OpenRaiser/Intern-Atlas huggingface dataset:
Hierarchical Constrained Stochastic Shortest Path Planning via Cost Budget Allocation
Venue: arXiv cs.AI 2022
Year: 2022

Stochastic sequential decision making often requires hierarchical structure in the problem where each high-level action should be further planned with primitive states and actions. In addition, many real-world applications require a plan that satisfies constraints on the secondary costs such as risk measure or fuel consumption. In this paper, we propose a hierarchical constrained stochastic shortest path problem (HC-SSP) that meets those two crucial requirements in a single framework. Although HC-SSP provides a useful framework to model such planning requirements in many real-world applications, the resulting problem has high complexity and makes it difficult to find an optimal solution fast which prevents user from applying it to real-time and risk-sensitive applications. To address this problem, we present an algorithm that iteratively allocates cost budget to lower level planning problems based on branch-and-bound scheme to find a feasible solution fast and incrementally update the incumbent solution. We demonstrate the proposed algorithm in an evacuation scenario and prove the advantage over a state-of-the-art mathematical programming based approach.
What is Proxy Discrimination?
Venue: arXiv cs.LG 2022
Year: 2022

The near universal condemnation of proxy discrimination hides a disagreement over what it is. This work surveys various notions of proxy and proxy discrimination found in prior work and represents them in a common framework. These notions variously turn on statistical dependencies, causal effects, and intentions. It discusses the limitations and uses of each notation and of the concept as a whole.
The Meta-Turing Test
Venue: arXiv cs.AI 2022
Year: 2022

We propose an alternative to the Turing test that removes the inherent asymmetry between humans and machines in Turing's original imitation game. In this new test, both humans and machines judge each other. We argue that this makes the test more robust against simple deceptions. We also propose a small number of refinements to improve further the test. These refinements could be applied also to Turing's original imitation game.
Hierarchical Collaborative Hyper-parameter Tuning
Venue: arXiv cs.LG 2022
Year: 2022

Hyper-parameter Tuning is among the most critical stages in building machine learning solutions. This paper demonstrates how multi-agent systems can be utilized to develop a distributed technique for determining near-optimal values for any arbitrary set of hyper-parameters in a machine learning model. The proposed method employs a distributedly formed hierarchical agent-based architecture for the cooperative searching procedure of tuning hyper-parameter values. The presented generic model is used to develop a guided randomized agent-based tuning technique, and its behavior is investigated in both machine learning and global function optimization applications. According the empirical results, the proposed model outperformed both of its underlying randomized tuning strategies in terms of classification error and function evaluations, notably in higher number of dimensions.
Unsupervised machine learning for physical concepts
Venue: arXiv cs.LG 2022
Year: 2022

In recent years, machine learning methods have been used to assist scientists in scientific research. Human scientific theories are based on a series of concepts. How machine learns the concepts from experimental data will be an important first step. We propose a hybrid method to extract interpretable physical concepts through unsupervised machine learning. This method consists of two stages. At first, we need to find the Betti numbers of experimental data. Secondly, given the Betti numbers, we use a variational autoencoder network to extract meaningful physical variables. We test our protocol on toy models and show how it works.
Subspace Learning Machine (SLM): Methodology and Performance
Venue: arXiv cs.LG 2022
Year: 2022

Inspired by the feedforward multilayer perceptron (FF-MLP), decision tree (DT) and extreme learning machine (ELM), a new classification model, called the subspace learning machine (SLM), is proposed in this work. SLM first identifies a discriminant subspace, $S^0$, by examining the discriminant power of each input feature. Then, it uses probabilistic projections of features in $S^0$ to yield 1D subspaces and finds the optimal partition for each of them. This is equivalent to partitioning $S^0$ with hyperplanes. A criterion is developed to choose the best $q$ partitions that yield $2q$ partitioned subspaces among them. We assign $S^0$ to the root node of a decision tree and the intersections of $2q$ subspaces to its child nodes of depth one. The partitioning process is recursively applied at each child node to build an SLM tree. When the samples at a child node are sufficiently pure, the partitioning process stops and each leaf node makes a prediction. The idea can be generalized to regression, leading to the subspace learning regressor (SLR). Furthermore, ensembles of SLM/SLR trees can yield a stronger predictor. Extensive experiments are conducted for performance benchmarking among SLM/SLR trees, ensembles and classical classifiers/regressors.
Statistical Characterization of Closed-Loop Latency at the Mobile Edge
Venue: arXiv cs.NI 2022
Year: 2022

The stringent timing and reliability requirements in mission-critical applications require a detailed statistical characterization of the latency. Teleoperation is a representative use case, in which a human operator (HO) remotely controls a robot by exchanging command and feedback signals. We present a framework to analyze the latency of a closed-loop teleoperation system consisting of three entities: HO, robot located in remote environment, and a Base Station (BS) with Mobile edge Computing (MEC) capabilities. A model of each component of the system is used to analyze the closed-loop latency and decide upon the optimal compression strategy. The closed-form expression of the distribution of the closed-loop latency is difficult to estimate, such that suitable upper and lower bounds are obtained. We formulate a non-convex optimization problem to minimize the closed-loop latency. Using the obtained upper and lower bound on the closed-loop latency, a computationally efficient procedure to optimize the closed-loop latency is presented. The simulation results reveal that compression of sensing data is not always beneficial, while system design based on average performance leads to under-provisioning and may cause performance degradation. The applicability of the proposed analysis is much wider than teleoperation, for systems whose latency budget consists of many components.
Collaborative Multi-Radars Tracking by Distributed Auctions
Venue: arXiv cs.MA 2022
Year: 2022

In this paper, we present an algorithm which lies in the domain of task allocation for a set of static autonomous radars with rotating antennas. It allows a set of radars to allocate in a fully decentralized way a set of active tracking tasks according to their location, considering that a target can be tracked by several radars, in order to improve accuracy with which the target is tracked. The allocation algorithm proceeds through a collaborative and fully decentralized auction protocol, using a collaborative auction protocol (Consensus Based Bundle Auction algorithm). Our algorithm is based on a double use of our allocation protocol among the radars. The latter begin by allocating targets, then launch a second round of allocation if theyhave resources left, in order to improve accuracy on targets already tracked. Our algorithm is also able to adapt to dynamism, i.e. to take into account the fact that the targets are moving and that the radar(s) most suitable for Tracking them changes as the mission progresses. To do this, the algorithm is restarted on a regular basis, to ensure that a bid made by a radar can decrease when the target moves away from it. Since our algorithm is based on collaborative auctions, it does not plan the following rounds, assuming that the targets are not predictable enough for this. Our algorithm is however based on radars capable of anticipating the positions of short-term targets, thanks to a Kalman filter. The algorithm will be illustrated based on a multi-radar tracking scenario where the radars, autonomous, must follow a set of targets in order to reduce the position uncertainty of the targets. Standby aspects will not be considered in this scenario. It is assumed that the radars can pick up targets in active pursuit, with an area ofuncertainty corresponding to their distance.
Human-Robot Interface to Operate Robotic Systems via Muscle Synergy-Based Kinodynamic Information Transfer
Venue: arXiv cs.RO 2022
Year: 2022

When a human performs a given specific task, it has been known that the central nervous system controls modularized muscle group, which is called muscle synergy. For human-robot interface design problem, therefore, the muscle synergy can be utilized to reduce the dimensionality of control signal as well as the complexity of classifying human posture and motion. In this paper, we propose an approach to design a human-robot interface which enables a human operator to transfer a kinodynamic control command to robotic systems. A key feature of the proposed approach is that the muscle synergy and corresponding activation curve are employed to calculate a force generated by a tool at the robot end effector. A test bed for experiments consisted of two armband type surface electromyography sensors, an RGB-d camera, and a Kinova Gen2 robotic manipulator to verify the proposed approach. The result showed that both force and position commands could be successfully transferred to the robotic manipulator via our muscle synergy-based kinodynamic interface.
AutoLC: Search Lightweight and Top-Performing Architecture for Remote Sensing Image Land-Cover Classification
Venue: arXiv cs.CV 2022
Year: 2022

Land-cover classification has long been a hot and difficult challenge in remote sensing community. With massive High-resolution Remote Sensing (HRS) images available, manually and automatically designed Convolutional Neural Networks (CNNs) have already shown their great latent capacity on HRS land-cover classification in recent years. Especially, the former can achieve better performance while the latter is able to generate lightweight architecture. Unfortunately, they both have shortcomings. On the one hand, because manual CNNs are almost proposed for natural image processing, it becomes very redundant and inefficient to process HRS images. On the other hand, nascent Neural Architecture Search (NAS) techniques for dense prediction tasks are mainly based on encoder-decoder architecture, and just focus on the automatic design of the encoder, which makes it still difficult to recover the refined mapping when confronting complicated HRS scenes. To overcome their defects and tackle the HRS land-cover classification problems better, we propose AutoLC which combines the advantages of two methods. First, we devise a hierarchical search space and gain the lightweight encoder underlying gradient-based search strategy. Second, we meticulously design a lightweight but top-performing decoder that is adaptive to the searched encoder of itself. Finally, experimental results on the LoveDA land-cover dataset demonstrate that our AutoLC method outperforms the state-of-art manual and automatic methods with much less computational consumption.
Machine Learning to Support Triage of Children at Risk for Epileptic Seizures in the Pediatric Intensive Care Unit
Venue: arXiv cs.LG 2022
Year: 2022

Objective: Epileptic seizures are relatively common in critically-ill children admitted to the pediatric intensive care unit (PICU) and thus serve as an important target for identification and treatment. Most of these seizures have no discernible clinical manifestation but still have a significant impact on morbidity and mortality. Children that are deemed at risk for seizures within the PICU are monitored using continuous-electroencephalogram (cEEG). cEEG monitoring cost is considerable and as the number of available machines is always limited, clinicians need to resort to triaging patients according to perceived risk in order to allocate resources. This research aims to develop a computer aided tool to improve seizures risk assessment in critically-ill children, using an ubiquitously recorded signal in the PICU, namely the electrocardiogram (ECG). Approach: A novel data-driven model was developed at a patient-level approach, based on features extracted from the first hour of ECG recording and the clinical data of the patient. Main results: The most predictive features were the age of the patient, the brain injury as coma etiology and the QRS area. For patients without any prior clinical data, using one hour of ECG recording, the classification performance of the random forest classifier reached an area under the receiver operating characteristic curve (AUROC) score of 0.84. When combining ECG features with the patients clinical history, the AUROC reached 0.87. Significance: Taking a real clinical scenario, we estimated that our clinical decision support triage tool can improve the positive predictive value by more than 59% over the clinical standard.
Recurrent Encoder-Decoder Networks for Vessel Trajectory Prediction with Uncertainty Estimation
Venue: arXiv cs.CV 2022
Year: 2022

Recent deep learning methods for vessel trajectory prediction are able to learn complex maritime patterns from historical Automatic Identification System (AIS) data and accurately predict sequences of future vessel positions with a prediction horizon of several hours. However, in maritime surveillance applications, reliably quantifying the prediction uncertainty can be as important as obtaining high accuracy. This paper extends deep learning frameworks for trajectory prediction tasks by exploring how recurrent encoder-decoder neural networks can be tasked not only to predict but also to yield a corresponding prediction uncertainty via Bayesian modeling of epistemic and aleatoric uncertainties. We compare the prediction performance of two different models based on labeled or unlabeled input data to highlight how uncertainty quantification and accuracy can be improved by using, if available, additional information on the intention of the ship (e.g., its planned destination).
RustSEG -- Automated segmentation of corrosion using deep learning
Venue: arXiv cs.CV 2022
Year: 2022

The inspection of infrastructure for corrosion remains a task that is typically performed manually by qualified engineers or inspectors. This task of inspection is laborious, slow, and often requires complex access. Recently, deep learning based algorithms have revealed promise and performance in the automatic detection of corrosion. However, to date, research regarding the segmentation of images for automated corrosion detection has been limited, due to the lack of availability of per-pixel labelled data sets which are required for model training. Herein, a novel deep learning approach (termed RustSEG) is presented, that can accurately segment images for automated corrosion detection, without the requirement of per-pixel labelled data sets for training. The RustSEG method will first, using deep learning techniques, determine if corrosion is present in an image (i.e. a classification task), and then if corrosion is present, the model will examine what pixels in the original image contributed to that classification decision. Finally, the method can refine its predictions into a pixel-level segmentation mask. In ideal cases, the method is able to generate precise masks of corrosion in images, demonstrating that the automated segmentation of corrosion without per-pixel training data is possible, addressing a significant hurdle in automated infrastructure inspection.
TextMatcher: Cross-Attentional Neural Network to Compare Image and Text
Venue: arXiv cs.CV 2022
Year: 2022

We study a novel multimodal-learning problem, which we call text matching: given an image containing a single-line text and a candidate text transcription, the goal is to assess whether the text represented in the image corresponds to the candidate text. We devise the first machine-learning model specifically designed for this problem. The proposed model, termed TextMatcher, compares the two inputs by applying a cross-attention mechanism over the embedding representations of image and text, and it is trained in an end-to-end fashion. We extensively evaluate the empirical performance of TextMatcher on the popular IAM dataset. Results attest that, compared to a baseline and existing models designed for related problems, TextMatcher achieves higher performance on a variety of configurations, while at the same time running faster at inference time. We also showcase TextMatcher in a real-world application scenario concerning the automatic processing of bank cheques.
Comparison of Brick and Project Haystack to Support Smart Building Applications
Venue: arXiv cs.CY 2022
Year: 2022

Enabling buildings with Smart Building applications will help to achieve the ongoing efficient commissioning of buildings, ultimately attaining peak performance in energy use and improved occupant health and comfort, at minimum cost. For these technologies to be scalable data ontology must be adopted to semantically represent data generated by building mechanical systems, acting as conduit for connection to Smart Building applications. The viability of Brick and Project Haystack ontologies, as found by industry and academia, prompted a quantitative comparison of completeness and expressiveness using a case study with an industry ontology as the baseline. Additionally, a qualitative comparison was completed using key ontology qualities outlined in literature. A recommendation of Brick is made based on results. Brick achieved higher assessment values in completeness and expressiveness achieving 59% and 100% respectively, as compared to Haystacks 43% and 96%. Additionally, Brick exhibited five of six desirable qualities, where Haystack exhibited only three. The recommendation of the appropriate ontology forms the basis for longer-term Smart Building application development, which will support innovative approaches to sustainability in building operations across scale, as well as next-generation building controls and automation strategies.
High-Speed Imaging Receiver Design for 6G Optical Wireless Communications: A Rate-FOV Trade-Off
Venue: arXiv cs.IT 2022
Year: 2022

The design of a compact high-speed and wide field of view (FOV) receiver is challenging due to the presence of two well-known trade-offs. The first one is the area-bandwidth trade-off of photodetectors (PDs) and the second one is the gain-FOV trade-off due to the use of optics. The combined effects of these two trade-offs imply that the achievable data rate of an imaging optical receiver is limited by its FOV, i.e., a rate-FOV trade-off. To control the area-bandwidth trade-off, an array of small PDs can be used instead of a single PD. Moreover, in practice, a large-area lens is required to ensure sufficient power collection, which in turn limits the receiver FOV (i.e., gain-FOV trade-off). We propose an imaging receiver design in the form of an array of arrays. To achieve a reasonable receiver FOV, we use individual focusing lens for each PD array rather than a single collection lens for the whole receiver. The proposed array of arrays structure provides an effective method to control both gain-FOV trade-off (via an array of lenses) and area-bandwidth trade-off (via arrays of PDs). We first derive a tractable analytical model for the SNR of an array of PDs where the maximum ratio combining has been employed. Then, we extend the model for the proposed array of arrays structure and the accuracy of the analytical model is verified based on several Optic Studio-based simulations. Next, we formulate an optimization problem to maximize the achievable data rate of the imaging receiver subject to a minimum required FOV. The optimization problem is solved for two commonly used modulation techniques, namely, OOK and direct current biased optical orthogonal frequency division multiplexing with variable rate quadrature amplitude modulation. It is demonstrated that a data rate of ~ 24 Gbps with a FOV of 15 is achievable using OOK with a total receiver size of 2 cm by 2 cm.
Explainable Computational Creativity
Venue: arXiv cs.HC 2022
Year: 2022

Human collaboration with systems within the Computational Creativity (CC) field is often restricted to shallow interactions, where the creative processes, of systems and humans alike, are carried out in isolation, without any (or little) intervention from the user, and without any discussion about how the unfolding decisions are taking place. Fruitful co-creation requires a sustained ongoing interaction that can include discussions of ideas, comparisons to previous/other works, incremental improvements and revisions, etc. For these interactions, communication is an intrinsic factor. This means giving a voice to CC systems and enabling two-way communication channels between them and their users so that they can: explain their processes and decisions, support their ideas so that these are given serious consideration by their creative collaborators, and learn from these discussions to further improve their creative processes. For this, we propose a set of design principles for CC systems that aim at supporting greater co-creation and collaboration with their human collaborators.
Abductive Reasoning in Intuitionistic Propositional Logic via Theorem Synthesis
Venue: arXiv cs.LO 2022
Year: 2022

With help of a compact Prolog-based theorem prover for Intuitionistic Propositional Logic, we synthesize minimal assumptions under which a given formula formula becomes a theorem. After applying our synthesis algorithm to cover basic abductive reasoning mechanisms, we synthesize conjunctions of literals that mimic rows of truth tables in classical or intermediate logics and we abduce conditional hypotheses that turn the theorems of classical or intermediate logics into theorems in intuitionistic logic. One step further, we generalize our abductive reasoning mechanism to synthesize more expressive sequent premises using a minimal set of canonical formulas, to which arbitrary formulas in the calculus can be reduced while preserving their provability. Organized as a self-contained literate Prolog program, the paper supports interactive exploration of its content and ensures full replicability of our results.