bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Antifouling Effectiveness of the Novel Organic Coating for Adherent Species in the Seawater of the coastal area of Ha Long City, Quang Ninh Province (Vietnam)
Authors: Nguyen Xuan Thai , Dao Phi Hung , Nguyen Thuy Chinh , Nguyen Anh Hiep , Pham Minh Quan , Do Huu Nghi , Do Cong Thung , Nguyen Van Quan , Thai Hoang
Published: May 2024

This paper presents the results of a 12-month testing period in seawater at Vung Oan, Ha Long City marine area, Quang Ninh Province (Vietnam), assessing the fouling organism’s removal effectiveness of polysiloxane (PS)/Ag-Zn zeolite/Cu2O nanocomposite coatings. This experiment is a substantial base to evaluate impact of Ag-Zn/zeolite and Cu2O nanoparticles (NPs) on growth of bacteria and macro-fouling organisms compared to a control coating without the above additives. The results indicated that the coating loaded with Ag-Zn/zeolite and Cu2O NPs exhibited a lower bacterial count than the coating without biocide additives. Specifically, the bacterial count was 9.6 × 105 for the coating with biocide and 2 × 107 for the coating without biocide. Regarding macro-fouling species, the analysis and identification of organisms attached to the coating samples revealed the presence of three fouling species: Perna viridis, Balanus amphitrite, and Haliclona cinerea. Modiolus barbatus, Nereis sp., and Xanthidae were only observed on surface of the control samples, suggesting that Ag-Zn/zeolite and Cu2O NPs can impede the development of Modiolus barbatus, Nereis sp., and Xanthidae on the coating surface. Furthermore, the average weight of macro-fouling organisms on the coating containing Ag-Zn/zeolite and Cu2O NPs was significantly lower (90 g/sample) than that of the macro-fouling organisms on the control coating (333 g/sample). Notably, one of the three samples with the Ag-Zn/zeolite and Cu2O NPs had the lowest weight of macro-fouling organisms, measuring only 35 g. Based on these findings, it can be concluded that polysiloxane/Ag-Zn/zeolite/Cu2O nanocomposite coatings show promise as antifouling paints for marine work applications.
Status and impacts to sea turtles in Vietnam
Authors: Nguyen Dang Ngai , Pham Van Chien
Published: May 2024

Sea turtles face a great danger of extinction because of human impacts, and the number of turtle occurrence is decreasing. By interviewing fishermen, coastal residents and authorities combined with field surveys in some places where turtles regularly lay eggs, such as Con Dao and Ninh Thuan, the results show that 05 species of sea turtles were found in Vietnam: The Green (Chelonia mydas), Hawksbill (Eretmochelys imbricata), Olive Ridley (Lepidochelys olivacea), Loggerhead (Caretta caretta) and Leatherback (Dermochelys coriacea). Among them, Green turtles are the most abundant and distributed in all surveyed localities; Hawksbill and Olive Ridley were smaller in number; and Loggerhead and Leatherback were the two species with the least number. Only 03 species remaining to breed on the beaches: Green, Olive Ridley, and Leatherback. The Hawksbill turtles used to lay eggs in the past but have been no longer recorded. Con Dao is a home for the largest number of breeding Green turtles. The reasons for the decline of turtles in Vietnam were the collection of eggs and nesting turtles, fisheries bycatch, development of coastal infrastructure and waste pollution, habitat degradation, and illegal trade in sea turtle products.
Antimicrobial secondary metabolites from a marine-derived fungus \(\textit{Penicillium}\) sp. OM07
Authors: Phi Thi Dao , Nguyen Thuy Linh , Vu Van Nam , Vu Thi Quyen , Nguyen Mai Anh , Vu Thi Thu Huyen , Le Thi Hong Minh , Pham Van Cuong , Doan Thi Mai Huong
Published: May 2024

Eight compounds, diketopiperazine dimer WIN 64821 (1), ergosterol peroxide (2), ergosterol (3), 3β,5α,9α-trihydroxyergosta-7,22-dien-6-one (4), 3,4-dihydroxy-6,7-dimethyl-quinolin-2-carboxylic (5), norhaman (6), dihydrocitrinin (7), and phenol A acid (8) were isolated and characterized from the culture broth of the marine-derived Penicillium sp. OM07 strain was isolated from sediment collecting at Son Cha, Hue, Vietnam. Their structures were determined by analyses of MS and NMR data. All compounds were evaluated for their antimicrobial activity against a panel of clinically significant microorganisms. Most showed high antifungal activity against Candida albicans ATCC10231 strain with MIC values ranging from 8 µg/mL to 256 µg/mL. All compounds had inhibitory activity against from one to three Gram-positive tested strains with MIC values from 64–256 µg/mL.
Continuity and Change in Islamic Religious Authority
Authors: Alatas Ismail Fajrie
Published: May 2024

This article aims to elaborate on the debate surrounding continuity and change in Islamic religious authority. The discussion begins by establishing a framework for understanding the analytical definition of authority before delving into the dimensions that experience continuity and change. Arendt defines authority as a relationship, signifying a connection to a specific discursive tradition, knowledge, and history. Additionally, it involves a relationship with others who may recognize, accept, or contest this connection. Building upon Arendt’s definition, the article deconstructs the nature of this relationship, emphasizing three constitutive elements: the notion of a past, the connection to the foundational past, and the ability to convey and enhance that model. This includes the augmentation of the foundational past and the ability to elicit obedience without resorting to coercion. What sets authority apart is its unique capacity to foster obedience through a relationship, free from coercion. To identify the constituents of authority, the article explores the question of continuity and change in a more productive manner by focusing on each of its three constitutive elements.
Voice of Resilience: Unveiling the Humanitarian Tragedy of Palestinian Women in the Lens of Umm Sa'd
Authors: Hidayah Nur , Burhani Nasih
Published: May 2024

This article aims to discuss the humanitarian crisis that Palestinian women refugees have to face in the novel Umm Sa‘d by Ghassan Kanafani. This humanitarian crisis is a result of the Israeli-Palestinian conflict since the 1948 Nakba incident, which continues to this day. This article reveals and analyzes the humanitarian crisis faced by Palestinian women in this novel using the state-ibuism approach and qualitative descriptive methods. State-ibuism is defined as the ideology of women’s actions in taking care of their family, group, or state to support male power. Kanafani’s main idea for Palestinian women to rebuild the Palestinian national narrative is a pro-natal movement using their wombs. Their wombs are positioned as a battlefield for the Israel and Palestine movements to determine the definition of their nation. This article contributes to providing an understanding of the role of Palestinian women in rebuilding the idea of a Palestinian national narrative, which makes the womb of Palestinian women a tool and a strategy for population dominance through statistical politics.
In Search for Muslim Ethics of Calvinism: Improving the Quality of Public Life through the Incorporation of Religious Narratives into the State Policies in Indonesia
Authors: Hilmy Masdar , Syafaq Hammis
Published: May 2024

This article deals with an exploration of how Islam is translated into Calvinist ethics into the state policies in order to improve the quality of public life in Indonesia by revisiting the foundation of “mild secularism” adopted by the founding fathers of this country. Along with this argument, religious ethics can be utilized in improving the quality of public life by revitalizing religious doctrines to support the betterment of public life quality. In doing so, reactivating the mechanisms of inductive rational thinking such as ijtihad or tajdīd is badly needed in order to deconstruct old-fashioned understanding of religious teachings prevalent in society regarding all issues contradictory to the betterment of public life quality such as Human Development Index (HDI), life expectancy index, health issues, and so forth. However, this huge undertaking is only possible by means of a concerted collaboration across institutions both within state structure and outside of it.
Artificial Intelligence Efforts in Addressing the Humanitarian Crisis: A Study on Rohingya Refugees
Authors: Mutaqin Enjen Zaenal , Yahya Muhamad Slamet , Fian Khafifatul
Published: May 2024

Efforts to address the humanitarian crises among Rohingya refugees, including violence against women, human trafficking, and other inequalities, have been insufficient. This paper explores how artificial intelligence (AI) can help mitigate these issues through a qualitative approach. Data was gathered via questionnaires and supported by various relevant sources, followed by thorough analysis. The findings indicate that AI can play a significant role in addressing humanitarian crises by improving mental health services, detecting potential security threats, aiding in language translation, and enhancing cybersecurity. However, the use of AI also presents challenges, such as biased or inaccurate data, threats to data security, reduced human skills, privacy concerns, and job displacement. The Qur’an encourages the use of the universe’s resources wisely, suggesting that technology, when used judiciously, aligns with Islamic teachings. Thus, AI technology can be beneficial if applied responsibly and within ethical guidelines.
Pengaruh Model Pembelajaran Knisley untuk Meningkatkan Kemampuan Pemahaman Konseptual Siswa
Authors: Icha Romadhoni Ardamevia Rizky , Farida Nur , Suwanti Vivi
Published: May 2024

This research aims to find out the impact of Knisley’s learning model to improve students’ conceptual understanding ability of circle material. Knisley’s learning model is based on well-structured knowledge to relate new concepts to students’ previous knowledge and experiences. This study is quantitative research, using quasi-experimental research methods, and the research design uses a non-equivalent control group design. In the pretest, it was obtained that tcount<ttable (1,426<2,01063) and sig. (2-tailed) > 0,05 where 0,160 > 0,05, so from the results of the pretest, it was concluded that there was no difference in conceptual understanding between the control class and the experimental class on the circle material at MTs An-Nur Bululawang. In the posttest, tcount> ttable (2.105 > 2.01063) and sig. (2-tailed) < 0.05 where 0.041 < 0.05 as a result of the post-test questions, it was determined that the control and experimental classes at MTs An-Nur Bululawang had a different conceptual grasp of the circular material. The Knisley learning paradigm can improve students' conceptual comprehension skills, according to the findings of this study, and is effective if applied in students’ mathematics learning.
Pengembangan Media Papan Hitung Untuk Meningkatkan Hasil Belajar Materi Operasi Hitung Penjumlahan Siswa Madrasah Ibtidaiyah
Authors: Fina Atifatul Husna  , Nadlir
Published: May 2024

The use of mathematics learning media in the lower class is necessary because low-class students are not yet able to think abstractly, which is a need for visualization in reals form. The purpose of this study was to improve student learning outcomes in addition to calculation operations with the help of counting board media. The development model used is the ADDIE model, with the sample being 20 students in grade 1. Data collection techniques used questionnaires, interviews, observations, tests, and documentation. The results of this validation related to the feasibility of design experts amounting to 88.33% including a very feasible category, material experts amounting to 84.17% including a feasible category, and learning experts amounting to 92.5% including a very feasible category. Then the results of the field trial obtained an N-Gain value of 0.73 which is included in the high category. So that the development of the counting board media can improve student learning outcomes in addition to operation material for MINU Unggulan Wali Songo Bojonegoro students.
The Development of Mathematics Learning Media Course Modules integrated with Riau Malay Culture on Mathematics Education Students of State Islamic University Sultan Syarif Kasim Riau
Authors: Noviarni Noviarni , Hasibuan Ismail Mulia , Nurdin Erdawati , Amelia Rizki
Published: May 2024

The outcomes of creating a media program for mathematics education students at UIN Suska Riau that incorporates Riau Malay culture are presented in this article. This study employed the Plomp paradigm of developmental research, utilizing questionnaires, interview summaries, and module performance analysis. The data analysis results indicated that the mathematics learning media module based on Riau Malay culture was classified as very valid with an average validity of 87.15%; the practicality level percentage in the small group trial was 75% (practical); following revision, the practicality level percentage in the limited group trial was 88.8% (very practical). Based on the results of the module performance that the ability of students who use the module is better than the ability of students who do not use the module, it means that the Riau Malay culture-based mathematics learning media module is declared effective with t count = 5.13 greater than t table = 1.73 where the average experimental class (85.26) is higher than the control class (78.79). This is supported by the results of interviews that show students who use the module are directed and helped greatly in preparing integrated mathematics learning media of Riau Malay culture. These results are based on the performance results of the module, which show that the ability of students who use the module is better than the ability of students who do not use it.
Analisis Persepsi Peserta Didik Tentang Aktivitas Mengajar Guru Matematika Menggunakan Metode K-Means Clustering
Authors: Kaloka Tesdiq Prigel , Pratomo Danang Yugo
Published: May 2024

Education is an important aspect of human resource development. A conducive atmosphere during the learning process is very important. This atmosphere can be assessed by students' perceptions of the teaching activities of mathematics teachers. The criteria in teaching activities are material assessment, characteristics understanding, ability to manage to learn, ability to evaluate learning, and student satisfaction. The level of student satisfaction can be determined using the results of teaching activities. In this study, we focus on determining the percentage of student satisfaction. We use K-Means Clustering to analyze the result and the Elbow Method to determine the number of clusters. For the results, we got 3 clusters with the first cluster consisting of 92 students who are quite satisfied, the second cluster with 50 satisfied students, and the third cluster having 49 students not satisfied. If we transform the result to percentage, then we have 26,18% satisfied students, 48,17% quite satisfied, and 26,65% dissatisfied.
Logistic regression in approximation of the results of gas chromatographic analysis of thermally unstable compounds
Authors:   St. Petersburg State University, Institute of Chemistry, Zenkevich Igor G. , Zambrano Daniela Morocho ,   St. Petersburg State University, Institute of Chemistry
Published: May 2024

Regularities and peculiarities of gas chromatographic analysis of thermally unstable compounds were considered on the example of mixture of the reaction products of isopropylbenzene (cumene) free-radical chlorination. The principal constituent in this mixture is (1-chloro-1-methylethyl)benzene, which has the lowest thermal stability, and is partially converted to a-methylstyrene, the only product of its thermal destruction at the chromatograph injector temperatures up to 300 °С. Nevertheless, the results of the study confirms that the gas chromatographic analysis of chloroalkylarenes is possible without their decomposition with the injector temperatures up to 200 °С, even if the analytes contain chlorine atoms at the tertiary carbon atoms and in the “benzylic” positions relative to the aromatic fragment. Similar control of thermal stability of analytes can be recommended for other samples contained potentially unstable constituents. It is shown that thermal decomposition of thermally unstable constituents of samples cannot be revealed from the results of gas chromatographic analysis with capillary columns using variations of their absolute peak areas. Such task can be solved only by using relative peak areas calculated in respect to thermally stable compounds. The dependencies of relative peak areas of unstable constituents vs. temperature (descending), as well as those of their decomposition products (ascending) are characterized by presence of two limits. Low temperature limits correspond to the real content of unstable constituents or their decomposition products is the samples, while the upper limits – to the composition of such samples at their hypothetically complete destruction. Such dependencies can be approximated by logistic regression equation if sampling into capillary columns is carried out at relatively high split ratios (approx. not less than 10 : 1). At lower split ratios the temperature dependencies of peak areas of unstable constituents and products of their transformation are strongly distorted by so-called sample’s composition discrimination effects that make impossible data approximation using logistic regression.
Determination of 2,4-dichlorophenoxyacetic acid and its metabolite in soils by gas chromatography-mass spectrometry after pre-concentration using magnetic charcoal based on rice husk
Authors:   Voronezh State University of Engineering Technologies, Sypko K. S. ,   North Caucasian Federal University, Gubin A. S. ,   Voronezh State University of Engineering Technologies, Sukhanov P. T. ,   Voronezh State University of Engineering Technologies, Kushnir A. A. ,   Voronezh State University of Engineering Technologies, Pugacheva I. N. ,   Voronezh State University of Engineering Technologies
Published: May 2024

A method for determining 2,4-dichlorophenoxyacetic acid (2,4-D) and its metabolite 2,4-dichlorophenol (2,4-DСР) in soils is proposed. 2,4-D and 2,4-DСР were extracted from the soil by an alkali solution. The analytes were recovered from the extract using a sorbent based on magnetite nanoparticles and coal obtained by burning rice husk, and desorbed by methanol. 2,4-D and 2,4-DСР in the concentrate thus obtained were converted to methyl esters and determined using the GC-MS method. The quantification limit (LOQ) of 2,4-D and 2,4-DCP were 3.0 μg/kg and 0.08 μg/kg, respectively. Leached black soil (from the border of Stavropol and Krasnodar Territories) was selected as a real object for analyzing the distribution of the herbicide "Ballerina" (ethylhexyl ester of 2,4-D) and its degradation product. A day after applying the herbicide, the concentration of 2,4-D in the surface layer of the soil was 119 μg/kg. Atmospheric precipitation has a significant effect on motion of 2,4-D along the soil profile. The most significant decrease in the concentration of 2,4-D was observed between 3 and 10 days after application of the herbicide. A month after application of the herbicide, the concentration of 2,4-D was 31, 18 and 11 μg/kg at the depths of 10, 30 and 50 cm, respectively; no 2,4-D was detected in the surface soil layer. Detectable amounts of 2,4-DСР appeared on 16-th day after application of the herbicide; 2,4-DСР degrades much slower than 2,4-D. After 1.5 months, the concentration of 2,4-DСР was 0.53, 0.45 and 0.22 μg/kg at the depths of 10, 30 and 50 cm, respectively. At the same time, 2,4-D was not detected throughout the soil profile.
Interactions in Al2O3 - graphene oxide composite: XPS study
Authors:   Institute of Physics and Technology, Ural Federal University, Kukharenko A. I. ,   M.N. Mikheev Institute of Metal Physics, Russian Academy of Sciences, Ural Branch, Cholakh S. O. ,   Institute of Physics and Technology, Ural Federal University, Kurmaev E. Z. ,   Institute of Physics and Technology, Ural Federal University,   M.N. Mikheev Institute of Metal Physics, Russian Academy of Sciences, Ural Branch, Zhidkov I. S. ,   Institute of Physics and Technology, Ural Federal University,   M.N. Mikheev Institute of Metal Physics, Russian Academy of Sciences, Ural Branch
Published: May 2024

The structure of an aluminum oxide/graphene oxide (GO) composite was studied using X-ray photoelectron spectroscopy (XPS). High-energy resolved XPS measurements of Al 2p-core level spectra revealed the formation of Al–O–C bonds, which indicated the occurrence of interfacial reactions between Al2O3 and GO in the process of composite synthesis. An increase in intensity in the near-edge region of XPS valence band (VB) spectrum was observed with increasing GO concentration, indicating possible contribution of the electronic states of carbon. Filling the electronic states on the edge of VB allows to associate GO doping with the loss of dielectric properties of the original Al2O3 compound. Addition of graphene oxide to Al2O3 ceramics changed conductive properties of the composite due to formation of new chemical bonds.
Application of cold wet road concrete mix with modified dispersed viscous bitumen for operative patch works
Authors: Kochetkov Andrey , Vasiliev Yuri , Malazonia Georgy
Published: May 2024

One of the directions of road surfaces operative patch works provision in spring period is the possibility of materials and technologies development of warehoused cold wet road concrete mix with clamp storage period is up to several years according to the technology of scientific school of professor Gornayev Nikolay Alekseevich of Saratov State Technical University.

In this article the questions of cold wet road concrete mix application with modified dispersed viscous bitumen for patch work in Kuvandyk town of Orenburg region are considered. The time to repair the pothole after its discovery was 10 minutes.

According to road workers with many years of experience, manual laying of asphalt mix with dispersed bitumen was not different in labour intensity from work with hot mix, while working conditions with cold mix were much better.

Working mixture compaction brings water from the composition to the surface of the arranged asphalt concrete layer and, further, evaporates, and particles of viscous bitumen in the mineral material shell are destroyed and at the limits give the usual film coating of mineral particles in the form of mastic, various types of asphalt concrete and other asphalt materials. Two patents have been obtained by the authors on this technology. When the repair results were examined after three years, the facility was found to be in good condition.
Human-AI Interaction in the Age of Large Language Models
Authors: Yang Diyi
Published: May 2024

Large language models (LLMs) have revolutionized the way humans interact with AI systems, transforming a wide range of fields and disciplines. In this talk, I share two distinct approaches to empowering human-AI interaction using LLMs. The first one explores how LLMstransform computational social science, and how human-AI collaboration can reduce costs and improve the efficiency of social science research. The second part looks at social skill learning via LLMs by empowering therapists and learners with LLM-empowered feedback and deliberative practices. These two works demonstrate how human-AI collaboration via LLMs can empower individuals and foster positive change. We conclude by discussing how LLMs enable collaborative intelligence by redefining the interactions between humans and AI systems.
Concept-Guided LLM Agents for Human-AI Safety Codesign
Authors: Geissler Florian , Roscher Karsten , Trapp Mario
Published: May 2024

Generative AI is increasingly important in software engineering, including safety engineering, where its use ensures that software does not cause harm to people. This also leads to high quality requirements for generative AI. Therefore, the simplistic use of Large Language Models (LLMs) alone will not meet these quality demands. It is crucial to develop more advanced and sophisticated approaches that can effectively address the complexities and safety concerns of software systems. Ultimately, humans must understand and take responsibility for the suggestions provided by generative AI to ensure system safety. To this end, we present an efficient, hybrid strategy to leverage LLMs for safety analysis and Human-AI codesign. In particular, we develop a customized LLM agent that uses elements of prompt engineering, heuristic reasoning, and retrieval-augmented generation to solve tasks associated with predefined safety concepts, in interaction with a system model graph. The reasoning is guided by a cascade of micro-decisions that help preserve structured information. We further suggest a graph verbalization which acts as an intermediate representation of the system model to facilitate LLM-graph interactions. Selected pairs of prompts and responses relevant for safety analytics illustrate our method for the use case of a simplified automated driving system.
Exploring Failure Cases in Multimodal Reasoning About Physical Dynamics
Authors: Ghaffari Sadaf , Krishnaswamy Nikhil
Published: May 2024

In this paper, we present an exploration of LLMs' abilities to problem solve with physical reasoning in situated environments. We construct a simple simulated environment and demonstrate examples of where, in a zero-shot setting, both text and multimodal LLMs display atomic world knowledge about various objects but fail to compose this knowledge in correct solutions for an object manipulation and placement task. We also use BLIP, a vision-language model trained with more sophisticated cross-modal attention, to identify cases relevant to object physical properties that that model fails to ground. Finally, we present a procedure for discovering the relevant properties of objects in the environment and propose a method to distill this knowledge back into the LLM.
Fusing Domain-Specific Content from Large Language Models into Knowledge Graphs for Enhanced Zero Shot Object State Classification
Authors: Gouidis Filippos , Papantoniou Katerina , Papoutsakis Konstantinos , Patkos Theodore , Argyros Antonis , Plexousakis Dimitris
Published: May 2024

Domain-specific knowledge can significantly contribute to addressing a wide variety of vision tasks. However, the generation of such knowledge entails considerable human labor and time costs. This study investigates the potential of Large Language Models (LLMs) in generating and providing domain-specific information through semantic embeddings. To achieve this, an LLM is integrated into a pipeline that utilizes Knowledge Graphs and pre-trained semantic vectors in the context of the Vision-based Zero-shot Object State Classification task. We thoroughly examine the behavior of the LLM through an extensive ablation study. Our findings reveal that the integration of LLM-based embeddings, in combination with general-purpose pre-trained embeddings, leads to substantial performance improvements. Drawing insights from this ablation study, we conduct a comparative analysis against competing models, thereby highlighting the state-of-the-art performance achieved by the proposed approach.
Enhancing Knowledge Graph Consistency through Open Large Language Models: A Case Study
Authors: Padia Ankur , Ferraro Francis , Finin Tim
Published: May 2024

High-quality knowledge graphs (KGs) play a crucial role in many applications. However, KGs created by automated information extraction systems can suffer from erroneous extractions or be inconsistent with provenance/source text. It is important to identify and correct such problems. In this paper, we study leveraging the emergent reasoning capabilities of large language models (LLMs) to detect inconsistencies between extracted facts and their provenance. With a focus on ``open'' LLMs that can be run and trained locally, we find that few-shot approaches can yield an absolute performance gain of 2.5-3.4% over the state-of-the-art method with only 9% of training data. We examine the LLM architectures' effect and show that Decoder-Only models underperform Encoder-Decoder approaches. We also explore how model size impacts performance and counterintuitively find that larger models do not result in consistent performance gains. Our detailed analyses suggest that while LLMs can improve KG consistency, the different LLM models learn different aspects of KG consistency and are sensitive to the number of entities involved.
A Framework for Enhancing Behavioral Science Research with Human-Guided Language Models
Authors: Scheuerman Jaelle , Acklin Dina
Published: May 2024

Many behavioral science studies result in large amounts of unstructured data sets that are costly to code and analyze, requiring multiple reviewers to agree on systematically chosen concepts and themes to categorize responses. Large language models (LLMs) have potential to support this work, demonstrating capabilities for categorizing, summarizing, and otherwise organizing unstructured data. In this paper, we consider that although LLMs have the potential to save time and resources performing coding on qualitative data, the implications for behavioral science research are not yet well understood. Model bias and inaccuracies, reliability, and lack of domain knowledge all necessitate continued human guidance. New methods and interfaces must be developed to enable behavioral science researchers to efficiently and systematically categorize unstructured data together with LLMs. We propose a framework for incorporating human feedback into an annotation workflow, leveraging interactive machine learning to provide oversight while improving a language model's predictions over time.
Advancing Ontology Alignment in the Labor Market: Combining Large Language Models with Domain Knowledge
Authors: Snijder Lucas L. , Smit Quirine T. S. , De Boer Maaike H. T.
Published: May 2024

One of the approaches to help the demand and supply problem in the labor market domain is to change from degree-based hiring to skill-based hiring. The link between occupations, degrees and skills is captured in domain ontologies such as ESCO in Europe and O*NET in the US. Several countries are also building or extending these ontologies. The alignment of the ontologies is important, as it should be clear how they all relate. Aligning two ontologies by creating a mapping between them is a tedious task to do manually, and with the rise of generative large language models like GPT-4, we explore how language models and domain knowledge can be combined in the matching of the instances in the ontologies and in finding the specific relation between the instances (mapping refinement). We specifically focus on the process of updating a mapping, but the methods could also be used to create a first-time mapping. We compare the performance of several state-of-the-art methods such as GPT-4 and fine-tuned BERT models on the mapping between ESCO and O*NET and ESCO and CompetentNL (the Dutch variant) for both ontology matching and mapping refinement. Our findings indicate that: 1) Match-BERT-GPT, an integration of BERT and GPT, performs best in ontology matching, while 2) TaSeR outperforms GPT-4, albeit marginally, in the task of mapping refinement. These results show that domain knowledge is still important in ontology alignment, especially in the updating of a mapping in our use cases in the labor domain.
Building Communication Efficient Asynchronous Peer-to-Peer Federated LLMs with Blockchain
Authors: Balija Sree Bhargavi , Nanda Amitash , Sahoo Debashis
Published: May 2024

Large language models (LLM) have gathered attention with the advent of ChatGPT. However, developing personalized LLM models faces challenges in real-world applications due to data scarcity and privacy concerns. Federated learning addresses these issues, providing collaborative training while preserving the client’s data. Although it has made significant progress, federated learning still faces ongoing challenges, such as communication efficiency, heterogeneous data, and privacy-preserving methods. This paper presents a novel, fully decentralized federated learning framework for LLMs to address these challenges. We utilize different blockchain-federated LLM (BC-FL) algorithms, effectively balancing the trade-off between latency and accuracy in a decentralized-federated learning environment. Additionally, we address the challenge of communication overhead in peer-to-peer networks by optimizing the path for weight transfer and mitigating node anomalies. We conducted experiments to evaluate memory usage and latency in server and serverless environments. Our results demonstrate a decrease in latency by 5X and a 13% increase in accuracy for serverless cases. Comparisons between synchronous and asynchronous scenarios revealed a 76% reduction in information passing time for the latter. The PageRank method is most efficient in eliminating anomalous nodes for better performance of the global federated LLM model. The code is available on GitHub (https://github.com/Sreebhargavibalijaa/Federated_finetuning_LLM-s_p2p_environment)
Is Federated Learning Still Alive in the Foundation Model Era?
Authors: Baracaldo Nathalie
Published: May 2024

Federated learning (FL) has arisen as an alternative to collecting large amounts of data in a central place to train a machine learning (ML) model. FL is privacy-friendly, allowing multiple parties to collaboratively train an ML model without exchanging or transmitting their training data. For this purpose, an aggregator iteratively coordinates the training process among parties, and parties simply share with the aggregator model updates, which contain information pertinent to the model such as neural network weights. Besides privacy, generalization has been another key driver for FL: parties who do not have enough data to train a good performing model by themselves can now engage in FL to obtain an ML model suitable for their tasks. Products and real applications in the industry and consumer space have demonstrated the power of this learning paradigm.

Recently, foundation models have taken the AI community by storm, promising to solve the shortage of labeled data. A foundation model is a powerful model that can be recycled for a variety of use cases by applying techniques such as zero-shot learning and full or parameter-efficient fine tuning. The premise is that the amount of data required to fine tune a foundation model for a new task is much smaller than fully training a traditional model from scratch. The reason why this is the case is that a good foundation model has already learned relevant general representations, and thus, adapting it to a new task only requires a minimal number of additional samples. This raises the question: Is FL still alive in the era of foundation models?

In this talk, I will address this question. I will present some use cases where FL is very much alive. In these use cases, finding a foundation model with a desired representation is difficult if not impossible. With this pragmatic point of view, I hope to shed some light into a real use case where disparate private data is available in isolation at different parties and where labels may be located at a single party that doesn’t have any other information, making it impossible for a single party to train a model on its own. Furthermore, in some vertically-partitioned scenarios, cleaning data is not an option due to privacy-related reasons and it is not clear how to apply foundation models. Finally, I will also go over a few other requirements that are often overlooked, such as unlearning of data and its implications for the lifecycle management of FL and systems based on foundation models.
Revolutionizing AI-Assisted Education with Federated Learning: A Pathway to Distributed, Privacy-Preserving, and Debiased Learning Ecosystems
Authors: Hridi Anurata Prabha , Sahay Rajeev , Hosseinalipour Seyyedali , Akram Bita
Published: May 2024

The majority of current research on the application of artificial intelligence (AI) and machine learning (ML) in science, technology, engineering, and mathematics (STEM) education relies on centralized model training architectures. Typically, this involves pooling data at a centralized location alongside an ML model training module, such as a cloud server. However, this approach necessitates transferring student data across the network, leading to privacy concerns. In this paper, we explore the application of federated learning (FL), a highly recognized distributed ML technique, within the educational ecosystem. We highlight the potential benefits FL offers to students, classrooms, and institutions. Also, we identify a range of technical, logistical, and ethical challenges that impede the sustainable implementation of FL in the education sector. Finally, we discuss a series of open research directions, focusing on nuanced aspects of FL implementation in educational contexts. These directions aim to explore and address the complexities of applying FL in varied educational settings, ensuring its deployment is technologically sound, beneficial, and equitable for all stakeholders involved.
You Can Have Your Cake and Eat It Too: Ensuring Practical Robustness and Privacy in Federated Learning
Authors: Sheybani Nojan , Koushanfar Farinaz
Published: May 2024

Inherently, federated learning (FL) robustness is very challenging to guarantee, especially when trying to maintain privacy. Compared to standard ML settings, FL's open training process allows for malicious clients to easily go under the radar. Alongside this, malicious clients can easily collude to attack the training process continuously, and without detection. FL models are also still susceptible to attacks on standard ML training procedures. This massive attack surface makes balancing the tradeoff between utility, practicality, robustness, and privacy extremely challenging. While there have been proposed defenses to attacks using popular privacy-preserving primitives, such as fully homomorphic encryption, they often face trouble balancing an all-important question that is present in all privacy-preserving systems: How much utility and practicality am I willing to give up to ensure privacy and robustness? In this work, we discuss a practical approach towards secure and robust FL and the challenges that face this field of emerging research.
Ethical Considerations of Generative AI: A Survey Exploring the Role of Decision Makers in the Loop
Authors: Parra Bautista Yohn Jairo , Theran Carlos , Aló Richard
Published: May 2024

We explore the foresighted concerns that Norbert Wiener voiced in 1960 about the potential of machines to learn and create strategies that could not be anticipated, drawing parallels to the fable "The Sorcerer's Apprentice" by Goethe. The progress in artificial intelligence (AI) has brought these worries back to the forefront, as shown by a survey AI Impacts conducted in 2022 with more than 700 machine learning researchers. This survey found a five percentage probability that advanced AI might cause "extremely adverse" outcomes, including the possibility of human extinction. Importantly, the introduction of OpenAI's ChatGPT, powered by GPT-4, has led to a surge in entrepreneurial activities, highlighting the ease of use of large language models (LLMs).AI's potential for adverse outcomes, such as military control and unregulated AI races, is explored alongside concerns about AI's role in governance, healthcare, media portrayal, and surpassing human intelligence. Given their transformative impact on content creation, the prominence of generative AI tools such as ChatGPT is noted. The societal assessment of Artificial Intelligence (AI) has grown increasingly intricate and pressing in tandem with the rapid evolution of this technology. As AI continues to advance at a swift pace, the need to comprehensively evaluate its societal implications has become more complex and urgent, necessitating a thorough examination of its potential impact on various domains such as governance, healthcare, media portrayal, and surpassing human intelligence. This assessment is crucial in addressing ethical concerns related to bias, data misuse, technical limitations, and transparency gaps, and in integrating ethical and legal principles throughout AI algorithm lifecycles to ensure alignment with societal well-being. Furthermore, the urgency of addressing the societal implications of AI is underscored by the need for healthcare workforce upskilling and ethical considerations in the era of AI-assisted medicine, emphasizing the critical importance of integrating societal well-being into the development and deployment of AI technologies. Our study entails an examination of the ethical quandaries and obstacles presented when developing methods to evaluate and predict the broader societal impacts of AI on decision-making processes involving the generating of images, videos, and textual content.
Modes of Tracking Mal-Info in Social Media with AI/ML Tools to Help Mitigate Harmful GenAI for Improved Societal Well Being
Authors: Skumanich Andy , Kim Han Kyul
Published: May 2024

A rapidly developing threat to societal well-being is from misinformation widely spread on social media. Even more concerning is ”mal-info” (malicious) which is amplified on certain social networks. Now there is an additional dimension to that threat, which is the use of Generative AI to deliberately augment the mis-info and mal-info. This paper highlights some of the ”fringe” social media channels which have a high level of mal-info as characterized by our AI/ML algorithms. We discuss various channels and focus on one in particular, ”GAB”, as representative of the potential negative impacts. We outline some of the current mal-info as an example. We capture elements, and observe the trends in time. We provide a set of AI/ML modes which can characterize the mal-info and allow for capture, tracking, and potentially for
responding or for mitigation. We highlight the concern about malicious agents using GenAI for deliberate mal-info messaging specifically to disrupt societal well being. We suggest the characterizations presented as a methodology for initiating a more deliberate and quantitative approach to address these harmful aspects of social media which would adversely impact societal well being.

The article highlights the potential for ”mal-info,” including disinfo, cyberbullying, and hate speech, to disrupt segments of society. The amplification of mal-info can result in serious real-world consequences such as mass shootings. Despite attempts to introduce moderation on major platforms like Facebook and to some extent on X/Twitter, there are now growing social networks such as Gab, Gettr, and Bitchute that offer completely unmoderated spaces. This paper presents an introduction
to these platforms and the initial results of a semiquantitative analysis of Gab’s posts. The paper examines several characterization modes using text analysis. The paper emphasizes the developing dangerous use of generative AI algorithms by Gab and other fringe platforms, highlighting the risks to societal well being. This article aims to lay the foundation for capturing, monitoring, and mitigating these risks.
The Impacts of Text-to-Image Generative AI on Creative Professionals According to Prospective Generative AI Researchers: Insights from Japan
Authors: Yin Ho Sharon Chee , Ema Arisa , Tajmel Tanja
Published: May 2024

The growing interest in Japan to implement text-to-image (T2I) generative artificial intelligence (GenAI) technologies in creative workflows has raised concern over what ethical and social implications these technologies will have on creative professionals. Our pilot study is the first to discuss what social and ethical oversights may emerge regarding such issues from prospective Japanese researchers – computer science (CS) graduate students studying in Japan. Given that these students are the primary demographic hired to work at research and development (R&D) labs at the forefront of such innovations in Japan, any social and ethical oversight on such issues may unequip them as future knowledge experts who will play a pivotal role in helping shape Japan’s policies regarding image generating AI technologies.
An Analysis Method for the Impact of GenAI Code Suggestions on Software Engineers’ Thought Processes
Authors: Yonekawa Takahiro , Yamano Hiroko , Sakata Ichiro
Published: May 2024

Interactive generative AI can be used in software programming to generate sufficient quality of code. Software developers can utilize the output code of generative AI as well as website resources from search engine results. In this research, we present a framework for defining states of programming activity and for capturing the actions of developers in a time series.
We also describe a scheme for analyzing the thought process of software developers by using a graph structure to describe state transitions. By applying these means, we showed that it is feasible to analyze the effects of changes in the development environment on programming activities.
Bridging the Gap: Diversity Initiatives in AI Education
Authors: Evans Ryan , Sinha Neelu
Published: May 2024

This position paper highlights the critical need to enhance diversity in artificial intelligence (AI) education, focusing on K-8 students. As AI increasingly shapes our societal landscape, ensuring equitable access and participation in AI-related fields is essential. However, the current AI education landscape lacks inclusivity, resulting in underrepresentation and limited opportunities for marginalized groups such as racial and ethnic minorities, women, individuals with disabilities, and those from economically disadvantaged backgrounds. The paper advocates for a comprehensive approach to address diversity gaps in AI education. This involves revising curricula to include diverse perspectives, integrating AI knowledge into core subject areas, and utilizing machine learning (ML) to enhance learning across disciplines. Educators can create inclusive learning environments by incorporating culturally relevant examples and interactive activities showcasing AI's positive impact on diverse communities.
Furthermore, promoting diversity in AI education requires investment in teacher training and resources. Educators need support to implement inclusive teaching methods, understand cultural nuances, and address implicit biases. Bridging the digital gap is also crucial, as access to technology and hands-on AI experience ensures equal opportunities for all students regardless of socioeconomic back-ground. By embracing diversity and inclusivity in AI education at the K-8 level, we can cultivate a future generation of AI professionals and informed citizens who leverage technology to address diverse community needs.
A Human-Centric Approach towards Equity and Inclusion in AI Education
Authors: Mehrotra Swati , Sinha Neelu
Published: May 2024

Artificial Intelligence (AI) has become pervasive in modern lives, with AI generative tools driving further transformation. However, a notable issue persists: the underrepresentation of females and individuals from ethnic and racial minorities in the tech industry. Despite generally positive attitudes toward technology among young students, this enthusiasm often does not extend to aspirations for careers in the field. To address this disparity, many schools in the United States are now offering computer science and AI courses at the high school level. Nevertheless, students from underrepresented groups often feel disconnected from these subjects, leading to low enrollment rates. Research underscores that students' career aspirations are solidified between the ages of 10-14 yrs, highlighting the importance of engaging them with computer science and computing skills during this formative period. Leveraging the Bourdieusian concept of social capital, this paper proposes educational interventions tailored for elementary schools. By nurturing students' technical social capital, these interventions aim to foster an inclusive ecosystem from an early age, when aspirations are taking shape. Ultimately, the goal is to enhance the accessibility of computer science education and related skills, empowering young students from underrepresented groups to pursue higher studies and careers in computer science and AI fields.