OpenRaiser/Intern-Atlas huggingface dataset:
Learning Fine-grained Fact-Article Correspondence in Legal Cases
Venue: arXiv cs.CL 2021
Year: 2021

Automatically recommending relevant law articles to a given legal case has attracted much attention as it can greatly release human labor from searching over the large database of laws. However, current researches only support coarse-grained recommendation where all relevant articles are predicted as a whole without explaining which specific fact each article is relevant with. Since one case can be formed of many supporting facts, traversing over them to verify the correctness of recommendation results can be time-consuming. We believe that learning fine-grained correspondence between each single fact and law articles is crucial for an accurate and trustworthy AI system. With this motivation, we perform a pioneering study and create a corpus with manually annotated fact-article correspondences. We treat the learning as a text matching task and propose a multi-level matching network to address it. To help the model better digest the content of law articles, we parse articles in form of premise-conclusion pairs with random forest. Experiments show that the parsed form yielded better performance and the resulting model surpassed other popular text matching baselines. Furthermore, we compare with previous researches and find that establishing the fine-grained fact-article correspondences can improve the recommendation accuracy by a large margin. Our best system reaches an F1 score of 96.3%, making it of great potential for practical use. It can also significantly boost the downstream task of legal decision prediction, increasing the F1 score by up to 12.7%.
Relative Position Estimation Between Two UWB Devices with IMUs
Venue: arXiv cs.RO 2021
Year: 2021

For a team of robots to work collaboratively, it is crucial that each robot have the ability to determine the position of their neighbors, relative to themselves, in order to execute tasks autonomously. This letter presents an algorithm for determining the three-dimensional relative position between two mobile robots, each using nothing more than a single ultra-wideband transceiver, an accelerometer, a rate gyro, and a magnetometer. A sliding window filter estimates the relative position at selected keypoints by combining the distance measurements with acceleration estimates, which each agent computes using an on-board attitude estimator. The algorithm is appropriate for real-time implementation, and has been tested in simulation and experiment, where it comfortably outperforms standard estimators. A positioning accuracy of less than 1 meter is achieved with inexpensive sensors.
A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI
Venue: arXiv cs.AI 2021
Year: 2021

Existing approaches for generating human-aware agent behaviors have considered different measures of interpretability in isolation. Further, these measures have been studied under differing assumptions, thus precluding the possibility of designing a single framework that captures these measures under the same assumptions. In this paper, we present a unifying Bayesian framework that models a human observer's evolving beliefs about an agent and thereby define the problem of Generalized Human-Aware Planning. We will show that the definitions of interpretability measures like explicability, legibility and predictability from the prior literature fall out as special cases of our general framework. Through this framework, we also bring a previously ignored fact to light that the human-robot interactions are in effect open-world problems, particularly as a result of modeling the human's beliefs over the agent. Since the human may not only hold beliefs unknown to the agent but may also form new hypotheses about the agent when presented with novel or unexpected behaviors.
Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?
Venue: arXiv cs.CL 2021
Year: 2021

Language models trained on billions of tokens have recently led to unprecedented results on many NLP tasks. This success raises the question of whether, in principle, a system can ever ``understand'' raw text without access to some form of grounding. We formally investigate the abilities of ungrounded systems to acquire meaning. Our analysis focuses on the role of ``assertions'': textual contexts that provide indirect clues about the underlying semantics. We study whether assertions enable a system to emulate representations preserving semantic relations like equivalence. We find that assertions enable semantic emulation of languages that satisfy a strong notion of semantic transparency. However, for classes of languages where the same expression can take different values in different contexts, we show that emulation can become uncomputable. Finally, we discuss differences between our formal model and natural language, exploring how our results generalize to a modal setting and other semantic relations. Together, our results suggest that assertions in code or language do not provide sufficient signal to fully emulate semantic representations. We formalize ways in which ungrounded language models appear to be fundamentally limited in their ability to ``understand''.
An Accurate and Efficient Large-scale Regression Method through Best Friend Clustering
Venue: arXiv cs.LG 2021
Year: 2021

As the data size in Machine Learning fields grows exponentially, it is inevitable to accelerate the computation by utilizing the ever-growing large number of available cores provided by high-performance computing hardware. However, existing parallel methods for clustering or regression often suffer from problems of low accuracy, slow convergence, and complex hyperparameter-tuning. Furthermore, the parallel efficiency is usually difficult to improve while striking a balance between preserving model properties and partitioning computing workloads on distributed systems. In this paper, we propose a novel and simple data structure capturing the most important information among data samples. It has several advantageous properties supporting a hierarchical clustering strategy that is irrelevant to the hardware parallelism, well-defined metrics for determining optimal clustering, balanced partition for maintaining the compactness property, and efficient parallelization for accelerating computation phases. Then we combine the clustering with regression techniques as a parallel library and utilize a hybrid structure of data and model parallelism to make predictions. Experiments illustrate that our library obtains remarkable performance on convergence, accuracy, and scalability.
Fuzzy Classification of Multi-intent Utterances
Venue: arXiv cs.CL 2021
Year: 2021

Current intent classification approaches assign binary intent class memberships to natural language utterances while disregarding the inherent vagueness in language and the corresponding vagueness in intent class boundaries. In this work, we propose a scheme to address the ambiguity in single-intent as well as multi-intent natural language utterances by creating degree memberships over fuzzified intent classes. To our knowledge, this is the first work to address and quantify the impact of the fuzzy nature of natural language utterances over intent category memberships. Additionally, our approach overcomes the sparsity of multi-intent utterance data to train classification models by using a small database of single intent utterances to generate class memberships over multi-intent utterances. We evaluate our approach over two task-oriented dialog datasets, across different fuzzy membership generation techniques and approximate string similarity measures. Our results reveal the impact of lexical overlap between utterances of different intents, and the underlying data distributions, on the fuzzification of intent memberships. Moreover, we evaluate the accuracy of our approach by comparing the defuzzified memberships to their binary counterparts, across different combinations of membership functions and string similarity measures.
Focusing on Shadows for Predicting Heightmaps from Single Remotely Sensed RGB Images with Deep Learning
Venue: arXiv cs.CV 2021
Year: 2021

Estimating the heightmaps of buildings and vegetation in single remotely sensed images is a challenging problem. Effective solutions to this problem can comprise the stepping stone for solving complex and demanding problems that require 3D information of aerial imagery in the remote sensing discipline, which might be expensive or not feasible to require. We propose a task-focused Deep Learning (DL) model that takes advantage of the shadow map of a remotely sensed image to calculate its heightmap. The shadow is computed efficiently and does not add significant computation complexity. The model is trained with aerial images and their Lidar measurements, achieving superior performance on the task. We validate the model with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest Lidar dataset. Our work suggests that the proposed DL architecture and the technique of injecting shadows information into the model are valuable for improving the heightmap estimation task for single remotely sensed imagery.
Fast MILP-based Task and Motion Planning for Pick-and-Place with Hard/Soft Constraints of Collision-Free Route
Venue: arXiv cs.RO 2021
Year: 2021

We present new models of optimization-based task and motion planning (TAMP) for robotic pick-and-place (P&P), which plan action sequences and motion trajectory with low computational costs. We improved an existing state-of-the-art TAMP model integrated with the collision avoidance, which is formulated as a mixed-integer linear programing (MILP) problem. To enable the MILP solver to search for solutions efficiently, we introduced two approaches leveraging features of collision avoidance in robotic P&P. The first approach reduces number of binary variables, which are related to the collision avoidance of delivery objects, by reformulating them as continuous variables with additional hard constraints. These hard constraints maintain consistency by conditionally propagating binary values, which are related to the carry action state and collision avoidance of robots, to the reformulated continuous variables. The second approach is more aware of the branch-and-bound method, which is the fundamental algorithm of modern MILP solvers. This approach guides the MILP solver to find integer solutions with shallower branching by adding a soft constraint, which softly restricts a robot's routes around delivery objects. We demonstrate the effectiveness of the proposed approaches with a modern MILP solver.
An Online Scheduling Algorithm for Energy Minimization in Wireless Powered Mobile Edge Computing Networks
Venue: arXiv cs.NI 2021
Year: 2021

The integration of Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT), which is usually referred to as Wireless Powered Mobile Edge Computing (WP-MEC), has been recognized as a promising technique to enhance the lifetime and computation capacity of wireless devices (WDs). Compared to the conventional battery-powered MEC networks, WP-MEC brings new challenges to the computation scheduling problem because we have to jointly optimize the resource allocation in WPT and computation offloading. In this paper, we consider the energy minimization problem for WP-MEC networks with multiple WDs and multiple access points. We design an online algorithm by transforming the original problem into a series of deterministic optimization problems based on the Lyapunov optimization theory. To reduce the time complexity of our algorithm, the optimization problem is relaxed and decomposed into several independent subproblems. After solving each subproblem, we adjust the computed values of variables to obtain a feasible solution. Extensive simulations are conducted to validate the performance of the proposed algorithm.
Enriched Attention for Robust Relation Extraction
Venue: arXiv cs.CL 2021
Year: 2021

The performance of relation extraction models has increased considerably with the rise of neural networks. However, a key issue of neural relation extraction is robustness: the models do not scale well to long sentences with multiple entities and relations. In this work, we address this problem with an enriched attention mechanism. Attention allows the model to focus on parts of the input sentence that are relevant to relation extraction. We propose to enrich the attention function with features modeling knowledge about the relation arguments and the shortest dependency path between them. Thus, for different relation arguments, the model can pay attention to different parts of the sentence. Our model outperforms prior work using comparable setups on two popular benchmarks, and our analysis confirms that it indeed scales to long sentences with many entities.
Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans
Venue: arXiv cs.CR 2021
Year: 2021

Medical health care centers are envisioned as a promising paradigm to handle the massive volume of data of COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training the model in a single organization, which is most common weakness due to the privacy and security of raw data communication. To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models without leakage of data privacy. The blockchain ledger technology provides the decentralization of federated learning model without any central server. The proposed homomorphic encryption scheme encrypts and decrypts the gradients of model to preserve the privacy. More precisely, the proposed framework: i) train the local model by a novel capsule network to segmentation and classify COVID-19 images, ii) then use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, and finally the model is shared over a decentralized platform through proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing in the decentralized network. The conducted experimental resultsdemonstrate the performance of the proposed scheme.
Continental-scale land cover mapping at 10 m resolution over Europe (ELC10)
Venue: arXiv cs.CV 2021
Year: 2021

Widely used European land cover maps such as CORINE are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a high resolution (10 m) land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A Random Forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across 8 land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10-m land cover maps including S2GLC and FROM-GLC10. We found that atmospheric correction of Sentinel-2 and speckle filtering of Sentinel-1 imagery had minimal effect on enhancing classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The conversion of LUCAS points into homogenous polygons under the Copernicus module increased accuracy by <1%, revealing that Random Forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies - the difference between 5K and 50K LUCAS points is only 3% (86 vs 89%). At 10-m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g. tree planting).
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
Venue: arXiv cs.IR 2021
Year: 2021

Transformer encoding networks have been proved to be a powerful tool of understanding natural languages. They are playing a critical role in native ads service, which facilitates the recommendation of appropriate ads based on user's web browsing history. For the sake of efficient recommendation, conventional methods would generate user and advertisement embeddings independently with a siamese transformer encoder, such that approximate nearest neighbour search (ANN) can be leveraged. Given that the underlying semantic about user and ad can be complicated, such independently generated embeddings are prone to information loss, which leads to inferior recommendation quality. Although another encoding strategy, the cross encoder, can be much more accurate, it will lead to huge running cost and become infeasible for realtime services, like native ads recommendation. In this work, we propose hybrid encoder, which makes efficient and precise native ads recommendation through two consecutive steps: retrieval and ranking. In the retrieval step, user and ad are encoded with a siamese component, which enables relevant candidates to be retrieved via ANN search. In the ranking step, it further represents each ad with disentangled embeddings and each user with ad-related embeddings, which contributes to the fine-grained selection of high-quality ads from the candidate set. Both steps are light-weighted, thanks to the pre-computed and cached intermedia results. To optimize the hybrid encoder's performance in this two-stage workflow, a progressive training pipeline is developed, which builds up the model's capability in the retrieval and ranking task step-by-step. The hybrid encoder's effectiveness is experimentally verified: with very little additional cost, it outperforms the siamese encoder significantly and achieves comparable recommendation quality as the cross encoder.
Semiotic Aggregation in Deep Learning
Venue: arXiv cs.LG 2021
Year: 2021

Convolutional neural networks utilize a hierarchy of neural network layers. The statistical aspects of information concentration in successive layers can bring an insight into the feature abstraction process. We analyze the saliency maps of these layers from the perspective of semiotics, also known as the study of signs and sign-using behavior. In computational semiotics, this aggregation operation (known as superization) is accompanied by a decrease of spatial entropy: signs are aggregated into supersign. Using spatial entropy, we compute the information content of the saliency maps and study the superization processes which take place between successive layers of the network. In our experiments, we visualize the superization process and show how the obtained knowledge can be used to explain the neural decision model. In addition, we attempt to optimize the architecture of the neural model employing a semiotic greedy technique. To the extent of our knowledge, this is the first application of computational semiotics in the analysis and interpretation of deep neural networks.
On sequentiality and well-bracketing in the $π$-calculus
Venue: arXiv cs.LO 2021
Year: 2021

The $π$-calculus is used as a model for programming languages. Its contexts exhibit arbitrary concurrency, making them very discriminating. This may prevent validating desirable behavioural equivalences in cases when more disciplined contexts are expected. In this paper we focus on two such common disciplines: sequentiality, meaning that at any time there is a single thread of computation, and well-bracketing, meaning that calls to external services obey a stack-like discipline. We formalise the disciplines by means of type systems. The main focus of the paper is on studying the consequence of the disciplines on behavioural equivalence. We define and study labelled bisimilarities for sequentiality and well-bracketing. These relations are coarser than ordinary bisimilarity. We prove that they are sound for the respective (contextual) barbed equivalence, and also complete under a certain technical condition. We show the usefulness of our techniques on a number of examples, that have mainly to do with the representation of functions and store.
An Extensive Study on Smell-Aware Bug Localization
Venue: arXiv cs.SE 2021
Year: 2021

Bug localization is an important aspect of software maintenance because it can locate modules that should be changed to fix a specific bug. Our previous study showed that the accuracy of the information retrieval (IR)-based bug localization technique improved when used in combination with code smell information. Although this technique showed promise, the study showed limited usefulness because of the small number of: 1) projects in the dataset, 2) types of smell information, and 3) baseline bug localization techniques used for assessment. This paper presents an extension of our previous experiments on Bench4BL, the largest bug localization benchmark dataset available for bug localization. In addition, we generalized the smell-aware bug localization technique to allow different configurations of smell information, which were combined with various bug localization techniques. Our results confirmed that our technique can improve the performance of IR-based bug localization techniques for the class level even when large datasets are processed. Furthermore, because of the optimized configuration of the smell information, our technique can enhance the performance of most state-of-the-art bug localization techniques.
FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Monocular 3D object detection is an important task for autonomous driving considering its advantage of low cost. It is much more challenging than conventional 2D cases due to its inherent ill-posed property, which is mainly reflected in the lack of depth information. Recent progress on 2D detection offers opportunities to better solving this problem. However, it is non-trivial to make a general adapted 2D detector work in this 3D task. In this paper, we study this problem with a practice built on a fully convolutional single-stage detector and propose a general framework FCOS3D. Specifically, we first transform the commonly defined 7-DoF 3D targets to the image domain and decouple them as 2D and 3D attributes. Then the objects are distributed to different feature levels with consideration of their 2D scales and assigned only according to the projected 3D-center for the training procedure. Furthermore, the center-ness is redefined with a 2D Gaussian distribution based on the 3D-center to fit the 3D target formulation. All of these make this framework simple yet effective, getting rid of any 2D detection or 2D-3D correspondence priors. Our solution achieves 1st place out of all the vision-only methods in the nuScenes 3D detection challenge of NeurIPS 2020. Code and models are released at https://github.com/open-mmlab/mmdetection3d.