Reviewerly/exHarmony huggingface dataset:
THE REALITY OF TEACHERS' KNOWLEDGE OF DYSLEXIA IN THE ELEMENTARY STAGES IN PUBLIC SCHOOLS IN RAMALLAH AND AL-BIREH GOVERNORATE

The study aims to discover the reality of teachers' knowledge of dyslexia in the elementary stage in public schools in Ramallah and Al-Bireh governorate. The study sample included 76 male and female teachers in schools that have a resource room. The researcher designed a questionnaire by referring to the conceptual network on the subject of dyslexia and the arbitration of a group of specialized professors. The scale has a high degree of validity and reliability and consists of 65 items distributed over five dimensions (symptoms of reading difficulties, reading comprehension, writing, behavioral and psychomotor indicators, spelling, and reading aloud). The results of the study concluded as follows: There are no statistically significant differences between the average responses of the respondents according to the variable of gender, educational qualification and years of experience
Sustainable Learning and the Difficulties of Integral Child Development: Case Study in Children of an Educational Center in Cuenca, Ecuador

This paper presents the results of an investigation carried out on a group of children at the initial education level (between 3 and 5 years old) from an educational center in Ecuador. The goal was to determine the incidence of sustainable learning in the difficulties of comprehensive child development within the context of the pandemic caused by the COVID-19 virus. During the investigation, quantitative and qualitative methods were used, involving teachers and parents. Among the main findings, it is highlighted that there is an appreciable lack of knowledge about the milestones of child development by parents and teachers, and also deficient knowledge about comprehensive child development was evidenced, which represents a risk, since these factors could prevent an early detection of learning difficulties in children and, consequently, a late intervention of support so that they can overcome them. In addition, important recommendations are presented that teachers at this level, educational institutions, and children's families can apply to improve the learning process.
Invited Symposium Workspaces Reexamined: Implications for Design, Management, and Employee Wellbeing

The rise in remote work arrangements has accelerated interest in the design of employee workspaces across geographic locations. The rapid expansion of workspaces beyond organizational structures highlights the need for developing a person-centric perspective on work design that takes into account how workers perceive and craft their physical work environment and utilize new technologies, so that strategies for ensuring worker well-being and desired worker outcomes may be developed. The goal of this symposium is to introduce an interdisciplinary approach to this person-centric perspective that draws upon foundational concepts in psychology and architecture, and to delineate a future research agenda with practical implications for organizations and workers in the workplace of the future.
Analyzing Cognitive Load Associated with Manual Text Classification Task Using Eye Tracking

The accuracy of machine learning-based automated text classification systems, such as spam filters and search engine results, heavily depends on the quality of manual text classification. However, the cognitive demands of manual text classification tasks, particularly when dealing with challenging or difficult-to-comprehend texts, have not been extensively explored in previous studies. This research aims to address this gap by investigating the cognitive load associated with manual text classification tasks through analyzing eye tracking data. In this study, 30 participants performed manual text classification tasks while their ocular parameters were recorded using an eye tracker. The findings of this study revealed that ocular parameters recorded through eye tracking provided valuable insights into the cognitive load experienced during manual text classification tasks. Furthermore, it was observed that complex narratives led to higher cognitive load estimation. Moreover, native English-speaking participants exhibited lower cognitive load, compared to non-native English speakers.
Hilbert Basis Activation Function for Neural Network

Artificial neural networks (NNs) have shown remarkable success in a wide range of machine learning tasks. The activation function is a crucial component of NNs, as it introduces non-linearity and enables the network to learn complex representations. In this paper, we propose a novel activation function based on Hilbert basis, a mathematical concept from algebraic geometry. We formulate the Hilbert basis activation function and investigate its properties. We also compare its performance with popular activation functions such as ReLU and sigmoid through experiments on MNIST dataset under LeNet architecture. Our results show that the Hilbert basis activation function can improve the performance of NNs, achieving competitive accuracy and robustness via probability analysis.
Beyond plain toxic: building datasets for detection of flammable topics and inappropriate statements

Toxicity on the Internet is an acknowledged problem. It includes a wide range of actions from the use of obscene words to offenses and hate speech toward particular users or groups of people. However, there also exist other types of inappropriate messages which are usually not viewed as toxic as they do not contain swear words or explicit offenses. Such messages can contain covert toxicity or generalizations, incite harmful actions (crime, suicide, drug use), and provoke “heated” discussions. These messages are often related to particular sensitive topics, e.g. politics, sexual minorities, or social injustice. Such topics tend to yield toxic emotional reactions more often than other topics, e.g. cars or computing. At the same time, not all messages within “flammable” topics are inappropriate. This work focuses on automatically detecting inappropriate language in natural texts. This is crucial for monitoring user-generated content and developing dialogue systems and AI assistants. While many works focus on toxicity detection, we highlight the fact that texts can be harmful without being toxic or containing obscene language. Blind censorship based on keywords is a common approach to address these issues, but it limits a system’s functionality. This work proposes a safe and effective solution to serve broad user needs and develop necessary resources and tools. Thus, machinery for inappropriateness detection could be useful (i) for making communication on the Internet safer, more productive, and inclusive by flagging truly inappropriate content while not banning messages blindly by topic; (ii) for detection of inappropriate messages generated by automatic systems, e.g. neural chatbots, due to biases in training data; (iii) for debiasing training data for language models (e.g. BERT and GPT-2). Towards this end, in this work, we present two text collections labeled according to a binary notion of inappropriateness (124,597 samples) and a multinomial notion of sensitive topic (33,904 samples). Assuming that the notion of inappropriateness is common among people of the same culture, we base our approach on a human intuitive understanding of what is not acceptable and harmful. To devise an objective view of inappropriateness, we define it in a data-driven way through crowdsourcing. Namely, we run a large-scale annotation study asking workers if a given chatbot-generated utterance could harm the reputation of the company that created this chatbot. High values of inter-annotator agreement suggest that the notion of inappropriateness exists and can be uniformly understood by different people. To define the notion of a sensitive topic in an objective way we use guidelines suggested by specialists in the Legal and PR departments of a large company. We use the collected datasets to train inappropriateness and sensitive topic classifiers employing both classic and Transformer-based models.
Dynamic Text: How to Effectively Embed Emotions in Text-based Computer-Mediated Communication

Despite being an effective mode of communication, Computer-Mediated Communication (CMC), particularly regarding text-based exchanges, still has inherent limitations compared to in-person communication. The objective of the current study is to review the previous studies which identified and addressed the challenges, including difficulty in embedding emotional information in text effectively. Previous studies demonstrated changes in text properties might deliver emotional information as well as the two phases of text encodings could be completed simultaneously by interaction designs. The study also suggests future studies to find the emotional meaning of text features and to develop novel applications using machine learning technologies.
The Impact of Example-Based Xai on Trust in Highly-Technical Populations

We examined the impact of explainable artificial intelligence on trust in a highly-technical population applied to a high-risk domain. Specifically, we examined the effect of an example-based explainable machine learning system on trust for data analysts working for a pipeline inspection company. This study compared a baseline interface with no explanation to two example-based explainable interfaces. We found that showing examples from multiple classes significantly increased trust compared to the other interfaces. Also, enabling the user to override the ML agent’s decision is a bigger factor for trust for this technical population than the amount of explanation shown in the interface.
Usability of Ranked-Choice Voting Paper Ballots

Ranked choice voting (RCV) is a method of voting where individuals rank their choices for each race in an election, rather than selecting a single candidate. For paper ballot implementations, there are three basic formats: bubble grid, column, and handwritten. This study aimed to understand which format produces the best outcomes with the fewest errors. Using a between-subjects design, we measured voting time, errors, success rates, and overall subjective usability using the System Usability Scale (SUS). Results showed that the handwritten format took significantly longer to complete. However, the handwritten ballot errors were recoverable and generally did not invalidate the ballots. The column and bubble grid ballots had significant errors, with fewer errors on the bubble grid format. There was no significant difference in SUS scores, nor any contribution of demographics across ballot types. Ballot format directly dictates the time taken to vote, usability, and overall voter success.
Robust Q‐learning‐based multi‐objective sheep flock optimizer with a Cauchy operator for effective path planning in unmanned aerial vehicles

Summary Unmanned aerial vehicle (UAV) path planning can be treated as a nondeterministic polynomial (NP) hard concern or an optimization problem. The conventional approaches are unable to effectively handle these issues due to discontinuity, non‐linearity, multi‐modality, and inseparability. On the other hand, meta‐heuristic algorithms are effective at tackling these issues because they are simple, adaptable, and derivation free. To enhance the performance in a variety of challenging circumstances, this paper proposes a novel Q‐learning‐based multi‐objective sheep flock optimizer with a Cauchy operator (Q‐MOSFO‐CA) to solve the constrained UAV path planning issues. The multi‐objective functions considered here are costs and constraints (threat, terrain, turning, climbing, and gliding constraints) to determine the feasible and optimal path. To avoid the probability of falling into the local optimum and to address the shortcoming of unbalanced convergence and also to maintain the exploitation and exploration capability, the Cauchy operator (CA) is integrated with the sheep flock optimization (SFO) algorithm. The Q‐learning model is introduced to balance both the global and local searches. Here, the exploration model performs the global search whereas the exploitation model performs the local search to attain an optimal solution. In the simulation scenario, the statistical analysis is conducted under two scenarios, and some essential measures such as the number of iterations at convergence (NIC), evaluation time (ET), energy consumption, and convergence analysis are determined. The proposed method obtains NIC of 1305 and 1436, ET of 12.8 and 15.2 s, and energy consumption of 20,600 and 21,465 J for both Scenarios 1 and 2, respectively.
Effective factors of icons searching performance based on visual attention capture for the nuclear power plants

Abstract With the rapid development of digital and intelligent information systems, the display of visual information on interfaces has become an important challenge in the field of human‐computer interaction, as well as the human‐computer interface of nuclear power plants. This paper examines effective factors affecting visual attention capture based on icons related to the human‐computer interface of nuclear power plants. Experiments were conducted on multiple factors of visual search tasks with reference to information features to analyze information display factors affecting visual cognitive performance. The experimental results showed that the three factors, icon shape, feature display, and target position, have a great effect on information searching performance. The main effect of reaction time on three factors all reached a significance level congruent with the effect on fixation time and pupil response. Eye gaze indexes of feature display factors presented an increasing trend, which indicates that the information block spends more time on cognitive processing than icon features in the human‐computer interface of nuclear power plants. Pupil diameter was dilated when searching icons under difficult visual cognition conditions, and when searching information blocks, pupils were dilated more than searching single icons. Therefore, shape highlighting of data icons in the human‐computer interface of nuclear power plants can effectively improve the attention capture of the operator's visual search, and the ease of icon discrimination is an influential factor in visual attention capture.
Exploring named entity recognition and relation extraction for ontology and medical records integration

The available natural language data in electronic health records is of noteworthy interest to health research and development. Nevertheless, their manual analysis is not feasible and poses a challenge to accessing valuable information in these records. This paper presents an approach to automatically extract information from these unstructured medical records using Domain Entity Recognition and Relation Extraction, structuring the results through a domain ontology. We developed our work in the oncology domain, an attention-demanding field. The main contribution of this work lies in integrating multiple resources in a complete methodology to accomplish this task. We developed a new entity and relation annotated dataset of medical evolutions in Brazilian Portuguese, containing 1622 documents, 146,769 entities, and 111,716 relations. We attained 78.24 % accuracy for entity and relation extraction in the exams domain. Healthcare specialists evaluated the approach regarding entity recognition and relation extraction positively and considered the methodology valuable to health professionals.
Non-IID always Bad? Semi-Supervised Heterogeneous Federated Learning with Local Knowledge Enhancement

Federated learning (FL) is important for privacy-preserving services by training models without collecting raw user data. Most FL algorithms assume all data is annotated, which is impractical due to the high cost of labeling data in real applications. To alleviate the reliance on labeled data, semi-supervised federated learning (SSFL) has been proposed to utilize unlabeled data on clients to improve model performance. However, most existing methods either have privacy issues which share models trained on other clients, or generate pseudo-labels for unlabeled local datasets with the global model, which is usually biased towards the global data distribution. The latter may lead to sub-optimal accuracy of pseudo-labels, due to the gap between the local data distribution and the global model, especially in non-IID settings. In this paper, we propose a semi-supervised heterogeneous federated learning method with local knowledge enhancement, called FedLoKe, which aims to train an accurate global model from both labeled and unlabeled local data with non-IID distributions. Specifically, in FedLoKe, the server maintains a global model to capture global data distribution, and each client learns a local model to capture local data distribution. Since the distribution captured by the local model is aligned with the local data distribution, we utilize it to generate high-accuracy pseudo-labels of the unlabeled dataset for global model training. To prevent the local model from severely overfitting the small number of local labeled data, we further use the exponential moving average and apply the global model to generate pseudo-labels for local modeling training. Experiments on four datasets show the effectiveness of FedLoKe. Our code is available at: https://github.com/zcfinal/FedLoKe.
Secure Cloud Based IoT Data Storage

With the development of emerging technologies, the rate of security and performance of data storage problems are increasing daily and becomes basic necessity of every organizations to store and manage sensitive data in both cloud and IoT environments. Cloud computing is an advanced innovation in which individual can drive work by using several applications. It offers privilege to store data on cloud server while ensuring secrecy of cloud that become a significant problem now a days. Therefore, In the light of previous research study we aims to find various issues and challenges in existing cloud system and provide a better method to store the data in cloud. In our research, we proposed method to improve the secrecy of data storage by using AES encryption with PQC (NTRU) and SHA-512 with modern homomorphic (FHE) cryptographic algorithms. Furthermore, by employing this hybrid technique the performance of cloud storage can be enhance by ensuring the confidentiality and integrity. Furthermore, our proposed method has high performance, throughput and resistance against various attacks to store the large amount of data in cloud.
FINRule: Feature Interactive Neural Rule Learning

Though neural networks have achieved impressive prediction performance, it's still hard for people to understand what neural networks have learned from the data. The black-box property of neural networks already becomes one of the main obstacles preventing from being applied to many high-stakes applications, such as finance and medicine that have critical requirement on the model transparency and interpretability. In order to enhance the explainability of neural networks, we propose a neural rule learning method-Feature Interactive Neural Rule Learning (FINRule) to incorporate the expressivity of neural networks and the interpretability of rule-based systems. Specifically, we conduct rule learning as differential discrete combination encoded by a feedforward neural network, in which each layer acts as a logical operator of explainable decision conditions. The first hidden layer can act as sharable atomic conditions which are connected to next hidden layer for formulating decision rules. Moreover, we propose to represent both atomic condition and rules with contextual embeddings, with aim to enrich the expressivity power by capturing high-order feature interactions. We conduct comprehensive experiments on real-world datasets to validate both effectiveness and explainability of the proposed method.
DOR: a novel dual-observation-based approach for recommendation systems

Abstract As online social media platforms continue to proliferate, users are faced with an overwhelming amount of information, making it challenging to filter and locate relevant information. While personalized recommendation algorithms have been developed to help, most existing models primarily rely on user behavior observations such as viewing history, often overlooking the intricate connection between the reading content and the user’s prior knowledge and interest. This disconnect can consequently lead to a paucity of diverse and personalized recommendations. In this paper, we propose a novel approach to tackle the multifaceted issue of recommendation. We introduce the Dual-Observation-based approach for the Recommendation (DOR) system, a novel model leveraging dual observation mechanisms integrated into a deep neural network. Our approach is designed to identify both the core theme of an article and the user’s unique engagement with the article, considering the user’s belief network, i.e., a reflection of their personal interests and biases. Extensive experiments have been conducted using real-world datasets, in which the DOR model was compared against a number of state-of-the-art baselines. The experimental results explicitly demonstrate the reliability and effectiveness of the DOR model, highlighting its superior performance in news recommendation tasks.
A Mix-up Strategy to Enhance Adversarial Training with Imbalanced Data

Adversarial training has been proven to be one of the most effective techniques to defend against adversarial examples. The majority of existing adversarial training methods assume that every class in the training data is equally distributed. However, in reality, some classes often have a large number of training data while others only have a very limited amount. Recent studies have shown that the performance of adversarial training will degrade drastically if the training data is imbalanced. In this paper, we propose a simple yet effective framework to enhance the robustness of DNN models under imbalanced scenarios. Our framework, Imb-Mix, first augments the training dataset by generating multiple adversarial examples for samples in the minority classes. This is done by first adding random noise to the original adversarial examples created by one specific adversarial attack method. It then constructs Mixup-mimic mixed examples upon the augmented dataset used by adversarial training. In addition, we theoretically prove the regularization effect of our Mixup-mimic mixed examples generation technique in Imb-Mix. Extensive experiments on various imbalanced datasets verify the effectiveness of the proposed framework.
A Graph Neural Network Model for Concept Prerequisite Relation Extraction

In recent years, with the emergence of online learning platforms and e-learning resources, many documents are available for a particular topic. For a better learning experience, the learner often needs to know and learn first the prerequisite concepts for a given concept. Traditionally, the identification of such prerequisite concepts is done manually by subject experts, which in turn, often limits self-paced learning. Recently, machine learning models have found encouraging success for the task, obviating manual effort. In this paper, we propose a graph neural network based approach that leverages node attention over a heterogeneous graph to extract the prerequisite concepts for a given concept. Experiments on a set of benchmark data show that the proposed model outperforms the existing models by large margins almost always, making the model a new state-of-the-art for the task.