Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Detection of Learning Disorders on a Large Scale Dataset of Eye Movement Records

Early detection of dyslexia and learning disorders is vital for avoiding a learning disability, as well as supporting dyslexic students by tailoring academic programs to their needs. Several studies have investigated using supervised algorithms to screen dyslexia vs control subjects; however, the data size and the conditions of data acquisition were their most significant limitation. In the current study, we leverage a large dataset, containing 4243 time series of eye movement records from children across Europe. These datasets were derived from various tests such as saccade, vergence, and reading tasks. Furthermore, our methods were evaluated with realistic test data, including real-life biases such as noise, eye tracking misalignment, and similar pathologies among non-scholar difficulty classes. In addition, we present a novel convolutional neural network architecture, adapted to our time series classification problem, that is intended to generalize on a small annotated dataset and to handle a high-resolution signal (1024 point). Our architecture achieved a precision of 80.20% and a recall of 75.1%, when trained on the vergence dataset, and a precision of 77.2% and a recall of 77.5% when trained on the saccade dataset. Finally, we performed a comparison using our ML approach, a second architecture developed for a similar problem, and two other methods that we investigated that use deep learning algorithms to predict dyslexia.
Approaching literary texts via online learning during COVID-19 pandemic in Jordan: Challenges and obstacles

This study investigates students’ attitude toward using asynchronous and synchronous online learning in reading literary texts at the University of Jordan-Aqaba. It examines how certain demographic variables, including gender, place of residence and the available technological means, affect their attitudes towards such a learning method in the Jordanian context. A descriptive, analytic design was used to explore students’ reception and acceptance of learning via online platforms. One hundred and forty two students majoring in English Literature from the University of Jordan-Aqaba participated in the questionnaire. We used a simple random sampling (SRS) in which each member of the population had an equal chance to respond. We distributed a questionnaire of 23 statements to participants on December 11th, 2020, via Microsoft Forms. The statements were in a multiple-choice format, using Likert scale’s five levels. This study concludes that there is no statistically significant differences at (a≤0.05) among students’ attitudes towards using online learning that can be attributed to gender, place of living, and technological tools. Though, we argue that the high number of students using mobile phone has contributed to their moderate response towards the shift to online learning. While this paper is a case study that does not intend to generalize, interrogating the efficacy of online learning is essential in understanding ethical and professional issues that may arise in the learning process. It also helps us forge new ways to deal with these issues in the future.
Challenges and prospects of online education in the context of barrier-free access

Given the trends of barrier-free education, it is relevant to study the peculiarities of online education through the prism of characterising its prospects and challenges. The aim of the study is to provide a thorough analysis of the challenges and prospects of online education in the context of educational accessibility. Methods: the study uses a mixed methodological framework that combines both quantitative and qualitative research methods. Research design: quantitative data were collected directly through online surveys, and qualitative data were collected through in-depth interviews. The study used questions based on the Likert scale. A total of 250 people took part in the study: 150 teachers and 100 students. All of them had different experience of using digital technologies in online education. Results: online education is a promising area, as it is based on the elements of barrier-free access, but there are a number of problems on the way to further development that determine promising areas for its improvement. The main challenges include technological problems, low material, and technical base, unequal access to technology, lack of direct contact between students and teachers, low level of adaptation to digital technologies. Limitations: Due to the chosen sampling method, the results may not be fully generalisable to different population groups, including those in other countries. Conclusions: promising areas for further development of online education include improving the material and technical base, developing adaptive platforms, creating content with an emphasis on accessibility, developing digital skills and information competence of both students and teachers.
Revolutionizing Retail: Dinzo- An Advanced and Secure Online Shopping Platform

This paper explores the development and implementation of Dinzo, an innovative e-commerce platform integrating a proprietary search algorithm, a custom API, and advanced security measures to redefine the online shopping experience.
User Interface Analysis of PeduliLindungi Application to Improve User Experience with The Heuristic Evaluation Method

The Covid-19 pandemic limits human activities every day. The spread of the virus is uncontrollable, it can attack anyone, anytime, and anywhere. As a solution, the use of the PeduliLindungi application technology developed by the Ministry of Communication and Information in collaboration with other relevant Ministries and Institutions is used to help track the spread of viruses and understand preventive measures to stop their spread. Research was conducted on the appearance of the application's User Interface using the Heuristic Evaluation method to measure its usefulness. However, there are several problems experienced by users, namely the information is not updated (vaccines and Covid-19 tests), the language used is inconsistent, and others. Questionnaire testing with 10 questions based on 10 Heuristic Evaluation criteria was distributed via Google Form to 33 respondents. Then, carry out validity and reliability tests with the research results obtained Valid and Reliable. as well as testing the hypothesis of all Learnability, Efficiency, Memorability, and Error Prevention factors, based on usability and user satisfaction aspects, questionnaire items all test results are accepted and there are no serious problems. However, there are a number of things that need to be improved so that the application can run more optimally.
Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects

Planning as heuristic search is one of the most successful approaches to classical planning but unfortunately, it does not trivially extend to Generalized Planning (GP); GP aims to compute algorithmic solutions that are valid for a set of classical planning instances from a given domain, even if these instances differ in their number of objects, the initial and goal configuration of these objects and hence, in the number (and possible values) of the state variables. State-space search, as it is implemented by heuristic planners, becomes then impractical for GP. In this paper we adapt the planning as heuristic search paradigm to the generalization requirements of GP, and present the first native heuristic search approach to GP. First, the paper introduces a new pointer-based solution space for GP that is independent of the number of classical planning instances in a GP problem and the size of those instances (i.e. the number of objects, state variables and their domain sizes). Second, the paper defines an upgraded version of our GP algorithm, called Best-First Generalized Planning (BFGP), that implements a best-first search in our pointer-based solution space for GP. Lastly, the paper defines a set of evaluation and heuristic functions for BFGP that asses the structural complexity of the candidate GP solutions, as well as their fitness to a given input set of classical planning instances. The computation of these evaluation and heuristic functions does not require grounding states or actions in advance. Therefore our GP as heuristic search approach can handle large sets of state variables with large numerical domains, e.g. integers.
Investigating accounting professionals’ intention to adopt blockchain technology

Purpose Blockchain’s potential is so significant that business activities across all industries can be drastically altered. Furthermore, the characteristics of blockchain appear to be well-suited to accounting requirements. However, accounting professionals’ attitude and intention toward blockchain adoption are not clear, particularly in India. Thus, this study aims to investigate and evaluate accountants’ intention to adopt blockchain technology in accounting activities. Design/methodology/approach This study examined and assessed accountants’ intention to use blockchain in accounting. To effectively measure usage intention, this study extended the unified theory of acceptance and use of technology (UTAUT) model by including context-specific constructs. To empirically test and validate the proposed model, data were collected from “369” professional accountants in India. Findings The findings revealed that facilitating conditions, performance expectancy and initial trust had a significant impact on adoption. Furthermore, the regulatory framework materially moderated the association between usage intention and its predictors. Originality/value These findings provide new empirical evidence about the impact of different predictors of usage intention by extending the UTAUT model. Relevant stakeholders can refer to this pioneering study to increase the adoption of blockchain as an efficient and trustworthy system among professional accountants, particularly in developing countries such as India.
Multimodal Hinglish Tweet Dataset for Deep Pragmatic Analysis

Wars, conflicts, and peace efforts have become inherent characteristics of regions, and understanding the prevailing sentiments related to these issues is crucial for finding long-lasting solutions. Twitter/`X’, with its vast user base and real-time nature, provides a valuable source to assess the raw emotions and opinions of people regarding war, conflict, and peace. This paper focuses on collecting and curating hinglish tweets specifically related to wars, conflicts, and associated taxonomy. The creation of said dataset addresses the existing gap in contemporary literature, which lacks comprehensive datasets capturing the emotions and sentiments expressed by individuals regarding wars, conflicts, and peace efforts. This dataset holds significant value and application in deep pragmatic analysis as it enables future researchers to identify the flow of sentiments, analyze the information architecture surrounding war, conflict, and peace effects, and delve into the associated psychology in this context. To ensure the dataset’s quality and relevance, a meticulous selection process was employed, resulting in the inclusion of explanable 500 carefully chosen search filters. The dataset currently has 10,040 tweets that have been validated with the help of human expert to make sure they are correct and accurate.
On the interaction between the search parameters and the nature of the search problems in search‐based model‐driven engineering

Abstract The use of search‐based software engineering to address model‐driven engineering activities (SBMDE) is becoming more popular. Many maintenance tasks can be reformulated as a search problem, and, when those tasks are applied to software models, the search strategy has to retrieve a model fragment. There are no studies on the influence of the search parameters when applied to software models. This article evaluates the impact of different search parameter values on the performance of an evolutionary algorithm whose population is in the form of software models. Our study takes into account the nature of the model fragment location problems (MFLPs) in which the evolutionary algorithm is applied. The evaluation searches 1895 MFLPs (characterized through five measures that define MFLPs) from two industrial case studies and uses 625 different combinations of search parameter values. The results show that the impact on the performance when varying the population size, the replacement percentage, or the crossover rate produces changes of around 30% in performance. With regard to the nature of the problems, the size of the search space has the largest impact. Search parameter values and the nature of the MFLPs influence the performance when applying an evolutionary algorithm to perform fragment location on models. Search parameter values have a greater effect on precision values, and the nature of the MFLPs has a greater effect on recall values. Our results should raise awareness of the relevance of the search parameters and the nature of the problems for the SBMDE community.
A machine reading comprehension framework for recognizing emotion cause in conversations

Recognizing Emotion Cause in Conversations (RECC) is a key issue in modeling human cognitive processes, involving Conversational Causal Emotion Entailment task (C2E2) and Conversational Causal Span Extraction task (C2SE). Previous emotion cause extraction research has been concentrated at the clause level, detecting if the cause is in the text, which may not describe the underlying causes in texts well. In order to address this issue, we suggest a novel approach that can recognize emotion cause spans. These spans can often represent or imply the causes for controlling emotions. In this paper, we use a Machine Reading Comprehension framework to Recognize the Emotion Cause in Conversations (MRC-RECC), at both the span level and clause level simultaneously. Specifically, we use two types of queries to build the associations between the two different subtasks: emotion causal entailment task and emotion causal span extraction task. Our framework can recognize emotion cause more effectively by using joint learning to make these two tasks complement each other. Experiments demonstrate that our MRC-RECC provides state-of-the-art performances, which can reason more emotion causes in conversation texts. The code can be found at https://github.com/Guangzidetiaoyue/MRC-RECCON.
Exploring weakly-supervised image manipulation localization with tampering Edge-based class activation map

In recent investigations concerning manipulated image localization, prevailing methodologies have relied on Convolutional Neural Networks (CNNs) to facilitate supervised learning predicated upon manipulated images and their corresponding ground-truth annotations. Nevertheless, these methods produce high false detection rates when detecting unaltered images due to the limited availability of authentic images. In this paper, we initiate our inquiry by scrutinizing the distribution characteristics of class activation maps within the context of image manipulation detection and the inherent patterns of alterations within this domain. We then introduce the novel concept of a tampering Edge-based Class Activation Map (EdgeCAM) tailored for detecting tampering within manipulated images. Leveraging EdgeCAM, we formulate a framework for localizing tampered regions in images through a weakly-supervised approach, relying solely on image-level annotations. Ultimately, we establish a fundamental benchmark for weakly-supervised image manipulation localization by utilizing genuine tampered images collected from real-world scenarios. The empirical results highlight the notable efficacy of our proposed method, outperforming current weakly-supervised methods for detecting image manipulation by a significant margin of 12.7% on the average combined F1 metrics across various generic datasets. This result also achieves performance comparable to supervised methods for image manipulation localization.
Technology acceptance and transparency demands for toxic language classification – interviews with moderators of public online discussion fora

Many online discussion providers consider using algorithm-based moderation software to support their employees in moderating toxic communication. Such technology is also attractive for public discussion providers, including public administration and public service media. To ensure successful implementation, however, it is crucial that moderators can correctly understand and use the software according to context-specific workplace requirements. This exploratory case study sheds light on the technology acceptance of algorithm-based moderation software by moderators in German public administration and public service media. Specifically, we focus on the moderators’ user characteristics and workplace requirement perceptions as preconditions for technology acceptance. We combined twelve structured qualitative interviews with moderators with an enhanced cognitive walkthrough (ECW) of an algorithm-based moderation dashboard. Additionally, stimuli of two different transparency mechanisms were added to the interview. Our findings suggest that transparency is one of the most requested characteristics of algorithm-based moderation software and, when met, transparency is beneficial for the acceptance of automated content classification in these systems. However, the findings suggest that different AI perceptions and technology commitment among moderators corresponded with different transparency motives related to the moderation system. We assume that addressing those differing motives by different transparency mechanisms may positively affect technology acceptance.
Overview of big data information privacy protection technology and challenges

With the rapid development of advanced technologies like the mobile internet, cloud computing, and the Internet of Things, the era of big data is gradually upon us. In this era, big data has been widely utilized in various fields like business, finance, social networking, and healthcare, and the value of data has become increasingly prominent. Data has become a new factor of production, the digital economy has become a new engine of development, and an important driving force for promoting social progress and economic development. However, with the advancement of big data technology, the risks of data leakage and misuse have increased. Personal data information leakage or misuse can lead to personal privacy infringement and fraud, such as the Sina Weibo data leak in March 2020 where attackers breached part of the Sina Weibo database, obtaining sensitive data including real names, website usernames, genders, locations, and phone numbers, impacting 538 million Weibo users. Therefore, there is an increasing demand for data information privacy protection. This paper will explore the core technologies and application scenarios in data information privacy protection and investigate data privacy protection in real-life settings.
China Educational Resources Analysis Based on GINI Coefficients

This article is based on the Gini coefficient to analyze the current educational status in China, and the Gini coefficient is calculated by the educational resources in different provinces. To diversify the sample, the work includes four different educational parameters: number of graduate students, number of institutions of higher learning, and number of high schools and middle schools. Based on the Chinese identity data from 2013 to 2021, this work calculates a line plot of the Gini coefficient, applying the correlation between the Lorentz curve and dynamic conditions. Concerning the different levels of education developments in China, it can draw the following conclusions: the Gini coefficient on the number of postgraduate students and institutions of higher learning is decreasing year by year, and the Gini coefficient on the number of high schools and middle schools appear an opposite trend. Regarding China’s basic national conditions, the research results show that postgraduate and institutions of higher learning education resources are more equal. Still, the trend for middle and high school is on the contrary.
Text classification by BERT-Capsules

This paper presents a model that integrates a BERT encoder with a Capsule network, eliminating the traditional fully connected layer designed for downstream classification tasks in BERT in favor of a capsule layer. This capsule layer consists of three main modules: the representation module, the probability module, and the reconstruction module. It transforms the final hidden layer output of BERT into the final activation capsule probabilities to classify the text. By applying the model to sentiment analysis and text classification tasks, and comparing the test results with various BERT variants, the performance across all metrics was found to be superior. Observing the model’s handling of multiple entities and complex relationships, sentences with high ambiguity were extracted to observe the probability distribution of all capsules and compared with RNN-Capsule. It was found that the activation capsule probabilities for BERT-Capsule were significantly higher than the rest, and more pronounced than RNN-Capsule, indicating the model’s exceptional ability to process ambiguous information.
Fault Diagnosis of Linear Guide Rails Based on SSTG Combined with CA-DenseNet

Monitoring the status of linear guide rails is essential because they are important components in linear motion mechanical production. Thus, this paper proposes a new method of conducting the fault diagnosis of linear guide rails. First, synchrosqueezing transform (SST) combined with Gaussian high-pass filter, termed as SSTG, is proposed to process vibration signals of linear guide rails and obtain time-frequency images, thus helping realize fault feature visual enhancement. Next, the coordinate attention (CA) mechanism is introduced to promote the DenseNet model and obtain the CA-DenseNet deep learning framework, thus realizing accurate fault classification. Comparison experiments with other methods reveal that the proposed method has a high classification accuracy of up to 95.0%. The experimental results further demonstrate the effectiveness and robustness of the proposed method for the fault diagnosis of linear guide rails.
Security Against Attacks to Cryptographic Algorithms by Quantum Computer – And Why Multiple Different Solutions Are Needed

This work describes and compares different ways to protect your systems against attacks by quantum computers. The comparison is done from a security and an implementation point of view.
The Effect of Acting Programs on Adults with Disabilities

Virtual acting programs are effective and have the potential to benefit adults with disabilities in many ways. Participants in both delivery models showed improvement in key skills such as improved speaking and reduction of social isolation. Recreational acting courses on virtual platforms increase accessibility, which is especially important for the population of people with physical disabilities, whose mobility can be limited, and may improve their emotional wellbeing. Additional research should be conducted to determine the effectiveness of other recreational activities for those with disabilities when delivered virtually