Reviewerly/exHarmony huggingface dataset:
Joint Recurrent Actor-Critic Model for Partially Observable Control

In many real-world robotic control tasks, limited sensors with noise could affect the perceptual abilities of agents. Attempts to solve these partially observable (PO) control tasks using value-based deep reinforcement learning (RL) with recurrent neural networks (RNN) often suffer from two prominent problems: recurrent update inefficiency and overestimation bias of value approximation. In this paper, we propose an off-policy model-free deep RL algorithm, named joint recurrent actor-critic (JRAC), to tackle these two problems simultaneously. First, our algorithm develops the stored encoded state with an episode mask to implement step-based experience replay for efficient recurrent update. Then a pair of critics with independent LSTM parts is introduced to reduce overestimation. We further design a joint actor-critic architecture by sharing the RNN for the actor and the critic, which improves computational efficiency and accelerates the training process without causing intolerable instabilities. Our experiments show that the proposed algorithm can effectively deal with partial observability and performs well on several complicated PO control tasks.
Facial Expression Recognition Based on TripletLoss and Attention Mechanism

The paper proposes a facial expression recognition method called Triplet-Loss Attention Network (TAN), which aims to address the problem of large intra-class and inter-class distances in facial expression recognition. The method uses a self-attention mechanism to calculate the weight of triplet expression samples, which helps in determining the influence of key regions on expression recognition. The method uses images with attention weights above a certain threshold as training samples to form new hard triplets. The distance between high-weight samples in the three sets of samples is calculated using the Mahalanobis distance formula, and the difference in distance between high-weight groups of triplets is calculated using the Maharaja Loss function. The Triplet Loss is mainly used as the loss function in TAN, and the model is jointly optimized using Triplet Loss, Mahalanobis Loss, and Cross Entropy Loss functions to improve the performance of the model in facial expression recognition. Experimental results show that TAN performs well in alleviating the intra-class and inter-class distance problem and has good robustness and generalization performance. On the RAF-DB and FERPlus datasets, TAN achieves recognition accuracies of 88.40% and 88.73%, respectively, which is 1.37% and 0.18% higher than the previous state-of-the-art methods.
Research on Video Detection Method of Mudslide based on Inflated 3D Convolutional Neural Network

Mudslide is a common natural disaster in mountainous areas, causing harm to roads and railroads and structures. To address this problem, this paper adopts the automatic video recognition approach, which utilizes widely installed video surveillance equipment to detect the changes of mudslides, so as to identify the mudslide diffuse flow disaster in the video monitoring area to achieve early warning. Firstly, the deep learning model is trained with weakly labeled mudslide video files, and the spatio-temporal feature learning method, i.e., inflated 3D convolutional network, is combined in the model, which results in a higher correctness rate of training and detection; secondly, the model is shown to have a recognition accuracy of 86% through the testing of the relevant datasets, which can be used as an effective complement to the traditional method of mudslide early warning.
The change and development of journalism in the age of artificial intelligence

With the continuous innovation and development of information technology means such as big data and artificial intelligence, the news cultural ecology has been changed to a certain extent, and the production mode of traditional news has been completely subverted and reconstructed. Under the influence of the era of artificial intelligence, the news and advertising industry is facing unprecedented transition changes, natural language generation, speech recognition, virtual agent has become the main trend of the development of the times, journalism gradually shows the development characteristics of technological thinking. Based on this, this article briefly discusses the reform and development strategies of journalism in the era of artificial intelligence, in order to provide experience and help for journalists, and help artificial intelligence technology to empower and liberate the productivity of journalism.
On the Legal Regulation of Infringement of Public Figures' Emoji Portrait Infringement

Ge You sued E-Dragon Travel for unlawful use of the photo of "Ge You lying down" for advertisement and sought that E-Dragon Travel apologize to him and reimburse him for the losses he suffered. Ge You's lawsuit against E-Dragon Tourism drew attention to celebrity emoticons once more. Language, in certain ways, can no longer adapt to the needs of human language in the age the Internet. Emoji is a new form of communication that has gained extensive societal attention, as well as being "noticed" and "loved" by businesses. As a result, several forms of infringement appear one after the other. First and foremost, the concept and characteristics of the public figure emoticon picture right are explored in this work. The classification of emoticons is provided, and the public figure emoticon image infringement may or has been constructed to assess the problem. Finally, it makes practicable solutions to preserve the portrait right of prominent figures' emoticons in China by combining China's practical experience and the great experience of international nations.
Successful Adoption of DevOps Practices in Software Development in DoD Acquisition Programs—The CREATE Example

A growing number of articles describe the challenges of adopting DevOps for DoD software development. A recent paper in this journal discussed DevOps adoption challenges within the acquisition programs of the Navy. For embedded software, these challenges have been overwhelming to date. However, for standalone applications, the story is different, even though the lingering impact of DoD Instruction 5000 and other DoD cultural practices remains problematic. The HPCMP CREATE™ program, or simply CREATE in this paper, is part of the portfolio of the DoD High Performance Computing Modernization Program (HPCMP). It has successfully adapted DevOps concepts and agile software development practices to develop a family of software applications that enable system-scale virtual prototyping and testing analysis for major DoD acquisition programs. This study describes the main enabling practices that made this possible.
Intelligence Augmentation via Human-AI Symbiosis

Intelligence augmentation (IA) facilitates a new systems perspective to frame the value outcome of the interaction between human and AI agents. The factors that can optimize this collaborative integration of the multi-agent system are investigated and discussed. Different kinds of knowledge approaches are met in various contexts to create an optimized IA system in service settings. In this respect, AI agents are not just tools but rather co-creators of value that can influence human agents' learning cycles. Hence, humans' effective interaction with AI agents produces a learning effect that can empower humans' interpretative capability. This chapter focuses on IA and shows that IA is not only a theoretical paradigm but also serves as a platform to facilitate the transition from smart services to wise service innovation to the benefit of both the multi-agent system benefitting service organizations and the consumers too. Potential challenges are also discussed from a societal viewpoint.
Artificial Intelligence Method for the Analysis of Marketing Scientific Literature

A machine-based research reading methodology specific to the academic discipline of marketing science is introduced, focused on the text mining of scientific texts, analysis and predictive writing, by adopting artificial intelligence developments from other research fields in particular materials and chemical science. It is described how marketing research can be extracted from documents, classified and tokenised in individual words. This is conducted by applying text-mining with named entity recognition together with entity normalisation for large-scale information extraction of published scientific literature. Both a generic methodology for overall marketing science analysis as well as a narrowed-down contextualised method for delimited marketing topics are detailed. Automated literature review is discussed as well as potential automated formulation of hypotheses and how AI can assist in the transfer of marketing research knowledge to practice, in particular to startups, as they can benefit from AI powered science-based decision making. Recommendations for next steps are made.
Exploratory Cluster Analysis Using Self-Organizing Maps

As the volume and complexity of data streams continue to increase, exploratory cluster analysis is becoming increasingly important. In this chapter, the authors explore the use of artificial neural networks (ANNs), particularly self-organizing maps (SOMs), for this purpose. They propose additional methodologies, including concept drift detection, as well as distributed and collaborative learning strategies and introduce a new open-source Java ANN library, designed to support practical applications of SOMs across various domains. By following our tutorial, users will gain practical insights into visualizing and analyzing these challenging datasets, enabling them to harness the full potential of our approach in their own projects. Overall, this chapter aims to provide readers with a comprehensive understanding of SOMs and their place within the broader context of artificial neural networks. Furthermore, we offer practical guidance on the effective development and utilization of these models in real-world applications.
Causality

Judea Pearl's ladder of causation framework has dramatically influenced the understanding of causality in computer science. Despite artificial intelligence (AI) advancements, grasping causal relationships remains challenging, emphasizing the causal revolution's significance in improving AI's understanding of cause and effect. The work presents a novel taxonomy of causal inference methods, clarifying diverse approaches for inferring causality from data. It highlights the implications of causality in responsible AI and explainable AI (xAI), addressing bias in AI systems. The chapter points out causality as the next step in AI for creating new questions, developing causal tools, and clarifying opaque models with xAI approaches. The work clarifies causal models' significance and implications in various AI subareas.
Ethics, Big Data and Artificial Intelligence: Exploring Academic Works in the Educational Landscape

Big Data and Artificial Intelligence (AI) confer substantial advancements across diverse sectors of society, education included. However, it is imperative to extend the discourse surrounding these interventions to align with ethical research principles, data usage, and prevailing legislation. Consequently, this study endeavors to scrutinize scholarly literature spanning the interval between 2011 and 2022, concentrating on the ethical facets recommended for inquiries that meld big data and artificial intelligence within educational contexts. Employing a bibliographic research approach, the inquiry was conducted on the CAPES Periodicals Portal, utilizing descriptors such as “ethics,” “big data,” “artificial intelligence,” and “education.“ Out of a corpus of 84 articles, nine were incorporated into this research subsequent to the application of inclusion and exclusion criteria. These works encompass both empirical and theoretical contributions, interlinking big data and AI with educational settings. Researchers employ documentary sources, questionnaires, and their individual pedagogical experiences for data collection. Methodologically, these studies lean towards techniques such as descriptive content analysis, descriptive statistical analysis, confirmatory factor analysis, and textual data mining for data analysis. With regard to ethical principles spotlighted in the studies, salient themes include responsibility, transparency, reliability, and privacy. The outcomes suggest that crucial lacunae still exist, particularly concerning aspects like informed consent and other pivotal ethical protocols.
On the Use of ChatGPT to Support Requirements Engineering Teaching and Learning Process

The use of artificial intelligence (AI) in education is an emerging trend that offers multiple opportunities and challenges. In this article, we present an exploratory study on the use of ChatGPT, an AI-based tool that allows generating responses to questions posed by the user, as support for project-based learning in the Requirements Engineering course. The objective of the study is to identify the advantages and disadvantages of using ChatGPT in this context, as well as to provide recommendations for its implementation and improvement. To this end, a survey was conducted with 27 students who used ChatGPT in the course, and their responses were analyzed using statistical and qualitative techniques. The study results show that students perceived ChatGPT as a useful and novel tool that facilitated the generation and understanding of concepts, models and tools related to requirements engineering, as well as the simulation of interviews with value chain actors. However, some limitations and aspects to improve using ChatGPT were also identified, such as the accuracy and consistency of the generated responses, the adequate contextualization of information, and the ability to customize and adjust the tool. The study concludes that ChatGPT has great potential to support project-based learning in the Requirements Engineering course, but also requires technical and pedagogical improvements to optimize its performance and effectiveness.
Criteria of Pedagogical Practices in Times of Pandemic: A Systematic Review

The COVID-19 pandemic has posed significant challenges to the field of education, requiring pedagogical practices to swiftly evolve from technological and socioformative perspectives, as well as the incorporation of other pedagogical practices that dominated the teacher-student interaction during the pandemic to ensure the continuity of ongoing academic cycles. The objective is to characterize the criteria that can be derived from pedagogical practices during the 2020–2022 pandemic period. A qualitative and quantitative evaluation of authors’ contributions was conducted through a systematic review based on the PRISMA 2020 guidelines using SCOPUS and Web of Science (WoS) databases. Boolean equations based on keywords were employed for publication filtering. Cohen's Kappa index was calculated to assess review concordance. The analysis reveals that pedagogical practices influence the deficient technological inclusion of teachers’ skills. Socioformation shows a shift from traditional characteristics in teaching processes. Additionally, there is openness to the inclusion of new pedagogical practices associated with digital education and virtual environments. Technology and methodology integrate co-evaluation, feedback, and interactive methodologies as alternatives for enhancing acquired and inherent technological skills in virtual classrooms using technological platforms.
Academic Performance During the Pandemic with ICT Was Better Before and After It

The research carried out at the Armed Forces University ESPE, Santo Domingo Campus, aimed to explore the impact of changing teaching methodologies using ICT on the academic performance of Information Technology Engineering students before, during, and after the COVID-19 pandemic. For the analysis, a random sample of academic performance from three student cohorts during different periods, with a one-year difference between them, was taken. In accordance with various authors, a one-way analysis of variance (ANOVA) test was employed due to the characteristics of the sample, with the purpose of assessing whether significant differences in performance means existed between the groups; additionally, other tests were used for the same purpose. The sample was validated for normality, homoscedasticity, and independence of samples before conducting the ANOVA test. The results unveiled significant differences in the average academic performance among the groups. Further analysis, utilizing the Tukey test, demonstrated that the academic performance of students during the pandemic differed significantly from the other groups. However, there were no significant differences between the groups before and after the pandemic. This led to the conclusion that the post-pandemic teaching methodology might not have capitalized on the positive aspects of employing ICT to enhance students’ academic performance.
Identifying Master’s Program Web Pages with Machine Learning

In the journey of education and professional growth, there is a wide variety of postgraduate programs and specializations offered by different universities and training centers, many of which have a significant online presence. Simultaneously, for the institutions offering these programs, it is of interest to gather information about the competition in order to develop their study offering strategies accordingly. Likewise, for most individuals interested in pursuing these studies, identifying these programs on various published web pages proves to be challenging. This work presents a tool for detecting relevant information from web page content, allowing the identification of master’s programs. One of the primary challenges addressed in this research is the technological issue of accurately and efficiently extracting information from diverse web sources, given the variability in webpage structures and formats. The tool is based on the analysis of various Machine Learning and Deep Learning models, along with the application of web scraping techniques. Tests and validations were conducted on different models, with the best result being achieved by the deep recurrent neural networks model, which reached a 91.95% F1-score metric. The use of automated tools for selection, gathering, and classification of information enhances learning by enabling a greater focus on points of interest. Therefore, when searching for master’s degree programs, people would avoid the tedious task of manual searching and sorting. Instead, they can focus on deeply analyzing and comparing the options. This focus on exploration and analysis would enhance their understanding of the options and promote more informed decisions.
Gender Inclusion from the Art for Children

Art presents a technological solution to talk about gender, providing educational innovation through open educational resources. This work presents an Instructional Methodological Class for teachers of Basic Education, where they reflect on gender identity, from art, for students of nine years of age, which promotes inclusion in the classroom. The applied methodology is inspired by educational references such as Paolo Freire and Luis Camnitzer. Three classes were designed based on international artistic references, who discursively and technically expose gender deconstruction stereotypes such as Sarah Lucas, Cindy Sherman, and Yasumasa Morimura. In this case, educational innovation consists on the creation of three hand-made prototypes, one in each session. Additionally, these resources are available for teachers on open repositories through QR codes. By applying the pilot classes in two schools: one public and one private, the results showed that this age group does not use gender stereotypes negatively. This is an initial step to validate the assessments through survey and self-assessment, to obtain real data for which triangulation may be used in the future.
Methodological Actions for the Electronic Configuration of the Educa Plus Platform: Promoting Interactive Learning

This work implements the use of the “Educa Plus” platform to achieve interactive and meaningful learning in the field of chemistry. The research is developed through an analysis that combines quantitative and qualitative methods, using a conceptual framework and research variables within the educational field. These variables are examined through a study that includes a statistical analysis with the aim of analyzing the factors involved in the pedagogical methodology. The main purpose of this article is to present a concrete proposal: a manual of activities that improves the teaching-learning process through the use of technology and the promotion of teamwork. The intention is to provide educators with practical tools that allow them to make the most of available technological resources and create a collaborative learning environment.
Advancements in AI-driven multilingual comprehension for social robot interactions: An extensive review

&lt;abstract&gt;&lt;p&gt;In the digital era, human-robot interaction is rapidly expanding, emphasizing the need for social robots to fluently understand and communicate in multiple languages. It is not merely about decoding words but about establishing connections and building trust. However, many current social robots are limited to popular languages, serving in fields like language teaching, healthcare and companionship. This review examines the AI-driven language abilities in social robots, providing a detailed overview of their applications and the challenges faced, from nuanced linguistic understanding to data quality and cultural adaptability. Last, we discuss the future of integrating advanced language models in robots to move beyond basic interactions and towards deeper emotional connections. Through this endeavor, we hope to provide a beacon for researchers, steering them towards a path where linguistic adeptness in robots is seamlessly melded with their capacity for genuine emotional engagement.&lt;/p&gt;&lt;/abstract&gt;
EITGAN: A Transformation-based Network for recovering adversarial examples

&lt;abstract&gt;&lt;p&gt;Adversarial examples have been shown to easily mislead neural networks, and many strategies have been proposed to defend them. To address the problem that most transformation-based defense strategies will degrade the accuracy of clean images, we proposed an Enhanced Image Transformation Generative Adversarial Network (EITGAN). Positive perturbations were employed in the EITGAN to counteract adversarial effects while enhancing the classified performance of the samples. We also used the image super-resolution method to mitigate the effect of adversarial perturbations. The proposed method does not require modification or retraining of the classifier. Extensive experiments demonstrated that the enhanced samples generated by the EITGAN effectively defended against adversarial attacks without compromising human visual recognition, and their classification performance was superior to that of clean images.&lt;/p&gt;&lt;/abstract&gt;
Attending the ASIS&amp;amp;T Annual Meeting in London, England? Need to “Cover” a Class You are Teaching this Fall? Hold an Information Science Video Festival!

All educators occasionally wrestle with the matter of travel or other interruptions during the academic semester that take them away from the classroom. This posting offers a turnkey way for information science teachers to productively fill the gap: Hold an Information Science Video Festival!