OpenRaiser/Intern-Atlas huggingface dataset:
DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data
Venue: arXiv cs.LG 2020
Year: 2020

Counterfactual prediction is about predicting outcome of the unobserved situation from the data. For example, given patient is on drug A, what would be the outcome if she switch to drug B. Most of existing works focus on modeling counterfactual outcome based on static data. However, many applications have time-varying confounding effects such as multiple treatments over time. How to model such time-varying effects from longitudinal observational data? How to model complex high-dimensional dependency in the data? To address these challenges, we propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) by incorporating recurrent neural networks into two-phase adjustments for the existence of time-varying confounding in modern longitudinal data. In phase I cohort reweighting we fit one network for emitting time dependent inverse probabilities of treatment, use them to generate a pseudo balanced cohort. In phase II outcome progression, we input the adjusted data to the subsequent predictive network for making counterfactual predictions. We evaluate DeepRite on both synthetic data and a real data collected from sepsis patients in the intensive care units. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data that can be better aligned with the standard guidelines for management of sepsis thanks to its applicability to create balanced cohorts.
A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models
Venue: arXiv cs.CL 2020
Year: 2020

Word representation has always been an important research area in the history of natural language processing (NLP). Understanding such complex text data is imperative, given that it is rich in information and can be used widely across various applications. In this survey, we explore different word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS). We describe a variety of text representation methods, and model designs have blossomed in the context of NLP, including SOTA LMs. These models can transform large volumes of text into effective vector representations capturing the same semantic information. Further, such representations can be utilized by various machine learning (ML) algorithms for a variety of NLP related tasks. In the end, this survey briefly discusses the commonly used ML and DL based classifiers, evaluation metrics and the applications of these word embeddings in different NLP tasks.
Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection
Venue: arXiv cs.LG 2020
Year: 2020

The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness. This paper aims to introduce a novel method to detect abnormalities based on internal cross-correlation parameters of the vehicle. Before the implementation of Machine Learning, the detection of abnormalities were manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. Nowadays, it is possible to train a Dynamic Bayesian Network (DBN) model to automatically evaluate and detect when the vehicle is potentially misbehaving. In this paper, different scenarios have been set in order to train and test a switching DBN for Perimeter Monitoring Task using a semantic segmentation for the DBN model and Hellinger Distance metric for abnormality measurements.
How to Not Get Caught When You Launder Money on Blockchain?
Venue: arXiv cs.CR 2020
Year: 2020

The number of blockchain users has tremendously grown in recent years. As an unintended consequence, e-crime transactions on blockchains has been on the rise. Consequently, public blockchains have become a hotbed of research for developing AI tools to detect and trace users and transactions that are related to e-crime. We argue that following a few select strategies can make money laundering on blockchain virtually undetectable with most of the existing tools and algorithms. As a result, the effective combating of e-crime activities involving cryptocurrencies requires the development of novel analytic methodology in AI.
Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation
Venue: arXiv cs.CL 2020
Year: 2020

Utterance classification performance in low-resource dialogue systems is constrained by an inevitably high degree of data imbalance in class labels. We present a new end-to-end pairwise learning framework that is designed specifically to tackle this phenomenon by inducing a few-shot classification capability in the utterance representations and augmenting data through an interpolation of utterance representations. Our approach is a general purpose training methodology, agnostic to the neural architecture used for encoding utterances. We show significant improvements in macro-F1 score over standard cross-entropy training for three different neural architectures, demonstrating improvements on a Virtual Patient dialogue dataset as well as a low-resourced emulation of the Switchboard dialogue act classification dataset.
Slicing a single wireless collision channel among throughput- and timeliness-sensitive services
Venue: arXiv cs.IT 2020
Year: 2020

The fifth generation (5G) wireless system has a platform-driven approach, aiming to support heterogeneous connections with very diverse requirements. The shared wireless resources should be sliced in a way that each user perceives that its requirement has been met. Heterogeneity challenges the traditional notion of resource efficiency, as the resource usage has cater for, e.g. rate maximization for one user and timeliness requirement for another user. This paper treats a model for radio access network (RAN) uplink, where a throughput-demanding broadband user shares wireless resources with an intermittently active user that wants to optimize the timeliness, expressed in terms of latency-reliability or Age of Information (AoI). We evaluate the trade-offs between throughput and timeliness for Orthogonal Multiple Access (OMA) as well as Non-Orthogonal Multiple Access (NOMA) with successive interference cancellation (SIC). We observe that NOMA with SIC, in a conservative scenario with destructive collisions, is just slightly inferior to that of OMA, which indicates that it may offer significant benefits in practical deployments where the capture effect is frequently encountered. On the other hand, finding the optimal configuration of NOMA with SIC depends on the activity pattern of the intermittent user, to which OMA is insensitive.
A Study on Efficiency in Continual Learning Inspired by Human Learning
Venue: arXiv cs.LG 2020
Year: 2020

Humans are efficient continual learning systems; we continually learn new skills from birth with finite cells and resources. Our learning is highly optimized both in terms of capacity and time while not suffering from catastrophic forgetting. In this work we study the efficiency of continual learning systems, taking inspiration from human learning. In particular, inspired by the mechanisms of sleep, we evaluate popular pruning-based continual learning algorithms, using PackNet as a case study. First, we identify that weight freezing, which is used in continual learning without biological justification, can result in over $2\times$ as many weights being used for a given level of performance. Secondly, we note the similarity in human day and night time behaviors to the training and pruning phases respectively of PackNet. We study a setting where the pruning phase is given a time budget, and identify connections between iterative pruning and multiple sleep cycles in humans. We show there exists an optimal choice of iteration v.s. epochs given different tasks.
A Visuospatial Dataset for Naturalistic Verb Learning
Venue: arXiv cs.CL 2020
Year: 2020

We introduce a new dataset for training and evaluating grounded language models. Our data is collected within a virtual reality environment and is designed to emulate the quality of language data to which a pre-verbal child is likely to have access: That is, naturalistic, spontaneous speech paired with richly grounded visuospatial context. We use the collected data to compare several distributional semantics models for verb learning. We evaluate neural models based on 2D (pixel) features as well as feature-engineered models based on 3D (symbolic, spatial) features, and show that neither modeling approach achieves satisfactory performance. Our results are consistent with evidence from child language acquisition that emphasizes the difficulty of learning verbs from naive distributional data. We discuss avenues for future work on cognitively-inspired grounded language learning, and release our corpus with the intent of facilitating research on the topic.
Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading
Venue: arXiv cs.CV 2020
Year: 2020

Diabetes is one of the most common disease in individuals. \textit{Diabetic retinopathy} (DR) is a complication of diabetes, which could lead to blindness. Automatic DR grading based on retinal images provides a great diagnostic and prognostic value for treatment planning. However, the subtle differences among severity levels make it difficult to capture important features using conventional methods. To alleviate the problems, a new deep learning architecture for robust DR grading is proposed, referred to as SEA-Net, in which, spatial attention and channel attention are alternatively carried out and boosted with each other, improving the classification performance. In addition, a hybrid loss function is proposed to further maximize the inter-class distance and reduce the intra-class variability. Experimental results have shown the effectiveness of the proposed architecture.
Collaborative Method for Incremental Learning on Classification and Generation
Venue: arXiv cs.CV 2020
Year: 2020

Although well-trained deep neural networks have shown remarkable performance on numerous tasks, they rapidly forget what they have learned as soon as they begin to learn with additional data with the previous data stop being provided. In this paper, we introduce a novel algorithm, Incremental Class Learning with Attribute Sharing (ICLAS), for incremental class learning with deep neural networks. As one of its component, we also introduce a generative model, incGAN, which can generate images with increased variety compared with the training data. Under challenging environment of data deficiency, ICLAS incrementally trains classification and the generation networks. Since ICLAS trains both networks, our algorithm can perform multiple times of incremental class learning. The experiments on MNIST dataset demonstrate the advantages of our algorithm.
Prediction-Based Power Oversubscription in Cloud Platforms
Venue: arXiv cs.DC 2020
Year: 2020

Datacenter designers rely on conservative estimates of IT equipment power draw to provision resources. This leaves resources underutilized and requires more datacenters to be built. Prior work has used power capping to shave the rare power peaks and add more servers to the datacenter, thereby oversubscribing its resources and lowering capital costs. This works well when the workloads and their server placements are known. Unfortunately, these factors are unknown in public clouds, forcing providers to limit the oversubscription so that performance is never impacted. In this paper, we argue that providers can use predictions of workload performance criticality and virtual machine (VM) resource utilization to increase oversubscription. This poses many challenges, such as identifying the performance-critical workloads from black-box VMs, creating support for criticality-aware power management, and increasing oversubscription while limiting the impact of capping. We address these challenges for the hardware and software infrastructures of Microsoft Azure. The results show that we enable a 2x increase in oversubscription with minimum impact to critical workloads.
Conversation Graph: Data Augmentation, Training and Evaluation for Non-Deterministic Dialogue Management
Venue: arXiv cs.CL 2020
Year: 2020

Task-oriented dialogue systems typically rely on large amounts of high-quality training data or require complex handcrafted rules. However, existing datasets are often limited in size considering the complexity of the dialogues. Additionally, conventional training signal inference is not suitable for non-deterministic agent behaviour, i.e. considering multiple actions as valid in identical dialogue states. We propose the Conversation Graph (ConvGraph), a graph-based representation of dialogues that can be exploited for data augmentation, multi-reference training and evaluation of non-deterministic agents. ConvGraph generates novel dialogue paths to augment data volume and diversity. Intrinsic and extrinsic evaluation across three datasets shows that data augmentation and/or multi-reference training with ConvGraph can improve dialogue success rates by up to 6.4%.
Group-Harmonic and Group-Closeness Maximization -- Approximation and Engineering
Venue: arXiv cs.DS 2020
Year: 2020

Centrality measures characterize important nodes in networks. Efficiently computing such nodes has received a lot of attention. When considering the generalization of computing central groups of nodes, challenging optimization problems occur. In this work, we study two such problems, group-harmonic maximization and group-closeness maximization both from a theoretical and from an algorithm engineering perspective. On the theoretical side, we obtain the following results. For group-harmonic maximization, unless $P=NP$, there is no polynomial-time algorithm that achieves an approximation factor better than $1-1/e$ (directed) and $1-1/(4e)$ (undirected), even for unweighted graphs. On the positive side, we show that a greedy algorithm achieves an approximation factor of $λ(1-2/e)$ (directed) and $λ(1-1/e)/2$ (undirected), where $λ$ is the ratio of minimal and maximal edge weights. For group-closeness maximization, the undirected case is $NP$-hard to be approximated to within a factor better than $1-1/(e+1)$ and a constant approximation factor is achieved by a local-search algorithm. For the directed case, however, we show that, for any $ε<1/2$, the problem is $NP$-hard to be approximated within a factor of $4|V|^{-ε}$. From the algorithm engineering perspective, we provide efficient implementations of the above greedy and local search algorithms. In our experimental study we show that, on small instances where an optimum solution can be computed in reasonable time, the quality of both the greedy and the local search algorithms come very close to the optimum. On larger instances, our local search algorithms yield results with superior quality compared to existing greedy and local search solutions, at the cost of additional running time. We thus advocate local search for scenarios where solution quality is of highest concern.
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection
Venue: arXiv cs.LG 2020
Year: 2020

The evolution of Intelligent Transportation Systems in recent times necessitates the development of self-awareness in agents. Before the intensive use of Machine Learning, the detection of abnormalities was manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. This paper aims to introduce a novel method to develop self-awareness in autonomous vehicles that mainly focuses on detecting abnormal situations around the considered agents. Multi-sensory time-series data from the vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN) models used for future state prediction and the detection of dynamic abnormalities. Moreover, an initial level collective awareness model that can perform joint anomaly detection in co-operative tasks is proposed. The GNG algorithm learns the DBN models' discrete node variables; probabilistic transition links connect the node variables. A Markov Jump Particle Filter (MJPF) is applied to predict future states and detect when the vehicle is potentially misbehaving using learned DBNs as filter parameters. In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models.
Multilayer Clustered Graph Learning
Venue: arXiv cs.LG 2020
Year: 2020

Multilayer graphs are appealing mathematical tools for modeling multiple types of relationship in the data. In this paper, we aim at analyzing multilayer graphs by properly combining the information provided by individual layers, while preserving the specific structure that allows us to eventually identify communities or clusters that are crucial in the analysis of graph data. To do so, we learn a clustered representative graph by solving an optimization problem that involves a data fidelity term to the observed layers, and a regularization pushing for a sparse and community-aware graph. We use the contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph. The regularization is based on a measure of graph sparsification called "effective resistance", coupled with a penalization of the first few eigenvalues of the representative graph Laplacian matrix to favor the formation of communities. The proposed optimization problem is nonconvex but fully differentiable, and thus can be solved via the projected gradient method. Experiments show that our method leads to a significant improvement w.r.t. state-of-the-art multilayer graph learning algorithms for solving clustering problems.
"What, not how": Solving an under-actuated insertion task from scratch
Venue: arXiv cs.RO 2020
Year: 2020

Robot manipulation requires a complex set of skills that need to be carefully combined and coordinated to solve a task. Yet, most ReinforcementLearning (RL) approaches in robotics study tasks which actually consist only of a single manipulation skill, such as grasping an object or inserting a pre-grasped object. As a result the skill ('how' to solve the task) but not the actual goal of a complete manipulation ('what' to solve) is specified. In contrast, we study a complex manipulation goal that requires an agent to learn and combine diverse manipulation skills. We propose a challenging, highly under-actuated peg-in-hole task with a free, rotational asymmetrical peg, requiring a broad range of manipulation skills. While correct peg (re-)orientation is a requirement for successful insertion, there is no reward associated with it. Hence an agent needs to understand this pre-condition and learn the skill to fulfil it. The final insertion reward is sparse, allowing freedom in the solution and leading to complex emerging behaviour not envisioned during the task design. We tackle the problem in a multi-task RL framework using Scheduled Auxiliary Control (SAC-X) combined with Regularized Hierarchical Policy Optimization (RHPO) which successfully solves the task in simulation and from scratch on a single robot where data is severely limited.
Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors
Venue: arXiv cs.CV 2020
Year: 2020

Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 $dB$. The algorithm filters background activity noise and extracts objects using robust Hough transform technique. The depth of each detected object is computed by triangulating 2D features extracted utilising LC-Harris. Finally, asynchronous adaptive collision avoidance (AACA) algorithm is applied for effective avoidance. Qualitative evaluation is compared using event-camera and traditional camera.
A brief overview of swarm intelligence-based algorithms for numerical association rule mining
Venue: arXiv cs.NE 2020
Year: 2020

Numerical Association Rule Mining is a popular variant of Association Rule Mining, where numerical attributes are handled without discretization. This means that the algorithms for dealing with this problem can operate directly, not only with categorical, but also with numerical attributes. Until recently, a big portion of these algorithms were based on a stochastic nature-inspired population-based paradigm. As a result, evolutionary and swarm intelligence-based algorithms showed big efficiency for dealing with the problem. In line with this, the main mission of this chapter is to make a historical overview of swarm intelligence-based algorithms for Numerical Association Rule Mining, as well as to present the main features of these algorithms for the observed problem. A taxonomy of the algorithms was proposed on the basis of the applied features found in this overview. Challenges, waiting in the future, finish this paper.
Poster: Benchmarking Financial Data Feed Systems
Venue: arXiv cs.PF 2020
Year: 2020

Data-driven solutions for the investment industry require event-based backend systems to process high-volume financial data feeds with low latency, high throughput, and guaranteed delivery modes. At vwd we process an average of 18 billion incoming event notifications from 500+ data sources for 30 million symbols per day and peak rates of 1+ million notifications per second using custom-built platforms that keep audit logs of every event. We currently assess modern open source event-processing platforms such as Kafka, NATS, Redis, Flink or Storm for the use in our ticker plant to reduce the maintenance effort for cross-cutting concerns and leverage hybrid deployment models. For comparability and repeatability we benchmark candidates with a standardized workload we derived from our real data feeds. We have enhanced an existing light-weight open source benchmarking tool in its processing, logging, and reporting capabilities to cope with our workloads. The resulting tool wrench can simulate workloads or replay snapshots in volume and dynamics like those we process in our ticker plant. We provide the tool as open source. As part of ongoing work we contribute details on (a) our workload and requirements for benchmarking candidate platforms for financial feed processing; (b) the current state of the tool wrench.
Systematic literature review protocol Identification and classification of feature modeling errors
Venue: arXiv cs.SE 2020
Year: 2020

Context: The importance of feature modeling languages for software product lines and the planning stage for a systematic literature review. Objective: A protocol for carrying out a systematic literature review about the evidence for identifying and classifying the errors in feature modeling languages. Method: The definition of a protocol to conduct a systematic literature review according to the guidelines of B. Kitchenham. Results: A validated protocol to conduct a systematic literature review. Conclusions: A proposal for the protocol definition of a systematic literature review about the identification and classification of errors in feature modeling was built. Initial results show that the effects and results for solving these errors should be carried out.