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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhai, Jianyang Mai, Zi-Feng Zheng, Dongyi Wang, Chang-Dong Zheng, Xiawu Li, Hui Yang, Feidiao Tian, Yonghong Sun Yat-sen University China Pengcheng Laboratory China Guangdong Key Laboratory of Big Data Analysis and Processing China School of Informatics Xiamen University China Peking University China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have demonstrated powerful performance in sequential recommendation due to their robust language modeling and comprehension capabilities. In such paradigms, the item texts of interaction sequences are formulated as sentences, and LLMs are utilized to learn language representations or directly generate target item texts by incorporating instructions. Despite their promise, these methods solely focus on modeling the mapping from sequential texts to target items, neglecting the relationship between the items in an interaction sequence. This results in a failure to learn the transition patterns between items, which reflect the dynamic change in user preferences and are crucial for predicting the next item. To tackle this issue, we propose a novel framework for mapping the sequential item texts to the sequential item IDs, named ST2SI. Specifically, we first introduce multi-query input and item linear projection (ILP) to model the conditional probability distribution of items. Then, we further propose ID alignment to address misalignment between item texts and item IDs by instruction tuning. Finally, we propose efficient ILP tuning to adapt flexibly to different scenarios, requiring only training a linear layer to achieve competitive performance. Extensive experiments on six real-world datasets show that our approach outperforms the best baselines by 7.33% in NDCG@10, 4.65% in Recall@10, and 8.42% in MRR. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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A Fast Tongue Detection and Location Algorithm in Natural Environment

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Computers, Materials & Continua 2022年第12期73卷 4727-4742页

作者： Lei Zhu Guojiang Xin Xin Wang Changsong Ding Hao Liang Qilei Chen School of Informatics Hunan University of Chinese MedicineChangsha410208China TCM Big Data Analysis Laboratory of Hunan Changsha410208China Department of Computer Science University of Massachusetts LowellLowell01854USA

The collection and extraction of tongue images has always been an important part of intelligent tongue *** present,the collection of tongue images generally needs to be completed in a sealed,stable light environment,which is not conducive to the promotion of extensive tongue image and intelligent tongue *** response to the problem,a newalgorithm named GCYTD(GELU-CA-YOLO Tongue Detection)is proposed to quickly detect and locate the tongue in a natural environment,which can greatly reduce the restriction of the tongue image collection *** algorithm is based on the YOLO(You Only Look Once)V4-tiny network model to detect the ***,the GELU(Gaussian Error Liner Units)activation function is integrated into the model to improve the training speed and reduce the number of model parameters;then,the CA(Coordinate Attention)mechanism is integrated into the model to enhance the detection precision and improve the failure tolerance of the *** with the other classical algorithms,Experimental results show thatGCYTD algorithm has a better performance on the tongue images of all types in terms of training speed,tongue detection speed and detection precision,*** lighter model can contribute on deploying the tongue detection model on small mobile terminals.

关键词： Tongue detection YOLO V4-tiny CA mechanism GELU

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Road Segmentation with U-Net Architecture Using Jetson AGX Xavier For Autonomous Vehicle 16

Road Segmentation with U-Net Architecture Using Jetson AGX X...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Gunawan Fadillah, Muhammad Fikri Prakasa, Esa Sugiarto, Bambang Suharsono, Teguh Nurhadi Sukmana, Rini Nuraini Universitas Sangga Buana Faculty of Engineering Department of Informatics Engineering Bandung Indonesia Research Center for Data and Information Sciences Brin Image and Video Data Analysis Research Group Bandung Indonesia

ISBN: (纸本)9781665455947

Autonomous Vehicle is a technology that has been often discussed in the last few years in the category of research and industry. This technology is able to sense the surrounding environment and control the vehicle autonomously without any human intervention. In its implementation, this technology requires a lot of information, especially the road track that will be passed. Because of that, the thing that must be considered is to segment the road first. The aim of this research is to develop a method that can segment the roads to produce a model that can recognize the road track as well. This research uses Convolutional Neural Network (CNN) with U-Net architecture. The datasets have a form of car trips video recordings from the dashboard camera, which are then extracted into a frame. After this process, it is annotated or manual segmentation using Supervisely to be used as a reference for training and testing. From the results of the calculation process with the confusion matrix, the accuracy of the U-net architecture gets a value of 95%, precision value is 81%, recall value is 92%, F1-Score value is 86% IOU value is 76%. Followed by testing the model in real-time using Jetson AGX Xavier, this tool is specially designed to develop artificial intelligence with high specifications. The test is carried out with two types of testing. The first test with an RGB background produces an FPS of 0.17, and the second test without an RGB background gets an FPS in the range of 0.55-0.67. © 2022 IEEE.

关键词： Video recording

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Fuzzy Logical-Linguistic analysis of data Quality in Financial Information Systems 16

Fuzzy Logical-Linguistic Analysis of Data Quality in Financi...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Lomazov, Alexander Lomazov, Vadim Anichin, Vladislav Petrosov, David Belgorod State Agricultural University Department of Applied Informatics and Mathematics Belgorod Russia Belgorod State Agricultural University Department of Economics Belgorod Russia Financial University Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

The proposed approach to the analysis of data quality is based on the use of the apparatus of fuzzy logical-linguistic analysis by L. Zadeh. The indicators are presented in the form of linguistic variables, the semantics of which are set by experts. The constructed hierarchy of indicators makes it possible to increase the scientific validity of decisions while ensuring the quality of data in financial systems. © 2023 IEEE.

关键词： Semantics

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What's Distributive Justice Got to Do with It? Rethinking Algorithmic Fairness from the Perspective of Approximate Justice 24

What's Distributive Justice Got to Do with It? Rethinking Al...

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Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society

作者： Corinna Hertweck Christoph Heitz Michele Loi Institute for Data Analysis and Process Design Zurich University of Applied Sciences Winterthur Switzerland and Department of Informatics University of Zurich Zurich Switzerland Institute for Data Analysis and Process Design Zurich University of Applied Sciences Winterthur Switzerland AlgorithmWatch Berlin Germany

In the field of algorithmic fairness, many fairness criteria have been proposed. Oftentimes, their proposal is only accompanied by a loose link to ideas from moral philosophy - which makes it difficult to understand when the proposed criteria should be used to evaluate the fairness of a decision-making system. More recently, researchers have thus retroactively tried to tie existing fairness criteria to philosophical concepts. Group fairness criteria have typically been linked to egalitarianism, a theory of distributive justice. This makes it tempting to believe that fairness criteria mathematically represent ideals of distributive justice and this is indeed how they are typically portrayed. In this paper, we will discuss why the current approach of linking algorithmic fairness and distributive justice is too simplistic and, hence, insufficient. We argue that in the context of imperfect decision-making systems - which is what we deal with in algorithmic fairness - we should not only care about what the ideal distribution of benefits/harms among individuals would look like but also about how deviations from said ideal are distributed. Our claim is that algorithmic fairness is concerned with unfairness in these deviations. This requires us to rethink the way in which we, as algorithmic fairness researchers, view distributive justice and use fairness criteria.

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Machine Learning Models and Technology for Classification of Forest on Satellite data 20

Machine Learning Models and Technology for Classification of...

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20th International Conference on Smart Technologies, EUROCON 2023

作者： Salii, Yevhenii Kuzin, Volodymyr Hohol, Anton Kussul, Nataliia Yailymova, Hanna National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Mathematical Modeling and Data Analysis Kyiv Ukraine National University of 'Kyiv-Mohyla Academy' Department of Informatics Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Space Information Technologies and Systems Space Research Institute NASU-SSAU Department of Mathematical Modeling and Data Analysis Kyiv Ukraine

ISBN: (纸本)9781665463973

The paper deals with the problem of semantic segmentation of satellite imagery to deliver forest type map with high resolution. To solve the problem, we propose 4 machine learning models. Two of them are based on Random Forest (RF) and other two - on Convolutional Neural Network (CNN) - U-Net. As an input we use 2 images of Sentinel-2 (one for summer and one for winter, 4 spectral bands from each). As an output (labels) we use the Copernicus Forest Type dataset for 2018 year. Our models showed promising results on validation data. Of all models the one based on U-Net ended up being the most efficient in forest classification with overall accuracy 91.7%. At the same time the best RF model scored with 86.5%. After comparing the results, in order to check our model transferability we created and compared forest map of northern part of Kyiv region of 2018 and 2022. The experiment confirmed the robustness of the model and it's scalability. The developed models have been implemented in the cloud platform specialized on satellite data - CREODIAS. The developed map can provide valuable data for foresters, biologists, or other researchers to make decisions about forest management and conservation, as well as to ensure that Europe's forests are managed in an ecologically sustainable way. © 2023 IEEE.

关键词： Machine learning

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A CALIBRATION TEST FOR EVALUATING SET-BASED EPISTEMIC UNCERTAINTY REPRESENTATIONS

arXiv

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arXiv 2025年

作者： Jürgens, Mira Mortier, Thomas Hüllermeier, Eyke Bengs, Viktor Waegeman, Willem Department of Data Analysis and Mathematical Modeling Ghent University Belgium Department of Environment Ghent University Belgium Department of Informatics Munich Center for Machine Learning LMU Munich Germany

The accurate representation of epistemic uncertainty is a challenging yet essential task in machine learning. A widely used representation corresponds to convex sets of probabilistic predictors, also known as credal sets. One popular way of constructing these credal sets is via ensembling or specialized supervised learning methods, where the epistemic uncertainty can be quantified through measures such as the set size or the disagreement among members. In principle, these sets should contain the true data-generating distribution. As a necessary condition for this validity, we adopt the strongest notion of calibration as a proxy. Concretely, we propose a novel statistical test to determine whether there is a convex combination of the set’s predictions that is calibrated in distribution. In contrast to previous methods, our framework allows the convex combination to be instance dependent, recognizing that different ensemble members may be better calibrated in different regions of the input space. Moreover, we learn this combination via proper scoring rules, which inherently optimize for calibration. Building on differentiable, kernel-based estimators of calibration errors, we introduce a nonparametric testing procedure and demonstrate the benefits of capturing instance-level variability on of synthetic and real-world experiments. © 2025, CC BY.

关键词： Self-supervised learning

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Illuminating Router Vendor Diversity Within Providers and Along Network Paths

arXiv

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arXiv 2023年

作者： Albakour, Taha Gasser, Oliver Beverly, Robert Smaragdakis, Georgios TU Berlin Germany Center for Measurement and Analysis of Network Data Max Planck Institute for Informatics Germany Delft University of Technology Netherlands

The Internet architecture has facilitated a multi-party, distributed, and heterogeneous physical infrastructure where routers from different vendors connect and inter-operate via IP. Such vendor heterogeneity can have important security and policy implications. For example, a security vulnerability may be specific to a particular vendor and implementation, and thus will have a disproportionate impact on particular networks and paths if exploited. From a policy perspective, governments are now explicitly banning particular vendors—or have threatened to do so. Despite these critical issues, the composition of router vendors across the Internet remains largely opaque. Remotely identifying router vendors is challenging due to their strict security posture, indistinguishability due to code sharing across vendors, and noise due to vendor mergers. We make progress in overcoming these challenges by developing LFP, a tool that improves the coverage, accuracy, and efficiency of router fingerprinting as compared to the current state-of-the-art. We leverage LFP to characterize the degree of router vendor homogeneity within networks and the regional distribution of vendors. We then take a path-centric view and apply LFP to better understand the potential for correlated failures and fate-sharing. Finally, we perform a case study on inter- and intra-United States data paths to explore the feasibility to make vendor-based routing policy decisions, i.e., whether it is possible to avoid a particular vendor given the current infrastructure. © 2023, CC BY.

关键词： Network security

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Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System 1

Using Arduino Iot Modules As A Low Cost Environmental Resear...

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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Magableh, Murad Marie, Ziyad Mohamed, Rajina R. Bin Ibrahim, Mohd Hafizuddin Jusoh, Julaily Aida Kumar, Rohit Irbid National University Faculty of Business and Financial Science MIS BI and Data Analysis Department Irbid Jordan Al-Istiqlal University Faculty of Administrative Sciences and Informatics Public Administration Department Palestine College of Computing Dan Informatics Universiti Tenaga Nasional Malaysia Politeknik Kuala Terenngganu Department of Electrical Engineering Kuala Terengganu20200 Malaysia UniSZA Faculty of Informatics and Computing Malaysia School of Computer Science and Engineering Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

关键词： Internet of things

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