate the extent to which the semblance of syntactic structure captured by language models adheres to a surface-syntactic or deep syntactic style of analysis, and whether the patterns are consistent across different languages.We apply a probe for extracting directed dependency trees to BERT and ELMo models trained on 13 different languages, probing for two different syntactic annotation styles: Universal Dependencies (UD), prioritizing deep syntactic relations, and Surface-Syntactic Universal Dependencies (SUD), focusing on surface structure.We find that both models exhibit a preference for UD over SUD -with interesting variations across languages and layers -and that the strength of this preference is correlated with differences in tree shape.
MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction

The ambiguity in image matching is one of main factors decreasing the quality of the 3D model reconstructed by PatchMatch based multiple view stereo. In this paper, we present a novel method, matching ambiguity reduced multiple view stereo (MARMVS) to address this issue. The MARMVS handles the ambiguity in image matching process with three newly proposed strategies: 1) The matching ambiguity is measured by the differential geometry property of image surface with epipolar constraint, which is used as a critical criterion for optimal scale selection of every single pixel with corresponding neighbouring images. 2) The depth of every pixel is initialized to be more close to the true depth by utilizing the depths of its surrounding sparse feature points, which yields faster convergency speed in the following PatchMatch stereo and alleviates the ambiguity introduced by self similar structures of the image. 3) In the last propagation of the PatchMatch stereo, higher priorities are given to those planes with the related 2D image patch possesses less ambiguity, this strategy further propagates a correctly reconstructed surface to raw texture regions. In addition, the proposed method is very efficient even running on consumer grade CPUs, due to proper parameterization and discretization in the depth map computation step. The MARMVS is validated on public benchmarks, and experimental results demonstrate competing performance against the state of the art.
Sequential 3D Human Pose and Shape Estimation From Point Clouds

This work addresses the problem of 3D human pose and shape estimation from a sequence of point clouds. Existing sequential 3D human shape estimation methods mainly focus on the template model fitting from a sequence of depth images or the parametric model regression from a sequence of RGB images. In this paper, we propose a novel sequential 3D human pose and shape estimation framework from a sequence of point clouds. Specifically, the proposed framework can regress 3D coordinates of mesh vertices at different resolutions from the latent features of point clouds. Based on the estimated 3D coordinates and features at the low resolution, we develop a spatial-temporal mesh attention convolution (MAC) to predict the 3D coordinates of mesh vertices at the high resolution. By assigning specific attentional weights to different neighboring points in the spatial and temporal domains, our spatial-temporal MAC can capture structured spatial and temporal features of point clouds. We further generalize our framework to the real data of human bodies with a weakly supervised fine-tuning method. The experimental results on SURREAL, Human3.6M, DFAUST and the real detailed data demonstrate that the proposed approach can accurately recover the 3D body model sequence from a sequence of point clouds.
Self-weighted Multi-view Fuzzy Clustering

Since the data in each view may contain distinct information different from other views as well as has common information for all views in multi-view learning, many multi-view clustering methods have been designed to use these information (including the distinct information for each view and the common information for all views) to improve the clustering performance. However, previous multi-view clustering methods cannot effectively detect these information so that difficultly outputting reliable clustering models. In this article, we propose a fuzzy, sparse, and robust multi-view clustering method to consider all kinds of relations among the data (such as view importance, view stability, and view diversity), which can effectively extract both distinct information and common information as well as balance these two kinds of information. Moreover, we devise an alternating optimization algorithm to solve the resulting objective function as well as prove that our proposed algorithm achieves fast convergence. It is noteworthy that existing multi-view clustering methods only consider a part of the relations, and thus are a special case of our proposed framework. Experimental results on synthetic datasets and real datasets show that our proposed method outperforms the state-of-the-art clustering methods in terms of evaluation metrics of clustering such as clustering accuracy, normalized mutual information, purity, and adjusted rand index.
Head2Head++: Deep Facial Attributes Re-Targeting

Facial video re-targeting is a challenging problem aiming to modify the facial attributes of a target subject in a seamless manner by a driving monocular sequence. We leverage the 3D geometry of faces and Generative Adversarial Networks (GANs) to design a novel deep learning architecture for the task of facial and head reenactment. Our method is different to purely 3D model-based approaches, or recent image-based methods that use Deep Convolutional Neural Networks (DCNNs) to generate individual frames. We manage to capture the complex non-rigid facial motion from the driving monocular performances and synthesise temporally consistent videos, with the aid of a sequential Generator and an ad-hoc Dynamics Discriminator network. We conduct a comprehensive set of quantitative and qualitative tests and demonstrate experimentally that our proposed method can successfully transfer facial expressions, head pose and eye gaze from a source video to a target subject, in a photo-realistic and faithful fashion, better than other state-of-the-art methods. Most importantly, our system performs end-to-end reenactment in nearly real-time speed (18 fps).
Quantum Computing for Enhancing Grid Security

This paper introduces quantum computing as a necessary and viable tool in addressing the needs of a modernized power grid. The application of quantum computing in enhancing physical security of the grid - an increasingly difficult problem to solve- is investigated. A comparative study based on mathematically proven computing performance measures shows the merits of the proposed method and further unveils the potential benefits of quantum computing in improving grid performance.
SRPOL’s System for the IWSLT 2020 End-to-End Speech Translation Task

This paper describes the submission to IWSLT 2020 (Ansari et al., 2020) End-to-End speech translation task by Samsung R&D Institute, Poland.We took part in the offline End-to-End English to German TED lectures translation task.We based our solution on our last year's submission (Potapczyk et al., 2019).We used a slightly altered Transformer(Vaswani et al., 2017) architecture with ResNet-like(He et al., 2016) convolutional layer preparing the audio input to Transformer encoder.To improve the model's quality of translation we introduced two regularization techniques and trained on machine translated Librispeech(Panayotov et al., 2015) corpus in addition to iwsltcorpus, TEDLIUM2(Rousseau et al., 2014) and Must C(Di Gangi et al., 2019) corpora.Our best model scored almost 3 BLEU higher than last year's model.To segment 2020 test set we used exactly the same procedure as last year.
VHP

Locality sensitive hashing (LSH) is a widely practiced c -approximate nearest neighbor( c -ANN) search algorithm in high dimensional spaces. The state-of-the-art LSH based algorithm searches an unbounded and irregular space to identify candidates, which jeopardizes the efficiency. To address this issue, we introduce the concept of virtual hypersphere partitioning. The core idea is to impose a virtual hypersphere, centered at the query, in the original feature space and only examine points inside the hypersphere. The search space of a hypersphere is isotropic and bounded, and thus more efficient than the existing one. In practice, we use multiple physical hyperspheres with different radii in corresponding projection subspaces to emulate the single virtual hypersphere. We also developed a principled method to compute the hypersphere radii for given success probability. Based on virtual hypersphere partitioning, we propose a novel disk-based indexing and searching scheme VHP to answer c -ANN queries. In the indexing phase, VHP stores LSH projections with independent B + -trees. To process a query, VHP keeps increasing the radii of physical hyperspheres co-ordinately, which in effect amounts to enlarging the virtual hypersphere, to accommodate more candidates until the success probability is met. Rigorous theoretical analysis shows that the proposed algorithm supports c -ANN search for arbitrarily small c ≥ 1 with probability guarantee. Extensive experiments on a variety of datasets, including the billion-scale ones, demonstrate that VHP could achieve different tradeoffs between efficiency and accuracy, and achieves up to 2x speedup in running time over the state-of-the-art methods.