Reviewerly/exHarmony huggingface dataset:
Transformers Generalize to the Semantics of Logics

We show that neural networks can learn the semantics of propositional and linear-time temporal logic (LTL) from imperfect training data. Instead of only predicting the truth value of a formula, we use a Transformer architecture to predict the solution for a given formula, e.g., a variable assignment for a formula in propositional logic. Most formulas have many solutions and the training data thus depends on the particularities of the generator. We make the surprising observation that while the Transformer does not perfectly predict the generator's output, it still produces correct solutions to almost all formulas, even when its prediction deviates from the generator. It appears that it is easier to learn the semantics of the logics than the particularities of the generator. We observe that the Transformer preserves this semantic generalization even when challenged with formulas of a size it has never encountered before. Surprisingly, the Transformer solves almost all LTL formulas in our test set including those for which our generator timed out.
Learning Discrete Structured Representations by Adversarially Maximizing  Mutual Information

We propose learning discrete structured representations from unlabeled data by maximizing the mutual information between a structured latent variable and a target variable. Calculating mutual information is intractable in this setting. Our key technical contribution is an adversarial objective that can be used to tractably estimate mutual information assuming only the feasibility of cross entropy calculation. We develop a concrete realization of this general formulation with Markov distributions over binary encodings. We report critical and unexpected findings on practical aspects of the objective such as the choice of variational priors. We apply our model on document hashing and show that it outperforms current best baselines based on discrete and vector quantized variational autoencoders. It also yields highly compressed interpretable representations.
Analysis of the self projected matching pursuit algorithm

The convergence and numerical analysis of a low memory implementation of the Orthogonal Matching Pursuit greedy strategy, which is termed Self Projected Matching Pursuit, is presented. This approach renders an iterative way of solving the least squares problem with much less storage requirement than direct linear algebra techniques. Hence, it is appropriate for solving large linear systems. The analysis highlights its suitability within the class of well posed problems.
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments

The causal relationships among a set of random variables are commonly represented by a Directed Acyclic Graph (DAG), where there is a directed edge from variable $X$ to variable $Y$ if $X$ is a direct cause of $Y$. From the purely observational data, the true causal graph can be identified up to a Markov Equivalence Class (MEC), which is a set of DAGs with the same conditional independencies between the variables. The size of an MEC is a measure of complexity for recovering the true causal graph by performing interventions. We propose a method for efficient iteration over possible MECs given intervention results. We utilize the proposed method for computing MEC sizes and experiment design in active and passive learning settings. Compared to previous work for computing the size of MEC, our proposed algorithm reduces the time complexity by a factor of $O(n)$ for sparse graphs where $n$ is the number of variables in the system. Additionally, integrating our approach with dynamic programming, we design an optimal algorithm for passive experiment design. Experimental results show that our proposed algorithms for both computing the size of MEC and experiment design outperform the state of the art.
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs

We consider distributions arising from a mixture of causal models, where each model is represented by a directed acyclic graph (DAG). We provide a graphical representation of such mixture distributions and prove that this representation encodes the conditional independence relations of the mixture distribution. We then consider the problem of structure learning based on samples from such distributions. Since the mixing variable is latent, we consider causal structure discovery algorithms such as FCI that can deal with latent variables. We show that such algorithms recover a union of the component DAGs and can identify variables whose conditional distribution across the component DAGs vary. We demonstrate our results on synthetic and real data showing that the inferred graph identifies nodes that vary between the different mixture components. As an immediate application, we demonstrate how retrieval of this causal information can be used to cluster samples according to each mixture component.
Graph-based Nearest Neighbor Search: From Practice to Theory

Graph-based approaches are empirically shown to be very successful for the nearest neighbor search (NNS). However, there has been very little research on their theoretical guarantees. We fill this gap and rigorously analyze the performance of graph-based NNS algorithms, specifically focusing on the low-dimensional (d << \log n) regime. In addition to the basic greedy algorithm on nearest neighbor graphs, we also analyze the most successful heuristics commonly used in practice: speeding up via adding shortcut edges and improving accuracy via maintaining a dynamic list of candidates. We believe that our theoretical insights supported by experimental analysis are an important step towards understanding the limits and benefits of graph-based NNS algorithms.
QEScalor: Quantitative Elastic Scaling Framework in Distributed Streaming Processing

Recently, researchers usually use the elastic scaling techniques as a powerful means of the distributed stream processing systems to deal with the high-speed data stream which arrives continuously and fluctuates constantly. The existing methods allocate the same amount of resources to the instances of the same operator, but they ignore the correlation between the operator performance and resource provision. It may lead to the waste of the resources caused by the over-provision or the huge overhead of the scheduling caused by the under-provision. To solve the above problems, we present a quantitative elastic scaling framework, named QEScalor, to allocate resources for the operator instances quantitatively based on the actual performance requirements. The experimental results show that compared with the existing works, the QEScalor can not only achieve resource-efficient elastic scaling with lower cost, but also it can enhance the total performance of the DSPAs.
Marine Environmental Monitoring Data Based on Information Visualization

Li, J., 2020. Marine environmental monitoring data based on information visualization. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 530–534. Coconut Creek (Florida), ISSN 0749-0208.When the current method was used to analyze the marine environmental monitoring data, it was time-consuming, and the error of analysis results was too large, leading to low efficiency of data collection and low accuracy of data analysis. Therefore, an analysis method of marine environment monitoring data based on information visualization was put forward. The application portal layer, visualization service layer, grid middleware layer and visualization resource layer were used to build the monitoring platform architecture. The monitoring for marine environment data was realized through the grid platform implementation mechanism, information sharing service and visualization service. Through the evaluation equation, thematic map and evaluation report, the marine environmental monitoring data was analyzed. Experimental results show that the proposed method has high efficiency in data acquisition and high accuracy in data analysis.
Flexible distribution-free conditional predictive bands using density estimators.

Conformal methods create prediction bands that control average coverage under no assumptions besides i.i.d. data. Besides average coverage, one might also desire to control conditional coverage, that is, coverage for every new testing point. However, without strong assumptions, conditional coverage is unachievable. Given this limitation, the literature has focused on methods with asymptotical conditional coverage. In order to obtain this property, these methods require strong conditions on the dependence between the target variable and the features. We introduce two conformal methods based on conditional density estimators that do not depend on this type of assumption to obtain asymptotic conditional coverage: Dist-split and CD-split. While Dist-split asymptotically obtains optimal intervals, which are easier to interpret than general regions, CD-split obtains optimal size regions, which are smaller than intervals. CD-split also obtains local coverage by creating a data-driven partition of the feature space that scales to high-dimensional settings and by generating prediction bands locally on the partition elements. In a wide variety of simulated scenarios, our methods have a better control of conditional coverage and have smaller length than previously proposed methods.
Unsupervised Discovery of Object Landmarks via Contrastive Learning

Given a collection of images, humans are able to discover landmarks of the depicted objects by modeling the shared geometric structure across instances. This idea of geometric equivariance has been widely used for unsupervised discovery of object landmark representations. In this paper, we develop a simple and effective approach based on contrastive learning of invariant representations. We show that when a deep network is trained to be invariant to geometric and photometric transformations, representations from its intermediate layers are highly predictive of object landmarks. Furthermore, by stacking representations across layers in a hypercolumn their effectiveness can be improved. Our approach is motivated by the phenomenon of the gradual emergence of invariance in the representation hierarchy of a deep network. We also present a unified view of existing equivariant and invariant representation learning approaches through the lens of contrastive learning, shedding light on the nature of invariances learned. Experiments on standard benchmarks for landmark discovery, as well as a challenging one we propose, show that the proposed approach surpasses prior state-of-the-art.
Depth perception based on monochromatic shape encode-decode structured light method

This paper presents a depth perception system based on one-shot coding structured light method. The designed monochromatic pattern is robust against occlusion and sharp deformation, suitable for dynamic scene, and less sensitive for color interference, with symbol density spectrum (SDS) rule, legitimately increasing number of feature points inside element which further enhancing resolution. For the decoding stage, firstly, the proposed adaptive-symmetry-template (ASD) can robustly detect feature points, secondly, in virtue of transfer learning, using DenseNet to identify element label, thirdly, graph topological structure and look-up-table (LUT) help reducing decoding time. The experiments show that the encoding method increases resolution fairly a lot and for several complex objects, the decoding method can achieve high accuracy and keep robustness.
A MAS-Based Approach for POI Group Recommendation in LBSN

Location-based recommender systems (LBRS) suggest friends, events, and places considering information about geographical locations. These recommendations can be made to individuals but also to groups of users, which implies satisfying the group as a whole. In this work, we analyze different alternatives for POI group recommendations based on a multi-agent system consisting of negotiating agents that represent a group of users. The results obtained thus far indicate that our multi-agent approach outperforms traditional aggregation approaches, and that the usage of LBSN information helps to improve both the quality of the recommendations and the efficiency of the recommendation process.
Digital Inequality in Education in Argentina

This paper – as a part of a broader research on the policies and practices of digital literacy in relation to inclusion-, seeks to identify the central tensions in teachers´ practices related to the use of digital technologies in education. In Argentina, these tensions have increased, the moment when the pandemic compelled the educational system at all levels, to switch from the physical to the virtual classroom. Despite the recent date of these events, in this study we aim to reflect on the first findings. We will continue this research through interviews and reports analysis, in the coming period.
Developing an android-based learning media on human auditory system for junior high school students

Abstract The purpose of this research was to produce an Android-based learning media on Human Auditory System for VIII graders of Junior High School. The product is packed in the Application Package File (.apk) format and was triumphantly developed using the Research and Development model consisting of several stages: Analysis, Design, Development, Implementation, and Evaluation. The application contains Objectives, Materials, Simulation, and Interactive Evaluation. The product entitled “Human Auditory System” could be accessed on the Google Play Store. The application was assessed by materials experts, media experts, and teachers. Moreover, the product has gone through three stages of evaluation in VIII Grade of Junior High School students involving six students in One-to-One Trial, 12 students in Small-Scale Trial, and 30 students in Field Trial. The results showed that this product was conveniently used as the learning media for Human Auditory Systems in VIII Grade of Junior High School with a validity value of 4.22 (excellent), and a practicality score of 4.31 (excellent).
Anomaly Detection in Activities of Daily Living with Linear Drift

Anomalyq detection in Activities of Daily Living (ADL) plays an important role in e-health applications. An abrupt change in the ADL performed by a subject might indicate that she/he needs some help. Another important issue related with e-health applications is the case where the change in ADL undergoes a linear drift, which occurs in cognitive decline, Alzheimer's disease or dementia. This work presents a novel method for detecting a linear drift in ADL modelled as circular normal distributions. The method is based on techniques commonly used in Statistical Process Control and, through the selection of a convenient threshold, is able to detect and estimate the change point in time when a linear drift started. Public datasets have been used to assess whether ADL can be modelled by a mixture of circular normal distributions. Exhaustive experimentation was performed on simulated data to assess the validity of the change detection algorithm, the results showing that the difference between the real change point and the estimated change point was $4.90_{+3.17}^{-1.98}$ days on average. ADL can be modelled using a mixture of circular normal distributions. A new method to detect anomalies following a linear drift is presented. Exhaustive experiments showed that this method is able to estimate the change point in time for processes following a linear drift.
Comparative study of graphic-based tag clouds: theory and experimental evaluation for information search

Purpose Graphic-based tag clouds aim to visually represent tag content and tag structure, and then to better represent tagged information for later search. However, few studies have clarified the features among varied visualization approaches involved in graphic-based tag clouds and compared them for the purpose of information search. Design/methodology/approach After reviewing four kinds of graphic-based tag clouds, an experimental demonstration was conducted in our study to verify how user performs in information search for a general seeking task by using them. Precision ratio, recall ratio, clicks on search and time for search were four variables tested in the experiment. Also, two supplementary tests were respectively carried out to manifest how graphic-based tag clouds contributed to the identification of target tags and tag clusters. Findings The experimental results showed that compared to tag content visual tag structure was more important to find related tags from tag clouds for information search. In addition, tag clouds that visually represented the semantic relationships within tags could make user more confident about their search result and carry out a shorter learning process during searching, which signified a tag-based information search path when visual elements were applied. Originality/value This research is one of the first to illustrate systematically the graphic-based tag clouds and their impacts on information search. The research findings could suggest on how to build up more effective and interactive tag clouds and make proposition for the design of search user interface by using graphic-based tag clouds.
Cuatro décadas de políticas para integrar las tecnologías digitales en el aula en Cataluña: acciones, logros y fracasos

In most of the European countries, the implementation of digital technologies has been driven through government education policies. In this contribution, we will focus on the analysis of the role of actions promoted by the Government of Catalonia from the 80s to the present taking into account the international studies about digital education. Numerous initiatives and programs have been supported in Catalonia over the years without, in many cases, a lack of continuity and evaluation of the results obtained. However, the new initiatives on digital education in Catalonia open new expectations. For the first time, the focus is not only the integration of digital technology, but also the improvement of education and not focused exclusively on the use of digital technologies.
Biotouch: a framework based on behavioral biometrics and location for continuous authentication on mobile banking applications

This article describes a multimodal framework based on behavioral biometrics and location for continuous user authentication in mobile banking applications. The proposed framework joins the typing and screen slide behavior patterns with the GPS location of the user in the authentication process. During the performance evaluation experiments, we used more than 6 different machine learning algorithms. Main results show an average accuracy between 78 and 91%.
Building reproducible, reusable, and robust machine learning software

We have seen significant achievements with machine learning in recent years. Yet reproducing results for state-of-the-art deep learning methods is seldom straightforward. High variance of some methods can make learning particularly difficult. Furthermore, results can be brittle to even minor perturbations in the domain or experimental procedure. In this talk, I will review challenges that arise in experimental techniques and reporting procedures in deep learning, with a particular focus on reinforcement learning. I will also describe several recent results and guidelines designed to make future results more reproducible, reusable and robust.
Positive Semidefinite Matrix Factorization: A Connection With Phase Retrieval and Affine Rank Minimization

Positive semidefinite matrix factorization (PSDMF) expresses each entry of a nonnegative matrix as the inner product of two positive semidefinite (psd) matrices. When all these psd matrices are constrained to be diagonal, this model is equivalent to nonnegative matrix factorization. Applications include combinatorial optimization, quantum-based statistical models, and recommender systems, among others. However, despite the increasing interest in PSDMF, only a few PSDMF algorithms were proposed in the literature. In this work, we provide a collection of tools for PSDMF, by showing that PSDMF algorithms can be designed based on phase retrieval (PR) and affine rank minimization (ARM) algorithms. This procedure allows a shortcut in designing new PSDMF algorithms, as it allows to leverage some of the useful numerical properties of existing PR and ARM methods to the PSDMF framework. Motivated by this idea, we introduce a new family of PSDMF algorithms based on iterative hard thresholding (IHT). This family subsumes previously-proposed projected gradient PSDMF methods. We show that there is high variability among PSDMF optimization problems that makes it beneficial to try a number of methods based on different principles to tackle difficult problems. In certain cases, our proposed methods are the only algorithms able to find a solution. In certain other cases, they converge faster. Our results support our claim that the PSDMF framework can inherit desired numerical properties from PR and ARM algorithms, leading to more efficient PSDMF algorithms, and motivate further study of the links between these models.
Avoiding Vendor-Lockin in Cloud Monitoring Using Generic Agent Templates

Cloud computing passed the hype cycle long ago and firmly established itself as a future technology since then. However, to utilize the cloud optimally, and therefore, as cost-efficiently as possible, a continuous monitoring is key to prevent an over- or under-commissioning of resources. However, selecting a suitable monitoring solution is a challenging task. Monitoring agents that collect monitoring data are spread across the monitored IT environment. Therefore, the possibility of vendor lock-ins leads to a lack of flexibility when the cloud environment or the business needs change. To handle these challenges, we introduce generic agent templates that are applicable to many monitoring systems and support a replacement of monitoring systems. Solution-specific technical details of monitoring agents are abstracted from and system administrators only need to model generic agents, which can be transformed into solution-specific monitoring agents. The transformation logic required for this process is provided by domain experts to not further burden system administrators. Furthermore, we introduce an agent lifecycle to support the system administrator with the management and deployment of generic agents.
Threat of Adversarial Attacks on Face Recognition: A Comprehensive Survey.

Face recognition (FR) systems have demonstrated outstanding verification performance, suggesting suitability for real-world applications, ranging from photo tagging in social media to automated border control (ABC). In an advanced FR system with deep learning-based architecture, however, promoting the recognition efficiency alone is not sufficient and the system should also withstand potential kinds of attacks designed to target its proficiency. Recent studies show that (deep) FR systems exhibit an intriguing vulnerability to imperceptible or perceptible but natural-looking adversarial input images that drive the model to incorrect output predictions. In this article, we present a comprehensive survey on adversarial attacks against FR systems and elaborate on the competence of new countermeasures against them. Further, we propose a taxonomy of existing attack and defense strategies according to different criteria. Finally, we compare the presented approaches according to techniques' characteristics.