OpenRaiser/Intern-Atlas huggingface dataset:
Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT
Venue: arXiv cs.CL 2021
Year: 2021

Retrieval-based dialogue systems select the best response from many candidates. Although many state-of-the-art models have shown promising performance in dialogue response selection tasks, there is still quite a gap between R@1 and R@10 performance. To address this, we propose to leverage linguistic coordination (a phenomenon that individuals tend to develop similar linguistic behaviors in conversation) to rerank the N-best candidates produced by BERT, a state-of-the-art pre-trained language model. Our results show an improvement in R@1 compared to BERT baselines, demonstrating the utility of repairing machine-generated outputs by leveraging a linguistic theory.
FastRIFE: Optimization of Real-Time Intermediate Flow Estimation for Video Frame Interpolation
Venue: arXiv cs.CV 2021
Year: 2021

The problem of video inter-frame interpolation is an essential task in the field of image processing. Correctly increasing the number of frames in the recording while maintaining smooth movement allows to improve the quality of played video sequence, enables more effective compression and creating a slow-motion recording. This paper proposes the FastRIFE algorithm, which is some speed improvement of the RIFE (Real-Time Intermediate Flow Estimation) model. The novel method was examined and compared with other recently published algorithms. All source codes are available at https://gitlab.com/malwinq/interpolation-of-images-for-slow-motion-videos
Diagnosing Transformers in Task-Oriented Semantic Parsing
Venue: arXiv cs.CL 2021
Year: 2021

Modern task-oriented semantic parsing approaches typically use seq2seq transformers to map textual utterances to semantic frames comprised of intents and slots. While these models are empirically strong, their specific strengths and weaknesses have largely remained unexplored. In this work, we study BART and XLM-R, two state-of-the-art parsers, across both monolingual and multilingual settings. Our experiments yield several key results: transformer-based parsers struggle not only with disambiguating intents/slots, but surprisingly also with producing syntactically-valid frames. Though pre-training imbues transformers with syntactic inductive biases, we find the ambiguity of copying utterance spans into frames often leads to tree invalidity, indicating span extraction is a major bottleneck for current parsers. However, as a silver lining, we show transformer-based parsers give sufficient indicators for whether a frame is likely to be correct or incorrect, making them easier to deploy in production settings.
Hierarchical Transformer Encoders for Vietnamese Spelling Correction
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we propose a Hierarchical Transformer model for Vietnamese spelling correction problem. The model consists of multiple Transformer encoders and utilizes both character-level and word-level to detect errors and make corrections. In addition, to facilitate future work in Vietnamese spelling correction tasks, we propose a realistic dataset collected from real-life texts for the problem. We compare our method with other methods and publicly available systems. The proposed method outperforms all of the contemporary methods in terms of recall, precision, and f1-score. A demo version is publicly available.
Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training
Venue: arXiv cs.CV 2021
Year: 2021

Well-annotated medical images are costly and sometimes even impossible to acquire, hindering landmark detection accuracy to some extent. Semi-supervised learning alleviates the reliance on large-scale annotated data by exploiting the unlabeled data to understand the population structure of anatomical landmarks. The global shape constraint is the inherent property of anatomical landmarks that provides valuable guidance for more consistent pseudo labelling of the unlabeled data, which is ignored in the previously semi-supervised methods. In this paper, we propose a model-agnostic shape-regulated self-training framework for semi-supervised landmark detection by fully considering the global shape constraint. Specifically, to ensure pseudo labels are reliable and consistent, a PCA-based shape model adjusts pseudo labels and eliminate abnormal ones. A novel Region Attention loss to make the network automatically focus on the structure consistent regions around pseudo labels. Extensive experiments show that our approach outperforms other semi-supervised methods and achieves notable improvement on three medical image datasets. Moreover, our framework is flexible and can be used as a plug-and-play module integrated into most supervised methods to improve performance further.
Measuring global properties of neural generative model outputs via generating mathematical objects
Venue: arXiv cs.LG 2021
Year: 2021

We train deep generative models on datasets of reflexive polytopes. This enables us to compare how well the models have picked up on various global properties of generated samples. Our datasets are complete in the sense that every single example, up to changes of coordinate, is included in the dataset. Using this property we also perform tests checking to what extent the models are merely memorizing the data. We also train models on the same dataset represented in two different ways, enabling us to measure which form is easiest to learn from. We use these experiments to show that deep generative models can learn to generate geometric objects with non-trivial global properties, and that the models learn some underlying properties of the objects rather than simply memorizing the data.
Fast zone-based algorithms for reachability in pushdown timed automata
Venue: arXiv cs.FL 2021
Year: 2021

Given the versatility of timed automata a huge body of work has evolved that considers extensions of timed automata. One extension that has received a lot of interest is timed automata with a, possibly unbounded, stack, also called the pushdown timed automata (PDTA) model. While different algorithms have been given for reachability in different variants of this model, most of these results are purely theoretical and do not give rise to efficient implementations. One main reason for this is that none of these algorithms (and the implementations that exist) use the so-called zone-based abstraction, but rely either on the region-abstraction or other approaches, which are significantly harder to implement. In this paper, we show that a naive extension of the zone based reachability algorithm for the control state reachability problem of timed automata is not sound in the presence of a stack. To understand this better we give an inductive rule based view of the zone reachability algorithm for timed automata. This alternate view allows us to analyze and adapt the rules to also work for pushdown timed automata. We obtain the first zone-based algorithm for PDTA which is terminating, sound and complete. We implement our algorithm in the tool TChecker and perform experiments to show its efficacy, thus leading the way for more practical approaches to the verification of pushdown timed systems.
Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving, by triggering a fallback behavior if a target accuracy cannot be guaranteed. We introduce a new uncertainty measure based on disagreeing predictions as measured by a dissimilarity function. We propose to estimate this dissimilarity by training a deep neural architecture in parallel to the task-specific network. It allows this observer to be dedicated to the uncertainty estimation, and let the task-specific network make predictions. We propose to use self-supervision to train the observer, which implies that our method does not require additional training data. We show experimentally that our proposed approach is much less computationally intensive at inference time than competing methods (e.g. MCDropout), while delivering better results on safety-oriented evaluation metrics on the CamVid dataset, especially in the case of glare artifacts.
Network Activities Recognition and Analysis Based on Supervised Machine Learning Classification Methods Using J48 and Naïve Bayes Algorithm
Venue: arXiv cs.CR 2021
Year: 2021

Network activities recognition has always been a significant component of intrusion detection. However, with the increasing network traffic flow and complexity of network behavior, it is becoming more and more difficult to identify the specific behavior quickly and accurately by user network monitoring software. It also requires the system security staff to pay close attention to the latest intrusion monitoring technology and methods. All of these greatly increase the difficulty and complexity of intrusion detection tasks. The application of machine learning methods based on supervised classification technology would help to liberate the network security staff from the heavy and boring tasks. A finetuned model would accurately recognize user behavior, which could provide persistent monitoring with a relative high accuracy and good adaptability. Finally, the results of network activities recognition by J48 and Naïve Bayes algorithms are introduced and evaluated.
The Feature-First Block Model
Venue: arXiv cs.LG 2021
Year: 2021

Labelled networks are an important class of data, naturally appearing in numerous applications in science and engineering. A typical inference goal is to determine how the vertex labels (or features) affect the network's structure. In this work, we introduce a new generative model, the feature-first block model (FFBM), that facilitates the use of rich queries on labelled networks. We develop a Bayesian framework and devise a two-level Markov chain Monte Carlo approach to efficiently sample from the relevant posterior distribution of the FFBM parameters. This allows us to infer if and how the observed vertex-features affect macro-structure. We apply the proposed methods to a variety of network data to extract the most important features along which the vertices are partitioned. The main advantages of the proposed approach are that the whole feature-space is used automatically and that features can be rank-ordered implicitly according to impact.
Incremental Learning of Probabilistic Movement Primitives (ProMPs) for Human-Robot Cooperation
Venue: arXiv cs.RO 2021
Year: 2021

For a successful deployment of physical Human-Robot Cooperation (pHRC), humans need to be able to teach robots new motor skills quickly. Probabilistic movement primitives (ProMPs) are a promising method to encode a robot's motor skills learned from human demonstrations in pHRC settings. However, most algorithms to learn ProMPs from human demonstrations operate in batch mode, which is not ideal in pHRC. In this paper we propose a new learning algorithm to learn ProMPs incrementally in pHRC settings. Our algorithm incorporates new demonstrations sequentially as they arrive, allowing humans to observe the robot's learning progress and incrementally shape the robot's motor skill. A built in forgetting factor allows for corrective demonstrations resulting from the human's learning curve or changes in task constraints. We compare the performance of our algorithm to existing batch ProMP algorithms on reference data generated from a pick-and-place task at our lab. Furthermore, we show in a proof of concept study on a Franka Emika Panda how the forgetting factor allows us to adopt changes in the task. The incremental learning algorithm presented in this paper has the potential to lead to a more intuitive learning progress and to establish a successful cooperation between human and robot faster than training in batch mode.
An unsupervised machine-learning checkpoint-restart algorithm using Gaussian mixtures for particle-in-cell simulations
Venue: arXiv cs.DC 2021
Year: 2021

We propose an unsupervised machine-learning checkpoint-restart (CR) lossy algorithm for particle-in-cell (PIC) algorithms using Gaussian mixtures (GM). The algorithm features a particle compression stage and a particle reconstruction stage, where a continuum particle distribution function is constructed and resampled, respectively. To guarantee fidelity of the CR process, we ensure the exact preservation of charge, momentum, and energy for both compression and reconstruction stages, everywhere on the mesh. We also ensure the preservation of Gauss' law after particle reconstruction. As a result, the GM CR algorithm is shown to provide a clean, conservative restart capability while potentially affording orders of magnitude savings in input/output requirements. We demonstrate the algorithm using a recently developed exactly energy- and charge-conserving PIC algorithm on physical problems of interest, with compression factors $\gtrsim75$ with no appreciable impact on the quality of the restarted dynamics.
DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future
Venue: arXiv cs.RO 2021
Year: 2021

Combination of machine learning (for generating machine intelligence), computer vision (for better environment perception), and robotic systems (for controlled environment interaction) motivates this work toward proposing a vision-based learning framework for intelligent robot control as the ultimate goal (vision-based learning robot). This work specifically introduces deep reinforcement learning as the the learning framework, a General-purpose framework for AI (AGI) meaning application-independent and platform-independent. In terms of robot control, this framework is proposing specifically a high-level control architecture independent of the low-level control, meaning these two required level of control can be developed separately from each other. In this aspect, the high-level control creates the required intelligence for the control of the platform using the recorded low-level controlling data from that same platform generated by a trainer. The recorded low-level controlling data is simply indicating the successful and failed experiences or sequences of experiments conducted by a trainer using the same robotic platform. The sequences of the recorded data are composed of observation data (input sensor), generated reward (feedback value) and action data (output controller). For experimental platform and experiments, vision sensors are used for perception of the environment, different kinematic controllers create the required motion commands based on the platform application, deep learning approaches generate the required intelligence, and finally reinforcement learning techniques incrementally improve the generated intelligence until the mission is accomplished by the robot.
Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agents that could defeat professional players in StarCraft~II. However, existing approaches to tackle full games have high computational costs, usually requiring the use of thousands of GPUs and CPUs for weeks. This paper has two main contributions to address this issue: 1) We introduce Gym-$μ$RTS (pronounced "gym-micro-RTS") as a fast-to-run RL environment for full-game RTS research and 2) we present a collection of techniques to scale DRL to play full-game $μ$RTS as well as ablation studies to demonstrate their empirical importance. Our best-trained bot can defeat every $μ$RTS bot we tested from the past $μ$RTS competitions when working in a single-map setting, resulting in a state-of-the-art DRL agent while only taking about 60 hours of training using a single machine (one GPU, three vCPU, 16GB RAM). See the blog post at https://wandb.ai/vwxyzjn/gym-microrts-paper/reports/Gym-RTS-Toward-Affordable-Deep-Reinforcement-Learning-Research-in-Real-Time-Strategy-Games--Vmlldzo2MDIzMTg and the source code at https://github.com/vwxyzjn/gym-microrts-paper
Distribution Matching for Machine Teaching
Venue: arXiv cs.LG 2021
Year: 2021

Machine teaching is an inverse problem of machine learning that aims at steering the student learner towards its target hypothesis, in which the teacher has already known the student's learning parameters. Previous studies on machine teaching focused on balancing the teaching risk and cost to find those best teaching examples deriving the student model. This optimization solver is in general ineffective when the student learner does not disclose any cue of the learning parameters. To supervise such a teaching scenario, this paper presents a distribution matching-based machine teaching strategy. Specifically, this strategy backwardly and iteratively performs the halving operation on the teaching cost to find a desired teaching set. Technically, our strategy can be expressed as a cost-controlled optimization process that finds the optimal teaching examples without further exploring in the parameter distribution of the student learner. Then, given any a limited teaching cost, the training examples will be closed-form. Theoretical analysis and experiment results demonstrate this strategy.
A Survey on Anomaly Detection for Technical Systems using LSTM Networks
Venue: arXiv cs.LG 2021
Year: 2021

Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure. As the causes of anomalies are often unknown due to complex system dynamics, efficient anomaly detection is necessary. Conventional detection approaches rely on statistical and time-invariant methods that fail to address the complex and dynamic nature of anomalies. With advances in artificial intelligence and increasing importance for anomaly detection and prevention in various domains, artificial neural network approaches enable the detection of more complex anomaly types while considering temporal and contextual characteristics. In this article, a survey on state-of-the-art anomaly detection using deep neural and especially long short-term memory networks is conducted. The investigated approaches are evaluated based on the application scenario, data and anomaly types as well as further metrics. To highlight the potential of upcoming anomaly detection techniques, graph-based and transfer learning approaches are also included in the survey, enabling the analysis of heterogeneous data as well as compensating for its shortage and improving the handling of dynamic processes.
Adapting Behaviors via Reactive Synthesis
Venue: arXiv cs.FL 2021
Year: 2021

In the \emph{Adapter Design Pattern}, a programmer implements a \emph{Target} interface by constructing an \emph{Adapter} that accesses an existing \emph{Adaptee} code. In this work, we present a reactive synthesis interpretation to the adapter design pattern, wherein an algorithm takes an \emph{Adaptee} and a \emph{Target} transducers, and the aim is to synthesize an \emph{Adapter} transducer that, when composed with the {\em Adaptee}, generates a behavior that is equivalent to the behavior of the {\em Target}. One use of such an algorithm is to synthesize controllers that achieve similar goals on different hardware platforms. While this problem can be solved with existing synthesis algorithms, current state-of-the-art tools fail to scale. To cope with the computational complexity of the problem, we introduce a special form of specification format, called {\em Separated GR($k$)}, which can be solved with a scalable synthesis algorithm but still allows for a large set of realistic specifications. We solve the realizability and the synthesis problems for Separated GR($k$), and show how to exploit the separated nature of our specification to construct better algorithms, in terms of time complexity, than known algorithms for GR($k$) synthesis. We then describe a tool, called SGR($k$), that we have implemented based on the above approach and show, by experimental evaluation, how our tool outperforms current state-of-the-art tools on various benchmarks and test-cases.
Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture
Venue: arXiv cs.LG 2021
Year: 2021

A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered feature-based representation employed in traditional machine learning based solutions. The seizure detection system utilises only convolutional layers in order to process the multichannel time domain signal and is designed to exploit the large amount of weakly labelled data in the training stage. The system performance is assessed on a large database of continuous EEG recordings of 834h in duration; this is further validated on a held-out publicly available dataset and compared with two baseline SVM based systems. The developed system achieves a 56% relative improvement with respect to a feature-based state-of-the art baseline, reaching an AUC of 98.5%; this also compares favourably both in terms of performance and run-time. The effect of varying architectural parameters is thoroughly studied. The performance improvement is achieved through novel architecture design which allows more efficient usage of available training data and end-to-end optimisation from the front-end feature extraction to the back-end classification. The proposed architecture opens new avenues for the application of deep learning to neonatal EEG, where the performance becomes a function of the amount of training data with less dependency on the availability of precise clinical labels.
A Sum-of-Ratios Multi-Dimensional-Knapsack Decomposition for DNN Resource Scheduling
Venue: arXiv cs.DC 2021
Year: 2021

In recent years, to sustain the resource-intensive computational needs for training deep neural networks (DNNs), it is widely accepted that exploiting the parallelism in large-scale computing clusters is critical for the efficient deployments of DNN training jobs. However, existing resource schedulers for traditional computing clusters are not well suited for DNN training, which results in unsatisfactory job completion time performance. The limitations of these resource scheduling schemes motivate us to propose a new computing cluster resource scheduling framework that is able to leverage the special layered structure of DNN jobs and significantly improve their job completion times. Our contributions in this paper are three-fold: i) We develop a new resource scheduling analytical model by considering DNN's layered structure, which enables us to analytically formulate the resource scheduling optimization problem for DNN training in computing clusters; ii) Based on the proposed performance analytical model, we then develop an efficient resource scheduling algorithm based on the widely adopted parameter-server architecture using a sum-of-ratios multi-dimensional-knapsack decomposition (SMD) method to offer strong performance guarantee; iii) We conduct extensive numerical experiments to demonstrate the effectiveness of the proposed schedule algorithm and its superior performance over the state of the art.