Reviewerly/exHarmony huggingface dataset:
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming

We propose a new method to approximate the posterior distribution of probabilistic programs by means of computing guaranteed bounds. The starting point of our work is an interval-based trace semantics for a recursive, higher-order probabilistic programming language with continuous distributions. Taking the form of (super-/subadditive) measures, these lower/upper bounds are non-stochastic and provably correct: using the semantics, we prove that the actual posterior of a given program is sandwiched between the lower and upper bounds (soundness); moreover the bounds converge to the posterior (completeness). As a practical and sound approximation, we introduce a weight-aware interval type system, which automatically infers interval bounds on not just the return value but also weight of program executions, simultaneously. We have built a tool implementation, called GuBPI, which automatically computes these posterior lower/upper bounds. Our evaluation on examples from the literature shows that the bounds are useful, and can even be used to recognise wrong outputs from stochastic posterior inference procedures.
Machine Translation from Signed to Spoken Languages: State of the Art and Challenges

Automatic translation from signed to spoken languages is an interdisciplinary research domain, lying on the intersection of computer vision, machine translation and linguistics. Nevertheless, research in this domain is performed mostly by computer scientists in isolation. As the domain is becoming increasingly popular - the majority of scientific papers on the topic of sign language translation have been published in the past three years - we provide an overview of the state of the art as well as some required background in the different related disciplines. We give a high-level introduction to sign language linguistics and machine translation to illustrate the requirements of automatic sign language translation. We present a systematic literature review to illustrate the state of the art in the domain and then, harking back to the requirements, lay out several challenges for future research. We find that significant advances have been made on the shoulders of spoken language machine translation research. However, current approaches are often not linguistically motivated or are not adapted to the different input modality of sign languages. We explore challenges related to the representation of sign language data, the collection of datasets, the need for interdisciplinary research and requirements for moving beyond research, towards applications. Based on our findings, we advocate for interdisciplinary research and to base future research on linguistic analysis of sign languages. Furthermore, the inclusion of deaf and hearing end users of sign language translation applications in use case identification, data collection and evaluation is of the utmost importance in the creation of useful sign language translation models. We recommend iterative, human-in-the-loop, design and development of sign language translation models.
Active Learning Polynomial Threshold Functions

We initiate the study of active learning polynomial threshold functions (PTFs). While traditional lower bounds imply that even univariate quadratics cannot be non-trivially actively learned, we show that allowing the learner basic access to the derivatives of the underlying classifier circumvents this issue and leads to a computationally efficient algorithm for active learning degree-$d$ univariate PTFs in $\tilde{O}(d^3\log(1/\varepsilon\delta))$ queries. We also provide near-optimal algorithms and analyses for active learning PTFs in several average case settings. Finally, we prove that access to derivatives is insufficient for active learning multivariate PTFs, even those of just two variables.
A domain specific language for data-centric infographics: technical report

The production process of data-centric infographics entails problems related to a disconnection between the supporting software environments. We investigate those problems and redesigns this process following the model-driven paradigm. We have designed a domain-specific language to model infographics, and implemented an interpreter that generates the infographics automatically. To inform the engineering of the DSL and interpreter, we have analysed 58 infographics reporting on ethical, social and environmental accounting, and 10 infographic design tools. We then have tested our proposal by means of modelling a sample of 10 infographics and generating them. We have also conducted a controlled experiment to assess the extent to which the generated infograhics are as visually attractive as the original ones and whether the participants can identify whether the infographic they see is original or generated. This report provides the details of this research project: conceptual model, collected data, analysis, and supporting instruments.
Label conditioned segmentation

Semantic segmentation is an important task in computer vision that is often tackled with convolutional neural networks (CNNs). A CNN learns to produce pixel-level predictions through training on pairs of images and their corresponding ground-truth segmentation labels. For segmentation tasks with multiple classes, the standard approach is to use a network that computes a multi-channel probabilistic segmentation map, with each channel representing one class. In applications where the image grid size (e.g., when it is a 3D volume) and/or the number of labels is relatively large, the standard (baseline) approach can become prohibitively expensive for our computational resources. In this paper, we propose a simple yet effective method to address this challenge. In our approach, the segmentation network produces a single-channel output, while being conditioned on a single class label, which determines the output class of the network. Our method, called label conditioned segmentation (LCS), can be used to segment images with a very large number of classes, which might be infeasible for the baseline approach. We also demonstrate in the experiments that label conditioning can improve the accuracy of a given backbone architecture, likely, thanks to its parameter efficiency. Finally, as we show in our results, an LCS model can produce previously unseen fine-grained labels during inference time, when only coarse labels were available during training. We provide all of our code here: https://github.com/tym002/Label-conditioned-segmentation
De-rendering 3D Objects in the Wild

With increasing focus on augmented and virtual reality applications (XR) comes the demand for algorithms that can lift objects from images and videos into representations that are suitable for a wide variety of related 3D tasks. Large-scale deployment of XR devices and applications means that we cannot solely rely on supervised learning, as collecting and annotating data for the unlimited variety of objects in the real world is infeasible. We present a weakly supervised method that is able to decompose a single image of an object into shape (depth and normals), material (albedo, reflectivity and shininess) and global lighting parameters. For training, the method only relies on a rough initial shape estimate of the training objects to bootstrap the learning process. This shape supervision can come for example from a pretrained depth network or - more generically - from a traditional structure-from-motion pipeline. In our experiments, we show that the method can successfully de-render 2D images into a decomposed 3D representation and generalizes to unseen object categories. Since in-the-wild evaluation is difficult due to the lack of ground truth data, we also introduce a photo-realistic synthetic test set that allows for quantitative evaluation.
Bounded Space Differentially Private Quantiles

Estimating the quantiles of a large dataset is a fundamental problem in both the streaming algorithms literature and the differential privacy literature. However, all existing private mechanisms for distribution-independent quantile computation require space at least linear in the input size $n$. In this work, we devise a differentially private algorithm for the quantile estimation problem, with strongly sublinear space complexity, in the one-shot and continual observation settings. Our basic mechanism estimates any $\alpha$-approximate quantile of a length-$n$ stream over a data universe $\mathcal{X}$ with probability $1-\beta$ using $O\left( \frac{\log (|\mathcal{X}|/\beta) \log (\alpha \epsilon n)}{\alpha \epsilon} \right)$ space while satisfying $\epsilon$-differential privacy at a single time point. Our approach builds upon deterministic streaming algorithms for non-private quantile estimation instantiating the exponential mechanism using a utility function defined on sketch items, while (privately) sampling from intervals defined by the sketch. We also present another algorithm based on histograms that is especially suited to the multiple quantiles case. We implement our algorithms and experimentally evaluate them on synthetic and real-world datasets.
METHODOLOGICAL ACTIVITIES OF PEDAGOGICAL CONSULTATION ON OVERCOMING EXISTING PROBLEMS IN THE EDUCATIONAL PROCESS OF CHILDREN WITH SPECIAL EDUCATIONAL NEEDS/ՄԱՆԿԱՎԱՐԺԱԿԱՆ ԽՈՐՀՐԴԱՏՎՈՒԹՅԱՆ ՄԵԹՈԴԱԿԱՆ ԳՈՐԾԱՌՈՒՅԹՆԵՐԸ՝ ՈՒՂՂՎԱԾ ԿԱՊԿՈւ ԵՐԵԽԱՆԵՐԻ ԿՐԹԱԿԱՆ ԳՈՐԾԸՆԹԱՑՈՒՄ ԱՌԿԱ ՀԻՄՆԱԽՆԴԻՐՆԵՐԻ ՀԱՂԹԱՀԱՐՄԱՆԸ

The article presents the existing problems in the educational process of children in need of special learning conditions. The types, methods of pedagogical counseling are also considered. The purpose of this paper is to emphasize the methodological significance of pedagogical counseling in the context of universal inclusion in the effective implementation of the activities of various subjects of the educational process./Հոդվածում ներկայացվում են ԿԱՊԿՈւ երեխաների կրթական գործընթացում առկա հիմնախնդիրները: Դիտարկվում են նաև մանկավարժական խորհրդատվության իրականացման ձևերը, եղանակ-ները և մեթոդները: Հոդվածի նպատակն է՝ ընդգծել մանկավարժական խորհրդատվության մեթոդական նշանակությունը կրթական գործընթացի տարբեր սուբյեկտների գործունեության արդյունավետ իրականացման ընթացքում՝ համընդհանուր ներառականության պայմաններում:
Putting People in their Place: Monocular Regression of 3D People in Depth

Given an image with multiple people, our goal is to directly regress the pose and shape of all the people as well as their relative depth. Inferring the depth of a person in an image, however, is fundamentally ambiguous without knowing their height. This is particularly problematic when the scene contains people of very different sizes, e.g. from infants to adults. To solve this, we need several things. First, we develop a novel method to infer the poses and depth of multiple people in a single image. While previous work that estimates multiple people does so by reasoning in the image plane, our method, called BEV, adds an additional imaginary Bird's-Eye-View representation to explicitly reason about depth. BEV reasons simultaneously about body centers in the image and in depth and, by combing these, estimates 3D body position. Unlike prior work, BEV is a single-shot method that is end-to-end differentiable. Second, height varies with age, making it impossible to resolve depth without also estimating the age of people in the image. To do so, we exploit a 3D body model space that lets BEV infer shapes from infants to adults. Third, to train BEV, we need a new dataset. Specifically, we create a "Relative Human" (RH) dataset that includes age labels and relative depth relationships between the people in the images. Extensive experiments on RH and AGORA demonstrate the effectiveness of the model and training scheme. BEV outperforms existing methods on depth reasoning, child shape estimation, and robustness to occlusion. The code and dataset are released for research purposes.
Unsupervised Complementary-aware Multi-process Fusion for Visual Place  Recognition

A recent approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques simultaneously. However, selecting the optimal set of techniques to use in a specific deployment environment a-priori is a difficult and unresolved challenge. Further, to the best of our knowledge, no method exists which can select a set of techniques on a frame-by-frame basis in response to image-to-image variations. In this work, we propose an unsupervised algorithm that finds the most robust set of VPR techniques to use in the current deployment environment, on a frame-by-frame basis. The selection of techniques is determined by an analysis of the similarity scores between the current query image and the collection of database images and does not require ground-truth information. We demonstrate our approach on a wide variety of datasets and VPR techniques and show that the proposed dynamic multi-process fusion (Dyn-MPF) has superior VPR performance compared to a variety of challenging competitive methods, some of which are given an unfair advantage through access to the ground-truth information.
Understanding the propagation of excitations in quantum spin chains with different kind of interactions

The dynamical behaviour of the quantum state of different quantum spin chains, with designed site dependent interaction strengths, is analyzed when the initial state belongs to the one excitation subspace. It is shown that the inhomogeneous chains are able to transfer excitations with near perfect fidelity. This behaviour is found for two very different spin chain Hamiltonians. The first one is the ferromagnetic Heisenberg Hamiltonian with nearest neighbor interactions, the second one describes a chain with long range anisotropic interactions which are ferromagnetic in the $z$ direction and antiferromagnetic in the $(x,y)$ plane. It is shown that both designed chains have in common a partially ordered spectrum and well localized eigenvectors. This physical trait unifies the description of both kind of systems.
TRIP: Trustless Coercion-Resistant In-Person Voter Registration

Remote electronic voting is convenient and flexible, but presents risks of coercion and vote buying. One promising mitigation strategy enables voters to give a coercer fake voting credentials, which silently cast votes that do not count. However, current proposals make problematic assumptions during credential issuance, such as relying on a trustworthy registrar, on trusted hardware, or on voters interacting with multiple registrars. We present TRIP, the first voter registration scheme that addresses these challenges by leveraging the physical security of in-person interaction. Voters use a kiosk in a privacy booth to print real and fake paper credentials, which appear indistinguishable to others. Voters interact with only one authority, need no trusted hardware during credential issuance, and need not trust the registrar except when actually under coercion. For verifiability, each credential includes an interactive zero-knowledge proof, which is sound in real credentials and unsound in fake credentials. Voters learn the difference by observing the order of printing steps, and need not understand the technical details. We prove formally that TRIP satisfies coercion-resistance and verifiability. In a user study with 150 participants, 83% successfully used TRIP.
Lattice-Based Methods Surpass Sum-of-Squares in Clustering

Clustering is a fundamental primitive in unsupervised learning which gives rise to a rich class of computationally-challenging inference tasks. In this work, we focus on the canonical task of clustering $d$-dimensional Gaussian mixtures with unknown (and possibly degenerate) covariance. Recent works (Ghosh et al.\ '20; Mao, Wein '21; Davis, Diaz, Wang '21) have established lower bounds against the class of low-degree polynomial methods and the sum-of-squares (SoS) hierarchy for recovering certain hidden structures planted in Gaussian clustering instances. Prior work on many similar inference tasks portends that such lower bounds strongly suggest the presence of an inherent statistical-to-computational gap for clustering, that is, a parameter regime where the clustering task is \textit{statistically} possible but no \textit{polynomial-time} algorithm succeeds. One special case of the clustering task we consider is equivalent to the problem of finding a planted hypercube vector in an otherwise random subspace. We show that, perhaps surprisingly, this particular clustering model \textit{does not exhibit} a statistical-to-computational gap, even though the aforementioned low-degree and SoS lower bounds continue to apply in this case. To achieve this, we give a polynomial-time algorithm based on the Lenstra--Lenstra--Lovasz lattice basis reduction method which achieves the statistically-optimal sample complexity of $d+1$ samples. This result extends the class of problems whose conjectured statistical-to-computational gaps can be "closed" by "brittle" polynomial-time algorithms, highlighting the crucial but subtle role of noise in the onset of statistical-to-computational gaps.
Expert System for Early Detection and Diagnosis of Central Nervous Diseases in Humans with Forward Chaining and Backward Chaining Methods Using Interactive Multimedia

The tasks of the nervous system contained in the human body include diagnosing, analyzing and conveying information. This information that has been obtained will be combined with the sensory system, and will then be integrated in the brain and signals, then transmitted again to the motor system and autonomic pathways to control movement, visceral activity, and endocrine function. All actions are controlled by nerves which are interconnected to form a signaling network consisting of the sensory system and the motor system. The nervous system has several functions, including immunity and supporting function and modulating the activity of the nervous system. There are many types of neurological diseases found in the medical world, but due to a lack of information and knowledge about the disease. So the researchers made this study using forward and backward chaining reasoning methods, along with neurological disease therapy, ways of healing, and methods of treatment. This system is made so that sufferers can easily understand about neurological diseases, and understand the types of central nervous diseases, along with their healing therapies, and how to treat them. This system provides several solutions to prevent neurological diseases according to the type of disease. The system development methodology uses the waterfall method. Application software used to create this application program is Dreamweaver MX, Adobe Flash CS3, XAMPP, Adobe Photoshop, MySQL, Opera, Mozilla Firefox. The result of this research is an application program that can help users to find out the types of central nervous disease and provide extensive information about central nervous disease and find out how to treat and cure them.
Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds

We consider distributed optimization methods for problems where forming the Hessian is computationally challenging and communication is a significant bottleneck. We leverage randomized sketches for reducing the problem dimensions as well as preserving privacy and improving straggler resilience in asynchronous distributed systems. We derive novel approximation guarantees for classical sketching methods and establish tight concentration results that serve as both upper and lower bounds on the error. We then extend our analysis to the accuracy of parameter averaging for distributed sketches. Furthermore, we develop unbiased parameter averaging methods for randomized second order optimization for regularized problems that employ sketching of the Hessian. Existing works do not take the bias of the estimators into consideration, which limits their application to massively parallel computation. We provide closed-form formulas for regularization parameters and step sizes that provably minimize the bias for sketched Newton directions. Additionally, we demonstrate the implications of our theoretical findings via large scale experiments on a serverless cloud computing platform.
Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection

Foreign substances on the road surface, such as rainwater or black ice, reduce the friction between the tire and the surface. The above situation will reduce the braking performance and make difficult to control the vehicle body posture. In that case, there is a possibility of property damage at least. In the worst case, personal damage will be occured. To avoid this problem, a road anomaly detection model is proposed based on vehicle driving noise. However, the prior proposal does not consider the extra noise, mixed with driving noise, and skipping calculations for moments without vehicle driving. In this paper, we propose a simple driving event extraction method and noise reduction method for improving computational efficiency and anomaly detection performance.
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach

Black box optimization requires specifying a search space to explore for solutions, e.g. a d-dimensional compact space, and this choice is critical for getting the best results at a reasonable budget. Unfortunately, determining a high quality search space can be challenging in many applications. For example, when tuning hyperparameters for machine learning pipelines on a new problem given a limited budget, one must strike a balance between excluding potentially promising regions and keeping the search space small enough to be tractable. The goal of this work is to motivate -- through example applications in tuning deep neural networks -- the problem of predicting the quality of search spaces conditioned on budgets, as well as to provide a simple scoring method based on a utility function applied to a probabilistic response surface model, similar to Bayesian optimization. We show that the method we present can compute meaningful budget-conditional scores in a variety of situations. We also provide experimental evidence that accurate scores can be useful in constructing and pruning search spaces. Ultimately, we believe scoring search spaces should become standard practice in the experimental workflow for deep learning.
On the Evolution of the MCTS Upper Confidence Bounds for Trees by Means of Evolutionary Algorithms in the Game of Carcassonne

Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The MCTS's popularity is based on its extraordinary results in the challenging two-player based game Go, a game considered much harder than Chess and that until very recently was considered infeasible for Artificial Intelligence methods. The success of MCTS depends heavily on how the tree is built and the selection process plays a fundamental role in this. One particular selection mechanism that has proved to be reliable is based on the Upper Confidence Bounds for Trees, commonly referred as UCT. The UCT attempts to nicely balance exploration and exploitation by considering the values stored in the statistical tree of the MCTS. However, some tuning of the MCTS UCT is necessary for this to work well. In this work, we use Evolutionary Algorithms (EAs) to evolve mathematical expressions with the goal to substitute the UCT mathematical expression. We compare our proposed approach, called Evolution Strategy in MCTS (ES-MCTS) against five variants of the MCTS UCT, three variants of the star-minimax family of algorithms as well as a random controller in the Game of Carcassonne. We also use a variant of our proposed EA-based controller, dubbed ES partially integrated in MCTS. We show how the ES-MCTS controller, is able to outperform all these 10 intelligent controllers, including robust MCTS UCT controllers.