Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Children's Free Play in Primary Schools

Various technologies (e.g., tablets, toolkits, and digital toys) are used in schools. However, they are often designed to introduce new play practices for serving pre-defined learning purposes. In this study, we are interested in constructive play 'in the wild' and how technologies can be integrated more organically into the ways young school children are already playing. This paper presents 4 one-week ethnographic study in four early primary school classrooms (children aged 5-7). The aim is to gain insights in children's free play and identify design opportunities for technology serving children's constructive play. Our findings illustrate children's interactions with resources and peers during free play, which often involve imitations and dynamically change between being solitary and social. We observed that children's constructive play was often associated with other forms of play. On this basis, we suggest three design implications for technologies that support and encourage constructive play during fee play in schools.
Ensembled Outlier Detection using Multi-Variable Correlation in WSN through Unsupervised Learning Techniques

Outlier detection in Wireless Sensor Networks is a crucial aspect in IoT, since cheap sensors tend to be seriously exposed to errors and inaccuracies. Hence, there is the need of a solution to improve the quality of the data without increasing the cost of the sensors. In Big Data paradigms, it is difficult to exploit the temporal correlation of sensors since Big Data architectures and technologies do not process data in order. In this paper, a complete study of multi-variable based outlier detection is carried out. Firstly, three known unsupervised algorithms are analysed (Elliptic Envelope, Isolation Forest and Local Outlier Factor) and are tested in a big data architecture. Secondly, an ensemble outlier detector (EOD) is created with the outputs of these algorithms and it is compared, in a Lab environment, with previous results for different parameters of contamination of the training set. The analysis of the results show that for correlated variables, multi-variable EOD has a very good detection rate with a very low false alarm rate. Finally, the EOD is used in a real world scenario in the city of Barcelona and the results are analysed using spectral-decomposition techniques which indicate that EOD has a good performance in a real case.
Supporting the Acquisition of Programming Skills with Program Construction Patterns

A central topic to computer science education is the training of novice programmers. Novice programming skills have already been mapped to hierarchical levels, and expert programming skills have been measured based on task performance. But didactical instructions and individual support to acquire expert programming skills have not been provided in detail. I propose the investigation of structural and semantic patterns in program construction sequences in an IDE-based learning analytics setting. I aim to provide a more fine-grained assessment of programming skills, to enable skill assessments during programming tasks, and to support the individual acquisition of programming skills.
Gray-Box Models for Performance Assessment of Spark Applications

: Big data applications are among the most suitable applications to be executed on cluster resources because of their high requirements of computational power and data storage. Correctly sizing the resources devoted to their execution does not guarantee they will be executed as expected. Nevertheless, their execution can be affected by perturbations which can change the expected execution time. Identifying when these types of issue occurred by comparing their actual execution time with the expected one is mandatory to identify potentially critical situations and to take the appropriate steps to prevent them. To fulﬁll this objective, accurate estimates are necessary. In this paper, machine learning techniques coupled with a posteriori knowledge are exploited to build performance estimation models. Experimental results show how the models built with the proposed approach are able to outperform a reference state-of-the-art method (i.e., Ernest method), reducing in some scenarios the error from the 221.09-167.07% to 13.15-30.58%.
GUI-Guided Repair of Mobile Test Scripts

Graphical User Interface (GUI) testing has been the focus of mobile app testing. Manual test cases, containing valuable human knowledge about the apps under test, are often coded as scripts to enable automated and repeated execution for test cost reduction. Unfortunately, many test scripts may become broken due to changes made during app updates. Broken test scripts are expected to be updated for reuse; however, the maintenance cost can be high if large numbers of test scripts require manual repair. We propose an approach named METER to repairing broken test scripts automatically when mobile apps are updated. METER novelly leverages computer vision techniques to infer GUI changes between two versions from screenshots and uses the GUI changes to guide the repair of test scripts. In experiments conducted on 18 Android apps, METER was able to repair 78.3% broken test scripts.
Formulation and Branch-and-cut algorithm for the Minimum Cardinality Balanced and Connected Clustering Problem

Given a graph G = (V ,E), integer bounds l ,u : 0 ≤ l ≤ u and positive integer weights assigned to the vertices V ofG , the Minimum Cardinality Balanced and Connected Clustering Problem (MCBCCP) consists of finding a minimum cardinality partitioning of the vertices ofV so that (a) each set in the partition induces a connected subgraph of G and (b) the sum of the weights of the vertices in each set belongs to the interval [l ,u]. In this paper, we present an integer programming formulation, valid inequalities and a Branch-and-cut algorithm for MCBCCP. Our computational experiments suggest that the inequalities investigated here help the overall performance of the algorithm. In addition to the one tested here, we discuss other formulations, following different modeling arguments. Some of them are based on MCBCCP’s connections to other combinatorial optimization problems found in the literature.
Investigating Mobile Applications Quality in Official and Third-party Marketplaces

One of the winning factors of Android was the use of the Java programming language and the XML language for application development. Furthermore, the open-source license and the availability of reverse engineering tools stimulated the proliferation of third-party markets where users can download for free repackaged version of commercial app, facilitating the phenomenon of plagiarism. In this paper we present an empirical study aimed to define whether there are differences from the quality point of view in Android applications available in the official market and in third-party ones, investigating whether supervised and unsupervised models built with a set of features belonging to four categories (i.e., dimensional, complexity, object oriented and Android) are effective in app store detection.
Revisiting Pseudo Invariant Calibration Sites (PICS) Over Sand Deserts for Vicarious Calibration of Optical Imagers at 20 km and 100 km Scales

In-flight assessment of the radiometric performances of space-borne instruments can be achieved by means of vicarious calibration over Pseudo-Invariant Calibration Sites (PICS). PICS are chosen for the high temporal stability of their surface optical properties combined with a high spatial homogeneity. A first list of the main desert PIC sites was identified 20 years ago for the calibration of medium/coarse spatial resolution instruments in the solar spectral range (400–2500 nm). They are located in the Saharan desert and in the Arabian Peninsula. Six of them have since been endorsed by the CEOS/WGCV/IVOS as reference Calibration/Validation test sites. In this study, we have revisited the list of desert PIC sites at the global scale with the aim of (1) assessing if these twenty PICS are still “optimal”, in terms of temporal stability and spatial uniformity, and using up-to-date multi-spectral remote sensing data, and (2) identifying new calibration sites distributed over other areas of the world. We verified that the original sites remain very relevant, although alternate locations in their close vicinity have slightly better characteristics. We proposed four additional targets with similar characteristics, some of which may offer easier logistical access. In order to support radiative transfer simulations of satellite sensor measurements over the sites, we assessed the abilities of several semi-empirical models to reproduce the spectro-directional signatures of six IVOS sites and the four new candidate sites, and we derived climatologies of the main atmospheric properties (trace gas column load and aerosol optical depth).
Noisy Dual Principal Component Pursuit

Dual Principal Component Pursuit (DPCP) is a recently proposed non-convex optimization based method for learning subspaces of high relative dimension from noiseless datasets contaminated by as many outliers as the square of the number of inliers. Experimentally, DPCP has proved to be robust to noise and outperform the popular RANSAC on 3D vision tasks such as road plane detection and relative poses estimation from three views. This paper extends the global optimality and convergence theory of DPCP to the case of data corrupted by noise, and further demonstrates its robustness using synthetic and real data.
Data-efficient Neural Text Compression with Interactive Learning

Neural sequence-to-sequence models have been successfully applied to text compression. However, these models were trained on huge automatically induced parallel corpora, which are only available for a few domains and tasks. In this paper, we propose a novel interactive setup to neural text compression that enables transferring a model to new domains and compression tasks with minimal human supervision. This is achieved by employing active learning, which intelligently samples from a large pool of unlabeled data. Using this setup, we can successfully adapt a model trained on small data of 40k samples for a headline generation task to a general text compression dataset at an acceptable compression quality with just 500 sampled instances annotated by a human.
Workspace-based Virtual Networks: A Clean Slate Approach to Slicing Cloud Networks

Cloud Computing has brought a new vision about what applications might expect from underlying networks. Software Defined Networking, in this context, provides techniques to make networks more flexible to application requirements as well as to virtual networks, which help implement the provisioning of physical resources to support tenants. Cloud-hosted applications assume that networks have high throughput, but do not consider competition through the medium, handled by the switches. This paper aims to present the concept of Workspace as a new paradigm to manage the underlying resources in terms of meeting the communication requirements. We introduce a clean-slate approach based on horizontal addressing that has Workspaces as logical links capable of being parameterized at the level of medium access control. In addition, an overview of a new network element designed to allow the parameterization of Workspaces directly on the hardware is given.
Robust Semantic Parsing with Adversarial Learning for Domain Generalization

This paper addresses the issue of generalization for Semantic Parsing in an adversarial framework. Building models that are more robust to inter-document variability is crucial for the integration of Semantic Parsing technologies in real applications. The underlying question throughout this study is whether adversarial learning can be used to train models on a higher level of abstraction in order to increase their robustness to lexical and stylistic variations. We propose to perform Semantic Parsing with a domain classification adversarial task, covering various use-cases with or without explicit knowledge of the domain. The strategy is first evaluated on a French corpus of encyclopedic documents, annotated with FrameNet, in an information retrieval perspective. This corpus constitutes a new public benchmark, gathering documents from various thematic domains and various sources. We show that adversarial learning yields improved results when using explicit domain classification as the adversarial task. We also propose an unsupervised domain discovery approach that yields equivalent improvements. The latter is also evaluated on a PropBank Semantic Role Labeling task on the CoNLL-2005 benchmark and is shown to increase the model’s generalization capabilities on out-of-domain data.
Predicate Exchange: Inference with Declarative Knowledge

Programming languages allow us to express complex predicates, but existing inference methods are unable to condition probabilistic models on most of them. To support a broader class of predicates, we develop an inference procedure called predicate exchange, which softens predicates. A soft predicate quantifies the extent to which values of model variables are consistent with its hard counterpart. We substitute the likelihood term in the Bayesian posterior with a soft predicate, and develop a variant of replica exchange MCMC to draw posterior samples. We implement predicate exchange as a language agnostic tool which performs a nonstandard execution of a probabilistic program. We demonstrate the approach on sequence models of health and inverse rendering.
CloudCity: A Live Environment for the Management of Cloud Infrastructures

Cloud computing has emerged as the de facto approach for providing services over the Internet. Although having increased popularity, challenges arise in the management of such environments, especially when the cloud service providers are constantly evolving their services and technology stack in order to maintain position in a demanding market. This usually leads to a combination of different services, each one managed individually, not providing a big picture of the architecture. In essence, the end state will be too many resources under management in an overwhelming heterogeneous environment. An infrastructure that has considerable growth will not be able to avoid its increasing complexity. Thus, this papers introduces liveness as an attempt to increase the feedback-loop to the developer in the management of cloud architectures. This aims to ease the process of developing and integrating cloud-based systems, by giving the possibility to understand the system and manage it in an interactive and immersive experience, thus perceiving how the infrastructure reacts to change. This approach allows the real-time visualization of a cloud infrastructure composed of a set of Amazon Web Services resources, using visual city metaphors.
Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning

We study high-dimensional estimators with the trimmed `1 penalty, which leaves the h largest parameter entries penalty-free. While optimization techniques for this nonconvex penalty have been studied, the statistical properties have not yet been analyzed. We present the first statistical analyses for M -estimation, and characterize support recovery, `∞ and `2 error of the trimmed `1 estimates as a function of the trimming parameter h. Our results show different regimes based on how h compares to the true support size. Our second contribution is a new algorithm for the trimmed regularization problem, which has the same theoretical convergence rate as difference of convex (DC) algorithms, but in practice is faster and finds lower objective values. Empirical evaluation of `1 trimming for sparse linear regression and graphical model estimation indicate that trimmed `1 can outperform vanilla `1 and non-convex alternatives. Our last contribution is to show that the trimmed penalty is beneficial beyond M -estimation, and yields promising results for two deep learning tasks: input structures recovery and network sparsification.
On the Methods for Recalibrating Geostationary Longwave Channels Using Polar Orbiting Infrared Sounders

This study presents a common recalibration method that has been applied to geostationary imagers’ infrared (IR) and water vapour (WV) channel measurements, referred to as the multi-sensor infrared channel calibration (MSICC) method. The method relies on data of the Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infrared Sounder (AIRS), and High-Resolution Infrared Radiation Sounder (HIRS/2) on polar orbiting satellites. The geostationary imagers considered here are VISSR/JAMI/IMAGER on JMA’s GMS/MTSAT series and MVIRI/SEVIRI on EUMETSAT’s METEOSAT series. IASI hyperspectral measurements are used to determine spectral band adjustment factors (SBAF) that account for spectral differences between the geostationary and polar orbiting satellite measurements. A new approach to handle the spectral gaps of AIRS measurements using IASI spectra is developed and demonstrated. Our method of recalibration can be directly applied to the lowest level of geostationary measurements available, i.e., digital counts, to obtain recalibrated radiances. These radiances are compared against GSICS-corrected radiances and are validated against SEVIRI radiances, both during overlapping periods. Significant reduction in biases have been observed for both IR and WV channels, 4% and 10%, respectively compared to the operational radiances.
Investigating the Effects of Gender Bias on GitHub

Diversity, including gender diversity, is valued by many software development organizations, yet the field remains dominated by men. One reason for this lack of diversity is gender bias. In this paper, we study the effects of that bias by using an existing framework derived from the gender studies literature.We adapt the four main effects proposed in the framework by posing hypotheses about how they might manifest on GitHub,then evaluate those hypotheses quantitatively. While our results how that effects of gender bias are largely invisible on the GitHub platform itself, there are still signals of women concentrating their work in fewer places and being more restrained in communication than men.
An Encoding Strategy Based Word-Character LSTM for Chinese NER

A recently proposed lattice model has demonstrated that words in character sequence can provide rich word boundary information for character-based Chinese NER model. In this model, word information is integrated into a shortcut path between the start and the end characters of the word. However, the existence of shortcut path may cause the model to degenerate into a partial word-based model, which will suffer from word segmentation errors. Furthermore, the lattice model can not be trained in batches due to its DAG structure. In this paper, we propose a novel word-character LSTM(WC-LSTM) model to add word information into the start or the end character of the word, alleviating the influence of word segmentation errors while obtaining the word boundary information. Four different strategies are explored in our model to encode word information into a fixed-sized representation for efficient batch training. Experiments on benchmark datasets show that our proposed model outperforms other state-of-the-arts models.
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator

We propose a class of novel variance-reduced stochastic conditional gradient methods. By adopting the recent stochastic path-integrated differential estimator technique (SPIDER) of Fang et al. (2018) for the classical Frank-Wolfe (FW) method, we introduce SPIDER-FW for finite-sum minimization as well as the more general expectation minimization problems. SPIDER-FW enjoys superior complexity guarantees in the non-convex setting, while matching the best known FW variants in the convex case. We also extend our framework à la conditional gradient sliding (CGS) of Lan & Zhou (2016), and propose SPIDER-CGS.
Self-similar Epochs: Value in arrangement

Optimization of machine learning models is commonly performed through stochastic gradient updates on randomly ordered training examples. This practice means that sub-epochs comprise of independent random samples of the training data that may not preserve informative structure present in the full data. We hypothesize that the training can be more effective with {\em self-similar} arrangements that potentially allow each epoch to provide benefits of multiple ones. We study this for "matrix factorization" -- the common task of learning metric embeddings of entities such as queries, videos, or words from example pairwise associations. We construct arrangements that preserve the weighted Jaccard similarities of rows and columns and experimentally observe training acceleration of 3\%-37\% on synthetic and recommendation datasets. Principled arrangements of training examples emerge as a novel and potentially powerful enhancement to SGD that merits further exploration.
A Study of Latent Structured Prediction Approaches to Passage Reranking

The structured output framework provides a helpful tool for learning to rank problems. In this paper, we propose a structured output approach which regards rankings as latent variables. Our approach addresses the complex optimization of Mean Average Precision (MAP) ranking metric. We provide an inference procedure to find the max-violating ranking based on the decomposition of the corresponding loss. The results of our experiments on WikiQA and TREC13 datasets show that our reranking based on structured prediction is a promising research direction.
Influence of pump parameters on two-pump optical parametric amplification

An analysis of two-pump optical parametric amplifiers is proposed in order to evidence strategies to project and develop flat two-pump fiber parametric amplifiers (2P-OPAs). Amplifier gain profile is evaluated through a model that takes into account the four-wave mixing process with pump depletion. Disturbing influence of degenerate three-wave mixing processes is separately addressed. Through the analysis, important 2P-OPA project guidelines are obtained. Theoretical forecasts are confirmed by experimental measurements on different 2P-OPAs, realized by changing power and spectral allocations of the two pumps, obtained by Raman amplification.
Supporting the Statistical Analysis of Variability Models

Variability models are broadly used to specify the configurable features of highly customizable software. In practice, they can be large, defining thousands of features with their dependencies and conflicts. In such cases, visualization techniques and automated analysis support are crucial for understanding the models. This paper contributes to this line of research by presenting a novel, probabilistic foundation for statistical reasoning about variability models. Our approach not only provides a new way to visualize, describe and interpret variability models, but it also supports the improvement of additional state-of-the-art methods for software product lines; for instance, providing exact computations where only approximations were available before, and increasing the sensitivity of existing analysis operations for variability models. We demonstrate the benefits of our approach using real case studies with up to 17,365 features, and written in two different languages (KConfig and feature models).