Reviewerly/exHarmony huggingface dataset:
Dynamic backup workers for parallel machine learning

The most popular framework for distributed training of machine learning models is the (synchronous) parameter server (PS). This paradigm consists of $n$ workers, which iteratively compute updates of the model parameters, and a stateful PS, which waits and aggregates all updates to generate a new estimate of model parameters and sends it back to the workers for a new iteration. Transient computation slowdowns or transmission delays can intolerably lengthen the time of each iteration. An efficient way to mitigate this problem is to let the PS wait only for the fastest $n-b$ updates, before generating the new parameters. The slowest $b$ workers are called backup workers. The optimal number $b$ of backup workers depends on the cluster configuration and workload, but also (as we show in this paper) on the hyper-parameters of the learning algorithm and the current stage of the training. We propose DBW, an algorithm that dynamically decides the number of backup workers during the training process to maximize the convergence speed at each iteration. Our experiments show that DBW 1) removes the necessity to tune $b$ by preliminary time-consuming experiments, and 2) makes the training up to a factor $3$ faster than the optimal static configuration.
Towards Automated Website Classification by Deep Learning

In recent years, the interest in Big Data sources has been steadily growing within the Official Statistic community. The Italian National Institute of Statistics (Istat) is currently carrying out several Big Data pilot studies. One of these studies, the ICT Big Data pilot, aims at exploiting massive amounts of textual data automatically scraped from the websites of Italian enterprises in order to predict a set of target variables (e.g. e-commerce) that are routinely observed by the traditional ICT Survey. In this paper, we show that Deep Learning techniques can successfully address this problem. Essentially, we tackle a text classification task: an algorithm must learn to infer whether an Italian enterprise performs e-commerce from the textual content of its website. To reach this goal, we developed a sophisticated processing pipeline and evaluated its performance through extensive experiments. Our pipeline uses Convolutional Neural Networks and relies on Word Embeddings to encode raw texts into grayscale images (i.e. normalized numeric matrices). Web-scraped texts are huge and have very low signal to noise ratio: to overcome these issues, we adopted a framework known as False Positive Reduction, which has seldom (if ever) been applied before to text classification tasks. Several original contributions enable our processing pipeline to reach good classification results. Empirical evidence shows that our proposal outperforms all the alternative Machine Learning solutions already tested in Istat for the same task.
A PCA-like Autoencoder

An autoencoder is a neural network which data projects to and from a lower dimensional latent space, where this data is easier to understand and model. The autoencoder consists of two sub-networks, the encoder and the decoder, which carry out these transformations. The neural network is trained such that the output is as close to the input as possible, the data having gone through an information bottleneck : the latent space. This tool bears significant ressemblance to Principal Component Analysis (PCA), with two main differences. Firstly, the autoencoder is a non-linear transformation, contrary to PCA, which makes the autoencoder more flexible and powerful. Secondly, the axes found by a PCA are orthogonal, and are ordered in terms of the amount of variability which the data presents along these axes. This makes the interpretability of the PCA much greater than that of the autoencoder, which does not have these attributes. Ideally, then, we would like an autoencoder whose latent space consists of independent components, ordered by decreasing importance to the data. In this paper, we propose an algorithm to create such a network. We create an iterative algorithm which progressively increases the size of the latent space, learning a new dimension at each step. Secondly, we propose a covariance loss term to add to the standard autoencoder loss function, as well as a normalisation layer just before the latent space, which encourages the latent space components to be statistically independent. We demonstrate the results of this autoencoder on simple geometric shapes, and find that the algorithm indeed finds a meaningful representation in the latent space. This means that subsequent interpolation in the latent space has meaning with respect to the geometric properties of the images.
Virtual KITTI 2

This paper introduces an updated version of the well-known Virtual KITTI dataset which consists of 5 sequence clones from the KITTI tracking benchmark. In addition, the dataset provides different variants of these sequences such as modified weather conditions (e.g. fog, rain) or modified camera configurations (e.g. rotated by 15 degrees). For each sequence, we provide multiple sets of images containing RGB, depth, class segmentation, instance segmentation, flow, and scene flow data. Camera parameters and poses as well as vehicle locations are available as well. In order to showcase some of the dataset's capabilities, we ran multiple relevant experiments using state-of-the-art algorithms from the field of autonomous driving. The dataset is available for download at https://europe.naverlabs.com/Research/Computer-Vision/Proxy-Virtual-Worlds.
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection

This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning (Bengio et al. 2009; Zaremba and Sutskever 2014). We propose a new method to integrate HRL, experience replay and effective subgoal selection through an implicit curriculum design based on human expertise to support sample-efficient learning and enhance interpretability of the agent's behavior. Human expertise remains indispensable in many areas such as medicine (Buch, Ahmed, and Maruthappu 2018) and law (Cath 2018), where interpretability, explainability and transparency are crucial in the decision making process, for ethical and legal reasons. Our method simplifies the complex task sets for achieving the overall objectives by decomposing them into subgoals at different levels of abstraction. Incorporating relevant subjective knowledge also significantly reduces the computational resources spent in exploration for RL, especially in high speed, changing, and complex environments where the transition dynamics cannot be effectively learned and modelled in a short time. Experimental results in two StarCraft II (SC2) (Vinyals et al. 2017) minigames demonstrate that our method can achieve better sample efficiency than flat and end-to-end RL methods, and provides an effective method for explaining the agent's performance.
Neural Logic Networks

Recent years have witnessed the great success of deep neural networks in many research areas. The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of logical reasoning. However, the concrete ability of logical reasoning is critical to many theoretical and practical problems. In this paper, we propose Neural Logic Network (NLN), which is a dynamic neural architecture that builds the computational graph according to input logical expressions. It learns basic logical operations as neural modules, and conducts propositional logical reasoning through the network for inference. Experiments on simulated data show that NLN achieves significant performance on solving logical equations. Further experiments on real-world data show that NLN significantly outperforms state-of-the-art models on collaborative filtering and personalized recommendation tasks.
Hitting Sets and Reconstruction for Dense Orbits in $\text{VP}_e$ and $ΣΠΣ$ Circuits

In this paper we study polynomials in $\text{VP}_e$ (polynomial-sized formulas) and in $ΣΠΣ$ (polynomial-size depth-$3$ circuits) whose orbits, under the action of the affine group $\text{GL}_n^{\text{aff}}(\mathbb{F})$, are $\mathit{dense}$ in their ambient class. We construct hitting sets and interpolating sets for these orbits as well as give reconstruction algorithms. As $\text{VP}=\text{VNC}^2$, our results for $\text{VP}_e$ translate immediately to $\text{VP}$ with a quasipolynomial blow up in parameters. If any of our hitting or interpolating sets could be made $\mathit{robust}$ then this would immediately yield a hitting set for the superclass in which the relevant class is dense, and as a consequence also a lower bound for the superclass. Unfortunately, we also prove that the kind of constructions that we have found (which are defined in terms of $k$-independent polynomial maps) do not necessarily yield robust hitting sets.
Proq: Projection-based Runtime Assertions for Debugging on a Quantum Computer

In this paper, we propose Proq, a runtime assertion scheme for testing and debugging quantum programs on a quantum computer. The predicates in Proq are represented by projections (or equivalently, closed subspaces of the state space), following Birkhoff-von Neumann quantum logic. The satisfaction of a projection by a quantum state can be directly checked upon a small number of projective measurements rather than a large number of repeated executions. On the theory side, we rigorously prove that checking projection-based assertions can help locate bugs or statistically assure that the semantic function of the tested program is close to what we expect, for both exact and approximate quantum programs. On the practice side, we consider hardware constraints and introduce several techniques to transform the assertions, making them directly executable on the measurement-restricted quantum computers. We also propose to achieve simplified assertion implementation using local projection technique with soundness guaranteed. We compare Proq with existing quantum program assertions and demonstrate the effectiveness and efficiency of Proq by its applications to assert two ingenious quantum algorithms, the Harrow-Hassidim-Lloyd algorithm and Shor's algorithm.
Noise in Classification

This chapter considers the computational and statistical aspects of learning linear thresholds in presence of noise. When there is no noise, several algorithms exist that efficiently learn near-optimal linear thresholds using a small amount of data. However, even a small amount of adversarial noise makes this problem notoriously hard in the worst-case. We discuss approaches for dealing with these negative results by exploiting natural assumptions on the data-generating process.
A multipurpose robotic glove designed to teach sign language through guided manual motions

Projections show the numbers of deaf and deafblind individuals are quickly increasing, as both the number of deaf and blind individuals are expected to double within the next 50 years. Additionally, there is limited support for deafblind individuals, as they have little sensory access to the world. Most existing solutions are inadequate because they are either not portable, readily accessible or are very expensive. The goal of our project is to create a low cost, accessible solution for those with these disabilities: a portable device that utilizes the sense of touch to teach sign language, instead of conventional sign language learning methods that rely on sight. By doing this, a more immersive and personalized experience is created. The implementation of this solution is two-pronged: first, the physical portion of the solution is equipped with servo motors that control the pulling and release of a cord threaded through rings on the fingers that moves the user's hands into various sign language positions, which is closely modeled after human hand anatomy. Secondly, feasibility is determined for an AI algorithm to take in data from an external camera to efficiently add new signs to the glove, and also to track the user's sign language patterns and rate the user's accuracy with various signs. Through these mechanisms the user is engaged in learning new signs and expanding their sign language vocabulary, all without their sense of sight. We were successful in creating a working prototype analyzed through hand-tracking mechanisms, and achieving a 91-92% accuracy for the American Sign Language alphabet.
Anahad Naad and Pictorial Resonance: The Halo and Sonic Vibration in Sikh Art

The halo is a familiar symbol across Buddhist, Christian, and Mughal art, but its role in Sikh visual media has yet to be acknowledged and examined. This article studies the Sikh halo in relation to key philosophical concepts of dasam dwar (the tenth door/opening, located at the crown of the head) and anahad naad (an embodied perception of an internally heard music). When viewed from a Sikh perspective, the halo emerges as an external marker of an internal attunement to the divine one. As spectators, we attune to the latent sonic frequencies of the halo, which reflect the spiritual attainment of Sikh Gurus and other holy figures. Sikh art allows the inner/outer dialectic of the halo to come into view: to our eyes, the halo signals spiritual realization, while to our ears, it invites us to contemplate the intensity of sonic vibration that resounds through and emits from the divine body in tandem with the expressive kirtan of Bhai Mardana and his rabab (a plucked chordophone). &#x0D; &#x0D; Drawing on select artwork, I invite spectators to experience the sonic dimension through attention to the unique positioning of the halo in relation to the rabab. My critical focus on sonic vibration as suggested by a symbolic connection between the halo and anahad naad allows a concentration at the head to offer a new vantage point for understanding the iconographical presence of this symbol. This article centers Sikh art within the long history of the halo while highlighting how this symbol establishes a unique epistemological system of expressive meaning.
Using Outstanding Translated Children's Books in the School Setting to Promote an Understanding of Other Countries and Cultures

The importance of translated children's books to the literary and cultural development of children is well documented, and the study of other countries is an important part of the school curriculum across grade levels. Therefore, purposes of the research were: (a) to share results of research on outstanding translated children's books published from 1990-2000 and provide a bibliography of these books that can be used by librarians, teachers, and students in a study of other countries; and (b) to present an application of a collaborative study on the use of translated books by a middle school librarian and teachers in a real-life setting.
Incremental Heuristic Search in Games: The Quest for Speed

Robot path-planning methods can be used in real-time computer games but then need to run fast to ensure that the game characters move in real time, an issue addressed by incremental heuristic search methods. In this paper, we demon- strate how one can speed up D* Lite, an incremental heuristic search method that implements planning with the freespace assumption to move game characters in initially unknown or partially unknown terrain to given goal coordinates. We speed up D* Lite by implementing its priority queue with buckets rather than a binary heap. This non-trivial change reduces its runtime by a factor of two and effectively doubles the number of game characters that real-time computer games can afford.
One-Way Analysis Of Variance

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.
Flagpoles anyone? Causal and explanatory asymmetries

This paper discusses some procedures developed in recent work in machine learning for inferring causal direction from observational data. The role of independence and invariance assumptions is emphasized. Several familiar examples including Hempel’s flagpole problem are explored in the light of these ideas. The framework is then applied to problems having to do with explanatory direction in non-causal explanation.
Correspondence-based analogies for choosing problem representations

Mathematics and computing students learn new concepts and fortify their expertise by solving problems. The representation of a problem, be it through algebra, diagrams, or code, is key to understanding and solving it. Multiple-representation interactive environments are a promising approach, but the task of choosing an appropriate representation is largely placed on the user. We propose a new method to recommend representations based on correspondences: conceptual links between domains. Correspondences can be used to analyse, identify, and construct analogies even when the analogical target is unknown. This paper explains how correspondences build on probability theory and Gentner's structure-mapping framework; proposes rules for semi-automated correspondence discovery; and describes how correspondences can explain and construct analogies.
Emotion-based analysis of programming languages on Stack Overflow

When developing a software engineering project, selecting the most appropriate programming language is a crucial step. Most often, feeling at ease with the possible options becomes almost as relevant as the technical features of the language. Therefore, it appears to be worth analyzing the role that the emotional component plays in this process. In this article, we analyze the trend of the emotions expressed by developers in 2018 on the Stack Overflow platform in posts concerning 26 programming languages. To do so, we propose a learning model trained by distant supervision and the comparison of two different classifier architectures.
A New Validity Index in Overlapping Clusters for Medical Images

Detecting automatically overlapping structures is a major issue in segmentation. In addition, the assessment of the quality of the clusters produced by fuzzy segmentation algorithms is one of the challenging tasks in segmentation process. To address this issue, a wide variety of functions called validity indexes have been proposed and developed. The overlap phenomenon constitute a source of failure for most of these indexes, in this context we propose a new cluster validity index VECS to recognize the optimal number of clusters adapted to the fuzzy c-means algorithm; based on the entropy, on the partition coefficient functions and on two criteria: the compactness within the classes and the inter-classes separation. We conducted experiments on medical imaging data sets, including simulated brain magnetic resonance imaging (MRI) and leukemia images. The experimental results reveal that, our VECS validity index outperform the other existing methods and has greater ability to identify the appropriate number of segments on high overlapped data sets.
Class-wise Dynamic Graph Convolution for Semantic Segmentation

Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-attention mechanism. In order to avoid potential misleading contextual information aggregation in previous works, we propose a class-wise dynamic graph convolution (CDGC) module to adaptively propagate information. The graph reasoning is performed among pixels in the same class. Based on the proposed CDGC module, we further introduce the Class-wise Dynamic Graph Convolution Network(CDGCNet), which consists of two main parts including the CDGC module and a basic segmentation network, forming a coarse-to-fine paradigm. Specifically, the CDGC module takes the coarse segmentation result as class mask to extract node features for graph construction and performs dynamic graph convolutions on the constructed graph to learn the feature aggregation and weight allocation. Then the refined feature and the original feature are fused to get the final prediction. We conduct extensive experiments on three popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012 and COCO Stuff, and achieve state-of-the-art performance on all three benchmarks.
DigComp: Methodological frame for measuring digital competencies

This review paper is based on a desk study of the Digital Competence Framework -DigComp developed by the European Commission.The framework was based on the results of a survey of a sample of the European Union population, which showed that a large percentage had underdeveloped digital skills.The aim of this paper is to review the current state of the literature, which is based on the existing DigComp framework, its fields and skills within those areas, which covers the territory of the Republic of Serbia in addition to the other countries.Besides that, a comparison was made of certain areas of the DigComp 2.0 framework with previous ones (DigComp 1.0), and a brief overview of their implementation in practice.It was found that there was space for further study of this topic, and this contributed to the identification of directions for future research.The next step will be to measure students' digital skills.
Secure Computation of the $$k^{\mathrm {th}}$$-Ranked Element in a Star Network

We consider the problem of securely computing the $$k^{\mathrm {th}}$$ -ranked element in a sequence of n private integers distributed among n parties. The $$k^{\mathrm {th}}$$ -ranked element (e.g., minimum, maximum, median) is of particular interest in collaborative benchmarking and auctions. Previous secure protocols for the $$k^{\mathrm {th}}$$ -ranked element require a communication channel between each pair of parties. A server model naturally fits with the client-server architecture of Internet applications in which clients are connected to the server and not to other clients. It simplifies secure computation by reducing the number of rounds and improves its performance and scalability. In this paper, we propose different approaches for privately computing the $$k^{\mathrm {th}}$$ -ranked element in the server model, using either garbled circuits or threshold homomorphic encryption. Our schemes have a constant number of rounds and can compute the $$k^{\mathrm {th}}$$ -ranked element within seconds for up to 50 clients in a WAN.