Reviewerly/exHarmony huggingface dataset:
On the Evaluation of Contextual Embeddings for Zero-Shot Cross-Lingual Transfer Learning.

Pre-trained multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning, where multilingual BERT is fine-tuned on some source language (typically English) and evaluated on a different target language. However, published results for baseline mBERT zero-shot accuracy vary as much as 17 points on the MLDoc classification task across four papers. We show that the standard practice of using English dev accuracy for model selection in the zero-shot setting makes it difficult to obtain reproducible results on the MLDoc and XNLI tasks. English dev accuracy is often uncorrelated (or even anti-correlated) with target language accuracy, and zero-shot cross-lingual performance varies greatly within the same fine-tuning run and between different fine-tuning runs. We recommend providing oracle scores alongside the zero-shot results: still fine-tune using English, but choose a checkpoint with the target dev set. Reporting this upper bound makes results more consistent by avoiding the variation from bad checkpoints.
[DC] Resolving Cue Conflicts in Augmented Reality

Virtual objects in augmented realty (AR) often appear ”floaty”–a visual indication that augmented objects are poorly integrated into real world environments through shading. As a result, it is difficult to determine where objects are positioned in space. However, accurate depth perception is crucial for many applications in AR. I begin investigating this issue by evaluating different shading techniques for shadows, because shadows represent an important depth cue for creating a point of contact with surfaces beneath objects in three-dimensional space. As my research progresses, I hope to extend my investigation to other relative depth cues that may further resolve cue conflicts present in AR displays. My goal is to develop graphics based solutions to mitigate cue conflicts that result in perceptual uncertainty, especially in optical see-through (OST) AR displays. Resolving perceptual limitations will allow us to provide guidelines for software and hardware development of both future and current AR devices.
PhyAR: Determining the Utility of Augmented Reality for Physics Education in the Classroom

Physics is frequently cited as a difficult roadblock and hindrance to retention in STEM majors. In this paper, we present the results of a study exploring the potential utility and use cases of augmented reality in secondary and post secondary physics courses. To gather meaningful information, we developed PhyAR, prototype physics education application in augmented reality. We collected feedback and opinions from a qualitative study of university students with STEM backgrounds. Our findings point towards a clear desire to see the use of more interactive 3D AR content in physics courses.
A Bio-Inspired Goal-Directed Visual Navigation Model for Aerial Mobile Robots

Reliably navigating to a distant goal remains a major challenge in robotics. In contrast, animals such as rats and pigeons can perform goal-directed navigation with great reliability. Evidence from neural science and ethology suggests that various species represent the spatial space as a topological template, with which they can actively evaluate future navigation uncertainty and plan reliable/safe paths to distant goals. While topological navigation models have been deployed in mobile robots, relatively little inspiration has drawn upon biology in terms of topological mapping and active path planning. In this paper, we propose a novel bio-inspired topological navigation model, which consists of topological map construction, active path planning and path execution, for aerial mobile robots with visual landmark recognition and compass orientation capability. To mimic the topological spatial representation, the model firstly builds the topological nodes based on the reliability of visual landmarks, and constructs the edges based on the compass accuracy. Then a reward diffusion algorithm akin to animals’ path evaluation process is developed. The diffusion process takes the topological structure and landmark reliability into consideration, which helps the agent to construct the path with visually reliable nodes. In the path execution process, the agent combines orientation guidance and landmark recognition to estimate its position. To evaluate the performance of the proposed navigation model, a systematic series of experiments were conducted in a range of challenging and varied real-world visual environments. The results show that the proposed model generates animal-like navigation behaviours, which avoids travelling across large visually aliased areas, such as forest and water regions, and achieves higher localization accuracy than navigating on the shortest paths.
BlockNet: A Deep Neural Network for Block-Based Motion Estimation Using Representative Matching

Owing to the limitations of practical realizations, block-based motion is widely used as an alternative for pixel-based motion in video applications such as global motion estimation and frame rate up-conversion. We hereby present BlockNet, a compact but effective deep neural architecture for block-based motion estimation. First, BlockNet extracts rich features for a pair of input images. Then, it estimates coarse-to-fine block motion using a pyramidal structure. In each level, block-based motion is estimated using the proposed representative matching with a simple average operator. The experimental results show that BlockNet achieved a similar average end-point error with and without representative matching, whereas the proposed matching incurred 18% lower computational cost than full matching.
Machine Learning Algorithms for Diamond Price Prediction

Precious stones like diamond are in high demand in the investment market due to their monetary rewards. Thus, it is of utmost importance to the diamond dealers to predict the accurate price. However, the prediction process is difficult due to the wide variation in the diamond stones sizes and characteristics. In this paper, several machine learning algorithms were used to help in predicting diamond price, among them Liner regression, Random forest regression, polynomial regression, Gradient descent and Neural network. After training several models, testing their accuracy and analyzing their results, it turns out that the best of them is the random forest regression.
Quantum Generative Adversarial Network: A Survey

Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits some problems, such as barren plateaus, unstable gradient, model collapse, absent complete scientific evaluation system, etc. How to improve the theory of QGAN and apply it that have attracted some researcher. In this paper, we comprehensively and deeply review recently proposed GAN and QAGN models and their applications, and we discuss the existing problems and future research trends of QGAN.
Traffic Sign Recognition Using a Synthetic Data Training Approach

Traffic Sign Recognition (TSR) is a crucial component in many automotive applications, such as driver assistance, sign maintenance, and vehicle autonomy. In this paper, we present an efficient approach to training a machine learning-based TSR solution. In our choice of recognition method, we have opted for convolutional neural networks, which have demonstrated best-in-class performance in previous works on TSR. One of the challenges related to training deep neural networks is the requirement for a large amount of training data. To circumvent the tedious process of acquiring and manually labelling real data, we investigate the use of synthetically generated images. Our networks, trained on only synthetic data, are capable of recognising traffic signs in challenging real-world footage. The classification results achieved on the GTSRB benchmark are seen to outperform existing state-of-the-art solutions.
The Best-Worst Method for Resource Allocation and Task Scheduling in Cloud Computing

In cloud computing, service providers and service consumers often pursue conflicting benefits. The task-scheduling problem in particular requires the consideration of all characteristics affecting the scheduling process that are related to the two parties involved. The literature proposes that scheduling tasks and resource allocation problems can be structured and mathematically represented by adopting Multi-Criteria Decision-Making (MCDM) methods. This paper introduces a model to effectively prioritize tasks in a cloud environment based on consumer preferences and pre-defined criteria. The model adopts the Best-Worst Method (BWM) as a light MCDM method for structuring task-scheduling problems, accommodating consumer preferences, and providing reliable and scientific results for prioritizing tasks in the cloud. Furthermore, the model gives consumers the flexibility to change their preferences and offers a dynamic way to deal with the task-scheduling problem. The paper also provides a step-by-step example to guide consumers on the benefits of the proposed model.
Conditional Driving from Natural Language Instructions

Widespread adoption of self-driving cars will depend not only on their safety but largely on their ability to interact with human users. Just like human drivers, self-driving cars will be expected to understand and safely follow natural-language directions that suddenly alter the pre-planned route according to user's preference or in presence of ambiguities, particularly in locations with poor or outdated map coverage. To this end, we propose a language-grounded driving agent implementing a hierarchical policy using recurrent layers and gated attention. The hierarchical approach enables us to reason both in terms of high-level language instructions describing long time horizons and low-level, complex, continuous state/action spaces required for real-time control of a self-driving car. We train our policy with conditional imitation learning from realistic language data collected from human drivers and navigators. Through quantitative and interactive experiments within the CARLA framework, we show that our model can successfully interpret language instructions and follow them safely, even when generalizing to previously unseen environments. Code and video are available at this https URL.
Recommendation System for Prediction of Tumour in Cells Using Machine Learning Approach

In today’s world cancer is a familiar disease to everyone, people are keen to know the way and approaches for the better diagnosis of the cancer diseases in the early stages, cancer occurs in the different parts of the body which are having the auspicious behaviours of the cancer cell. One of the major cancerous subject where the whole world is affected that is tumour, which is found in the breast region, where more than millions of people are resulting to death by these breast tumours. So the study is made under the face of machine learning algorithms and strengthening the performance of these approaches which tends to classify and prediction based on the data. Therefore, these approaches of the machine learning creates the significance model for the prediction of the cancerous cells and the analysis of those tumour cells in the subject of the breast cancer. This model tends to the development of the recommender system, which helps to bring out the condition whether the breast cancer is benign or malignant. This system use to have of K- nearest neighbour (KNN) algorithm approaches with the univariate and multivariate analysis.
Collaborative Aspects of Collecting and Reflecting on Behavioral Data

Direct observation of behavior provides a unique type of data for reflecting on during a process of behavioral intervention. This study focuses on practitioners who specialize in operationalizing, recording, and monitoring behavior using data collection through paper-and-pencil or, increasingly, mobile computing. Applying an action research approach, we conducted fieldwork to understand observational data collection among practitioners providing children with special education support for behavioral needs. We present a model of collaborative data collection, which describes how practices are situated in the process of collecting data that are useful for reflection by teams of practitioners. We discuss how computer-assisted data collection could promote more systematic and rigorous practices, and design considerations for the collaborative aspects of collecting and reflecting on behavioral data. This study builds on research describing the practices of individuals who track their own behavioral data, and improves our understanding of informal documentation practices in organizations.
Snowball: Iterative Model Evolution and Confident Sample Discovery for Semi-Supervised Learning on Very Small Labeled Datasets

In this work, we develop a joint sample discovery and iterative model evolution method for semi-supervised learning on very small labeled training sets. We propose a master-teacher-student model framework to provide multi-layer guidance during the model evolution process with multiple iterations and generations. The teacher model is constructed by performing an exponential moving average of the student models obtained from past training steps. The master network combines the knowledge of the student and teacher models with additional access to newly discovered samples. The master and teacher models are then used to guide the training of the student network by enforcing the consistency between their predictions of unlabeled samples and evolve all models when more and more samples are discovered. Our extensive experiments demonstrate that the process of discovering confident samples from the unlabeled dataset, once coupled with the master-teacher-student network evolution, can significantly improve the overall semi-supervised learning performance. For example, on the CIFAR-10 dataset, with a small set of 250 labeled samples, our method achieves an error rate of 11.58%, more than 38% lower than Mean-Teacher (49.91%). When coupled with the MixMatch augmentation and loss function, the improvements are also significant.
Seizing Requirements Engineering Issues through Supervised Learning Techniques

In recent years, the popularity of machine learning techniques has grown due to the availability of larges volumes of data and the increased processing capacity of computers. Despite the inherent value of these techniques, few studies have attempted to summarize how machine learning algorithms, especially supervised learning have contributed to task automation and resolving challenges in Requirements Engineering. This paper proposes a systematic mapping of the literature to identify and analyze proposals which employ supervised learning in Requirements Engineering between 2002-2018. The goal of this research is to identify trends, datasets, and methods used. Thirty-three studies were selected based on defined inclusion and exclusion criteria. The results show that researches using these techniques focuses on eight broad categories: detection of linguistic problems in requirements documents and artifacts written in natural language, classification of document content, traceability, effort estimation, requirements analysis, failures prediction, quality and detection of business rules. The most used supervised learning algorithms were Support Vector Machine, Naive Bayes, Decision Tree, K-Nearest Neighbour, and Random Forest. Twenty-five public and twenty-eight private data sources were identified. Among the most used public data sources are Predictor Models in Software Engineering, iTrust Electronic Health Care System and Metric Data Program from NASA.
Managing long-term experiment data: a repository for soil and agricultural research

Agricultural long-term experiments (LTEs) are most valuable research infrastructures to reveal the effects of agricultural measures in the long run. However, information about existing LTEs is scattered and the data are often not easy to access. A repository for LTE data and metadata can ensure that a particular LTE can be found easily and access to the data is simplified. “BonaRes” is short for “Soil as a Sustainable Resource for the Bioeconomy.” In this funding initiative of the German Federal Ministry for Education and Research (BMBF) one focus is on the acquisition, storage, and provision of data from LTEs in Germany and beyond. For better findability and reusability of these data, the BonaRes Data Repository was established. The BonaRes Data Portal is an important component, which serves as a central access point for data managed within the BonaRes Data Repository. It aims at providing information about LTEs in Germany as well as LTEs' research data for free reuse. The definition of LTE in the context of BonaRes is a minimum duration of 20 years, a static design and focus on a scientific issue in the context of soil and yield. According to this definition, in total 200 LTEs across Germany were identified. Information was compiled and published in a dynamic GIS-based web application. Research data in the BonaRes Data Repository will be provided according to the FAIR principles. Standards are an important component of this concept. The BonaRes Data Centre compiled, discussed, and published a list of more than 600 standards, regulations, code lists, and controlled vocabularies in the field of soil, agricultural, and data science. Before research data are transferred to the BonaRes Data Repository, the appropriate data policy must be approved by the respective institution's head on behalf of the responsible institution. Metadata has to be provided to ensure a proper discovery, reuse, and analysis of the research data. The first LTE, of which research data are published for free reuse through BonaRes, is the “V140” (1963–today) from Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany.
Picsar (Physics Augmented Reality) as a Learning Media to Practice Abstract Thinking Skills in Atomic Model

Abstract The research aimed at developing an augmented reality-based learning media to train abstract thinking skills that are feasible (practical and effective). Development research using the ADDIE model (Analysis, Design, Develop, Implementation, and Evaluation) guided the research methodology. Specifically, a one-shot case study was utilised in the research trial design. In the Spring Semester 2019, thirty three students from private high school in Surabaya implied the Picsar (Physics Augmented Reality). Data collection techniques used the form of observation sheet learning to assess the practicality of the media, assessment sheets, and student response questionnaires to assess the effectiveness of the media. The results of the research obtained some important points: 1) the percentage of learning implementation was 98.14% with a very good category; 2) the percentage of students’ abstract thinking skills indicated in varying numbers: 100% of proportional reasoning with very good categories, 66.67% of probabilistic reasoning with good categories, 75.76% combinatorial reasoning with good categories, and 66.67% of correlation reasoning with a good category), and the percentage of students’ responses by 92.76% with a very good category.
Assisted Music Creation with Flow Machines: Towards New Categories of New

This chapter reflects on about 10 years of research in AI-assisted music composition, in particular during the Flow Machines project. We reflect on the motivations for such a project, its background, its main results and impact, both technological and musical, several years after its completion.
Stronger and Faster Wasserstein Adversarial Attacks

Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to small, imperceptible perturbations known as adversarial attacks. While the majority of existing attacks focus on measuring perturbations under the $\ell_p$ metric, Wasserstein distance, which takes geometry in pixel space into account, has long been known to be a suitable metric for measuring image quality and has recently risen as a compelling alternative to the $\ell_p$ metric in adversarial attacks. However, constructing an effective attack under the Wasserstein metric is computationally much more challenging and calls for better optimization algorithms. We address this gap in two ways: (a) we develop an exact yet efficient projection operator to enable a stronger projected gradient attack; (b) we show that the Frank-Wolfe method equipped with a suitable linear minimization oracle works extremely fast under Wasserstein constraints. Our algorithms not only converge faster but also generate much stronger attacks. For instance, we decrease the accuracy of a residual network on CIFAR-10 to $3.4\%$ within a Wasserstein perturbation ball of radius $0.005$, in contrast to $65.6\%$ using the previous Wasserstein attack based on an \emph{approximate} projection operator. Furthermore, employing our stronger attacks in adversarial training significantly improves the robustness of adversarially trained models.
Evaluating the Performance of Reinforcement Learning Algorithms

Performance evaluations are critical for quantifying algorithmic advances in reinforcement learning. Recent reproducibility analyses have shown that reported performance results are often inconsistent and difficult to replicate. In this work, we argue that the inconsistency of performance stems from the use of flawed evaluation metrics. Taking a step towards ensuring that reported results are consistent, we propose a new comprehensive evaluation methodology for reinforcement learning algorithms that produces reliable measurements of performance both on a single environment and when aggregated across environments. We demonstrate this method by evaluating a broad class of reinforcement learning algorithms on standard benchmark tasks.
Intrinsic Reward Driven Imitation Learning via Generative Model

Imitation learning in a high-dimensional environment is challenging. Most inverse reinforcement learning (IRL) methods fail to outperform the demonstrator in such a high-dimensional environment, e.g., Atari domain. To address this challenge, we propose a novel reward learning module to generate intrinsic reward signals via a generative model. Our generative method can perform better forward state transition and backward action encoding, which improves the module's dynamics modeling ability in the environment. Thus, our module provides the imitation agent both the intrinsic intention of the demonstrator and a better exploration ability, which is critical for the agent to outperform the demonstrator. Empirical results show that our method outperforms state-of-the-art IRL methods on multiple Atari games, even with one-life demonstration. Remarkably, our method achieves performance that is up to 5 times the performance of the demonstration.