Reviewerly/exHarmony huggingface dataset:
Transferring Procedural Knowledge Across Commonsense Tasks

Stories about everyday situations are an essential part of human communication, motivating the need to develop AI agents that can reliably understand these stories. Despite the long list of supervised methods for story completion and procedural understanding, current AI fails to generalize its procedural reasoning to unseen stories. This paper is based on the hypothesis that the generalization can be improved by associating downstream prediction with fine-grained modeling and the abstraction of procedural knowledge in stories. To test this hypothesis, we design LEAP: a comprehensive framework that reasons over stories by jointly considering their (1) overall plausibility, (2) conflict sentence pairs, and (3) participant physical states. LEAP integrates state-of-the-art modeling architectures, training regimes, and augmentation strategies based on natural and synthetic stories. To address the lack of densely annotated training data on participants and their physical states, we devise a robust automatic labeler based on semantic parsing and few-shot prompting with large language models. Our experiments with in- and out-of-domain tasks reveal insights into the interplay of architectures, training regimes, and augmentation strategies. LEAP’s labeler consistently improves performance on out-of-domain datasets, while our case studies show that the dense annotation supports explainability.
Model-Based Reinforcement Learning with Multi-Step Plan Value Estimation

A promising way to improve the sample efficiency of reinforcement learning is model-based methods, in which many explorations and evaluations can happen in the learned models to save real-world samples. However, when the learned model has a non-negligible model error, sequential steps in the model are hard to be accurately evaluated, limiting the model’s utilization. This paper proposes to alleviate this issue by introducing multi-step plans into policy optimization for model-based RL. We employ the multi-step plan value estimation, which evaluates the expected discounted return after executing a sequence of action plans at a given state, and updates the policy by directly computing the multi-step policy gradient via plan value estimation. The new model-based reinforcement learning algorithm MPPVE (Model-based Planning Policy Learning with Multi-step Plan Value Estimation) shows a better utilization of the learned model and achieves a better sample efficiency than state-of-the-art model-based RL approaches. The code is available at https://github.com/HxLyn3/MPPVE.
Introduction to Matplotlib

Matplotlib is a Python graphing and plotting library that can generate a variety of different types of graph or chart in a variety of different formats. It can be used to generate line charts, scatter graphs, heat maps, bar charts, pie charts and 3D plots. It can even support animations and interactive displays.
Studying the robustness of quantum random walk search on Hypercube against phase errors in the traversing coin by semi-empirical methods

In this work we obtain semi-empirical expressions for the probability to find solution of Quantum random walk search algorithm on hypercube when the traversing coin is constructed by generalized Householder reflection and an additional phase shift. Calculations are made for specific relations between coin phases (first from the reflection and second from the shift), that are obtained in our previous works. The quantum algorithm could be made more robust against phase errors, if an experimental implementation preserves those relations. The results from numerical simulations of quantum random walk search algorithm on hypercube are used to find an empirical formulas for algorithm’s probability to find solution. Those formulas, obtained by fitting data to a suitable function, are used to make prognosis about the algorithm’s robustness when dimension of the coin is too large to be simulated on classical computer. Here, we explicitly show all steps of the method used in the paper, while also discuss its advantages and limitations. The goal of this work is to help the experimental implementation of the quantum random walk search algorithm by giving evaluation of the algorithm’s robustness and the obtained probability to find a solution when our design of the walking coin is used.
Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence–Powered Graphical User Interface for Intensive Care Unit Instability Decision Support

Abstract Background Critical instability forecast and treatment can be optimized by artificial intelligence (AI)-enabled clinical decision support. It is important that the user-facing display of AI output facilitates clinical thinking and workflow for all disciplines involved in bedside care. Objectives Our objective is to engage multidisciplinary users (physicians, nurse practitioners, physician assistants) in the development of a graphical user interface (GUI) to present an AI-derived risk score. Methods Intensive care unit (ICU) clinicians participated in focus groups seeking input on instability risk forecast presented in a prototype GUI. Two stratified rounds (three focus groups [only nurses, only providers, then combined]) were moderated by a focus group methodologist. After round 1, GUI design changes were made and presented in round 2. Focus groups were recorded, transcribed, and deidentified transcripts independently coded by three researchers. Codes were coalesced into emerging themes. Results Twenty-three ICU clinicians participated (11 nurses, 12 medical providers [3 mid-level and 9 physicians]). Six themes emerged: (1) analytics transparency, (2) graphical interpretability, (3) impact on practice, (4) value of trend synthesis of dynamic patient data, (5) decisional weight (weighing AI output during decision-making), and (6) display location (usability, concerns for patient/family GUI view). Nurses emphasized having GUI objective information to support communication and optimal GUI location. While providers emphasized need for recommendation interpretability and concern for impairing trainee critical thinking. All disciplines valued synthesized views of vital signs, interventions, and risk trends but were skeptical of placing decisional weight on AI output until proven trustworthy. Conclusion Gaining input from all clinical users is important to consider when designing AI-derived GUIs. Results highlight that health care intelligent decisional support systems technologies need to be transparent on how they work, easy to read and interpret, cause little disruption to current workflow, as well as decisional support components need to be used as an adjunct to human decision-making.
Social Media as a Marketing Tool

Purpose: The study focused to conduct a comprehensive comparative analysis between social media marketing and Traditional media marketing.&#x0D; &#x0D; Theoretical Framework: The theoretical framework for this research on the comparative analysis of traditional media and social media consists of a series of contrasting statements that explore key dimensions like attractive advertising, informative advertising, reliability advertising etc. aiming to elucidate the distinct characteristics and implications of each marketing approach.&#x0D; &#x0D; Design/Methodology/Approach: The study is an exploratory based. The primary data is collected from 363 customers in Manipur using structured questionnaires. The data are analyzed using proper statistical tools- IBM SPSS Statistics 25 to determine which approach is more preferred by customers in the market situation of Manipur.&#x0D; &#x0D; Findings: Research results found that there is a significant difference between social media marketing and Traditional media marketing. The social media marketing is more preferred by customers over Traditional media marketing.&#x0D; &#x0D; Conclusion: Based on the research findings, the study intends contribute to a deeper understanding of the evolving landscape of marketing strategies and assists businesses in making informed decisions regarding choice of marketing channel in a small state like Manipur.&#x0D; &#x0D; Originality/ Value: The study lies in the holistic exploration of the contrasting dynamic between social media marketing and traditional media marketing. The study also enhances our insights into optimizing marketing decisions and harnessing the synergies between these two distinct yet interconnected avenues in the evolving landscape of media strategies.
Decolonizing creativity in the digital era

This paper explores the importance of decolonizing creativity in the digital era within the context of the platformised creator economy. We argue that technology has largely been designed for Western audiences by Western audiences, thereby overlooking an enormous segment of global Internet users who exist outside of the Global North. To take a truly inclusive approach to design research, it is imperative to consider local impacts and vernacular realities of designed platforms. Therefore, we share the results of fieldwork conducted with young people in India to produce a non-Western, decolonial perspective on digital creativity and algorithmic cultures. In sharing these findings, we also introduce a framework for other scholars to use when synthesizing globally-minded research on digital creativity: Creativity As… Access, Identity, Expression, and Data. In sharing deep insights into local realities pertaining to these topics, this paper contributes to ongoing discourse surrounding digital creativity and algorithmic cultures by offering a non-Western perspective. We compare these results with relevant findings in the West to reveal creative universalisms as well as unique local cultural needs. Through the latter, we highlight the importance of designing platforms with relevant audiences in mind and considering local vernacular realities when making design decisions.
Real Time Face Recognition for Mobile Application Based on Mobilenetv2

Real-time facial recognition is one of the technologies with significant applications in a variety of contexts, including supporting the process of employee attendance. Attendance is a crucial aspect of company administration that influences productivity and operational effectiveness. Traditional attendance mechanisms are susceptible to fraud and errors, so businesses must adopt digital attendance solutions. Face recognition with landmark-based anti-spoofing using MobileNetV2 on mobile devices is intended to be an innovative solution for attendance management. This system employs CNN with MobileNetV2 architecture to detect and identify employee faces in real time. MobileNetV2 is advantageous because it makes efficient use of mobile device resources without sacrificing precision. The research results demonstrate the extraction of eye and lip landmark points with the Blazeface model integrated in the Dart programming language using the Flutter framework. The implementation of the mobile system yields an application called FaceON that can aid in the prevention of potential fraud by employing anti-spoofing techniques. Before a visage can be verified, users must overcome obstacles such as winks and smiles. The contribution of this study is that this system is a dependable and innovative solution for employee attendance management in the digital age
Decoding the Threat Landscape : ChatGPT, FraudGPT, and WormGPT in Social Engineering Attacks

In the ever-evolving realm of cybersecurity, the rise of generative AI models like ChatGPT, FraudGPT, and WormGPT has introduced both innovative solutions and unprecedented challenges. This research delves into the multifaceted applications of generative AI in social engineering attacks, offering insights into the evolving threat landscape using the blog mining technique. Generative AI models have revolutionized the field of cyberattacks, empowering malicious actors to craft convincing and personalized phishing lures, manipulate public opinion through deepfakes, and exploit human cognitive biases. These models, ChatGPT, FraudGPT, and WormGPT, have augmented existing threats and ushered in new dimensions of risk. From phishing campaigns that mimic trusted organizations to deepfake technology impersonating authoritative figures, we explore how generative AI amplifies the arsenal of cybercriminals. Furthermore, we shed light on the vulnerabilities that AI-driven social engineering exploits, including psychological manipulation, targeted phishing, and the crisis of authenticity. To counter these threats, we outline a range of strategies, including traditional security measures, AI-powered security solutions, and collaborative approaches in cybersecurity. We emphasize the importance of staying vigilant, fostering awareness, and strengthening regulations in the battle against AI-enhanced social engineering attacks. In an environment characterized by the rapid evolution of AI models and a lack of training data, defending against generative AI threats requires constant adaptation and the collective efforts of individuals, organizations, and governments. This research seeks to provide a comprehensive understanding of the dynamic interplay between generative AI and social engineering attacks, equipping stakeholders with the knowledge to navigate this intricate cybersecurity landscape.
Twitter Sentiment Geographical Index Dataset

Abstract Promoting well-being is one of the key targets of the Sustainable Development Goals at the United Nations. Many national and city governments worldwide are incorporating Subjective Well-Being (SWB) indicators into their agenda, to complement traditional objective development and economic metrics. In this study, we introduce the Twitter Sentiment Geographical Index (TSGI), a location-specific expressed sentiment database with SWB implications, derived through deep-learning-based natural language processing techniques applied to 4.3 billion geotagged tweets worldwide since 2019. Our open-source TSGI database represents the most extensive Twitter sentiment resource to date, encompassing multilingual sentiment measurements across 164 countries at the admin-2 (county/city) level and daily frequency. Based on the TSGI database, we have created a web platform allowing researchers to access the sentiment indices of selected regions in the given time period.
Inclusive metaverse in media arts education: New innovative-methodological proposal for inclusive school

&lt;p&gt;The paper briefly describes the inclusive school places the greatest emphasis on inclusive innovation, which revolves around fostering knowledge of media arts. The main goal is to investigate and analyze the incorporation of the metaverse through the complementary subject of media arts for inclusive educational purposes. This proposal was allowed to incorporate the virtual and three-dimensional environment used for students and teachers, exploring and interacting with various digital worlds. The analysis of the results was carried out through the contents of the Venn diagram and additional creation, attending to criteria of investigative interest of inclusive metaverse to demonstrate and represent the new preliminary ideas through the educational development of media arts and virtual environments. There are debates about the analysis of inclusive metaverse as the most advanced engineering and artistic technology project, which allows you to incorporate the methodological proposal associated with the complementary subject of media arts together with the interactive tools, making visible in materiality more complex didactics of students of different levels of school education in general. In conclusion, the methodological proposal of media arts for the inclusive metaverse, each student, each teacher, or each educational system could transform the next generation of participation and motivation towards school, depending on the artistic and technological expression that students had learned by using educational gamification, that is, they are better prepared to face the art technology and challenges of the future, bringing with them a spirit of innovation and collaboration, marking a path in the world of digital art.&lt;/p&gt;
Multiclass malaria parasite recognition based on transformer models and a generative adversarial network

Malaria is an extremely infectious disease and a main cause of death worldwide. Microscopic examination of thin slide serves as a common method for the diagnosis of malaria. Meanwhile, the transformer models have gained increasing popularity in many regions, such as computer vision and natural language processing. Transformers also offer lots of advantages in classification task, such as Fine-grained Feature Extraction, Attention Mechanism etc. In this article, we propose to assist the medical professionals by developing an effective framework based on transformer models and a generative adversarial network for multi-class plasmodium classification and malaria diagnosis. The Generative Adversarial Network is employed to generate extended training samples from multiclass cell images, with the aim of enhancing the robustness of the resulting model. We aim to optimize plasmodium classification to achieve an exact balance of high accuracy and low resource consumption. A comprehensive comparison of the transformer models to the state-of-the-art methods proves their efficiency in the classification of malaria parasite through thin blood smear microscopic images. Based on our findings, the Swin Transformer model and MobileVit outperform the baseline architectures in terms of precision, recall, F1-score, specificity, and FPR on test set (the data was divided into train: validation: test splits). It is evident that the Swin Transformer achieves superior detection performance (up to 99.8% accuracy), while MobileViT demonstrates lower memory usage and shorter inference times. High accuracy empowers healthcare professionals to conduct precise diagnoses, while low memory usage and short inference times enable the deployment of predictive models on edge devices with limited computational and memory resources.
ANALYSIS OF TEACHER STRATEGIES IN TEACHING ENGLISH USING DIFFERENTIATED LEARNING

This research aims to analyze the teachers’ strategies for teaching English using differentiated learning. The researcher used a case study as the method. The subjects were two teachers who are the driving teacher or Guru Penggerak. Interviews and observation were utilized to collect the data. The data was analysed using triangulation. The result shows differentiated learning is the process of modifying instruction to match the needs of individual students, and as a solution to students' requirements, the teacher presents them with multiple possibilities for absorbing information it is important to implement because in one class there are various needs of the students that should be fulfilled. Strategies used by the teachers to accommodate the student's needs are from the content, process, and product. The content, process, and product should be different based on the students' needs but on the same topic. Another strategy used by the teachers to ensure the student's participation is to make the students busy with the task and the target that should be fulfilled. Thus, this research concludes that the implementation of differentiated learning in class is effective that can help the students to understand the material according to the students’ needs. For further research, the researcher might be investigated the effectiveness of differentiated learning for student outcomes and for the school, to facilitate workshops or training for the teachers about differentiated learning.
Moiré Synaptic Transistor for Homogeneous-Architecture Reservoir Computing

Reservoir computing has been considered as a promising intelligent computing paradigm for effectively processing complex temporal information. Exploiting tunable and reproducible dynamics in the single electronic device have been desired to implement the “reservoir” and the “readout” layer of reservoir computing system. Two-dimensional moiré materials, with an artificial lattice constant many times larger than the atomic length scale, are one type of most studied artificial quantum materials in community of material science and condensed-matter physics over the past years. These materials are featured with gate-tunable periodic potential and electronic correlation, thus varying the electric field allows the electrons in the moiré potential per unit cell to exhibit distinct and reproducible dynamics, showing great promise in robust reservoir computing. Here, we report that a moiré synaptic transistor can be used to implement the reservoir computing system with a homogeneous reservoir-readout architecture. The synaptic transistor is fabricated based on an h-BN/bilayer graphene/h-BN moiré heterostructure, exhibiting ferroelectricity-like hysteretic gate voltage dependence of resistance. Varying the magnitude of the gate voltage enables the moiré transistor to switch between long-term memory and short-term memory with nonlinear dynamics. By employing the short- and long-term memories as the reservoir nodes and weights of the readout layer, respectively, we construct a full-moiré physical neural network and demonstrate that the classification accuracy of 90.8% can be achieved for the MNIST (Modified National Institute of Standards and Technology) handwritten digits database. Our work would pave the way towards the development of neuromorphic computing based on moiré materials.
TransRender: a transformer-based boundary rendering segmentation network for stroke lesions

Vision transformer architectures attract widespread interest due to their robust representation capabilities of global features. Transformer-based methods as the encoder achieve superior performance compared to convolutional neural networks and other popular networks in many segmentation tasks for medical images. Due to the complex structure of the brain and the approximate grayscale of healthy tissue and lesions, lesion segmentation suffers from over-smooth boundaries or inaccurate segmentation. Existing methods, including the transformer, utilize stacked convolutional layers as the decoder to uniformly treat each pixel as a grid, which is convenient for feature computation. However, they often neglect the high-frequency features of the boundary and focus excessively on the region features. We propose an effective method for lesion boundary rendering called TransRender, which adaptively selects a series of important points to compute the boundary features in a point-based rendering way. The transformer-based method is selected to capture global information during the encoding stage. Several renders efficiently map the encoded features of different levels to the original spatial resolution by combining global and local features. Furthermore, the point-based function is employed to supervise the render module generating points, so that TransRender can continuously refine the uncertainty region. We conducted substantial experiments on different stroke lesion segmentation datasets to prove the efficiency of TransRender. Several evaluation metrics illustrate that our method can automatically segment the stroke lesion with relatively high accuracy and low calculation complexity.