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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Adapting YOLOv8 as a Vision-Based Animal Detection System to Facilitate Herding 18th

Adapting YOLOv8 as a Vision-Based Animal Detection System to...

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18th International Conference on Hybrid artificial intelligence Systems

作者： Riego del Castillo, Virginia Garcia Sierra, Juan Felipe Sanchez-Gonzalez, Lidia Univ Leon Dept Ingn Mecan Informat & Aerosp Leon 24071 Spain

ISBN: (纸本)9783031407246;9783031407253

In this work, the YOLOv8 model is adapted to a specific problem in order to increase its performance. Thus, a vision-based system is developed to provide perceptual information to a robot to detect animals in the environment and to be able to perform herding tasks. For this purpose, a dataset is created by selecting animal images from the public AP10K dataset, as well as sheep images acquired by a camera attached to a 4-legged robot. Three different configurations of YOLOv8 are considered: nano, medium and extra-large, trained on the COCO dataset. Its fine-tuning with the animal image dataset shows an improvement in performance achieved not only from the point of view of the robot, but also from the point of view of a drone or a person. The best results are obtained with the YOLOv8 medium configuration when it is trained with the dataset that includes images of the robot's view.

关键词： Precision Livestock Farming Computer vision 4-legged robot Animal detection

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Weak Argumentation Semantics and Unsafe Odd Cycles: Results and a Conjecture 18th

Weak Argumentation Semantics and Unsafe Odd Cycles: Results ...

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18th European Conference on Logics in artificial intelligence (JELIA)

作者： Dyrkolbotn, Sjur K. Western Norway Univ Appl Sci Dept Civil Engn Bergen Norway

ISBN: (纸本)9783031436185;9783031436192

Some semantics for argumentation, including the newly introduced weakly admissible semantics, allow us to ignore attacks from arguments that are perceived as problematic. A key intuition motivating such semantics is that arguments that indirectly attack themselves may be problematic in such a way that this is justified. In this paper, we formalise this intuition and provide a class of semantics that are weakly admissible, coincide with the stable semantics on a large class of argumentation frameworks that admit stable sets, and only ignore attacks from arguments on unsafe cycles of odd length. We also show that no member of our class of semantics coincide with the semantics that takes all subset of -maximal weakly admissible sets as extensions. However, we show that this semantics satisfies an even stronger property, if the following conjecture is true: if an argumentation framework has no non-empty weakly admissible sets, then every argument lies on an unsafe odd cycle.

关键词： Semantics

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A Feature Selection and Association Rule Approach to Identify Genes Associated with Metastasis and Low Survival in Sarcoma 18th

A Feature Selection and Association Rule Approach to Identif...

引用

18th International Conference on Hybrid artificial intelligence Systems

作者： Lourdes Linares-Barrera, M. Martinez-Ballesteros, Maria Garcia-Heredia, Jose M. Riquelme, Jose C. Univ Seville Dept Lenguajes & Sistemas Informat Seville 41012 Spain Univ Seville Dept Bioquim Vegetal Biol Mol Seville 41012 Spain

ISBN: (纸本)9783031407246;9783031407253

Sarcomas are rare mesodermal tumors of heterogeneous nature and have a higher incidence in children. The relative 5-year survival rate for patients with metastatic sarcoma is usually low. Standard treatment for sarcomas involves surgical resection, and investigating the genetic basis of these tumors through genome-wide analysis is crucial due to their rarity and late diagnosis. This work proposes a methodology that combines preprocessing, feature selection and association rule mining to identify relevant genes and significant relationships in biological data from sarcoma patients. Our study aims to identify the relationships between metastasis-associated genes and patient survival of less than 5 years. The proposed approach was applied to a sarcoma dataset containing data on gene expression, metastasis occurrence, and survival time, revealing a set of biologically relevant gene interactions associated with sarcoma metastasis and low survival rates. The combined use of these techniques can facilitate the identification of biomarkers or gene signatures associated with the disease and provide insight into the underlying biological mechanisms involved in sarcomas.

关键词： feature selection association rules gene expression sarcoma

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Future Augmentation with Self-distillation in Recommendation 1

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Liu, Chong Xie, Ruobing Liu, Xiaoyang Wang, Pinzheng Zheng, Rongqin Zhang, Lixin Li, Juntao Xia, Feng Lin, Leyu Tencent WeChat Beijing Peoples R China OPPO Shenzhen Peoples R China Soochow Univ Suzhou Peoples R China Tencent Shenzhen Peoples R China

ISBN: (数字)9783031434273

ISBN: (纸本)9783031434266;9783031434273

Sequential recommendation (SR) aims to provide appropriate items a user will click according to the user's historical behavior sequence. Conventional SR models are trained under the next item prediction task, and thus should deal with two challenges, including the data sparsity of user feedback and the variability and irregularity of user behaviors. Different from natural language sequences in NLP, user behavior sequences in recommendation are much more personalized, irregular, and unordered. Therefore, the current user preferences extracted from user historical behaviors may also have correlations with the next-k (i.e., future clicked) items besides the classical next-1 (i.e., current clicked) item to be predicted. Inspired by this phenomenon, we propose a novel Future augmentation with self-distillation in recommendation (FASRec). It considers future clicked items as augmented positive signals of the current clicks in training, which addresses both data sparsity and behavior irregularity and variability issues. To denoise these augmented future clicks, we further adopt a self-distillation module with the exponential moving average strategy, considering soft labels of self-distillation as confidence for more accurate augmentations. In experiments, FASRec achieves significant and consistent improvements on both offline and online evaluations with different base SR models, confirming its effectiveness and universality. FASRec has been deployed on a widely-used recommendation feed in Tencent. The source codes are in https://***/FASRec/FASRec.

关键词： Future augmentation Self-distillation Recommendation

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CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data Using Normalizing Flows 27th

CeFlow: A Robust and Efficient Counterfactual Explanation Fr...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Tri Dung Duong Li, Qian Xu, Guandong Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW Australia Curtin Univ Sch Elect Engn Comp & Math Sci Perth WA Australia

ISBN: (纸本)9783031333767;9783031333774

Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as instructions to guide end users on how to observe the desired results by altering samples. Although state-of-the-art counterfactual explanation methods are proposed to use variational autoencoder (VAE) to achieve promising improvements, they suffer from two major limitations: 1) the counterfactuals generation is prohibitively slow, which prevents algorithms from being deployed in interactive environments;2) the counterfactual explanation algorithms produce unstable results due to the randomness in the sampling procedure of variational autoencoder. In this work, to address the above limitations, we design a robust and efficient counterfactual explanation framework, namely CeFlow, which utilizes normalizing flows for the mixed-type of continuous and categorical features. Numerical experiments demonstrate that our technique compares favorably to state-of-the-art methods. We release our source code (https://***/tridungduong16/***) for reproducing the results.

关键词： Counterfactual explanation Normalizing flow Interpretable machine learning

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Removing Ambiguity in Natural Language for Generating Self-Join Queries 22nd

Removing Ambiguity in Natural Language for Generating Self-J...

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22nd International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Sawant, Pradnya Sonawane, Kavita St Francis Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9783031425073;9783031425080

Databases systems are used almost everywhere in modern life and a prior knowledge of query languages like SQL is required to interact with these systems. Generally, relational databases are very useful for storing a significant amount of the world's data. But the user must know a query language to access this data efficiently. Nowadays, Natural Language Processing is used in almost every field of human-to-machine interaction. Mapping natural language into the form of structured query Language (SQL) has many benefits. An interface that uses natural language processing allows the user to interact with the relational database as if the communication is happening between human beings. There are different models available for mapping the natural language to SQL addressing various types of SQL queries. But some of the SQL queries like self join where the table needs to join with itself for generating the results, can not be handled by these existing state-of-theart models as they require some common sense knowledge. And because of these ambiguities in natural language, converting natural language to SQL becomes a complex task. In this paper, a novel approach using word sense disambiguation, for understanding the context of the natural language question addressing self-join queries is being proposed. This proposed approach can be integrated with existing text to SQL models to understand the natural language questions which require common sense knowledge.

关键词： Word Sense Disambiguation Self-Join Synset Glosses

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From Fragmented Data to Collective intelligence: A Data Fabric Approach for University Knowledge Management 15th

From Fragmented Data to Collective Intelligence: A Data Fabr...

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15th International Conference on Computational Collective intelligence (ICCCI)

作者： Nguyen, Lan T. K. Nguyen, Hoa N. Nguyen, Son H. VNU Univ Social Sci & Human Hanoi Vietnam VNU Univ Engn & Technol Hanoi Vietnam VNU Digital Knowledge Hanoi Vietnam

ISBN: (纸本)9783031414558;9783031414565

Liberal education-especially in universities-requires knowledge management. It soon becomes a trend, improving corporate performance and employee retention. In nine institutions and hundreds of departments and majors, Vietnam National University in Hanoi has around 2,500 researchers. Thus, knowledge management manages massive amounts of fragmented data and knowledge from many departments, disciplines, multidisciplines, geographies, and unevenly distributed systems. We propose a data fabric strategy for managing university knowledge by integrating and harmonizing fragmented data across systems and sources. Data-driven decision making promotes collective intelligence by creating a unified and complete picture of institutional knowledge. The data fabric technique powers vDFKM, a knowledge management platform that goes beyond metadata and the knowledge graph paradigm in integrating, exploiting, and reusing knowledge. The paradigm includes KM-based Applications, KM Coordinator, and Runtime and Data Infrastructure. A VNU Digital Knowledge Hub (VNU-DKH) prototype promotes the vDFKM technique. Initial experimental findings show that VNU-DKH can model knowledge representation of learning resources more efficiently and improve knowledge integration across universities.

关键词： Data Fabric Knowledge Management Knowledge Integration Knowledge Graph Active Metadata

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Utilization of Vision Transformer for Classification and Ranking of Video Distortions 10th

Utilization of Vision Transformer for Classification and Ran...

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10th IAPR TC3 workshop on artificial Neural Networks for Pattern Recognition (ANNPR)

作者： AlDahoul, Nouar Karim, Hezerul Abdul Tan, Myles Joshua Toledo Multimedia Univ Fac Engn Cyberjaya Malaysia Univ St La Salle Dept Nat Sci Bacolod Philippines Univ St La Salle Dept Chem Engn Bacolod Philippines Univ St La Salle Dept Elect Engn Bacolod Philippines

ISBN: (纸本)9783031206498;9783031206504

The perceptual quality of video surveillance footage has impacts on several tasks involved in the surveillance process, such as the detection of anomalous objects. The videos captured by a camera are prone to various distortions such as noise, smoke, haze, low or uneven illumination, blur, rain, and compression, which affect visual quality. Automatic identification of distortions is important when enhancing video quality. Video quality assessment involves two stages: (1) classification of distortions affecting the video frames and (2) ranking of these distortions. A novel video dataset was utilized for training, validating, and testing. Working with this dataset was challenging because it included nine categories of distortions and four levels of severity. The greatest challenge was the availability of multiple types of distortions in the same video. The work presented in this paper addresses the problem of multi-label distortion classification and ranking. A vision transformer was used for feature learning. The experiment showed that the proposed solution performed well in terms of F1 score of single distortion (77.9%) and F1 score of single and multiple distortions (69.9%). Moreover, the average accuracy of level classification was 62% with an average F1 score of 61%.

关键词： Distortion classification and ranking Multi-label classification Video quality assessment Vision transformer

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kNN-Embed: Locally Smoothed Embedding Mixtures for Multi-interest Candidate Retrieval 27th

kNN-Embed: Locally Smoothed Embedding Mixtures for Multi-int...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： El-Kishky, Ahmed Markovich, Thomas Leung, Kenny Portman, Frank Haghighi, Aria Xiao, Ying Twitter Cortex San Francisco CA 94103 USA

ISBN: (纸本)9783031333798;9783031333804

Candidate retrieval is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate items are then ranked and pruned in later stages of recommender systems using a more complex ranking model. As the top of the recommendation funnel, it is important to retrieve a high-recall candidate set to feed into downstream ranking models. A common approach is to leverage approximate nearest neighbor (ANN) search from a single dense query embedding;however, this approach this can yield a low-diversity result set with many near duplicates. As users often have multiple interests, candidate retrieval should ideally return a diverse set of candidates reflective of the user's multiple interests. To this end, we introduce kNN-Embed, a general approach to improving diversity in dense ANN-based retrieval. kNN-Embed represents each user as a smoothed mixture over learned item clusters that represent distinct "interests" of the user. By querying each of a user's mixture component in proportion to their mixture weights, we retrieve a high-diversity set of candidates reflecting elements from each of a user's interests. We experimentally compare kNN-Embed to standard ANN candidate retrieval, and show significant improvements in overall recall and improved diversity across three datasets. Accompanying this work, we open source a large Twitter follow-graph dataset (https://***/datasets/Twitter/TwitterFollowGraph), to spur further research in graph-mining and representation learning for recommender systems.

关键词： candidate retrieval embedding nearest neighbor, diversity

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Choose Your Colour: Tree Interpolation for Quantified Formulas in SMT 1

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29th International Conference on Automated Deduction (CADE)

作者： Henkel, Elisabeth Hoenicke, Jochen Schindler, Tanja Univ Freiburg Freiburg Germany Certora Tel Aviv Israel Univ Liege Liege Belgium

ISBN: (数字)9783031384998

ISBN: (纸本)9783031384998;9783031384981

We present a generic tree-interpolation algorithm in the SMT context with quantifiers. The algorithm takes a proof of unsatisfiability using resolution and quantifier instantiation and computes interpolants (which may contain quantifiers). Arbitrary SMT theories are supported, as long as each theory itself supports tree interpolation for its lemmas. In particular, we show this for the theory combination of equality with uninterpreted functions and linear arithmetic. The interpolants can be tweaked by virtually assigning each literal in the proof to interpolation partitions (colouring the literals) in arbitrary ways. The algorithm is implemented in SMTInterpol.

关键词： Tree Interpolation Quantified Formulas SMT

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