bluuebunny/crossref_metadata_2025 huggingface dataset:
Biomechanical approach to dental orthopedic treatment of patients with postoperative defect of the upper jaw
Authors:   City Polyclinic No. 209 of Moscow Department of Health, Shanidze Zurab , Muslov Sergey ,   A.I. Evdokimov Moscow State Medical-Dental University of the Ministry of Health of the Russian Federation, Arutyunov Anatoly ,   Central Research Institute of Dentistry and Oral and Maxillofacial Surgery of the Ministry of Health of the Russian  Federation, Astashina Nataliya ,   Academician E.A. Wagner Perm State Medical University of Ministry of Health of Russia, Arutyunov Sergey ,   A.I. Evdokimov Moscow State Medical-Dental University of the Ministry of Health of the Russian Federation
Published: January 2024

Replacement of postoperative maxillary defect of oncological origin in patients with complete absence of teeth is a difficult task. Based on the results of mechanical tests of structural materials and computer simulation of the biomechanics of the jaw prosthesis-obturator, the authors created improved obturator designs with specified design characteristics, which ensured the effectiveness of dental orthopedic treatment of this category of patients.
In vitro wear investigation of polyether ether ketone biopolymer for total knee replacement
Authors:   Rashtreeya Vidyalaya College of Engineering, Kumar Sandeep , Rajeswara Rao K.V.S. , Yalamalle Sunil
Published: January 2024

Nowadays, the average life span of knee prosthesis used in total knee replacement is approximately 10 to 15 years. The prevention of failure for these implants has been a constant struggle for orthopaedic surgeons and engineers. The reason behind for implant failures includes wear, infection, instability and stiffness. The major causes of failure are wear and tear of prosthesis. The most common biomaterials used in total knee replacement rubbing against a mating component of ultra-high-molecular-weight polyethylene are titanium alloy Ti6AI4V and alloy CoCrMo. The main drawback is that implants are still restrained by tiny ultra-high molecular weight poly debris generated by long term friction between femoral component and polyethylene articulating surface. Hence, the attempt is made to reduce the wear rate by replacing with high wear-resisting biomaterial. This research investigated whether polyether ether ketone was the appropriate material for total knee replacement by examining wear performance against Ti6AI4V. The experiments are conducted with different loading and speeds. The results are optimized by developing the regression equation using analysis of variance.
Effect of Air Polishing on the Treatment of Peri-Implant Diseases: A Systematic Review and Meta-Analysis
Authors: Bi Jiarui , Khoshkam Vahid , Hunter Mylea , Cho Christopher , Kar Kian
Published: January 2024

Peri-implant diseases have become one of the notable biological complications of postrehabilitation with implant-supported restorations. Effective modalities for decontamination of biofilm deposits around implant surfaces are critical for resolution of the inflammation. Air polishing is one of the recommended clinical methods for treating peri-implant diseases. This systematic review assessed clinical evidence on efficacy of using air polishing technology for the management of peri-implant diseases, including peri-implant mucositis and peri-implantitis. Four electronic databases from January 1990 to December 2022 were searched to identify the relative human randomized clinical trials that applied air polishing for nonsurgical and surgical treatment of peri-implant mucositis and peri-implantitis. Twelve articles were selected. For treating peri-implant mucositis, air polishing showed a comparable effect to ultrasonic scaling in the reduction of bleeding on probing (BOP) and probing pocket depth (PPD). The nonsurgical approach of air polishing in treating peri-implantitis varied in the reduction of BOP, PPD, and clinical attachment level (CAL) in evaluated studies. Air polishing in the surgical treatment of peri-implantitis was comparable to mechanical cleaning, implantoplasty, and the use of Ti-brush, in regards to the significant reduction of BOP, PPD, and CAL, as well as the improvement of the bone level between baseline and follow-ups. The standardized mean difference with a 95% confidence interval of the studied parameters was estimated using the random effect model; however, statistical differences were not detected between air polishing and comparative modalities in the treatment of peri-implantitis. Within the limitations of this review, the application of air polishing did not result in more favorable outcomes in the treatment of peri-implant diseases compared to other modalities.
Level of Cognitive Domains Among Junior High School Students, University of Bohol, Tagbilaran City
Authors: Esto Michelle
Published: January 2024

Enhancing students' reading comprehension is a pivotal aspect of their academic development. This study focused on assessing the cognitive domain levels among Grades 7, 8, and 9 Junior High School students during the 2020-2021 academic year at the University of Bohol, with a sample size of 212 participants. Employing a descriptive survey design, a researcher-developed questionnaire served as the primary tool to gauge the cognitive domain proficiency of these students. Utilizing a random sampling technique ensured an unbiased selection of participants across the specified grades. The findings revealed that the majority of respondents exhibited a commendable level of reading comprehension. However, the study unveiled noteworthy disparities in mean scores between lower and higher-order cognitive domains among the participants. Furthermore, 52.83% (112 out of 212) of the students reported having diverse general references at home. The research employed a quantitative-descriptive-correlational method. Spearman rho analysis demonstrated a significant correlation (p < 0.05) between various levels of the cognitive domain, shedding light on the interconnected nature of these skills. These results provide crucial insights for educators and policymakers, emphasizing the necessity for targeted interventions aimed at enhancing higher-order thinking skills. Aligning with global literacy goals, this research underscores the importance of addressing cognitive disparities to foster a more comprehensive approach to reading comprehension education.
The open-access journal Biodiversity Observations: report for 2023
Authors: Underhill Les G , Navarro Rene A. , Loftie-Eaton Megan
Published: January 2024

Volume 13 of Biological Observation, published in 2023, contained 39 papers. The largest number of papers in a single volume was in the year 2016, when Volume 7 contained 103 papers.
The Effects of Chemical Element Symbols in Identifying 2D Chemical Structural Formulas
Authors:   National Kaohsiung Normal University, Kaohsiung, Taiwan, ROC, Huang Chin-Fei , Liu Chia-Ju ,   National Kaohsiung Normal University, Kaohsiung, Taiwan, ROC
Published: January 2024

This study aims to explore the effect of chemical element symbols in students’ identification of 2D chemical structural formulas. A chemical conceptual questionnaire, event-related potential experiments and interviews were administered to fifty university students in this study. The results revealed that high achieving students performed different brain activities and strategies to identify 2D figures (without chemical elements symbols inside) and 2D chemical structural formulas. However, low achieving students ignored the existence of chemical element symbols and performed similar brain activities and strategies when identifying 2D figures and chemical structural formulas. This paper discusses implications for new education.
Leadership Roles Displayed by Physical Education Teachers Working in Educational Institutions in Turkey
Authors:   University of Firat, School of Physical Education and Sport, Elazıg, Turkey, Karakaya Yunus Emre , Özdenk Çağrı ,   University of Firat, Institute of Health Sciences, Elazıg, Turkey, Karataş Özgür ,   University of Inönü, School of Physical Education and Sport, Malatya, Turkey
Published: January 2024

This study was done in order to determine the leadership roles displayed by physical education teachers working in primary and secondary education institutions in Turkey. The sample group of the study included 170 physical education teachers working in these educational institutions. So as to determine the ideas of the sample group, the scale form developed by Beycioğlu (2009) in order to determine the teachers’ perceptions and expectations related to teacher leadership was used. At the end of the study, a statistically significant relation was found in the ideas of the teachers in terms of school type variance in the “Institutional Development” dimension and age variance in the “Collaboration with Colleagues” and “Professional Development” dimensions. In all the dimensions, for gender, educational status and professional service period variances, no statistical significant relation could be found. As a result, it is determined that in order to increase leadership behaviors of the physical education teachers, measures should be taken by managers and studies about improving leadership behaviors with education supervisors’ counseling applications and inservice training for physical education teachers should be increased.
The Relations between Defence Mechanisms and Spiritual Involvement of Academic Staff Members as the Measure of Their Personal Maturity
Authors:   Jesuit University Ignatianum in Krakow, Seredyńska Anna
Published: January 2024

Researchers investigating human beings claim that everybody uses defence mechanisms. However, different people use different mechanisms. The most mature persons employ mature mechanisms, and those less mature and those in crisis situations use primitive mechanisms. The article presents a study on the relationship between the intensity of defence mechanisms and the level of spiritual involvement. Two groups of people are compared: one group consists of the members of the Christian Faculty Forum, and the other of people whose professional lives are not connected with teaching. The results obtained indicate a correlation between the involvement in one’s spiritual life and personal maturity measured with the use of mature defence mechanisms. The analysis of the results indicates that the development of spiritual life is connected with the development of personality. Involvement in spirituality and in spiritual health helps people reach maturity and deeper involvement in their pedagogical work.
Student Satisfaction Analysis with Study Programme by Satisfaction Pyramid Method
Authors:   VŠB-Technical University of Ostrava, Sadílek Tomáš
Published: January 2024

The paper describes the method of satisfaction measurement as one of the marketing techniques used for detecting student satisfaction with study programs. A method of Satisfaction Pyramid is presented, which is used for satisfaction analysis. The theoretical part of the paper describes a methodological approach to satisfaction measurement and the field part presents total satisfaction, Satisfaction Index and partial satisfaction with significant factors and the last part of the paper refers to the importance of partial factors. The aim of the article is to detect the level of student satisfaction with the most important subjects in the study programme and also to set the importance of every factor influencing total satisfaction. The student satisfaction analysis was realized at two Czech universities, which offer study programmes of adult education.
Scaling Carbon Footprinting: Challenges and Opportunities
Authors: Balaji Bharathan , Guest Geoffrey , Vunnava Venkata Sai Gargeya , Kramer Jared , Srinivasan Aravind , Taptich Michael
Published: January 2024

Rapid and continuous increase in greenhouse gas (GHG) emissions is warming our planet at unprecedented rates. Consumer products and services, including all aspects of the corresponding supply chain, contribute to more than 75% of these emissions. Attribution of GHG emissions to each product will drive awareness and change from individual consumers to large corporations that produce and own these products. However, accurate and standards-compliant accounting of carbon emissions for millions of products is challenging as it requires detailed manufacturing and supply chain data, and subject expertise in life cycle assessment (LCA). We posit that ideas from computer science and machine learning can alleviate bottlenecks in LCA, and that research contributions from this community will accelerate solutions for accurate carbon-footprint estimation as well as carbon-abatement strategies at scale. We present the principal components of an LCA study with a step-by-step walk-through. We elaborate upon the challenges to scale LCA, and identify the opportunities to innovate in this space with techniques such as information extraction, personalized recommendations, and decision-making under uncertainty.
Sustainable Inference of Remote Sensing Data by Recursive Semantic Segmentation – a Flood Extent Mapping Study
Authors: Brunschwiler Thomas , Claglüna Tobia , Muszynski Michal , Hölzer Tobias , Fraccaro Paolo , Zortea Maciel , Weiss Jonas
Published: January 2024

In times of climate change and large machine learning models with petabytes of training data, the demand for responsible AI methodologies is more pressing than ever. This is in-particular true for the vast amount of remote sensing data. Its value to explore and inform about earth processes is of paramount importance, especially in times of global warming. Thus, it is nearly ironic that such applications can be the cause of substantial green-house-gas emissions, through energy demands from compute and communication systems. Thus, this study aims at reducing the data transfer between data centers while maintaining near-real time insights from remote sensing data. A recursive inference approach is introduced consisting of three steps: i) Data pyramid preparation in the host data center (sequence of upscaled raster data). ii) The transfer of low-resolution images to the service data center, where a deep-learning model performs a semantic segmentation task, including an uncertainty estimation. Images of higher resolution are then requested and segmented in a recursive fashion, in areas of high uncertainty only. iii) Finally, the merging of the predictions at different resolutions is performed to result in the final pixel-wise segmentation at scale. The method is demonstrated on synthetic and real-world data for a flood mapping task. A U-Net encoder-decoder model is used for the semantic segmentation task, using Monte-Carlo dropout to result in the uncertainty map. The proof-of-concept demonstrated a 35-38% performance gain per transferred pixel compared to high-resolution image segmentation only. Further, we perform a scaling study to estimate the true potential of the recursive inference approach, indicating the potential to reduce a data transfer by up to 98%, considering four hierarchy levels in the data pyramid. With this study, we hope to have contributed a small but important step towards sustainable machine learning.
Generative AI and Discovery of Preferences for Single-Use Plastics Regulations
Authors: Hollauer Catharina , Garcelán Jorge , Ragam Nikhita , Vaish Tia , Isaac Asensio Omar
Published: January 2024

Given the heightened global awareness and attention to the negative externalities of plastics use, many state and local governments are considering legislation that will limit single-use plastics for consumers and retailers under extended producer responsibility laws. Considering the growing momentum of these single-use plastics regulations globally, there is a need for reliable and cost-effective measures of the public response to this rulemaking for inference and prediction. Automated computational approaches such as generative AI could enable real-time discovery of consumer preferences for regulations but have yet to see broad adoption in this domain due to concerns about evaluation costs and reliability across large-scale social data. In this study, we leveraged the zero and few-shot learning capabilities of GPT-4 to classify public sentiment towards regulations with increasing complexity in expert prompting. With a zero-shot approach, we achieved a 92% F1 score (s.d. 1%) and 91% accuracy (s.d. 1%), which resulted in three orders of magnitude lower research evaluation cost at 0.138 pennies per observation. We then use this model to analyze 5,132 tweets related to the policy process of the California SB-54 bill, which mandates user fees and limits plastic packaging. The policy study reveals a 12.4% increase in opposing public sentiment immediately after the bill was enacted with no significant changes earlier in the policy process. These findings shed light on the dynamics of public engagement with lower cost models for research evaluation. We find that public opposition to single-use plastics regulations becomes evident in social data only when a bill is effectively enacted.
Climate Resilience through AI-Driven Hurricane Damage Assessments
Authors: Singh Deepank Kumar , Hoskere Vedhus
Published: January 2024

Evolving hurricane patterns intensified by climate change are expected to exacerbate economic hardships on coastal communities. Climate resilience for these communities requires both the capability to recover rapidly from devastating storms, and the ability to develop an accurate and actionable understanding of vulnerabilities to reduce the impact of future storms. Available data from past storms can provide invaluable insight in addressing both these requirements. Post-disaster preliminary damage assessments (PDAs) are a crucial initial step toward a rapid recovery. They also provide the most accurate information on the performance of various types of dwellings after the storm. Traditional door-to-door inspection methods are time-consuming and can hinder efficient resource allocation by governments in the aftermath. To address this, researchers have proposed automated PDA frameworks, often utilizing data from satellites, combined with deep convolutional neural networks. However, before such frameworks can be adopted in practice, the accuracy and fidelity of predictions of damage level at the scale of an entire building must be comparable to human assessments. To bridge this gap, we present an innovative PDA framework that leverages Ultra-High-Resolution Aerial (UHRA) images alongside state-of-the-art transformer models for multi-class damage predictions across entire buildings. Our approach leverages vast amounts of unlabeled data to enhance accuracy of prediction and generalization capabilities. Through a series of experiments, we evaluate the influence of incorporating unlabeled data, and transformer models. By integrating UHRA images and semi-supervised transformer models, our findings indicate that this framework overcomes critical limitations associated with satellite imagery and traditional CNN models, achieving an 88% multiclass accuracy, ultimately leading to more precise, efficient, and reliable damage assessments that are a first step towards building more climate resilient societies.
Deep Learning Aerosol-Cloud Interactions from Satellite Imagery
Authors: Warburton Pierce , Shuler Kurtis , Patel Lekha
Published: January 2024

Satellite imagery can detect a wealth of ship tracks, temporary cloud trails created via cloud seeding by the emitted aerosols of large ships, a phenomenon that cannot be directly reproduced by global climate models. Ship tracks are satellite-observable examples of aerosol-cloud interactions, processes that constitute the largest uncertainty in climate forcing predictions, and when observed are also examples of Marine Cloud Brightening (MCB), a potential climate intervention strategy. Leveraging the large amount of observed ship track data to enhance understanding of aerosol-cloud interactions and the potentials of MCB is hindered by the computational infeasiblity of characterization from expensive physical models. In this paper, we focus on utilizing a cheaper physics-informed advection-diffusion surrogate to accurately emulate ship track behavior. As an indication of aerosol-cloud interaction behavior, we focus on learning the spreading behavior of ship tracks, neatly encoded in the emulator's spatio-temporal diffusion field. We train a convolutional LSTM to accurately learn the spreading behavior of simulated and satellite-masked ship tracks and discuss its potential in larger scale studies.
The Observable Mind: Enabling an Autonomous Agent Sharing Its Conscious Contents Using a Cognitive Architecture
Authors: Dong Daqi
Published: January 2024

We enable an autonomous agent sharing its artificial mind to its audiences like humans. This supports the autonomous human robot interactions relying on a cognitive architecture, LIDA, which explains and predicts how minds work and is used as the controllers of intelligent autonomous agents.
We argue that LIDA’s cognitive representations and processes may serve as the source of the mind content its agent shares out, autonomously.
We proposed a new description (sub) model into LIDA, letting its agent describing its conscious contents. Through this description, the agent’s mind is more observable so we can understand the agent’s entity and intelligence more directly. Also, this helps the agent explains its behaviors to its audiences so engage into its living society better.
We built an initial LIDA agent embedding with this description model. The agent shares its conscious content autonomously, reasonably explaining its behaviors.
Clarifying the Dialogue-Level Performance of GPT-3.5 and GPT-4 in Task-Oriented and Non-Task-Oriented Dialogue Systems
Authors: Iizuka Shinya , Mochizuki Shota , Ohashi Atsumoto , Yamashita Sanae , Guo Ao , Higashinaka Ryuichiro
Published: January 2024

Although large language models such as ChatGPT and GPT-4 have achieved superb performances in various natural language processing tasks, their dialogue performance is sometimes not very clear because the evaluation is often done on the utterance level where the quality of an utterance given context is the evaluation target. Our objective in this work is to conduct human evaluations of GPT-3.5 and GPT-4 to perform MultiWOZ and persona-based chat tasks in order to verify their dialogue-level performance in task-oriented and non-task-oriented dialogue systems. Our findings show that GPT-4 performs comparably with a carefully created rule-based system and has a significantly superior performance to other systems, including those based on GPT-3.5, in persona-based chat.
Awareness and Acceptance of Emerging Technology and Quadruped Robots in Dementia Care: A Survey Study
Authors: Morris Tyler , Wang Mengjun , Li Yan , Liu Songyan , Li Shuai , Zhao Xiaopeng
Published: January 2024

The rapid increase in the number of persons with Alzheimer’s Disease or related dementia has led many researchers to develop supplemental care to assist caregivers. One such form of care comes in the form of a quadruped robot that can interact with its environment to provide additional care. However, before such technology is fully implemented, researchers must understand how aware the public is of such technology for dementia care and how they perceive it. In this study, we surveyed 16 adults, all but one of which have been affected by dementia either directly or indirectly. We asked them questions regarding their attitude towards technology in healthcare and the quadruped robot that was demoed for them. It was found that people positively accept these robotic forms of dementia care, even if they do not have a comprehensive understanding of them. Furthermore, regarding the quadruped robot, people do perceive it positively but are not as confident in its ability to provide adequate care. They also have reservations about using robots to care for persons with dementia, mostly because of the lack of a “human touch,” and are afraid that robots might replace human caregivers altogether. From these results, researchers must do their best to not only develop the technology to be as robust as possible but keep the public informed of their research to bridge the gap between this revolutionary technology and its end users.
Towards an Ontology for Generating Behaviors for Socially Assistive Robots Helping Young Children
Authors: Yang Yuqi , Langer Allison , Howard Lauren , Marshall Peter J. , Wilson Jason R.
Published: January 2024

Socially assistive robots (SARs) have the potential to revolutionize educational experiences by providing safe, non-judgmental, and emotionally supportive environments for children's social development. The success of SARs relies on the synergy of different modalities, such as speech, gestures, and gaze, to maximize interactive experiences. This paper presents an approach for generating SAR behaviors that extend an upper ontology. The ontology may enable flexibility and scalability for adaptive behavior generation by defining key assistive intents, turn-taking, and input properties. We compare the generated behaviors with hand-coded behaviors that are validated through an experiment with young children. The results demonstrate that the automated approach covers the majority of manually developed behaviors while allowing for significant adaptations to specific circumstances. The technical framework holds the potential for broader interoperability in other assistive domains and facilitates the generation of context-dependent and socially appropriate robot behaviors.
Finding the Right Voice: Exploring the Impact of Gender Similarity and Gender-Role Congruity on the Efficacy of Automated Vehicle Explanations
Authors: Zhang Qiaoning , Yang X. Jessie , Robert Jr. Lionel P.
Published: January 2024

Automated Vehicles (AVs), acting as social robots, hold potential benefits for society. Prior research highlights how AV explanations can enhance passenger trust by clarifying the vehicle's reasoning and actions. However, an underexplored area is the impact of voice gender in AV explanations on this trust dynamic. To bridge this gap, our study, inspired by the gender-role congruity and similarity attraction theories, investigates the impacts of AV voice gender on user trust. The anticipated findings from our research are poised to play a critical role in designing AV explanations that enhance trust, thereby advancing the human-AV interaction landscape.
LabelUp: Rapid Image Labeling
Authors: Early Joseph , Kelly Eric , Scearce Jesse
Published: January 2024

MetroStar introduces "LabelUp," a transformative auto-labeling AI solution, custom-built for US government applications. Utilizing advanced transformer technology, LabelUp revolutionizes data labeling, significantly en-hancing operational AI models' efficiency for critical de-fense mechanisms. This innovative system promises over 800% workload reduction, facilitating rapid, precise la-beling with its intuitive low-code interface, featuring so-phisticated in-context-learning models. Compared to tra-ditional methods, LabelUp demonstrates staggering time and cost savings, redefining industry benchmarks. The paper further elucidates risk mitigation strategies, ensur-ing robust security and accuracy. In its prototype stage, LabelUp has shown significant potential, forecasting a breakthrough in image/video labeling processes. The white paper culminates with an invitation for a detailed government review and discussion.
A Hazard-Aware Metric for Ordinal Multi-Class Classification in Pathology
Authors: Jin David , Kapusta Ariel , Minot Patrick A. , Olson Niels H. , Rosenthal Joseph H. , Seheult Jansen N. , Stram Michelle
Published: January 2024

Artificial Intelligence (AI) for decision support and diagnosis in pathology could provide immense value to society, improving patient outcomes and alleviating workload demands on pathologists. However, this potential cannot be realized until sufficient methods for testing and evaluation of such AI systems are developed and adopted. We present a novel metric for evaluation of multi-class classification algorithms for pathology, Error Severity Index (ESI), to address the needs of pathologists and pathology lab managers in evaluating AI systems.
Mahalanobis-Aware Training for Out-of-Distribution Detection
Authors: Mclaughlin Connor , Matterer Jason , Yee Michael
Published: January 2024

While deep learning models have seen widespread success in controlled environments, there are still barriers to their adoption in open-world settings. One critical task for safe deployment is the detection of anomalous or out-of-distribution samples that may require human intervention. In this work, we present a novel loss function and recipe for training networks with improved density-based out-of-distribution sensitivity. We demonstrate the effectiveness of our method on CIFAR-10, notably reducing the false-positive rate of the relative Mahalanobis distance method on far-OOD tasks by over 50%.
On Integrating Generative Models into Cognitive Architectures for Improved Computational Sociocultural Representations
Authors: Dancy Christopher L. , Workman Deja
Published: January 2024

What might the integration of cognitive architectures and generative models mean for sociocultural representations within both systems? Beyond just integration, we see this question as paramount to understanding the potential wider impact of integrations between these two types of computational systems. Generative models, though an imperfect representation of the world and various con-texts, nonetheless may be useful as general world knowledge with careful considerations of sociocultural representations provided therein, including the represented sociocultural systems or, as we explain, genres of the Human. Thus, such an integration gives an opportunity to develop cognitive models that represent from the physiological/biological time scale to the social timescale and that more accurately represent the effects of ongoing sociocultural systems and structures on behavior. In addition, integrating these systems should prove useful to audit and test many generative models under more realistic cognitive uses and conditions. That is, we can ask what it means that people will likely be using knowledge from such models as knowledge for their own behavior and actions. We further discuss these perspectives and focus these perspectives using ongoing and potential work with (primarily) the ACT-R cognitive architecture. We also discuss issues with using generative models as a system for integration.
Exploiting Language Models as a Source of Knowledge for Cognitive Agents
Authors: Kirk James R. , Wray Robert E. , Laird John E.
Published: January 2024

Large language models (LLMs) provide capabilities far beyond sentence completion, including question answering, summarization, and natural-language inference. While many of these capabilities have potential application to cognitive systems, our research is exploiting language models as a source of task knowledge for cognitive agents, that is, agents realized via a cognitive architecture. We identify challenges and opportunities for using language models as an external knowledge source for cognitive systems and possible ways to improve the effectiveness of knowledge extraction by integrating extraction with cognitive architecture capabilities, highlighting with examples from our recent work in this area.
A Proposal for a Language Model Based Cognitive Architecture
Authors: Knowles Kobe , Witbrock Michael , Dobbie Gillian , Yogarajan Vithya
Published: January 2024

Large Language Models (LLMs) have shown impressive performance on a wide variety of tasks. However, apparent limitations hinder their performance, especially on tasks that require multiple steps of reasoning or compositionality. Arguably, the primary sources of these limitations are the decoding strategy and how the models are trained. We propose, and provide a general description of, an architecture that combines LLMs and cognitive architectures, called Language Model based Cognitive Architecture (LMCA), to overcome these limitations. We draw an analogy between this architecture and "fast" and "slow" thinking in human cognition.
Growing an Embodied Generative Cognitive Agent
Authors: Lynn Spencer K. , Loyall Bryan , Niehaus James
Published: January 2024

An evolutionary perspective on embodiment puts maintenance of physiology within a functional envelope as the brain’s base goal, with all other goals as refinements. Thus, all goals have physiological perturbation for their motivation and allostatic recovery as their signal of fulfillment. From this account, two entailments emerge. First, an object’s properties are not intrinsic to the object but a situated function of the morphology of the object and the affordances required by the goal. Second, categories do not exist without reference to some goal; they are constructed at the time of perception by blending prior conceptual knowledge to create an understanding of the perception with respect to the goal. Our thesis is that generative large language model (LLM) architectures are part of the solution to creating artificial organic-like cognitive architectures, but that LLMs as currently trained are generative only at a surface-level of behavior rather than deeper levels of cognition and, furthermore, that generative architectures must be coupled with an embodied cognitive agent architecture, which suggests both the additional levels at which generativity must operate and capabilities that the combined architecture must support.
The Grounding Problem: An Approach to the Integration of Cognitive and Generative Models
Authors: Maher Mary Lou , Ventura Dan , Magerko Brian
Published: January 2024

The integration of cognitive and neural AI paradigms is a promising direction for overcoming the limitations of current deep learning models, but how to effect this integration is an open question. We propose that the key to this challenge lies in addressing the question of grounding. We adopt a cognitive perspective on grounding, and identify five types of grounding that are relevant for AI systems. We discuss ways that grounding in both cognitive and neural AI systems can facilitate the integration of these two paradigms, illustrating with examples in the domains of computational creativity and education. Because grounding is not only a technical problem but also a social and ethical one, requiring the collaboration and participation of multiple stakeholders, prosecuting such a research program is both timely and challenging.
Cognitive Architecture toward Common Ground Sharing among Humans and Generative AIs: Trial Modeling on Model-Model Interaction in Tangram Naming Task
Authors: Morita Junya , Yui Tatsuya , Amaya Takeru , Higashinaka Ryuichiro , Takeuchi Yugo
Published: January 2024

For generative AIs to be trustworthy, establishing transparent common grounding with humans is essential. As a preparation toward human-model common grounding, this study examines the process of model-model common grounding. In this context, common ground is defined as a cognitive framework shared among agents in communication, enabling the connection of symbols exchanged between agents to the meanings inherent in each agent. This connection is facilitated by a shared cognitive framework among the agents involved. In this research, we focus on the tangram naming task (TNT) as a testbed to examine the common-ground-building process. Unlike previous models designed for this task, our approach employs generative AIs to visualize the internal processes of the model. In this task, the sender constructs a metaphorical image of an abstract figure within the model and generates a detailed description based on this image. The receiver interprets the generated description from the partner by constructing another image and reconstructing the original abstract figure. Preliminary results from the study show an improvement in task performance beyond the chance level, indicating the effect of the common cognitive framework implemented in the models. Additionally, we observed that incremental backpropagations leveraging successful communication cases for a component of the model led to a statistically significant increase in performance. These results provide valuable insights into the mechanisms of common grounding made by generative AIs, improving human communication with the evolving intelligent machines in our future society.
A Neuro-Mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization
Authors: Ororbia Alexander G. , Kelly Mary Alexandria
Published: January 2024

Over the last few years, large neural generative models, capable of synthesizing semantically rich passages of text or producing complex images, have recently emerged as a popular representation of what has come to be known as ``generative artificial intelligence'' (generative AI). Beyond opening the door to new opportunities as well as challenges for the domain of statistical machine learning, the rising popularity of generative AI brings with it interesting questions for Cognitive Science, which seeks to discover the nature of the processes that underpin minds and brains as well as to understand how such functionality might be acquired and instantianted in biological (or artificial) substrate. With this goal in mind, we argue that a promising research program lies in the crafting of cognitive architectures, a long-standing tradition of the field, cast fundamentally in terms of neuro-mimetic generative building blocks. Concretely, we discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition in terms of Hebbian adaptation operating in service of optimizing a variational free energy.
Automating Knowledge Acquisition for Content-Centric Cognitive Agents Using LLMs
Authors: Oruganti Sanjay , Nirenburg Sergei , English Jesse , McShane Marjorie
Published: January 2024

The paper describes a system that uses large language model (LLM) technology to support automatic learning of new entries in an intelligent agent’s semantic lexicon. The process is bootstrapped by an existing non-toy lexicon and a natural language generator that converts formal, ontologically-grounded representations of meaning into natural language sentences. The learning method involves a sequence of LLM requests and includes an automatic quality control step. To date, this learning method has been applied to learning multiword expressions whose meanings are equivalent to those of transitive verbs in the agent’s lexicon. The experiment demonstrates the benefits of a hybrid learning architecture that integrates knowledge-based methods and resources with both traditional data analytics and LLMs.
Leveraging Conflict to Bridge Cognitive Reasoning and Generative Algorithms
Authors: Raja Anita , Leshchenko Alisa , Kim Jihie
Published: January 2024

Autonomous agents require the ability to identify and adapt to unexpected conditions. Real-world environments are rarely stationary, making it problematic for agents operating in such environments to learn efficient policies. There is therefore a need for a general framework capable of detecting when an agent has encountered novel conditions, and determining how it should adjust its actions. In this position paper we propose a framework that couples cognitive reasoning and generative algorithms by leveraging conflict detection to adjust to unexpected dynamics. Specifically, we propose that a metacognitive conflict resolution mechanism is necessary; such a mechanism would balance the use of commonsense and deliberative reasoning to allow the agent to navigate novel conditions.
Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis
Authors: Romero Oscar J. , Zimmerman John , Steinfeld Aaron , Tomasic Anthony
Published: January 2024

This paper explores the integration of two AI subdisciplines employed in the development of artificial agents that exhibit intelligent behavior: Large Language Models (LLMs) and Cognitive Architectures (CAs). We present three integration approaches, each grounded in theoretical models and supported by preliminary empirical evidence. The modular approach, which introduces four models with varying degrees of integration, makes use of chain-of-thought prompting, and draws inspiration from augmented LLMs, the Common Model of Cognition, and the simulation theory of cognition. The agency approach, motivated by the Society of Mind theory and the LIDA cognitive architecture, proposes the formation of agent collections that interact at micro and macro cognitive levels, driven by either LLMs or symbolic components. The neuro-symbolic approach, which takes inspiration from the CLARION cognitive architecture, proposes a model where bottom-up learning extracts symbolic representations from an LLM layer and top-down guidance utilizes symbolic representations to direct prompt engineering in the LLM layer. These approaches aim to harness the strengths of both LLMs and CAs, while mitigating their weaknesses, thereby advancing the development of more robust AI systems. We discuss the tradeoffs and challenges associated with each approach.
Opportunities and Challenges in Applying Generative Methods to Exploring and Validating the Common Model of Cognition
Authors: Sibert Catherine , Steffen Sonke
Published: January 2024

Dynamic Causal Modeling (DCM), a generative method for fitting large scale functional connectivity data, has provided a method of validating the architectural network structure proposed by the Common Model of Cognition (CMC). As the CMC expands, however, different methods will be required to handle the increased model complexity. While other generative methods exist that can deal with networks containing larger numbers of modules and connections, and even investigate the plausibility of different connections, a method for comparing these alternative structures will still be needed to make strong conclusions about the connection of the CMC and its variants to the true structure underlying human cognition.
Bridging Generative Networks with the Common Model of Cognition
Authors: West Robert L. , Eckler Spencer , Conway-Smith Brendan , Turcas Nico , Tomkins-Flanagan Eilene , Kelly Mary Alexandria
Published: January 2024

This article presents a theoretical framework for adapting the Common Model of Cognition to large generative network models within the field of artificial intelligence. This can be accomplished by restructuring modules within the Common Model into shadow production systems that are peripheral to a central production system, which handles higher-level reasoning based on the shadow productions’ output. Implementing this novel structure within the Common Model allows for a seamless connection between cognitive architectures and generative neural networks.
Communication-Efficient Pseudo Value-Based Random Forests for Federated Survival Analysis
Authors: Rahman Md Mahmudur , Purushotham Sanjay
Published: January 2024

Federated Survival Analysis (FSA) is an emerging technique for analyzing decentralized survival data while preserving data privacy and providing more generalized survival predictions. Existing FSA methods often rely on deep learning models, which can be computationally expensive and require substantial data and communication rounds. Recent research has demonstrated that ensemble-based approaches like random survival forests can achieve comparable performance to deep learning models with a single communication round within a federated learning (FL) framework, especially when dealing with small, decentralized datasets. However, these approaches have yet to address the challenges specific to FSA, such as data heterogeneity, non-uniform censoring, and competing risks. To address these challenges, we propose FedPRF, an FL framework for survival analysis based on Federated Pseudo Values (FPV) based random forests model, we call it RFpseudo. FedPRF introduces FPV to handle issues related to censoring and effectively address the unique challenges of FSA. FedPRF is computationally efficient, requiring only two communication rounds across clients: (i) computing FPV and (ii) aggregating a subset of trees trained locally at clients. Extensive experiments on distributed survival data with a single event and multiple competing events demonstrate that FedPRF achieves performance close to the gold-standard centralized training setting and outperforms deep learning-based FSA approaches. Importantly, FedPRF maintains the interpretability of centrally trained survival models. Furthermore, it is scalable to large-scale data and highly distributed settings with numerous clients.
Survival Prediction via Deep Attention-Based Multiple-Instance Learning Networks with Instance Sampling
Authors: Tarkhan Aliasghar , Nguyen Trung Kien , Simon Noah , Dai Jian
Published: January 2024

Survival prediction via training deep neural networks with giga-pixel whole-slide images (WSIs) is challenging due to the lack of time annotation at the pixel level or patch (instance). Multiple instance learning (MIL), as a typical weakly supervised learning method, aims to resolve this challenge by using only the slide-level time. The attention-based MIL method leverages and enhances performance by weighting the instances based on their contribution to predicting the outcome. A WSI typically contains hundreds of thousands of image patches. Training a deep neural network with thousands of image patches per slide is computationally expensive and time-consuming. To tackle this issue, we propose an adaptive-learning strategy where we sample a subset of informative instances/patches more often to train the deep survival neural networks. We also present other sampling strategies and compare them with our proposed sampling strategy. Using both real-world and synthesized WSIs for survival, we show that sampling strategies significantly can significantly reduce computing time while result in no or negligible performance loss. We also discuss the benefits of each instance sampling strategy in different scenarios.
Forgetful Large Language Models: Lessons Learned from Using LLMs in Robot Programming
Authors: Chen Juo-Tung , Huang Chien-Ming
Published: January 2024

Large language models offer new ways of empowering people to program robot applications-namely, code generation via prompting. However, the code generated by LLMs is susceptible to errors. This work reports a preliminary exploration that empirically characterizes common errors produced by LLMs in robot programming. We categorize these errors into two phases: interpretation and execution. In this work, we focus on errors in execution and observe that they are caused by LLMs being “forgetful” of key information provided in user prompts. Based on this observation, we propose prompt engineering tactics designed to reduce errors in execution. We then demonstrate the effectiveness of these tactics with three language models: ChatGPT, Bard, and LLaMA-2. Finally, we discuss lessons learned from using LLMs in robot programming and call for the benchmarking of LLM-powered end-user development of robot applications.