bluuebunny/crossref_metadata_2025 huggingface dataset:
Difficulties and Anxiety Of 9th-Grade Students in Facing Final Exam During a Pandemic
Authors: Ega Shavira Lintang , Sumbaji Putranto
Published: May 2024

This research aims to explain students’ difficulties in learning mathematics, the factors that cause learning difficulties, and student anxiety in facing school final exams during a pandemic and explain the design of a good mathematics learning system that should be carried out during a pandemic according to the results of this research. This research is descriptive qualitative research that was conducted in one of the junior high schools in the Tangerang district in 2021 with the research sample being 9th-grade students. For getting the data, this research used questionnaires, interviews, and documentation techniques. The result of this study found some of the main problems that happened to students in facing school final exams include students' unpreparedness for online learning, lack of interest in learning in students, lack of understanding of the material, feeling lazy to take part in online learning, difficulties in learning mathematics and the anxiety of students themselves in facing the final school exam. Making a good and comfortable learning system by paying more attention to the mental readiness of students and making teaching materials in the form of audio-visual or video can be one of the right solutions. In addition, face-to-face learning can provide motivation to students to increase their confidence. Thus, their anxiety in facing the final school exam can be overcome.
Pembelajaran Berbantuan Aplikasi Graspable Math pada Materi SPLDV untuk Melatih Pemahaman Matematis Siswa
Authors: Taofik Agna Ilma , Priatna Nanang
Published: May 2024

This study aims to describe the use of Graspable Math learning media in the material of the Two-Variable Linear Equation System (SPLDV) to train mathematical understanding skills. This study was conducted on 8th-grade students with as many as six people as subjects using a qualitative methodology. The instruments used were math comprehension test sheets and interview guidelines. The results of this study show that the Graspable Math application can be used as a practical medium for students' mathematics learning. This implies that the use of the Graspable Math application on SPLDV material at the junior high school level can help students' understanding of mathematics, especially in algebraic material which includes operations and substitution between tribes
Investigating Pedagogical Content Knowledge and Decision-Making on Algebraic Tasks
Authors: Mastuti Ajeng Gelora
Published: May 2024

Pedagogical Content Knowledge (PCK) tasks and decision-making are closely related to teaching and learning mathematics. This study aims to investigate teacher PCK in decision-making on different algebra tasks and to see activity in teacher-student interactions. This research is qualitative, with a research approach used as a case study. The participants in this study were only one teacher with different characteristics in mathematics tasks. Data collection consisted of primary research data from class observations, teacher interview transcripts, and researcher notes, while documents such as teacher notes were secondary data. Data analysis was carried out directly based on class observations and interviews. The findings in this study reveal teacher PCK through teacher decision-making regarding algebraic tasks in Swartz & Perkins (2016) three theories of thinking: generating ideas, clarifying ideas, and assessing the reasonableness of ideas.
Analisis Kebutuhan Pengembangan Instrumen Penilaian Kemampuan Berpikir Geometri Berbasis Website
Authors: Fardiana Rina , Supriyadi  , Djuniadi
Published: May 2024

The ability to think geometrically is hierarchical so students need to master the initial level of thinking before they are going to the next level. Geometry learning strategies need to consider the level of students' thinking abilities. Assessment with paper-based tests has weaknesses in terms of practicality and does not develop students' technological literacy in the digitalization era. This study aims to analyze the need for the development of a website-based instrument to assess the geometric thinking skills of elementary school students referring to Van Hiele's theory. This research is a literature review with a qualitative approach. The articles reviewed in this research are articles that discuss thinking skills based on Van Hiele's theory with research subjects of elementary school students and published from 2021 to 2023. Data were analyzed by Miles and Huberman's interactive model. The results showed that a web-based instrument for assessing the geometric thinking abilities of elementary school students based on Van Hiele's theory was needed to systematically assess students' geometric thinking abilities then could be followed up with learning activities according to students' abilities. The level of thinking of elementary school students assessed is from visualization to abstraction which is adapted to the material and curriculum of the elementary school.
Development of organizational and technological construction diagrams of railway track facilities with the use of artificial neural networks
Authors: Polyanskiy Aleksey
Published: May 2024

The article is devoted to the research of the development procedure of organizational and technological construction diagrams of railway track facilities with the use of artificial neural networks. Organizational and technological diagrams allow to present the construction technological process in a visual form, determine the sequence of the facility erection in parts and methods of work production. In the digitalization conditions of organizational and technological documentation development there is a need for the rapid formation of visual materials for the construction works implementation. It is the visual aspects that allows to effectively assess the development and control of the technological processes implementation. The solution of these problems is possible by improving the existing engineering and technical support system of railway construction through the introduction of an engineering-intellectual support subsystem of technological processes of railway track facilities construction. One of the subsystem’s tasks is the effective use of automation tools with the artificial intelligence elements.

The development procedure analysis of organizational and technological diagrams allowed to determine the artificial neural network application possibilities to obtain a visual representation of the technological process taking into account its spatial partitioning into grips. For this purpose the neural network structure, the program module realizing the algorithm of its training, the lists of input data and output parameters were developed. For the neural network training the data sample prepared in advance in the coded format based on the existing organizational and technological diagrams of railway track facilities construction was used.

Based on the theoretical study results, practical development aspects of organizational and technological diagrams with the use of neural network on the example of the railway roadbed embankments erection are presented.

The results given in the article are obtained in the course of dissertation research carried out by the author.
Investigational studies of asphalt concrete elastic moduli for pavement design
Authors: Kosenko Natalya , Goryachev Mikhail , Dmitriev Sergei , Yarkin Sergei
Published: May 2024

In recent years in the Russian Federation there has been a significant expansion of road materials variety used on highways. However, there is a lack of harmonization of new asphalt concrete of Western European format, with regulatory documents for the non-rigid pavement design, in terms of their design characteristics assignment. In this regard, there is a need to determine and justify the numerical values of the new asphalt concrete mechanical performance used for the non-rigid pavement design when tested for strength.

In previous publications authors have presented a new graph-analytical approach to justify the elasticity design moduli of asphalt concrete from GOST R 58406.2-2020. This paper is a continuation of that development and includes the empirical verification results of the analytical method accuracy. Tests were carried out to determine the static laboratory elasticity modulus of asphalt concrete under uniaxial bending loading of bar specimens of some asphalt concrete types from GOST R 58406.2 and best corresponding ones from GOST 9128. To the values obtained, during the experiment, the calculation method developed by the authors earlier was applied. The article presents a study aimed at experimental confirmation of analytically justified calculated values of asphalt concrete elastic moduli according to GOST R 58406.2-2021 for the pavement design.
Toward Risk Frameworks for Autonomous Systems that Take Societal Safety-related Benefits into Account
Authors: Bass Ellen J. , Weber Steven
Published: May 2024

Current risk frameworks such as probabilistic risk analy-sis methodologies do not take societal safety-related benefits into account. To inform human-AI collaborative system development, this manuscript highlights the need for updated risk frameworks and suggestions for relevant considerations.
Subjectivity in Unsupervised Machine Learning Model Selection
Authors: Chen Wanyi , Cummings Mary
Published: May 2024

Model selection is a necessary step in unsupervised machine learning. Despite numerous criteria and metrics, model selection remains subjective. A high degree of subjectivity may lead to questions about repeatability and reproducibility of various machine learning studies and doubts about the robustness of models deployed in the real world. Yet, the impact of modelers' preferences on model selection outcomes remains largely unexplored. This study uses the Hidden Markov Model as an example to investigate the subjectivity involved in model selection. We asked 33 participants and three Large Language Models (LLMs) to make model selections in three scenarios. Results revealed variability and inconsistencies in both the participants’ and the LLMs' choices, especially when different criteria and metrics disagree. Sources of subjectivity include varying opinions on the importance of different criteria and metrics, differing views on how parsimonious a model should be, and how the size of a dataset should influence model selection. The results underscore the importance of developing a more standardized way to document subjective choices made in model selection processes.
On Replacing Humans with Large Language Models in Voice-Based Human-in-the-Loop Systems
Authors: Huang Shih-Hong , Huang Ting-Hao 'Kenneth'
Published: May 2024

It is easy to assume that Large Language Models (LLMs) will seamlessly take over applications, especially those that are largely automated. In the case of conversational voice assistants, commercial systems have been widely deployed and used over the past decade. However, are we indeed on the cusp of the future we envisioned? There exists a social-technical gap between what people want to accomplish and the actual capability of technology. In this paper, we present a case study comparing two voice assistants built on Amazon Alexa: one employing a human-in-the-loop workflow, the other utilizes LLM to engage in conversations with users. In our comparison, we discovered that the issues arising in current human-in-the-loop and LLM systems are not identical. However, the presence of a set of similar issues in both systems leads us to believe that focusing on the interaction between users and systems is crucial, perhaps even more so than focusing solely on the underlying technology itself. Merely enhancing the performance of the workers or the models may not adequately address these issues. This observation prompts our research question: What are the overlooked contributing factors in the effort to improve the capabilities of voice assistants, which might not have been emphasized in prior research?
Personalised Course Recommender: Linking Learning Objectives and Career Goals through Competencies
Authors: Beutling Nils , Spahic-Bogdanovic Maja
Published: May 2024

This paper presents a Knowledge-Based Recommender System (KBRS) that aims to align course recommendations with students' career goals in the field of information systems. The developed KBRS uses the European Skills, Competences, qualifications, and Occupations (ESCO) ontology, course descriptions, and a Large Language Model (LLM) such as ChatGPT 3.5 to bridge course content with the skills required for specific careers in information systems. In this context, no reference is made to the previous behavior of students. The system links course content to the skills required for different careers, adapts to students' changing interests, and provides clear reasoning for the courses proposed. An LLM is used to extract learning objectives from course descriptions and to map the promoted competency. The system evaluates the degree of relevance of courses based on the number of job-related skills supported by the learning objectives. This recommendation is supported by information that facilitates decision-making. The paper describes the system's development, methodology and evaluation and highlights its flexibility, user orientation and adaptability. It also discusses the challenges that arose during the development and evaluation of the system.
GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding
Authors: Dernbach Stefan , Agarwal Khushbu , Zuniga Alejandro , Henry Michael , Choudhury Sutanay
Published: May 2024

Integrating large language models with knowledge graphs derived from domain-specific data represents an important advancement towards more powerful and factual reasoning. As these models grow more capable, it is crucial to enable them to perform multi-step inferences over real-world knowledge graphs while minimizing hallucination. While large language models excel at conversation and text generation, their ability to reason over domain-specialized graphs of interconnected entities remains limited. For example, can we query a model to identify the optimal contact in a professional network for a specific goal, based on relationships and attributes in a private database? The answer is no – such capabilities lie beyond current methods. However, this question underscores a critical technical gap that must be addressed. Many high-value applications in areas such as science, security, and e-commerce rely on proprietary knowledge graphs encoding unique structures, relationships, and logical constraints. We introduce a fine-tuning framework for developing Graph-aligned Language Models (GaLM) that transforms a knowledge graph into an alternate text representation with labeled question-answer pairs. We demonstrate that grounding the models in specific graph-based knowledge expands the models’ capacity for structure-based reasoning. Our methodology leverages the large-language model's generative capabilities to create the dataset and proposes an efficient alternate to retrieval-augmented generation styled methods.
Can LLMs Answer Investment Banking Questions? Using Domain-Tuned Functions to Improve LLM Performance on Knowledge-Intensive Analytical Tasks
Authors: Harvel Nicholas , Haiek Felipe Bivort , Ankolekar Anupriya , Brunner David James
Published: May 2024

Large Language Models (LLMs) can increase the productivity of general-purpose knowledge work, but accuracy is a concern, especially in professional settings requiring domain-specific knowledge and reasoning. To evaluate the suitability of LLMs for such work, we developed a benchmark of 16 analytical tasks representative of the investment banking industry. We evaluated LLM performance without special prompting, with relevant information provided in the prompt, and as part of a system giving the LLM access to domain-tuned functions for information retrieval and planning. Without access to functions, state-of-the-art LLMs performed poorly, completing two or fewer tasks correctly. Access to appropriate domain-tuned functions yielded dramatically better results, although performance was highly sensitive to the design of the functions and the structure of the information they returned. The most effective designs yielded correct answers on 12 out of 16 tasks. Our results suggest that domain-specific functions and information structures, by empowering LLMs with relevant domain knowledge and enabling them to reason in domain-appropriate ways, may be a powerful means of adapting LLMs for use in demanding professional settings.
Rule-Based Explanations of Machine Learning Classifiers Using Knowledge Graphs
Authors: Menis Mastromichalakis Orfeas , Dervakos Edmund , Chortaras Alexandros , Stamou Giorgos
Published: May 2024

The use of symbolic knowledge representation and reasoning as a way to resolve the lack of transparency of machine learning classifiers is a research area that has lately gained a lot of traction. In this work, we use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier escaping the constraints of using the features of raw data as a means to express the explanations, providing a promising solution to the problem of the understandability of explanations. In particular, given a description of the application domain of the classifier in the form of a knowledge graph, we introduce a novel theoretical framework for representing explanations of its operation, in the form of query-based rules expressed in the terminology of the knowledge graph. This allows for explaining opaque black-box classifiers, using terminology and information that is independent of the features of the classifier and its domain of application, leading to more understandable explanations but also allowing the creation of different levels of explanations according to the final end-user.
What Can Computers Do Now? Dreyfus Revisited for the Third Wave of Artificial Intelligence
Authors: Schuering Ben , Schmid Thomas
Published: May 2024

In recent years, artificial intelligence (AI) has seen significant advances that have in fact exceeded even optimistic prognoses. Using data-driven AI, namely deep learning techniques, it has been demonstrated that computers may now be equipped with abilities of remarkable scope and quality, such as solving image and text processing tasks at human level. Large language models, in particular, have sparked debates regarding opportunities and challenges of this rapidly developing area. Will remaining fundamental challenges of data-driven AI, such as factual or logical mistakes, be overcome for good if complemented and hybridized with symbolic AI techniques, such as knowledge representation and reasoning? Will systems of artificial general intelligence (AGI) emerge from this, possessing common sense and in fact completing the decades-old quest for AI that motivated the raise of the field in the 1950s? In the light of these questions, we review the likewise, decades-old philosophical debate about capabilities and limitations of computers from a hybrid AI point of view. Here, we discuss how hybrid AI is coming closer to disproving Hubert Dreyfus’ famous statements regarding what computers can not do. At the same time, we shed light on a lesser discussed challenge for hybrid AI: the possibility that its developers might be its biggest limiters.
Resource-aware Federated Data Analytics in Edge-Enabled IoT Systems
Authors: Khamfroush Hana
Published: May 2024

In a resource constrained environment like Internet-of-Things (IoT) systems, it is critical to make optimal decisions on how much resources
to allocate pre-processing and how much to allocate to model training, and which specific combination of preprocessing and learning should be selected.
This talk first, provides an overview of some initial steps we took towards developing federated data pre-processing in IoT environments, and then a
visionary overview of potential research problems related to developing an integrated resource-aware and Quality-of-Service (QoS)-aware data pre-processing and model training system is provided.
Towards Fault-Tolerant Federated and Distributed Machine Learning
Authors: Koyejo Sanmi
Published: May 2024

Machine learning (ML) models are routinely trained and deployed among distributed devices, e.g., learning with geo-distributed data centers and federated learning with mobile devices. Such shared computing platforms are susceptible to hardware, software, communication errors, and security concerns. This talk will outline some of the threat models in distributed learning, along with robust learning methods proposed to augment the fault tolerance of distributed machine learning, showing both theoretical and empirical evidence of robustness to benign and adversarial attacks.
Towards Robust Multi-Agent Reinforcement Learning
Authors: Mitra Aritra
Published: May 2024

Stochastic gradient descent (SGD) is at the heart of large-scale distributed machine learning paradigms such as federated learning (FL). In these applications, the task of training high-dimensional weight vectors is distributed among several workers that exchange information over networks of limited bandwidth. While parallelization at such an immense scale helps to reduce the computational burden, it creates several other challenges: delays, asynchrony, and most importantly, a significant communication bottleneck. The popularity and success of SGD can be attributed in no small part to the fact that it is extremely robust to such deviations from ideal operating conditions. Inspired by these findings, we ask: Are common reinforcement learning (RL)
algorithms also robust to similarly structured perturbations? Perhaps surprisingly, despite the recent surge of interest in multi-agent/federated RL, almost nothing is known about the above question. This paper collects some of our recent results in filling this void.
Now It Sounds Like You: Learning Personalized Vocabulary On Device
Authors: Shenoy Ashish , Wang Sid , Chuang Pierce , Nguyen John
Published: May 2024

In recent years, Federated Learning (FL) has shown significant advancements in its ability to perform various natural language processing (NLP) tasks. This work focuses on applying personalized FL for on-device language modeling. Due to limitations of memory and latency, these models cannot support the complexity of sub-word tokenization or beam search decoding, resulting in the decision to deploy a closed-vocabulary language model. However, closed-vocabulary models are unable to handle out-of-vocabulary (OOV) words belonging to specific users. To address this issue, We propose a novel technique called "OOV expansion" that improves OOV coverage and increases model accuracy while minimizing the impact on memory and latency. This method introduces a personalized "OOV adapter" that effectively transfers knowledge from a central model and learns word embedding for personalized vocabulary. OOV expansion significantly outperforms standard FL personalization methods on a set of common FL benchmarks.
Advancing Neuro-Inspired Lifelong Learning for Edge with Co-Design
Authors: Soures Nicholas , Karia Vedant , Kudithipudi Dhireesha
Published: May 2024

Lifelong learning, which refers to an agent's ability to continuously learn and enhance its performance over its lifespan, is a significant challenge in artificial intelligence (AI), that biological systems tackle efficiently. This challenge is further exacerbated when AI is deployed in untethered environments with strict energy and latency constraints.
We take inspiration from neural plasticity and investigate how to leverage and build energy-efficient lifelong learning machines. Specifically, we study how a combination of neural plasticity mechanisms, namely neuromodulation, synaptic consolidation, and metaplasticity, enhance the continual learning capabilities of AI models. We further co-design architectures that leverage compute-in-memory topologies and sparse spike-based communication with quantization for the edge. Aspects of this co-design can be transferred to federated lifelong learning scenarios.
Federated Variational Inference: Towards Improved Personalization and Generalization
Authors: Vedadi Elahe , Dillon Joshua V. , Mansfield Philip Andrew , Singhal Karan , Afkanpour Arash , Morningstar Warren Richard
Published: May 2024

Conventional federated learning algorithms train a single global model by leveraging all participating clients’ data. However, due to heterogeneity in client generative distributions and predictive models, these approaches may not appropriately approximate the predictive process, converge to an optimal state, or generalize to new clients. We study personalization and generalization in stateless cross-device federated learning setups assuming heterogeneity in client data distributions and predictive models. We first propose a hierarchical generative model and formalize it using Bayesian Inference. We then approximate this process using Variational Inference to train our model efficiently. We call this algorithm Federated Variational Inference (FedVI). We use PAC-Bayes analysis to provide generalization bounds for FedVI. We evaluate our model on FEMNIST and CIFAR-100 image classification and show that FedVI beats the state-of-the-art on both tasks.
Generative AI Applications in Helping Children with Speech Language Issues
Authors: Qin Helen
Published: May 2024

This paper reports how generative AI can help children with specific language impairment (SLI) issues by developing an AI-assisted tool to support children with challenges in phonological development in English, especially children with English as the secondary language in the United States. Children from bilingual families often experience challenges in developing proficiency in English pronunciation and communication, which has been exacerbated by remote learning during the pandemic and led to learning loss. School-aged children with speech problems require timely intervention because children with language disorders find it difficult to communicate with others, leading to social isolation and academic difficulties. The needed intervention is often delayed due to the high cost of speech services and the shortage of Speech and Language Pathologists (SLPs). Individuals with a history of SLI have an increased risk of unemployment. An AI-assisted Phonological Development (AI-PD) tool was prototyped, aiming to alleviate these challenges by assisting caregivers in evaluating children's phonological development, assisting SLPs in lesson preparation, and mitigating the severe shortage of SLPs.
How Can Large Language Models Enable Better Socially Assistive Human-Robot Interaction: A Brief Survey
Authors: Shi Zhonghao , Landrum Ellen , O'Connell Amy , Kian Mina , Pinto-Alva Leticia , Shrestha Kaleen , Zhu Xiaoyuan , Matarić Maja J
Published: May 2024

Socially assistive robots (SARs) have shown great success in providing personalized cognitive-affective support for user populations with special needs such as older adults, children with autism spectrum disorder (ASD), and individuals with mental health challenges. The large body of work on SAR demonstrates its potential to provide at-home support that complements clinic-based interventions delivered by mental health professionals, making these interventions more effective and accessible. However, there are still several major technical challenges that hinder SAR-mediated interactions and interventions from reaching human-level social intelligence and efficacy. With the recent advances in large language models (LLMs), there is an increased potential for novel applications within the field of SAR that can significantly expand the current capabilities of SARs. However, incorporating LLMs introduces new risks and ethical concerns that have not yet been encountered, and must be carefully be addressed to safely deploy these more advanced systems. In this work, we aim to conduct a brief survey on the use of LLMs in SAR technologies, and discuss the potentials and risks of applying LLMs to the following three major technical challenges of SAR: 1) natural language dialog; 2) multimodal understanding; 3) LLMs as robot policies.
AI Health Agents: Pathway2vec, ReflectE, Category Theory, and Longevity
Authors: Swan Melanie , Kido Takashi , Roland Eric , Dos Santos Renato P.
Published: May 2024

Health Agents are introduced as the concept of a personalized AI health advisor overlay for continuous health monitoring (e.g. 1000x/minute) medical-grade smartwatches and wearables for “healthcare by app” instead of “sickcare by appointment.” Individuals can customize the level of detail in the information they view. Health Agents “speak” natural language to humans and formal language to the computational infrastructure, possibly outputting the mathematics of personalized homeostatic health as part of their reinforcement learning agent behavior. As an AI health interface, the agent facilitates the management of precision medicine as a service. Healthy longevity is a high-profile area characterized by the increasing acceptance of medical intervention, longevity biotech venture capital investment, and global priority as 2 billion people will be over 65 in 2050. Aging hallmarks, biomarkers, and clocks provide a quantitative measure for intervention. Some of the leading interventions include metformin, rapamycin, spermidine, NAD+/sirtuins, alpha-ketoglutarate, and taurine. AI-driven digital biology, longevity medicine, and Web3 personalized healthcare come together in the idea of Health Agents. This Web3 genAI tool for automated health management, specifically via digital-biological twins and pathway2vec approaches, demonstrates human-AI intelligence amplification and works towards healthy longevity for global well-being.
What Is a Correct Output by Generative AI From the Viewpoint of Well-Being? – Perspective From Sleep Stage Estimation –
Authors: Takadama Keiki
Published: May 2024

This paper explores an answer to the question of “what is a correct output by generative AI from the viewpoint of well-being?” and discusses an effectiveness of taking account of a biological rhythm for this issue. Concretely, this paper focuses on an estimation of the REM sleep stage as one of sleep stages, and compared its estimations based on random forest as one of the machine learning methods and the ultradian rhythm as one of the biological rhythms. From the human subject experiment, the following implications have been revealed: (1) the REM sleep stage is wrongly estimated in many areas by random forest; and (2) the integration of the REM sleep stage estimation based on the biological rhythm with that based on random forest improves the F-score of the estimated REM sleep stage.
The Psychosocial Impacts of Generative AI Harms
Authors: Vassel Faye-Marie , Shieh Evan , Sugimoto Cassidy R. , Monroe-White Thema
Published: May 2024

The rapid emergence of generative Language Models (LMs) has led to growing concern about the impacts that their unexamined adoption may have on the social well-being of diverse user groups. Meanwhile, LMs are increasingly being adopted in K-20 schools and one-on-one student settings with minimal investigation of potential harms associated with their deployment. Motivated in part by real-world/everyday use cases (e.g., an AI writing assistant) this paper explores the potential psychosocial harms of stories generated by five leading LMs in response to open-ended prompting. We extend findings of stereotyping harms analyzing a total of 150K 100-word stories related to student classroom interactions. Examining patterns in LM-generated character demographics and representational harms (i.e., erasure, subordination, and stereotyping) we highlight particularly egregious vignettes, illustrating the ways LM-generated outputs may influence the experiences of users with marginalized and minoritized identities, and emphasizing the need for a critical understanding of the psychosocial impacts of generative AI tools when deployed and utilized in diverse social contexts.
AI-Assisted Talk: A Narrative Review on the New Social and Conversational Landscape
Authors: Vo Kevin
Published: May 2024

In this ongoing narrative review, I summarize the existing body of literature on the role of artificial intelligence in mediating human communication, focusing on how it is currently transforming our communication patterns. Moreover, this re-view uniquely contributes by critically analyzing potential future shifts in these patterns, particularly in light of the advancing capabilities of artificial intelligence. Special emphasis is placed on the implications of emerging generative AI technologies, projecting how they might redefine the landscape of human interaction.
Evaluating Large Language Models with RAG Capability: A Perspective from Robot Behavior Planning and Execution
Authors: Yamanaka Jin , Kido Takashi
Published: May 2024

After the significant performance of Large Language Models (LLMs) was revealed, their capabilities were rapidly expanded with techniques such as Retrieval Augmented Generation (RAG). Given their broad applicability and fast development, it's crucial to consider their impact on social systems. On the other hand, assessing these advanced LLMs poses challenges due to their extensive capabilities and the complex nature of social systems.

In this study, we pay attention to the similarity between LLMs in social systems and humanoid robots in open environments. We enumerate the essential components required for controlling humanoids in problem solving which help us explore the core capabilities of LLMs and assess the effects of any deficiencies within these components. This approach is justified because the effectiveness of humanoid systems has been thoroughly proven and acknowledged. To identify needed components for humanoids in problem-solving tasks, we create an extensive component framework for planning and controlling humanoid robots in an open environment. Then assess the impacts and risks of LLMs for each component, referencing the latest benchmarks to evaluate their current strengths and weaknesses. Following the assessment guided by our framework, we identified certain capabilities that LLMs lack and concerns in social systems.
Enhancing AI Education at an MSI: A Design-Based Research Approach
Authors: Bhattacharya Sambit , Czejdo Bogdan , Zulli Rebecca A. , Smith Adrienne A.
Published: May 2024

While students are often passionate about their chosen fields, they often have limited awareness of the profound impact of AI technologies on their professions. In order to advance efforts in building subject-relevant AI literacy among undergraduate students studying Computer Science and non-Computer Science (Criminal Justice and Forensic Science) it is imperative to engage in rigorous efforts to develop and study curricular infusion of Artificial Intelligence topics. Using a Design-Based Research model, the project team and the external evaluators studied the first iteration of the module development and implementation. Using data collected through surveys, focus groups, critical review, and reflection exercises the external evaluation team produced findings that informed the project team in revising and improving their materials and approach for the second iteration. These efforts can help educators and the AI module developers tailor their AI curriculum to address these specific areas, ensuring that students develop a more accurate understanding of applications of AI in their future career field.
Leveraging Generative Artificial Intelligence to Broaden Participation in Computer Science
Authors: Jayachandran Devang , Maldikar Pranit , Love Tyler S. , Blum Jeremy J.
Published: May 2024

Generative Artificial Intelligence (AI) was incorporated into a competitive programming event that targeted undergraduate students, including those with little programming experience. The competition incorporated a range of challenge design approaches that promoted meaningful interaction with generative AI system, even while keeping the challenge difficulty level to an appropriate level. An analysis of survey responses and competition data showed that this format lowered barriers to participation, successfully engaged students throughout the competition, and increased the likelihood that they would participate in a similar event. In an extension of this work, a professional development workshop for high school teachers is being developed, along with a contest for high school students. Participant surveys and logs of interaction with the contest and generative AI systems will be analyzed to measure the effect of generative AI on student self-efficacy and suggest ways to integrate generative AI instruction into computer science curriculum.
Modeling Human-Like Acquisition of Language and Concepts
Authors: Lindes Peter , Jones Steven
Published: May 2024

Humans acquire language and related concepts in a trajectory over a lifetime. Concepts for simple interaction with the world are learned before language. Later, words are learned to name these concepts along with structures needed to represent larger meanings. Eventually, language advances to where it can drive the learning of new concepts. Throughout this trajectory a language processing capability uses architectural mechanisms to process language using the knowledge already acquired. We assume that this growing body of knowledge is made up of small units of form-meaning mapping that can be composed in many ways, suggesting that these units are learned incrementally from experience. In prior work we have built a system to comprehend human language within an autonomous robot using knowledge in such units developed by hand. Here we propose a research program to develop the ability of an artificial agent to acquire this knowledge incrementally and autonomously from its experience in a similar trajectory. We then propose a strategy for evaluating this human-like learning system using a large benchmark created as a tool for training deep learning systems. We expect that our human-like learning system will produce better task performance from training on only a small subset of this benchmark.
Teaching Functions with Gaussian Process Regression
Authors: Malaviya Maya , Ho Mark K.
Published: May 2024

Humans are remarkably adaptive instructors who adjust advice based on their estimations about a learner’s prior knowledge and current goals. Many topics that people teach, like goal-directed behaviors, causal systems, categorization, and time-series patterns, have an underlying commonality: they map inputs to outputs through an unknown function. This project builds upon a Gaussian process (GP) regression model that describes learner behavior as they search the hypothesis space of possible underlying functions to find the one that best fits their current data. We extend this work by implementing a teacher model that reasons about a learner’s GP regression in order to provide specific information that will help them form an accurate estimation of the function.
Task-driven Risk-bounded Hierarchical Reinforcement Learning Based on Iterative Refinement
Authors: Parimi Viraj , Hong Sungkweon , Williams Brian
Published: May 2024

Deep Reinforcement Learning (DRL) has garnered substantial acclaim for its versatility and widespread applications across diverse domains. Aligned with human-like learning, DRL is grounded in the fundamental principle of learning from interaction, wherein agents dynamically adjust behavior based on environmental feedback in the form of rewards. This iterative trial-and-error process, mirroring human learning, underscores the importance of observation, experimentation, and feedback in shaping understanding and behavior. DRL agents, trained to navigate complex surroundings, refine their knowledge through hierarchical and abstract representations, empowered by deep neural networks. These representations enable efficient handling of long-horizon tasks and flexible adaptation to novel situations, akin to the human ability to construct mental models for comprehending complex concepts and predicting outcomes. Hence, abstract representation building emerges as a critical aspect in the learning processes of both artificial agents and human learners, particularly in long-horizon tasks.

Furthermore, human decision-making, deeply rooted in evolutionary history, exhibits a remarkable capacity to balance the tradeoff between risk and cost across various domains. This cognitive process involves assessing potential negative consequences, evaluating factors such as the likelihood of adverse outcomes, severity of potential harm, and overall uncertainty. Humans intuitively gauge inherent risks and adeptly weigh associated costs, extending beyond monetary expenses to include time, effort, and opportunity costs. The nuanced ability of humans to consider the tradeoff between risk and cost highlights the complexity and adaptability of human decision-making, a skill lacking in typical DRL agents. Principles like these derived from human-like learning present an avenue for inspiring advancements in DRL, fostering the development of more adaptive and intelligent artificial agents.

Motivated by these observations and focusing on practical challenges in robotics, our efforts target risk-aware stochastic sequential decision-making problem which is crucial for tasks with extended time frames and varied strategies. A novel integration of model-based conditional planning with DRL is proposed, inspired by hierarchical techniques. This approach breaks down complex tasks into manageable subtasks(motion primitives), ensuring safety constraints and informed decision-making. Unlike existing methods, our approach addresses motion primitive improvement iteratively, employing diverse prioritization functions to guide the search process effectively. This risk-bounded planning algorithm seamlessly integrates conditional planning and motion primitive learning, prioritizing computational efforts for enhanced efficiency within specified time limits.
A Model of Cognizing Supporting the Origination of Cognizing in Nature
Authors: Pogossian Edward M.
Published: May 2024

Our model of cognizing roots in developmental psychology by Jean Piaget, follows researchers in modeling cognizing by solvers of combinatorial games, enriches object–oriented representatives of realities by input classifiers and relationships in English, while tends to be consistent with questioning the origination of cognizing in nature.
Let us introduce the basics of the model, provide arguments for its adequacy, followed by those supporting the origination of cognizing.
Exploring the Gap: The Challenge of Achieving Human-like Generalization for Concept-based Translation Instruction Using Large Language Models
Authors: Qian Ming , Kong Chuiqing
Published: May 2024

Our study utilizes concept description instructions and few-shot learning examples to examine the effectiveness of a large language model (GPT-4) in generating Chinese-to-English translations that embody related translation concepts. We discovered that human language experts possess superior abductive reasoning skills compared to GPT-4. Therefore, it is crucial for humans to employ abductive reasoning to craft more detailed instructions and infuse additional logic into exemplary prompts, a step essential for guiding a large language model effectively, in contrast to the more intuitive understanding a human expert might have. This approach would make the prompt engineering process more complicated and less human-like. Emphasizing domain-specific abductive reasoning stands out as a crucial aspect of human-like learning that AI/ML systems based on large language models should aim to replicate.
Human-Like Learning of Social Reasoning via Analogy
Authors: Rabkina Irina
Published: May 2024

Neurotypical adult humans are impeccably good social reasoners. Despite the occasional faux pas, we know how to interact in most social settings and how to consider others' points of view. Young children, on the other hand, do not. Social reasoning, like many of our most important skills, is learned.

Much like human children, AI agents are not good social reasoners. While some algorithms can perform some aspects of social reasoning, we are a ways off from AI that can interact naturally and appropriately in the broad range of settings that people can. In this talk, I will argue that learning social reasoning via the same processes used by people will help AI agents reason--and interact--more like people do. Specifically, I will argue that children learn social reasoning via analogy, and that AI agents should, too. I will present evidence from cognitive modeling experiments demonstrating the former and AI experiments demonstrating the latter. I will also propose future directions for social reasoning research that both demonstrate the need for robust, human-like social reasoning in AI and test the utility of common approaches.
Turtle-like Geometry Learning: How Humans and Machines Differ in Learning Turtle Geometry
Authors: Rismanchian Sina , Doroudi Shayan , Razeghi Yasaman
Published: May 2024

While object recognition is one of the prevalent affordances of humans' perceptual systems, even human infants can prioritize a place system over the object recognition system, that is used when navigating. This ability, combined with active learning strategies can make humans fast learners of Turtle Geometry, a notion introduced about four decades ago. We contrast humans' performances and learning strategies with large visual language models (LVLMs) and as we show, LVLMs fall short of humans in solving Turtle Geometry tasks. We outline different characteristics of human-like learning in the domain of Turtle Geometry that are fundamentally unparalleled in state-of-the-art deep neural networks and can inform future research directions in the field of artificial intelligence.
Do Large Language Models Learn to Human-Like Learn?
Authors: Roberts Jesse
Published: May 2024

Human-like learning refers to the learning done in the lifetime of the individual. However, the architecture of the human brain has been developed over millennia and represents a long process of evolutionary learning which could be viewed as a form of pre-training. Large language models (LLMs), after pre-training on large amounts of data, exhibit a form of learning referred to as in-context learning (ICL). Consistent with human-like learning, LLMs are able to use ICL to perform novel tasks with few examples and to interpret the examples through the lens of their prior experience. I examine the constraints which typify human-like learning and propose that LLMs may learn to exhibit human-like learning simply by training on human generated text.
Comparing Human Behavior to an Optimal Policy for Innovation
Authors: Zhao Bonan , Vélez Natalia , Griffiths Thomas L.
Published: May 2024

Human learning does not stop at solving a single problem. Instead, we seek new challenges, define new goals, and come up with new ideas. Unlike the classic explore-exploit trade-off between known and unknown options, making new tools or generating new ideas is not about collecting data from existing unknown options, but rather about create new options out of what is currently available. We introduce a discovery game designed to study how rational agents make decisions about pursuing innovations, where discovering new ideas is a process of combining existing ideas in an open-ended compositional space. We derive optimal policies of this decision problem formalized as a Markov decision process, and compare people's behaviors to the model predictions in an online behavioral experiment. We found evidence that people both innovate rationally, guided by potential returns in this discovery game, and under- and over-explore systematically in different settings.