Reviewerly/exHarmony huggingface dataset:
Refined TV-<i>L</i> <sup>1</sup> Optical Flow Estimation Using Joint Filtering

Though the accuracy and robustness of optical flow has been dramatically enhanced over the past few years, the issue of edge-blurring near the image and motion boundaries has remained a challenge in flow field estimation. In this paper, we propose a refined total variation with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> norm (TV- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ) optical flow estimation approach using joint filtering, named JOF. First, we divide the image into three categorized regions: mutual-structure regions, inconsistent structure regions, and smooth regions. The mutual-structure guided filter for optical flow estimation is constructed by extracting the mutual-structure regions of the flow field. Second, the refined TV- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> optical flow model is proposed by incorporating the non-local term and mutual-structure guided filter objective function into the classical TV- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> energy function. Furthermore, the novel TV- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> optical flow objective function is minimized using a joint filtering program composed of a weighted median filter and a mutual-structure guided filter to optimize the estimated flow field during the coarse-to-fine optical flow computation scheme. Finally, we compare the proposed JOF method with several state-of-the-art approaches including variational and deep learning based optical flow models using the Middlebury, MPI-Sintel, and UCF101 test databases. The evaluation results indicate that the proposed method has high accuracy and good robustness for flow field computation and, especially, the significant benefit of edge-preserving.
A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification

The sentiment analysis used in this study is the process of classifying text into two classes, namely negative and positive classes. The classification method used is Support Vector Machine (SVM). The successful classification of the SVM method depends on the soft margin coefficient C, as well as the σ parameter of the kernel function. Therefore we need a combination of SVM parameters that are appropriate for classifying film opinion data using the SVM method. This study uses the Firefly method as an SVM parameter optimization method. The dataset used in this study is public opinion data on several films. The results of this study indicate that the Firefly Algorithm (FA) can be used to find optimal parameters in the SVM classifier. This is evidenced by the results of SVM system testing using 2179 data with nine SVM parameter combinations resulting in 85% highest accuracy, while the FA-SVM system with nine population and generation combinations produces the highest accuracy of 88%. The second test results using 1200 data using the same combination as the one test, the SVM method produces the highest accuracy of 87%, while the FA-SVM method produces the highest accuracy of 89%.
IoT and Blockchain Paradigms for EV Charging System

In this research work, we apply the Internet of Things (IoT) paradigm with a decentralized blockchain approach to handle the electric vehicle (EV) charging process in shared spaces, such as condominiums. A mobile app handles the user authentication mechanism to initiate the EV charging process, where a set of sensors are used for measuring energy consumption, and based on a microcontroller, establish data communication with the mobile app. A blockchain handles financial transitions, and this approach can be replicated to other EV charging scenarios, such as public charging systems in a city, where the mobile device provides an authentication mechanism. A user interface was developed to visualize transactions, gather users’ preferences, and handle power charging limitations due to the usage of a shared infrastructure. The developed approach was tested in a shared space with three EVs using a charging infrastructure for a period of 3.5 months.
Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning

Maximum entropy deep reinforcement learning (RL) methods have been demonstrated on a range of challenging continuous tasks. However, existing methods either suffer from severe instability when training on large off-policy data or cannot scale to tasks with very high state and action dimensionality such as 3D humanoid locomotion. Besides, the optimality of desired Boltzmann policy set for non-optimal soft value function is not persuasive enough. In this paper, we first derive soft policy gradient based on entropy regularized expected reward objective for RL with continuous actions. Then, we present an off-policy actor-critic, model-free maximum entropy deep RL algorithm called deep soft policy gradient (DSPG) by combining soft policy gradient with soft Bellman equation. To ensure stable learning while eliminating the need of two separate critics for soft value functions, we leverage double sampling approach to making the soft Bellman equation tractable. The experimental results demonstrate that our method outperforms in performance over off-policy prior methods.
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss

Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial examples. In this paper, we improve the robustness of DNNs by utilizing techniques of Distance Metric Learning. Specifically, we incorporate Triplet Loss, one of the most popular Distance Metric Learning methods, into the framework of adversarial training. Our proposed algorithm, Adversarial Training with Triplet Loss (AT2L), substitutes the adversarial example against the current model for the anchor of triplet loss to effectively smooth the classification boundary. Furthermore, we propose an ensemble version of AT2L, which aggregates different attack methods and model structures for better defense effects. Our empirical studies verify that the proposed approach can significantly improve the robustness of DNNs without sacrificing accuracy. Finally, we demonstrate that our specially designed triplet loss can also be used as a regularization term to enhance other defense methods.
Partial Label Learning with Unlabeled Data

Partial label learning deals with training examples each associated with a set of candidate labels, among which only one label is valid. Previous studies typically assume that the candidate label sets are provided for all training examples. In many real-world applications such as video character classification, however, it is generally difficult to label a large number of instances and there exists much data left to be unlabeled. We call this kind of problem semi-supervised partial label learning. In this paper, we propose the SSPL method to address this problem. Specifically, an iterative label propagation procedure between partial label examples and unlabeled instances is employed to disambiguate the candidate label sets of partial label examples as well as assign valid labels to unlabeled instances. The importance of unlabeled instances increases adaptively as the number of iteration increases, since they carry richer labeling information. Finally, unseen instances are classified based on the minimum reconstruction error on both partial label and unlabeled instances. Experiments on real-world data sets clearly validate the effectiveness of the proposed SSPL method.
Paraphrase Diversification Using Counterfactual Debiasing

The problem of generating a set of diverse paraphrase sentences while (1) not compromising the original meaning of the original sentence, and (2) imposing diversity in various semantic aspects, such as a lexical or syntactic structure, is examined. Existing work on paraphrase generation has focused more on the former, and the latter was trained as a fixed style transfer, such as transferring from positive to negative sentiments, even at the cost of losing semantics. In this work, we consider style transfer as a means of imposing diversity, with a paraphrasing correctness constraint that the target sentence must remain a paraphrase of the original sentence. However, our goal is to maximize the diversity for a set of k generated paraphrases, denoted as the diversified paraphrase (DP) problem. Our key contribution is deciding the style guidance at generation towards the direction of increasing the diversity of output with respect to those generated previously. As pre-materializing training data for all style decisions is impractical, we train with biased data, but with debiasing guidance. Compared to state-of-the-art methods, our proposed model can generate more diverse and yet semantically consistent paraphrase sentences. That is, our model, trained with the MSCOCO dataset, achieves the highest embedding scores, .94/.95/.86, similar to state-of-the-art results, but with a lower mBLEU score (more diverse) by 8.73%.
Inferring Substitutable Products with Deep Network Embedding

On E-commerce platforms, understanding the relationships (e.g., substitute and complement) among products from user's explicit feedback, such as users' online transactions, is of great importance to boost extra sales. However, the significance of such relationships is usually neglected by existing recommender systems. In this paper, we propose a semisupervised deep embedding model, namely, Substitute Products Embedding Model (SPEM), which models the substitutable relationships between products by preserving the second-order proximity, negative first-order proximity and semantic similarity in a product co-purchasing graph based on user's purchasing behaviours. With SPEM, the learned representations of two substitutable products align closely in the latent embedding space. Extensive experiments on real-world datasets are conducted, and the results verify that our model outperforms state-of-the-art baselines.
Grading Uncompilable Programs

Evaluators wish to test candidates on their ability to propose the correct algorithmic approach to solve programming problems. Recently, several automated systems for grading programs have been proposed, but none of them address uncompilable codes. We present the first approach to grade uncompilable codes and provide semantic feedback on them using machine learning. We propose two methods that allow us to derive informative semantic features from programs. One of this approach makes the program compilable by correcting errors, while the other relaxes syntax/grammar rules to help parse uncompilable codes. We compare the relative efficacy of these approaches towards grading. We finally combine them to build an algorithm which rivals the accuracy of experts in grading programs. Additionally, we show that the models learned for compilable codes can be reused for uncompilable codes. We present case studies, where companies are able to hire more efficiently by deploying our technology.
Sequence Generation: From Both Sides to the Middle

The encoder-decoder framework has achieved promising process for many sequence generation tasks, such as neural machine translation and text summarization. Such a framework usually generates a sequence token by token from left to right, hence (1) this autoregressive decoding procedure is time-consuming when the output sentence becomes longer, and (2) it lacks the guidance of future context which is crucial to avoid under-translation. To alleviate these issues, we propose a synchronous bidirectional sequence generation (SBSG) model which predicts its outputs from both sides to the middle simultaneously. In the SBSG model, we enable the left-to-right (L2R) and right-to-left (R2L) generation to help and interact with each other by leveraging interactive bidirectional attention network. Experiments on neural machine translation (En-De, Ch-En, and En-Ro) and text summarization tasks show that the proposed model significantly speeds up decoding while improving the generation quality compared to the autoregressive Transformer.
Classification with Label Distribution Learning

Label Distribution Learning (LDL) is a novel learning paradigm, aim of which is to minimize the distance between the model output and the ground-truth label distribution. We notice that, in real-word applications, the learned label distribution model is generally treated as a classification model, with the label corresponding to the highest model output as the predicted label, which unfortunately prompts an inconsistency between the training phrase and the test phrase. To solve the inconsistency, we propose in this paper a new Label Distribution Learning algorithm for Classification (LDL4C). Firstly, instead of KL-divergence, absolute loss is applied as the measure for LDL4C. Secondly, samples are re-weighted with information entropy. Thirdly, large margin classifier is adapted to boost discrimination precision. We then reveal that theoretically LDL4C seeks a balance between generalization and discrimination. Finally, we compare LDL4C with existing LDL algorithms on 17 real-word datasets, and experimental results demonstrate the effectiveness of LDL4C in classification.
Generalized Lazy Search for Robot Motion Planning:Interleaving Search and Edge Evaluation via Event-Based Toggles

Lazy search algorithms can efficiently solve problems where edge evaluation is the bottleneck in computation, as is the case for robotic motion planning. The optimal algorithm in this class, LazySP, lazily restricts edge evaluation to only the shortest path. Doing so comes at the expense of search effort, i.e., LazySP must recompute the search tree every time an edge is found to be invalid. This becomes prohibitively expensive when dealing with large graphs or highly cluttered environments. Our key insight is the need to balance both edge evaluation and search effort to minimize the total planning time. Our contribution is two-fold. First, we propose a framework, Generalized Lazy Search (GLS), that seamlessly toggles between search and evaluation to prevent wasted efforts. We show that for a choice of toggle, GLS is provably more efficient than LazySP. Second, we leverage prior experience of edge probabilities to derive GLS policies that minimize expected planning time. We show that GLS equipped with such priors significantly outperforms competitive baselines for many simulated environments in R2,SE(2) and 7-DoF manipulation.
Aspect-Based Sentiment Classification with Attentive Neural Turing Machines

Aspect-based sentiment classification aims to identify sentiment polarity expressed towards a given opinion target in a sentence. The sentiment polarity of the target is not only highly determined by sentiment semantic context but also correlated with the concerned opinion target. Existing works cannot effectively capture and store the inter-dependence between the opinion target and its context. To solve this issue, we propose a novel model of Attentive Neural Turing Machines (ANTM). Via interactive read-write operations between an external memory storage and a recurrent controller, ANTM can learn the dependable correlation of the opinion target to context and concentrate on crucial sentiment information. Specifically, ANTM separates the information of storage and computation, which extends the capabilities of the controller to learn and store sequential features. The read and write operations enable ANTM to adaptively keep track of the interactive attention history between memory content and controller state. Moreover, we append target entity embeddings into both input and output of the controller in order to augment the integration of target information. We evaluate our model on SemEval2014 dataset which contains reviews of Laptop and Restaurant domains and Twitter review dataset. Experimental results verify that our model achieves state-of-the-art performance on aspect-based sentiment classification.
Multi-view learning-based data proliferator for boosting classification using highly imbalanced classes

Multi-view data representation learning explores the relationship between the views and provides rich complementary information that can improve computer-aided diagnosis. Specifically, existing machine learning methods devised to automate neurological disorder diagnosis using brain data provided new insights into how a particular disorder such as autism spectrum disorder (ASD) alters the brain construct. However, the performance of machine learning methods highly depends on the size of the training samples from both classes. In a real-world clinical setting, such medical data is very expensive and challenging to collect, might (i) suffer from several limitations such as imbalanced classes and (ii) have non-heterogeneous distribution when derived from multi-view brain representations. To the best of our knowledge, the problem of imbalanced and multi-view data classification remains unexplored in the field of network neuroscience. To fill this gap, we propose a Multi-View LEArning-based data Proliferator (MV-LEAP) that enables the classification of imbalanced multi-view representations. MV-LEAP comprises two key steps. First, a manifold learning-based proliferator, which enables to generate synthetic data for each view, is developed to handle imbalanced data. Second, a multi-view manifold data alignment leveraging tensor canonical correlation analysis is proposed to map all original and proliferated (i.e., synthesized) views into a shared subspace where their distributions are aligned for the target classification task. We evaluated our method on imbalanced multi-view ASD vs. normal control (NC) connectomic datasets with imbalanced classes. Overall, MV-LEAP achieved the best classification results in comparison with baseline data synthesis methods.
RLXSS: Optimizing XSS Detection Model to Defend Against Adversarial Attacks Based on Reinforcement Learning

With the development of artificial intelligence, machine learning algorithms and deep learning algorithms are widely applied to attack detection models. Adversarial attacks against artificial intelligence models become inevitable problems when there is a lack of research on the cross-site scripting (XSS) attack detection model for defense against attacks. It is extremely important to design a method that can effectively improve the detection model against attack. In this paper, we present a method based on reinforcement learning (called RLXSS), which aims to optimize the XSS detection model to defend against adversarial attacks. First, the adversarial samples of the detection model are mined by the adversarial attack model based on reinforcement learning. Secondly, the detection model and the adversarial model are alternately trained. After each round, the newly-excavated adversarial samples are marked as a malicious sample and are used to retrain the detection model. Experimental results show that the proposed RLXSS model can successfully mine adversarial samples that escape black-box and white-box detection and retain aggressive features. What is more, by alternately training the detection model and the confrontation attack model, the escape rate of the detection model is continuously reduced, which indicates that the model can improve the ability of the detection model to defend against attacks.
The blue monkey: A new nature inspired metaheuristic optimization algorithm

This paper introduces a study to Blue Monkey (BM) algorithm, which is a new metaheuristic algorithm optimization based on the performance of blue monkey swarms in nature. The BM algorithm identifies how many males in one group. Normally, outside the season of the breeding, the groups of blue monkeys have only one adult male like other forest guenons. In addition to related patas monkeys (Erythrocebus patas). Forty-three of well-known test functions, which used in the area of optimization are used as benchmark to check BM algorithm, in addition, BM verified by a comparative performance check with Artificial-Bee-Colony (ABC), Gravitational Search Algorithm (GSA), Biogeography-Based Optimizer (BBO), and Particle Swarm Optimization (PSO). The obtained results demonstrated that BM algorithm is competitive compared with the selected metaheuristic algorithms; also, BM is able to converge towards the global optimal through optimization problems. Further, this algorithm is very efficient in field of dissolving real problems with restrictions and unidentified search space. It should be mentioned that the BM algorithm has some variables and it can obtain better results. in many test functions comparing with other algorithms.
M-BERT: Injecting Multimodal Information in the BERT Structure

Multimodal language analysis is an emerging research area in natural languageprocessing that models language in a multimodal manner. It aims to understandlanguage from the modalities of text, visual, and acoustic by modeling bothintra-modal and cross-modal interactions. BERT (Bidirectional EncoderRepresentations from Transformers) provides strong contextual languagerepresentations after training on large-scale unlabeled corpora. Fine-tuningthe vanilla BERT model has shown promising results in building state-of-the-artmodels for diverse NLP tasks like question answering and language inference.However, fine-tuning BERT in the presence of information from other modalitiesremains an open research problem. In this paper, we inject multimodalinformation within the input space of BERT network for modeling multimodallanguage. The proposed injection method allows BERT to reach a new state of theart of $84.38\%$ binary accuracy on CMU-MOSI dataset (multimodal sentimentanalysis) with a gap of 5.98 percent to the previous state of the art and 1.02percent to the text-only BERT.
Underwater Image Enhancement Based on Removing Light Source Color and Dehazing

Typical underwater images exhibit poor visibility because of light scattering and absorption in turbid water.To resolve the ill-posed problem, a novel underwater image enhancement method based on Removing Light Source Color and Dehazing(RLSCD) is proposed.Scene depth, which is assumed strongly correlated with attenuations, is often ignored in most of the previous methods.A new scene depth estimation, which takes the attenuations of the different light conditions into account, is first presented.Additionally, the background light is estimated based on gray open operation and scene depth to avoid that the pixels in the white objects and artificial lighting regions may be wrongly estimated as the background light.With the estimated background light and transmission map, the dehazed image is achievable.Moreover, the theoretical analysis of the Lambertian Model indicates that the disturbance of the light source color in dehazed image can be estimated, this phenomenon enables us to correct color distortion and light overcompensation by removing the disturbance.Experimental results demonstrate the RLSCD method outperforms state-of-theart methods in terms of relatively genuine color, increased contrast, and brightness.