Reviewerly/exHarmony huggingface dataset:
Making Decisions with Knowledge Base Repairs

Building large knowledge bases (KBs) is a fundamental task for automated reasoning and intelligent applications. Needing the interaction between domain and modeling knowledge, it is also error-prone. In fact, even well-maintained KBs are often found to lead to unwanted conclusions. We deal with two kinds of decisions associated with faulty KBs. First, which portions of the KB (and their conclusions) can still be trusted? Second, which is the correct way to repair the KB? Our solution to both problems is based on storing all the information about repairs in a compact data structure.
Aspects of Intercultural Communication in IT: Convergence of Communication and Computing in the Global World of Interconnectedness

The paper focuses on the influence of culture and communication on the way IT processes are managed. The global world is very much intercultural and interconnected, mostly due to the vast impact of the Internet communication. To communicate efficiently, the intercultural aspects of information transferred by computerized information systems must be taken into account. There are many issues which have to be addressed by the IT departments in companies if they want to be successful and competitive. The paper stresses the importance of interculturality in the global IT world and tries to find key issues which are crucial to stability of IT processes in companies. There are also very important issues connected to computational linguistics and machine learning which can be utilized when developing tools for intercultural global world because both computational linguistics and machine learning can bring new ideas and issues to areas such as eLearning. In eLearning processes the use of artificial intelligence though computational linguistics and machine learning can eliminate the potential bottlenecks which we are now facing globally.
Health State Modeling and Prediction based on Hidden Markov Models

The collective health clinic data of people in a society is surmised to have a variety of characteristic health states and certain dynamics governing health state changes over time. Given such a collection of samples, we propose a way of estimating a set of health states in dynamic context using the tool of hidden Markov model (HMM). We also present a method of predicting the future health states based on the Markov dynamics or a set of state duration statistics derived from the model parameters and data clusters. In the proposed method we design a number of HMMs, each for a set of sequences with a particular disease history of interest. They are used to predict the future health states on the basis of corresponding diseases or health problems. Health state prediction as a service can be presented with a set of potential future states and over a number of years into the future. Experimental results have shown a baseline performance of 48\% for single year prediction with a model of sixteen states with single hypothesis for the apoplexy history data set. The performance quickly increases to 70\% with two hypotheses. When applied to multiple year prediction, the accuracy decreases by about 12 percentage points or less over the course of five years.
Keyword Extraction Based on word Synonyms Using WORD2VEC

Nowadays, the data revealed by the online individuals are increasing exponentially. The raw information that increasing data holds, transformed into meaningful outputs using machine learning and deep learning methods. Generally, supervised learning methods are used for information extraction and classification. Supervised learning is based on the training set that classification algorithms are trained. In the proposed approach, keyword extraction solution is proposed to classify text data more convenient. The developed solution is based on the Word2Vec algorithm, which works by taking into consideration the semantic meaning of the words unlike general approaches that based on word frequency. A new approach, word embedding algorithm named “Word2Vec”, works by calculating the word weights, semantic relationship, and the final weights of vectors. The obtained keywords are trained with Naive Bayes and Decision Trees methods and the performance of the proposed method is shown by classification example.
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data

Continuous-time Bayesian Networks (CTBNs) represent a compact yet powerful framework for understanding multivariate time-series data. Given complete data, parameters and structure can be estimated efficiently in closed-form. However, if data is incomplete, the latent states of the CTBN have to be estimated by laboriously simulating the intractable dynamics of the assumed CTBN. This is a problem, especially for structure learning tasks, where this has to be done for each element of a super-exponentially growing set of possible structures. In order to circumvent this notorious bottleneck, we develop a novel gradient-based approach to structure learning. Instead of sampling and scoring all possible structures individually, we assume the generator of the CTBN to be composed as a mixture of generators stemming from different structures. In this framework, structure learning can be performed via a gradient-based optimization of mixture weights. We combine this approach with a new variational method that allows for a closed-form calculation of this mixture marginal likelihood. We show the scalability of our method by learning structures of previously inaccessible sizes from synthetic and real-world data.
Multiclass Performance Metric Elicitation

Metric Elicitation is a principled framework for selecting the performance metric that best reflects implicit user preferences. However, available strategies have so far been limited to binary classification. In this paper, we propose novel strategies for eliciting multiclass classification performance metrics using only relative preference feedback. We also show that the strategies are robust to both finite sample and feedback noise.
An Empirical Analysis of Moroccan Dialectal User-Generated Text

With the increase of web use in Morocco today, Internet has become an important source of information. Specifically, across social media, Moroccan people use several languages in their communication leaving behind unstructured user-generated text that present several opportunities for Natural Language Processing. Among languages found in this data, Moroccan Dialectal Arabic stands with an important content and several features. In this paper, we investigate online written text generated by Moroccan users in social media with an emphasis on Moroccan Dialectal Arabic. For this purpose, we follow several steps, using some tools such as a language identification system, in order to conduct a deep study of this data. The most interesting findings that have emerged is the use of code switching, multi-script and low amount of words in Moroccan UGT text.
Quantum state protection in finite-temperature environment via quantum gates

We propose a protocol to protect the quantum states and entanglements from finite-temperature thermal noise via quantum gates. Compared to the common protocols protecting the quantum states and entanglements by using weak measurements and their reversals, no time-consuming weak measurements are needed in the present protocol and consequently, it is much faster. We also discuss the possible implementation of the protocol in cavity QED system.
On the Cross-Finger Similarity of Vein Patterns

Biometric recognition based on finger-vein patterns is gaining more and more attention, as several approaches have been recently proposed to extract discriminative features from vascular structures. In this paper we investigate the similarity between vein patterns of symmetric fingers of the left and right hand of a subject. More in detail, we analyze the performance achievable when using symmetric fingers and geometry- or deep-learning-based feature extraction methods for recognition. A database with acquisitions from left and right index, medium, and ring fingers of 106 subjects is exploited for experimental tests.
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels

We revisit the classic randomized sketch of a tensor product of $q$ vectors $x_i\in\mathbb{R}^n$. The $i$-th coordinate $(Sx)_i$ of the sketch is equal to $\prod_{j = 1}^q \langle u^{i, j}, x^j \rangle / \sqrt{m}$, where $u^{i,j}$ are independent random sign vectors. Kar and Karnick (JMLR, 2012) show that if the sketching dimension $m = \Omega(\epsilon^{-2} C_{\Omega}^2 \log (1/\delta))$, where $C_{\Omega}$ is a certain property of the point set $\Omega$ one wants to sketch, then with probability $1-\delta$, $\|Sx\|_2 = (1\pm \epsilon)\|x\|_2$ for all $x\in\Omega$. However, in their analysis $C_{\Omega}^2$ can be as large as $\Theta(n^{2q})$, even for a set $\Omega$ of $O(1)$ vectors $x$. We give a new analysis of this sketch, providing nearly optimal bounds. Namely, we show an upper bound of $m = \Theta \left (\epsilon^{-2} \log(n/\delta) + \epsilon^{-1} \log^q(n/\delta) \right ),$ which by composing with CountSketch, can be improved to $\Theta(\epsilon^{-2}\log(1/(\delta \epsilon)) + \epsilon^{-1} \log^q (1/(\delta \epsilon))$. For the important case of $q = 2$ and $\delta = 1/\poly(n)$, this shows that $m = \Theta(\epsilon^{-2} \log(n) + \epsilon^{-1} \log^2(n))$, demonstrating that the $\epsilon^{-2}$ and $\log^2(n)$ terms do not multiply each other. We also show a nearly matching lower bound of $m = \Omega(\eps^{-2} \log(1/(\delta)) + \eps^{-1} \log^q(1/(\delta)))$. In a number of applications, one has $|\Omega| = \poly(n)$ and in this case our bounds are optimal up to a constant factor. This is the first high probability sketch for tensor products that has optimal sketch size and can be implemented in $m \cdot \sum_{i=1}^q \textrm{nnz}(x_i)$ time, where $\textrm{nnz}(x_i)$ is the number of non-zero entries of $x_i$. Lastly, we empirically compare our sketch to other sketches for tensor products, and give a novel application to compressing neural networks.
Long Short Term Memory Networks for Light Field View Synthesis

Because light field devices have a limited angular resolution, artificially reconstructing intermediate views is an interesting task. In this work, we propose a novel way to solve this problem using deep learning. In particular, the use of Long Short Term Memory Networks on a plane sweep volume is proposed. The approach has the advantage of having very few parameters and can be run on sequences with arbitrary length. We show that our approach yields results that are competitive with the state-of-the-art for dense light fields. Experimental results also show promising results with light fields with wider baselines.
Application of an Intelligent Request Distribution Broker in Two-Layer Cloud-Based Web System

Nowadays, a significant part of human activity is supported by information systems, especially Web systems, hosted in the Web clouds. In the article, we attempt to answer the question whether the cooperation of the non-intelligent and intelligent HTTP request distribution strategies eliminates the shortcomings of these strategies and increases the quality of request servicing in a Web cloud. We present the strategies used, a test bed and the results of the conducted experiments. At the end of the article we discuss results and present final conclusions.
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data

Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to certain shape constraints (such as nonnegativity). Here we propose Deep Random Splines, a flexible class of random functions obtained by transforming Gaussian noise through a deep neural network whose output are the parameters of a spline. Unlike Gaussian processes, Deep Random Splines allow us to readily enforce shape constraints while inheriting the richness and tractability of deep generative models. We also present an observational model for point process data which uses Deep Random Splines to model the intensity function of each point process and apply it to neural population data to obtain a low-dimensional representation of spiking activity. Inference is performed via a variational autoencoder that uses a novel recurrent encoder architecture that can handle multiple point processes as input. We use a newly collected dataset where a primate completes a pedaling task, and observe better dimensionality reduction with our model than with competing alternatives.
Generalization in multitask deep neural classifiers: a statistical physics approach

A proper understanding of the striking generalization abilities of deep neural networks presents an enduring puzzle. Recently, there has been a growing body of numerically-grounded theoretical work that has contributed important insights to the theory of learning in deep neural nets. There has also been a recent interest in extending these analyses to understanding how multitask learning can further improve the generalization capacity of deep neural nets. These studies deal almost exclusively with regression tasks which are amenable to existing analytical techniques. We develop an analytic theory of the nonlinear dynamics of generalization of deep neural networks trained to solve classification tasks using softmax outputs and cross-entropy loss, addressing both single task and multitask settings. We do so by adapting techniques from the statistical physics of disordered systems, accounting for both finite size datasets and correlated outputs induced by the training dynamics. We discuss the validity of our theoretical results in comparison to a comprehensive suite of numerical experiments. Our analysis provides theoretical support for the intuition that the performance of multitask learning is determined by the noisiness of the tasks and how well their input features align with each other. Highly related, clean tasks benefit each other, whereas unrelated, clean tasks can be detrimental to individual task performance.
Generating Cross-Domain Text Classification Corpora from Social Media Comments

In natural language processing (NLP), cross-domain text classification problems like cross-topic, cross-genre or cross-language authorship attribution are characterized by having different contexts for training and testing data. That is, learning algorithms which are trained on the specific properties of the training data have to make predictions on test data which comprises substantially different properties. To this end, the corpora that are used for analyses in cross-domain problems are limited in size and variation, decreasing the expressive power and generalizability of the proposed solutions. In this paper, we present a methodological framework and toolset for dynamically creating cross-domain datasets by utilizing millions of Reddit comments. We show that different types of cross-domain datasets such as cross-topic or cross-lingual corpora can be constructed, and demonstrate a wide variety of use cases, including previously unfeasible analyses like cross-lingual authorship attribution on original, non-translated texts. Using state-of-the-art authorship attribution methods, we show the potential of a cross-topic corpus generated by our framework when compared to the corpora that were used in related approaches, and enable the advance of research previously limited by corpora availability.
Causal Unfoldings

In the context of models with mixed nondeterministic and probabilistic choice, we present a concurrent model based on partial orders, more precisely Winskel's event structures. We study its relationship with the interleaving-based model of Segala's probabilistic automata. Lastly, we use this model to give a truly concurrent semantics to an extension of CCS with probabilistic choice, and relate this concurrent semantics to the usual interleaving semantics, thus generalising existing results on CCS, event structures and labelled transition systems.
Accurate Layerwise Interpretable Competence Estimation

Estimating machine learning performance “in the wild” is both an important and unsolved problem. In this paper, we seek to examine, understand, and predict the pointwise competence of classification models. Our contributions are twofold: First, we establish a statistically rigorous definition of competence that generalizes the common notion of classifier confidence; second, we present the ALICE (Accurate Layerwise Interpretable Competence Estimation) Score, a pointwise competence estimator for any classifier. By considering distributional, data, and model uncertainty, ALICE empirically shows accurate competence estimation in common failure situations such as class-imbalanced datasets, out-of-distribution datasets, and poorly trained models. Our contributions allow us to accurately predict the competence of any classification model given any input and error function. We compare our score with state-of-the-art confidence estimators such as model confidence and Trust Score, and show significant improvements in competence prediction over these methods on datasets such as DIGITS, CIFAR10, and CIFAR100.
Artificial Neural Networks

The previous chapter discussed various terminology related to Machine Learning and some of the metrics used to check the accuracy of the model. This chapter discusses the concepts of neural networks.
The Label Complexity of Active Learning from Observational Data

Counterfactual learning from observational data involves learning a classifier on an entire population based on data that is observed conditioned on a selection policy. This work considers this problem in an active setting, where the learner additionally has access to unlabeled examples and can choose to get a subset of these labeled by an oracle. Prior work on this problem uses disagreement-based active learning, along with an importance weighted loss estimator to account for counterfactuals, which leads to a high label complexity. We show how to instead incorporate a more efficient counterfactual risk minimizer into the active learning algorithm. This requires us to modify both the counterfactual risk to make it amenable to active learning, as well as the active learning process to make it amenable to the risk. We provably demonstrate that the result of this is an algorithm which is statistically consistent as well as more label-efficient than prior work.
Coresets for Archetypal Analysis

Archetypal analysis represents instances as linear mixtures of prototypes (the archetypes) that lie on the boundary of the convex hull of the data. Archetypes are thus often better interpretable than factors computed by other matrix factorization techniques. However, the interpretability comes with high computational cost due to additional convexity-preserving constraints. In this paper, we propose efficient coresets for archetypal analysis. Theoretical guarantees are derived by showing that quantization errors of k-means upper bound archetypal analysis; the computation of a provable absolute-coreset can be performed in only two passes over the data. Empirically, we show that the coresets lead to improved performance on several data sets.
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks

The advent of deep learning algorithms for mobile devices and sensors has led to a dramatic expansion in the availability and number of systems trained on a wide range of machine learning tasks, creating a host of opportunities and challenges in the realm of transfer learning. Currently, most transfer learning methods require some kind of control over the systems learned, either by enforcing constraints during the source training, or through the use of a joint optimization objective between tasks that requires all data be co-located for training. However, for practical, privacy, or other reasons, in a variety of applications we may have no control over the individual source task training, nor access to source training samples. Instead we only have access to features pre-trained on such data as the output of black-boxes.'' For such scenarios, we consider the multi-source learning problem of training a classifier using an ensemble of pre-trained neural networks for a set of classes that have not been observed by any of the source networks, and for which we have very few training samples. We show that by using these distributed networks as feature extractors, we can train an effective classifier in a computationally-efficient manner using tools from (nonlinear) maximal correlation analysis. In particular, we develop a method we refer to as maximal correlation weighting (MCW) to build the required target classifier from an appropriate weighting of the feature functions from the source networks. We illustrate the effectiveness of the resulting classifier on datasets derived from the CIFAR-100, Stanford Dogs, and Tiny ImageNet datasets, and, in addition, use the methodology to characterize the relative value of different source tasks in learning a target task.
Tailorable Remote Assistance with RemoteAssistKit: A Study of and Design Response to Remote Assistance in the Manufacturing Industry

In this paper we present our findings from interviewing manufacturing employees about their remote assistance practices and requirements to remote assistance technology. We found that their needs were rather heterogeneous regarding mobility, camera setups and location of guidance. We built the prototype RemoteAssistKit (RAK) as a design response to their heterogeneous needs. RAK is a tailorable remote assistance solution, where different mobility modes, camera setups and locations of guidance can be explored and interchanged using a graphical user interface. RAK thus moves some of the design responsibility from the designer to the users, who are empowered to tailor remote assistance to their needs and preferences.
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs

Reinforcement Learning (RL) algorithms typically require millions of environment interactions to learn successful policies in sparse reward settings. Hindsight Experience Replay (HER) was introduced as a technique to increase sample efficiency by reimagining unsuccessful trajectories as successful ones by altering the originally intended goals. However, it cannot be directly applied to visual environments where goal states are often characterized by the presence of distinct visual features. In this work, we show how visual trajectories can be hallucinated to appear successful by altering agent observations using a generative model trained on relatively few snapshots of the goal. We then use this model in combination with HER to train RL agents in visual settings. We validate our approach on 3D navigation tasks and a simulated robotics application and show marked improvement over baselines derived from previous work.