OpenRaiser/Intern-Atlas huggingface dataset:
Doppler velocity-based algorithm for Clustering and Velocity Estimation of moving objects
Venue: arXiv cs.RO 2021
Year: 2021

We propose a Doppler velocity-based cluster and velocity estimation algorithm based on the characteristics of FMCW LiDAR which achieves highly accurate, single-scan, and real-time motion state detection and velocity estimation. We prove the continuity of the Doppler velocity on the same object. Based on this principle, we achieve the distinction between moving objects and stationary background via region growing clustering algorithm. The obtained stationary background will be used to estimate the velocity of the FMCW LiDAR by the least-squares method. Then we estimate the velocity of the moving objects using the estimated LiDAR velocity and the Doppler velocity of moving objects obtained by clustering. To ensure real-time processing, we set the appropriate least-squares parameters. Meanwhile, to verify the effectiveness of the algorithm, we create the FMCW LiDAR model on the autonomous driving simulation platform CARLA for spawning data. The results show that our algorithm can process at least a 4.5million points and estimate the velocity of 150 moving objects per second under the arithmetic power of the Ryzen 3600x CPU, with a motion state detection accuracy of over 99% and estimated velocity accuracy of 0.1 m/s.
Distinguishing Transformative from Incremental Clinical Evidence: A Classifier of Clinical Research using Textual features from Abstracts and Citing Sentences
Venue: arXiv cs.CL 2021
Year: 2021

In clinical research and clinical decision-making, it is important to know if a study changes or only supports the current standards of care for specific disease management. We define such a change as transformative and a support as incremental research. It usually requires a huge amount of domain expertise and time for humans to finish such tasks. Faculty Opinions provides us with a well-annotated corpus on whether a research challenges or only confirms established research. In this study, a machine learning approach is proposed to distinguishing transformative from incremental clinical evidence. The texts from both abstract and a 2-year window of citing sentences are collected for a training set of clinical studies recommended and labeled by Faculty Opinions experts. We achieve the best performance with an average AUC of 0.755 (0.705-0.875) using Random Forest as the classifier and citing sentences as the feature. The results showed that transformative research has typical language patterns in citing sentences unlike abstract sentences. We provide an efficient tool for identifying those clinical evidence challenging or only confirming established claims for clinicians and researchers.
One Bad Apple Spoils the Bunch: Transaction DoS in MimbleWimble Blockchains
Venue: arXiv cs.CR 2021
Year: 2021

As adoption of blockchain-based systems grows, more attention is being given to privacy of these systems. Early systems like BitCoin provided few privacy features. As a result, systems with strong privacy guarantees, including Monero, Zcash, and MimbleWimble have been developed. Compared to BitCoin, these cryptocurrencies are much less understood. In this paper, we focus on MimbleWimble, which uses the Dandelion++ protocol for private transaction relay and transaction aggregation to provide transaction content privacy. We find that in combination these two features make MimbleWimble susceptible to a new type of denial-of-service attacks. We design, prototype, and evaluate this attack on the Beam network using a private test network and a network simulator. We find that by controlling only 10% of the network nodes, the adversary can prevent over 45% of all transactions from ending up in the blockchain. We also discuss several potential approaches for mitigating this attack.
Joint Activity Detection and Channel Estimation in Cell-Free Massive MIMO Networks with Massive Connectivity
Venue: arXiv cs.IT 2021
Year: 2021

Cell-free massive MIMO is one of the key technologies for future wireless communications, in which users are simultaneously and jointly served by all access points (APs). In this paper, we investigate the minimum mean square error (MMSE) estimation of effective channel coefficients in cell-free massive MIMO systems with massive connectivity. To facilitate the theoretical analysis, only single measurement vector (SMV) based MMSE estimation is considered in this paper, i.e., the MMSE estimation is performed based on the received pilot signals at each AP separately. Inspired by the decoupling principle of replica symmetric postulated MMSE estimation of sparse signal vectors with independent and identically distributed (i.i.d.) non-zero components, we develop the corresponding decoupling principle for the SMV based MMSE estimation of sparse signal vectors with independent and non-identically distributed (i.n.i.d.) non-zero components, which plays a key role in the theoretical analysis of SMV based MMSE estimation of the effective channel coefficients in cell-free massive MIMO systems with massive connectivity. Subsequently, based on the obtained decoupling principle of MMSE estimation, likelihood ratio test and the optimal fusion rule, we perform user activity detection based on the received pilot signals at only one AP, or cooperation among the entire set of APs for centralized or distributed detection. Via theoretical analysis, we show that the error probabilities of both centralized and distributed detection tend to zero when the number of APs tends to infinity while the asymptotic ratio between the number of users and pilots is kept constant. We also investigate the asymptotic behavior of oracle estimation in cell-free massive MIMO systems with massive connectivity via random matrix theory.
The probabilistic Weisfeiler-Leman algorithm
Venue: arXiv cs.CC 2021
Year: 2021

A probabilistic version of the Weisfeiler-Leman algorithm for computing the coherent closure of a colored graph is suggested. The algorithm is Monte Carlo and runs in time $ O(n^{1+ω}\log^2 n) $, where $ n $ is the number of vertices of the graph and $ ω< 2.273 $ is the matrix multiplication exponent.
SimViT: Exploring a Simple Vision Transformer with sliding windows
Venue: arXiv cs.CV 2021
Year: 2021

Although vision Transformers have achieved excellent performance as backbone models in many vision tasks, most of them intend to capture global relations of all tokens in an image or a window, which disrupts the inherent spatial and local correlations between patches in 2D structure. In this paper, we introduce a simple vision Transformer named SimViT, to incorporate spatial structure and local information into the vision Transformers. Specifically, we introduce Multi-head Central Self-Attention(MCSA) instead of conventional Multi-head Self-Attention to capture highly local relations. The introduction of sliding windows facilitates the capture of spatial structure. Meanwhile, SimViT extracts multi-scale hierarchical features from different layers for dense prediction tasks. Extensive experiments show the SimViT is effective and efficient as a general-purpose backbone model for various image processing tasks. Especially, our SimViT-Micro only needs 3.3M parameters to achieve 71.1% top-1 accuracy on ImageNet-1k dataset, which is the smallest size vision Transformer model by now. Our code will be available in https://github.com/ucasligang/SimViT.
Automatic extraction of requirements expressed in industrial standards : a way towards machine readable standards ?
Venue: arXiv cs.IR 2021
Year: 2021

The project, under industrial funding, presented in this publication aims at the semantic analysis of a normative document describing requirements applicable to electrical appliances. The objective of the project is to build a semantic approach to extract and automatically process information related to the requirements contained in the standard. To this end, the project has been divided into three parts, covering the analysis of the requirements document, the extraction of relevant information and creation of the ontology and the comparison with other approaches. The first part of our work deals with the analysis of the requirements document under study. The study focuses on the specificity of the sentence structure, the use of particular words and vocabulary related to the representation of the requirements. The aim is to propose a representation facilitating the extraction of information, used in the second part of the study. In the second part, the extraction of relevant information is conducted in two ways: manual (the ontology being built by hand), semi-automatic (using semantic annotation software and natural language processing techniques). Whatever the method used, the aim of this extraction is to create the concept dictionary, then the ontology, enriched as the document is scanned and understood by the system. Once the relevant terms have been identified, the work focuses on identifying and representing the requirements, separating the textual writing from the information given in the tables. The automatic processing of requirements involves the extraction of sentences containing terms identified as relevant to a requirement. The identified requirement is then indexed and stored in a representation that can be used for query processing.
Overview of Quantum Key Distribution Technique within IPsec Architecture
Venue: arXiv cs.CR 2021
Year: 2021

Quantum Key Distribution (QKD) is an approach for establishing symmetrical binary keys between distant users in an information-theoretically secure way. In this paper we provide an overview of existing solutions that integrate QKD within the most popular architecture for establishing secure communications in modern IP (Internet Protocol) networks - IPsec (Internet Protocol security). The provided overview can be used to further design the integration of QKD within the IPsec architecture striving for a standardized solution.
An Experimental Evaluation and Investigation of Waves of Misery in R-trees
Venue: arXiv cs.DB 2021
Year: 2021

Waves of misery is a phenomenon where spikes of many node splits occur over short periods of time in tree indexes. Waves of misery negatively affect the performance of tree indexes in insertion-heavy workloads.Waves of misery have been first observed in the context of the B-tree, where these waves cause unpredictable index performance. In particular, the performance of search and index-update operations deteriorate when a wave of misery takes place, but is more predictable between the waves. This paper investigates the presence or lack of waves of misery in several R-tree variants, and studies the extent of which these waves impact the performance of each variant. Interestingly, although having poorer query performance, the Linear and Quadratic R-trees are found to be more resilient to waves of misery than both the Hilbert and R*-trees. This paper presents several techniques to reduce the impact in performance of the waves of misery for the Hilbert and R*-trees. One way to eliminate waves of misery is to force node splits to take place at regular times before nodes become full to achieve deterministic performance. The other way is that upon splitting a node, do not split it evenly but rather at different node utilization factors. This allows leaf nodes not to fill at the same pace. We study the impact of two new techniques to mitigate waves of misery after the tree index has been constructed, namely Regular Elective Splits (RES, for short) and Unequal Random Splits (URS, for short). Our experimental investigation highlights the trade-offs in performance of the introduced techniques and the pros and cons of each technique.
Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs
Venue: arXiv cs.GT 2021
Year: 2021

Active Directory is the default security management system for Windows domain networks. We study the shortest path edge interdiction problem for defending Active Directory style attack graphs. The problem is formulated as a Stackelberg game between one defender and one attacker. The attack graph contains one destination node and multiple entry nodes. The attacker's entry node is chosen by nature. The defender chooses to block a set of edges limited by his budget. The attacker then picks the shortest unblocked attack path. The defender aims to maximize the expected shortest path length for the attacker, where the expectation is taken over entry nodes. We observe that practical Active Directory attack graphs have small maximum attack path lengths and are structurally close to trees. We first show that even if the maximum attack path length is a constant, the problem is still $W[1]$-hard with respect to the defender's budget. Having a small maximum attack path length and a small budget is not enough to design fixed-parameter algorithms. If we further assume that the number of entry nodes is small, then we derive a fixed-parameter tractable algorithm. We then propose two other fixed-parameter algorithms by exploiting the tree-like features. One is based on tree decomposition and requires a small tree width. The other assumes a small number of splitting nodes (nodes with multiple out-going edges). Finally, the last algorithm is converted into a graph convolutional neural network based heuristic, which scales to larger graphs with more splitting nodes.
Gradient Leakage Attack Resilient Deep Learning
Venue: arXiv cs.LG 2021
Year: 2021

Gradient leakage attacks are considered one of the wickedest privacy threats in deep learning as attackers covertly spy gradient updates during iterative training without compromising model training quality, and yet secretly reconstruct sensitive training data using leaked gradients with high attack success rate. Although deep learning with differential privacy is a defacto standard for publishing deep learning models with differential privacy guarantee, we show that differentially private algorithms with fixed privacy parameters are vulnerable against gradient leakage attacks. This paper investigates alternative approaches to gradient leakage resilient deep learning with differential privacy (DP). First, we analyze existing implementation of deep learning with differential privacy, which use fixed noise variance to injects constant noise to the gradients in all layers using fixed privacy parameters. Despite the DP guarantee provided, the method suffers from low accuracy and is vulnerable to gradient leakage attacks. Second, we present a gradient leakage resilient deep learning approach with differential privacy guarantee by using dynamic privacy parameters. Unlike fixed-parameter strategies that result in constant noise variance, different dynamic parameter strategies present alternative techniques to introduce adaptive noise variance and adaptive noise injection which are closely aligned to the trend of gradient updates during differentially private model training. Finally, we describe four complementary metrics to evaluate and compare alternative approaches.
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification
Venue: arXiv cs.LG 2021
Year: 2021

Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions especially when they are applied to sensitive domains involving valuable resource allocation, such as education, loan, and employment. It is crucial to conduct fairness testing before DNNs are reliably deployed to such sensitive domains, i.e., generating as many instances as possible to uncover fairness violations. However, the existing testing methods are still limited from three aspects: interpretability, performance, and generalizability. To overcome the challenges, we propose NeuronFair, a new DNN fairness testing framework that differs from previous work in several key aspects: (1) interpretable - it quantitatively interprets DNNs' fairness violations for the biased decision; (2) effective - it uses the interpretation results to guide the generation of more diverse instances in less time; (3) generic - it can handle both structured and unstructured data. Extensive evaluations across 7 datasets and the corresponding DNNs demonstrate NeuronFair's superior performance. For instance, on structured datasets, it generates much more instances (~x5.84) and saves more time (with an average speedup of 534.56%) compared with the state-of-the-art methods. Besides, the instances of NeuronFair can also be leveraged to improve the fairness of the biased DNNs, which helps build more fair and trustworthy deep learning systems.
CABACE: Injecting Character Sequence Information and Domain Knowledge for Enhanced Acronym and Long-Form Extraction
Venue: arXiv cs.CL 2021
Year: 2021

Acronyms and long-forms are commonly found in research documents, more so in documents from scientific and legal domains. Many acronyms used in such documents are domain-specific and are very rarely found in normal text corpora. Owing to this, transformer-based NLP models often detect OOV (Out of Vocabulary) for acronym tokens, especially for non-English languages, and their performance suffers while linking acronyms to their long forms during extraction. Moreover, pretrained transformer models like BERT are not specialized to handle scientific and legal documents. With these points being the overarching motivation behind this work, we propose a novel framework CABACE: Character-Aware BERT for ACronym Extraction, which takes into account character sequences in text and is adapted to scientific and legal domains by masked language modelling. We further use an objective with an augmented loss function, adding the max loss and mask loss terms to the standard cross-entropy loss for training CABACE. We further leverage pseudo labelling and adversarial data generation to improve the generalizability of the framework. Experimental results prove the superiority of the proposed framework in comparison to various baselines. Additionally, we show that the proposed framework is better suited than baseline models for zero-shot generalization to non-English languages, thus reinforcing the effectiveness of our approach. Our team BacKGProp secured the highest scores on the French dataset, second-highest on Danish and Vietnamese, and third-highest in the English-Legal dataset on the global leaderboard for the acronym extraction (AE) shared task at SDU AAAI-22.
PerCQA: Persian Community Question Answering Dataset
Venue: arXiv cs.CL 2021
Year: 2021

Community Question Answering (CQA) forums provide answers for many real-life questions. Thanks to the large size, these forums are very popular among machine learning researchers. Automatic answer selection, answer ranking, question retrieval, expert finding, and fact-checking are example learning tasks performed using CQA data. In this paper, we present PerCQA, the first Persian dataset for CQA. This dataset contains the questions and answers crawled from the most well-known Persian forum. After data acquisition, we provide rigorous annotation guidelines in an iterative process, and then the annotation of question-answer pairs in SemEvalCQA format. PerCQA contains 989 questions and 21,915 annotated answers. We make PerCQA publicly available to encourage more research in Persian CQA. We also build strong benchmarks for the task of answer selection in PerCQA by using mono- and multi-lingual pre-trained language models
Deeper Clinical Document Understanding Using Relation Extraction
Venue: arXiv cs.CL 2021
Year: 2021

The surging amount of biomedical literature & digital clinical records presents a growing need for text mining techniques that can not only identify but also semantically relate entities in unstructured data. In this paper we propose a text mining framework comprising of Named Entity Recognition (NER) and Relation Extraction (RE) models, which expands on previous work in three main ways. First, we introduce two new RE model architectures -- an accuracy-optimized one based on BioBERT and a speed-optimized one utilizing crafted features over a Fully Connected Neural Network (FCNN). Second, we evaluate both models on public benchmark datasets and obtain new state-of-the-art F1 scores on the 2012 i2b2 Clinical Temporal Relations challenge (F1 of 73.6, +1.2% over the previous SOTA), the 2010 i2b2 Clinical Relations challenge (F1 of 69.1, +1.2%), the 2019 Phenotype-Gene Relations dataset (F1 of 87.9, +8.5%), the 2012 Adverse Drug Events Drug-Reaction dataset (F1 of 90.0, +6.3%), and the 2018 n2c2 Posology Relations dataset (F1 of 96.7, +0.6%). Third, we show two practical applications of this framework -- for building a biomedical knowledge graph and for improving the accuracy of mapping entities to clinical codes. The system is built using the Spark NLP library which provides a production-grade, natively scalable, hardware-optimized, trainable & tunable NLP framework.
Stance Quantification: Definition of the Problem
Venue: arXiv cs.CL 2021
Year: 2021

Stance detection is commonly defined as the automatic process of determining the positions of text producers, towards a target. In this paper, we define a research problem closely related to stance detection, namely, stance quantification, for the first time. We define stance quantification on a pair including (1) a set of natural language text items and (2) a target. At the end of the stance quantification process, a triple is obtained which consists of the percentages of the number of text items classified as Favor, Against, Neither, respectively, towards the target in the input pair. Also defined in the current paper is a significant subproblem of the stance quantification problem, namely, multi-target stance quantification. We believe that stance quantification at the aggregate level can lead to fruitful results in many application settings, and furthermore, stance quantification might be the sole stance related analysis alternative in settings where privacy concerns prevent researchers from applying generic stance detection.
Stepping Back to SMILES Transformers for Fast Molecular Representation Inference
Venue: arXiv cs.CE 2021
Year: 2021

In the intersection of molecular science and deep learning, tasks like virtual screening have driven the need for a high-throughput molecular representation generator on large chemical databases. However, as SMILES strings are the most common storage format for molecules, using deep graph models to extract molecular feature from raw SMILES data requires an SMILES-to-graph conversion, which significantly decelerates the whole process. Directly deriving molecular representations from SMILES is feasible, yet there exists a performance gap between the existing unpretrained SMILES-based models and graph-based models at large-scale benchmark results, while pretrain models are resource-demanding at training. To address this issue, we propose ST-KD, an end-to-end \textbf{S}MILES \textbf{T}ransformer for molecular representation learning boosted by \textbf{K}nowledge \textbf{D}istillation. In order to conduct knowledge transfer from graph Transformers to ST-KD, we have redesigned the attention layers and introduced a pre-transformation step to tokenize the SMILES strings and inject structure-based positional embeddings. Without expensive pretraining, ST-KD shows competitive results on latest standard molecular datasets PCQM4M-LSC and QM9, with $3\text{-}14\times$ inference speed compared with existing graph models.