Reviewerly/exHarmony huggingface dataset:
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks.

We study the problem of causal discovery through targeted interventions. Starting from few observational measurements, we follow a Bayesian active learning approach to perform those experiments which, in expectation with respect to the current model, are maximally informative about the underlying causal structure. Unlike previous work, we consider the setting of continuous random variables with non-linear functional relationships, modelled with Gaussian process priors. To address the arising problem of choosing from an uncountable set of possible interventions, we propose to use Bayesian optimisation to efficiently maximise a Monte Carlo estimate of the expected information gain.
Intelligent Evaluation Model of Bilingual Teaching Quality based on Network Resource Sharing

Aiming at the poor application performance of traditional model for bilingual teaching quality evaluation, this paper proposes a model construction method of bilingual teaching quality evaluation based on network resource sharing. The evaluation indicators are selected reasonably, and an intelligent evaluation model of bilingual teaching quality based on network resource sharing is constructed. The evaluation indicators in the model are compared and weighted by analytic hierarchy process (AHP), and a fuzzy comprehensive evaluation matrix is established. The bilingual teaching quality evaluation has been realised. The experimental results show that the fluctuation range of evaluation error is between 1.6 and 1.7, and the evaluation error is small. Through the questionnaire survey, it is found that the time for teachers to compile teaching plans is shortened by 0.4 hours, the students' scores are improved by 1.5 points, the quality of bilingual teaching is improved, and the satisfaction of teachers and students is improved.
A Track Fusion Method Based on Converted Measurements in Multistatic Sonar Systems

Tracking with multistatic sonar measurements is challenging due to the fact that the measurements are nonlinear functions of the Cartesian state. To obtain a global estimation of the target position, the existing approaches are to fuse local estimations of the target position or expand the measurement vector to be global. But those methods may cause large amounts of calculations due to the need for many Kalman filters or a high dimension of the measurement vector. The paper converted measurements from measurement coordinates to Cartesian coordinates to obtain the global estimation of the target position based on a Kalman filter and avoiding the high dimension problem, which means a low calculation cost. The performance of the proposed method is demonstrated by different simulated scenarios.
Control Synthesis for Fuzzy Systems with Local Nonlinear Models Subject to Actuator Saturation

This paper presents a new method to design non-parallel distributed compensation (non-PDC) laws for Takagi- Sugeno fuzzy systems with local nonlinear models subject to actuator saturation. Based on specific congruence transformations, the local stabilization conditions are derived using Lyapunov stability theorem. The local design framework is established through an effective treatment of the sector-bound conditions for the input-saturation phenomenon and the nonlinearities in the consequents of the fuzzy systems. Using a non-quadratic Lyapunov function candidate, the control design is reformulated as an LMI-based optimization problem with a line search over a parameter, which can be effectively solved with convex optimization techniques. In particular, we theoretically prove that the new control method is less conservative compared to that derived from a standard non-PDC controller. Illustrative examples are provided to point out the interests of the proposed control method.
Automatic proficiency classification in L2 Portuguese

We present the first experiments on automatic proficiency classification for L2 Portuguese. For the experiments, we take advantage of a new version of the NLI-PT dataset, a compilation of L2 Portuguese texts written by learners. We use supervised learning and we approach the task as a classification problem, using the CEFR scale. Different linguistic features are tested, combined with different algorithms. With the best model, we get an accuracy of 72%, a result in line with previous experiments with other languages.
Assessing Architectural Similarity in Populations of Deep Neural Networks

Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the synthesis of increasingly efficient architectures over successive generations. Despite recent research showing the efficacy of multi-parent evolutionary synthesis, little has been done to directly assess architectural similarity between networks during the synthesis process for improved parent network selection. In this work, we present a preliminary study into quantifying architectural similarity via the percentage overlap of architectural clusters. Results show that networks synthesized using architectural alignment (via gene tagging) maintain higher architectural similarities within each generation, potentially restricting the search space of highly efficient network architectures.
Modified Scale-Space Analysis in Frequency Domain Based on Adaptive Multiscale Gaussian Filter for Saliency Detection

The salient region is an area in the image that is paid attention from a human. It is a distinctive feature from neighbors such as color, shape or pattern. Saliency detection is a model that imitates the human visual system to perceive the scene. It has been widely used in many vision systems. Many papers use the smoothing or suppressing technique to extract a desirable output as so-called saliency map. Even though most of these researches achieve saliency detection based on the filter, the size of the filter is fixed. This leads to a filter ineffective when applying to the whole area of each image. In order to solve this issue, an adaptive multiscale Gaussian filter (MSS) for scale-space analysis in the frequency domain is proposed. The proposed filter is extended from an adaptive median filter which is a powerful method to remove the noise from the input image. This paper proposes the method that offers the appropriate filter to suppress the repeated pattern spectrum of each region in each image before extracting the saliency map. The experimental result shows that the proposed method outperforms the baseline methods, which includes HFT, Itti, SAL, SR and SUN.
Shape Control of Discrete Generalized Gaussian Distributions for Frequency-Domain Audio Coding

Entropy coding, which is an essential part of audio compression, is always required to manage the tradeoffs between compression efficiency and computational complexity, and the strategy to achieve them highly depends on the distributions of inputs. In this paper, we present a method of controlling them for enhancing the compression efficiency of Golomb-Rice (GR) encoding, one of the simplest entropy coding methods optimal for Laplacian distributions. We will show that the proposed invertible and low-complexity mapping of integers enables the GR encoding to assign nearly the optimal code length for a wider range of distributions, generalized Gaussian distributions, maintaining low computational cost. A simulation by random numbers reveals that the proposed coder based on this scheme works about 6 times faster than the state-of-the-art arithmetic coder for Gaussian-distributed integers maintaining the increase in relative redundancy around 2.6%, which is much lower than that of a conventional GR coder. Additionally, an application to a practical speech and audio coding scheme is presented, and an objective evaluation for real speech and audio signals confirms the advantages of the proposed method in compression. The method is expected to widen the capability of low-complexity entropy coding, providing us with more flexible codec designs.
A Drag Model-LIDAR-IMU Fault-Tolerance Fusion Method for Quadrotors

In this paper, a drag model-aided fault-tolerant state estimation method is presented for quadrotors. Firstly, the drag model accuracy was improved by modeling an angular rate related item and an angular acceleration related item, which are related with flight maneuver. Then the drag model, light detection and ranging (LIDAR), and inertial measurement unit (IMU) were fused based on the Federal Kalman filter frame. In the filter, the LIDAR estimation fault was detected and isolated, and the disturbance to the drag model was estimated and compensated. Some experiments were carried out, showing that the velocity and position estimation were improved compared with the traditional LIDAR/IMU fusion scheme.
Explaining Cataloging to a Six Year Old?

Cataloging is complex, both in the formal and informal sense. The standards used in cataloging, as well as the larger ecosystem in which catalogers work including the models developed to undergird their work, are increasing in complexity to the point where catalogers themselves may be unclear, and explaining the work to outsiders, including administrators and those new to the profession, is increasingly difficult. Ultimately, the training and continuing education and all around intellectual effort required to stay on top of the field of cataloging is prodigious. Any possible lessening of the complexity, while maintaining quality, should be sought.
Dynamic Trajectory Planning for Autonomous Driving Based on Fluid Simulation

This paper describes the use of Lattice Boltzmann Method for dynamic autonomous driving trajectory planning. By simulating fluid flow on roads as a two-dimensional tubes in a small area around ego car, a vector map is created which can be used as a direct basis for driving trajectory. To tailor the phenomena of fluid flow for legal driving trajectory, the simulated fluid source is placed at an offset angle while dynamically following target car and road borders generate additional potential field. Experiments have shown that fluid simulation needs to propagate relatively to car driven distance for the algorithm to be feasible. During evaluation such ratio has been found and approximated.
The Universe: Boiling ‘bouillon’ of Quantons

This article was published in the Leonov's book: Quantum Energetics. Volume 1. Theory of Superunification. Cambridge International Science Publishing, 2010, pp. 9-55. The theory of Superunification is based on my discovery in 1996 of a quantum of space-time (quanton) and superstrong electromagnetic interaction (SEI) – the fifth fundamental force (Superforce). SEI is a global electromagnetic field that permeates our universe. This field consists of quantons and represents a quantized space-time. In the region of the ultra microworld of quantons, we can observe their microscopic vibrations and rotation that resemble a boiling “bouillon” of quantons. Quanton consists of four integers quarks: two magnetic and two electric. Quarks have no mass. This article shows that the Superforce (fifth force) unites all known fundamental forces from a single position: gravitation, electromagnetism, nuclear and electroweak forces. The theory of Superunification is new quantum physics; it unites the general theory of relativity (GR) and quantum theory. Our universe is not flat, but it is curved according to Einstein. This fact explains the accelerated motion of galaxies from the center of the universe to its periphery by the forces of the global antigravity.
Inferring Dynamical Systems with Long-Range Dependencies through Line Attractor Regularization.

Vanilla RNN with ReLU activation have a simple structure that lends itself to systematic dynamical systems analysis and interpretation, but they suffer from the exploding vs. vanishing gradients problem. Recent attempts to retain this simplicity while alleviating the gradient problem are based on proper initialization schemes or orthogonality/unitary constraints on the RNN's recurrence matrix, which, however, comes with limitations to its expressive power with regards to dynamical systems phenomena like chaos or multi-stability. Here, we instead suggest a regularization scheme that pushes part of the RNN's latent subspace toward a line attractor configuration that enables long short-term memory and arbitrarily slow time scales. We show that our approach excels on a number of benchmarks like the sequential MNIST or multiplication problems, and enables reconstruction of dynamical systems which harbor widely different time scales.
Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas

The Rohingya refugee crisis is one of the biggest humanitarian crises of modern times with more than 600,000 Rohingyas rendered homeless according to the United Nations High Commissioner for Refugees. While it has received sustained press attention globally, no comprehensive research has been performed on social media pertaining to this large evolving crisis. In this work, we construct a substantial corpus of YouTube video comments (263,482 comments from 113,250 users in 5,153 relevant videos) with an aim to analyze the possible role of AI in helping a marginalized community. Using a novel combination of multiple Active Learning strategies and a novel active sampling strategy based on nearest-neighbors in the comment-embedding space, we construct a classifier that can detect comments defending the Rohingyas among larger numbers of disparaging and neutral ones. We advocate that beyond the burgeoning field of hate-speech detection, automatic detection of \emph{help-speech} can lend voice to the voiceless people and make the internet safer for marginalized communities.
A Trust Model for Cloud: Results from a Survey

This research proposes a cloud consumer trust model that focuses on decision making and the value of our data. The value consumers place on data plays a critical role in how consumers trust. The research was validated with a questionnaire and statistical power analysis. Few cloud trust models appear to be validated against real world data, and none, that we found, accomplish an a-priori statistical power analysis. Results indicated consumers were interested in data location, third party access and most surprisingly unbiased/expert organization recommendations were more important than family and friends recommendations.
Making Iot Security Policies Relevant, Inclusive and Practical for People: A Multi-Dimensional Method

Growing amounts of research on IoT and its implications for security, privacy, economy and society has been carried out to inform policies and design. However, ordinary people who are citizens and users of these emerging technologies have rarely been involved in the processes that inform these policies, governance mechanisms and design due to the institutionalised processes that prioritise objective knowledge over subjective ones. People's subjective experiences are often discarded. This priority is likely to further widen the gap between people, technology policies and design as technologies advance towards delegated human agencies, which decreases human interfaces in technology-mediated relationships with objects, systems, services, trade and other (often) unknown third-party beneficiaries. Such a disconnection can have serious implications for policy implementation, especially when it involves human limitations. To address this disconnection, we argue that a space for people to meaningfully contribute their subjective knowledge - experienceto complex technology policies that, in turn, shape their experience and well-being needs to be constructed. To this end, our paper contributes the design and pilot implementation of a method to reconnect and involve people in IoT security policymaking and development.
Toward the Development of a Low-Cost High-Precision Instrumented Mini-Solar Sensor

In this work the development of a low-cost solar sensor capable of locating the position of the sun through photosensors is presented. In addition, the sensor is integrated with the instrumentation to provide the data for a solar position algorithm. As preliminary results, we present the methodology followed for the development of the sensor, as well as the theoretical characterization carried out through the Tonatiuh software.
Can 1figr Figure into Your Assessment? A Case Study on the Use of 1Figr for A Serials Review

Over the years, libraries have taken numerous approaches to assess their serial subscriptions. When the Texas A&M University Libraries made the strategic decision to introduce a sustainable, iterative approach to reviewing serials they employed a new tool to the task, 1Science’s 1Figr product, which includes reports that combine traditionally used usage and citation analysis data together with new open access metrics. This case study examines the reports and how they were employed in the review to help the Libraries’ make substantive collection decisions. This article should help libraries get a better understanding of what 1Figr is and how it can be used so that libraries can make an informed decision as to whether the product would be a worthwhile investment for them.
A class of optimization problems motivated by rank estimators in robust regression

A rank estimator in robust regression is a minimizer of a function which depends (in addition to other factors) on the ordering of residuals but not on their values. Here we focus on the optimization aspects of rank estimators. We distinguish two classes of functions: the class with a continuous and convex objective function (CCC), which covers the class of rank estimators known from statistics, and also another class (GEN), which is far more general. We propose efficient algorithms for both classes. For GEN we propose an enumerative algorithm that works in polynomial time as long as the number of regressors is O(1). The proposed algorithm utilizes the special structure of arrangements of hyperplanes that occur in our problem and is superior to other known algorithms in this area. For the continuous and convex case, we propose an unconditionally polynomial algorithm finding the exact minimizer, unlike the heuristic or approximate methods implemented in statistical packages.
A Conceptual Modelling Approach to Visualising Linked Data

Increasing numbers of Linked Open Datasets are being published, and many possible data visualisations may be appropriate for a user’s given exploration or analysis task over a dataset. Users may therefore find it difficult to identify visualisations that meet their data exploration or analyses needs. We propose an approach that creates conceptual models of groups of commonly used data visualisations, which can be used to analyse the data and users’ queries so as to automatically generate recommendations of possible visualisations. To our knowledge, this is the first work to propose a conceptual modelling approach to recommending visualisations for Linked Data.
Named Entity Recognition in Chinese Electronic Medical Records Based on the Model of Bidirectional Long Short-Term Memory with a Conditional Random Field Layer.

Named entity recognition in electronic medical records is of great significance to the construction of medical knowledge maps. This paper proposes a model of bidirectional Long Short-Term Memory with a conditional random field layer(BiLSTM-CRF). In terms of simultaneously identifying 5 types of clinical entities from CCKS2018 Chinese EHRs corpus, the BiLSTM-CRF model finally achieved better performance than the baseline CRF model (F-score of 84.23% vs 82.49%).
Scoring-Aggregating-Planning: Learning task-agnostic priors from interactions and sparse rewards for zero-shot generalization

Humans can learn task-agnostic priors from interactive experience and utilize the priors for novel tasks without any finetuning. In this paper, we propose Scoring-Aggregating-Planning (SAP), a framework that can learn task-agnostic semantics and dynamics priors from arbitrary quality interactions under sparse reward and then plan on unseen tasks in zero-shot condition. The framework finds a neural score function for local regional state and action pairs that can be aggregated to approximate the quality of a full trajectory; moreover, a dynamics model that is learned with self-supervision can be incorporated for planning. Many previous works that leverage interactive data for policy learning either need massive on-policy environmental interactions or assume access to expert data while we can achieve a similar goal with pure off-policy imperfect data. Instantiating our framework results in a generalizable policy to unseen tasks. Experiments demonstrate that the proposed method can outperform baseline methods on a wide range of applications including gridworld, robotics tasks, and video games.
Illumination-Based Data Augmentation for Robust Background Subtraction

A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In this paper, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly, but also features semantic transformations of illumination which enhance the generalisation of the model. It successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask that is randomly generated. Such data allows us to effectively train an illumination-invariant deep learning model for BGS. Experimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place. The source code of the project is made publicly available at this https URL.
The Art of Tokenization: Blockchain Affordances and the Invention of Future Milieus

Ten years after the introduction of the Bitcoin protocol, an increasing number of art-tech startups and more or less independent initiatives have begun to explore second-generation blockchains such as Ethereum and the emergent practice of tokenization (i.e., the issuance of new cryptoassets primarily to self-fund decentralized projects) as a means to intervene in the structures and processes underlying the rampant financialization of art. Yet amidst the volatility of the cryptocurrency market, tokenization has been critiqued as a way to reinscribe and proliferate current financial logics in this new space. Acknowledging such critiques, in this essay I foreground the novelty of cryptotokens and blockchains by exploring different examples of how tokenization has been deployed in the art market-milieu. In spite of recent attempts to extend the scarcity-based paradigm to blockchains, I argue that cryptotokens do introduce differences in kind in the ways in which value generation and distribution are expressed and accounted for in digital environments. In this context, artistic approaches to tokenization can illuminate new aspects of the affordances of these technologies, toward the disintermediation of art production and its networked value from the current institutional-financial milieu. This can open up new ways to reimagine and reprogram financial and social relations, and gesture toward new opportunities and challenges for a practice of digital design focused on the ideation and realization of cryptoeconomic systems.
Safe Return Path Mapping for Drone Applications

In a time of maturation of the drone market, these platforms are becoming increasingly more available and perform a myriad of tasks. In autonomous missions, the stage between the end of the mission and the landing at the Launch Point is of critical importance for the overall success of the mission being performed. In order to increase safety, this paper proposes a solution that uses flight data from previous missions to create a network of safe paths to be used when the drone is returning to the Launch Point. Search algorithms are used to process the network and find the shortest distance path.