OpenRaiser/Intern-Atlas huggingface dataset:
Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction
Venue: arXiv cs.CL 2021
Year: 2021

We investigate the problem of Chinese Grammatical Error Correction (CGEC) and present a new framework named Tail-to-Tail (\textbf{TtT}) non-autoregressive sequence prediction to address the deep issues hidden in CGEC. Considering that most tokens are correct and can be conveyed directly from source to target, and the error positions can be estimated and corrected based on the bidirectional context information, thus we employ a BERT-initialized Transformer Encoder as the backbone model to conduct information modeling and conveying. Considering that only relying on the same position substitution cannot handle the variable-length correction cases, various operations such substitution, deletion, insertion, and local paraphrasing are required jointly. Therefore, a Conditional Random Fields (CRF) layer is stacked on the up tail to conduct non-autoregressive sequence prediction by modeling the token dependencies. Since most tokens are correct and easily to be predicted/conveyed to the target, then the models may suffer from a severe class imbalance issue. To alleviate this problem, focal loss penalty strategies are integrated into the loss functions. Moreover, besides the typical fix-length error correction datasets, we also construct a variable-length corpus to conduct experiments. Experimental results on standard datasets, especially on the variable-length datasets, demonstrate the effectiveness of TtT in terms of sentence-level Accuracy, Precision, Recall, and F1-Measure on tasks of error Detection and Correction.
Transferable Adversarial Examples for Anchor Free Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result. The vulnerability has led to a surge of research in this direction, including adversarial attacks on object detection networks. However, previous studies are dedicated to attacking anchor-based object detectors. In this paper, we present the first adversarial attack on anchor-free object detectors. It conducts category-wise, instead of previously instance-wise, attacks on object detectors, and leverages high-level semantic information to efficiently generate transferable adversarial examples, which can also be transferred to attack other object detectors, even anchor-based detectors such as Faster R-CNN. Experimental results on two benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance and transferability.
Corporate core values and social responsibility: What really matters to whom
Venue: arXiv cs.CL 2021
Year: 2021

This study uses an innovative measure, the Semantic Brand Score, to assess the interest of stakeholders in different company core values. Among others, we focus on corporate social responsibility (CSR) core value statements, and on the attention they receive from five categories of stakeholders (customers, company communication teams, employees, associations and media). Combining big data methods and tools of Social Network Analysis and Text Mining, we analyzed about 58,000 Italian tweets and found that different stakeholders have different prevailing interests. CSR gets much less attention than expected. Core values related to customers and employees are in the foreground.
Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement
Venue: arXiv cs.CL 2021
Year: 2021

Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training. Unfortunately, the scale of current annotated datasets is relatively limited, which cannot provide sufficient support for models to capture useful indicators from causal statements, especially for handing those new, unseen cases. To alleviate this problem, we propose a novel approach, shortly named CauSeRL, which leverages external causal statements for event causality identification. First of all, we design a self-supervised framework to learn context-specific causal patterns from external causal statements. Then, we adopt a contrastive transfer strategy to incorporate the learned context-specific causal patterns into the target ECI model. Experimental results show that our method significantly outperforms previous methods on EventStoryLine and Causal-TimeBank (+2.0 and +3.4 points on F1 value respectively).
PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support
Venue: arXiv cs.CL 2021
Year: 2021

Great research interests have been attracted to devise AI services that are able to provide mental health support. However, the lack of corpora is a main obstacle to this research, particularly in Chinese language. In this paper, we propose PsyQA, a Chinese dataset of psychological health support in the form of question and answer pair. PsyQA is crawled from a Chinese mental health service platform, and contains 22K questions and 56K long and well-structured answers. Based on the psychological counseling theories, we annotate a portion of answer texts with typical strategies for providing support, and further present in-depth analysis of both lexical features and strategy patterns in the counseling answers. We also evaluate the performance of generating counseling answers with the generative pretrained models. Results show that utilizing strategies enhances the fluency and helpfulness of generated answers, but there is still a large space for future research.
Lymph Node Graph Neural Networks for Cancer Metastasis Prediction
Venue: arXiv cs.LG 2021
Year: 2021

Predicting outcomes, such as survival or metastasis for individual cancer patients is a crucial component of precision oncology. Machine learning (ML) offers a promising way to exploit rich multi-modal data, including clinical information and imaging to learn predictors of disease trajectory and help inform clinical decision making. In this paper, we present a novel graph-based approach to incorporate imaging characteristics of existing cancer spread to local lymph nodes (LNs) as well as their connectivity patterns in a prognostic ML model. We trained an edge-gated Graph Convolutional Network (Gated-GCN) to accurately predict the risk of distant metastasis (DM) by propagating information across the LN graph with the aid of soft edge attention mechanism. In a cohort of 1570 head and neck cancer patients, the Gated-GCN achieves AUROC of 0.757 for 2-year DM classification and $C$-index of 0.725 for lifetime DM risk prediction, outperforming current prognostic factors as well as previous approaches based on aggregated LN features. We also explored the importance of graph structure and individual lymph nodes through ablation experiments and interpretability studies, highlighting the importance of considering individual LN characteristics as well as the relationships between regions of cancer spread.
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual Transfer
Venue: arXiv cs.CL 2021
Year: 2021

Multilingual pre-trained models have achieved remarkable performance on cross-lingual transfer learning. Some multilingual models such as mBERT, have been pre-trained on unlabeled corpora, therefore the embeddings of different languages in the models may not be aligned very well. In this paper, we aim to improve the zero-shot cross-lingual transfer performance by proposing a pre-training task named Word-Exchange Aligning Model (WEAM), which uses the statistical alignment information as the prior knowledge to guide cross-lingual word prediction. We evaluate our model on multilingual machine reading comprehension task MLQA and natural language interface task XNLI. The results show that WEAM can significantly improve the zero-shot performance.
Internal Shortest Absent Word Queries in Constant Time and Linear Space
Venue: arXiv cs.DS 2021
Year: 2021

Given a string $T$ of length $n$ over an alphabet $Σ\subset \{1,2,\ldots,n^{O(1)}\}$ of size $σ$, we are to preprocess $T$ so that given a range $[i,j]$, we can return a representation of a shortest string over $Σ$ that is absent in the fragment $T[i]\cdots T[j]$ of $T$. We present an $O(n)$-space data structure that answers such queries in constant time and can be constructed in $O(n\log_σn)$ time.
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data
Venue: arXiv cs.CL 2021
Year: 2021

Among ubiquitous multimodal data in the real world, text is the modality generated by human, while image reflects the physical world honestly. In a visual understanding application, machines are expected to understand images like human. Inspired by this, we propose a novel self-supervised learning method, named Text-enhanced Visual Deep InfoMax (TVDIM), to learn better visual representations by fully utilizing the naturally-existing multimodal data. Our core idea of self-supervised learning is to maximize the mutual information between features extracted from multiple views of a shared context to a rational degree. Different from previous methods which only consider multiple views from a single modality, our work produces multiple views from different modalities, and jointly optimizes the mutual information for features pairs of intra-modality and inter-modality. Considering the information gap between inter-modality features pairs from data noise, we adopt a \emph{ranking-based} contrastive learning to optimize the mutual information. During evaluation, we directly use the pre-trained visual representations to complete various image classification tasks. Experimental results show that, TVDIM significantly outperforms previous visual self-supervised methods when processing the same set of images.
Acoustic-based Object Detection for Pedestrian Using Smartphone
Venue: arXiv cs.HC 2021
Year: 2021

Walking while using a smartphone is becoming a major pedestrian safety concern as people may unknowingly bump into various obstacles that could lead to severe injuries. In this paper, we propose ObstacleWatch, an acoustic-based obstacle collision detection system to improve the safety of pedestrians who are engaged in smartphone usage while walking. ObstacleWatch leverages the advanced audio hardware of the smartphone to sense the surrounding obstacles and infers fine-grained information about the frontal obstacle for collision detection. In particular, our system emits well-designed inaudible beep signals from the smartphone built-in speaker and listens to the reflections with the stereo recording of the smartphone. By analyzing the reflected signals received at two microphones, ObstacleWatch is able to extract fine-grained information of the frontal obstacle including the distance, angle, and size for detecting the possible collisions and to alert users. Our experimental evaluation under two real-world environments with different types of phones and obstacles shows that ObstacleWatch achieves over 92% accuracy in predicting obstacle collisions with distance estimation errors at about 2 cm. Results also show that ObstacleWatch is robust to different sizes of objects and is compatible with different phone models with low energy consumption.
A Continuous Liveness Detection System for Text-independent Speaker Verification
Venue: arXiv cs.CR 2021
Year: 2021

Voice authentication is drawing increasing attention and becomes an attractive alternative to passwords for mobile authentication. Recent advances in mobile technology further accelerate the adoption of voice biometrics in an array of diverse mobile applications. However, recent studies show that voice authentication is vulnerable to replay attacks, where an adversary can spoof a voice authentication system using a pre-recorded voice sample collected from the victim. In this paper, we propose VoiceLive, a liveness detection system for both text-dependent and text-independent voice authentication on smartphones. VoiceLive detects a live user by leveraging the user's unique vocal system and the stereo recording of smartphones. In particular, utilizing the built-in gyroscope, loudspeaker, and microphone, VoiceLive first measures the smartphone's distance and angle from the user, then it captures the position-specific time-difference-of-arrival (TDoA) changes in a sequence of phoneme sounds to the two microphones of the phone, and uses such unique TDoA dynamic which doesn't exist under replay attacks for liveness detection. VoiceLive is practical as it doesn't require additional hardware but two-channel stereo recording that is supported by virtually all smartphones. Our experimental evaluation with 12 participants and different types of phones shows that VoiceLive achieves over 99% detection accuracy at around 1% Equal Error Rate (EER) on the text-dependent system and around 99% accuracy and 2% EER on the text-independent one. Results also show that VoiceLive is robust to different phone positions, i.e. the user is free to hold the smartphone with distinct distances and angles.
Relational Analysis of Sensor Attacks on Cyber-Physical Systems
Venue: arXiv cs.CR 2021
Year: 2021

Cyber-physical systems, such as self-driving cars or autonomous aircraft, must defend against attacks that target sensor hardware. Analyzing system design can help engineers understand how a compromised sensor could impact the system's behavior; however, designing security analyses for cyber-physical systems is difficult due to their combination of discrete dynamics, continuous dynamics, and nondeterminism. This paper contributes a framework for modeling and analyzing sensor attacks on cyber-physical systems, using the formalism of hybrid programs. We formalize and analyze two relational properties of a system's robustness. These relational properties respectively express (1) whether a system's safety property can be influenced by sensor attacks, and (2) whether a system's high-integrity state can be affected by sensor attacks. We characterize these relational properties by defining an equivalence relation between a system under attack and the original unattacked system. That is, the system satisfies the robustness properties if executions of the attacked system are appropriately related to executions of the unattacked system. We present two techniques for reasoning about the equivalence relation and thus proving the relational properties for a system. One proof technique decomposes large proof obligations to smaller proof obligations. The other proof technique adapts the self-composition technique from the literature on secure information-flow, allowing us to reduce reasoning about the equivalence of two systems to reasoning about properties of a single system. This technique allows us to reuse existing tools for reasoning about properties of hybrid programs, but is challenging due to the combination of discrete dynamics, continuous dynamics, and nondeterminism. To evaluate, we present three case studies motivated by real design flaws in existing cyber-physical systems.
Component Stability in Low-Space Massively Parallel Computation
Venue: arXiv cs.DS 2021
Year: 2021

We study the power and limitations of component-stable algorithms in the low-space model of Massively Parallel Computation (MPC). Recently Ghaffari, Kuhn and Uitto (FOCS 2019) introduced the class of component-stable low-space MPC algorithms, which are, informally, defined as algorithms for which the outputs reported by the nodes in different connected components are required to be independent. This very natural notion was introduced to capture most (if not all) of the known efficient MPC algorithms to date, and it was the first general class of MPC algorithms for which one can show non-trivial conditional lower bounds. In this paper we enhance the framework of component-stable algorithms and investigate its effect on the complexity of randomized and deterministic low-space MPC. Our key contributions include: 1) We revise and formalize the lifting approach of Ghaffari, Kuhn and Uitto. This requires a very delicate amendment of the notion of component stability, which allows us to fill in gaps in the earlier arguments. 2) We also extend the framework to obtain conditional lower bounds for deterministic algorithms and fine-grained lower bounds that depend on the maximum degree $Δ$. 3) We demonstrate a collection of natural graph problems for which non-component-stable algorithms break the conditional lower bound obtained for component-stable algorithms. This implies that, for both deterministic and randomized algorithms, component-stable algorithms are conditionally weaker than the non-component-stable ones. Altogether our results imply that component-stability might limit the computational power of the low-space MPC model, at least in certain contexts, paving the way for improved upper bounds that escape the conditional lower bound setting of Ghaffari, Kuhn, and Uitto.
Convolutional Neural Network(CNN/ConvNet) in Stock Price Movement Prediction
Venue: arXiv cs.NE 2021
Year: 2021

With technological advancements and the exponential growth of data, we have been unfolding different capabilities of neural networks in different sectors. In this paper, I have tried to use a specific type of Neural Network known as Convolutional Neural Network(CNN/ConvNet) in the stock market. In other words, I have tried to construct and train a convolutional neural network on past stock prices data and then tried to predict the movement of stock price i.e. whether the stock price would rise or fall, in the coming time.
LyricJam: A system for generating lyrics for live instrumental music
Venue: arXiv cs.SD 2021
Year: 2021

We describe a real-time system that receives a live audio stream from a jam session and generates lyric lines that are congruent with the live music being played. Two novel approaches are proposed to align the learned latent spaces of audio and text representations that allow the system to generate novel lyric lines matching live instrumental music. One approach is based on adversarial alignment of latent representations of audio and lyrics, while the other approach learns to transfer the topology from the music latent space to the lyric latent space. A user study with music artists using the system showed that the system was useful not only in lyric composition, but also encouraged the artists to improvise and find new musical expressions. Another user study demonstrated that users preferred the lines generated using the proposed methods to the lines generated by a baseline model.
A Survey on Optimal Transport for Machine Learning: Theory and Applications
Venue: arXiv cs.LG 2021
Year: 2021

Optimal Transport (OT) theory has seen an increasing amount of attention from the computer science community due to its potency and relevance in modeling and machine learning. It introduces means that serve as powerful ways to compare probability distributions with each other, as well as producing optimal mappings to minimize cost functions. In this survey, we present a brief introduction and history, a survey of previous work and propose directions of future study. We will begin by looking at the history of optimal transport and introducing the founders of this field. We then give a brief glance into the algorithms related to OT. Then, we will follow up with a mathematical formulation and the prerequisites to understand OT. These include Kantorovich duality, entropic regularization, KL Divergence, and Wassertein barycenters. Since OT is a computationally expensive problem, we then introduce the entropy-regularized version of computing optimal mappings, which allowed OT problems to become applicable in a wide range of machine learning problems. In fact, the methods generated from OT theory are competitive with the current state-of-the-art methods. We follow this up by breaking down research papers that focus on image processing, graph learning, neural architecture search, document representation, and domain adaptation. We close the paper with a small section on future research. Of the recommendations presented, three main problems are fundamental to allow OT to become widely applicable but rely strongly on its mathematical formulation and thus are hardest to answer. Since OT is a novel method, there is plenty of space for new research, and with more and more competitive methods (either on an accuracy level or computational speed level) being created, the future of applied optimal transport is bright as it has become pervasive in machine learning.
Traversing Steep and Granular Martian Analog Slopes With a Dynamic Quadrupedal Robot
Venue: arXiv cs.RO 2021
Year: 2021

Celestial bodies such as the Moon and Mars are mainly covered by loose, granular soil, a notoriously challenging terrain to traverse with (wheeled) robotic systems. Here, we present experimental work on traversing steep, granular slopes with the dynamically walking quadrupedal robot SpaceBok. To adapt to the challenging environment, we developed passive-adaptive planar feet and optimized grouser pads to reduce sinkage and increase traction on planar and inclined granular soil. Single-foot experiments revealed that a large surface area of 110cm2 per foot reduces sinkage to an acceptable level even on highly collapsible soil (ES-1). Implementing several 12mm grouser blades increases traction by 22% to 66% on granular media compared to grouser-less designs. Together with a terrain-adapting walking controller, we validate - for the first time - static and dynamic locomotion on Mars analog slopes of up to 25°(the maximum of the testbed). We evaluated the performance between point- and planar feet and static and dynamic gaits regarding stability (safety), velocity, and energy consumption. We show that dynamic gaits are energetically more efficient than static gaits but are riskier on steep slopes. Our tests also revealed that planar feet's energy consumption drastically increases when the slope inclination approaches the soil's angle of internal friction due to shearing. Point feet are less affected by slippage due to their excessive sinkage, but in turn, are prone to instabilities and tripping. We present and discuss safe and energy-efficient global path-planning strategies for accessing steep topography on Mars based on our findings.
Unified Performance Analysis of Reconfigurable Intelligent Surface Empowered Free Space Optical Communications
Venue: arXiv cs.IT 2021
Year: 2021

Reconfigurable intelligent surface (RIS) is an excellent use case for line-of-sight (LOS) based technologies such as free-space optical (FSO) communications. In this paper, we analyze the performance of RIS-empowered FSO (RISE-FSO) systems by unifying Fisher-Snedecor (F), Gamma-Gamma (GG), and Malaga (M) distributions for atmospheric turbulence with zero-boresight pointing errors over deterministic as well as random path-loss in foggy conditions with heterodyne detection (HD) and intensity modulation/direct detection (IM/DD) methods. By deriving the probability density function (PDF) and cumulative distribution function (CDF) of the direct-link (DL) with the statistical effect of atmospheric turbulence, pointing errors, and random fog, we develop exact expressions of PDF and CDF of the resultant channel for the RISE-FSO system. Using the derived statistical results, we present exact expressions of outage probability, average bit-error-rate (BER), ergodic capacity, and moments of signal-to-noise ratio (SNR) for both DL-FSO and RISE-FSO systems. We also develop an asymptotic analysis of the outage probability and average BER and derive the diversity order of the considered systems. We validate the analytical expressions using Monte-Carlo simulations and demonstrate the performance scaling of the FSO system with the number of RIS elements for various turbulence channels, detection techniques, and weather conditions.