Reviewerly/exHarmony huggingface dataset:
Analysis of Reading Skills of Class III Students with Group Learning Method at Elementary School

In learning Indonesian, each student has different abilities. One of the problems that elementary school (SD) students often encounter in their daily lives is problems related to not reading fluently. Because unwittingly fluent reading is often found in human activities in carrying out various activities in daily life, elementary school students are no exception. To describe the results of the third grade students reading skills with the group learning method at SDN Modong Tulangan, namely through the learning process students are expected to have the ability to think and interact to be able to solve problems in everyday life. Based on this, in this study the researchers used descriptive qualitative research with an approach to students. Qualitative research is research that produces descriptive data in the form of written and spoken words from people and behaviors observed in the field. The population in this study was 19 students divided into 9 female students and 10 male students in grade III at SDN Modong Tulangan. The instruments used are test questions, student response questionnaires, interviews, and observation sheets. The results showed that the application of group learning to reading skills was found in the teacher's teaching and learning activities which began by providing guidance to students through explanations from Indonesian books that made me smart, then students began to apply according to the explanations of the class teacher to use Indonesian books to make me smarter. Thorough and correct. The use of group learning can attract students' attention by encouraging students to learn to read which makes students' reading skills very good.
Improving the Ability to Recognize the Letters through the Interactive Video Media "Mari Mengenal Huruf" in Group A Kindergarten

Cognitive ability is one of the developmental aspects of children who are experiencing maturity and need to be developed. In the cognitive development of early childhood there is one development that must be developed, namely recognizing letters. The introduction of the letters of the alphabet in early childhood helps children to recognize various kinds of letters of the alphabet. Regarding the letter recognition program, it can be done using interactive video media "Let's Get to Know the Letters". The purpose of this study was to improve the ability to recognize letters of the alphabet in Group A children in PKK 1 Jodokan Kindergarten, Beji District. This research is a Classroom Action Research. Based on the data analysis carried out, it was concluded that the ability to recognize the letters of the alphabet in group A in Pre-cycle obtained an average value of 35%, while in Cycle I the percentage was 48.33%, which means less than the maximum. Then giving the Cycle II action, the percentage of the average value becomes 93%, so that the average gain in cycle I and cycle II has increased again by 44.67%. The conclusion of this research is that through viewing the interactive video "Let's Get to Know the Letters" can increase the ability to recognize the letters of the alphabet.
Practical Implementation of Encoding Range Top-2 Queries

Abstract We design a practical variant of an encoding for range Top-2 query (RT2Q) and evaluate its performance. Given an array $A[1,n]$ of $n$ elements from a total order, the range Top-2 encoding problem is to construct a data structure that answers ${\textsf{RT2Q}}{}$, which returns the positions of the first and second largest elements within a given range of $A$, without accessing the array $A$ at query time. We design the following two implementations: (i) an implementation based on an alternative representation of Davoodi et al.’s [Phil. Trans. Royal Soc. A, 2016] data structure, which supports queries efficiently. Experimental results show that our implementation is efficient in practice and gives improved time-space trade-offs compared with the indexing data structures (which keep the original array $A$ as part of the data structure) for range maximum queries. (ii) Another implementation based on Jo et al.’s ${\textsf{RT2Q}}{}$ encoding on $2 \times n$ array [CPM, 2016], which can be constructed in $O(n)$ time. We compare our encoding with Gawrychowski and Nicholson’s optimal encoding [ICALP, 2015] and show that in most cases, our encoding shows faster construction time while using a competitive space in practice.
Threshold Homomorphic Encryption From Provably Secure NTRU

Abstract Homomorphic Encryption (HE) supports computation on encrypted data without the need to decrypt, enabling secure outsourcing of computing to an untrusted cloud. Motivated by application scenarios where private information is offered by different data owners, Multi-Key Homomorphic Encryption (MKHE) and Threshold Homomorphic Encryption (ThHE) were proposed. Unlike MKHE, ThHE schemes do not require expensive ciphertext extension procedures and are therefore as efficient as their underlying single-key HE schemes. In this work, we propose a novel NTRU-type ThHE scheme which caters to the computation scenarios with pre-defined participants. In addition to inheriting the simplicity of NTRU scheme, our construction has no expensive relinearization and correspondingly no costly evaluation keys. Controlling noise to make it increase linearly and then using a wide key distribution, our scheme is immune to the subfield lattice attacks and its security follows from the hardness of the standard R-LWE problem. Finally, based on the {0,1}-linear secret sharing and noise flooding techniques, we design a single round distributed threshold decryption protocol, where the decryption is able to be completed even when only given a subset (say $t$-out-of-$k$) of partial decryptions. To the best of our knowledge, our construction is the first NTRU-type ThHE scheme.
Modelling and visualizing information entropy in Python

Abstract This paper examines the specifics of structural evolution in large systems and diverse distribution of resource in the context of the traditional Information Entropy concepts. An original and comprehensive methodology for modelling and visualizing the production of maximal and minimal levels of entropy is developed in Python/Pycharm environment.
A Comprehensive Analysis of Automated Threat Modeling Solution Company: Threat Modeler Software, Inc.

Purpose: Effective Security Threat modeling in an enterprise depends on the efficient tools used for modeling. The Threat modeling tool market has multiple players that provide platforms to automate the Threat modeling process in enterprises. Threat Modeler Software, Inc. is one such platform provider company. The paper aims to explore the company and its platform's features. Design/Methodology/Approach: This paper explores the documentation available on Threat Modeler Software, Inc. to understand the features, working principles, and company information. Features are further explored by performing hands-on Threat modeling using the trial edition of the platform. SWOC analysis of the company is performed to analyze the factors affecting the company as a Threat modeling platform provider. Findings/Result: Threat Modeler Software, Inc.’s platform has innovative features that enable automated Threat modeling. SWOC analysis has identified some of the challenges that the company has. The competitor list showed commercial and open-source competitors in the race to create Threat modeling platforms. The innovative culture of the Threat Modeler Software, Inc. must continue to provide new features making the Threat modeling experience unique. Originality/Value: This paper studies Threat Modeler platform's architecture and explores important features of the platform. Capabilities of the features and their importance is studied. SWOC analysis is performed to identify factors affecting the company. A list of different threat modeling platforms is built to understand the current competitors for Threat Modeler Software, Inc. Paper Type: Case study.
Moving-Target Intelligent Tutoring System for Marksmanship Training

Abstract Intelligent tutoring systems (ITSs) may augment military training systems and mitigate existing limitations in training personnel and resources. A study was conducted to investigate the effectiveness of an embedded rifle marksmanship ITS for Moving Targets (MT-ITS). MT-ITS has two main components: (1) a Smart Sight System that provides a perceptual cue to help trainees adjust their point of aim to account for a target’s speed, direction of movement, and distance, and (2) a performance-based algorithm that delivers shooting performance feedback to trainees. The MT-ITS was tested in an experiment where participants engaged moving targets in a virtual shooting range. Moving targets were presented at different speeds, direction of movement, and distances. Two types of marksmanship training were compared: with ITS and without ITS (a standard training). The ITS training group produced better hit rate and aiming accuracy scores than the standard training group, requiring less practice to achieve asymptotic results. Implications for the design of embedded trainers with ITS for marksmanship specifically and for training motor skills in general are discussed in the context of future research directions.
A Computational Neural Network Model for College English Grammar Correction

For the error correction of English grammar, if there are errors in the semantic units (words and sentences), it will inevitably affect the subsequent text analysis and semantic understanding, and ultimately reduce the overall performance of the practical application system. Therefore, intelligent error detection and correction of the word and grammatical errors in English texts is one of the key and difficult points of natural language processing. This exploration innovatively combines a computational neural model with college grammar error correction to improve the accuracy of college grammar error correction. It studies the computational neural model in English grammar error correction based on a neural network named Knowledge and Neural machine translation powered College English Grammar Typo Correction (KNGTC). First, the Recurrent Neural Network is introduced, and the overall structure of the English grammatical error correction neural model is constructed. Moreover, the supervised training of Attention is discussed, and the experimental environment and experimental data are given. The results show that KNGTC has high accuracy in college English grammar correction, and the accuracy of this model in CET-4 and CET-6 writing can reach 82.69%. The English grammar error correction model based on the computational neural network has perfect function and strong error correction ability. The optimization and perfection of the model can improve students' English grammar level, which has certain practical value. After years of continuous optimization and improvement, English grammar error correction technology has entered a performance bottleneck. This mode's construction can break the current technology's limitations and bring a better user experience. Therefore, it is very valuable to study the error correction model of English grammar in practical application.
Progressive privacy-preserving batch retrieval of lung CT image sequences based on edge-cloud collaborative computation

A computer tomography image (CI) sequence can be regarded as a time-series data that is composed of a great deal of nearby and similar CIs. Since the computational and I/O costs of similarity measure, encryption, and decryption calculation during a similarity retrieval of the large CI sequences (CIS) are extremely high, deploying all retrieval tasks in the cloud, however, will lead to excessive computing load on the cloud, which will greatly and negatively affect the retrieval performance.To tackle the above challenges, the paper proposes a progressive privacy-preserving Batch Retrieval scheme for the lung CISs based on edge-cloud collaborative computation called the BRS method. There are four supporting techniques to enable the BRS method, such as: 1) batch similarity measure for CISs, 2) CIB-based privacy preserving scheme, 3) uniform edge-cloud index framework, and 4) edge buffering.The experimental results reveal that our method outperforms the state-of-the-art approaches in terms of efficiency and scalability, drastically reducing response time by lowering network communication costs while enhancing retrieval safety and accuracy.
Comparative analysis of imaging diagnostic models for tubular basophilia and mineralization of kidney

Abstract Background Now that it is possible to efficiently classify and save tissue images of laboratory animals using whole-slide imaging, many diagnostic models are being developed through transfer learning with Convolutional Neural Network (CNN). In this study, transfer learning was performed to gain toxicopathological knowledge using CNN models such as InceptionV3 and Xception. For the classification of tubular basophilia and mineralization, two representative background lesions that commonly occur in toxicological studies, accuracies of diagnosis were compared using MobileNetV2, Xception and InceptionV3. For the simultaneous detection of the two lesions, the accuracy was analysed using You Only Look Once version 4 (YOLOv4). Results The accuracy of the classification models was as follows: MobileNetV2 (epoch 50, accuracy: 98.57%) &gt; Xception (epoch 70, accuracy: 97.47%) &gt; InceptionV3 (epoch 70, accuracy: 89.62%). In the case of object detection, the accuracy of YOLOv4 was 98.62% at epoch 3000. Conclusions Among the classification models, MobileNetV2 had the best accuracy despite applying a lower epoch than InceptionV3 and Xception. The object detection model, YOLOv4, accurately and simultaneously diagnosed tubular basophilia and mineralization, with an accuracy of 98.62% at epoch 3000.
New Language Identification and Sentiment Analysis Modules for Social Media Communication

The style and vocabulary of social media communication, such as chats, discussions or comments, differ vastly from standard languages. Specifically in internal business communication, the texts contain large amounts of language mixins, professional jargon and occupational slang, or colloquial expressions. Standard natural language processing tools thus mostly fail to detect basic text processing attributes such as the prevalent language of a message or communication or their sentiment. In the presented paper, we describe the development and evaluation of new modules specifically designed for language identification and sentiment analysis of informal business communication inside a large international company. Besides the details of the module architectures, we offer a detailed comparison with other state-of-the-art tools for the same purpose and achieve an improvement of 10–13 % in accuracy with selected problematic datasets.
Disentangle Then Calibrate: Selective Treasure Sharing for Generalized Rare Disease Diagnosis

Annotated images for rare disease diagnosis are extremely hard to collect. Therefore, identifying rare diseases based on scarce amount of data is of far-reaching significance. Existing methods target only at rare diseases diagnosis, while neglect to preserve the performance of common disease diagnosis. To address this issue, we first disentangle the features of common diseases into a disease-shared part and a disease-specific part, and then employ the disease-shared features alone to enrich rare-disease features, without interfering the discriminability of common diseases. In this paper, we propose a new setting, i.e., generalized rare disease diagnosis to simultaneously diagnose common and rare diseases. A novel selective treasure sharing (STS) framework is devised under this setting, which consists of a gradient-induced disentanglement (GID) module and a distribution-targeted calibration (DTC) module. The GID module disentangles the common-disease features into disease-shared channels and disease-specific channels based on the gradient agreement across different diseases. Then, the DTC module employs only disease-shared channels to enrich rare-disease features via distribution calibration. Hence, abundant rare-disease features are generated to alleviate model overfitting and ensure a more accurate decision boundary. Extensive experiments conducted on two medical image classification datasets demonstrate the superior performance of the proposed STS framework.
A Novel Deep Learning System for Breast Lesion Risk Stratification in Ultrasound Images

This paper presents a novel deep learning system to classify breast lesions in ultrasound images into benign and malignant and into Breast Imaging Reporting and Data System (BI-RADS) six categories simultaneously. A multitask soft label generating architecture is proposed to improve the classification performance, in which task-correlated labels are obtained from a dual-task teacher network and utilized to guide the training of a student model. In student model, a consistency supervision mechanism is embedded to constrain that a prediction of BI-RADS is consistent with the predicted pathology result. Moreover, a cross-class loss function that penalizes different degrees of misclassified items with different weights is introduced to make the prediction of BI-RADS closer to the annotation. Experiments on our private and two public datasets show that the proposed system outperforms current state-of-the-art methods, demonstrating the great potential of our method in clinical diagnosis.
…and That's What Gets Results: HCI Methods in OzCHI Publications

What is HCI as a discipline? There is little data available on the analytical and methodological practices of the HCI community, particularly in generalist venues. This paper builds on recent research that has started to examine OzCHI as an HCI venue. While other examinations of HCI venues have considered the relative popularity of research topics and themes, we examine the methods being adopted by OzCHI authors. This is the first paper for over a decade to examine the methodologies being used at an HCI venue. We present a description of the relative balance of qualitative and quantitative work at OzCHI, and a close examination of the relative prevalence of a range of research methods. We also examine the relationships between analytical approach and gender, geography and—relying on recent changes to OzCHI—paper length. We find that the qualitative-quantitative split is not gender neutral, and has impact on page length of a paper, but is not affected by geography. Drawing on these insights we provide advice and guidance for future OzCHI organisers, and a research agenda for better understanding HCI as a discipline.
Development of E Module Based on Android in Basic Graphics Design Course

This study aims to development and feasibility of e modul based on Android in the basic subjects of graphic design for class X Multimedia at SMK PGRI 2 Sidoarjo. This research was conducted at SMK PGRI 2 Sidoarjo with research subjects in class X Multimedia who received Basic Graphic Design subjects. In this study are research and development method of R and D uses the ADDIE model which consists of analysis, design, development, implementation, and evaluation. However, in this study, only three stages were applied, namely the analysis, design, development stages, while the implementation dissemination and evaluation stages were not carried out due to the limited time of the study and the impact of the Covid 19 outbreak. The android-based e-module has met the validity criteria by obtaining an average score from all aspects of the assessment, the media expert validator is 88 percent which is in the category suitable for use with revisions, and the material expert validator is 74 percent in the category suitable for use with revisions so that based on the assessment experts have been appropriate and suitable to be used as learning media in Basic Graphic Design subjects
Shaping Romance: Mediating Intimacy for Co-located Couples

During recent years multiple studies focused on how to mediate the intimacy of couples over distance by researching various intimacy aspects, such as physical contact and disclosure. At the same time, mediating intimacy for co-located couples remains relatively unexplored. Our paper focuses on this and presents an empirical field study involving 13 co-located couples that interacted with a technology probe titled ‘Shaping Romance’. In short, our qualitative findings show that technology can mediate intimacy by allowing partners to look inwards and reflect on their own desires, look outwards and reflect on the desires of their partner, and look at the whole by remembering, acting and validating. Our contributions to HCI are the technological intervention itself, our findings which highlight limitations and opportunities technology has for mediating the intimacy of co-located couples, and a design space full of dilemmas that we present for future researchers and designers.
Design Opportunities to Enhance Children's Engagement with Nature in Bhutan: A Working Field Theory

Bhutan is known to the world for its rich natural biodiversity. However, with urbanization, children living in urban areas are increasingly spending their time indoors. There is potential to understand how children engage with nature and explore the design of suitable technologies to motivate children to be outdoors. We conducted an exploratory study with 11 parents and 12 children (7-8 years old) living in urban Bhutan to understand how children with their family engage with nature. We found that children experience nature physically with adults’ presence and design elements such as fantasy, sensory, curiosity, and game play with friends and siblings can enhance nature engagement. Concurrently, we found that technologies can be disruptive when children are outdoors with their friends. The contribution of this paper is a deeper understanding of how children experience nature, which is visualized through a working field theory, and a discussion of design opportunities to enhance nature interactions.
NVP: A Flexible and Efficient Processor Architecture for Accelerating Diverse Computer Vision Tasks including DNN

Compared with the CPUs and GPUs, the AI accelerators are able to achieve higher performance and energy efficiency for accelerating the DNNs. However, besides the DNNs, the computer vision also involves other tasks such as conventional image filtering and stereo matching. These tasks are not supported by the AI accelerators. In addition, the newly proposed DNN structures are not supported by the existing AI accelerators, making them difficult to catch up with the ever-evolving AI algorithms. To address this challenge, the Google has proposed the Pixel Visual Core (PVC) processor with a flexible architecture to accelerate diverse computer vision tasks including the DNNs while achieving higher efficiency. However, the architecture of the PVC is not well optimized, leading to limited energy efficiency. In this brief, we have proposed a flexible and efficient processor architecture (named NVP) with several design techniques to address the limitations of the PVC. The NVP is able to accelerate diverse computer vision tasks including DNN structures, conventional image filtering and stereo matching, while achieving significantly improved energy efficiency than the PVC and comparable energy efficiency with the AI accelerators.