Reviewerly/exHarmony huggingface dataset:
Generative Text Secret Sharing with Topic-Controlled Shadows

Secret image sharing has been extensively and thoroughly researched. However, in the social network environment, shadow images are subject to compression or noise pollution during uploading and transmitting, which makes it challenging to recover secrets losslessly. Texts are more suited for transmission in social networks as shadows because of the broad variety of application scenarios and inherent robustness. Through a secret sharing technique of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>k</mi> <mo>,</mo> <mi>n</mi> </mrow> </mfenced> </math> threshold, a secret is encrypted as <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>n</mi> </math> shadows, where any <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>k</mi> </math> or more shadows can recover the secret, while less than <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>k</mi> </math> cannot obtain any information on the secret. In this article, we propose a generative text secret sharing scheme with topic-controlled shadows, which encrypts a secret message as a number of semantically natural shadow texts and controls the topics of shadow texts using bag-of-words models during text generation by the language model. This study also proposes two goal programming models to improve the shadow texts’ topic relevance and fluency. The shadow texts of the proposed scheme satisfy loss tolerance, semantic comprehensibility, topic controllability, and robustness. An ablation study, comparative test, and anti-detection experiment verify the effectiveness of the proposed scheme.
Data Augmentation and Random Multi-Model Deep Learning for Data Classification

In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models.The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples.Deep learning (DL) models have a lot of parameters, and they frequently overfit.Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL.Nevertheless, reliable data collection is a major limiting factor.Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of normalization in batches.In this paper, we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning (RMDL) which uses the association approaches of multi-DL to yield random models for classification.We present a methodology for using Generative Adversarial Networks (GANs) to generate images for data augmenting.Through experiments, we discover that samples generated by GANs when fed into RMDL improve both accuracy and model efficiency.Experimenting across both MNIST and CIAFAR-10 datasets show that, error rate with proposed approach has been decreased with different random models.
The Utilization of Eggshell as Reinforcing Material of Heat Cured Acrylic Resin Denture Base

Heat-cured acrylic resin is easily fractured and can be prevented by adding reinforcing materials of duck eggshell powder. Furthermore, it can be used as a biocompatible reinforcing material due to the high content of CaCO3. This study aims to determine the effect of adding 2.5% and 5% duck eggshells as reinforcement for the denture base with heat-cured acrylic resin on the transverse strength. The method is an laboratory experiments with a post-test only control design conducted on January 2022. The samples were made by adding a reinforcing agent of 2.5% and 5% heat-cured acrylic resin powder’s weight in a mould sized 65 x 10 x 2.5 ± 0.5 mm with 11 samples in each group. The curing process was executed at a temperature of 70°C for 90 minutes before increasing to 100°C for 30 minutes, and the transverse strength of acrylic resin was measured using the Universal Testing Machine. The sample tested using univariate analysis with the results of the average transverse strength in the control group 66.59 ± 1.88 MPa, after adding 2.5% duck eggshell 72.61 ± 3.05 MPa and 5% duck eggshell 86.14 ± 4.09. The one-way ANOVA and LSD tests showed significant results with p-value = 0.001 (p &lt; 0.05) and 0.001 (p &lt;0.05). The highest transverse strength value of heat-cured acrylic resin was found in the 5% duck eggshell group, followed by 2.5%, and lastly, the control group.
Deep features for image copy move forgery detection

In this modern era, there has been a increase in the copying and moving of illegalities, distributing them, and then tempered images. These days even the most secured data sometimes go under devastating forgery. Copy forgery is seen to be the easiest copying method without leaving any obvious traces of manipulating an image’s content.Like working on the pixels of an image by geometrical and illumination techniques which are either cut or copied or copied to some other place of the identical image. In this paper, we propose copy paste forgery detection of an image by extracting and grouping SIFT key points for analyzation. The first step is to find suspicious clusters of key points and patches to roughly estimate the dimensionality of SIFT. Secondly, it finds pixel level, scale, and color by comparingsimilar neighborhoods of matching duplicated regions. The results prove to be giving good performance scores, computational timing, and complexity
Enhancing Student Motivation: Articulate Storyline Learning Media Experiment

This research investigates the impact of utilizing the learning media "Articulate Storyline" on the motivation of fourth-grade students in SDN Sekardangan. Recognizing the prevalence of motivation issues among students, this study employs an experimental approach with a "one group pretest posttest design" to examine the relationship between learning media and student motivation. The data was collected through a social studies learning motivation questionnaire administered to the research sample. The findings demonstrate a significant positive effect of implementing Articulate Storyline on student learning, highlighting its potential as an effective tool for enhancing motivation in educational settings. The implications of these results offer valuable insights for educators and policymakers aiming to foster a conducive learning environment. &#x0D; Highlights: &#x0D; &#x0D; The use of Articulate Storyline learning media significantly enhances student motivation.&#x0D; Experimental study explores the impact of Articulate Storyline on fourth-grade students' motivation.&#x0D; Findings highlight the positive influence of learning media on student motivation.&#x0D; &#x0D; Keywords: Articulate Storyline, learning media, student motivation, experimental study, fourth-grade students.
Users' Expectations, Experiences, and Concerns With COVID Alert, an Exposure-Notification App

We conducted semi-structured interviews with 20 users of Canada's exposure-notification app, COVID Alert. We identified several types of users' mental models for the app. Participants' concerns were found to correlate with their level of understanding of the app. Compared to a centralized contact-tracing app, COVID Alert was favored for its more efficient notification delivery method, its higher privacy protection, and its optional level of cooperation. Based on our findings, we suggest decision-makers rethink the app's privacy-utility trade-off and improve its utility by giving users more control over their data. We also suggest technology companies build and maintain trust with the public. Further, we recommend increasing diagnosed users' motivation to notify the app and encouraging exposed users to follow the guidelines. Last, we provide design suggestions to help users with Unsound and Innocent mental models to better understand the app.
Empowering Learning: Simple Tool Practicum-Based POE Model for Student Outcomes

This pre-experimental quantitative study aimed to investigate the impact of a POE (Predict, Observe, Explain) learning model based on a simple tool practicum on student learning outcomes. The research was conducted at SMP Negeri 2 Gedangan, with one experimental class and two replication classes. Non-probability random sampling was employed to select the participants. The assessment instrument consisted of multiple-choice questions to measure learning outcomes. Data analysis included the N-Gain test and paired sample t-test. The findings revealed that the N-Gain scores for all three classes were in the moderate category, and the paired sample t-test showed a significant increase in learning outcomes (p &lt; 0.05) in each class. The results suggest that implementing the simple tool practicum-based POE learning model positively influences student learning outcomes. This study contributes to the field of education by highlighting an effective approach to enhance student learning through hands-on experiences and the application of the POE model. &#x0D; Highlights:&#x0D; &#x0D; The study focuses on enhancing student learning outcomes through a POE learning model and a simple tool practicum.&#x0D; The research employs a quantitative approach, utilizing the N-Gain test to assess the effectiveness of the intervention.&#x0D; The findings demonstrate a significant improvement in student learning outcomes, highlighting the potential of the simple tool practicum-based POE learning model.&#x0D; &#x0D; Keywords: Student learning outcomes, POE learning model, Simple tool practicum, Quantitative research, N-Gain test.
Multimedia Boosts Fourth Graders' Reading Comprehension: Experimental Evidence

This research aimed to investigate the impact of interactive multimedia on the reading comprehension skills of fourth-grade students at SDN Ketegan. The study involved 30 students and employed a one-group pretest-posttest design with a quantitative experimental approach. Data were collected through pre-test and post-test description questions. Statistical analysis revealed a significant improvement in students' reading comprehension skills, with a mean pretest score of 57.08 and a posttest score of 84.50. These findings indicate the positive influence of interactive multimedia on students' reading skills. The results emphasize the potential of interactive multimedia as an effective tool for enhancing reading comprehension abilities among elementary school students.&#x0D; Highlights:&#x0D; &#x0D; The study investigates the impact of interactive multimedia on reading comprehension skills of fourth-grade students.&#x0D; A quantitative experimental approach is used to gather data and analyze the results.&#x0D; The findings demonstrate a significant improvement in students' reading comprehension abilities after exposure to interactive multimedia.&#x0D; &#x0D; Keywords: Reading comprehension, Fourth grade students, Interactive multimedia, Quantitative experimental study, SDN Ketegan.
Channel Attention TextCNN with Feature Word Extraction for Chinese Sentiment Analysis

Chinese short text sentiment analysis can help understand society’s views on various hot topics. Many existing sentiment analysis methods are based on sentiment dictionaries. Still, sentiment dictionaries are easily affected by subjective factors. They require a lot of time to build as well as maintenance to prevent obsolescence. For the aim of extracting rich information within texts more effectively, we propose a Channel Attention TextCNN with Feature Word Extraction model (CAT-FWE). The feature word extraction module helps us choose words that affect the sentiment of reviews. Then, these words are integrated with multi-level semantic information to enhance the information of sentences. In addition, the channel attention textCNN module that is a promotion of traditional TextCNN tends to pay more attention to those meaningful features. It eliminates the impacts of features that do not make any sense effectively. We apply our CAT-FWE model to both fine-grained classification and binary classification tasks for Chinese short texts. Experiment results show that it can improve the performance of emotion recognition.
Are We Healthier Together? Two Strategies for Supporting Macronutrient Assessment Skills and How the Crowd Can Help (or Not)

Learning macronutrient assessment skills can support improved health outcomes and overall wellbeing. We conducted two Mechanical Turk studies to investigate how users might benefit from the crowd's input in macronutrient assessment education. We first determined whether the wisdom of the crowd alone would provide users with enough insight to arrive at accurate macronutrient estimates. Next, we tested two methods of teaching macronutrient assessment skills (Comparison and Decomposition) and analyzed their effectiveness. Results from these studies indicate that while the crowd alone may not be sufficient to support this type of education, users may yet benefit from access to community-generated photos and labels while they use either the Comparison or Decomposition strategy.
Shame on Who? Experimentally Reducing Shame During Political Arguments on Twitter

Online political arguments have a reputation for being futile exchanges, partially because people often respond more punitively to those who do not share their views, a phenomenon called ingroup bias. We explore how ingroup bias affects political disagreements online, and how respect can mitigate its effects. Towards this goal, we conducted an experiment on Twitter systematically varying respectful versus neutral disagreement language across people who did and did not share views. We found that people who do not share views were most likely to reply to disagreements, and neutral disagreements generated more discussions than respectful disagreements. However, we also found that using respectful language increased respectful language received in return, and it reduced the effects of ingroup bias across conversations with people who do and do not share the same views. We conclude with recommendations to promote respectful language on social media and build shame resiliency online, such as designs that encourages thoughtful engagement and a peer support network that allows users to share shame experiences online.
Requirement Analysis and Teaching Strategies for Advanced English Writing Course Based on Blended Learning Algorithm

To make advanced English writing course in colleges and universities better service economic development, the teaching status quo of advanced English writing in several colleges and universities in Xi'an is studied through the questionnaire survey, and suggestions from students are collected and analyzed based on the theory of requirement analysis. The questionnaire survey results show that 60% of students take advanced English writing course to be more competitive in their professional fields. However, teaching in advanced English writing at this stage cannot meet their requirements for academic paper writing, and 50% of the students are less satisfied with the current advanced English writing course. They hope that academic English can be incorporated into comprehensive English, so that they can improve their ability to use academic English. The requirement analysis on advanced English writing reveals that students hope to have a more comprehensive teaching mode for English writing, and put forward different requirements for teachers' qualifications, class size, teaching mode, and teaching content. In response to these requirements, teaching strategies on advanced English writing are discussed, so as to establish a more complete teaching system for advanced English writing. This study is of great significance for research on advanced English writing course in China.
Learning-Based Ordering Characters on Ancient Document

Digitalizing and translating a scanned document image entails detecting the characters using a detector and translating the characters in the order they were detected with a translator. However, it is impossible to translate these characters correctly because the detector often detects them in any order. As a result, since it is critical to organize the recognized characters for proper translation, we propose ordering characters from documents with multiple variations using the strength of the learning-based model that learns the necessary operations from the data. In this task, it is difficult to order the characters written on antique handwritten documents that have deviations such as a bent or split line, as opposed to official records that have lines placed uprightly one by one. Because dealing with these many variants using a human-designed algorithm is problematic, we arrange characters printed on papers with diverse variations by taking advantage of a training model that can learn the appropriate function from data. Our method outputs both line id and y-axis and combines them to assign the sequential index. It is difficult to train using simply local regions because sequential character indexes in a large range include long-range dependencies. To solve this problem, we use network architecture to expand the receptive field as wide as possible. The network must learn to give various indexes to characters in similar places for each document because the number and area of characters vary for each document. We offer the ground truth assign method based on the absolute position to assign similar indexes to characters in similar places. Furthermore, even if the network uses absolute ground truth, the network may assign the incorrect line if the center coordinates of characters are biased in one direction. As a result, we employed the Region of Interest (ROI) from the pretrained coordinate layer, which contains position and trend information. We used the modified edit distance to compare the similarity of character indexes from the ground truth and our technique. In addition, we computed the modified fisher criterion to assess the degree of the clustering line. Consequently, our edit distance is just 0.43 times that of the human-designed algorithm, and our fisher criterion is 1.46 times that of the human-designed algorithm, improving the performance of human-designed algorithm.
"Kabootar": Towards Informal, Trustworthy, and Community-Based FinTech for Marginalized Immigrants

Financial technology (FinTech) platforms often exclude certain countries from their services due to global political conflicts. As a result, immigrants from these neglected countries struggle with transferring money to and from their homeland through formal mechanisms. Instead, they get involved in informal transnational transactions that, while flexible, are often risky and full of hassles. We looked into this issue through an online survey (n=127) and engaged with multiple stakeholders (n=16), including the Iranian immigrant community in Canada, to co-design an application called ?Kabootar' that matches senders and receivers of money across borders. In this application, a sender-receiver pair is matched with a pertinent pair sending money in the opposite direction. By facilitating two intra-national transactions in local currencies instead of two relatively complicated inter-national transactions, the need for money to cross borders is eliminated while staying within the boundaries of the law. Our user study (n=13) revealed several tensions in users trusting such informal transnational transactions. This work contributes to CSCW, HCI, and social computing's growing scholarship in personalized and collaborative computing technologies by advocating for a novel design approach based on collaboration and informality and extends their scope to the domain of FinTech for politically marginalized communities.
Designing Conversational Evaluation Tools

Conversational agents (CAs) provide opportunities for improving the interaction in evaluation surveys. To investigate if and how a user-centered conversational evaluation tool impacts users' response quality and their experience, we build EVA - a novel conversational course evaluation tool for educational scenarios. In a field experiment with 128 students, we compared EVA against a static web survey. Our results confirm prior findings from literature about the positive effect of conversational evaluation tools in the domain of education. Second, we then investigate the differences between a voice-based and text-based conversational human-computer interaction of EVA in the same experimental set-up. Against our prior expectation, the students of the voice-based interaction answered with higher information quality but with lower quantity of information compared to the text-based modality. Our findings indicate that using a conversational CA (voice and text-based) results in a higher response quality and user experience compared to a static web survey interface.
Optical encoder neural network: a CNN-based optical encoder for robot localization

We tackle the problem of robot localization by means of a deep learning (DL) approach. Convolutional neural networks, mainly devised for image analysis, are at the core of the proposed solution. An optical encoder neural network (OE-net) is devised to give back the relative pose of a robot by processing consecutive images. A monocular camera, fastened on the robot and oriented toward the floor, collects the vision data. The OE-net takes a pair of the acquired consecutive images as input and provides the relative pose information. The neural network is trained using a supervised learning approach. This preliminary study, made on synthetic images, suggests that a convolutional network and hence a DL approach can be a viable complement to the traditional visual odometry for robot ego-motion estimation. The obtained outcomes look very promising.
Self-attention relational modeling and background suppression for weakly supervised temporal action localization

Weakly supervised temporal action localization aims to locate the start and end boundaries of action instances and recognize the corresponding categories. Classical methods include random erasure, attention mechanism, and cross-temporal graph relationship modeling. Despite their great progress, there are still two challenges: localization integrity and background interference. Therefore, we propose a framework with self-attention relationship modeling and background suppression to address these issues. First, the input features of background frames are suppressed by the filtering module, which prevents interference from background noise. Second, a self-attention mechanism is designed to model the relationship between different segments in the video, which refines action features to encourage smoother temporal classification scores for completeness localization. Finally, under the guidance of classified loss Lact, the refined segment features and foreground weights are further combined in an attention-weighted pool to achieve video-level prediction. The algorithm is experimentally verified on THUMOS14 and ActivityNet1.2 datasets and compared with other relevant literature, which proves its feasibility and effectiveness.
Online Learning in Times of COVID-19 With Engineering Students From Marginalized Rural Areas in Mexico

COVID-19 has affected education worldwide, in Mexico. In this study, the impact of the extraordinary online modality because of the pandemic on the accreditation of courses for engineering students was evaluated, of the 64 students officially registered in three courses of microbiology, only 90% registered on the Moodle platform to take the online course, 71% of the dynamics were delivered, 67% of the final project and 80% of the delivery of the team’s activities were not reached, there was an approval of 54%. Some problems were that videoconferences were not attended and assignments were not turned in.