Reviewerly/exHarmony huggingface dataset:
Multi-task learning for abstractive text summarization with key information guide network

Abstract Neural networks based on the attentional encoder-decoder model have good capability in abstractive text summarization. However, these models are hard to be controlled in the process of generation, which leads to a lack of key information. And some key information, such as time, place, and people, is indispensable for humans to understand the main content. In this paper, we propose a key information guide network for abstractive text summarization based on a multi-task learning framework. The core idea is to automatically extract the key information that people need most in an end-to-end way and use it to guide the generation process, so as to get a more human-compliant summary. In our model, the document is encoded into two parts: results of the normal document encoder and the key information encoding, and the key information includes the key sentences and the keywords. A multi-task learning framework is introduced to get a more sophisticated end-to-end model. To fuse the key information, we propose a novel multi-view attention guide network to obtain the dynamic representations of the source text and the key information. In addition, the dynamic representations are incorporated into the abstractive module to guide the process of summary generation. We evaluate our model on the CNN/Daily Mail dataset and experimental results show that our model leads to significant improvements.
TIME: Text and Image Mutual-Translation Adversarial Networks

Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework. While previous methods tackle the T2I problem as a uni-directional task and use pre-trained language models to enforce the image--text consistency, TIME requires neither extra modules nor pre-training. We show that the performance of G can be boosted substantially by training it jointly with D as a language model. Specifically, we adopt Transformers to model the cross-modal connections between the image features and word embeddings, and design an annealing conditional hinge loss that dynamically balances the adversarial learning. In our experiments, TIME achieves state-of-the-art (SOTA) performance on the CUB dataset (Inception Score of 4.91 and Fréchet Inception Distance of 14.3 on CUB), and shows promising performance on MS-COCO dataset on image captioning and downstream vision-language tasks.
Abusive Language Detection: A Comprehensive Review

Objectives:To provide an organised literature on the detection of Abusive language on Twitter using natural language processing (NLP).Methods: In this study, the survey has been conducted on different methods and research conducted on the types of Abusive language used in social media, why it is important?How it has been detected in real time social media platforms and the performance metrics that are used by researchers in evaluating the performance of the detection of abusive language on Twitter by the users.Results: Giving an organised review of past methodologies, including methods, important features and core algorithms, this study arranges and depicts the present condition about this area.The study also talks about the intricacy of hate speech idea which is characterised in numerous stages ad settings.This area of study has an obvious potential for societal effect, especially in digital media and online networks.A crucial step in propelling automatic hate speech detection is the advancement and systemisation of common assets, for example, clarified data sets in numerous dialects, rules, and calculations.Conclusion: This survey study contains all the relevant references related to detection of abusive language on social media using NLP and machine learning methods.Ultimately, it can be as source of references to the other researchers in finding the literatures that are relevant to their research area in the detection of Abusive language on Twitter.adding to the engendering of abusive language too.Since then, this sort of biased conversation might be amazingly destructive to people, so, social companies as well as government can make profit from identification and anticipation tools.This study contributes a solution to this issue by giving a methodological representation of work already done on this specific topic.In this study, the problem is framed by defining it, outlining identifying strategies and resources.A systematic approach is adopted which critically investigate and analyse both theoretical and practical aspects.
GeoQoE-Vanet: QoE-Aware Geographic Routing Protocol for Video Streaming over Vehicular Ad-hoc Networks

Video streaming is one of the challenging issues in vehicular ad-hoc networks (VANETs) due to their highly dynamic topology and frequent connectivity disruptions. Recent developments in the routing protocol methods used in VANETs have contributed to improvements in the quality of experience (QoE) of the received video. One of these methods is the selection of the next-hop relay vehicle. In this paper, a QoE-aware geographic protocol for video streaming over VANETs is proposed. The selection process of the next relay vehicle is based on a correlated formula of QoE and quality of service (QoS) factors to enhance the users’ QoE. The simulation results show that the proposed GeoQoE-Vanet outperforms both GPSR and GPSR-2P protocols in providing the best end-user QoE of video streaming service.
MODELING AND PERFORMANCE ANALYSIS OF CLOUD COMPUTING LOAD BALANCING ALGORITHMS

Currently, the widespread use of cloud computing has led to an increase in the load on cloud servers. The large number of services provided requires a large amount of traffic. This article discusses load balancing algorithms in cloud computing and analyzes their effectiveness. The effectiveness of the algorithms is evaluated by comparing the response time and cost of the VM for three different regions.
Multiphoton Graph States from a Solid-State Single-Photon Source

Photonic graph states are underlying resources for one-way optical quantum computation, quantum error correction, fundamental testing of quantum mechanics, and quantum communication networks. Most existing works, however, are based on the spontaneous parametric down-conversion sources that intrinsically suffer from probabilistic generation and double pair components. Here, we create two important classes of graph states, a polarization-encoded four-photon Greenberger–Horne–Zeilinger (GHZ) state and a linear cluster state, by actively demultiplexing a deterministic single-photon source from a semiconductor quantum dot embedded in a micropillar. A state fidelity of 0.790 ± 0.009 (0.763 ± 0.004) and a count rate of ∼13 Hz are observed for the four-photon GHZ (cluster) state. The results constitute a new route toward the multiphoton entanglement with deterministic single-photon sources.
Image Captioning using Deep Learning: A Systematic Literature Review

Auto Image captioning is defined as the process of generating captions or textual descriptions for images based on the contents of the image.It is a machine learning task that involves both natural language processing (for text generation) and computer vision (for understanding image contents).Auto image captioning is a very recent and growing research problem nowadays.Day by day various new methods are being introduced to achieve satisfactory results in this field.However, there are still lots of attention required to achieve results as good as a human.This study aims to find out in a systematic way that what different and recent methods and models are used for image captioning using deep learning?What methods are implemented to use those models?And what methods are more likely to give good results.For doing so we have performed a systematic literature review on recent studies from 2017 to 2019 from wellknown databases (Scopus, Web of Sciences, IEEEXplore).We found a total of 61 prime studies relevant to the objective of this research.We found that CNN is used to understand image contents and find out objects in an image while RNN or LSTM is used for language generation.The most commonly used datasets are MS COCO used in all studies and flicker 8k and flicker 30k.The most commonly used evaluation matrix is BLEU (1 to 4) used in all studies.It is also found that LSTM with CNN has outperformed RNN with CNN.We found that the two most promising methods for implementing this model are Encoder Decoder, and attention mechanism and a combination of them can help in improving results to a good scale.This research provides a guideline and recommendation to researchers who want to contribute to auto image captioning.
Incomplete data classification with view-based decision tree

Data quality issues may bring serious problems in data analysis. For instance, missing values could decrease the accuracy of the classification. As traditional classification approaches can only be applied to complete data sets, we present a generic classification model for incomplete data where existing classification methods can be effectively incorporated. Firstly, we generate complete views from the incomplete data by choosing proper subsets of attributes based on Information Gain measure. Then we use these selected views to obtain multiple base classifiers. Finally, the base classifies are effectively combined as a final classifier with a decision tree. Extensive experiments results on real data sets demonstrate that the proposed method outperforms existing approaches.
Solar Photocatalytic Degradation of Organic Contaminants in Landfill Leachate using TiO2 Nanoparticles by RSM and ANN

In the present study, artificial neural network (ANN) and response surface methodology (RSM) models were used to investigate the heterogeneous photocatalysis performance in removal of chemical oxygen demand (COD) from landfill leachate using compound parabolic collector.Effect of the three parameters, i.e. pH, catalyst dosage and irradiation time were studied for COD removal efficiency and these parameters are optimized by the RSM.The optimum values of pH 5, the dosage of 0.75 g/L and irradiation time of 100 minutes is capable to remove 32.19% of COD from the leachate.A good agreement is shown by the analysis of variance for the regression coefficient R 2 for predicted value (0.92268) and adjusted value (0.9776).The proposed RSM and ANN model R 2 values were found to be 0.9882 and 0.9974 respectively, which confirms the ideality of RSM and ANN.The results also confirm that the input and output data from RSM could be appropriate to build the ANN model.Further BOD 5 /COD ratio is studied for the biodegradability of leachate and it was found that increase of biodegradability value from 0.17 to 0.47 was at pH 3, catalyst dosage of 1 g/L and irradiation time of 150 minutes.
Few-shot Human Motion Prediction via Learning Novel Motion Dynamics

Human motion prediction is a task where we anticipate future motion based on past observation. Previous approaches rely on the access to large datasets of skeleton data, and thus are difficult to be generalized to novel motion dynamics with limited training data. In our work, we propose a novel approach named Motion Prediction Network (MoPredNet) for few-short human motion prediction. MoPredNet can be adapted to predicting new motion dynamics using limited data, and it elegantly captures long-term dependency in motion dynamics. Specifically, MoPredNet dynamically selects the most informative poses in the streaming motion data as masked poses. In addition, MoPredNet improves its encoding capability of motion dynamics by adaptively learning spatio-temporal structure from the observed poses and masked poses. We also propose to adapt MoPredNet to novel motion dynamics based on accumulated motion experiences and limited novel motion dynamics data. Experimental results show that our method achieves better performance over state-of-the-art methods in motion prediction.
Evaluating Explanation Methods for Neural Machine Translation

Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods.Word Alignment Error Rate can be used as such a metric that matches human understanding, however, it can not measure explanation methods on those target words that are not aligned to any source word.This paper thereby makes an initial attempt to evaluate explanation methods from an alternative viewpoint.To this end, it proposes a principled metric based on fidelity in regard to the predictive behavior of the NMT model.As the exact computation for this metric is intractable, we employ an efficient approach as its approximation.On six standard translation tasks, we quantitatively evaluate several explanation methods in terms of the proposed metric and we reveal some valuable findings for these explanation methods in our experiments.
The Unstoppable Rise of Computational Linguistics in Deep Learning

In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures.We focus on the importance of variable binding and its instantiation in attention-based models, and argue that Transformer is not a sequence model but an induced-structure model.This perspective leads to predictions of the challenges facing research in deep learning architectures for natural language understanding.
Self-supervised Monocular Depth and Visual Odometry Learning with Scale-consistent Geometric Constraints

The self-supervised learning-based depth and visual odometry (VO) estimators trained on monocular videos without ground truth have drawn significant attention recently. Prior works use photometric consistency as supervision, which is fragile under complex realistic environments due to illumination variations. More importantly, it suffers from scale inconsistency in the depth and pose estimation results. In this paper, robust geometric losses are proposed to deal with this problem. Specifically, we first align the scales of two reconstructed depth maps estimated from the adjacent image frames, and then enforce forward-backward relative pose consistency to formulate scale-consistent geometric constraints. Finally, a novel training framework is constructed to implement the proposed losses. Extensive evaluations on KITTI and Make3D datasets demonstrate that, i) by incorporating the proposed constraints as supervision, the depth estimation model can achieve state-of-the-art (SOTA) performance among the self-supervised methods, and ii) it is effective to use the proposed training framework to obtain a uniform global scale VO model.
A Linguistic Perspective on Radar Micro-Doppler Analysis of American Sign Language

Although users of American Sign Language (ASL) comprise a significant minority in the U.S. and Canada, people in the Deaf community have been unable to benefit from many new technologies, which depend upon vocalized speech, and are designed for hearing individuals. While video has led to tremendous advances in ASL recognition, concerns over invasion of privacy have limited its use for in-home smart environments. This work presents initial work on the use of RF sensors, which can protect user privacy, for the purpose of ASL recognition. The new offerings of 2D/3D RF data representations and optical flow are presented. The fractal complexity of ASL is shown to be greater than that of daily activities - a relationship consistent with linguistic analysis conducted using video.
Channel-Level Variable Quantization Network for Deep Image Compression

Deep image compression systems mainly contain four components: encoder, quantizer, entropy model, and decoder. To optimize these four components, a joint rate-distortion framework was proposed, and many deep neural network-based methods achieved great success in image compression. However, almost all convolutional neural network-based methods treat channel-wise feature maps equally, reducing the flexibility in handling different types of information. In this paper, we propose a channel-level variable quantization network to dynamically allocate more bitrates for significant channels and withdraw bitrates for negligible channels. Specifically, we propose a variable quantization controller. It consists of two key components: the channel importance module, which can dynamically learn the importance of channels during training, and the splitting-merging module, which can allocate different bitrates for different channels. We also formulate the quantizer into a Gaussian mixture model manner. Quantitative and qualitative experiments verify the effectiveness of the proposed model and demonstrate that our method achieves superior performance and can produce much better visual reconstructions.
Multi Task Mutual Learning for Joint Sentiment Classification and Topic Detection

Recently, advances in neural network approaches have achieved many successes in both sentiment classification and probabilistic topic modeling.On the one hand, latent topics derived from the global context of documents could be helpful in capturing more accurate word semantics and hence could potentially improve the sentiment classification accuracy.On the other hand, the word-level attention vectors obtained during the learning of sentiment classifiers could carry word-level polarity information and can be used to guide the discovery of topics in topic modeling.This paper proposes a multi-task learning framework which jointly learns a sentiment classifier and a topic model by making the word-level latent topic distributions in the topic model to be similar to the word-level attention vectors in sentiment classifiers through mutual learning.Experimental results on the Yelp and IMDB datasets verify the superior performance of the proposed framework over strong baselines on both sentiment classification and topic modeling.The proposed framework also extracts more interpretable topics compared to other conventional topic models and neural topic models.
Multiobjective planning for spacecraft reorientation under complex pointing constraints

The spacecraft is required to optimize some performance indexes during the attitude maneuver in many space missions. Moreover, the presence of the bounded and pointing constraints will greatly reduce the feasible attitude space. It is difficult to solve the optimal spacecraft reorientation under complex multiple constraints. The notion of the rotational-path geometric-reasonableness is proposed to evaluate the quality of the maneuver path in terms of its geometric characteristics, which involve the path length and path unwinding in this paper. Considering the rotational-path geometric-reasonableness and energy consumption, this paper addresses the multiobjective spacecraft reorientation planning problem with constraints and proposes a new dynamic-iteration-based multiobjective planning (DIMP) method. Through the iterative quadratic programming approach with the dynamic termination criteria, the proposed method first generates a multiobjective suboptimal solution that does not consider the pointing constraints. Then the final solution under the keep-in and keep-out constraints is yielded via multiple rotational-path decomposition and iterative planning. Simulation results demonstrate the high efficiency of the proposed method, which can obtain a high-quality and low-energy solution.
Should All Cross-Lingual Embeddings Speak English?

Most of recent work in cross-lingual word embeddings is severely Anglocentric.The vast majority of lexicon induction evaluation dictionaries are between English and another language, and the English embedding space is selected by default as the hub when learning in a multilingual setting.With this work, however, we challenge these practices.First, we show that the choice of hub language can significantly impact downstream lexicon induction and zero-shot POS tagging performance.Second, we both expand a standard Englishcentered evaluation dictionary collection to include all language pairs using triangulation, and create new dictionaries for under-represented languages. 1Evaluating established methods over all these language pairs sheds light into their suitability for aligning embeddings from distant languages and presents new challenges for the field.Finally, in our analysis we identify general guidelines for strong cross-lingual embedding baselines, that extend to language pairs that do not include English.
Better AMR-To-Text Generation with Graph Structure Reconstruction

AMR-to-text generation is a challenging task of generating texts from graph-based semantic representations. Recent studies formalize this task a graph-to-sequence learning problem and use various graph neural networks to model graph structure. In this paper, we propose a novel approach that generates texts from AMR graphs while reconstructing the input graph structures. Our model employs graph attention mechanism to aggregate information for encoding the inputs. Moreover, better node representations are learned by optimizing two simple but effective auxiliary reconstruction objectives: link prediction objective which requires predicting the semantic relationship between nodes, and distance prediction objective which requires predicting the distance between nodes. Experimental results on two benchmark datasets show that our proposed model improves considerably over strong baselines and achieves new state-of-the-art.
Composing Elementary Discourse Units in Abstractive Summarization

In this paper, we argue that elementary discourse unit (EDU) is a more appropriate textual unit of content selection than the sentence unit in abstractive summarization.To well handle the problem of composing EDUs into an informative and fluent summary, we propose a novel summarization method that first designs an EDU selection model to extract and group informative EDUs and then an EDU fusion model to fuse the EDUs in each group into one sentence.We also design the reinforcement learning mechanism to use EDU fusion results to reward the EDU selection action, boosting the final summarization performance.Experiments on CNN/Daily Mail have demonstrated the effectiveness of our model.
schuBERT: Optimizing Elements of BERT

Transformers (Vaswani et al., 2017) have gradually become a key component for many state-of-the-art natural language representation models.A recent Transformer based model-BERT (Devlin et al., 2018) achieved state-of-the-art results on various natural language processing tasks, including GLUE, SQuAD v1.1, and SQuAD v2.0.This model however is computationally prohibitive and has a huge number of parameters.In this work we revisit the architecture choices of BERT in efforts to obtain a lighter model.We focus on reducing the number of parameters yet our methods can be applied towards other objectives such FLOPs or latency.We show that much efficient light BERT models can be obtained by reducing algorithmically chosen correct architecture design dimensions rather than reducing the number of Transformer encoder layers.In particular, our schuBERT gives 6.6% higher average accuracy on GLUE and SQuAD datasets as compared to BERT with three encoder layers while having the same number of parameters.