Reviewerly/exHarmony huggingface dataset:
Generating Multiple Diverse Responses for Short-Text Conversation

Neural generative models have become popular and achieved promising performance on short-text conversation tasks. They are generally trained to build a 1-to-1 mapping from the input post to its output response. However, a given post is often associated with multiple replies simultaneously in real applications. Previous research on this task mainly focuses on improving the relevance and informativeness of the top one generated response for each post. Very few works study generating multiple accurate and diverse responses for the same post. In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously. A reinforcement learning algorithm is designed to solve our model. Experiments on two short-text conversation tasks validate that the multiple responses generated by our model obtain higher quality and larger diversity compared with various state-ofthe-art generative models.
Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences

Statistical models of the human body surface are generally learned from thousands of high-quality 3D scans in predefined poses to cover the wide variety of human body shapes and articulations. Acquisition of such data requires expensive equipment, calibration procedures, and is limited to cooperative subjects who can understand and follow instructions, such as adults. We present a method for learning a statistical 3D Skinned Multi-Infant Linear body model (SMIL) from incomplete, low-quality RGB-D sequences of freely moving infants. Quantitative experiments show that SMIL faithfully represents the RGB-D data and properly factorizes the shape and pose of the infants. To demonstrate the applicability of SMIL, we fit the model to RGB-D sequences of freely moving infants and show, with a case study, that our method captures enough motion detail for General Movements Assessment (GMA), a method used in clinical practice for early detection of neurodevelopmental disorders in infants. SMIL provides a new tool for analyzing infant shape and movement and is a step towards an automated system for GMA.
Credit Card Fraud Detection Using Autoencoder Model in Unbalanced Datasets

Fraudulent credit card transaction is still one of problems that face the companies and banks sectors; it causes them to lose billions of dollars every year. The design of efficient algorithm is one of the most important challenges in this area. This paper aims to propose an efficient approach that automatic detects fraud credit card related to insurance companies using deep learning algorithm called Autoencoders. The effectiveness of the proposed method has been proved in identifying fraud in actual data from transactions made by credit cards in September 2013 by European cardholders. In addition, a solution for data unbalancing is provided in this paper, which affects most current algorithms. The suggested solution relies on training for the autoencoder for the reconstruction normal data. Anomalies are detected by defining a reconstruction error threshold and considering the cases with a superior threshold as anomalies. The algorithm's performance was able to detected fraudulent transactions between 64% at the threshold = 5, 79% at the threshold = 3 and 91% at threshold= 0.7, it is better in performance compare with logistic regression 57% in unbalanced dataset.
Incorporating Source Syntax into Transformer-Based Neural Machine Translation

Transformer-based neural machine translation (NMT) has recently achieved state-ofthe-art performance on many machine translation tasks.However, recent work (Raganato and Tiedemann, 2018;Tang et al., 2018;Tran et al., 2018) has indicated that Transformer models may not learn syntactic structures as well as their recurrent neural network-based counterparts, particularly in low-resource cases.In this paper, we incorporate constituency parse information into a Transformer NMT model.We leverage linearized parses of the source training sentences in order to inject syntax into the Transformer architecture without modifying it.We introduce two methods: a multi-task machine translation and parsing model with a single encoder and decoder, and a mixed encoder model that learns to translate directly from parsed and unparsed source sentences.We evaluate our methods on low-resource translation from English into twenty target languages, showing consistent improvements of 1.3 BLEU on average across diverse target languages for the multi-task technique.We further evaluate the models on full-scale WMT tasks, finding that the multi-task model aids low-and medium-resource NMT but degenerates high-resource English→German translation.
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games

We study the global convergence of policy optimization for finding the Nash equilibria (NE) in zero-sum linear quadratic (LQ) games. To this end, we first investigate the landscape of LQ games, viewing it as a nonconvex-nonconcave saddle-point problem in the policy space. Specifically, we show that despite its nonconvexity and nonconcavity, zero-sum LQ games have the property that the stationary point of the objective function with respect to the linear feedback control policies constitutes the NE of the game. Building upon this, we develop three projected nested-gradient methods that are guaranteed to converge to the NE of the game. Moreover, we show that all these algorithms enjoy both globally sublinear and locally linear convergence rates. Simulation results are also provided to illustrate the satisfactory convergence properties of the algorithms. To the best of our knowledge, this work appears to be the first one to investigate the optimization landscape of LQ games, and provably show the convergence of policy optimization methods to the NE. Our work serves as an initial step toward understanding the theoretical aspects of policy-based reinforcement learning algorithms for zero-sum Markov games in general.
Speedup your analytics

Database and big data analytics systems such as Hadoop and Spark have a large number of configuration parameters that control memory distribution, I/O optimization, parallelism, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators struggle to understand and tune them to achieve good performance. In this tutorial, we review existing approaches on automatic parameter tuning for databases, Hadoop, and Spark, which we classify into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We describe the foundations of different automatic parameter tuning algorithms and present pros and cons of each approach. We also highlight real-world applications and systems, and identify research challenges for handling cloud services, resource heterogeneity, and real-time analytics.
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks

We present Ordinary Differential Equation Variational Auto-Encoder (ODE2VAE), a latent second order ODE model for high-dimensional sequential data. Leveraging the advances in deep generative models, ODE2VAE can simultaneously learn the embedding of high dimensional trajectories and infer arbitrarily complex continuous-time latent dynamics. Our model explicitly decomposes the latent space into momentum and position components and solves a second order ODE system, which is in contrast to recurrent neural network (RNN) based time series models and recently proposed black-box ODE techniques. In order to account for uncertainty, we propose probabilistic latent ODE dynamics parameterized by deep Bayesian neural networks. We demonstrate our approach on motion capture, image rotation, and bouncing balls datasets. We achieve state-of-the-art performance in long term motion prediction and imputation tasks.
Research on automatic location and recognition of insulators in substation based on YOLOv3

With the development of a smart grid, the automatic location of power equipment is becoming a trend. In this study, a method for automatic location identification and diagnosis of external power insulation equipment based on YOLOv3 is proposed. This deep learning algorithm is used to extract the characteristics of image data under the visible light channel of the insulator. It learns and trains the collected data to realise the rapid location identification and frame selection of the external insulation equipment and extract discharge characteristics of the target box under the ultraviolet channel. According to the number of photons and the spot area information, the operating status of the equipment is determined. The results show that the YOLOv3 algorithm with a training rate of 0.005 achieved a fast convergence of the location recognition model. The average recognition accuracy was 88.7% and the average detection time was 0.0182 s. The combination of visible light path insulator target recognition and ultraviolet light path diagnosis can realise a lean and intelligent diagnosis of power equipment. This method had good real-time performance, accuracy, and robustness to the background. It provides a new concept for intelligent diagnosis and location analysis of power equipment.
Artifact-free deconvolution in light field microscopy

The sampling patterns of the light field microscope (LFM) are highly depth-dependent, which implies non-uniform recoverable lateral resolution across depth.Moreover, reconstructions using state-of-the-art approaches suffer from strong artifacts at axial ranges, where the LFM samples the light field at a coarse rate.In this work, we analyze the sampling patterns of the LFM, and introduce a flexible light field point spread function model (LFPSF) to cope with arbitrary LFM designs.We then propose a novel aliasing-aware deconvolution scheme to address the sampling artifacts.We demonstrate the high potential of the proposed method on real experimental data.
Cloud federation formation using genetic and evolutionary game theoretical models

This paper proposes an approach based on genetic algorithms and evolutionary game theory in order to study the problem of forming highly profitable federated clouds, while maintaining stability among the members in the presence of dynamic strategies (i.e. cloud providers joining and/or leaving federations) that might result in decreased Quality of Service (QoS). Cloud federation helps cloud providers to take advantage of the available unused virtual machines. It allows the providers to combine their resources in order to serve a larger pool of requests that could not have been served otherwise. We tackle the problem of forming federations while maximizing the total profit they yield using a Genetic Algorithm. However, the main problem may rise after the federation formation where many cloud providers, due to the dynamicity, may be tempted to reallocated their resources into other federations for seeking better payoff. Such an act may lead to a decrease in the QoS and cause a drop in the profit earned by the federations. Thus, we extend the genetic model as an evolutionary game, which aims to improve the profit while maintaining stability among federations. Experiments were conducted using CloudHarmony real-world dataset and benchmarked with Sky federation model previously introduced in the literature. Both the genetic and evolutionary game theoretical models outperform the benchmarked one. The evolutionary game model gave better results in terms of profit and QoS’s due to its mechanism of reaching a stable state, in which no provider has incentive to reallocate his resources into different federations.
Ship Target Detection Algorithm Based on Improved Faster R-CNN

Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is proposed. In the proposed method, the image downscaling method is used to enhance the useful information of the ship image. The scene narrowing technique is used to construct the target regional positioning network and the Faster R-CNN convolutional neural network into a hierarchical narrowing network, aiming at reducing the target detection search scale and improving the computational speed of Faster R-CNN. Furthermore, deep cooperation between main network and subnet is realized to optimize network parameters after researching Faster R-CNN with subject narrowing function and selecting texture features and spatial difference features as narrowed sub-networks. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm.
MIRAGE: Mobile-app Traffic Capture and Ground-truth Creation

Network traffic analysis, i.e., the umbrella of procedures for distilling information from network traffic, represents the enabler for highly-valuable profiling information, other than being the workhorse for several key network management tasks. While it is currently being revolutionized in its nature by the rising share of traffic generated by mobile and hand-held devices, existing design solutions are mainly evaluated on private traffic traces, and only a few public datasets are available, thus clearly limiting repeatability and further advances on the topic. To this end, this paper introduces and describes MIRAGE, a reproducible architecture for mobile-app traffic capture and ground-truth creation. The outcome of this system is MIRAGE-2019, a human-generated dataset for mobile traffic analysis (with associated ground-truth) having the goal of advancing the state-of-the-art in mobile app traffic analysis. A first statistical characterization of the mobile-app traffic in the dataset is provided in this paper. Still, MIRAGE is expected to be capitalized by the networking community for different tasks related to mobile traffic analysis.
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation

This paper highlights the impressive utility of multi-parallel corpora for transfer learning in a one-to-many low-resource neural machine translation (NMT) setting.We report on a systematic comparison of multistage finetuning configurations, consisting of (1) pretraining on an external large (209k-440k) parallel corpus for English and a helping target language, (2) mixed pre-training or fine-tuning on a mixture of the external and low-resource (18k) target parallel corpora, and (3) pure finetuning on the target parallel corpora.Our experiments confirm that multi-parallel corpora are extremely useful despite their scarcity and content-wise redundancy thus exhibiting the true power of multilingualism.Even when the helping target language is not one of the target languages of our concern, our multistage finetuning can give 3-9 BLEU score gains over a simple one-to-one model.
Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection

We present the shared task on Fine-Grained Propaganda Detection, which was organized as part of the NLP4IF workshop at EMNLP-IJCNLP 2019.There were two subtasks.FLC is a fragment-level task that asks for the identification of propagandist text fragments in a news article and also for the prediction of the specific propaganda technique used in each such fragment (18-way classification task).SLC is a sentence-level binary classification task asking to detect the sentences that contain propaganda.A total of 12 teams submitted systems for the FLC task, 25 teams did so for the SLC task, and 14 teams eventually submitted a system description paper.For both subtasks, most systems managed to beat the baseline by a sizable margin.The leaderboard and the data from the competition are available at http://propaganda.qcri.org/nlp4if-shared-task/.
Multi-Task Driven Feature Models for Thermal Infrared Tracking

Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific discriminative features and fine-grained correlation features for TIR tracking. Specifically, we first use an auxiliary classification network to guide the generation of TIR-specific discriminative features for distinguishing the TIR objects belonging to different classes. Second, we design a fine-grained aware module to capture more subtle information for distinguishing the TIR objects belonging to the same class. These two kinds of features complement each other and recognize TIR objects in the levels of inter-class and intra-class respectively. These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task. In addition, we develop a large-scale TIR training dataset to train the network for adapting the model to the TIR domain. Extensive experimental results on three benchmarks show that the proposed algorithm achieves a relative gain of 10% over the baseline and performs favorably against the state-of-the-art methods. Codes and the proposed TIR dataset are available at https://github.com/QiaoLiuHit/MMNet.
Adversarial Deep Network Embedding for Cross-Network Node Classification

In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. The existing domain adaptation algorithms generally fail to model the network structural information, and the current network embedding models mainly focus on single-network applications. Thus, both of them cannot be directly applied to solve the cross-network node classification problem. This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information. In ACDNE, the deep network embedding module utilizes two feature extractors to jointly preserve attributed affinity and topological proximities between nodes. In addition, a node classifier is incorporated to make node representations label-discriminative. Moreover, an adversarial domain adaptation technique is employed to make node representations network-invariant. Extensive experimental results demonstrate that the proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification.
Independence Promoted Graph Disentangled Networks

We address the problem of disentangled representation learning with independent latent factors in graph convolutional networks (GCNs). The current methods usually learn node representation by describing its neighborhood as a perceptual whole in a holistic manner while ignoring the entanglement of the latent factors. However, a real-world graph is formed by the complex interaction of many latent factors (e.g., the same hobby, education or work in social network). While little effort has been made toward exploring the disentangled representation in GCNs. In this paper, we propose a novel Independence Promoted Graph Disentangled Networks (IPGDN) to learn disentangled node representation while enhancing the independence among node representations. In particular, we firstly present disentangled representation learning by neighborhood routing mechanism, and then employ the Hilbert-Schmidt Independence Criterion (HSIC) to enforce independence between the latent representations, which is effectively integrated into a graph convolutional framework as a regularizer at the output layer. Experimental studies on real-world graphs validate our model and demonstrate that our algorithms outperform the state-of-the-arts by a wide margin in different network applications, including semi-supervised graph classification, graph clustering and graph visualization.
DenseLightNet: A Light-Weight Vehicle Detection Network for Autonomous Driving

In recent years, vehicle detectors built on deep convolutional neural network (DCNN) have been widely used in autonomous driving. Under the complex conditions of road traffic, the detector is expected to run in high speed and high accuracy. However, due to the limited computing power and storage space on the autonomous vehicle, the deployment of advanced DCNN detectors is often restricted. The design of lightweight and powerful detectors is in a great desire. Recently, group convolution, as a novel convolution algorithm, has been proposed to reduce the floating-point operations and make the detection network lighter and faster. However, in practice, it is found that the increase of group number does not always boost the detection speed, but sometimes leads to the performance degradation. In addition, the existing guidelines for network design do not indicate how to choose the group number in the group convolution in order to maximize the overall detection speed. To this end, in this paper, we propose three new guidelines to determine the valid range of the group number, and design a lightweight detection network-DenseLightNet-based on these new design criteria. The proposed detector runs at a speed of three times faster than the current real-time detector YoloV3, while holding a much smaller model size.
Siamese capsule networks with global and local features for text classification

Text classification is a popular research topic in the field of natural language processing and provides wide applications. The existing text classification methods based on deep neural networks can completely extract the local features of text. The text classification models constructed based on these methods yield good experimental results. However, these methods generally ignore the global semantic information of different categories of text and global spatial distance between categories. To some extent, this adversely affects the accuracy of classification. In this study, to address this problem, Siamese capsule networks with global and local features were proposed. A Siamese network was used to glean information about the global semantic differences between categories, which could more accurately represent the semantic distance between different categories. A global memory mechanism was established to store global semantic features, which were then incorporated into the text classification model. Capsule vectors were used to obtain the spatial position relationships of local features, thereby improving the representation capabilities of the features. The experimental results showed that the proposed model achieved better results and performed significantly better on six different public datasets, as compared with ten baseline algorithms.
Differential Evolution Algorithm for Single Objective Bound-Constrained Optimization: Algorithm j2020

In this paper, a new algorithm is presented to deal with real parameter single-objective optimization problems, which are often complex and computationally very expensive. The proposed algorithm (j2020) is based on the self-adaptive differential evolution algorithms jDE and jDE100. Our algorithm uses two populations like jDE100, while jDE uses only one population. It uses a crowding mechanism, which is not being used in previous algorithms, and a mechanism to choose vectors in the mutation operation from both subpopulations. We provide the obtained results for each benchmark function for four dimension scenarios as required by the organizers of the special session for Single Objective Bound-Constrained optimization. We also compare the obtained results with the original DE and jSO algorithms on the largest dimension scenario.