Reviewerly/exHarmony huggingface dataset:
Phase unwrapping based on adaptive image in-painting of fringe patterns in measuring gear tooth flanks by laser interferometry

Phase unwrapping in regions of abnormal fringes remains an unresolved issue. In this paper, we present an approach that combines an image-inpainting strategy based on an adaptive window which is obtained according to the density and orientation of fringe patterns and a quality-guided algorithm for phase unwrapping. First, a threshold is set to a quality map to detect the target region. Second, the target region is filled with new phase values by the adaptive image-inpainting method. Then, a quality-guided phase unwrapping algorithm is applied to this newly generated wrapped phase map. Finally, postprocessing of the unwrapped result is performed. The method is validated through several simulation and experiments. The results demonstrate that the proposed algorithm is effective in the presence of abnormal fringes.
Future English Learning: Chatbots and Artificial Intelligence

Advancements in robotic research have enabled robots to assist humans in many ways. Chatbots have been considered useful in many areas and research has increasingly focused on applying this technology to language education. The purpose of this study is to report on and review different types of intelligent chatbots in terms of language learning. The findings reveal that there are few chatbot programs that allow for direct interaction between chatbots and humans through voice recognition systems or texting for the purpose of learning foreign languages. Researchers have investigated the limited use of AI in education fields, including chatbot applications aimed at improving English teaching and learning. Based on their empirical studies, chatbots have proven to have some positive effects on students’ communication skills largely by their effect on expanding the quantity of their interactions, meaning negotiation, increasing their motivation, and on raising their interest in learning. Thus, this study proposes that chatbots can enrich language inputs and bring opportunities for language learners to raise communicative competence. More studies should be conducted to develop chatbots for learning foreign languages. Based on the findings of this study, suggestions for future research directions concerning chatbots in the realm of language education are presented, and further pedagogical implications are discussed.
Cross-lingual Information Retrieval with BERT.

Multiple neural language models have been developed recently, e.g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking. In this paper, we explore the use of the popular bidirectional language model, BERT, to model and learn the relevance between English queries and foreign-language documents in the task of cross-lingual information retrieval. A deep relevance matching model based on BERT is introduced and trained by finetuning a pretrained multilingual BERT model with weak supervision, using home-made CLIR training data derived from parallel corpora. Experimental results of the retrieval of Lithuanian documents against short English queries show that our model is effective and outperforms the competitive baseline approaches.
Learning to Manipulate Object Collections Using Grounded State Representations

We propose a method for sim-to-real robot learning which exploits simulator state information in a way that scales to many objects. We first train a pair of encoder networks to capture multi-object state information in a latent space. One of these encoders is a CNN, which enables our system to operate on RGB images in the real world; the other is a graph neural network (GNN) state encoder, which directly consumes a set of raw object poses and enables more accurate reward calculation and value estimation. Once trained, we use these encoders in a reinforcement learning algorithm to train image-based policies that can manipulate many objects. We evaluate our method on the task of pushing a collection of objects to desired tabletop regions. Compared to methods which rely only on images or use fixed-length state encodings, our method achieves higher success rates, performs well in the real world without fine tuning, and generalizes to different numbers and types of objects not seen during training.
Three Tensions Between Personas and Complex Disability Identities

Literature on the challenges of building personas for marginalized populations has led to attempts to update or improve the format. Construction of personas for people with disabilities and intersecting identities are underexplored in this area. Drawing on past work on the importance of intersectionality in HCI, this paper presents results from a qualitative interview study of nine participants with a) multiple disabilities or b) a disability and one or more other marginalized identities. From these findings, the authors present considerations for designers or researchers interested in creating personas depicting users at the intersection of marginalized identities.
Interactive Event Sequence Prediction for Marketing Analysts

Timestamped event sequences are analyzed to tackle varied problems but have unique challenges in interpretation and analysis. Especially in event sequence prediction, it is difficult to convey the results due to the added uncertainty and complexity introduced by predictive models. In this work, we design and develop ProFlow, a visual analytics system for supporting analysts' workflow of exploring and predicting event sequences. Through an evaluation conducted with four data analysts in a real-world marketing scenario, we discuss the applicability and usefulness of ProFlow as well as its limitations and future directions.
Latest Trends in Recommender Systems applied in the medical domain

Recommender systems use algorithms to provide users with product or service recommendations. In recent times, these systems have been using Artificial Intelligence algorithms. However, choosing a suitable AI algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using AI algorithms often faces problems and raises questions that must be resolved. In this context developing recommender system in the medical domain are presented as complementary tools in decision-making processes in healthcare services, it allows to increase the usability of technologies and reduce information overload in processes. This paper presents a systematic review of the literature that analyze the use of AI algorithms in recommender system and identify new research opportunities.
Smart Platform for Cloud Service Providers

Cloud computing offers many types of computer related services without the direct active management of their users. Cloud Service Providers (CSPs) are responsible to manage these services such as placement of services in the cloud, resource allocation, network monitoring etc. The cloud service provider is required to monitor the network traffic, predict the dynamic traffic changes, and scale out the resources accordingly. We have proposed a platform for cloud service providers that automates the cloud management related services with following modules: (1) traffic monitoring, (2) traffic prediction, (3) virtual service instances placement and (4) traffic load balancing. We have used continuous and periodic approaches for traffic monitoring, Auto-Regressive Integrated Moving Average (ARIMA) model for traffic prediction, Randomized Weighted Majority Algorithm (RWMA) for virtual service instances placement and a threshold-based approach for load balancing. In this paper, we are presenting the performances of our cloud management platform, specially an evaluation of the algorithms used in above mentioned modules. Our results show that, using our proposed modules, the cloud management related services can be automated efficiently and reliably.
On the modelling and testing of a laboratory-scale Foucault pendulum as a precursor for the design of a high-performance measurement instrument

An integrated study is presented on the dynamic modelling and experimental testing of a mid-length Foucault pendulum with the aim of confirming insights from the literature on the reliable operation of this device and setting markers for future research in which the pendulum may be used for the measurement of relativistic effects due to terrestrial gravity. A tractable nonlinear mathematical model is derived for the dynamics of a practical laboratory Foucault pendulum and its performance with and without parametric excitation, and with coupling to long-axis torsion is investigated numerically for different geographical locations. An experimental pendulum is also tested, with and without parametric excitation, and it is shown that the model closely predicts the general precessional performance of the pendulum, for the case of applied parametric excitation of the length, when responding to the Newtonian rotation of the Earth. Many of the principal inherent performance limitations of Foucault pendulums from the literature have been confirmed and a general prescription for design is evolved, placing the beneficial effect of principal parametric resonance of this inherently nonlinear system in a central mitigating position, along with other assistive means of response moderation such as excitational phase control through electromagnetic pushing, enclosure, and the minimization of seismic and EMC noise. It is also shown, through a supporting analysis and calculation, that although the terrestrial measurement of the Lense-Thirring (LT) precession by means of a Foucault pendulum is certainly still within the realms of possibility, there remains a very challenging increase in resolution capability required, in the order of 2 × 109 to be sure of reliable detection, notwithstanding the removal of extraneous motions and interferences. This study sets the scene for a further investigation in the very near future in which these challenges are to be met, so that a new assault can be made on the terrestrial measurement of LT precession.
Seamless Integration of NoSQL class into the Database Curriculum

Since NoSQL emerged a decade ago, it has rapidly gained popularity and has been actively incorporated into data management solutions for big data. This phenomenon brings positive challenges to accommodate NoSQL topics in the database curriculum. This paper presents our experience of teaching a NoSQL class over the last three years. The course uses a comprehensive teaching methodology that combines lectures, hands-on assignments, projects, and research-based approaches. The methodology aims at both students' in-depth learning and seamless integration of NoSQL topics into the database curriculum. The teaching methodology and course contents are detailed. Student evaluations of teaching, assessment results, success stories, and challenges and lessons learned are presented.
Still Image-based Human Activity Recognition with Deep Representations and Residual Learning

Iterative Recognizing human activity in a scene is still a challenging and an important research area in the field of computer vision due to its various possible implementations on many fields including autonomous driving, bio medical, machine intelligent vision etc.Recently deep learning techniques have emerged and successfully deployed models for image recognition and classification, object detection, and speech recognition.Due to promising results the state of art deep learning techniques have replaced the traditional techniques.In this paper, a novel method is presented for human activity recognition based on pretrained Convolutional Neural Network (CNN) model utilized as feature extractor and deep representations are followed by Support Vector Machine (SVM) classifier for action recognition.It has been observed that previously learnt CNN knowledge from large scale data-set could be transferred to activity recognition task with limited training data.The proposed method is evaluated on publicly available stanford40 human action dataset, which includes 40 classes of actions and 9532 images.The comparative experiment results show that proposed method achieves better performance over conventional methods in term of accuracy and computational power.
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning

One popular trend in meta-learning is to learn from many training tasks a common initialization for a gradient-based method that can be used to solve a new task with few samples. The theory of meta-learning is still in its early stages, with several recent learning-theoretic analyses of methods such as Reptile [Nichol et al., 2018] being for convex models. This work shows that convex-case analysis might be insufficient to understand the success of meta-learning, and that even for non-convex models it is important to look inside the optimization black-box, specifically at properties of the optimization trajectory. We construct a simple meta-learning instance that captures the problem of one-dimensional subspace learning. For the convex formulation of linear regression on this instance, we show that the new task sample complexity of any initialization-based meta-learning algorithm is $\Omega(d)$, where $d$ is the input dimension. In contrast, for the non-convex formulation of a two layer linear network on the same instance, we show that both Reptile and multi-task representation learning can have new task sample complexity of $\mathcal{O}(1)$, demonstrating a separation from convex meta-learning. Crucially, analyses of the training dynamics of these methods reveal that they can meta-learn the correct subspace onto which the data should be projected.
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction

Structured prediction requires manipulating a large number of combinatorial structures, e.g., dependency trees or alignments, either as latent or output variables. Recently, the SparseMAP method has been proposed as a differentiable, sparse alternative to maximum a posteriori (MAP) and marginal inference. SparseMAP returns a combination of a small number of structures, a desirable property in some downstream applications. However, SparseMAP requires a tractable MAP inference oracle. This excludes, e.g., loopy graphical models or factor graphs with logic constraints, which generally require approximate inference. In this paper, we introduce LP-SparseMAP, an extension of SparseMAP that addresses this limitation via a local polytope relaxation. LP-SparseMAP uses the flexible and powerful domain specific language of factor graphs for defining and backpropagating through arbitrary hidden structure, supporting coarse decompositions, hard logic constraints, and higher-order correlations. We derive the forward and backward algorithms needed for using LP-SparseMAP as a hidden or output layer. Experiments in three structured prediction tasks show benefits compared to SparseMAP and Structured SVM.
Author profiling on bi-lingual tweets

The task of author profiling aims to distinguish the author’s profile traits from a given content. It has got potential applications in marketing, forensic analysis, fake profile detection, etc. In recent years, the usage of bi-lingual text has raised due to the global reach of social media tools as people prefer to use language that expresses their true feelings during online conversations and assessments. It has likewise impacted the use of bi-lingual (English and Roman-Urdu) text in the sub-continent (Pakistan, India, and Bangladesh) over social media. To develop and evaluate methods for bi-lingual author profiling, benchmark corpora are needed. The majority of previous efforts have focused on developing mono-lingual author profiling corpora for English and other languages. To fulfill this gap, this study aims to explore the problem of author profiling on bi-lingual data and presents a benchmark corpus of bi-lingual (English and Roman-Urdu) tweets. Our proposed corpus contains 339 author profiles and each profile is annotated with six different traits including age, gender, education level, province, language, and political party. As a secondary contribution, a range of deep learning methods, CNN, LSTM, Bi-LSTM, and GRU, are applied and compared on the three different bi-lingual corpora for age and gender identification, including our proposed corpus. Our extensive experimentation showed that the best results for both gender identification task (Accuracy = 0.882, F1-Measure = 0.839) and age identification (Accuracy = 0.735, F1-Measure = 0.739) are obtained using Bi-LSTM deep learning method. Our proposed bi-lingual tweets corpus is free and publicly available for research purposes.
Efficiently Learning Adversarially Robust Halfspaces with Noise

We study the problem of learning adversarially robust halfspaces in the distribution-independent setting. In the realizable setting, we provide necessary and sufficient conditions on the adversarial perturbation sets under which halfspaces are efficiently robustly learnable. In the presence of random label noise, we give a simple computationally efficient algorithm for this problem with respect to any $\ell_p$-perturbation.
Investigation of an Appropriate Location for Construction the Large-Scale Photovoltaic Power Plant in Southeastern Iran

This paper investigated a techno-economic evaluation of the one-axis tracker photovoltaic power plant with 10 megawatt capacity in southeast of Iran. The southeast region of Iran has a hot and dry climate and great potential of solar radiation. Reviewing solar radiation atlases and the average air temperature in this particular region, ten cities were selected as case studies. RETScreen software was used to calculate technical and economic parameters such as the cost of energy production, capacity factor, payback period, internal rate of return, net present value, benefit-cost ratio and gross reduction of greenhouse gas emissions. The results showed that using the one-axis tracker system increases the capacity factor of the power plant by approximate one-third relative to the fixed panel state. Moreover, the payback period of investment was achieved less than 4 years in all the studied cities. Considering all of the above parameters, we compared selected cities with each other from economical point of view. Finally, the results led to determine the best location for the large-scale photovoltaic power plant construction.
Towards Fast and Understandable Computations: Which “And”- and “Or”-Operations Can Be Represented by the Fastest (i.e., 1-Layer) Neural Networks? Which Activations Functions Allow Such Representations?

We want computations to be fast, and we want them to be understandable.As we show, the need for computations to be fast naturally leads to neural networks, with 1-layer networks being the fastest, and the need to be understandable naturally leads to fuzzy logic and to the corresponding "and"-and "or"-operations.Since we want our computations to be both fast and understandable, a natural question is: which "and"-and "or"-operations of fuzzy logic can be represented by the fastest (i.e., 1-layer) neural network?And a related question is: which activation functions allow such a representation?In this paper, we provide an answer to both questions: the only "and"-and "or"-operations that can be thus represented are max(0, a + b -1) and min(a + b, 1), and the only activations functions allowing such a representation are equivalent to the rectified linear function -the one used in deep learning.This result provides an additional explanation of why rectified linear neurons are so successful.With also show that with full 2-layer networks, we can compute practically any -27 -Kevin Alvarez et al."And"-and "Or"-Operations and Neural Networks "and"-and "or"-operation.
Controlled Text Generation with Adversarial Learning

In recent years, generative adversarial networks (GANs) have started to attain promising results also in natural language generation. However, the existing models have paid limited attention to the semantic coherence of the generated sentences. For this reason, in this paper we propose a novel network – the Controlled TExt generation Relational Memory GAN (CTERM-GAN) – that uses an external input to influence the coherence of sentence generation. The network is composed of three main components: a generator based on a Relational Memory conditioned on the external input; a syntactic discriminator which learns to discriminate between real and generated sentences; and a semantic discriminator which assesses the coherence with the external conditioning. Our experiments on six probing datasets have showed that the model has been able to achieve interesting results, retaining or improving the syntactic quality of the generated sentences while significantly improving their semantic coherence with the given input.
Approximate Inference for Fully Bayesian Gaussian Process Regression

Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function. The classical approach entails maximizing the marginal likelihood yielding fixed point estimates (an approach called \textit{Type II maximum likelihood} or ML-II). An alternative learning procedure is to infer the posterior over hyperparameters in a hierarchical specification of GPs we call \textit{Fully Bayesian Gaussian Process Regression} (GPR). This work considers two approximation schemes for the intractable hyperparameter posterior: 1) Hamiltonian Monte Carlo (HMC) yielding a sampling-based approximation and 2) Variational Inference (VI) where the posterior over hyperparameters is approximated by a factorized Gaussian (mean-field) or a full-rank Gaussian accounting for correlations between hyperparameters. We analyze the predictive performance for fully Bayesian GPR on a range of benchmark data sets.
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach.

In many learning settings, it is beneficial to augment the main features with pairwise interactions. Such interaction models can be often enhanced by performing variable selection under the so-called strong hierarchy constraint: an interaction is non-zero only if its associated main features are non-zero. Existing convex optimization based algorithms face difficulties in handling problems where the number of main features $p \sim 10^3$ (with total number of features $\sim p^2$). In this paper, we study a convex relaxation which enforces strong hierarchy and develop a highly scalable algorithm based on proximal gradient descent. We introduce novel screening rules that allow for solving the complicated proximal problem in parallel. In addition, we introduce a specialized active-set strategy with gradient screening for avoiding costly gradient computations. The framework can handle problems having dense design matrices, with $p = 50,000$ ($\sim 10^9$ interactions)---instances that are much larger than current state of the art. Experiments on real and synthetic data suggest that our toolkit hierScale outperforms the state of the art in terms of prediction and variable selection and can achieve over a 4900x speed-up.