OpenRaiser/Intern-Atlas huggingface dataset:
Stream-level Latency Evaluation for Simultaneous Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

Simultaneous machine translation has recently gained traction thanks to significant quality improvements and the advent of streaming applications. Simultaneous translation systems need to find a trade-off between translation quality and response time, and with this purpose multiple latency measures have been proposed. However, latency evaluations for simultaneous translation are estimated at the sentence level, not taking into account the sequential nature of a streaming scenario. Indeed, these sentence-level latency measures are not well suited for continuous stream translation resulting in figures that are not coherent with the simultaneous translation policy of the system being assessed. This work proposes a stream-level adaptation of the current latency measures based on a re-segmentation approach applied to the output translation, that is successfully evaluated on streaming conditions for a reference IWSLT task.
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP
Venue: arXiv cs.CL 2021
Year: 2021

Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks. In this paper, we explore whether and how such cross-task generalization ability can be acquired, and further applied to build better few-shot learners across diverse NLP tasks. We introduce CrossFit, a problem setup for studying cross-task generalization ability, which standardizes seen/unseen task partitions, data access during different learning stages, and the evaluation protocols. To instantiate different seen/unseen task partitions in CrossFit and facilitate in-depth analysis, we present the NLP Few-shot Gym, a repository of 160 diverse few-shot NLP tasks created from open-access NLP datasets and converted to a unified text-to-text format. Our analysis reveals that the few-shot learning ability on unseen tasks can be improved via an upstream learning stage using a set of seen tasks. We also observe that the selection of upstream learning tasks can significantly influence few-shot performance on unseen tasks, asking further analysis on task similarity and transferability.
LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
Venue: arXiv cs.CL 2021
Year: 2021

Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. In this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually-rich document understanding. To accurately evaluate LayoutXLM, we also introduce a multilingual form understanding benchmark dataset named XFUND, which includes form understanding samples in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and key-value pairs are manually labeled for each language. Experiment results show that the LayoutXLM model has significantly outperformed the existing SOTA cross-lingual pre-trained models on the XFUND dataset. The pre-trained LayoutXLM model and the XFUND dataset are publicly available at https://aka.ms/layoutxlm.
Emotion-Regularized Conditional Variational Autoencoder for Emotional Response Generation
Venue: arXiv cs.CL 2021
Year: 2021

This paper presents an emotion-regularized conditional variational autoencoder (Emo-CVAE) model for generating emotional conversation responses. In conventional CVAE-based emotional response generation, emotion labels are simply used as additional conditions in prior, posterior and decoder networks. Considering that emotion styles are naturally entangled with semantic contents in the language space, the Emo-CVAE model utilizes emotion labels to regularize the CVAE latent space by introducing an extra emotion prediction network. In the training stage, the estimated latent variables are required to predict the emotion labels and token sequences of the input responses simultaneously. Experimental results show that our Emo-CVAE model can learn a more informative and structured latent space than a conventional CVAE model and output responses with better content and emotion performance than baseline CVAE and sequence-to-sequence (Seq2Seq) models.
HalftimeHash: Modern Hashing without 64-bit Multipliers or Finite Fields
Venue: arXiv cs.DS 2021
Year: 2021

HalftimeHash is a new algorithm for hashing long strings. The goals are few collisions (different inputs that produce identical output hash values) and high performance. Compared to the fastest universal hash functions on long strings (clhash and UMASH) HalftimeHash decreases collision probability while also increasing performance by over 50%, exceeding 16 bytes per cycle. In addition, HalftimeHash does not use any widening 64-bit multiplications or any finite field arithmetic that could limit its portability.
User Behavior Discovery in the COVID-19 Era through the Sentiment Analysis of User Tweet Texts
Venue: arXiv cs.SI 2021
Year: 2021

The coronavirus disease (COVID-19) outbreak was declared a pandemic in March 2020 and since then it has had a significant effect on all aspects of life. Although we live in an information era, we do not have accurate information about this disease. Online social networks (OSNs) play a vital role in society, especially people who do not have trust in the government would tend to have more confidence in the evidence that is formed by social networks. The advantages of OSNs in the COVID-19 era are clear. For instance, social media enables people to connect with each other without the need for real-world face-to-face social interaction. Social media networks also act as a collective intelligence in the absence of world leadership. Therefore, in this study, considering the phenomenon of information diffusion in OSNs, we focus on the effects of COVID-19 on user sentiment and show the user behavior trend during the early months of the pandemic through mining and analyzing OSN data. Moreover, we propose a data-driven model to demonstrate how user sentiment changes over a period of time and how OSNs help us to obtain information on user behavior that is very important for the accurate prediction of future behavior. For this purpose, this study uses tweet texts about COVID-19 and the related network structure to extract significant features, and then presents a model attempting to provide a more comprehensive real picture of current and future conditions.
K-Means Clustering-Aided Non-Coherent Detection for Molecular Communications
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we consider non-coherent detection for molecular communication systems in the presence of inter-symbol-interference. In particular, we study non-coherent detectors based on memory-bits-based thresholds in order to achieve low bit-error-ratio (BER) transmission. The main challenge of realizing detectors based on memory-bits-based thresholds is to obtain the channel state information based only on the received signals. We tackle this issue by reformulating the thresholds through intermediate variables, which can be obtained by clustering multi-dimensional data from the received signals, and by using the K-means clustering algorithm. In addition to estimating the thresholds, we show that the transmitted bits can be retrieved from the clustered data. To reduce clustering errors, we propose iterative clustering methods from one-dimensional to multi-dimensional data, which are shown to reduce the BER. Simulation results are presented to verify the effectiveness of the proposed methods.
A Simple Mathematical Model of Politics (II)
Venue: arXiv cs.SI 2021
Year: 2021

In this paper, some main eigenvalues and eigenvectors of the politics matrix are investigated. The number of upper-class families in a society is the number of eigenvalues which are very close to 1. An algorithm to identify all the upper-class families from the right and left eigenvectors of those eigenvalues is developed.
Anytime Ranking on Document-Ordered Indexes
Venue: arXiv cs.IR 2021
Year: 2021

Inverted indexes continue to be a mainstay of text search engines, allowing efficient querying of large document collections. While there are a number of possible organizations, document-ordered indexes are the most common, since they are amenable to various query types, support index updates, and allow for efficient dynamic pruning operations. One disadvantage with document-ordered indexes is that high-scoring documents can be distributed across the document identifier space, meaning that index traversal algorithms that terminate early might put search effectiveness at risk. The alternative is impact-ordered indexes, which primarily support top-k disjunctions, but also allow for anytime query processing, where the search can be terminated at any time, with search quality improving as processing latency increases. Anytime query processing can be used to effectively reduce high-percentile tail latency which is essential for operational scenarios in which a service level agreement (SLA) imposes response time requirements. In this work, we show how document-ordered indexes can be organized such that they can be queried in an anytime fashion, enabling strict latency control with effective early termination. Our experiments show that processing document-ordered topical segments selected by a simple score estimator outperforms existing anytime algorithms, and allows query runtimes to be accurately limited in order to comply with SLA requirements.
On Design-time Security in IEC 61499 Systems: Conceptualisation, Implementation, and Feasibility
Venue: arXiv cs.CR 2021
Year: 2021

Cyber-attacks on Industrial Automation and Control Systems (IACS) are rising in numbers and sophistication. Embedded controller devices such as Programmable Logic Controllers (PLCs), which are central to controlling physical processes, must be secured against attacks on confidentiality, integrity and availability. The focus of this paper is to add design-level support for security in IACS applications, especially around inter-PLC communications. We propose an end-to-end solution to develop IACS applications with inherent, and parametric support for security. Built using the IEC 61499 Function Blocks standard, this solution allows us to annotate certain communications as 'secure' during design time. When the application is compiled, these annotations are transformed into a security layer that implements encrypted communication between PLCs. In this paper, we implement a part of this security layer focussed on confidentiality, called Confidentiality Layer for Function Blocks (CL4FB), which provides a range of encryption/decryption and secure key exchange functionalities. We study the impact of using CL4FB in IACS applications with real-time constraints. Through a case study focussing on protection functions in smart-grids, we show that varying levels of confidentiality can be achieved while also meeting hard real-time deadlines.
Mining Latent Structures for Multimedia Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommender systems. The majority of previous work focuses on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. Specifically, only collaborative item-item relationships are implicitly modeled through high-order item-user-item relations. Considering that items are associated with rich contents in multiple modalities, we argue that the latent semantic item-item structures underlying these multimodal contents could be beneficial for learning better item representations and further boosting recommendation. To this end, we propose a LATent sTructure mining method for multImodal reCommEndation, which we term LATTICE for brevity. To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs. Based on the learned latent graphs, we perform graph convolutions to explicitly inject high-order item affinities into item representations. These enriched item representations can then be plugged into existing collaborative filtering methods to make more accurate recommendations. Extensive experiments on three real-world datasets demonstrate the superiority of our method over state-of-the-art multimedia recommendation methods and validate the efficacy of mining latent item-item relationships from multimodal features.
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS
Venue: arXiv cs.CV 2021
Year: 2021

The recent progress of deep convolutional neural networks has enabled great success in single image super-resolution (SISR) and many other vision tasks. Their performances are also being increased by deepening the networks and developing more sophisticated network structures. However, finding an optimal structure for the given problem is a difficult task, even for human experts. For this reason, neural architecture search (NAS) methods have been introduced, which automate the procedure of constructing the structures. In this paper, we expand the NAS to the super-resolution domain and find a lightweight densely connected network named DeCoNASNet. We use a hierarchical search strategy to find the best connection with local and global features. In this process, we define a complexity-based penalty for solving image super-resolution, which can be considered a multi-objective problem. Experiments show that our DeCoNASNet outperforms the state-of-the-art lightweight super-resolution networks designed by handcraft methods and existing NAS-based design.
Metadata Normalization
Venue: arXiv cs.LG 2021
Year: 2021

Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. While these techniques normalize feature distributions by standardizing with batch statistics, they do not correct the influence on features from extraneous variables or multiple distributions. Such extra variables, referred to as metadata here, may create bias or confounding effects (e.g., race when classifying gender from face images). We introduce the Metadata Normalization (MDN) layer, a new batch-level operation which can be used end-to-end within the training framework, to correct the influence of metadata on feature distributions. MDN adopts a regression analysis technique traditionally used for preprocessing to remove (regress out) the metadata effects on model features during training. We utilize a metric based on distance correlation to quantify the distribution bias from the metadata and demonstrate that our method successfully removes metadata effects on four diverse settings: one synthetic, one 2D image, one video, and one 3D medical image dataset.
DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss
Venue: arXiv cs.LG 2021
Year: 2021

Deep Learning has become a very valuable tool in different fields, and no one doubts the learning capacity of these models. Nevertheless, since Deep Learning models are often seen as black boxes due to their lack of interpretability, there is a general mistrust in their decision-making process. To find a balance between effectiveness and interpretability, Explainable Artificial Intelligence (XAI) is gaining popularity in recent years, and some of the methods within this area are used to generate counterfactual explanations. The process of generating these explanations generally consists of solving an optimization problem for each input to be explained, which is unfeasible when real-time feedback is needed. To speed up this process, some methods have made use of autoencoders to generate instant counterfactual explanations. Recently, a method called Deep Guided Counterfactual Explanations (DGCEx) has been proposed, which trains an autoencoder attached to a classification model, in order to generate straightforward counterfactual explanations. However, this method does not ensure that the generated counterfactual instances are close to the data manifold, so unrealistic counterfactual instances may be generated. To overcome this issue, this paper presents Distribution Aware Deep Guided Counterfactual Explanations (DA-DGCEx), which adds a term to the DGCEx cost function that penalizes out of distribution counterfactual instances.
Strategies for Democratization of Supercomputing: Availability, Accessibility and Usability of High Performance Computing for Education and Practice of Big Data Analytics
Venue: arXiv cs.CY 2021
Year: 2021

There has been an increasing interest in and growing need for high performance computing (HPC), popularly known as supercomputing, in domains such as textual analytics, business domains analytics, forecasting and natural language processing (NLP), in addition to the relatively mature supercomputing domains of quantum physics and biology. HPC has been widely used in computer science (CS) and other traditionally computation intensive disciplines, but has remained largely siloed away from the vast array of social, behavioral, business and economics disciplines. However, with ubiquitous big data, there is a compelling need to make HPC technologically and economically accessible, easy to use, and operationally democratized. Therefore, this research focuses on making two key contributions, the first is the articulation of strategies based on availability, accessibility and usability for the demystification and democratization of HPC, based on an analytical review of Caliburn, a notable supercomputer at its inception. The second contribution is a set of principles for HPC adoption based on an experiential narrative of HPC usage for textual analytics and NLP of social media data from a first time user perspective. Both, the HPC usage process and the output of the early stage analytics are summarized. This research study synthesizes expert input on HPC democratization strategies, and chronicles the challenges and opportunities from a multidisciplinary perspective, of a case of rapid adoption of supercomputing for textual analytics and NLP. Deductive logic is used to identify strategies which can lead to efficacious engagement, adoption, production and sustained usage for research, teaching, application and innovation by researchers, faculty, professionals and students across a broad range of disciplines.
When to Build Quantum Software?
Venue: arXiv cs.SE 2021
Year: 2021

Despite ongoing advancements in quantum computing, businesses are still faced with the problem to decide if they would benefit from investing into this novel technology for building a business critical application. This uncertainty is not only owing to the limitations in the current state of the technology but also due to the gap between the level at which business applications are analyzed (e.g., using high level semi-formal languages) and the level at which quantum computing related information is currently available (e.g., formally specified computational problems, their algorithmic solutions with computational complexity theoretic analysis) to make informed decisions. To fill the discourse gap, in this paper, we present design of an interactive advisor, which augments users while deciding to invest into quantum software development as a plausible future option in their application context. Towards that we apply business process modeling and natural language similarity analysis using text-embeddings to associated business context with computational problems and formulate constraints in terms of quantum speedup and resource requirements to select software development platforms.
Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Face identification/recognition has significantly advanced over the past years. However, most of the proposed approaches rely on static RGB frames and on neutral facial expressions. This has two disadvantages. First, important facial shape cues are ignored. Second, facial deformations due to expressions can have an impact on the performance of such a method. In this paper, we propose a novel framework for dynamic 3D face identification/recognition based on facial keypoints. Each dynamic sequence of facial expressions is represented as a spatio-temporal graph, which is constructed using 3D facial landmarks. Each graph node contains local shape and texture features that are extracted from its neighborhood. For the classification/identification of faces, a Spatio-temporal Graph Convolutional Network (ST-GCN) is used. Finally, we evaluate our approach on a challenging dynamic 3D facial expression dataset.
Self-Paced Uncertainty Estimation for One-shot Person Re-Identification
Venue: arXiv cs.CV 2021
Year: 2021

The one-shot Person Re-ID scenario faces two kinds of uncertainties when constructing the prediction model from $X$ to $Y$. The first is model uncertainty, which captures the noise of the parameters in DNNs due to a lack of training data. The second is data uncertainty, which can be divided into two sub-types: one is image noise, where severe occlusion and the complex background contain irrelevant information about the identity; the other is label noise, where mislabeled affects visual appearance learning. In this paper, to tackle these issues, we propose a novel Self-Paced Uncertainty Estimation Network (SPUE-Net) for one-shot Person Re-ID. By introducing a self-paced sampling strategy, our method can estimate the pseudo-labels of unlabeled samples iteratively to expand the labeled samples gradually and remove model uncertainty without extra supervision. We divide the pseudo-label samples into two subsets to make the use of training samples more reasonable and effective. In addition, we apply a Co-operative learning method of local uncertainty estimation combined with determinacy estimation to achieve better hidden space feature mining and to improve the precision of selected pseudo-labeled samples, which reduces data uncertainty. Extensive comparative evaluation experiments on video-based and image-based datasets show that SPUE-Net has significant advantages over the state-of-the-art methods.
Direction-Aggregated Attack for Transferable Adversarial Examples
Venue: arXiv cs.LG 2021
Year: 2021

Deep neural networks are vulnerable to adversarial examples that are crafted by imposing imperceptible changes to the inputs. However, these adversarial examples are most successful in white-box settings where the model and its parameters are available. Finding adversarial examples that are transferable to other models or developed in a black-box setting is significantly more difficult. In this paper, we propose the Direction-Aggregated adversarial attacks that deliver transferable adversarial examples. Our method utilizes aggregated direction during the attack process for avoiding the generated adversarial examples overfitting to the white-box model. Extensive experiments on ImageNet show that our proposed method improves the transferability of adversarial examples significantly and outperforms state-of-the-art attacks, especially against adversarial robust models. The best averaged attack success rates of our proposed method reaches 94.6\% against three adversarial trained models and 94.8\% against five defense methods. It also reveals that current defense approaches do not prevent transferable adversarial attacks.