Reviewerly/exHarmony huggingface dataset:
Containerizing Visualization Software: Experiences and Best Practices

The standard process for software development has changed dramatically in the past decade. What was once a large effort of installing the same software across different systems has become much more streamlined with the rapid emergence and wide-scale adoption of Docker as the de facto container management ecosystem. Coincidentally, this has had an impact in the HPC and scientific computing community, allowing system maintainers to maintain and install packages with easier effort[12]. This can be seen through the adoption of containers on many large scale systems, including those supported by the Texas Advanced Computing Center (TACC), DOE, XSEDE, and the wider NSF community[23] [34] [15]. An extra layer of work is necessary when developing containers that require visualization technologies. This includes applications that require a windowing system such as X[9] to render GUIs. It can be further difficult when wanting to expose NVIDIA or GPU related capabilities to containers[32]. While the ability to containerize applications is widely available, there is no central resource for creating those with visualization requirements. Our work aims to consolidate common issues for visualization containers, including both similar and unique solutions. We give detail to the various ways we have worked at TACC to make visualization containers more available to researchers using HPC systems, made development easier, and the promise to being applied in lab research spaces. Our hope is to share similar challenges that other researchers may face, and providing possible solutions, so that further adoption of containers can be more easily developed.
Data Management Workflows in Interdisciplinary Highly Collaborative Research

Data curation is an important aspect in research projects. Effective data management is critical for data curation, and it not only contributes to the success of projects but makes research outputs findable, accessible, interoperable and reusable. We have examined interdisciplinary highly collaborative research (IHCR) practices in selected projects to propose data management workflows. This synopsis of work in progress discusses one of these workflows that helps locate information when there are multiple collaborators and the digital assets are spread across multiple storage systems and institutions.
Development of Low-Fidelity Virtual Replicas of Products for Usability Testing

Designers perform early-stage formative usability tests with low-fidelity prototypes to improve the design of new products. This low-tech prototype style reduces the manufacturing resources but limits the functions that can be assessed. Recent advances in technology enable designers to create low-fidelity 3D models for users to engage in a virtual environment. Three-dimensional models communicate design concepts and are not often used in formative usability testing. The proposed method discusses how to create a virtual replica of a product by assessing key human interaction steps and addresses the limitations of translating those steps into a virtual environment. In addition, the paper will provide a framework to evaluate the usability of a product in a virtual setting, with a specific emphasis on low-resource online testing in the user population. A study was performed to pilot the subject’s experience with the proposed approach and determine how the virtual online simulation impacted the performance. The study outcomes demonstrated that subjects were able to successfully interact with the virtual replica and found the simulation realistic. This method can be followed to perform formative usability tests earlier and incorporate subject feedback into future iterations of their design, which can improve safety and product efficacy.
A Neural-Symbolic Approach for Explanation Generation Based on Sub-concept Detection: An Application of Metric Learning for Low-Time-Budget Labeling

Abstract Deep learning methods, although effective in their assigned tasks, are mostly black-boxes with respect to their inner workings. For image classification with CNNs, there exists a variety of visual explanation methods that highlight parts of input images that were relevant for the classification result. But in many domains visual highlighting may not be expressive enough when the classification relies on complex relations within visual concepts. This paper presents an approach to enrich visual explanations with verbal local explanations, emphasizing important relational information. The proposed SymMetric algorithm combines metric learning and inductive logic programming (ILP). Labels given by a human for a small subset of important image parts are first generalized to a neighborhood of similar images using a learned distance metric. The information about labels and their spatial relations is then used to build background knowledge for ILP and ultimately to learn a first-order theory that locally explains the black-box with respect to the given image. The approach is evaluated with the Dogs vs. Cats data set demonstrating the generalization ability of metric learning and with Picasso Faces to illustrate recognition of spatial meaningful constellations of sub-concepts and creation of an expressive explanation.
Location Contact Tracing: Penetration, Privacy, Position, and Performance

The 2020 COVID-19 pandemic radically changed the world and how people interact, move, and behave. Following a lockdown that was imposed worldwide, although with different timing, Mobile Contact Tracing Apps (MCTAs) were proposed to digitally trace contacts between individuals while gradually releasing mobility constraints mandated to contain the spread of disease. General concern for privacy regarding the use of GPS data shifted the efforts toward distributed applications, which use Bluetooth technology to trace proximity and potential infections. Nonetheless, GPS data would help more health operators to understand where hotbeds are and to what extent the spread is progressing and at what pace. In addition to these issues, in this work we take a closer look at the major pillars of MCTA: Penetration, Privacy, Position, and Performance. We focus on (i) how the penetration rate affects the ability of a tracing application to work; (ii) the proposal of a novel method of tracing, which builds on the GPS technology; (iii) how the position of infections is beneficial to rapidly reduce the infection; and (iv) the discussion of the effects of such paradigms in different scenarios.
Machine Learning Algorithms

Human intelligence is deeply involved in creating efficient and faster systems that can work independently. Creation of such smart systems requires efficient training algorithms. Thus, the aim of this chapter is to introduce the readers with the concept of machine learning and the commonly employed learning algorithm for developing efficient and intelligent systems. The chapter gives a clear distinction between supervised and unsupervised learning methods. Each algorithm is explained with the help of suitable example to give an insight to the learning process.
Controlling StyleGANs using rough scribbles via one‐shot learning

Abstract This paper tackles the challenging problem of one‐shot semantic image synthesis from rough sparse annotations, which we call “ semantic scribbles .” Namely, from only a single training pair annotated with semantic scribbles, we generate realistic and diverse images with layout control over, for example, facial part layouts and body poses. We present a training strategy that performs pseudo labeling for semantic scribbles using the StyleGAN prior. Our key idea is to construct a simple mapping between StyleGAN features and each semantic class from a single example of semantic scribbles. With such mappings, we can generate an unlimited number of pseudo semantic scribbles from random noise to train an encoder for controlling a pretrained StyleGAN generator. Even with our rough pseudo semantic scribbles obtained via one‐shot supervision, our method can synthesize high‐quality images thanks to our GAN inversion framework. We further offer optimization‐based postprocessing to refine the pixel alignment of synthesized images. Qualitative and quantitative results on various datasets demonstrate improvement over previous approaches in one‐shot settings.
FRASHER – A framework for automated evaluation of similarity hashing

A challenge for digital forensic investigations is dealing with large amounts of data that need to be processed. Approximate matching (AM), a.k.a. similarity hashing or fuzzy hashing, plays a pivotal role in solving this challenge. Many algorithms have been proposed over the years such as ssdeep, sdhash, MRSH-v2, or TLSH, which can be used for similarity assessment, clustering of different artifacts, or finding fragments and embedded objects. To assess the differences between these implementations (e.g., in terms of runtime efficiency, fragment detection, or resistance against obfuscation attacks), a testing framework is indispensable and the core of this article. The proposed framework is called FRASHER (referring to a predecessor FRASH from 2013) and provides an up-to-date view on the problem of evaluating AM algorithms with respect to both the conceptual and the practical aspects. Consequently, we present and discuss relevant test case scenarios as well as release and demonstrate our framework allowing a comprehensive evaluation of AM algorithms. Compared to its predecessor, we adapt it to a modern environment providing better modularity and usability as well as more thorough testing cases.
Datastack: Unification of Heterogeneous Machine Learning Dataset Interfaces

Machine learning (ML) dataset preprocessing, cleaning, and integration into ML pipelines is often a cum-bersome endeavor that is susceptible to bugs and leads to unstructured code from the start. While existing frameworks for dataset integration often come with an extensive dataset repository, extending these repositories to new datasets is nontrivial due to lack of dataset retrieval, processing and iterator separation. To simplify the process of dataset integration, we present Datastack, an open-source framework that minimizes these efforts by providing well-defined interfaces that seamlessly integrate into existing machine learning frameworks. Inspired by stream processing frameworks such as Flink or Storm, Datastack decouples dataset-specific peculiarities such as custom data formats from the framework by introducing byte streams on an interface level. Furthermore, Datastack delivers dataset preprocessing functionalities such as stacking, splitting, and merging to alleviate error-prone data processing pipelines.
Online Procurement and Inventory Technology Based on Cloud Computing System

In order to explore the collaborative development of online procurement and inventory technology, this paper combines cloud computing technology to build online procurement and inventory management models, and builds a collaborative procurement computing experimental model with the help of a service system. Moreover, this paper clarifies the evolution problem of clustered supply chain network, and defines and constrains the model. In addition, this paper uses Agent technology to construct individual model and behavior model. Further, by complementing the advantages and disadvantages and mapping rules between Agent technology and Web service technology, the Agent model is mapped into a service model, which integrates the intelligence of Agent and the openness and cross-platform characteristics of Web services. Finally, this paper simulates the procurement and management model proposed in this paper through the simulation platform. The data analysis and research results show that the online procurement and inventory management technology based on the cloud computing system proposed in this paper can effectively promote the stable operation of procurement and inventory.
Comparing Data Mining Algorithms for Breast Cancer Diagnosis

Background: Early screening and diagnosis of breast cancer (BC) is critical for improving the quality of care and reducing the mortality rate. Objectives: This study aimed to construct and compare the performance of several machine learning (ML) algorithms in predicting BC. Methods: This descriptive and applied study included 1,052 samples (442 BC and 710 non-BC) with 30 features related to positive and negative BC diagnoses. The data mining (DM) process was implemented using the selected algorithm, including J-48 and random forest (RF) decision tree (DT), multilayer perceptron (MLP), Naïve Bayes (NB), Adaboost (AB), and logistics regression (LR) classifier. Then, we obtained the best algorithm by comparing their performances using the confusion matrix and area under the receiver operator characteristics (ROC) curve (AUC). Finally, we adopted the best model for BC prognosis. Results: The results of evaluating various DM algorithms revealed that the J-48 DT algorithm had the best performance (AUC = 0.922), followed by the AB, MLP, LR, and RF algorithms (AUC: 0.899, 0819, 0.716, and 0.703, respectively). Also, the NB algorithm achieved the lowest performance in this regard (AUC = 0.669). Conclusions: The ML presents a reasonable level of accuracy for an early diagnosis and screening of breast malignancies. Also, the empirical results showed that the J-48 DT algorithm yielded higher performance than other classifiers.
Thwarting Spam on Facebook

Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of these networks. Spammers often use certain techniques to deceive the OSN users for their own benefit. Facebook, one of the leading OSNs, is experiencing such crucial problems at an alarming rate. This chapter presents a methodology to segregate spam from legitimate posts using machine learning techniques: naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The textual, image, and video features are used together, which wasn't considered by the earlier researchers. Then, 1.5 million posts and comments are extracted from archival and real-time Facebook data, which is then pre-processed using RStudio. A total of 30 features are identified, out of which 10 are the best informative for identification of spam vs. ham posts. The entire dataset is shuffled and divided into three ratios, out of which 80:20 ratio of training and testing dataset provides the best result. Also, RF classifier outperforms NB and SVM by achieving overall F-measure 89.4% on the combined feature set.
Recognition of Persian/Arabic Handwritten Words Using a Combination of Convolutional Neural Networks and Autoencoder (AECNN)

Despite extensive research, recognition of Persian and Arabic manuscripts is still a challenging problem due to the complicated and irregular nature of writing, wide vocabulary, and diversity of handwritings. In Persian and Arabic words, letters are joined together, and signs such as dots are placed above or below letters. In the proposed approach, the words are first decomposed into their constituent subwords to enhance the recognition accuracy. Then the signs of subwords are extracted to develop a dictionary of main subwords and signs. The dictionary is then employed to train a classifier. Since the proposed recognition approach is based on unsigned subwords, the classifier may make a mistake in recognizing some subwords of a word. To overcome this, a new subword fusion algorithm is proposed based on the similarity of the main subwords and signs. Here, convolutional neural networks (CNNs) are utilized to train the classifier. An autoencoder (AE) network is employed to extract appropriate features. Thus, a hybrid network is developed and named AECNN. The known Iranshahr dataset, including nearly 17000 images of handwritten names of 503 cities of Iran, was employed to analyze and test the proposed approach. The resultant recognition accuracy is 91.09%. Therefore, the proposed approach is much more capable than the other methods known in the literature.
Fast and nanoscale-accuracy surface recovery of white-light scanning interferometry based on GPU-accelerated FFT-LMA-PSI algorithm

White-light scanning interferometry (WLSI) has been widely in the field of Nano-Micro topography measurement. In this study , to improve accuracy and robust of surface recovery, the Fast Fourier Transform-Levenberg Marquardt Algorithm-Phase Shift Interference (FFT-LMA-PSI) algorithm is first proposed to position zero optical path difference (OPD), and the FFT-LMA-PSI algorithm is a cascaded algorithm that involves FFT and multiple iterative optimization steps. In order to improve the computational efficiency, the GPU-accelerated FFT-LMA-PSI is developed and designed via compute unified device architecture (CUDA) C language. The speedup ratio of the proposed method are 28.6756&times;, 36.6950&times;, 41.9732&times;, 33.1928&times; for CCD pixel sizes of 128 &times; 128, 256 &times; 256, 512 &times; 512, 1024 &times; 1024. The standard deviation (&sigma;) error are 7.34 nm and 9.79 nm and the peak-valley (PV) error are 87.04 nm and 90.36 nm for the <i>Step </i>and<i> Sphere</i>, respectively. The experiment shows that the proposed method has good performance in calculation accuracy and efficiency.
E-Book Perceptions and Use: A Longitudinal Follow-Up Study

This article describes the results of a survey that gathered data on perceptions and use of e-books from undergraduate students, graduate students, faculty, and staff at two Maryland research universities in 2019. It follows two previous surveys in 2012 and 2014 by the same team of investigators, with slight changes in personnel, and compares results across time, by user affiliation with the university, and by STEM versus non-STEM disciplines. The study concludes with a discussion of the major findings and their implications for academic libraries and publishers, as well as areas for further inquiry.
Syntactic Information and Multiple Semantic Segments for Aspect-Based Sentiment Classification

Aspect-based sentiment classification (ASC) is a task to determine the sentiment polarities of specific aspects in a review. Syntactic information like dependency relation has been proven effective when extracting the sentiment features. On the other hand, multiple semantic segments in a review may influence the sentiment polarity. Thus, we propose a neural network based on dependency relation and structured attention (DRSAN) to fuse both dependency relation features and multiple semantic segments with different attention mechanisms. To verify the performance of DRSAN, we build a Chinese Mobile Phone Review (CMPR) dataset. To our knowledge, we are the first to explicitly integrate dependency relation and structured attention for the ASC task. The experimental results on SemEval 2014 Task 4, Twitter, CMPR, and several other cross-lingual and cross-domain datasets show that the proposed model outperforms all other benchmark models.
Smart Farming: Implementation of Industry 4.0 in the Agricultural Sector

Agriculture is a center that touches human life as well as other sectors in all countries. With the development of information technology, the agricultural sector also faces challenges and opportunities in realizing world food security. The development of Industry 4.0 was implemented in the agricultural sector as well as other sectors and was promoted to Farming 4.0. Industry 4.0 is a smart factory where smart digital devices are connected and that communicate with raw materials, semi-finished products, products, machines, tools, robots and people. The pace of technological change in the agricultural sector in the industrial era 4.0 is very high. However, these opportunities have not provided significant benefits in the agricultural sector. Farmers and various other actors need to get important information in line with the agriculture value chain. This paper discusses the successful implementation of Smart farming in several countries. With the many capabilities of Smart Farm, it is hoped that it will provide benefits for farmers and other agricultural actors in increasing agricultural production and their welfare.
Freezing of geometric discords for two qutrits in environments

Several measures of geometric discords have been recently proposed and mostly characterized in qubits. In this work, the dynamics of four measures of geometric discords is studied for two kinds of initial states in the two-qutrit model under environments. It is shown that four indicators can display freezing in Markovian regime and multiple freezing with multiple sudden changes in non-Markovian regime for suitable initial states and model parameters. Moreover, those measures evolve in a similar manner, but they differ from the decaying or increasing rate, the oscillatory amplitude and the time interval of freezing. However, they do not exhibit any freezing for the general Bell states. The dynamical hierarchy is discussed for those indicators. Those are helpful to understand the rich phenomena of discordant dynamics in open qutrits.
SMIRK: A machine learning-based pedestrian automatic emergency braking system with a complete safety case

SMIRK is a pedestrian automatic emergency braking system that facilitates research on safety-critical systems embedding machine learning components. As a fully transparent driver-assistance system, SMIRK can support future research on trustworthy AI systems, e.g., verification & validation, requirements engineering, and testing. SMIRK is implemented for the simulator ESI Pro-SiVIC with core components including a radar sensor, a mono camera, a YOLOv5 model, and an anomaly detector. ISO/PAS 21448 SOTIF guided the development, and we present a complete safety case for a restricted ODD using the AMLAS methodology. Finally, all training data used to train the perception system is publicly available.
Multimodal feature fusion and exploitation with dual learning and reinforcement learning for recipe generation

Recipes belong to long paragraphs with a cooking logic. To recipes from images and food names is more challenging in VQA (Visual Question Answering) due to the gap between images and texts. Although multimodal feature fusion, as a typical solver in VQA, is adopted in most situations for enhancing the accuracy, fused features obtained in this way can hardly provide guidance for keeping logic in produced texts. In this paper, ingredients are introduced to enhancing the relationship between food images and recipes, since they can reflect the cooking logic to a great extent, and dual learning is adopted to provide a complementary view by reconstructing ingredients from produced recipes. In order to make a full exploitation of ingredients for producing effective recipes, ingredients are fused into images and food names with an attention mechanism in the forward flow, and in the backward flow, a reconstructor is designed to reproduce ingredients from recipes. In addition, reinforcement learning is employed to guide ingredient reconstruction for preserving effective features in fused information explicitly. Extensive experiments demonstrate that more attention is allocated for producing effective recipes, and ablative study shows the reasonability of different components in the proposed method. • The problem of logic loss in sequential information generation is addressed in this paper. • Multimodal features in both forward and backward flows are exploited efficiently. • Dual learning provides a complementary view to enhance connections in inputs and outputs. • Reinforcement learning associates multimodal feature exploitation in a specific manner. • Extensive experiments and ablative studies demonstrate the superiority of our method.