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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Schiller, Benjamin Daxenberger, Johannes Waldis, Andreas Gurevych, Iryna summetix GmbH Germany Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany Information Systems Research Lab Lucerne University of Applied Sciences and Arts Switzerland

ISBN: (纸本)9798891761643

Topic-Dependent Argument Mining (TDAM), that is extracting and classifying argument components for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large TDAM datasets are rare and recognition of argument components requires expert knowledge. The task becomes even more difficult if it also involves stance detection of retrieved arguments. In this work, we investigate the effect of TDAM dataset composition in few- and zero-shot settings. Our findings show that, while fine-tuning is mandatory to achieve acceptable model performance, using carefully composed training samples and reducing the training sample size by up to almost 90% can still yield 95% of the maximum performance. This gain is consistent across three TDAM tasks on three different datasets. We also publish a new dataset and code for future benchmarking. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Automated Verification of Equivalence Properties in Advanced Logic Programs - Bachelor Thesis

arXiv

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arXiv 2023年

作者： Heuer, Jan University of Potsdam Knowledge Processing and Information Systems Germany

With the increase in industrial applications using Answer Set Programming, the need for formal verification tools, particularly for critical applications, has also increased. During the program optimisation process, it would be desirable to have a tool which can automatically verify whether an optimised subprogram can replace the original subprogram. Formally this corresponds to the problem of verifying the strong equivalence of two programs. In order to do so, the translation tool anthem was developed. It can be used in conjunction with an automated theorem prover for classical logic to verify that two programs are strongly equivalent. With the current version of anthem, only the strong equivalence of positive programs with a restricted input language can be verified. This is a result of the translation τ∗ implemented in anthem that produces formulas in the logic of here-and-there, which coincides with classical logic only for positive programs. This thesis extends anthem in order to overcome these limitations. First, the transformation σ∗ is presented, which transforms formulas from the logic of here-and-there to classical logic. A theorem formalises how σ∗ can be used to express equivalence in the logic of here- and-there in classical logic. Second, the translation τ∗ is extended to programs containing pools. Another theorem shows how σ∗ can be combined with τ∗ to express the strong equivalence of two programs in classical logic. With σ∗ and the extended τ∗, it is possible to express the strong equivalence of logic programs containing negation, simple choices, and pools. Both the extended τ∗ and σ∗ are implemented in a new version of anthem. Several examples of logic programs containing pools, negation, and simple choice rules, which the new version of anthem can translate to classical logic, are presented. Some available theorem provers (cvc4, princess, vampire, and zipperposition) are compared for their ability to verify the strong equivalence of the different

关键词： Application programs

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ViLLM-Eval: A Comprehensive Evaluation Suite for Vietnamese Large Language Models

arXiv

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arXiv 2024年

作者： Nguyen, Trong-Hieu Le, Anh-Cuong Nguyen, Viet-Cuong Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Hanoi Viet Nam HPC SYSTEMS Inc. Tokyo Japan

The rapid advancement of large language models (LLMs) necessitates the development of new benchmarks to accurately assess their capabilities. To address this need for Vietnamese, this work aims to introduce ViLLM-Eval, the comprehensive evaluation suite designed to measure the advanced knowledge and reasoning abilities of foundation models within a Vietnamese context. ViLLM-Eval consists of multiple-choice questions and predict next word tasks spanning various difficulty levels and diverse disciplines, ranging from humanities to science and engineering. A thorough evaluation of the most advanced LLMs on ViLLM-Eval revealed that even the best performing models have significant room for improvement in understanding and responding to Vietnamese language tasks. ViLLM-Eval is believed to be instrumental in identifying key strengths and weaknesses of foundation models, ultimately promoting their development and enhancing their performance for Vietnamese users1. This paper provides a thorough overview of ViLLM-Eval as part of the Vietnamese Large Language Model shared task, held within the 10th International Workshop on Vietnamese Language and Speech processing (VLSP 2023)2 © 2024, CC BY-NC-ND.

关键词： Computational linguistics

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Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web Search

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Journal of Computer Science & Technology 2021年第5期36卷 1167-1183页

作者： Dong-Hui Yang Zhen-Yu Li Xiao-Hui Wang Kavé Salamatian Gao-Gang Xie Network Technology Research Center Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Global Energy Interconnection Research Institute Co. Ltd.Beijing 102209China LISTIC Laboratory of Computer Science SystemsInformation and Knowledge ProcessingUniversitéSavoie Mont BlancChambéry 73011France Computer Network Information Center Chinese Academy of SciencesBeijing 100190China

Internet users heavily rely on web search engines for their intended *** major revenue of search engines is advertisements(or ads).However,the search advertising suffers from *** generate fake traffic which does not reach the intended audience,and increases the cost of the ***,it is critical to detect fraud in web *** studies solve this problem through fraudster detection(especially bots)by leveraging fraudsters'unique ***,they may fail to detect new means of fraud,such as crowdsourcing fraud,since crowd workers behave in part like normal *** this end,this paper proposes an approach to detecting fraud in web search from the perspective of fraudulent *** begin by using a unique dataset of 150 million web search logs to examine the discriminating features of fraudulent ***,we model the temporal correlation of fraudulent keywords as a graph,which reveals a very well-connected community ***,we design DFW(detection of fraudulent keywords)that mines the temporal correlations between candidate fraudulent keywords and a given list of *** particular,DFW leverages several refinements to filter out non-fraudulent keywords that co-occur with seeds *** evaluation using the search logs shows that DFW achieves high fraud detection precision(99%)and accuracy(93%).A further analysis reveals several typical temporal evolution patterns of fraudulent keywords and the co-existence of both bots and crowd workers as fraudsters for web search fraud.

关键词： community structure fraud analysis fraudulent keyword detection web search

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Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets

arXiv

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arXiv 2022年

作者： Schiller, Benjamin Daxenberger, Johannes Waldis, Andreas Gurevych, Iryna Summetix GmbH Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany Information Systems Research Lab Lucerne University of Applied Sciences and Arts Switzerland

Topic-Dependent Argument Mining (TDAM), that is extracting and classifying argument components for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large TDAM datasets are rare and recognition of argument components requires expert knowledge. The task becomes even more difficult if it also involves stance detection of retrieved arguments. In this work, we investigate the effect of TDAM dataset composition in few- and zero-shot settings. Our findings show that, while fine-tuning is mandatory to achieve acceptable model performance, using carefully composed training samples and reducing the training sample size by up to almost 90% can still yield 95% of the maximum performance. This gain is consistent across three TDAM tasks on three different datasets. We also publish a new dataset1 and code2 for future benchmarking. Copyright © 2022, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Exploiting Extended Reality Technologies for Educational Microscopy 17th

Exploiting Extended Reality Technologies for Educational Mic...

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17th International Conference on Virtual Reality and Augmented Reality, EuroVR 2020

作者： Theodoropoulou, Helena G. Kiourt, Chairi Lalos, Aris S. Koutsoudis, Anestis Paxinou, Evgenia Kalles, Dimitris Pavlidis, George Industrial Systems Institute Athena-Research and Innovation Center in Information Communication and Knowledge Technologies Patra Greece Institute for Language and Speech Processing Athena-Research and Innovation Center in Information Communication and Knowledge Technologies Xanthi Greece School of Science and Technology Hellenic Open University Patra Greece

ISBN: (纸本)9783030626549

Exploiting extended reality technologies in laboratory training enhances both teaching and learning experiences. It complements the existing traditional learning/teaching methods related to science, technology, engineering, arts and mathematics. In this work, we use extended reality technologies to create an interactive learning environment with dynamic educational content. The proposed learning environment can be used by students of all levels of education, to facilitate laboratory-based understanding of scientific concepts. We introduce a low-cost and user-friendly multi-platform system for mobile devices which, when coupled with edutainment dynamics, simulation, extended reality and natural hand movements sensing technologies such as hand gestures with virtual triggers, is expected to engage users and prepare them efficiently for the actual on-site laboratory experiments. The proposed system is evaluated by a group of experts and the results are analyzed in detail, indicating the positive attitude of the evaluators towards the adoption of the proposed system in laboratory educational procedures. We conclude the paper by highlighting the capabilities of extended reality and dynamic content management in educational microscopy procedures. © 2020, Springer Nature Switzerland AG.

关键词： Laboratories

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Optimized alcohol detection in postmortem vitreous humor using magnetic needle headspace single-drop microextraction and box-behnken design

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Journal of Chromatography A 2025年 1757卷

作者： Vahid Azizi Payman Hashemi Hamed Karami Mehrdad Moradi Vida Shiri-Ghaleh Department of Analytical Chemistry Faculty of Chemistry Lorestan University Khorramabad Iran Department of Petroleum Engineering Knowledge University Erbil 44001 Iraq Department of Signal and Information Processing for Sensing Systems Institute for Bioengineering of Catalonia (IBEC) The Barcelona Institute of Science and Technology Baldiri Reixac 10-12 Barcelona 08028 Spain Legal Medicine Research Center Legal Medicine Organization Tehran Iran

An innovative method for measuring alcohol levels in postmortem samples, particularly focusing on vitreous humor, was developed. The research examined the impact of a constant magnetic field on selective solvents in the single-drop microextraction technique (SDME), utilizing gas chromatography with flame ionization detector (GC-FID). The results indicated that using ethylene glycol (EtG) as solvent, the application of a magnetic field increased the efficiency of methanol and ethanol extractions from vitreous samples by up to 23.2 and 18.5 %, respectively. This method significantly improved extraction efficiency, linear range, detection limit, and accuracy compared to other techniques, making it a valuable approach for alcohol measurement in forensic medicine. The parameters were optimized using a Box-Behnken design of experiments. The optimal conditions included a temperature of 70 °C, an extraction time of 12 min, a drop volume of 2 µL, and a salt mass of 0.06 g. The method demonstrated a linear range of 10–1500 mg/L with limits of detection (LODs) ranging from 1.30–1.90 mg/L. Additionally, an R² value of 0.999 and a relative extraction efficiency of 93.4–106.6 % were achieved. By incorporating the innovative use of a magnetic field and the SDME method, this research significantly enhanced the accuracy and sensitivity of postmortem alcohol measurements, thereby advancing diagnostic techniques in forensic toxicology.

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Interdisciplinary Data Driven Production Process Analysis for the Internet of Production

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Procedia Manufacturing 2018年 26卷 1065-1076页

作者： R. Meyes H. Tercan T. Thiele A. Krämer J. Heinisch M. Liebenberg G. Hirt Ch. Hopmann G. Lakemeyer T. Meisen S. Jeschke Institute for Information Management in Mechanical Engineering RWTH Aachen University Dennewartstr. 27 52056 Aachen Germany Institute of Metal Forming RWTH Aachen University Intzestr. 10 52056 Aachen Germany Institute of Plastics Processing RWTH Aachen University Seffenter Weg 201 52056 Aachen Germany Knowledge-Based Systems Group RWTH Aachen University Ahornstraße 55 52056 Aachen Germany

Recent developments in the industrial field are strongly influenced by requirements of the fourth industrial revolution (I4.0) for modern Cyber-Physical Production systems (CPPS) and the coherent phenomenon of industrial big data (IBD). I4.0 is characterized by a growing amount of interdisciplinary work and cross-domain exchange of methods and knowledge. Similar to the development of the Internet of Things (IoT) for the consumer market, the emergence of an Internet of Production (IoP) in the industrial field is imminent. The future vision for an IoP is based on aggregated, multi-perspective and persistent data sets that can be seamlessly and semantically integrated to allow diagnosis and prediction in domain-specific real-time. In this paper, we demonstrate an exemplary scenario of collaborative cross-domain work, in which domain-experts from largely different fields of expertise, i.e. heavy plate rolling (HPR), injection molding (IM) and machine learning (ML), generate insights through data driven process analysis in two use cases. Specifically, in the HPR use case, reinforcement-learning was utilized to support the planning phase of the process aiming to reduce manual work load and to ultimately generate process plans that serve as a foundation for a simulation to calculate process results. On the contrary, in the IM use case, supervised-learning was utilized to learn a complex and computationally demanding finite element simulation model in order to predict process results for unknown process configurations, which can be used to optimize the process planning phase. While both use cases had the overall goal to utilize ML to gain new insights about the respective process, the actual ML application was utilized with reversed purpose. Particularly, in the HPR use case, ML was used to learn the process planning in order to calculate process results while in the IM use case, ML was used to predict process results in order to improve the process planning. We facilitate

关键词： Production Technology Industrial Big Data Internet of Production Heavy Plate Rolling Injection Molding Machine Lerarning

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Combined learning processes for injection moulding based on simulation and experimental data

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AIP Conference Proceedings 2019年第1期2139卷

作者： Christian Hopmann Sabina Jeschke Tobias Meisen Thomas Thiele Hasan Tercan Martin Liebenberg Julian Heinisch Matthias Theunissen 1Institute of Plastics Processing (IKV) at RWTH Aachen University Seffenter Weg 201 D-52074 Aachen Germany 2Institute of Information Management in Mechanical Engineering (IMA) - RWTH Aachen University Dennewart- straße 27 D-52068 Aachen Germany 3Knowledge-based Systems Group - RWTH Aachen University Ahornstraße 55 D-52056 Aachen Germany

Injection moulding enables the production of complex formed high-quality plastics parts in a single production step. To achieve a high and constant product quality, an appropriate process set-up with regards to product quality and process robustness is essential. A conventional process set-up requires expensive and time consuming experiments and know-how from the machine operator. One way to overcome this challenge is to make use of machine learning methods for process set-up. These methods can model the relationship between setting parameters and quality values and thus enable the identification of optimal working points. However, the training required for accurate modelling needs experimental data from extensive experiments for each process, too. Numerical simulation can predict quality values based on setting parameters without practical experiments. Whereas trends and the general dependencies between the parameters can be predicted with a satisfying accuracy, a certain discrepancy between the prediction of the simulation and the real process cannot be excluded. A combined approach using data from injection moulding simulations as well as experimental data appears promising to overcome the detriments of the solitary use of simulation for the training of machine learning algorithms. General dependencies could be gained from simulations without practical experiments and fine-tuning could be achieved by experimental trials with a minimal *** this paper, data obtained from a 2.5 D injection moulding simulation is compared with experimental data from a plate specimen and a complex formed injection moulded part. Therefore, central composed designs of experiments are used to identify differences in the effects and interdependencies of six setting parameters on quality values like part weight and dimensions. Furthermore, differences in the absolute values and the functionality of the effects are considered. On this basis, a combined machine learning concept using si

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Open data from the perspective of a business and a linked open data infrastructure

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Kyokai Joho Imeji Zasshi/Journal of the Institute of Image information and Television Engineers 2016年第11期70卷 862-866页

作者： Morikawa, Hiroaki Knowledge Computing Systems Project Artificial Intelligence Research Center Knowledge Information Processing Laboratory Fujitsu Laboratories Ltd. Kawasaki Japan

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