pwc-archive/papers-with-abstracts huggingface dataset:
Learning dynamic and hierarchical traffic spatiotemporal features with Transformer
Authors: Haoyang Yan, Xiaolei Ma
Date: 2021-04-12 00:00:00

Traffic forecasting is an indispensable part of Intelligent transportation systems (ITS), and long-term network-wide accurate traffic speed forecasting is one of the most challenging tasks. Recently, deep learning methods have become popular in this domain. As traffic data are physically associated with road networks, most proposed models treat it as a spatiotemporal graph modeling problem and use Graph Convolution Network (GCN) based methods. These GCN-based models highly depend on a predefined and fixed adjacent matrix to reflect the spatial dependency. However, the predefined fixed adjacent matrix is limited in reflecting the actual dependence of traffic flow. This paper proposes a novel model, Traffic Transformer, for spatial-temporal graph modeling and long-term traffic forecasting to overcome these limitations. Transformer is the most popular framework in Natural Language Processing (NLP). And by adapting it to the spatiotemporal problem, Traffic Transformer hierarchically extracts spatiotemporal features through data dynamically by multi-head attention and masked multi-head attention mechanism, and fuse these features for traffic forecasting. Furthermore, analyzing the attention weight matrixes can find the influential part of road networks, allowing us to learn the traffic networks better. Experimental results on the public traffic network datasets and real-world traffic network datasets generated by ourselves demonstrate our proposed model achieves better performance than the state-of-the-art ones.
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Authors: Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Zhihao Jia, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Dimitry Melts, Krishna Dhulipala, KR Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Guoqiang Jerry Chen, Manoj Krishnan, Avinash Nayak, Krishnakumar Nair, Bharath Muthiah, Mahmoud khorashadi, Pallab Bhattacharya, Petr Lapukhov, Maxim Naumov, Ajit Mathews, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, Vijay Rao
Date: 2021-04-12 00:00:00

Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX. We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup in terms of time to solution over previous systems. We achieve this by (i) designing the ZionEX platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers; (iv) adding high-performance core operators while retaining flexibility to support optimizers with fully deterministic updates (v) leveraging reduced precision communications, multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we develop and briefly comment on distributed data ingestion and other supporting services that are required for the robust and efficient end-to-end training in production environments.
Machine Learning Approach to Uncovering Residential Energy Consumption Patterns Based on Socioeconomic and Smart Meter Data
Authors: Wenjun Tang, Hao Wang, Xian-Long Lee, Hong-Tzer Yang
Date: 2021-04-12 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, feature selection

The smart meter data analysis contributes to better planning and operations for the power system. This study aims to identify the drivers of residential energy consumption patterns from the socioeconomic perspective based on the consumption and demographic data using machine learning. We model consumption patterns by representative loads and reveal the relationship between load patterns and socioeconomic characteristics. Specifically, we analyze the real-world smart meter data and extract load patterns by clustering in a robust way. We further identify the influencing socioeconomic attributes on load patterns to improve our method's interpretability. The relationship between consumers' load patterns and selected socioeconomic features is characterized via machine learning models. The findings are as follows. (1) Twelve load clusters, consisting of six for weekdays and six for weekends, exhibit a diverse pattern of lifestyle and a difference between weekdays and weekends. (2) Among various socioeconomic features, age and education level are suggested to influence the load patterns. (3) Our proposed analytical model using feature selection and machine learning is proved to be more effective than XGBoost and conventional neural network model in mapping the relationship between load patterns and socioeconomic features.
Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks
Authors: Louis Béthune, Thibaut Boissin, Mathieu Serrurier, Franck Mamalet, Corentin Friedrich, Alberto González-Sanz
Date: 2021-04-11 00:00:00
Tasks: General Classification, Misconceptions

Lipschitz constrained networks have gathered considerable attention in the deep learning community, with usages ranging from Wasserstein distance estimation to the training of certifiably robust classifiers. However they remain commonly considered as less accurate, and their properties in learning are still not fully understood. In this paper we clarify the matter: when it comes to classification 1-Lipschitz neural networks enjoy several advantages over their unconstrained counterpart. First, we show that these networks are as accurate as classical ones, and can fit arbitrarily difficult boundaries. Then, relying on a robustness metric that reflects operational needs we characterize the most robust classifier: the WGAN discriminator. Next, we show that 1-Lipschitz neural networks generalize well under milder assumptions. Finally, we show that hyper-parameters of the loss are crucial for controlling the accuracy-robustness trade-off. We conclude that they exhibit appealing properties to pave the way toward provably accurate, and provably robust neural networks.
Research on Optimization Method of Multi-scale Fish Target Fast Detection Network
Authors: Yang Liu, Shengmao Zhang, Fei Wang, Wei Fan, Guohua Zou, Jing Bo
Date: 2021-04-11 00:00:00

The fish target detection algorithm lacks a good quality data set, and the algorithm achieves real-time detection with lower power consumption on embedded devices, and it is difficult to balance the calculation speed and identification ability. To this end, this paper collected and annotated a data set named "Aquarium Fish" of 84 fishes containing 10042 images, and based on this data set, proposed a multi-scale input fast fish target detection network (BTP-yoloV3) and its optimization method. The experiment uses Depthwise convolution to redesign the backbone of the yoloV4 network, which reduces the amount of calculation by 94.1%, and the test accuracy is 92.34%. Then, the training model is enhanced with MixUp, CutMix, and mosaic to increase the test accuracy by 1.27%; Finally, use the mish, swish, and ELU activation functions to increase the test accuracy by 0.76%. As a result, the accuracy of testing the network with 2000 fish images reached 94.37%, and the computational complexity of the network BFLOPS was only 5.47. Comparing the YoloV3~4, MobileNetV2-yoloV3, and YoloV3-tiny networks of migration learning on this data set. The results show that BTP-Yolov3 has smaller model parameters, faster calculation speed, and lower energy consumption during operation while ensuring the calculation accuracy. It provides a certain reference value for the practical application of neural network.
Learning representations with end-to-end models for improved remaining useful life prognostics
Authors: Alaaeddine Chaoub, Alexandre Voisin, Christophe Cerisara, Benoît Iung
Date: 2021-04-11 00:00:00

The remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure. An accurate and reliable prognostic of the remaining useful life provides decision-makers with valuable information to adopt an appropriate maintenance strategy to maximize equipment utilization and avoid costly breakdowns. In this work, we propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the RUL. After normalization of all data, inputs are fed directly to an MLP layers for feature learning, then to an LSTM layer to capture temporal dependencies, and finally to other MLP layers for RUL prognostic. The proposed architecture is tested on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. Despite its simplicity with respect to other recently proposed models, the model developed outperforms them with a significant decrease in the competition score and in the root mean square error score between the predicted and the gold value of the RUL. In this paper, we will discuss how the proposed end-to-end model is able to achieve such good results and compare it to other deep learning and state-of-the-art methods.
Print Error Detection using Convolutional Neural Networks
Authors: Suyash Shandilya
Date: 2021-04-11 00:00:00

This paper discusses the need of an automated system for detecting print errors and the efficacy of Convolutional Neural Networks in such an application. We recognise the need of a dataset containing print error samples and propose a way to generate one artificially. We discuss the algorithms to generate such data along with the limitaions and advantages of such an apporach. Our final trained network gives a remarkable accuracy of 99.83\% in testing. We further evaluate how such efficiency was achieved and what modifications can be tested to further the results.
Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings
Authors: Chengjin Xu, Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann
Date: 2021-04-11 00:00:00
Tasks: Ensemble Learning, Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Knowledge Graph Embeddings, Link Prediction

Among the top approaches of recent years, link prediction using knowledge graph embedding (KGE) models has gained significant attention for knowledge graph completion. Various embedding models have been proposed so far, among which, some recent KGE models obtain state-of-the-art performance on link prediction tasks by using embeddings with a high dimension (e.g. 1000) which accelerate the costs of training and evaluation considering the large scale of KGs. In this paper, we propose a simple but effective performance boosting strategy for KGE models by using multiple low dimensions in different repetition rounds of the same model. For example, instead of training a model one time with a large embedding size of 1200, we repeat the training of the model 6 times in parallel with an embedding size of 200 and then combine the 6 separate models for testing while the overall numbers of adjustable parameters are same (6*200=1200) and the total memory footprint remains the same. We show that our approach enables different models to better cope with their expressiveness issues on modeling various graph patterns such as symmetric, 1-n, n-1 and n-n. In order to justify our findings, we conduct experiments on various KGE models. Experimental results on standard benchmark datasets, namely FB15K, FB15K-237 and WN18RR, show that multiple low-dimensional models of the same kind outperform the corresponding single high-dimensional models on link prediction in a certain range and have advantages in training efficiency by using parallel training while the overall numbers of adjustable parameters are same.
ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms
Authors: Huong Ha, Sunil Gupta, Santu Rana, Svetha Venkatesh
Date: 2021-04-11 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation

Machine learning models are being used extensively in many important areas, but there is no guarantee a model will always perform well or as its developers intended. Understanding the correctness of a model is crucial to prevent potential failures that may have significant detrimental impact in critical application areas. In this paper, we propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN). We develop a novel data augmentation method helping to train the BNN to achieve high accuracy. We also devise a theoretic information based sampling strategy to sample data points so as to achieve accurate estimations for the metrics of interest. Finally, we conduct an extensive set of experiments to test various machine learning models for different types of metrics. Our experiments show that the metrics estimations by our method are significantly better than existing baselines.
Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
Authors: Mario Di Mauro, Giovanni Galatro, Giancarlo Fortino, Antonio Liotta
Date: 2021-04-11 00:00:00
Tasks: Feature Correlation, feature selection, Intrusion Detection, Management, Network Intrusion Detection

Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats. Typically, ML algorithms are exploited to classify/recognize data traffic on the basis of statistical features such as inter-arrival times, packets length distribution, mean number of flows, etc. Dealing with the vast diversity and number of features that typically characterize data traffic is a hard problem. This results in the following issues: i) the presence of so many features leads to lengthy training processes (particularly when features are highly correlated), while prediction accuracy does not proportionally improve; ii) some of the features may introduce bias during the classification process, particularly those that have scarce relation with the data traffic to be classified. To this end, by reducing the feature space and retaining only the most significant features, Feature Selection (FS) becomes a crucial pre-processing step in network management and, specifically, for the purposes of network intrusion detection. In this review paper, we complement other surveys in multiple ways: i) evaluating more recent datasets (updated w.r.t. obsolete KDD 99) by means of a designed-from-scratch Python-based procedure; ii) providing a synopsis of most credited FS approaches in the field of intrusion detection, including Multi-Objective Evolutionary techniques; iii) assessing various experimental analyses such as feature correlation, time complexity, and performance. Our comparisons offer useful guidelines to network/security managers who are considering the incorporation of ML concepts into network intrusion detection, where trade-offs between performance and resource consumption are crucial.
Fine-Grained Attention for Weakly Supervised Object Localization
Authors: Junghyo Sohn, Eunjin Jeon, Wonsik Jung, Eunsong Kang, Heung-Il Suk
Date: 2021-04-11 00:00:00
Tasks: Object, Object Localization, Weakly-Supervised Object Localization

Although recent advances in deep learning accelerated an improvement in a weakly supervised object localization (WSOL) task, there are still challenges to identify the entire body of an object, rather than only discriminative parts. In this paper, we propose a novel residual fine-grained attention (RFGA) module that autonomously excites the less activated regions of an object by utilizing information distributed over channels and locations within feature maps in combination with a residual operation. To be specific, we devise a series of mechanisms of triple-view attention representation, attention expansion, and feature calibration. Unlike other attention-based WSOL methods that learn a coarse attention map, having the same values across elements in feature maps, our proposed RFGA learns fine-grained values in an attention map by assigning different attention values for each of the elements. We validated the superiority of our proposed RFGA module by comparing it with the recent methods in the literature over three datasets. Further, we analyzed the effect of each mechanism in our RFGA and visualized attention maps to get insights.
Conversational Semantic Role Labeling
Authors: Kun Xu, Han Wu, Linfeng Song, Haisong Zhang, Linqi Song, Dong Yu
Date: 2021-04-11 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Dialogue Understanding, Response Generation, Semantic Role Labeling, Sentence

Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence. Traditional SRL can fail to analyze dialogues because it only works on every single sentence, while ellipsis and anaphora frequently occur in dialogues. To address this problem, we propose the conversational SRL task, where an argument can be the dialogue participants, a phrase in the dialogue history or the current sentence. As the existing SRL datasets are in the sentence level, we manually annotate semantic roles for 3,000 chit-chat dialogues (27,198 sentences) to boost the research in this direction. Experiments show that while traditional SRL systems (even with the help of coreference resolution or rewriting) perform poorly for analyzing dialogues, modeling dialogue histories and participants greatly helps the performance, indicating that adapting SRL to conversations is very promising for universal dialogue understanding. Our initial study by applying CSRL to two mainstream conversational tasks, dialogue response generation and dialogue context rewriting, also confirms the usefulness of CSRL.
Enhancing Deep Neural Network Saliency Visualizations with Gradual Extrapolation
Authors: Tomasz Szandala
Date: 2021-04-11 00:00:00

In this paper, an enhancement technique for the class activation mapping methods such as gradient-weighted class activation maps or excitation backpropagation is proposed to present the visual explanations of decisions from convolutional neural network-based models. The proposed idea, called Gradual Extrapolation, can supplement any method that generates a heatmap picture by sharpening the output. Instead of producing a coarse localization map that highlights the important predictive regions in the image, the proposed method outputs the specific shape that most contributes to the model output. Thus, the proposed method improves the accuracy of saliency maps. The effect has been achieved by the gradual propagation of the crude map obtained in the deep layer through all preceding layers with respect to their activations. In validation tests conducted on a selected set of images, the faithfulness, interpretability, and applicability of the method are evaluated. The proposed technique significantly improves the localization detection of the neural networks attention at low additional computational costs. Furthermore, the proposed method is applicable to a variety deep neural network models. The code for the method can be found at https://github.com/szandala/gradual-extrapolation
A Graph Convolutional Neural Network based Framework for Estimating Future Citations Count of Research Articles
Authors: Abdul Wahid, Rajesh Sharma, Chandra Sekhara Rao Annavarapu
Date: 2021-04-11 00:00:00
Tasks: Articles

Scientific publications play a vital role in the career of a researcher. However, some articles become more popular than others among the research community and subsequently drive future research directions. One of the indicative signs of popular articles is the number of citations an article receives. The citation count, which is also the basis with various other metrics, such as the journal impact factor score, the $h$-index, is an essential measure for assessing a scientific paper's quality. In this work, we proposed a Graph Convolutional Network (GCN) based framework for estimating future research publication citations for both the short-term (1-year) and long-term (for 5-years and 10-years) duration. We have tested our proposed approach over the AMiner dataset, specifically on research articles from the computer science domain, consisting of more than 0.8 million articles.
Cross-Lingual Word Embedding Refinement by $\ell_{1}$ Norm Optimisation
Authors: Xutan Peng, Chenghua Lin, Mark Stevenson
Date: 2021-04-11 00:00:00
Tasks: Bilingual Lexicon Induction, Cross-Lingual Transfer, Cross-Lingual Word Embeddings, Natural Language Inference, Word Embeddings

Cross-Lingual Word Embeddings (CLWEs) encode words from two or more languages in a shared high-dimensional space in which vectors representing words with similar meaning (regardless of language) are closely located. Existing methods for building high-quality CLWEs learn mappings that minimise the $\ell_{2}$ norm loss function. However, this optimisation objective has been demonstrated to be sensitive to outliers. Based on the more robust Manhattan norm (aka. $\ell_{1}$ norm) goodness-of-fit criterion, this paper proposes a simple post-processing step to improve CLWEs. An advantage of this approach is that it is fully agnostic to the training process of the original CLWEs and can therefore be applied widely. Extensive experiments are performed involving ten diverse languages and embeddings trained on different corpora. Evaluation results based on bilingual lexicon induction and cross-lingual transfer for natural language inference tasks show that the $\ell_{1}$ refinement substantially outperforms four state-of-the-art baselines in both supervised and unsupervised settings. It is therefore recommended that this strategy be adopted as a standard for CLWE methods.
Disentangled Contrastive Learning for Learning Robust Textual Representations
Authors: Xiang Chen, Xin Xie, Zhen Bi, Hongbin Ye, Shumin Deng, Ningyu Zhang, Huajun Chen
Date: 2021-04-11 00:00:00
Tasks: Contrastive Learning

Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs. Intuitively, the representations should be similar in the feature space with subtle input permutations, while large variations occur with different meanings. This motivates us to investigate the learning of robust textual representation in a contrastive manner. However, it is non-trivial to obtain opposing semantic instances for textual samples. In this study, we propose a disentangled contrastive learning method that separately optimizes the uniformity and alignment of representations without negative sampling. Specifically, we introduce the concept of momentum representation consistency to align features and leverage power normalization while conforming the uniformity. Our experimental results for the NLP benchmarks demonstrate that our approach can obtain better results compared with the baselines, as well as achieve promising improvements with invariance tests and adversarial attacks. The code is available in https://github.com/zxlzr/DCL.