Reviewerly/exHarmony huggingface dataset:
Automatic construction and evaluation of macro-actions in reinforcement learning

In this paper, we propose a new subgoal-based method for automatic construction of useful macro-actions modeled with option framework. We propose a new community detection algorithm to provide an appropriate partitioning of the agent’ transition graph. Subgoals are considered as the border states of the transition graph communities and options are constructed for taking the agent from one community to other communities. Despite the importance of considering the effect of each macro-action on learning speed, there is no generic known mechanism for evaluating macro-actions in the literature. We show that using all of the detected macro-actions are not useful and even in a simple environment, the augmentation of the action space with useless or wrong macro-actions may easily worsen learning performance. We propose four different heuristics for evaluating options. We identify, in this way, inappropriate options and discard them from the agent choices. Experimental results show significant improvements in the speed of learning after pruning options.
Weakly Supervised Image Retrieval via Coarse-scale Feature Fusion and Multi-level Attention Blocks

In this paper, we propose an end-to-end Attention-Block network for image retrieval (ABIR), which greatly increases the retrieval accuracy without human annotations like bounding boxes. Specifically, our network utilizes coarse-scale feature fusion, which generates the attentive local features via combining the information from different intermediate layers. Detailed feature information is extracted with the application of two attention blocks. Extensive experiments show that our method outperforms the state-of-the-art by a significant margin on four public datasets for image retrieval tasks.
Learning with the Augmented Reality EduPARK Game-Like App: Its Usability and Educational Value for Primary Education

The EduPARK is a research and development project that intends to promote interdisciplinary mobile learning, supported by the development of an application to be used in an urban park, providing students’ involvement, motivation and engagement to enhance authentic and contextualized learning. The EduPARK project follows a qualitative interpretative methodology fitting in a design-based research, a useful framework for developing technology-enhanced learning resources comprising various cycles of prototype refinement: a game-like app for mobile devices, with Augmented Reality (AR) contents that follows geocaching principles. After those refinement stages, the final version of the app was released for the public in the Google Play Store; and around 50 activities were organized to collect systematic data to better understand mobile learning in outdoor settings, not only in formal but also in non- and informal contexts. To date, EduPARK has involved about 800 students from primary to higher education, 200 teachers and 60 tourists; however, this paper reports a survey study focused particularly on data of participants attending the first four years of primary education. A total of 290 students, organized in 73 teams, completed the game and expressed their opinion about the usability and the educational value of the app in a questionnaire applied after the activity; and automatic app loggings were collected. Results show that the EduPARK app achieved a good usability and has educational value for primary education students. The present work proposes a data collection tool, inspired in Brooke’s instrument, regarding the educational value of a game-based mobile AR resource.
Designing at the End of the World

We are living in a time of ecological and humanitarian crisis that requires imminent action from the joint fields of HCI and interaction design. In a very palpable way, we seem to be moving towards the "end of the world" (certainly, as we have known it). This workshop addresses three concrete end-of-world challenges - the end of nature, end of culture and end of the human - to contribute to a much-needed design research agenda and to build community in the process. The workshop will explore how the design of technology can support a fairer and more secure set of futures by considering these three end-states and what we, as participants (both contributing to futures and living with the outcomes), can offer to improve the options. Contributions to theory and practice will be welcome.
Designing and Implementing SVMs for High-Dimensional Knowledge Discovery Using FPGAs

Support Vector Machines (SVMs)represent a robust and valuable tool for Machine Learning. SVMs are resilient to overfitting and can provide useful information for high-dimensional data when sufficient hyperplane margin can be identified. However, SVMs require substantial computational load for large datasets. This paper discusses SVMs, analyzing SVM results, and classification of high-dimensional data for the purpose of Knowledge Discovery. Furthermore, this paper considers implementation of SVMs using Field Programmable Gate Arrays (FPGAs). The approach described in this paper applies to high performance, high throughput and scalable implementations for big data. The paper provides design data for FPGA implementation of SVMs. Finally, an example is provided is based on the Xilinx UltraScale+ FPGAs to illustrate the concepts in this paper.
Data-pooling and multi-task learning for enhanced performance of speech recognition systems in multiple low resourced languages

We present two approaches to improve the performance of automatic speech recognition (ASR) systems for Gujarati, Tamil and Telugu. In the first approach using data-pooling with phone mapping (DP-PM), a deep neural network (DNN) is trained to predict the senones for the target language; then we use the feature vectors and their alignments from other source languages to map the phones from the source to the target language. The lexicons of the source languages are then modified using this phone mapping and an ASR system for the target language is trained using both the target and the modified source data. This DP-PM approach gives relative improvements in word error rates (WER) of 5.1% for Gujarati, 3.1% for Tamil and 3.4% for Telugu, over the corresponding baseline figures. In the second approach using multi-task DNN (MT-DNN) modeling, we use feature vectors from all the languages and train a DNN with three output layers, each predicting the senones of one of the languages. Objective functions of the output layers are modified such that during training, only those DNN layers responsible for predicting the senones of a language are updated, if the feature vector belongs to that language. This MT-DNN approach achieves relative improvements in WER of 5.7%, 3.3% and 5.2% for Gujarati, Tamil and Telugu, respectively.
Dynamics of entanglement and non-classicality features of a single-mode nonlinear Jaynes–Cummings model

In this paper, we discuss a model of a nonlinear interaction between a λ-type four-level atom interacting with a cavity field. The analytical solution of the system under particular conditions has been obtained. Numerical treatments have been discussed and new features of the entanglement degree are obtained using entropy and Mandel Q-parameter. Moreover, we evaluate a few of their non-classical properties such as momentum increment, momentum diffusion, mean photon number and normal squeezing. The Kerr medium impact has been discussed through different phenomena. It is shown that Kerr-like medium parameter can be used as a controller parameter of the system dynamics.
FlowGraph

Temporally evolving graphs are an indispensable requisite of modern-day big data processing pipelines. Existing graph processing systems mostly focus on static graphs and lack the essential support for pattern detection and event processing in graph-shaped data. On the other hand, stream processing systems support event and pattern detection, but they are inadequate for graph processing. This work lies at the intersection of the graph and stream processing domains with the following objectives: (i) It introduces the syntax of a language for the detection of temporal patterns in large-scale graphs. (ii) It presents a novel data structure called distributed label store (DLS) to efficiently store graph computation results and discover temporal patterns within them. The proposed system, called FlowGraph, unifies graph-shaped data with stream processing by observing graph changes as a stream flowing into the system. It provides an API to handle temporal patterns that predicate on the results of graph computations with traditional graph computations.
Power law scaling of test error versus number of training images for deep convolutional neural networks

The highest accuracy in image classification is of utmost importance for the industrial application of algorithms based on convolutional neural networks. Empirically, it is sometimes possible to improve accuracy by increasing the size of the training set. In this work, the scaling of the test accuracy versus the size of the training set was studied for different networks. First, a network with a depth of few layers was initialized with random parameters and trained on subsets of images of variable size sampled from the MNIST dataset of handwritten digits and MNIST fashion dataset of clothes and accessories. The scaling of the accuracy versus the size of the training set may be described as the sum of two components: a power law and an offset independent on the size of the training set. Exponent of the power law appears to be the same in both dataset and independent on seeds, initial weights and number of convolutional filters. Then, the scaling of the accuracy versus the size of training set has been evaluated on a dataset of pictures of paintings, sacred icons and sculptures with the goal to correctly classify unknown pictures. The networks chosen are the ones implemented in the machine vision library Halcon 18.11, including two convolutional neural networks with unknown topology and Resnet50, pretrained on industrial images. The scaling of the accuracy versus the size of the training set seems to be compatible with the power law scaling observed on the few layers network trained on MNIST.
Large-Scale Architectural Asset Extraction from Panoramic Imagery

We present a system to extract architectural assets from large-scale collections of panoramic imagery. We automatically rectify and crop parts of the panoramic image that contain dominant planes, and then use object detection to extract assets such as façades and windows. We also provide various tools to identify attributes of the assets to determine the asset quality and index the assets for search. In addition, we propose a User Interface (UI) to visualize and query assets. Finally, we present applications for urban modeling and texture synthesis.
Contextual Word Embeddings and Topic Modeling in Healthy Dieting and Obesity

An alarming proportion of the US population is overweight. Obesity increases the risk of illnesses such as diabetes and cardiovascular diseases. In this paper, we propose the Contextual Word Embeddings (ContWEB) framework that aims to build contextual word embeddings on the relationship between obesity and healthy eating from the crowd domain (Twitter) and the expert domain (PubMed). For this purpose, our work is based on a pipeline model that consists of a chain of processing elements as follows: (1) to use term frequency and inverse document frequency (TF-IDF) and Word2Vec in the data collected from the crowd and expert domains; (2) to apply natural language processing (NLP) algorithms to the corpus; (3) to construct social word embeddings by sentiment analysis; (4) to discover the contextual word embeddings using co-occurrence and conditional probability; (5) to find an optimal number of topics in a topic modeling with the obesity and healthy dieting corpus; (6) to extract latent features extracted using Latent Dirichlet Allocation (LDA). The ContWEB framework has been implemented on Apache Spark and TensorFlow platforms. We have evaluated the ContWEB framework in terms of the effectiveness in contextual word embeddings constructed from the crowd and the expert domains. We conclude that the ContWEB framework would be useful in enhancing the decision-making process for healthy eating and obesity prevention.
Confirmation Based on Analogical Inference: Bayes Meets Jeffrey

Abstract Certain hypotheses cannot be directly confirmed for theoretical, practical, or moral reasons. For some of these hypotheses, however, there might be a workaround: confirmation based on analogical reasoning. In this paper we take up Dardashti, Hartmann, Thébault, and Winsberg’s (2019) idea of analyzing confirmation based on analogical inference Bayesian style. We identify three types of confirmation by analogy and show that Dardashti et al.’s approach can cover two of them. We then highlight possible problems with their model as a general approach to analogical inference and argue that these problems can be avoided by supplementing Bayesian update with Jeffrey conditionalization.
Searching source code fragments using incremental clustering

Summary Plagiarism is becoming an increasingly serious problem in academic environment. In this paper, we deal with a specific kind of plagiarism: source code plagiarism. In this case, there is no software available for detecting plagiarism on a larger scale (hundreds of student submissions every year). We propose algorithms for source code parsing and processing as a part of a complex system for plagiarism detection. A source code vectorization using characteristic vectors is a vital piece of the whole process, and k‐means algorithm helps with the classification and clustering of vectors. Student assignments are submitted regularly, and any plagiarism detection system needs to handle them as they come. For this reason, we propose a modified incremental k‐means algorithm and a method for determining the number of clusters. We also consider methods for vector search among clusters and suggest the use of conditional entropy to select the important vector elements used in the search algorithm. Our results show how the proposed algorithms and methods work on real student submissions.
A Prescription Trend Analysis using Medical Insurance Claim Big Data

Understanding the spread of diseases and the use of medicines is of practical importance for various organizations, such as medical providers, medical payers, and national governments. This study aims to detect the change in the prescription trends and to identify its cause through an analysis of Medical Insurance Claims (MICs), which comprise the specifications of medical fees charged to health insurers. Our approach is two-fold. (1) We propose a latent variable model that simulates the medication behavior of physicians to accurately reproduce monthly prescription time series from the MIC data, where prescription links between the diseases and medicines are missing. (2) We apply a state space model with intervention variables to decompose the monthly prescription time series into different components including seasonality and structural changes. Using a large dataset consisting of 3.5-year MIC records, we conduct experiments to evaluate our approach in terms of accuracy, usefulness, and efficiency. We also demonstrate three applications for our medical analysis.
Motivating Cybersecurity Advocates

Given modern society's dependence on technological infrastructure vulnerable to cyber-attacks, the need to expedite cybersecurity adoption is paramount. Cybersecurity advocates are a subset of security professionals who promote, educate about, and motivate adoption of security best practices and technologies as a major component of their jobs. Successfully recruiting and retaining advocates is of utmost importance. Accomplishing this requires an understanding of advocates' motivations and incentives and how these may differ from other cybersecurity professionals. As the first study of its kind, we interviewed 28 cybersecurity advocates to learn about their work motivations. Findings revealed several drivers for cybersecurity advocacy work, most of which were intrinsic motivators. Motivations included interest in the field, sense of duty, self-efficacy, evidence of impact, comradery, and, to a lesser degree, awards and monetary compensation. We leverage these insights for recommendations on how to frame cybersecurity advocacy as a profession that fuels these motivations and how to maintain this across advocates' careers.
An Intelligent Tutoring System and Teacher Dashboard to Support Mathematizing During Science Inquiry

Using mathematics is critical to science inquiry at the high school level and is predictive of students’ later success in STEM college majors and careers. Inq-ITS (Inquiry Intelligent Tutoring System) has recently added mathematizing functionalities in order to support students in the mathematical practices needed for scientific inquiry, and our teacher alerting dashboard, Inq-Blotter, is being extended to alert teachers in real-time to students’ difficulties with this practice. Mathematizing in science can be challenging as students must attend to multiple sources of information (i.e., graphs, data tables), as well as do graphing and modeling. In the present paper, I describe three studies on the use of Inq-Blotter to support students on mathematizing in which I: (1) explore students’ eye-movements and think-aloud protocols while mathematizing in Inq-ITS to identify the proportion of mathematizing difficulties that are related to knowledge acquisition processes versus other mathematical competencies (e.g., graph building and modeling), (2) examine if alerts within Inq-Blotter permit teachers to identify students who need help most (relative to teachers without access to alerts), and (3) identify whether teacher support based on alerts leads to improvements on students’ next opportunity to engage in mathematizing and examine the corresponding teacher discourse associated with students’ gains. These studies will indicate how to support students on mathematizing with intelligent technologies.
Modulation and phase-locking in nanocontact vortex oscillators

We have conducted experiments to probe how the dynamics of nanocontact vortex oscillators can be modulated by an external signal. We explore the phase-locking properties in both the commensurate and chaotic regimes, where chaos appears to impede phase-locking while a more standard behavior is seen in the commensurate phase. These different regimes correspond to how the periodicity of the vortex core reversal relates to the frequency of core gyration around the nanocontact; a commensurate phase appears when the reversal rate is an integer fraction of the gyration frequency, while a chaotic state appears when this ratio is irrational. External modulation where the power spectral density exhibits rich features appears due to the modulation between the external source frequency, gyration frequency, and core-reversal frequency. We explain these features with first- or second-order modulation between the three frequencies. Phase-locking is also visible between the external source frequency and internal vortex modes (gyration and core-reversal modes).
CogLearn: A Cognitive Graph-Oriented Online Learning System

We propose and implement a novel online learning system, called CogLearn, to support learner's self-awareness and reflective thinking, which urges a proper form of knowledge representation together with individual learner's cognitive status. We thus design and employ the machine learning techniques to estimate learner's cognitive status and identify educational relations to construct the desired knowledge representation, namely cognitive graph in our system. We further demonstrate the system by presenting two practical services, i.e., learning obstacle diagnosis and learning path planning, to demonstrate how the constructed cognitive graph effectively and adaptively supports individual system user's learning process.
Design of a null-screen for a compact corneal topographer

In this work we report the design of a null-screen for corneal topography. Here we assume that the corneal surface is an ellipsoid with a diameter of 12 mm and a radius of curvature of 7.8 mm. To avoid difficulties in the alignment of the test system due to the face contour (eyebrows, nose, or eyelids), we designed a compact conical null-screen with spots drawn in such a way that its image, which is formed by reflection on the test surface, it becomes an exact semi-radial array of circular spots if the surface is perfect. To validate our proposal, we perform topography measurements of a reference surface and some human corneas using a probabilistic algorithm. The results obtained with our algorithm were consistent and we can recover the shape of the surface with accuracy.
Exaggerated portrait caricatures synthesis

Recent developments on style transfer using convolution neural networks promote a series of computational art application, especially in generating various artistic styles images. However, the exaggerated portrait caricatures generation is still far from being perfect due to the fundamental challenges from the varied and extreme shape transformations of images. In this paper, we present a novel DeepExaggeration system to generate exaggerated portrait caricatures. First, the landmarks of portrait photos and exaggerated portrait caricatures are obtained for further geometric structural features extraction. Then, a generative model is designed to learn the mapping from the geometric structural features of portrait photos to those of exaggerated portrait caricature. After that, the exaggerated structural features can be generated by the designed generative model. Third, the generated exaggerated structural features are used for deforming the portrait photos to exaggerated portrait. Finally, we transfer the caricature style on the exaggerated portrait. Our method successfully relieves the difficulties from using convolution neural networks for geometric structural features extraction. Experimental results on exaggerated portrait caricatures synthesis demonstrate the effectiveness of our method. Moreover, we are the first to create the Magic Mirror Caricatures dataset by retrieving famous persons’ portrait photos and exaggerated portrait caricatures from the Internet.
Accelerometer Dense Trajectories for Activity Recognition and People Identification

This paper addresses the problem of activity recognition and people identification using accelerometer signals acquired by personal devices. Specifically, we propose a framework based on a Deep Neural Network that employs an efficient dense trajectory encoding to compute features. These Accelerometer Dense Trajectory (ADT) features, which are similar to those used for action recognition in the spatio-temporal domain of video data, densely map the accelerometer signals into three-dimensional normalised positions. To deal with the unordered nature and dimensional variation of trajectories associated with the classes, the proposed framework employs Fisher Vectors as a high order representation of the extracted features. We evaluate the proposed ADT features and framework on the Sphere2016 Challenge and WISDM datasets for activity recognition. For people identification, we employ the RecodGait dataset. For these two significantly different classification tasks, the performance evaluation results confirm the high descriptiveness of the proposed ADT features and the effectiveness of the proposed framework.