OpenRaiser/Intern-Atlas huggingface dataset:
Overview of CAPITEL Shared Tasks at IberLEF 2020: Named Entity Recognition and Universal Dependencies Parsing
Venue: arXiv cs.CL 2020
Year: 2020

We present the results of the CAPITEL-EVAL shared task, held in the context of the IberLEF 2020 competition series. CAPITEL-EVAL consisted on two subtasks: (1) Named Entity Recognition and Classification and (2) Universal Dependency parsing. For both, the source data was a newly annotated corpus, CAPITEL, a collection of Spanish articles in the newswire domain. A total of seven teams participated in CAPITEL-EVAL, with a total of 13 runs submitted across all subtasks. Data, results and further information about this task can be found at sites.google.com/view/capitel2020.
Petr4: Formal Foundations for P4 Data Planes
Venue: arXiv cs.PL 2020
Year: 2020

P4 is a domain-specific language for programming and specifying packet-processing systems. It is based on an elegant design with high-level abstractions like parsers and match-action pipelines that can be compiled to efficient implementations in software or hardware. Unfortunately, like many industrial languages, P4 has developed without a formal foundation. The P4 Language Specification is a 160-page document with a mixture of informal prose, graphical diagrams, and pseudocode. The P4 reference implementation is a complex system, running to over 40KLoC of C++ code. Clearly neither of these artifacts is suitable for formal reasoning. This paper presents a new framework, called Petr4, that puts P4 on a solid foundation. Petr4 consists of a clean-slate definitional interpreter and a calculus that models the semantics of a core fragment of P4. Throughout the specification, some aspects of program behavior are left up to targets. Our interpreter is parameterized over a target interface which collects all the target-specific behavior in the specification in a single interface. The specification makes ad-hoc restrictions on the nesting of certain program constructs in order to simplify compilation and avoid the possibility of nonterminating programs. We captured the latter intention in our core calculus by stratifying its type system, rather than imposing unnatural syntactic restrictions, and we proved that all programs in this core calculus terminate. We have validated the interpreter against a suite of over 750 tests from the P4 reference implementation, exercising our target interface with tests for different targets. We established termination for the core calculus by induction on the stratified type system. While developing Petr4, we reported dozens of bugs in the language specification and the reference implementation, many of which have been fixed.
A Distributed Algorithm for Overlapped Community Detection in Large-Scale Networks
Venue: arXiv cs.DC 2020
Year: 2020

Overlapped community detection in social networks has become an important research area with the increasing popularity and complexity of the networks. Most of the existing solutions are either centralized or parallel algorithms, which are computationally intensive - require complete knowledge of the entire networks. But it isn't easy to collect entire network data because the size of the actual networks may be prohibitively large. This may be a result of either privacy concerns or technological impediments. Performing in-network computation solves both problems utilizing the computational capability of the individual nodes of the network. Simultaneously, nodes communicate and share data with their neighbors via message passing, which may go a long way toward mitigating individual nodes' privacy concerns in the network. All the aforementioned concerns motivated us to design a decentralized or distributed technique to detect overlapped communities in a large-scale network. It is desirable because this technique does not offer a single point of failure, and the system as a whole can continue to function even when many of the nodes fail. In this paper, we address the overlapped community detection problem for large-scale networks. We present an efficient distributed algorithm, named DOCD, to identify the overlapped communities in the network. The DOCD algorithm's efficiency is verified with extensive simulation study on real network data such as Dolphin, Zachary karate club, Football club, and Facebook ego networks. We show that DOCD algorithm is capable of keeping the asymptotically same results with the existing classical centralized algorithms in terms of community modularity and the number of identified communities. The DOCD algorithm can also efficiently identify the overlapped nodes and overlapped communities with a small number of rounds of communication and computation.
A survey on practical adversarial examples for malware classifiers
Venue: arXiv cs.CR 2020
Year: 2020

Machine learning based solutions have been very helpful in solving problems that deal with immense amounts of data, such as malware detection and classification. However, deep neural networks have been found to be vulnerable to adversarial examples, or inputs that have been purposefully perturbed to result in an incorrect label. Researchers have shown that this vulnerability can be exploited to create evasive malware samples. However, many proposed attacks do not generate an executable and instead generate a feature vector. To fully understand the impact of adversarial examples on malware detection, we review practical attacks against malware classifiers that generate executable adversarial malware examples. We also discuss current challenges in this area of research, as well as suggestions for improvement and future research directions.
FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology
Venue: arXiv cs.LG 2020
Year: 2020

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++ based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases and test data can be found online on GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/.
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography
Venue: arXiv cs.LG 2020
Year: 2020

This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces. DNNs have been recently used for detecting the intended hand gesture through processing of surface electromyogram (sEMG) signals. The ultimate goal of these approaches is to realize high-performance controllers for prosthetic. However, although DNNs have shown superior accuracy than conventional methods when large amounts of data are available for training, their performance substantially decreases when data are limited. Collecting large datasets for training may be feasible in research laboratories, but it is not a practical approach for real-life applications. Therefore, there is an unmet need for the design of a modern gesture detection technique that relies on minimal training data while providing high accuracy. Here we propose an innovative and novel "Few-Shot Learning" framework based on the formulation of meta-learning, referred to as the FS-HGR, to address this need. Few-shot learning is a variant of domain adaptation with the goal of inferring the required output based on just one or a few training examples. More specifically, the proposed FS-HGR quickly generalizes after seeing very few examples from each class. The proposed approach led to 85.94% classification accuracy on new repetitions with few-shot observation (5-way 5-shot), 81.29% accuracy on new subjects with few-shot observation (5-way 5-shot), and 73.36% accuracy on new gestures with few-shot observation (5-way 5-shot).
Tree Embeddings for Hop-Constrained Network Design
Venue: arXiv cs.DS 2020
Year: 2020

Network design problems aim to compute low-cost structures such as routes, trees and subgraphs. Often, it is natural and desirable to require that these structures have small hop length or hop diameter. Unfortunately, optimization problems with hop constraints are much harder and less well understood than their hop-unconstrained counterparts. A significant algorithmic barrier in this setting is the fact that hop-constrained distances in graphs are very far from being a metric. We show that, nonetheless, hop-constrained distances can be approximated by distributions over "partial tree metrics." We build this result into a powerful and versatile algorithmic tool which, similarly to classic probabilistic tree embeddings, reduces hop-constrained problems in general graphs to hop-unconstrained problems on trees. We then use this tool to give the first poly-logarithmic bicriteria approximations for the hop-constrained variants of many classic network design problems. These include Steiner forest, group Steiner tree, group Steiner forest, buy-at-bulk network design as well as online and oblivious versions of many of these problems.
Generalized Constraints as A New Mathematical Problem in Artificial Intelligence: A Review and Perspective
Venue: arXiv cs.AI 2020
Year: 2020

In this comprehensive review, we describe a new mathematical problem in artificial intelligence (AI) from a mathematical modeling perspective, following the philosophy stated by Rudolf E. Kalman that "Once you get the physics right, the rest is mathematics". The new problem is called "Generalized Constraints (GCs)", and we adopt GCs as a general term to describe any type of prior information in modelings. To understand better about GCs to be a general problem, we compare them with the conventional constraints (CCs) and list their extra challenges over CCs. In the construction of AI machines, we basically encounter more often GCs for modeling, rather than CCs with well-defined forms. Furthermore, we discuss the ultimate goals of AI and redefine transparent, interpretable, and explainable AI in terms of comprehension levels about machines. We review the studies in relation to the GC problems although most of them do not take the notion of GCs. We demonstrate that if AI machines are simplified by a coupling with both knowledge-driven submodel and data-driven submodel, GCs will play a critical role in a knowledge-driven submodel as well as in the coupling form between the two submodels. Examples are given to show that the studies in view of a generalized constraint problem will help us perceive and explore novel subjects in AI, or even in mathematics, such as generalized constraint learning (GCL).
Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization
Venue: arXiv cs.LG 2020
Year: 2020

Neural combinatorial optimization (NCO) aims at designing problem-independent and efficient neural network-based strategies for solving combinatorial problems. The field recently experienced growth by successfully adapting architectures originally designed for machine translation. Even though the results are promising, a large gap still exists between NCO models and classic deterministic solvers, both in terms of accuracy and efficiency. One of the drawbacks of current approaches is the inefficiency of training on multiple problem sizes. Curriculum learning strategies have been shown helpful in increasing performance in the multi-task setting. In this work, we focus on designing a curriculum learning-based training procedure that can help existing architectures achieve competitive performance on a large range of problem sizes simultaneously. We provide a systematic investigation of several training procedures and use the insights gained to motivate application of a psychologically-inspired approach to improve upon the classic curriculum method.
Accessible Torque Bandwidth of a Series Elastic Actuator Considering the Thermodynamic Limitations
Venue: arXiv cs.RO 2020
Year: 2020

Within the scope of the paper, electromechanical and thermodynamic models are derived for a series elastic actuator and open loop and closed loop torque bandwidth parameters are analysed considering the thermodynamic behaviour of the actuator. It was observed that the closed loop torque bandwidth of the electromechanical subsystem of the actuator was not accessible in the entire torque reference amplitude range due to thermodynamic limitations. Therefore, a stator winding temperature estimation based adaptive controller is utilised and analysed to improve the accessibility of the controller based torque bandwidth. This paper implements the methodology on a HEBI Robotics X5-9 actuator as a case study.
Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent
Venue: arXiv cs.CV 2020
Year: 2020

Computer vision algorithms performance are near or superior to humans in the visual problems including object recognition (especially those of fine-grained categories), segmentation, and 3D object reconstruction from 2D views. Humans are, however, capable of higher-level image analyses. A clear example, involving theory of mind, is our ability to determine whether a perceived behavior or action was performed intentionally or not. In this paper, we derive an algorithm that can infer whether the behavior of an agent in a scene is intentional or unintentional based on its 3D kinematics, using the knowledge of self-propelled motion, Newtonian motion and their relationship. We show how the addition of this basic knowledge leads to a simple, unsupervised algorithm. To test the derived algorithm, we constructed three dedicated datasets from abstract geometric animation to realistic videos of agents performing intentional and non-intentional actions. Experiments on these datasets show that our algorithm can recognize whether an action is intentional or not, even without training data. The performance is comparable to various supervised baselines quantitatively, with sensible intentionality segmentation qualitatively.
Cross-lingual and Multilingual Spoken Term Detection for Low-Resource Indian Languages
Venue: arXiv cs.CL 2020
Year: 2020

Spoken Term Detection (STD) is the task of searching for words or phrases within audio, given either text or spoken input as a query. In this work, we use state-of-the-art Hindi, Tamil and Telugu ASR systems cross-lingually for lexical Spoken Term Detection in ten low-resource Indian languages. Since no publicly available dataset exists for Spoken Term Detection in these languages, we create a new dataset using a publicly available TTS dataset. We report a standard metric for STD, Mean Term Weighted Value (MTWV) and show that ASR systems built in languages that are phonetically similar to the target languages have higher accuracy, however, it is also possible to get high MTWV scores for dissimilar languages by using a relaxed phone matching algorithm. We propose a technique to bootstrap the Grapheme-to-Phoneme (g2p) mapping between all the languages under consideration using publicly available resources. Gains are obtained when we combine the output of multiple ASR systems and when we use language-specific Language Models. We show that it is possible to perform STD cross-lingually in a zero-shot manner without the need for any language-specific speech data. We plan to make the STD dataset available for other researchers interested in cross-lingual STD.
Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements
Venue: arXiv cs.RO 2020
Year: 2020

Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements. This paper presents Stochastic Process Anticipatory Navigation (SPAN), a framework that enables nonholonomic robots to navigate in environments with crowds, while anticipating and accounting for the motion patterns of pedestrians. To this end, we learn a predictive model to predict continuous-time stochastic processes to model future movement of pedestrians. Anticipated pedestrian positions are used to conduct chance constrained collision-checking, and are incorporated into a time-to-collision control problem. An occupancy map is also integrated to allow for probabilistic collision-checking with static obstacles. We demonstrate the capability of SPAN in crowded simulation environments, as well as with a real-world pedestrian dataset.
Goal-driven Command Recommendations for Analysts
Venue: arXiv cs.HC 2020
Year: 2020

Recent times have seen data analytics software applications become an integral part of the decision-making process of analysts. The users of these software applications generate a vast amount of unstructured log data. These logs contain clues to the user's goals, which traditional recommender systems may find difficult to model implicitly from the log data. With this assumption, we would like to assist the analytics process of a user through command recommendations. We categorize the commands into software and data categories based on their purpose to fulfill the task at hand. On the premise that the sequence of commands leading up to a data command is a good predictor of the latter, we design, develop, and validate various sequence modeling techniques. In this paper, we propose a framework to provide goal-driven data command recommendations to the user by leveraging unstructured logs. We use the log data of a web-based analytics software to train our neural network models and quantify their performance, in comparison to relevant and competitive baselines. We propose a custom loss function to tailor the recommended data commands according to the goal information provided exogenously. We also propose an evaluation metric that captures the degree of goal orientation of the recommendations. We demonstrate the promise of our approach by evaluating the models with the proposed metric and showcasing the robustness of our models in the case of adversarial examples, where the user activity is misaligned with selected goal, through offline evaluation.
Parameter Optimization for Loop Closure Detection in Closed Environments
Venue: arXiv cs.RO 2020
Year: 2020

Tuning parameters is crucial for the performance of localization and mapping algorithms. In general, the tuning of the parameters requires expert knowledge and is sensitive to information about the structure of the environment. In order to design truly autonomous systems the robot has to learn the parameters automatically. Therefore, we propose a parameter optimization approach for loop closure detection in closed environments which requires neither any prior information, e.g. robot model parameters, nor expert knowledge. It relies on several path traversals along the boundary line of the closed environment. We demonstrate the performance of our method in challenging real world scenarios with limited sensing capabilities. These scenarios are exemplary for a wide range of practical applications including lawn mowers and household robots.
Multi-View Dynamic Heterogeneous Information Network Embedding
Venue: arXiv cs.SI 2020
Year: 2020

Most existing Heterogeneous Information Network (HIN) embedding methods focus on static environments while neglecting the evolving characteristic of realworld networks. Although several dynamic embedding methods have been proposed, they are merely designed for homogeneous networks and cannot be directly applied in heterogeneous environment. To tackle above challenges, we propose a novel framework for incorporating temporal information into HIN embedding, denoted as Multi-View Dynamic HIN Embedding (MDHNE), which can efficiently preserve evolution patterns of implicit relationships from different views in updating node representations over time. We first transform HIN to a series of homogeneous networks corresponding to different views. Then our proposed MDHNE applies Recurrent Neural Network (RNN) to incorporate evolving pattern of complex network structure and semantic relationships between nodes into latent embedding spaces, and thus the node representations from multiple views can be learned and updated when HIN evolves over time. Moreover, we come up with an attention based fusion mechanism, which can automatically infer weights of latent representations corresponding to different views by minimizing the objective function specific for different mining tasks. Extensive experiments clearly demonstrate that our MDHNE model outperforms state-of-the-art baselines on three real-world dynamic datasets for different network mining tasks.
R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous Driving
Venue: arXiv cs.CV 2020
Year: 2020

For realizing safe autonomous driving, the end-to-end delays of real-time object detection systems should be thoroughly analyzed and minimized. However, despite recent development of neural networks with minimized inference delays, surprisingly little attention has been paid to their end-to-end delays from an object's appearance until its detection is reported. With this motivation, this paper aims to provide more comprehensive understanding of the end-to-end delay, through which precise best- and worst-case delay predictions are formulated, and three optimization methods are implemented: (i) on-demand capture, (ii) zero-slack pipeline, and (iii) contention-free pipeline. Our experimental results show a 76% reduction in the end-to-end delay of Darknet YOLO (You Only Look Once) v3 (from 1070 ms to 261 ms), thereby demonstrating the great potential of exploiting the end-to-end delay analysis for autonomous driving. Furthermore, as we only modify the system architecture and do not change the neural network architecture itself, our approach incurs no penalty on the detection accuracy.