Reviewerly/exHarmony huggingface dataset:
UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain

This article describes the participation of the UU TAILS team in the 2019 MEDIQA challenge intended to improve domain-specific models in medical and clinical NLP.The challenge consists of 3 tasks: medical language inference (NLI), recognizing textual entailment (RQE) and question answering (QA).Our team participated in tasks 1 and 2 and our best runs achieved a performance accuracy of 0.852 and 0.584 respectively for the test sets.The models proposed for task 1 relied on BERT embeddings and different ensemble techniques.For the RQE task, we trained a traditional multilayer perceptron network based on embeddings generated by the universal sentence encoder.
IITP-MT System for Gujarati-English News Translation Task at WMT 2019

We describe our submission to WMT 2019 News translation shared task for Gujarati-English language pair.We submit constrained systems, i.e, we rely on the data provided for this language pair and do not use any external data.We train Transformer based subword-level neural machine translation (NMT) system using original parallel corpus along with synthetic parallel corpus obtained through back-translation of monolingual data.Our primary systems achieve BLEU scores of 10.4 and 8.1 for Gujarati→English and English→Gujarati, respectively.We observe that incorporating monolingual data through back-translation improves the BLEU score significantly over baseline NMT and SMT systems for this language pair.
A Contrastive Analysis Between English and Indonesian Kinds of Sentences

The difference between English and Indonesia language become one of the hardest things to learn and to be understood. It could be seen by the grammar of the language and the system of communication between both languages. The aim of this study is to identify the difference and the similarity sentence between English and Indonesia Language and analysis contrastive between both languages. This study used a qualitative descriptive approach to find out the contrast between both of them. the sample in this study were 20 students of English Department Students in Victory University, especially the 2nd Semester. The result of this study was the main error of the students was in Declarative Sentence (DS), Negative Sentence (NS), Interrogative Sentence (IS) and Exclamatory Sentence (ES) i.e 92.86%. Based on the research, we found out that the students did those errors because the pattern of those sentences are different whereas the Imperative Sentence (IMS) has the same pattern with English.
Neural Gaussian Copula for Variational Autoencoder

Variational language models seek to estimate the posterior of latent variables with an approximated variational posterior.The model often assumes the variational posterior to be factorized even when the true posterior is not.The learned variational posterior under this assumption does not capture the dependency relationships over latent variables.We argue that this would cause a typical training problem called posterior collapse observed in all other variational language models.We propose Gaussian Copula Variational Autoencoder (VAE) to avert this problem.Copula is widely used to model correlation and dependencies of high-dimensional random variables, and therefore it is helpful to maintain the dependency relationships that are lost in VAE.The empirical results show that by modeling the correlation of latent variables explicitly using a neural parametric copula, we can avert this training difficulty while getting competitive results among all other VAE approaches.1
Content Modeling for Automated Oral Proficiency Scoring System

We developed an automated oral proficiency scoring system for non-native English speakers' spontaneous speech.Automated systems that score holistic proficiency are expected to assess a wide range of performance categories, and the content is one of the core performance categories.In order to assess the quality of the content, we trained a Siamese convolutional neural network (Siamese CNN) to model the semantic relationship between key points generated by experts and a test response.The correlation between human scores and Siamese CNN scores was comparable to human-human agreement (r = 0.63), and it was higher than the baseline content features.The inclusion of Siamese CNN-based feature to the existing state-of-the-art automated scoring model achieved a small but statistically significant improvement.However, the new model suffered from score inflation for long atypical responses with serious content issues.We investigated the reasons of this score inflation by analyzing the associations with linguistic features and identifying areas strongly associated with the score errors.
Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation

Attention mechanisms have seen some success for natural language processing downstream tasks in recent years and generated new stateof-the-art results.A thorough evaluation of the attention mechanism for the task of Argumentation Mining is missing.With this paper, we report a comparative evaluation of attention layers in combination with a bidirectional long short-term memory network, which is the current state-of-the-art approach for the unit segmentation task.We also compare sentencelevel contextualized word embeddings to pregenerated ones.Our findings suggest that for this task, the additional attention layer does not improve the performance.In most cases, contextualized embeddings do also not show an improvement on the score achieved by predefined embeddings.* The first two authors contributed equally.Their listing order is random.
Rewarding Coreference Resolvers for Being Consistent with World Knowledge

Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples.We show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases.Since relation extraction systems can rely on different forms of supervision and be biased in different ways, we obtain the best performance, improving over the state of the art, using multi-task reinforcement learning.
Color Image Retrieval Based on Quaternion and Deep Features

In this paper, aiming at the inefficient question of feature extraction and multi-feature fusion in existing algorithms, we propose a color image retrieval algorithm based on quaternion and deep features. Firstly, we replace the real part of the quaternion with the image information entropy to form a new image matrix, and extracting its moment invariant as the traditional features. Meanwhile, the deep features are extracted by the improved convolutional neural network. Secondly, we develop an effective features fusion procedure. Finally, retrieval results are obtained by Euclidean distance between image's fusion features. In order to demonstrate the effectiveness of our method, we have conducted extensive experiments on three well known datasets (INRIA Holidays dataset, University of Kentucky Benchmark, Oxford 5k).
Trainable Active Contour Model for Histological Image Segmentation

Lesions analysis of mucous glands, which depends on the glands segmentation in histological images, is an important task of surgical pathology.This paper presents a hybrid method of glands object segmentation in histological images, based on the trainable active contour model.The hybrid method combines the use of both modern convolutional neural networks and classical methods of mathematical image processing.Also, within this hybrid method a special postprocessing algorithm is implemented, which allows to correctly segment stucked glands in the image.The proposed method was tested on PATH-DT-MSU dataset and demonstrated good results.The average value of IoU for all test images is 0.81.
Aspect-Based Sentiment Analysis Using Grammatical Rules, Word Similarity and SentiCircle

Product reviews can be used as suggestion for companies to improve their services in this digitalization era.These reviews can be presented in more detail using aspect based sentiment analysis.In this research, aspect-based sentiment analysis method is proposed using grammatical rules, word similarity, and SentiCircle.This method began with extracting the candidate aspects rules based on phrase detection in constituency parse.The candidate aspects were categorized using word similarity.Word similarity calculated the similarity value between the candidate aspects and the keywords obtained from Wikipedia.To determine sentiment polarity, SentiCircle is used.SentiCircle can capture the contextual sentiment from the data.The result showed that the proposed method was able to categorize aspects correctly, with the highest f1-measure value of 84%, while sentiment analysis produced the highest f1-measure of 87%.
A Meta-Learning Framework for Learning Multi-User Preferences in QoE Optimization of DASH

Dynamic adaptive video streaming over hypertext transfer protocol (DASH) plays a key role in video transmission over the Internet. The conventional DASH adaptation approaches mainly focus on optimizing the overall quality of experience (QoE) for all client sides, neglecting the QoE diversity of different users. In this paper, we propose a meta-learning framework for multi-user preferences (MLMP) as a new DASH adaptation approach, which is able to optimize the diverse QoE of different users. Specifically, we first design a subjective experiment to analyze the difference of QoE preferences across users, in which QoE refers to the metrics of visual quality, fluctuation, and rebuffering events. Based on our findings, we formulate the QoE optimization of multi-user preferences as a multi-task deep reinforcement learning (DRL) problem. In our formulation, the QoE preference of each user is modeled in the overall QoE calculation via assigning the weights to the three QoE metrics. Then, the MLMP framework is developed to solve the proposed multi-task DRL problem, such that the preferences regarding visual quality, fluctuation, and rebuffering events can be optimized for different users in DASH adaptation. Finally, the simulation results show that the proposed approach outperforms state-of-the-art DASH adaptation approaches in satisfying the different users' QoE preferences regarding visual quality, fluctuation, and rebuffering events.
Double JPEG Compression Detection Based on Noise-Free DCT Coefficients Mixture Histogram Model

With the wide use of various image altering tools, digital image manipulation becomes very convenient and easy, which makes the detection of image originality and authenticity significant. Among various image tampering detection tools, double JPEG image compression detector, which is not sensitive to specific image tampering operation, has received large attention. In this paper, we propose an improved double JPEG compression detection method based on noise-free DCT (Discrete Cosine Transform) coefficients mixture histogram model. Specifically, we first extract the block-wise DCT coefficients histogram and eliminate the quantization noise which introduced by rounding and truncation operations. Then, for each DCT frequency, a posterior probability can be obtained by solving the DCT coefficients mixture histogram with a simplified model. Finally, the probabilities from all the DCT frequencies are accumulated to give the posterior probability of a DCT block being authentic or tampered. Extensive experimental results in both quantitative and qualitative terms prove the superiority of our proposed method when compared with the state-of-the-art methods.
Geology of the Neoproterozoic – early Cambrian Marwar Supergroup, Rajasthan: A synthesis

The Marwar Supergroup of the western India is one the largest Proterozoic sedimentary sequences of peninsular India.Due to its terminal Neoproterozoic-early Cambrian antiquity, these deposits are believed to hold potential clues to our understanding of the Proterozoic ocean, atmosphere, and origin of the complex multicellular life.Numerous paleobiological investigations in these largely fluvial and shallow marine deposits have brought out some intriguing results over the last decade.Similarly, chemostratigraphic studies have correlated the Bilara Group carbonates with Hanseran Group evaporites, and placed the middle and upper Marwar Supergroup alongside a host of late Ediacaran trans-Gondwanan basins.In this review, I have compiled the available lithostratigraphic, paleobiological, chemostratigraphic, and geochronological data from the Marwar Supergroup.In the wake of new age constraints for the Jodhpur Group, some of the trace fossil assemblages might require revisiting whereas new geochronologic data from within the supergroup would come in handy as we continue to look for the evidence of various Neoproterozoic global events in India.
3D Face Reconstruction From Volumes of Videos Using a Mapreduce Framework

As video blogs become favorable to the commonage, egocentric videos generate tremendous big video data, which capture a large number of interpersonal social events. There are significant challenges on retrieving rich social information, such as human identities, emotions and other interaction information from these massive video data. Limited methods have been proposed so far to address the issue of the unlabeled data. In this paper, we present a fully-automatic system retrieving both sparse 3D facial shape and dense 3D face, from which more face-related information can be predicted during social communication. First, we localize facial landmarks from 2D videos and retrieve sparse 3D shape from motion. Second, we apply the retrieved sparse 3D shape as a prior estimation of dense 3D face mesh. To deal with big social videos in a scalable manner, we design the proposed system on a Map/Reduce framework. Tested on FEI and BU-4DFE face datasets, we improve time efficiency by 92% and 73% respectively without accuracy loss.
A Deep Clustering Algorithm based on Gaussian Mixture Model

Abstract Clustering autonomously learns the implicit cluster structure in the original data without prior knowledge. The effect of ordinary clustering algorithms is not good to cluster high-dimensional data. In this paper, we propose a deep clustering algorithm based on Gaussian mixture model, which combines two models of stacked auto-encoder and Gaussian mixture model. This algorithm uses the expectation maximization algorithm of reducing dimension data feature to train Gaussian mixture and updates the data cluster so that the data is clustered in the feature space. The experimental results demonstrate that the proposed algorithm improves the clustering accuracy, and verifies the effectiveness of the algorithm.
POMP++: Facilitating Postmortem Program Diagnosis with Value-set Analysis

With the emergence of hardware-assisted processor tracing, execution traces can be logged with lower runtime overhead and integrated into the core dump. In comparison with an ordinary core dump, such a new post-crash artifact provides software developers and security analysts with more clues to a program crash. However, existing works only rely on the resolved runtime information, which leads to the limitation in data flow recovery within long execution traces. In this work, we propose POMP++, an automated tool to facilitate the analysis of post-crash artifacts. More specifically, POMP++ introduces a reverse execution mechanism to construct the data flow that a program followed prior to its crash. Furthermore, POMP++ utilizes Value-set Analysis, which helps to verify memory alias relation, to improve the ability of data flow recovery. With the restored data flow, POMP++ then performs backward taint analysis and highlights program statements that actually contribute to the crash. We have implemented POMP++ for Linux system on x86-32 platform, and tested it against various crashes resulting from 31 distinct real-world security vulnerabilities. The evaluation shows that, our work can pinpoint the root causes in 29 cases, increase the number of recovered memory addresses by 12 percent and reduce the execution time by 60 percent compared with existing reverse execution. In short, POMP++ can accurately and efficiently pinpoint program statements that truly contribute to the crashes, making failure diagnosis significantly convenient.
Group signature from lattices preserving forward security in dynamic setting

<p style='text-indent:20px;'>We propose the <i>first</i> lattice-based dynamic group signature scheme achieving <i>forward security</i>. Our scheme is proven to be secure against framing attack, misidentification attack and preserves anonymity under the learning with errors (${\mathsf{LWE}}$) and short integer solution (${\mathsf{SIS}}$) assumptions in the random oracle model. More interestingly, our setting allows the group manager to generate distinct certificates to distinct users which can be updated by the users themselves without any interaction with the group manager. Furthermore, our scheme is dynamic where signing key of a user is not fixed during the setup and is issued only at the joining time of the user.
Generators that read

Most discussions of procedural content generation have focused primarily on the artifacts that generators produce or the process by which these artifacts are created. Less focus, however, has been placed on the methods by which generators interpret their input. Many generators take complex input, act as part of a generative pipeline, are part of a mixed-initiative communication with the user, or otherwise need to take context into account during generation. In these cases, the process by which the generator reads and makes sense of its input is often just as interesting as the process by which it produces an output artifact. It is worthwhile to take a closer look at how generators read. Via a case study of two erasure poetry generators, we propose the concept of a generativist reading: a process of reading that produces generative models. Many existing generators have dual input/output or reading/writing processes that are presented as a monolithic unit, but our understanding of both processes and results is enriched when we clearly distinguish between how generators write and how they read.
PAI-FCNN: FPGA Based Inference System for Complex CNN Models

Convolutional Neural Network (CNN) models are becoming complex with advanced OPs and structures, which introduces design challenges for FPGA-based system. In this paper, we present the design of an FPGA-based CNN inference system, PAI-FCNN, to support modern complex CNN models. PAI-FCNN consists of scalable hardware design and a model reconstruction flow in software compiler. In this way, advanced OPs like Deconv, Conv with upsampling, Dilated Conv, Concatenation can be processed by PAI-FCNN with high performance and hardware efficiency. PAI-FCNN also incorporates reduced precision to boost computing capacity, and the emerging CNN-RNN (Recurrent Neural Network) hybrid models are supported. Our experiments on both PC and embedded FPGA platforms show that the system consistently performs in an efficient manner. PAI-FCNN achieves better throughput and power efficiency than GPU solutions.
Research on the Management Model of Marine Economic Information Resources in the Big Data Era Based on Cloud Computing

Zhao, Y.-N. and Zhao, H., 2019. Research on the management model of marine economic information resources in the big data era based on cloud computing. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 316–320. Coconut Creek (Florida), ISSN 0749-0208.Marine economic information resources are the basis for the planning, management, protection and rational utilization of marine resources, as well as the important support for economic development and marine development in coastal areas. It has become a very necessary task and a hot topic in academic research as how to carry out “big data” management of marine economic information resources and analyze and deal with “big data” efficiently and accurately through appropriate cloud resource scheduling strategies. Based on the analysis of the cloud computing resource scheduling system, this paper designs a dynamic scheduling framework for adaptive optimization of cloud computing resources by using the cloud computing and cloud service technology, and gives a specific structure and form of the framework system for marine information resources. The cloud management architecture has initially realized the digital cloud management of marine economic information resources, achieved the sharing of marine information resources and service resources, and made the selection and calling process of cloud resource scheduling methods more reasonable.
Multi-Graph Decoding for Code-Switching ASR

In the FAME!Project, a code-switching (CS) automatic speech recognition (ASR) system for Frisian-Dutch speech is developed that can accurately transcribe the local broadcaster's bilingual archives with CS speech.This archive contains recordings with monolingual Frisian and Dutch speech segments as well as Frisian-Dutch CS speech, hence the recognition performance on monolingual segments is also vital for accurate transcriptions.In this work, we propose a multi-graph decoding and rescoring strategy using bilingual and monolingual graphs together with a unified acoustic model for CS ASR.The proposed decoding scheme gives the freedom to design and employ alternative search spaces for each (monolingual or bilingual) recognition task and enables the effective use of monolingual resources of the high-resourced mixed language in low-resourced CS scenarios.In our scenario, Dutch is the high-resourced and Frisian is the low-resourced language.We therefore use additional monolingual Dutch text resources to improve the Dutch language model (LM) and compare the performance of single-and multi-graph CS ASR systems on Dutch segments using larger Dutch LMs.The ASR results show that the proposed approach outperforms baseline single-graph CS ASR systems, providing better performance on the monolingual Dutch segments without any accuracy loss on monolingual Frisian and code-mixed segments.
Cascaded ConvLSTMs Using Semantically-Coherent Data Synthesis for Video Object Segmentation

This paper proposes a simple yet effective and efficient method for video object segmentation. Most existing methods take the color image and the optical flow as input for discovering the salient object in terms of appearance and motion. We instead leverage a ResNet backbone as an appearance-characterization encoder for each frame at different scales, and a series of Convolutional Long Short-Term Memory units (ConvLSTMs) as a motion-modeling decoder at each corresponding scale. By imposing supervision over each scale, such modules can well tackle all scales of a moving object with an inevitable scale variance over time. Instead of following a Condition Random Fields based post-processing, we use a more effective and efficient cascade module to refine the model predictions. Most existing video object segmentation datasets have limited sizes because it is expensive and time-consuming to obtain pixel-wise annotations. To overcome the data-insufficiency issue when training the deep network, we propose a semantically-coherent data synthesis strategy to augment training sequences without any efforts. Extensive experiments and ablation studies on the DAVIS 2016 dataset validate our proposed method. Furthermore, our method without the cascade module achieves a real-time speed of 26 fps on a single GPU.