OpenRaiser/Intern-Atlas huggingface dataset:
Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data
Venue: arXiv cs.CL 2021
Year: 2021

Enabling empathetic behavior in Arabic dialogue agents is an important aspect of building human-like conversational models. While Arabic Natural Language Processing has seen significant advances in Natural Language Understanding (NLU) with language models such as AraBERT, Natural Language Generation (NLG) remains a challenge. The shortcomings of NLG encoder-decoder models are primarily due to the lack of Arabic datasets suitable to train NLG models such as conversational agents. To overcome this issue, we propose a transformer-based encoder-decoder initialized with AraBERT parameters. By initializing the weights of the encoder and decoder with AraBERT pre-trained weights, our model was able to leverage knowledge transfer and boost performance in response generation. To enable empathy in our conversational model, we train it using the ArabicEmpatheticDialogues dataset and achieve high performance in empathetic response generation. Specifically, our model achieved a low perplexity value of 17.0 and an increase in 5 BLEU points compared to the previous state-of-the-art model. Also, our proposed model was rated highly by 85 human evaluators, validating its high capability in exhibiting empathy while generating relevant and fluent responses in open-domain settings.
Automatic Difficulty Classification of Arabic Sentences
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we present a Modern Standard Arabic (MSA) Sentence difficulty classifier, which predicts the difficulty of sentences for language learners using either the CEFR proficiency levels or the binary classification as simple or complex. We compare the use of sentence embeddings of different kinds (fastText, mBERT , XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. Our best results have been achieved using fined-tuned Arabic-BERT. The accuracy of our 3-way CEFR classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification respectively and 0.71 Spearman correlation for regression. Our binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for sentence-pair semantic similarity classifier.
Usability Evaluation for Online Professional Search in the Dutch Archaeology Domain
Venue: arXiv cs.IR 2021
Year: 2021

This paper presents AGNES, the first information retrieval system for archaeological grey literature, allowing full-text search of these long archaeological documents. This search system has a web interface that allows archaeology professionals and scholars to search through a collection of over 60,000 Dutch excavation reports, totalling 361 million words. We conducted a user study for the evaluation of AGNES's search interface, with a small but diverse user group. The evaluation was done by screen capturing and a think aloud protocol, combined with a user interface feedback questionnaire. The evaluation covered both controlled use (completion of a pre-defined task) as well as free use (completion of a freely chosen task). The free use allows us to study the information needs of archaeologists, as well as their interactions with the search system. We conclude that: (1) the information needs of archaeologists are typically recall-oriented, often requiring a list of items as answer; (2) the users prefer the use of free-text queries over metadata filters, confirming the value of a free-text search system; (3) the compilation of a diverse user group contributed to the collection of diverse issues as feedback for improving the system. We are currently refining AGNES's user interface and improving its precision for archaeological entities, so that AGNES will help archaeologists to answer their research questions more effectively and efficiently, leading to a more coherent narrative of the past.
Correct-by-Construction Navigation Functions with Application to Sensor Based Robot Navigation
Venue: arXiv cs.RO 2021
Year: 2021

This paper brings together the concepts of navigation transformation and harmonic functions to form navigation functions that are correct-by-construction in the sense that no tuning is required. The form of the navigation function is explicitly related to the number of obstacles in the environment. This enables application of navigation functions for autonomous robot navigation in partially or fully unknown environments, with the capability of on-the-fly adjustment of the navigation function when new obstacles are discovered by the robot. Appropriate navigation controllers, applicable to robots with local, sector bounded sensing, are presented and analyzed for a~kinematic point-mass robot and then for the dynamic point-mass robot system. The closed form nature of the proposed navigation scheme provides for online, fast-feedback based navigation. In addition to the analytic guarantees, simulation studies are presented to verify the effectiveness of the methodology.
On the Termination of Some Biclique Operators on Multipartite Graphs
Venue: arXiv cs.DM 2021
Year: 2021

We define a new graph operator, called the weak-factor graph, which comes from the context of complex network modelling. The weak-factor operator is close to the well-known clique-graph operator but it rather operates in terms of bicliques in a multipartite graph. We address the problem of the termination of the series of graphs obtained by iteratively applying the weak-factor operator starting from a given input graph. As for the clique-graph operator, it turns out that some graphs give rise to series that do not terminate. Therefore, we design a slight variation of the weak-factor operator, called clean-factor, and prove that its associated series terminates for all input graphs. In addition, we show that the multipartite graph on which the series terminates has a very nice combinatorial structure: we exhibit a bijection between its vertices and the chains of the inclusion order on the intersections of the maximal cliques of the input graph.
Strong Approximate Consensus Halving and the Borsuk-Ulam Theorem
Venue: arXiv cs.GT 2021
Year: 2021

In the consensus halving problem we are given n agents with valuations over the interval $[0,1]$. The goal is to divide the interval into at most $n+1$ pieces (by placing at most n cuts), which may be combined to give a partition of $[0,1]$ into two sets valued equally by all agents. The existence of a solution may be established by the Borsuk-Ulam theorem. We consider the task of computing an approximation of an exact solution of the consensus halving problem, where the valuations are given by distribution functions computed by algebraic circuits. Here approximation refers to computing a point that $\varepsilon$-close to an exact solution, also called strong approximation. We show that this task is polynomial time equivalent to computing an approximation to an exact solution of the Borsuk-Ulam search problem defined by a continuous function that is computed by an algebraic circuit. The Borsuk-Ulam search problem is the defining problem of the complexity class BU. We introduce a new complexity class BBU to also capture an alternative formulation of the Borsuk-Ulam theorem from a computational point of view. We investigate their relationship and prove several structural results for these classes as well as for the complexity class FIXP.
Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples
Venue: arXiv cs.DB 2021
Year: 2021

Fuzzy similarity join is an important database operator widely used in practice. So far the research community has focused exclusively on optimizing fuzzy join \textit{scalability}. However, practitioners today also struggle to optimize fuzzy-join \textit{quality}, because they face a daunting space of parameters (e.g., distance-functions, distance-thresholds, tokenization-options, etc.), and often have to resort to a manual trial-and-error approach to program these parameters in order to optimize fuzzy-join quality. This key challenge of automatically generating high-quality fuzzy-join programs has received surprisingly little attention thus far. In this work, we study the problem of "auto-program" fuzzy-joins. Leveraging a geometric interpretation of distance-functions, we develop an unsupervised \textsc{Auto-FuzzyJoin} framework that can infer suitable fuzzy-join programs on given input tables, without requiring explicit human input such as labeled training data. Using \textsc{Auto-FuzzyJoin}, users only need to provide two input tables $L$ and $R$, and a desired precision target $τ$ (say 0.9). \textsc{Auto-FuzzyJoin} leverages the fact that one of the input is a reference table to automatically program fuzzy-joins that meet the precision target $τ$ in expectation, while maximizing fuzzy-join recall (defined as the number of correctly joined records). Experiments on both existing benchmarks and a new benchmark with 50 fuzzy-join tasks created from Wikipedia data suggest that the proposed \textsc{Auto-FuzzyJoin} significantly outperforms existing unsupervised approaches, and is surprisingly competitive even against supervised approaches (e.g., Magellan and DeepMatcher) when 50\% of ground-truth labels are used as training data.
ColoRadar: The Direct 3D Millimeter Wave Radar Dataset
Venue: arXiv cs.RO 2021
Year: 2021

Millimeter wave radar is becoming increasingly popular as a sensing modality for robotic mapping and state estimation. However, there are very few publicly available datasets that include dense, high-resolution millimeter wave radar scans and there are none focused on 3D odometry and mapping. In this paper we present a solution to that problem. The ColoRadar dataset includes 3 different forms of dense, high-resolution radar data from 2 FMCW radar sensors as well as 3D lidar, IMU, and highly accurate groundtruth for the sensor rig's pose over approximately 2 hours of data collection in highly diverse 3D environments.
"Sharks are not the threat humans are": Argument Component Segmentation in School Student Essays
Venue: arXiv cs.CL 2021
Year: 2021

Argument mining is often addressed by a pipeline method where segmentation of text into argumentative units is conducted first and proceeded by an argument component identification task. In this research, we apply a token-level classification to identify claim and premise tokens from a new corpus of argumentative essays written by middle school students. To this end, we compare a variety of state-of-the-art models such as discrete features and deep learning architectures (e.g., BiLSTM networks and BERT-based architectures) to identify the argument components. We demonstrate that a BERT-based multi-task learning architecture (i.e., token and sentence level classification) adaptively pretrained on a relevant unlabeled dataset obtains the best results
Formal Verification of Authenticated, Append-Only Skip Lists in Agda: Extended Version
Venue: arXiv cs.CR 2021
Year: 2021

Authenticated Append-Only Skiplists (AAOSLs) enable maintenance and querying of an authenticated log (such as a blockchain) without requiring any single party to store or verify the entire log, or to trust another party regarding its contents. AAOSLs can help to enable efficient dynamic participation (e.g., in consensus) and reduce storage overhead. In this paper, we formalize an AAOSL originally described by Maniatis and Baker, and prove its key correctness properties. Our model and proofs are machine checked in Agda. Our proofs apply to a generalization of the original construction and provide confidence that instances of this generalization can be used in practice. Our formalization effort has also yielded some simplifications and optimizations.
Volcano: Stateless Cache Side-channel Attack by Exploiting Mesh Interconnect
Venue: arXiv cs.CR 2021
Year: 2021

Cache side-channel attacks lead to severe security threats to the settings that a CPU is shared across users, e.g., in the cloud. The existing attacks rely on sensing the micro-architectural state changes made by victims, and this assumption can be invalidated by combining spatial (\eg, Intel CAT) and temporal isolation (\eg, time protection). In this work, we advance the state of cache side-channel attacks by showing stateless cache side-channel attacks that cannot be defeated by both spatial and temporal isolation. This side-channel exploits the timing difference resulted from interconnect congestion. Specifically, to complete cache transactions, for Intel CPUs, cache lines would travel across cores via the CPU mesh interconnect. Nonetheless, the mesh links are shared by all cores, and cache isolation does not segregate the traffic. An attacker can generate interconnect traffic to contend with the victim's on a mesh link, hoping that extra delay will be measured. With the variant delays, the attacker can deduce the memory access pattern of a victim program, and infer its sensitive data. Based on this idea, we implement Volcano and test it against the existing RSA implementations of JDK. We found the RSA private key used by a victim process can be partially recovered. In the end, we propose a few directions for defense and call for the attention of the security community.
Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm
Venue: arXiv cs.RO 2021
Year: 2021

Merging into the highway from the on-ramp is an essential scenario for automated driving. The decision-making under the scenario needs to balance the safety and efficiency performance to optimize a long-term objective, which is challenging due to the dynamic, stochastic, and adversarial characteristics. The Rule-based methods often lead to conservative driving on this task while the learning-based methods have difficulties meeting the safety requirements. In this paper, we propose an RL-based end-to-end decision-making method under a framework of offline training and online correction, called the Shielded Distributional Soft Actor-critic (SDSAC). The SDSAC adopts the policy evaluation with safety consideration and a safety shield parameterized with the barrier function in its offline training and online correction, respectively. These two measures support each other for better safety while not damaging the efficiency performance severely. We verify the SDSAC on an on-ramp merge scenario in simulation. The results show that the SDSAC has the best safety performance compared to baseline algorithms and achieves efficient driving simultaneously.
Exploring a Makeup Support System for Transgender Passing based on Automatic Gender Recognition
Venue: arXiv cs.HC 2021
Year: 2021

How to handle gender with machine learning is a controversial topic. A growing critical body of research brought attention to the numerous issues transgender communities face with the adoption of current automatic gender recognition (AGR) systems. In contrast, we explore how such technologies could potentially be appropriated to support transgender practices and needs, especially in non-Western contexts like Japan. We designed a virtual makeup probe to assist transgender individuals with passing, that is to be perceived as the gender they identify as. To understand how such an application might support expressing transgender individuals gender identity or not, we interviewed 15 individuals in Tokyo and found that in the right context and under strict conditions, AGR based systems could assist transgender passing.
CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes
Venue: arXiv cs.CV 2021
Year: 2021

For autonomous vehicles, an accurate calibration for LiDAR and camera is a prerequisite for multi-sensor perception systems. However, existing calibration techniques require either a complicated setting with various calibration targets, or an initial calibration provided beforehand, which greatly impedes their applicability in large-scale autonomous vehicle deployment. To tackle these issues, we propose a novel method to calibrate the extrinsic parameter for LiDAR and camera in road scenes. Our method introduces line features from static straight-line-shaped objects such as road lanes and poles in both image and point cloud and formulates the initial calibration of extrinsic parameters as a perspective-3-lines (P3L) problem. Subsequently, a cost function defined under the semantic constraints of the line features is designed to perform refinement on the solved coarse calibration. The whole procedure is fully automatic and user-friendly without the need to adjust environment settings or provide an initial calibration. We conduct extensive experiments on KITTI and our in-house dataset, quantitative and qualitative results demonstrate the robustness and accuracy of our method.
Testing Autonomous Systems with Believed Equivalence Refinement
Venue: arXiv cs.AI 2021
Year: 2021

Continuous engineering of autonomous driving functions commonly requires deploying vehicles in road testing to obtain inputs that cause problematic decisions. Although the discovery leads to producing an improved system, it also challenges the foundation of testing using equivalence classes and the associated relative test coverage criterion. In this paper, we propose believed equivalence, where the establishment of an equivalence class is initially based on expert belief and is subject to a set of available test cases having a consistent valuation. Upon a newly encountered test case that breaks the consistency, one may need to refine the established categorization in order to split the originally believed equivalence into two. Finally, we focus on modules implemented using deep neural networks where every category partitions an input over the real domain. We present both analytical and lazy methods to suggest the refinement. The concept is demonstrated in analyzing multiple autonomous driving modules, indicating the potential of our proposed approach.
CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays
Venue: arXiv cs.CV 2021
Year: 2021

We systematically evaluate the performance of deep learning models in the presence of diseases not labeled for or present during training. First, we evaluate whether deep learning models trained on a subset of diseases (seen diseases) can detect the presence of any one of a larger set of diseases. We find that models tend to falsely classify diseases outside of the subset (unseen diseases) as "no disease". Second, we evaluate whether models trained on seen diseases can detect seen diseases when co-occurring with diseases outside the subset (unseen diseases). We find that models are still able to detect seen diseases even when co-occurring with unseen diseases. Third, we evaluate whether feature representations learned by models may be used to detect the presence of unseen diseases given a small labeled set of unseen diseases. We find that the penultimate layer of the deep neural network provides useful features for unseen disease detection. Our results can inform the safe clinical deployment of deep learning models trained on a non-exhaustive set of disease classes.
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning
Venue: arXiv cs.RO 2021
Year: 2021

This letter compares the performance of four different, popular simulation environments for robotics and reinforcement learning (RL) through a series of benchmarks. The benchmarked scenarios are designed carefully with current industrial applications in mind. Given the need to run simulations as fast as possible to reduce the real-world training time of the RL agents, the comparison includes not only different simulation environments but also different hardware configurations, ranging from an entry-level notebook up to a dual CPU high performance server. We show that the chosen simulation environments benefit the most from single core performance. Yet, using a multi core system, multiple simulations could be run in parallel to increase the performance.
Synplex: A synthetic simulator of highly multiplexed histological images
Venue: arXiv cs.CV 2021
Year: 2021

Multiplex tissue immunostaining is a technology of growing relevance as it can capture in situ the complex interactions existing between the elements of the tumor microenvironment. The existence and availability of large, annotated image datasets is key for the objective development and benchmarking of bioimage analysis algorithms. Manual annotation of multiplex images, is however, laborious, often impracticable. In this paper, we present Synplex, a simulation system able to generate multiplex immunostained in situ tissue images based on user-defined parameters. This includes the specification of structural attributes, such as the number of cell phenotypes, the number and level of expression of cellular markers, or the cell morphology. Synplex consists of three sequential modules, each being responsible for a separate task: modeling of cellular neighborhoods, modeling of cell phenotypes, and synthesis of realistic cell/tissue textures. Synplex flexibility and accuracy are demonstrated qualitatively and quantitatively by generating synthetic tissues that simulate disease paradigms found in the real scenarios. Synplex is publicly available for scientific purposes, and we believe it will become a valuable tool for the training and/or validation of multiplex image analysis algorithms.
Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control
Venue: arXiv cs.LG 2021
Year: 2021

Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs) in monitoring and remote area surveillance applications has become widespread thanks to the price reduction and the increased capabilities of drones. The drones in the swarm need to cooperatively explore an unknown area, in order to identify and monitor interesting targets, while minimizing their movements. In this work, we propose a distributed Reinforcement Learning (RL) approach that scales to larger swarms without modifications. The proposed framework relies on the possibility for the UAVs to exchange some information through a communication channel, in order to achieve context-awareness and implicitly coordinate the swarm's actions. Our experiments show that the proposed method can yield effective strategies, which are robust to communication channel impairments, and that can easily deal with non-uniform distributions of targets and obstacles. Moreover, when agents are trained in a specific scenario, they can adapt to a new one with minimal additional training. We also show that our approach achieves better performance compared to a computationally intensive look-ahead heuristic.
The distance backbone of complex networks
Venue: arXiv cs.SI 2021
Year: 2021

Redundancy needs more precise characterization as it is a major factor in the evolution and robustness of networks of multivariate interactions. We investigate the complexity of such interactions by inferring a connection transitivity that includes all possible measures of path length for weighted graphs. The result, without breaking the graph into smaller components, is a distance backbone subgraph sufficient to compute all shortest paths. This is important for understanding the dynamics of spread and communication phenomena in real-world networks. The general methodology we formally derive yields a principled graph reduction technique and provides a finer characterization of the triangular geometry of all edges -- those that contribute to shortest paths and those that do not but are involved in other network phenomena. We demonstrate that the distance backbone is very small in large networks across domains ranging from air traffic to the human brain connectome, revealing that network robustness to attacks and failures seems to stem from surprisingly vast amounts of redundancy.