Reviewerly/exHarmony huggingface dataset:
Robustness Disparities in Commercial Face Detection

Facial detection and analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade. Critiques that focus on system performance analyze disparity of the system's output, i.e., how frequently is a face detected for different Fitzpatrick skin types or perceived genders. However, we focus on the robustness of these system outputs under noisy natural perturbations. We present the first of its kind detailed benchmark of the robustness of three such systems: Amazon Rekognition, Microsoft Azure, and Google Cloud Platform. We use both standard and recently released academic facial datasets to quantitatively analyze trends in robustness for each. Across all the datasets and systems, we generally find that photos of individuals who are older, masculine presenting, of darker skin type, or have dim lighting are more susceptible to errors than their counterparts in other identities.
Toward Written Error Correction with a Japanese-English Parallel Corpus: Data-Driven Learning in the Japanese EFL Classroom

The concept of data-driven learning (DDL) – direct student use of corpora – has been gaining attention among researchers and teachers. DDL gives learners the chance to take an inductive approach to learning by recognizing patterns in corpus data rather than following abstract rules. However, very few studies focus on presenting clear practices that other teachers can readily use in their classes, particularly related to written error correction. This paper begins by reviewing research pertaining to DDL in student writing as well as using Japanese-English parallel corpora. It then outlines a recent study by Jenifer Larson-Hall (2015), notes weaknesses in the study, and outlines a current project (McGuire &amp; Larson-Hall, under preparation).
Key Contributions in Clinical Research Informatics

Summary Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2020. Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between two section editors and the editorial team was organized to finally conclude on the selected four best papers. Results: Among the 877 papers published in 2020 and returned by the search, there were four best papers selected. The first best paper describes a method for mining temporal sequences from clinical documents to infer disease trajectories and enhancing high-throughput phenotyping. The authors of the second best paper demonstrate that the generation of synthetic Electronic Health Record (EHR) data through Generative Adversarial Networks (GANs) could be substantially improved by more appropriate training and evaluation criteria. The third best paper offers an efficient advance on methods to detect adverse drug events by computer-assisting expert reviewers with annotated candidate mentions in clinical documents. The large-scale data quality assessment study reported by the fourth best paper has clinical research informatics implications, in terms of the trustworthiness of inferences made from analysing electronic health records. Conclusions: The most significant research efforts in the CRI field are currently focusing on data science with active research in the development and evaluation of Artificial Intelligence/Machine Learning (AI/ML) algorithms based on ever more intensive use of real-world data and especially EHR real or synthetic data. A major lesson that the coronavirus disease 2019 (COVID-19) pandemic has already taught the scientific CRI community is that timely international high-quality data-sharing and collaborative data analysis is absolutely vital to inform policy decisions.
Generation of an E-learning Application Through Model Programming

Online learning or e-learning is a growing industry, whose beginnings date back to the 1980s and even before. The model of learning in history has always been quite simple. Until the early 2000s, education took place in a class of students with a teacher leading the class. The physical presence was not in question, and any other type of learning was, at best, questionable. Then the Internet appeared, and the rest is part of the story. On the other hand, the development of computer applications to ensure this type of education is also growing, but the problem is that each application is developed according to a given technology. Developing an e-learning application for each technology requires a lot of human resources and technical knowledge and always requires a migration of each application developed to other implementation platforms as needed. the model-driven architecture approach offers full or partial development of an e-learning application while remaining independent of the implementation platform of this application. Therefore, to solve this problem we propose a development of an e-learning application according to a model-driven architecture approach.
Exploiting Symmetry in High-Dimensional Dynamic Programming

We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The "curse of dimensionality" is avoided due to four complementary techniques: (1) exploiting symmetry in the approximate law of motion and the value function; (2) constructing a concentration of measure to calculate high-dimensional expectations using a single Monte Carlo draw from the distribution of idiosyncratic shocks; (3) sampling methods to ensure the model fits along manifolds of interest; and (4) selecting the most generalizable over-parameterized deep learning approximation without calculating the stationary distribution or applying a transversality condition. As an application, we solve a global solution of a multi-firm version of the classic Lucas and Prescott (1971) model of "investment under uncertainty." First, we compare the solution against a linear-quadratic Gaussian version for validation and benchmarking. Next, we solve nonlinear versions with aggregate shocks. Finally, we describe how our approach applies to a large class of models in economics.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
R2-D2: A Modular Baseline for Open-Domain Question Answering

This work presents a novel four-stage open-domain QA pipeline R2-D2 (Rank twice, reaD twice). The pipeline is composed of a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from all system's components. We demonstrate its strength across three open-domain QA datasets: NaturalQuestions, TriviaQA and EfficientQA, surpassing state-of-the-art on the first two. Our analysis demonstrates that: (i) combining extractive and generative reader yields absolute improvements up to 5 exact match and it is at least twice as effective as the posterior averaging ensemble of the same models with different parameters, (ii) the extractive reader with fewer parameters can match the performance of the generative reader on extractive QA datasets.
Awareness and Use of Cloud Computing: Its Implications in Selected Academic Libraries in Imo State, Nigeria

The study investigated awareness and use of cloud computing; its implications by libraries in selected academic libraries in Imo State, Nigeria. Four research questions were asked and answered. Also, three hypotheses were formulated and tested at 0.05 significance level. Survey research design was adopted. The total population of the study was 53 librarians, comprising professional staff of the library in Imo State University, Owerri, Federal Polytechnic, Nekede and Alvan Ikoku Federal College of Education, Owerri. The sample size was the entire population; hence the census enumeration sampling technique was employed. Rating scale was used for data collection. Data collected were presented in tables and column charts, and analyzed using mean, standard deviation. Testing of hypotheses was done using F-ratio (ANOVA) at the significance level of 0.05. Findings reveal that there is certain level of awareness on cloud computing technologies and models in the libraries studied. It was also discovered that cloud computing technology were used by libraries in the institutions studied, and economy of resource cost effectiveness and file sharing are some of the major positive implications of librarians adoption cloud computing technologies. However security and privacy, multiple taxation were also identified as major negative implications of cloud computing adoption by the librarians in discharging their functions in the libraries. Based on these findings, recommendations were made.
Balanced Crown Decomposition for Connectivity Constraints.

We introduce the balanced crown decomposition that captures the structure imposed on graphs by their connected induced subgraphs of a given size. Such subgraphs are a popular modeling tool in various application areas, where the non-local nature of the connectivity condition usually results in very challenging algorithmic tasks. The balanced crown decomposition is a combination of a crown decomposition and a balanced partition which makes it applicable to graph editing as well as graph packing and partitioning problems. We illustrate this by deriving improved approximation algorithms and kernelization for a variety of such problems.In particular, through this structure, we obtain the first constant-factor approximation for the Balanced Connected Partition (BCP) problem, where the task is to partition a vertex-weighted graph into k connected components of approximately equal weight. We derive a 3-approximation for the two most commonly used objectives of maximizing the weight of the lightest component or minimizing the weight of the heaviest component.
Intelligent Video Analytic Based Framework for Multi-view Video Summarization

A multi-view surveillance system captures the scenic details from a different perspective, defined by camera placements.The recorded data is used for feature extraction, which can be further utilized for various pattern-based analytic processes like object detection, event identification, and object tracking.In this proposed work, we present a method for creating a network of the optimal number of video cameras, to cover the maximum overlapping area under surveillance.In the proposed work, the focus is on developing algorithms for deciding efficient camera placement of multiple cameras at various junctions and intersections to generate a video summary based on the multiple views.Deep learning models like YOLO have been used for object detection based on the generation of a large number of bounding boxes and the associated search technique for generating rankings based on the views of the multiple cameras.Based on the view quality, the dominant views will be located.Further, keyframes are selected based on maximum frame coverage from these views.A video summary will be generated based on these keyframes.Thus, the video summary is generated through solving a multi-objective optimization problem based on keyframe importance evaluated using a maximum frame coverage.
Training Needs for the Digital Teacher: الاحتياجات التدريبية للمعلم الرقمي

Using digital technologies has become an urgent demand in this ever-changing world. The change in teachers’ role; from traditional to digital, requires systematic procedures and training. This study explores the importance of defining the training needs, the strategies of enclosing these needs, the types of training and the process of designing the training course. It also investigates the reasons that forces this change in the teacher’s role and the competencies and skills they should be equipped with.
Better Self-training for Image Classification Through Self-supervision

Self-training is a simple semi-supervised learning approach: Unlabelled examples that attract high-confidence predictions are labelled with their predictions and added to the training set, with this process being repeated multiple times. Recently, self-supervision—learning without manual supervision by solving an automatically-generated pretext task—has gained prominence in deep learning. This paper investigates three different ways of incorporating self-supervision into self-training to improve accuracy in image classification: self-supervision as pretraining only, self-supervision performed exclusively in the first iteration of self-training, and self-supervision added to every iteration of self-training. Empirical results on the SVHN, CIFAR-10, and PlantVillage datasets, using both training from scratch, and Imagenet-pretrained weights, show that applying self-supervision only in the first iteration of self-training can greatly improve accuracy, for a modest increase in computation time.
Hierarchical and Multimodal Classification of Images from Soil Remediation Reports

When soil remediation specialists clean up a new site, they have a long time manually revising digital reports previously written by other experts, where they look for necessary information in accordance with similar characteristics of polluted fields. Important information lies in tables, graphs, maps, drawings and their associated captions. Therefore, experts have to be able to quickly access these content-rich elements, instead of manually scrolling through each page of entire reports. Since this information is multimodal (image and text) and follows a semantically hierarchical structure, we propose a classification algorithm that takes these two constraints into account. In contrast to existing works using either multimodal system or hierarchical classification model, we explore the combination of state-of-the-art methods from multimodal systems (image and text modalities) and hierarchical classification systems. By this combination, we tackle the constraints of our classification process: small dataset, missing modalities, noisy data, and non-English corpus. Our evaluation shows that the multimodal hierarchical system outperforms the unimodal and that the performance of multimodal system with a joint combination of hierarchical classification and flat classification on different modalities provides promising results.
Using flashcards for English second language creative writing in Grade 1

Background: English Second Language (ESL) learners have difficulty constructing sentences due to internalising information in their home language and thereafter translating it into English. Learners who have difficulty speaking English generally encounter problems writing it, which hampers their creative writing ability.Objectives: The purpose of the research was to identify a teaching strategy to facilitate ESL learners with creative writing. This study explored the influence of flashcards on the creative writing skills of Grade 1 ESL learners and improved the researchers’ teaching practice.Method: This qualitative study depicted an action research design and utilised an inductive approach to data analysis. Convenience sampling was used when selecting the participants who were 31 Grade 1 learners in a school in Pietermaritzburg. The flashcards were used during the implementation stage of the action research process as an intervention to enhance learners’ creative writing skills.Findings: The findings indicate that learners who participated in the study had improved in their written assessments. There were three themes identified, which included misspelt words, incorrect use of tenses and ungrammatical sentence construction. Flashcards revealed the correct sentence writing techniques by depicting sentences. Learners’ written pieces were more logical and they participated actively during lessons. This enhanced the researcher’s teaching practice, which catered to both visual and auditory learners.Conclusion: The findings suggest that the use of flashcards had a positive effect on ESL learners’ creative writing skills. This encouraged participatory teaching and learning, which can be of benefit to many teachers seeking to engage learners using alternate learning styles.
A Recognition Algorithm for Signal Modulation Modes of Tactical Data Link

The identification of the modulation mode is the key to the identification and differentiation of data link signals. In view of this, for the modulation modes (π/4-DQPSK, MSK, QPSK, 8PSK, BPSK, OQPSK) of the U.S. military Link-11, Link-16, Link-22 and CDL tactical data link signals, this paper proposes a tactical data link signal modulation recognition algorithm based on joint features. It mainly studies the extraction of four feature parameters of each modulated signal under Gaussian channels : high-order cumulant, square spectrum, fourth-order spectrum and high-order cumulant of differential signals. After that, several common tactical data link modulated signals are identified and classified by the decision tree. Finally, the simulation experiments shows that the algorithm has low computational complexity and high recognition rate, and has strong reliability and accuracy.
How to Make a Secure Index for Searchable Symmetric Encryption, Revisited

Searchable symmetric encryption (SSE) enables clients to search encrypted data. Curtmola et al. (ACM CCS 2006) formalized a model and security notions of SSE and proposed two concrete constructions called SSE-1 and SSE-2. After the seminal work by Curtmola et al., SSE becomes an active area of encrypted search. In this paper, we focus on two unnoticed problems in the seminal paper by Curtmola et al. First, we show that SSE-2 does not appropriately implement Curtmola et al.'s construction idea for dummy addition. We refine SSE-2's (and its variants') dummy-adding procedure to keep the number of dummies sufficiently many but as small as possible. We then show how to extend it to the dynamic setting while keeping the dummy-adding procedure work well and implement our scheme to show its practical efficiency. Second, we point out that the SSE-1 can cause a search error when a searched keyword is not contained in any document file stored at a server and show how to fix it.
WHITE-BOX CARTOONIZATION USING AN EXTENDED GAN FRAMEWORK

In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos. The learning purposes of our system are based on three distinct representations: surface representation, structure representation, and texture representation. The surface representation refers to the smooth surface of the images. The structure representation relates to the sparse colour blocks and compresses generic content. The texture representation shows the texture, curves, and features in cartoon images. Generative Adversarial Network (GAN) framework decomposes the images into different representations and learns from them to generate cartoon images. This decomposition makes the framework more controllable and flexible which allows users to make changes based on the required output. This approach overcomes any previous system in terms of maintaining clarity, colours, textures, shapes of images yet showing the characteristics of cartoon images.
State of the Art and Challenges in Blockchain Applications

Blockchain is a decentralized infrastructure widely used in emerging digital cryptocurrencies. With the gradual acceptance of Bitcoin, it has attracted attention and research in this field. Blockchain technology has the characteristics of decentralization, and block data is basically non-tamperable and trustless, so, it is sought after by enterprises, especially financial institutions. Now, it has become a hot spot for research and applications following the Internet of Things, cloud computing, big data and artificial intelligence, and it has been listed as one of the biggest development trends for the next ten years by various researchers. Blockchain has the characteristics of decentralization, consensus mechanism, immutability, smart contracts, etc. Based on the analysis and comparison of the current state of blockchain research at home and abroad, and a brief introduction to the key technologies of the blockchain, this paper addresses the recent application progress of blockchain technology in recent years, and analyses the major current blockchain application problems, look forward to the future application prospects and development trends of the blockchain, and then provides useful motivation and reference for the future research and application of the blockchain.
MiikeMineStamps: A Long-Tailed Dataset of Japanese Stamps via Active Learning

Mining existing image datasets with rich information can help advance knowledge across domains in the humanities and social sciences. In the past, the extraction of this information was often prohibitively expensive and labor-intensive. AI can provide an alternative, making it possible to speed up the labeling and mining of large and specialized datasets via a human-in-the-loop method of active learning (AL). Although AL methods are helpful for certain scenarios, they present limitations when the set of classes is not known before labeling (i.e. an open-ended set) and the distribution of objects across classes is highly unbalanced (i.e. a long-tailed distribution). To address these limitations in object detection scenarios we propose a multi-step approach consisting of 1) object detection of a generic “object” class, and 2) image classification with an open class set and a long tail distribution. We apply our approach to recognizing stamps in a large compendium of historical documents from the Japanese company Mitsui Mi’ike Mine, one of the largest business archives in modern Japan that spans half a century, includes tens of thousands of documents, and has been widely used by labor historians, business historians, and others. To test our approach we produce and make publicly available the novel and expert-curated MiikeMineStamps dataset. This unique dataset consists of 5056 images of 405 different Japanese stamps, which to the best of our knowledge is the first published dataset of historical Japanese stamps. We hope that the MiikeMineStamps dataset will become a useful tool to further explore the application of AI methods to the study of historical documents in Japan and throughout the world of Chinese characters, as well as serve as a benchmark for image classification algorithms with an open-ended and highly unbalanced class set.