OpenRaiser/Intern-Atlas huggingface dataset:
Morpho-evolution with learning using a controller archive as an inheritance mechanism
Venue: arXiv cs.RO 2021
Year: 2021

The joint optimisation of body-plan and control via evolutionary processes can be challenging in rich morphological spaces in which offspring can have body-plans that are very different from either of their parents. This causes a potential mismatch between the structure of an inherited controller and the new body. To address this, we propose a framework that combines an evolutionary algorithm to generate body-plans and a learning algorithm to optimise the parameters of a neural controller. The topology of this controller is created once the body-plan of each offspring body-plan is generated. The key novelty of the approach is to add an external archive for storing learned controllers that map to explicit `types' of robots (where this is defined with respect the features of the body-plan). By learning from a controller with an appropriate structure inherited from the archive, rather than from a randomly initialised one, we show that both the speed and magnitude of learning increases over time when compared to an approach that starts from scratch, using two tasks and three environments. The framework also provides new insights into the complex interactions between evolution and learning.
CropGym: a Reinforcement Learning Environment for Crop Management
Venue: arXiv cs.LG 2021
Year: 2021

Nitrogen fertilizers have a detrimental effect on the environment, which can be reduced by optimizing fertilizer management strategies. We implement an OpenAI Gym environment where a reinforcement learning agent can learn fertilization management policies using process-based crop growth models and identify policies with reduced environmental impact. In our environment, an agent trained with the Proximal Policy Optimization algorithm is more successful at reducing environmental impacts than the other baseline agents we present.
A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs
Venue: arXiv cs.LG 2021
Year: 2021

We propose a combination of a variational autoencoder and a transformer based model which fully utilises graph convolutional and graph pooling layers to operate directly on graphs. The transformer model implements a novel node encoding layer, replacing the position encoding typically used in transformers, to create a transformer with no position information that operates on graphs, encoding adjacent node properties into the edge generation process. The proposed model builds on graph generative work operating on graphs with edge features, creating a model that offers improved scalability with the number of nodes in a graph. In addition, our model is capable of learning a disentangled, interpretable latent space that represents graph properties through a mapping between latent variables and graph properties. In experiments we chose a benchmark task of molecular generation, given the importance of both generated node and edge features. Using the QM9 dataset we demonstrate that our model performs strongly across the task of generating valid, unique and novel molecules. Finally, we demonstrate that the model is interpretable by generating molecules controlled by molecular properties, and we then analyse and visualise the learned latent representation.
Speech Quality Assessment in Crowdsourcing: Comparison Category Rating Method
Venue: arXiv cs.MM 2021
Year: 2021

Traditionally, Quality of Experience (QoE) for a communication system is evaluated through a subjective test. The most common test method for speech QoE is the Absolute Category Rating (ACR), in which participants listen to a set of stimuli, processed by the underlying test conditions, and rate their perceived quality for each stimulus on a specific scale. The Comparison Category Rating (CCR) is another standard approach in which participants listen to both reference and processed stimuli and rate their quality compared to the other one. The CCR method is particularly suitable for systems that improve the quality of input speech. This paper evaluates an adaptation of the CCR test procedure for assessing speech quality in the crowdsourcing set-up. The CCR method was introduced in the ITU-T Rec. P.800 for laboratory-based experiments. We adapted the test for the crowdsourcing approach following the guidelines from ITU-T Rec. P.800 and P.808. We show that the results of the CCR procedure via crowdsourcing are highly reproducible. We also compared the CCR test results with widely used ACR test procedures obtained in the laboratory and crowdsourcing. Our results show that the CCR procedure in crowdsourcing is a reliable and valid test method.
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
Venue: arXiv cs.HC 2021
Year: 2021

Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models. A critical, final step of AutoML is human selection of a final model from dozens of candidates. In current AutoML systems, selection is supported only by performance metrics. Prior work has shown that in practice, people evaluate ML models based on additional criteria, such as the way a model makes predictions. Comparison may happen at multiple levels, from types of errors, to feature importance, to how the model makes predictions of specific instances. We developed \tool{} to support interactive model comparison for AutoML by integrating multiple Explainable AI (XAI) and visualization techniques. We conducted a user study in which we both evaluated the system and used it as a technology probe to understand how users perform model comparison in an AutoML system. We discuss design implications for utilizing XAI techniques for model comparison and supporting the unique needs of data scientists in comparing AutoML models.
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Venue: arXiv cs.LG 2021
Year: 2021

Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, such as readmission at 30 days, mainly due to data sparsity issue. Consequently, classical machine learning methods, especially those that embed domain knowledge in handcrafted features, are often on par with, and sometimes outperform, deep learning approaches. In this paper, we illustrate how the potential of deep learning can be achieved by blending domain knowledge within deep learning architectures to predict adverse events at hospital discharge, including readmissions. More specifically, we introduce a learning architecture that fuses a representation of patient data computed by a self-attention based recurrent neural network, with clinically relevant features. We conduct extensive experiments on a large claims dataset and show that the blended method outperforms the standard machine learning approaches.
Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

In this work, we propose Behavior-Guided Actor-Critic (BAC), an off-policy actor-critic deep RL algorithm. BAC mathematically formulates the behavior of the policy through autoencoders by providing an accurate estimation of how frequently each state-action pair was visited while taking into consideration state dynamics that play a crucial role in determining the trajectories produced by the policy. The agent is encouraged to change its behavior consistently towards less-visited state-action pairs while attaining good performance by maximizing the expected discounted sum of rewards, resulting in an efficient exploration of the environment and good exploitation of all high reward regions. One prominent aspect of our approach is that it is applicable to both stochastic and deterministic actors in contrast to maximum entropy deep reinforcement learning algorithms. Results show considerably better performances of BAC when compared to several cutting-edge learning algorithms.
Amplitude, Phase, and Quadrant (APQ) Modulation for Indoor Visible Light Communications
Venue: arXiv cs.IT 2021
Year: 2021

The main challenge in visible light communications (VLC) is the low modulation bandwidth of light-emitting diodes (LEDs). This forms a barrier towards achieving high data rates. Moreover, the implementation of high order modulation schemes is restricted by the requirements of intensity modulation (IM) and direct detection (DD), which demand the use of real unipolar signals. In this paper, we propose a novel amplitude, phase and quadrant (APQ) modulation scheme that fits into the IM/DD restrictions in VLC systems. The proposed scheme decomposes the complex and bipolar symbols of high order modulations into three different symbols that carry the amplitude, phase and quadrant information of the intended symbol. The constructed symbols are assigned different power levels and are transmitted simultaneously, i.e. exploiting the entire bandwidth and time resources. The receiving terminal performs successive interference cancellation to extract and decode the three different symbols, and then uses them to decide the intended complex bipolar symbol. We evaluate the performance of the proposed APQ scheme in terms of symbol-error-rate and achievable system throughput for different setup scenarios. The obtained results are compared with generalized spatial shift keying (GSSK). The presented results show that APQ offers a higher reliability compared to GSSK across the simulation area, while providing lower hardware complexity.
Inverse Reinforcement Learning: A Control Lyapunov Approach
Venue: arXiv cs.LG 2021
Year: 2021

Inferring the intent of an intelligent agent from demonstrations and subsequently predicting its behavior, is a critical task in many collaborative settings. A common approach to solve this problem is the framework of inverse reinforcement learning (IRL), where the observed agent, e.g., a human demonstrator, is assumed to behave according to an intrinsic cost function that reflects its intent and informs its control actions. In this work, we reformulate the IRL inference problem to learning control Lyapunov functions (CLF) from demonstrations by exploiting the inverse optimality property, which states that every CLF is also a meaningful value function. Moreover, the derived CLF formulation directly guarantees stability of inferred control policies. We show the flexibility of our proposed method by learning from goal-directed movement demonstrations in a continuous environment.
A Probabilistic Framework for Lexicon-based Keyword Spotting in Handwritten Text Images
Venue: arXiv cs.IR 2021
Year: 2021

Query by String Keyword Spotting (KWS) is here considered as a key technology for indexing large collections of handwritten text images to allow fast textual access to the contents of these collections. Under this perspective, a probabilistic framework for lexicon-based KWS in text images is presented. The presentation aims at providing a tutorial view that helps to understand the relations between classical statements of KWS and the relative challenges entailed by these statements. More specifically, the development of the proposed framework makes it self-evident that word recognition or classification implicitly or explicitly underlies any formulation of KWS. Moreover, it clearly suggests that the same statistical models and training methods successfully used for handwriting text recognition can advantageously be used also for KWS, even though KWS does not generally require or rely on any kind of previously produced image transcripts. These ideas are developed into a specific, probabilistically sound approach for segmentation-free, lexicon-based, query-by-string KWS. Experiments carried out using this approach are presented, which support the consistency and general interest of the proposed framework. Several datasets, traditionally used for KWS benchmarking are considered, with results significantly better than those previously published for these datasets. In addition, results on two new, larger handwritten text image datasets are reported, showing the great potential of the methods proposed in this paper for indexing and textual search in large collections of handwritten documents.
Context-Aware Task Handling in Resource-Constrained Robots with Virtualization
Venue: arXiv cs.RO 2021
Year: 2021

Intelligent mobile robots are critical in several scenarios. However, as their computational resources are limited, mobile robots struggle to handle several tasks concurrently and yet guaranteeing real-timeliness. To address this challenge and improve the real-timeliness of critical tasks under resource constraints, we propose a fast context-aware task handling technique. To effectively handling tasks in real-time, our proposed context-aware technique comprises of three main ingredients: (i) a dynamic time-sharing mechanism, coupled with (ii) an event-driven task scheduling using reactive programming paradigm to mindfully use the limited resources; and, (iii) a lightweight virtualized execution to easily integrate functionalities and their dependencies. We showcase our technique on a Raspberry-Pi-based robot with a variety of tasks such as Simultaneous localization and mapping (SLAM), sign detection, and speech recognition with a 42% speedup in total execution time compared to the common Linux scheduler.
Compiler Toolchains for Deep Learning Workloads on Embedded Platforms
Venue: arXiv cs.PL 2021
Year: 2021

As the usage of deep learning becomes increasingly popular in mobile and embedded solutions, it is necessary to convert the framework-specific network representations into executable code for these embedded platforms. This paper consists of two parts: The first section is made up of a survey and benchmark of the available open source deep learning compiler toolchains, which focus on the capabilities and performance of the individual solutions in regard to targeting embedded devices and microcontrollers that are combined with a dedicated accelerator in a heterogeneous fashion. The second part explores the implementation and evaluation of a compilation flow for such a heterogeneous device and reuses one of the existing toolchains to demonstrate the necessary steps for hardware developers that plan to build a software flow for their own hardware.
A Constructive Logic with Classical Proofs and Refutations (Extended Version)
Venue: arXiv cs.LO 2021
Year: 2021

We study a conservative extension of classical propositional logic distinguishing between four modes of statement: a proposition may be affirmed or denied, and it may be strong or classical. Proofs of strong propositions must be constructive in some sense, whereas proofs of classical propositions proceed by contradiction. The system, in natural deduction style, is shown to be sound and complete with respect to a Kripke semantics. We develop the system from the perspective of the propositions-as-types correspondence by deriving a term assignment system with confluent reduction. The proof of strong normalization relies on a translation to System F with Mendler-style recursion.
Automatically Enforcing Fresh and Consistent Inputs in Intermittent Systems
Venue: arXiv cs.PL 2021
Year: 2021

Intermittently powered energy-harvesting devices enable new applications in inaccessible environments. Program executions must be robust to unpredictable power failures, introducing new challenges in programmability and correctness. One hard problem is that input operations have implicit constraints, embedded in the behavior of continuously powered executions, on when input values can be collected and used. This paper aims to develop a formal framework for enforcing these constraints. We identify two key properties -- freshness (i.e., uses of inputs must satisfy the same time constraints as in continuous executions) and temporal consistency (i.e., the collection of a set of inputs must satisfy the same time constraints as in continuous executions). We formalize these properties and show that they can be enforced using atomic regions. We develop Ocelot, an LLVM-based analysis and transformation tool targeting Rust, to enforce these properties automatically. Ocelot provides the programmer with annotations to express these constraints and infers atomic region placement in a program to satisfy them. We then formalize Ocelot's design and show that Ocelot generates correct programs with little performance cost or code changes.
Avocado Buying Trends in the United States Using SAC
Venue: arXiv cs.DC 2021
Year: 2021

The purpose of our paper is to analyze the dataset from Hass Avocado Board (HAB). The data features historical data on avocado prices and sales volume in multiple cities, states, and regions of the United States, ranging from 2015 to 2020. The paper consists of a mapped and calculated statistical analysis of the data over an established period. Using the cloud visualization tool SAP Analytics Cloud (SAC) to import and clean the data, we will complete comprehensive investigations and employ class lecture information as needed. Then, we will present insights using visualization, and time-series analysis. Our research intends to reveal insights into consumer buying statistics, such as the most popular type of avocado, preferences of organic to conventional and seasonality trends. The research is relevant as health-conscious trends have become increasingly popular, and avocado purchases indicate this.
Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes
Venue: arXiv cs.DB 2021
Year: 2021

Complex data pipelines are increasingly common in diverse applications such as BI reporting and ML modeling. These pipelines often recur regularly (e.g., daily or weekly), as BI reports need to be refreshed, and ML models need to be retrained. However, it is widely reported that in complex production pipelines, upstream data feeds can change in unexpected ways, causing downstream applications to break silently that are expensive to resolve. Data validation has thus become an important topic, as evidenced by notable recent efforts from Google and Amazon, where the objective is to catch data quality issues early as they arise in the pipelines. Our experience on production data suggests, however, that on string-valued data, these existing approaches yield high false-positive rates and frequently require human intervention. In this work, we develop a corpus-driven approach to auto-validate \emph{machine-generated data} by inferring suitable data-validation "patterns" that accurately describe the underlying data domain, which minimizes false positives while maximizing data quality issues caught. Evaluations using production data from real data lakes suggest that Auto-Validate is substantially more effective than existing methods. Part of this technology ships as an Auto-Tag feature in Microsoft Azure Purview.
Unveiling personnel movement in a larger indoor area with a non-overlapping multi-camera system
Venue: arXiv cs.CV 2021
Year: 2021

Surveillance cameras are widely applied for indoor occupancy measurement and human movement perception, which benefit for building energy management and social security. To address the challenges of limited view angle of single camera as well as lacking of inter-camera collaboration, this study presents a non-overlapping multi-camera system to enlarge the surveillance area and devotes to retrieve the same person appeared from different camera views. The system is deployed in an office building and four-day videos are collected. By training a deep convolutional neural network, the proposed system first extracts the appearance feature embeddings of each personal image, which detected from different cameras, for similarity comparison. Then, a stochastic inter-camera transition matrix is associated with appearance feature for further improving the person re-identification ranking results. Finally, a noise-suppression explanation is given for analyzing the matching improvements. This paper expands the scope of indoor movement perception based on non-overlapping multiple cameras and improves the accuracy of pedestrian re-identification without introducing additional types of sensors.
A Low-Cost Attack against the hCaptcha System
Venue: arXiv cs.CR 2021
Year: 2021

CAPTCHAs are a defense mechanism to prevent malicious bot programs from abusing websites on the Internet. hCaptcha is a relatively new but emerging image CAPTCHA service. This paper presents an automated system that can break hCaptcha challenges with a high success rate. We evaluate our system against 270 hCaptcha challenges from live websites and demonstrate that it can solve them with 95.93% accuracy while taking only 18.76 seconds on average to crack a challenge. We run our attack from a docker instance with only 2GB memory (RAM), 3 CPUs, and no GPU devices, demonstrating that it requires minimal resources to launch a successful large-scale attack against the hCaptcha system.
Not All Attention Is All You Need
Venue: arXiv cs.CL 2021
Year: 2021

Beyond the success story of pre-trained language models (PrLMs) in recent natural language processing, they are susceptible to over-fitting due to unusual large model size. To this end, dropout serves as a therapy. However, existing methods like random-based, knowledge-based and search-based dropout are more general but less effective onto self-attention based models, which are broadly chosen as the fundamental architecture of PrLMs. In this paper, we propose a novel dropout method named AttendOut to let self-attention empowered PrLMs capable of more robust task-specific tuning. We demonstrate that state-of-the-art models with elaborate training design may achieve much stronger results. We verify the universality of our approach on extensive natural language processing tasks.
Pyramidal Reservoir Graph Neural Network
Venue: arXiv cs.LG 2021
Year: 2021

We propose a deep Graph Neural Network (GNN) model that alternates two types of layers. The first type is inspired by Reservoir Computing (RC) and generates new vertex features by iterating a non-linear map until it converges to a fixed point. The second type of layer implements graph pooling operations, that gradually reduce the support graph and the vertex features, and further improve the computational efficiency of the RC-based GNN. The architecture is, therefore, pyramidal. In the last layer, the features of the remaining vertices are combined into a single vector, which represents the graph embedding. Through a mathematical derivation introduced in this paper, we show formally how graph pooling can reduce the computational complexity of the model and speed-up the convergence of the dynamical updates of the vertex features. Our proposed approach to the design of RC-based GNNs offers an advantageous and principled trade-off between accuracy and complexity, which we extensively demonstrate in experiments on a large set of graph datasets.