OpenRaiser/Intern-Atlas huggingface dataset:
A Graph Theory approach to assess nature's contribution to people at a global scale
Venue: arXiv cs.SI 2021
Year: 2021

Cultural Ecosystem Services (CES) assessment at large scales is crucial in marine ecosystems as they reflect key physical and cognitive interactions between humans and nature. The analysis of social media data with graph theory is a promising approach to provide global information on users' perceptions for different marine ecosystems. Fourteen areas were selected to illustrate the use of graph theory on social media data. The selected areas, known to protect key recreational, educational and heritage attributes of marine ecosystems, were investigated to identify variability in users' preferences. Instagram data (i.e., hashtags associated to photos) was extracted for each area allowing an in-depth assessment of the CES most appreciated by the users. Hashtags were analysed using network centrality measures to identify clusters of words, aspects not normally captured by traditional photo content analysis. The emergent properties of networks of hashtags were explored to characterise visitors' preferences (e.g., cultural heritage or nature appreciation), activities (e.g., diving or hiking), preferred habitats and species (e.g. forest, beach, penguins), and feelings (e.g., happiness or place identity). Network analysis on Instagram hashtags allowed delineating the users' discourse around a natural area, which provides crucial information for effective management of popular natural spaces for people.
A Single-Letter Upper Bound on the Mismatch Capacity via Multicast Transmission
Venue: arXiv cs.IT 2021
Year: 2021

We introduce a new analysis technique to derive a single-letter upper bound on the mismatch capacity of a stationary, single-user, memoryless channel with a decoding metric $q$. Our bound is obtained by considering a multicast transmission over a two-user broadcast channel with decoding metrics $q$ and $ρ$ at the receivers, referred to as $(q,ρ)$-surely degraded. This channel has the property that the intersection event of correct $q$-decoding of receiver $1$ and erroneous $ρ$-decoding of receiver $2$ has zero probability for any fixed-composition codebook of a certain composition $P$. Our bound holds in the strong converse sense of an exponential decay of the probability of correct decoding at rates above the bound. Further, we refine the proof and present a bound that is at least as tight as that of any choice of $ρ$. Several examples that demonstrate the strict improvement of our bound compared to previous results are analyzed. Finally, we detect equivalence classes of isomorphic channel-metric pairs $(W,q)$ that share the same mismatch capacity. We prove that if the class contains a matched pair, then our bound is tight and the mismatch capacity of the entire class is fully characterized and is equal to the LM rate, which is achievable by random coding, and may be strictly lower that the matched capacity.
Detection and Segmentation of Custom Objects using High Distraction Photorealistic Synthetic Data
Venue: arXiv cs.CV 2021
Year: 2021

We show a straightforward and useful methodology for performing instance segmentation using synthetic data. We apply this methodology on a basic case and derived insights through quantitative analysis. We created a new public dataset: The Expo Markers Dataset intended for detection and segmentation tasks. This dataset contains 5,000 synthetic photorealistic images with their corresponding pixel-perfect segmentation ground truth. The goal is to achieve high performance on manually-gathered and annotated real-world data of custom objects. We do that by creating 3D models of the target objects and other possible distraction objects and place them within a simulated environment. Expo Markers were chosen for this task, fitting our requirements of a custom object due to the exact texture, size and 3D shape. An additional advantage is the availability of this object in offices around the world for easy testing and validation of our results. We generate the data using a domain randomization technique that also simulates other photorealistic objects in the scene, known as distraction objects. These objects provide visual complexity, occlusions, and lighting challenges to help our model gain robustness in training. We are also releasing our manually-gathered datasets used for comparison and evaluation of our synthetic dataset. This white-paper provides strong evidence that photorealistic simulated data can be used in practical real world applications as a more scalable and flexible solution than manually-captured data. Code is available at the following address: https://github.com/DataGenResearchTeam/expo_markers
Boardroom Voting: Verifiable Voting with Ballot Privacy Using Low-Tech Cryptography in a Single Room
Venue: arXiv cs.CR 2021
Year: 2021

A boardroom election is an election that takes place in a single room -- the boardroom -- in which all voters can see and hear each other. We present an initial exploration of boardroom elections with ballot privacy and voter verifiability that use only "low-tech cryptography" without using computers to mark or collect ballots. Specifically, we define the problem, introduce several building blocks, and propose a new protocol that combines these blocks in novel ways. Our new building blocks include "foldable ballots" that can be rotated to hide the alignment of ballot choices with voting marks, and "visual secrets" that are easy to remember and use but hard to describe. Although closely seated participants in a boardroom election have limited privacy, the protocol ensures that no one can determine how others voted. Moreover, each voter can verify that their ballot was correctly cast, collected, and counted, without being able to prove how they voted, providing assurance against undue influence. Low-tech cryptography is useful in situations where constituents do not trust computer technology, and it avoids the complex auditing requirements of end-to-end cryptographic voting systems such as Prêt-à-Voter. This paper's building blocks and protocol are meant to be a proof of concept that might be tested for usability and improved.
A Development Cycle for Automated Self-Exploration of Robot Behaviors
Venue: arXiv cs.RO 2021
Year: 2021

In this paper we introduce Q-Rock, a development cycle for the automated self-exploration and qualification of robot behaviors. With Q-Rock, we suggest a novel, integrative approach to automate robot development processes. Q-Rock combines several machine learning and reasoning techniques to deal with the increasing complexity in the design of robotic systems. The Q-Rock development cycle consists of three complementary processes: (1) automated exploration of capabilities that a given robotic hardware provides, (2) classification and semantic annotation of these capabilities to generate more complex behaviors, and (3) mapping between application requirements and available behaviors. These processes are based on a graph-based representation of a robot's structure, including hardware and software components. A central, scalable knowledge base enables collaboration of robot designers including mechanical, electrical and systems engineers, software developers and machine learning experts. In this paper we formalize Q-Rock's integrative development cycle and highlight its benefits with a proof-of-concept implementation and a use case demonstration.
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future
Venue: arXiv cs.IT 2021
Year: 2021

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).
Rethinking Recurrent Neural Networks and Other Improvements for Image Classification
Venue: arXiv cs.CV 2021
Year: 2021

Over the long history of machine learning, which dates back several decades, recurrent neural networks (RNNs) have been used mainly for sequential data and time series and generally with 1D information. Even in some rare studies on 2D images, these networks are used merely to learn and generate data sequentially rather than for image recognition tasks. In this study, we propose integrating an RNN as an additional layer when designing image recognition models. We also develop end-to-end multimodel ensembles that produce expert predictions using several models. In addition, we extend the training strategy so that our model performs comparably to leading models and can even match the state-of-the-art models on several challenging datasets (e.g., SVHN (0.99), Cifar-100 (0.9027) and Cifar-10 (0.9852)). Moreover, our model sets a new record on the Surrey dataset (0.949). The source code of the methods provided in this article is available at https://github.com/leonlha/e2e-3m and http://nguyenhuuphong.me.
Many Order Types on Integer Grids of Polynomial Size
Venue: arXiv cs.CG 2021
Year: 2021

Two labeled point configurations $\{p_1,\ldots,p_n\}$ and $\{q_1,\ldots,q_n\}$ are of the same order type if, for every $i,j,k$, the triples $(p_i,p_j,p_k)$ and $(q_i,q_j,q_k)$ have the same orientation. In the 1980's, Goodman, Pollack and Sturmfels showed that (i) the number of order types on $n$ points is of order $4^{n+o(n)}$, (ii) all order types can be realized with double-exponential integer coordinates, and that (iii) certain order types indeed require double-exponential integer coordinates. In 2018, Caraballo, Díaz-Bá{ñ}ez, Fabila-Monroy, Hidalgo-Toscano, Lea{ñ}os, Montejano showed that at least $n^{3n+o(n)}$ order types can be realized on an integer grid of polynomial size. In this article, we improve their result by showing that at least $n^{4n+o(n)}$ order types can be realized on an integer grid of polynomial size, which is essentially best possible.
flexgrid2vec: Learning Efficient Visual Representations Vectors
Venue: arXiv cs.CV 2021
Year: 2021

We propose flexgrid2vec, a novel approach for image representation learning. Existing visual representation methods suffer from several issues, including the need for highly intensive computation, the risk of losing in-depth structural information and the specificity of the method to certain shapes or objects. flexgrid2vec converts an image to a low-dimensional feature vector. We represent each image with a graph of flexible, unique node locations and edge distances. flexgrid2vec is a multi-channel GCN that learns features of the most representative image patches. We have investigated both spectral and non-spectral implementations of the GCN node-embedding. Specifically, we have implemented flexgrid2vec based on different node-aggregation methods, such as vector summation, concatenation and normalisation with eigenvector centrality. We compare the performance of flexgrid2vec with a set of state-of-the-art visual representation learning models on binary and multi-class image classification tasks. Although we utilise imbalanced, low-size and low-resolution datasets, flexgrid2vec shows stable and outstanding results against well-known base classifiers. flexgrid2vec achieves 96.23% on CIFAR-10, 83.05% on CIFAR-100, 94.50% on STL-10, 98.8% on ASIRRA and 89.69% on the COCO dataset.
A Functional Model for Structure Learning and Parameter Estimation in Continuous Time Bayesian Network: An Application in Identifying Patterns of Multiple Chronic Conditions
Venue: arXiv cs.LG 2021
Year: 2021

Bayesian networks are powerful statistical models to study the probabilistic relationships among set random variables with major applications in disease modeling and prediction. Here, we propose a continuous time Bayesian network with conditional dependencies, represented as Poisson regression, to model the impact of exogenous variables on the conditional dependencies of the network. We also propose an adaptive regularization method with an intuitive early stopping feature based on density based clustering for efficient learning of the structure and parameters of the proposed network. Using a dataset of patients with multiple chronic conditions extracted from electronic health records of the Department of Veterans Affairs we compare the performance of the proposed approach with some of the existing methods in the literature for both short-term (one-year ahead) and long-term (multi-year ahead) predictions. The proposed approach provides a sparse intuitive representation of the complex functional relationships between multiple chronic conditions. It also provides the capability of analyzing multiple disease trajectories over time given any combination of prior conditions.
BasicBlocker: ISA Redesign to Make Spectre-Immune CPUs Faster
Venue: arXiv cs.CR 2021
Year: 2021

Recent research has revealed an ever-growing class of microarchitectural attacks that exploit speculative execution, a standard feature in modern processors. Proposed and deployed countermeasures involve a variety of compiler updates, firmware updates, and hardware updates. None of the deployed countermeasures have convincing security arguments, and many of them have already been broken. The obvious way to simplify the analysis of speculative-execution attacks is to eliminate speculative execution. This is normally dismissed as being unacceptably expensive, but the underlying cost analyses consider only software written for current instruction-set architectures, so they do not rule out the possibility of a new instruction-set architecture providing acceptable performance without speculative execution. A new ISA requires compiler and hardware updates, but these are happening in any case. This paper introduces BasicBlocker, a generic ISA modification that works for all common ISAs and that allows non-speculative CPUs to obtain most of the performance benefit that would have been provided by speculative execution. To demonstrate the feasibility of BasicBlocker, this paper defines a variant of the RISC-V ISA called BBRISC-V and provides a thorough evaluation on both a 5-stage in-order soft core and a superscalar out-of-order processor using an associated compiler and a variety of benchmark programs.
An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
Venue: arXiv cs.LG 2021
Year: 2021

Behavioral changes are the earliest signs of a mental disorder, but arguably, the dynamics of brain function gets affected even earlier. Subsequently, spatio-temporal structure of disorder-specific dynamics is crucial for early diagnosis and understanding the disorder mechanism. A common way of learning discriminatory features relies on training a classifier and evaluating feature importance. Classical classifiers, based on handcrafted features are quite powerful, but suffer the curse of dimensionality when applied to large input dimensions of spatio-temporal data. Deep learning algorithms could handle the problem and a model introspection could highlight discriminatory spatio-temporal regions but need way more samples to train. In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC). We pre-train the whole MILC model on unlabeled and unrelated healthy control data. We test our model on three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers and four different studies. Our algorithm outperforms existing self-supervised pre-training methods and provides competitive classification results to classical machine learning algorithms. Importantly, whole MILC enables attribution of subject diagnosis to specific spatio-temporal regions in the fMRI signal.
node2coords: Graph Representation Learning with Wasserstein Barycenters
Venue: arXiv cs.LG 2021
Year: 2021

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods do not learn representations that can be interpreted in a straightforward way and that are robust to perturbations to the graph structure. In this work, we address these two limitations by proposing node2coords, a representation learning algorithm for graphs, which learns simultaneously a low-dimensional space and coordinates for the nodes in that space. The patterns that span the low dimensional space reveal the graph's most important structural information. The coordinates of the nodes reveal the proximity of their local structure to the graph structural patterns. In order to measure this proximity by taking into account the underlying graph, we propose to use Wasserstein distances. We introduce an autoencoder that employs a linear layer in the encoder and a novel Wasserstein barycentric layer at the decoder. Node connectivity descriptors, that capture the local structure of the nodes, are passed through the encoder to learn the small set of graph structural patterns. In the decoder, the node connectivity descriptors are reconstructed as Wasserstein barycenters of the graph structural patterns. The optimal weights for the barycenter representation of a node's connectivity descriptor correspond to the coordinates of that node in the low-dimensional space. Experimental results demonstrate that the representations learned with node2coords are interpretable, lead to node embeddings that are stable to perturbations of the graph structure and achieve competitive or superior results compared to state-of-the-art methods in node classification.
Creating RESTful APIs over SPARQL endpoints using RAMOSE
Venue: arXiv cs.DB 2021
Year: 2021

Semantic Web technologies are widely used for storing RDF data and making them available on the Web through SPARQL endpoints, queryable using the SPARQL query language. While the use of SPARQL endpoints is strongly supported by Semantic Web experts, it hinders broader use of RDF data by common Web users, engineers and developers unfamiliar with Semantic Web technologies, who normally rely on Web RESTful APIs for querying Web-available data and creating applications over them. To solve this problem, we have developed RAMOSE, a generic tool developed in Python to create REST APIs over SPARQL endpoints. Through the creation of source-specific textual configuration files, RAMOSE enables the querying of SPARQL endpoints via simple Web RESTful API calls that return either JSON or CSV-formatted data, thus hiding all the intrinsic complexities of SPARQL and RDF from common Web users. We provide evidence that the use of RAMOSE to provide REST API access to RDF data within OpenCitations triplestores is beneficial in terms of the number of queries made by external users to such RDF data using the RAMOSE API compared with the direct access via the SPARQL endpoint. Our findings show the importance for suppliers of RDF data of having an alternative API access service, which enables its use by those with no (or little) experience in Semantic Web technologies and the SPARQL query language. RAMOSE can be used both to query any SPARQL endpoint and to query any other Web API, and thus it represents an easy generic technical solution for service providers who wish to create an API service to access Linked Data stored as RDF in a conventional triplestore.
On the Convergence of SGD with Biased Gradients
Venue: arXiv cs.LG 2021
Year: 2021

We analyze the complexity of biased stochastic gradient methods (SGD), where individual updates are corrupted by deterministic, i.e. biased error terms. We derive convergence results for smooth (non-convex) functions and give improved rates under the Polyak-Lojasiewicz condition. We quantify how the magnitude of the bias impacts the attainable accuracy and the convergence rates (sometimes leading to divergence). Our framework covers many applications where either only biased gradient updates are available, or preferred, over unbiased ones for performance reasons. For instance, in the domain of distributed learning, biased gradient compression techniques such as top-k compression have been proposed as a tool to alleviate the communication bottleneck and in derivative-free optimization, only biased gradient estimators can be queried. We discuss a few guiding examples that show the broad applicability of our analysis.
F*: An Interpretable Transformation of the F-measure
Venue: arXiv cs.LG 2021
Year: 2021

The F-measure, also known as the F1-score, is widely used to assess the performance of classification algorithms. However, some researchers find it lacking in intuitive interpretation, questioning the appropriateness of combining two aspects of performance as conceptually distinct as precision and recall, and also questioning whether the harmonic mean is the best way to combine them. To ease this concern, we describe a simple transformation of the F-measure, which we call F* (F-star), which has an immediate practical interpretation.