OpenRaiser/Intern-Atlas huggingface dataset:
Semi-supervised Neural Chord Estimation Based on a Variational Autoencoder with Latent Chord Labels and Features
Venue: arXiv cs.SD 2020
Year: 2020

This paper describes a statistically-principled semi-supervised method of automatic chord estimation (ACE) that can make effective use of music signals regardless of the availability of chord annotations. The typical approach to ACE is to train a deep classification model (neural chord estimator) in a supervised manner by using only annotated music signals. In this discriminative approach, prior knowledge about chord label sequences (model output) has scarcely been taken into account. In contrast, we propose a unified generative and discriminative approach in the framework of amortized variational inference. More specifically, we formulate a deep generative model that represents the generative process of chroma vectors (observed variables) from discrete labels and continuous features (latent variables), which are assumed to follow a Markov model favoring self-transitions and a standard Gaussian distribution, respectively. Given chroma vectors as observed data, the posterior distributions of the latent labels and features are computed approximately by using deep classification and recognition models, respectively. These three models form a variational autoencoder and can be trained jointly in a semi-supervised manner. The experimental results show that the regularization of the classification model based on the Markov prior of chord labels and the generative model of chroma vectors improved the performance of ACE even under the supervised condition. The semi-supervised learning using additional non-annotated data can further improve the performance.
Distilling neural networks into skipgram-level decision lists
Venue: arXiv cs.CL 2020
Year: 2020

Several previous studies on explanation for recurrent neural networks focus on approaches that find the most important input segments for a network as its explanations. In that case, the manner in which these input segments combine with each other to form an explanatory pattern remains unknown. To overcome this, some previous work tries to find patterns (called rules) in the data that explain neural outputs. However, their explanations are often insensitive to model parameters, which limits the scalability of text explanations. To overcome these limitations, we propose a pipeline to explain RNNs by means of decision lists (also called rules) over skipgrams. For evaluation of explanations, we create a synthetic sepsis-identification dataset, as well as apply our technique on additional clinical and sentiment analysis datasets. We find that our technique persistently achieves high explanation fidelity and qualitatively interpretable rules.
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI
Venue: arXiv cs.LG 2020
Year: 2020

We demonstrate a unified approach to rigorous design of safety-critical autonomous systems using the VerifAI toolkit for formal analysis of AI-based systems. VerifAI provides an integrated toolchain for tasks spanning the design process, including modeling, falsification, debugging, and ML component retraining. We evaluate all of these applications in an industrial case study on an experimental autonomous aircraft taxiing system developed by Boeing, which uses a neural network to track the centerline of a runway. We define runway scenarios using the Scenic probabilistic programming language, and use them to drive tests in the X-Plane flight simulator. We first perform falsification, automatically finding environment conditions causing the system to violate its specification by deviating significantly from the centerline (or even leaving the runway entirely). Next, we use counterexample analysis to identify distinct failure cases, and confirm their root causes with specialized testing. Finally, we use the results of falsification and debugging to retrain the network, eliminating several failure cases and improving the overall performance of the closed-loop system.
Qualitative Controller Synthesis for Consumption Markov Decision Processes
Venue: arXiv cs.FL 2020
Year: 2020

Consumption Markov Decision Processes (CMDPs) are probabilistic decision-making models of resource-constrained systems. In a CMDP, the controller possesses a certain amount of a critical resource, such as electric power. Each action of the controller can consume some amount of the resource. Resource replenishment is only possible in special reload states, in which the resource level can be reloaded up to the full capacity of the system. The task of the controller is to prevent resource exhaustion, i.e. ensure that the available amount of the resource stays non-negative, while ensuring an additional linear-time property. We study the complexity of strategy synthesis in consumption MDPs with almost-sure Büchi objectives. We show that the problem can be solved in polynomial time. We implement our algorithm and show that it can efficiently solve CMDPs modelling real-world scenarios.
DiResNet: Direction-aware Residual Network for Road Extraction in VHR Remote Sensing Images
Venue: arXiv cs.CV 2020
Year: 2020

The binary segmentation of roads in very high resolution (VHR) remote sensing images (RSIs) has always been a challenging task due to factors such as occlusions (caused by shadows, trees, buildings, etc.) and the intra-class variances of road surfaces. The wide use of convolutional neural networks (CNNs) has greatly improved the segmentation accuracy and made the task end-to-end trainable. However, there are still margins to improve in terms of the completeness and connectivity of the results. In this paper, we consider the specific context of road extraction and present a direction-aware residual network (DiResNet) that includes three main contributions: 1) An asymmetric residual segmentation network with deconvolutional layers and a structural supervision to enhance the learning of road topology (DiResSeg); 2) A pixel-level supervision of local directions to enhance the embedding of linear features; 3) A refinement network to optimize the segmentation results (DiResRef). Ablation studies on two benchmark datasets (the Massachusetts dataset and the DeepGlobe dataset) have confirmed the effectiveness of the presented designs. Comparative experiments with other approaches show that the proposed method has advantages in both overall accuracy and F1-score. The code is available at: https://github.com/ggsDing/DiResNet.
Evo* 2020 -- Late-Breaking Abstracts Volume
Venue: arXiv cs.NE 2020
Year: 2020

This volume contains the Late-Breaking Abstracts submitted to the Evo* 2020 Conference, that took place online, from 15 to 17 of April 2020. These papers where presented as short talks and also at the poster session of the conference together with other regular submissions. All of them present ongoing research and preliminary results investigating on the application of different approaches of Bioinspired Methods (mainly Evolutionary Computation) to different problems, most of them real world ones.
Unsupervised Severe Weather Detection Via Joint Representation Learning Over Textual and Weather Data
Venue: arXiv cs.LG 2020
Year: 2020

When observing a phenomenon, severe cases or anomalies are often characterised by deviation from the expected data distribution. However, non-deviating data samples may also implicitly lead to severe outcomes. In the case of unsupervised severe weather detection, these data samples can lead to mispredictions, since the predictors of severe weather are often not directly observed as features. We posit that incorporating external or auxiliary information, such as the outcome of an external task or an observation, can improve the decision boundaries of an unsupervised detection algorithm. In this paper, we increase the effectiveness of a clustering method to detect cases of severe weather by learning augmented and linearly separable latent representations.We evaluate our solution against three individual cases of severe weather, namely windstorms, floods and tornado outbreaks.
Using Containers to Create More Interactive Online Training and Education Materials
Venue: arXiv cs.CY 2020
Year: 2020

Containers are excellent hands-on learning environments for computing topics because they are customizable, portable, and reproducible. The Cornell University Center for Advanced Computing has developed the Cornell Virtual Workshop in high performance computing topics for many years, and we have always sought to make the materials as rich and interactive as possible. Toward the goal of building a more hands-on experimental learning experience directly into web-based online training environments, we developed the Cornell Container Runner Service, which allows online content developers to build container-based interactive edit and run commands directly into their web pages. Using containers along with CCRS has the potential to increase learner engagement and outcomes.
Voting Framework for Distributed Real-Time Ethernet based Dependable and Safe Systems
Venue: arXiv cs.DC 2020
Year: 2020

In many industrial sectors such as factory automation and process control sensor redundancy is required to ensure reliable and highly-available operation. Measured values from N-redundant sensors are typically subjected to some voting scheme to determine a value which is used in further processing. In this paper we present a voting framework which allows the sensors and the voting scheme to be configured at systemconfiguration time. The voting scheme is designed as a Real Time Ethernet profile. We describe the structure of the voting system and the design and verification of the framework. We argue the applicability of this sub-system based on a successful prototype implementation.
Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones?
Venue: arXiv cs.LG 2020
Year: 2020

One of the generally accepted views of modern deep learning is that increasing the number of parameters usually leads to better quality. The two easiest ways to increase the number of parameters is to increase the size of the network, e.g. width, or to train a deep ensemble; both approaches improve the performance in practice. In this work, we consider a fixed memory budget setting, and investigate, what is more effective: to train a single wide network, or to perform a memory split -- to train an ensemble of several thinner networks, with the same total number of parameters? We find that, for large enough budgets, the number of networks in the ensemble, corresponding to the optimal memory split, is usually larger than one. Interestingly, this effect holds for the commonly used sizes of the standard architectures. For example, one WideResNet-28-10 achieves significantly worse test accuracy on CIFAR-100 than an ensemble of sixteen thinner WideResNets: 80.6% and 82.52% correspondingly. We call the described effect the Memory Split Advantage and show that it holds for a variety of datasets and model architectures.
E-Health Sensitive Data Dissemination Exploiting Trust and Mobility of Users
Venue: arXiv cs.CY 2020
Year: 2020

E-health services handle a massive amount of sensitive data, requiring reliability and privacy. The advent of new technologies drives e-health services into their continuous provision outside traditional care institutions. This creates uncertain and unreliable conditions, resulting in the challenge of controlling sensitive user data dissemination. Then, there is a gap in sensitive data dissemination under situations requiring fast response (e.g., cardiac arrest). This obligates networks to provide reliable sensitive data dissemination under user mobility, dynamic network topology, and occasional interactions between the devices. In this article, we propose STEALTH, a system that employs social trust and communities of interest to address these challenges. STEALTH follows two steps: clustering and dissemination. In the first, STEALTH groups devices based on the interests of their users, forming communities of interest. A healthcare urgency launches the second, in which STEALTH disseminates user sensitive data to devices belonging to specific communities, subjected to the level of trust between devices. Simulation results demonstrate that STEALTH ensures data dissemination to people who can contribute toward an efficient service. STEALTH has achieved up to 97.14% of reliability in accessing sensitive data with a maximum latency of 170 ms, and up to 100% of availability during emergencies.
Network Coding Based on Byte-wise Circular Shift and Integer Addition
Venue: arXiv cs.IT 2020
Year: 2020

A novel implementation of a special class of Galois ring, in which the multiplication can be realized by a cyclic convolution, is applied to the construction of network codes. The primitive operations involved are byte-wise shifts and integer additions modulo a power of 2. Both of them can be executed efficiently in microprocessors. An illustration of how to apply this idea to array code is given at the end of the paper.
Success in creative careers depends little on product quality
Venue: arXiv cs.DL 2020
Year: 2020

In the recent article Janosov, Battiston, & Sinatra report that they separated the inputs of talent and luck in creative careers. They build on the previous work of Sinatra et al which introduced the Q-model. Under the model the popularity of different elements of culture is a product of two factors: a random factor and a Qfactor, or talent. The latter is fixed for an individual but randomly distributed among different people. This way they explain how some individuals can consistently produce high-impact work. They extract the Q-factors for different scientists, writers, and movie makers from statistical data on popularity of their work. However, in their article they reluctantly state that there is little correlation between popularity and quality ratings of of books and movies (correlation coefficients 0.022 and 0.15). I analyzed the data of the original Q-factor article and obtained a correlation between the citation-based Q-factor and Nobel Prize winning of merely 0.19. I also briefly review few other experiments that found a meager, sometimes even negative, correlation between popularity and quality of cultural products. I conclude that, if there is an ability associated with a high Q-factor it should be more of a marketing ability than an ability to produce a higher quality product. Janosov,
MineReduce: an approach based on data mining for problem size reduction
Venue: arXiv cs.AI 2020
Year: 2020

Hybrid variations of metaheuristics that include data mining strategies have been utilized to solve a variety of combinatorial optimization problems, with superior and encouraging results. Previous hybrid strategies applied mined patterns to guide the construction of initial solutions, leading to more effective exploration of the solution space. Solving a combinatorial optimization problem is usually a hard task because its solution space grows exponentially with its size. Therefore, problem size reduction is also a useful strategy in this context, especially in the case of large-scale problems. In this paper, we build upon these ideas by presenting an approach named MineReduce, which uses mined patterns to perform problem size reduction. We present an application of MineReduce to improve a heuristic for the heterogeneous fleet vehicle routing problem. The results obtained in computational experiments show that this proposed heuristic demonstrates superior performance compared to the original heuristic and other state-of-the-art heuristics, achieving better solution costs with shorter run times.
Exploring the Capabilities and Limits of 3D Monocular Object Detection -- A Study on Simulation and Real World Data
Venue: arXiv cs.CV 2020
Year: 2020

3D object detection based on monocular camera data is a key enabler for autonomous driving. The task however, is ill-posed due to lack of depth information in 2D images. Recent deep learning methods show promising results to recover depth information from single images by learning priors about the environment. Several competing strategies tackle this problem. In addition to the network design, the major difference of these competing approaches lies in using a supervised or self-supervised optimization loss function, which require different data and ground truth information. In this paper, we evaluate the performance of a 3D object detection pipeline which is parameterizable with different depth estimation configurations. We implement a simple distance calculation approach based on camera intrinsics and 2D bounding box size, a self-supervised, and a supervised learning approach for depth estimation. Ground truth depth information cannot be recorded reliable in real world scenarios. This shifts our training focus to simulation data. In simulation, labeling and ground truth generation can be automatized. We evaluate the detection pipeline on simulator data and a real world sequence from an autonomous vehicle on a race track. The benefit of simulation training to real world application is investigated. Advantages and drawbacks of the different depth estimation strategies are discussed.
Tight Bounds for Black Hole Search in Dynamic Rings
Venue: arXiv cs.DC 2020
Year: 2020

In this paper, we start the investigation of distributed computing by mobile agents in dangerous dynamic networks. The danger is posed by the presence in the network of a black hole BH, a harmful site that destroys all incoming agents without leaving any trace. The problem of determining the location of the black hole in a network, known as black hole search BHS, has been extensively studied in the literature, but always and only assuming that the network is static. At the same time, the existing results on mobile agents computing in dynamic networks never consider the presence of harmful sites. In this paper we start filling this research gap by studying black hole search in temporal rings, specifically focusing on 1-interval connectivity adversarial dynamics. The problem is solved if within finite time at least one agent survives and knows the location of BH. The main complexity parameters of BHS is the number of agents (called size) needed to solve the problem, and the number of moves (called cost) performed by the agents; in synchronous systems, such as temporal rings, an additional complexity measure is the amount of time until termination occurs. Feasibility and complexity depend on many parameters; in particular: whether the agents start from the same safe node or from possibly distinct safe locations, the size $n$ of the ring, whether or not $n$ is known, and the type of inter-agent communication (whiteboards, tokens, face-to-face, visual). In this paper, we provide a complete feasibility characterization for all instances of those parameters; all our algorithms are size optimal. Furthermore, we establish lower bounds on the cost (i.e., the number of moves) and time of size-optimal solutions for all instances of those parameters and show that our algorithms achieve those bound.
Local Gathering of Mobile Robots in Three Dimensions
Venue: arXiv cs.CG 2020
Year: 2020

In this work, we initiate the research about the Gathering problem for robots with limited viewing range in the three-dimensional Euclidean space. In the Gathering problem, a set of initially scattered robots is required to gather at the same position. The robots' capabilities are very restricted -- they do not agree on any coordinate system or compass, have a limited viewing range, have no memory of the past and cannot communicate. We study the problem in two different time models, in FSYNC (fully synchronized discrete rounds) and the continuous time model. For FSYNC, we introduce the 3D-Go-To-The-Center-strategy and prove a runtime of $Θ(n^2)$ that matches the currently best runtime bound for the same model in the Euclidean plane [SPAA'11]. Our main result is the generalization of contracting strategies (continuous time) from [Algosensors'17] to three dimensions. In contracting strategies, every robot that is located on the global convex hull of all robots' positions moves with full speed towards the inside of the convex hull. We prove a runtime bound of $O(Δ\cdot n^{3/2})$ for any three-dimensional contracting strategy, where $Δ$ denotes the diameter of the initial configuration. This comes up to a factor of $\sqrt{n}$ close to the lower bound of $Ω(Δ\cdot n)$ which is already true in two dimensions. In general, it might be hard for robots with limited viewing range to decide whether they are located on the global convex hull and which movement maintains the connectivity of the swarm, rendering the design of concrete contracting strategies a challenging task. We prove that the continuous variant of 3D-Go-To-The-Center is contracting and keeps the swarm connected. Moreover, we give a simple design criterion for three-dimensional contracting strategies that maintains the connectivity of the swarm and introduce an exemplary strategy based on this criterion.
System of Computer Modeling and Features of their use in the Educational Process of General Secondary Eeducation
Venue: arXiv cs.OH 2020
Year: 2020

The article analyzes the historical aspect of the formation of computer modeling as one of the perspective directions of educational process development. The notion of "system of computer modeling", conceptual model of system of computer modeling (SCMod), its components (mathematical, animation, graphic, strategic), functions, principles and purposes of use are grounded. The features of the organization of students work using SCMod, individual and group work, the formation of subject competencies are described; the aspect of students' motivation to learning is considered. It is established that educational institutions can use SCMod at different levels and stages of training and in different contexts, which consist of interrelated physical, social, cultural and technological aspects. It is determined that the use of SCMod in general secondary school would increase the capacity of teachers to improve the training of students in natural and mathematical subjects and contribute to the individualization of the learning process, in order to meet the pace, educational interests and capabilities of each particular student. It is substantiated that the use of SCMod in the study of natural-mathematical subjects contributes to the formation of subject competencies, develops the skills of analysis and decision-making, increases the level of digital communication, develops vigilance, raises the level of knowledge, increases the duration of attention of students. Further research requires the justification of the process of forming students' competencies in natural-mathematical subjects and designing cognitive tasks using SCMod.
Batch Codes from Affine Cartesian Codes and Quotient Spaces
Venue: arXiv cs.IT 2020
Year: 2020

Affine Cartesian codes are defined by evaluating multivariate polynomials at a cartesian product of finite subsets of a finite field. In this work we examine properties of these codes as batch codes. We consider the recovery sets to be defined by points aligned on a specific direction and the buckets to be derived from cosets of a subspace of the ambient space of the evaluation points. We are able to prove that under these conditions, an affine Cartesian code is able to satisfy a query of size up to one more than the dimension of the space of the ambient space.