OpenRaiser/Intern-Atlas huggingface dataset:
R2-D2: A Modular Baseline for Open-Domain Question Answering
Venue: arXiv cs.CL 2021
Year: 2021

This work presents a novel four-stage open-domain QA pipeline R2-D2 (Rank twice, reaD twice). The pipeline is composed of a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from all system's components. We demonstrate its strength across three open-domain QA datasets: NaturalQuestions, TriviaQA and EfficientQA, surpassing state-of-the-art on the first two. Our analysis demonstrates that: (i) combining extractive and generative reader yields absolute improvements up to 5 exact match and it is at least twice as effective as the posterior averaging ensemble of the same models with different parameters, (ii) the extractive reader with fewer parameters can match the performance of the generative reader on extractive QA datasets.
Monitoring the adoption of SPI-related best practices. An experience report
Venue: arXiv cs.SE 2021
Year: 2021

Software Process Improvement requires significant effort related not only to the identification of relevant issues and providing an adequate response to them, but also to the implementation and adoption of the changes. Best practices provide recommendations to software teams on how to address the identified objectives in practice, based on aggregated experience and knowledge. In the paper, we present the GEANT experience and observations from the process of adopting thebest practices, and present the setting we have been using.
BLESER: Bug Localization Based on Enhanced Semantic Retrieval
Venue: arXiv cs.SE 2021
Year: 2021

Static bug localization techniques that locate bugs at method granularity have gained much attention from both researchers and practitioners. For a static method-level bug localization technique, a key but challenging step is to fully retrieve the semantics of methods and bug reports. Currently, existing studies mainly use the same bag-of-word space to represent the semantics of methods and bug reports without considering structure information of methods and textual contexts of bug reports, which largely and negatively affects bug localization performance. To address this problem, we develop BLESER, a new bug localization technique based on enhanced semantic retrieval. Specifically, we use an AST-based code embedding model (capturing code structure better) to retrieve the semantics of methods, and word embedding models (capturing textual contexts better) to represent the semantics of bug reports. Then, a deep learning model is built on the enhanced semantic representations. During model building, we compare five typical word embedding models in representing bug reports and try to explore the usefulness of re-sampling strategies and cost-sensitive strategies in handling class imbalance problems. We evaluate our BLESER on five Java projects from the Defects4J dataset. We find that: (1) On the whole, the word embedding model ELMo outperformed the other four models (including word2vec, BERT, etc.) in facilitating bug localization techniques. (2) Among four strategies aiming at solving class imbalance problems, the strategy ROS (random over-sampling) performed much better than the other three strategies (including random under-sampling, Focal Loss, etc.). (3) By integrating ELMo and ROS into BLESER, at method-level bug localization, we could achieve MAP of 0.108-0.504, MRR of 0.134-0.510, and Accuracy@1 of 0.125-0.5 on five Defects4J projects.
On Event-Driven Knowledge Graph Completion in Digital Factories
Venue: arXiv cs.LG 2021
Year: 2021

Smart factories are equipped with machines that can sense their manufacturing environments, interact with each other, and control production processes. Smooth operation of such factories requires that the machines and engineering personnel that conduct their monitoring and diagnostics share a detailed common industrial knowledge about the factory, e.g., in the form of knowledge graphs. Creation and maintenance of such knowledge is expensive and requires automation. In this work we show how machine learning that is specifically tailored towards industrial applications can help in knowledge graph completion. In particular, we show how knowledge completion can benefit from event logs that are common in smart factories. We evaluate this on the knowledge graph from a real world-inspired smart factory with encouraging results.
Diagnostics-Guided Explanation Generation
Venue: arXiv cs.LG 2021
Year: 2021

Explanations shed light on a machine learning model's rationales and can aid in identifying deficiencies in its reasoning process. Explanation generation models are typically trained in a supervised way given human explanations. When such annotations are not available, explanations are often selected as those portions of the input that maximise a downstream task's performance, which corresponds to optimising an explanation's Faithfulness to a given model. Faithfulness is one of several so-called diagnostic properties, which prior work has identified as useful for gauging the quality of an explanation without requiring annotations. Other diagnostic properties are Data Consistency, which measures how similar explanations are for similar input instances, and Confidence Indication, which shows whether the explanation reflects the confidence of the model. In this work, we show how to directly optimise for these diagnostic properties when training a model to generate sentence-level explanations, which markedly improves explanation quality, agreement with human rationales, and downstream task performance on three complex reasoning tasks.
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking
Venue: arXiv cs.CV 2021
Year: 2021

Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments. As LiDARs provide accurate illumination-independent geometric depictions of the scene, performing these tasks using LiDAR point clouds provides reliable predictions. However, existing datasets lack diversity in the type of urban scenes and have a limited number of dynamic object instances which hinders both learning of these tasks as well as credible benchmarking of the developed methods. In this paper, we introduce the large-scale Panoptic nuScenes benchmark dataset that extends our popular nuScenes dataset with point-wise groundtruth annotations for semantic segmentation, panoptic segmentation, and panoptic tracking tasks. To facilitate comparison, we provide several strong baselines for each of these tasks on our proposed dataset. Moreover, we analyze the drawbacks of the existing metrics for panoptic tracking and propose the novel instance-centric PAT metric that addresses the concerns. We present exhaustive experiments that demonstrate the utility of Panoptic nuScenes compared to existing datasets and make the online evaluation server available at nuScenes.org. We believe that this extension will accelerate the research of novel methods for scene understanding of dynamic urban environments.
Video2Skill: Adapting Events in Demonstration Videos to Skills in an Environment using Cyclic MDP Homomorphisms
Venue: arXiv cs.AI 2021
Year: 2021

Humans excel at learning long-horizon tasks from demonstrations augmented with textual commentary, as evidenced by the burgeoning popularity of tutorial videos online. Intuitively, this capability can be separated into 2 distinct subtasks - first, dividing a long-horizon demonstration sequence into semantically meaningful events; second, adapting such events into meaningful behaviors in one's own environment. Here, we present Video2Skill (V2S), which attempts to extend this capability to artificial agents by allowing a robot arm to learn from human cooking videos. We first use sequence-to-sequence Auto-Encoder style architectures to learn a temporal latent space for events in long-horizon demonstrations. We then transfer these representations to the robotic target domain, using a small amount of offline and unrelated interaction data (sequences of state-action pairs of the robot arm controlled by an expert) to adapt these events into actionable representations, i.e., skills. Through experiments, we demonstrate that our approach results in self-supervised analogy learning, where the agent learns to draw analogies between motions in human demonstration data and behaviors in the robotic environment. We also demonstrate the efficacy of our approach on model learning - demonstrating how Video2Skill utilizes prior knowledge from human demonstration to outperform traditional model learning of long-horizon dynamics. Finally, we demonstrate the utility of our approach for non-tabula rasa decision-making, i.e, utilizing video demonstration for zero-shot skill generation.
Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction
Venue: arXiv cs.IR 2021
Year: 2021

Compliments and concerns in reviews are valuable for understanding users' shopping interests and their opinions with respect to specific aspects of certain items. Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. They lack explicit user attention and item property modeling, which however could provide valuable information beyond the ability to recommend items. Therefore, we propose a tightly coupled two-stage approach, including an Aspect-Sentiment Pair Extractor (ASPE) and an Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as concrete aspect-level evidence. Extensive experiments on seven real-world Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs which enable APRE to deliver superior accuracy over the leading baselines.
Leveraging Code Clones and Natural Language Processing for Log Statement Prediction
Venue: arXiv cs.SE 2021
Year: 2021

Software developers embed logging statements inside the source code as an imperative duty in modern software development as log files are necessary for tracking down runtime system issues and troubleshooting system management tasks. Prior research has emphasized the importance of logging statements in the operation and debugging of software systems. However, the current logging process is mostly manual and ad hoc, and thus, proper placement and content of logging statements remain as challenges. To overcome these challenges, methods that aim to automate log placement and log content, i.e., 'where, what, and how to log', are of high interest. Thus, we propose to accomplish the goal of this research, that is "to predict the log statements by utilizing source code clones and natural language processing (NLP)", as these approaches provide additional context and advantage for log prediction. We pursue the following four research objectives: (RO1) investigate whether source code clones can be leveraged for log statement location prediction, (RO2) propose a clone-based approach for log statement prediction, (RO3) predict log statement's description with code-clone and NLP models, and (RO4) examine approaches to automatically predict additional details of the log statement, such as its verbosity level and variables. For this purpose, we perform an experimental analysis on seven open-source java projects, extract their method-level code clones, investigate their attributes, and utilize them for log location and description prediction. Our work demonstrates the effectiveness of log-aware clone detection for automated log location and description prediction and outperforms the prior work.
Computational Polarization: An Information-theoretic Method for Resilient Computing
Venue: arXiv cs.IT 2021
Year: 2021

We introduce an error resilient distributed computing method based on an extension of the channel polarization phenomenon to distributed algorithms. The method leverages an algorithmic split operation that transforms two identical compute nodes to slow and fast workers, which parallels the channel split operation in Polar Codes. This operation preserves the average runtime, analogous to the conservation of Shannon capacity in channel polarization. By leveraging a recursive construction in a similar spirit to the Fast Fourier Transform, this method synthesizes virtual compute nodes with dispersed return time distributions, which we call computational polarization. We show that the runtime distributions form a functional martingale processes, identify their limiting distributions in closed-form expressions together with non-asymptotic convergence rates, and prove strong convergence results in Banach spaces. We provide an information-theoretic lower bound on the overall runtime of any coded computation method and show that the computational polarization approach asymptotically achieves the optimal runtime for computing linear functions. An important advantage is the near linear time decoding procedure, which is significantly cheaper than Maximum Distance Separable codes.
Dynamic Locomotion Teleoperation of a Wheeled Humanoid Robot Reduced Model with a Whole-Body Human-Machine Interface
Venue: arXiv cs.RO 2021
Year: 2021

Bilateral teleoperation provides humanoid robots with human planning intelligence while enabling the human to feel what the robot feels. It has the potential to transform physically capable humanoid robots into dynamically intelligent ones. However, dynamic bilateral locomotion teleoperation remains as a challenge due to the complex dynamics it involves. This work presents our initial step to tackle this challenge via the concept of wheeled humanoid robot locomotion teleoperation by body tilt. Specifically, we developed a force-feedback-capable whole-body human-machine interface (HMI), and designed a force feedback mapping and two teleoperation mappings that map the human's body tilt to the robot's velocity or acceleration. We compared the two mappings and studied the force feedback's effect via an experiment, where seven human subjects teleoperated a simulated robot with the HMI to perform dynamic target tracking tasks. The experimental results suggest that all subjects accomplished the tasks with both mappings after practice, and the force feedback improved their performances. However, the subjects exhibited two distinct teleoperation styles, which benefited from the force feedback differently. Moreover, the force feedback affected the subjects' preferences on the teleoperation mappings, though most subjects performed better with the velocity mapping.
Vulnerabilities and Attacks Against Industrial Control Systems and Critical Infrastructures
Venue: arXiv cs.CR 2021
Year: 2021

Critical infrastructures (CI) and industrial organizations aggressively move towards integrating elements of modern Information Technology (IT) into their monolithic Operational Technology (OT) architectures. Yet, as OT systems progressively become more and more interconnected, they silently have turned into alluring targets for diverse groups of adversaries. Meanwhile, the inherent complexity of these systems, along with their advanced-in-age nature, prevents defenders from fully applying contemporary security controls in a timely manner. Forsooth, the combination of these hindering factors has led to some of the most severe cybersecurity incidents of the past years. This work contributes a full-fledged and up-to-date survey of the most prominent threats against Industrial Control Systems (ICS) along with the communication protocols and devices adopted in these environments. Our study highlights that threats against CI follow an upward spiral due to the mushrooming of commodity tools and techniques that can facilitate either the early or late stages of attacks. Furthermore, our survey exposes that existing vulnerabilities in the design and implementation of several of the OT-specific network protocols may easily grant adversaries the ability to decisively impact physical processes. We provide a categorization of such threats and the corresponding vulnerabilities based on various criteria. As far as we are aware, this is the first time an exhaustive and detailed survey of this kind is attempted.
The Emotional Roller Coaster of Responding to Requirements Changes in Software Engineering
Venue: arXiv cs.SE 2021
Year: 2021

Background: A preliminary study we conducted showed that software practitioners respond to requirements changes(RCs) with different emotions, and that their emotions vary at stages of the RC handling life cycle, such as receiving, developing, and delivering RCs. Objective: We wanted to study more comprehensively how practitioners emotionally respond to RCs. Method: We conducted a world-wide survey with the participation of 201 software practitioners. In our survey, we used the Job-related Affective Well-being Scale (JAWS) and open-ended questions to capture participants emotions when handling RCs in their work and query about the different circumstances when they feel these emotions. We used a combined approach of statistical analysis, JAWS, and Socio-Technical Grounded Theory (STGT) for Data Analysis to analyse our survey data. Findings: We identified (1) emotional responses to RCs, i.e., the most common emotions felt by practitioners when handling RCs; (2) different stimuli -- such as the RC, the practitioner, team, manager, customer -- that trigger these emotions through their own different characteristics; (3)emotion dynamics, i.e., the changes in emotions during the project and RC handling life cycles; (4) distinct events where particular emotions are triggered:project milestones, and RC stages; (5) and time related matters that regulate the emotion dynamics. Conclusion: Practitioners are not pleased with receiving RCs all the time. Last minute RCs introduced closer to a deadline especially violate emotional well-being of practitioners. We present some practical recommendations for practitioners to follow, including a dual-purpose emotion-centric decision guide to help decide when to introduce or accept an RC, and some future key research directions.
Competence-based Curriculum Learning for Multilingual Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

Currently, multilingual machine translation is receiving more and more attention since it brings better performance for low resource languages (LRLs) and saves more space. However, existing multilingual machine translation models face a severe challenge: imbalance. As a result, the translation performance of different languages in multilingual translation models are quite different. We argue that this imbalance problem stems from the different learning competencies of different languages. Therefore, we focus on balancing the learning competencies of different languages and propose Competence-based Curriculum Learning for Multilingual Machine Translation, named CCL-M. Specifically, we firstly define two competencies to help schedule the high resource languages (HRLs) and the low resource languages: 1) Self-evaluated Competence, evaluating how well the language itself has been learned; and 2) HRLs-evaluated Competence, evaluating whether an LRL is ready to be learned according to HRLs' Self-evaluated Competence. Based on the above competencies, we utilize the proposed CCL-M algorithm to gradually add new languages into the training set in a curriculum learning manner. Furthermore, we propose a novel competenceaware dynamic balancing sampling strategy for better selecting training samples in multilingual training. Experimental results show that our approach has achieved a steady and significant performance gain compared to the previous state-of-the-art approach on the TED talks dataset.
Automated Security Assessment for the Internet of Things
Venue: arXiv cs.CR 2021
Year: 2021

Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inefficient. To address this problem, we propose an automated security assessment framework for IoT networks. Our framework first leverages machine learning and natural language processing to analyze vulnerability descriptions for predicting vulnerability metrics. The predicted metrics are then input into a two-layered graphical security model, which consists of an attack graph at the upper layer to present the network connectivity and an attack tree for each node in the network at the bottom layer to depict the vulnerability information. This security model automatically assesses the security of the IoT network by capturing potential attack paths. We evaluate the viability of our approach using a proof-of-concept smart building system model which contains a variety of real-world IoT devices and potential vulnerabilities. Our evaluation of the proposed framework demonstrates its effectiveness in terms of automatically predicting the vulnerability metrics of new vulnerabilities with more than 90% accuracy, on average, and identifying the most vulnerable attack paths within an IoT network. The produced assessment results can serve as a guideline for cybersecurity professionals to take further actions and mitigate risks in a timely manner.
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
Venue: arXiv cs.CL 2021
Year: 2021

Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge. Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models usually perform poorly when transfer to new domains with limited training samples. Therefore, building a knowledge-grounded dialogue system under the low-resource setting is a still crucial issue. In this paper, we propose a novel three-stage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base. To better cooperate with this framework, we devise a variant of Transformer with decoupled decoder which facilitates the disentangled learning of response generation and knowledge incorporation. Evaluation results on two benchmarks indicate that our approach can outperform other state-of-the-art methods with less training data, and even in zero-resource scenario, our approach still performs well.
An Effective Parallel Program Debugging Approach Based on Timing Annotation
Venue: arXiv cs.DC 2021
Year: 2021

We propose an effective parallel program debugging approach based on the timing annotation technique. With prevalent multi-core platforms, parallel programming is required to fully utilize the computing power. However, the non-determinism property and the associated concurrency bugs are notorious and remain to be great challenge to designers. We hence propose an effective program debugging approach using the timing annotation technique derived from the deterministic Multi-Core Instruction Set Simulation (MCISS) technology. We hence construct a deterministic execution environment for parallel program debugging and devise a few unique, effective and easy-to-use parallel debugging functions. We modify QEMU and GDB to implement and demonstrate our proposed idea. The usage of our debugger is almost identical to the conventional GDB debugger. Therefore, users may learn how to use the tool seamlessly.