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

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Automatic Text Extractive Summarization Based on Text Graph Representation and Attention Matrix 19th

Automatic Text Extractive Summarization Based on Text Graph ...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Lin, Yuan-Ching Ma, Jinwen Peking Univ Sch Math Sci Dept Informat & Computat Sci Beijing 1000871 Peoples R China Peking Univ LMAM Beijing 1000871 Peoples R China

ISBN: (纸本)9789819947515;9789819947522

Automatic text summarization via representing a text as a graph has been investigated for over ten years. With the developments of attention mechanism and Transformer on natural language processing (NLP), it is possible to make a connection between the graph and attention structure for a text. In this paper, we propose an attention matrix text graph model for extractive text summarization. Specifically, an attention matrix between all the sentences of the whole text is adopted as a weighted adjacent matrix of a fully connected graph of the text where each node represents a sentence, which can be computed using the pre-training language model. The GCN is further applied to the text graph model for classifying all the nodes and finding out the salient sentences from the text to generate a summary. It is demonstrated by the experimental results on two typical datasets that our proposed model can achieve a competitive result in comparison with state-of-the-art models.

关键词： Text summarization Graph convolutional network Attention mechanism

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Boosting Definability Bipartition Computation Using SAT Witnesses 18th

Boosting Definability Bipartition Computation Using SAT Witn...

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

作者： Lagniez, Jean-Marie Marquis, Pierre Univ Artois CNRS CRIL Lens France Inst Univ France Paris France

Bipartitioning the set of variables Var (Sigma) of a propositional formula Sigma w.r.t. definability consists in pointing out a bipartition of Var (Sigma) such that S defines the variables of O (outputs) in terms of ...

ISBN: (纸本)9783031436185;9783031436192

Bipartitioning the set of variables Var (Sigma) of a propositional formula Sigma w.r.t. definability consists in pointing out a bipartition < I, O > of Var (Sigma) such that S defines the variables of O (outputs) in terms of the variables in I (inputs), i.e., for every o is an element of O, there exists a formula Phi(o) over I such that o double left right arrow Phi(o) is a logical consequence of Sigma. The existence of Phi(o) given o, I, and Sigma is a coNP-complete problem, and as such, it can be addressed in practice using a SAT solver. From a computational perspective, definability bipartitioning has been shown as a valuable pre-processing technique for model counting, a key task for a number of AI problems involving probabilities. To maximize the benefits offered by such a preprocessing, one is interested in deriving subset-minimal bipartitions in terms of input variables, i.e., definability bipartitions < I, O > such that for every i is an element of I, < I \ {i}, O boolean OR {i}> is not a definability bipartition. We show how the computation of subset-minimal bipartitions can be boosted by leveraging not only the decisions furnished by SAT solvers (as done in previous approaches), but also the SAT witnesses (models and cores) justifying those decisions.

关键词： Automated reasoning including satisfiability checking and its extensions definability propositional logic

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Importance-Based Neuron Selective Distillation for Interference Mitigation in Multilingual Neural Machine Translation 16th

Importance-Based Neuron Selective Distillation for Interfere...

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Zhang, Jiarui Huang, Heyan Hu, Yue Guo, Ping Xie, Yuqiang Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Beijing Inst Technol Southeast Acad Informat Technol Putian Peoples R China

ISBN: (纸本)9783031402913;9783031402920

Multilingual neural machine translation employs a single model to translate multiple languages, enabling efficient cross-lingual transferability through shared parameters. However, multilingual training suffers from negative language interference, especially interference with high-resource languages. Existing approaches generally use language-specific modules to distinguish heterogeneous characteristics among different languages but suffer from the parameter explosion problem. In this paper, we propose a "divide and conquer" multilingual translation training method based on the importance of neurons that can mitigate negative language interference effectively without adding additional parameters. The key technologies can be summarized as estimation, pruning, distillation, and fine-tuning. Specifically, we estimate the importance of existing pre-trained model neurons, dividing them into the important ones representing general knowledge of each language and the unimportant ones representing individual knowledge of each low-resource language. Then, we prune the pre-trained model, retaining only the important neurons, and train the pruned model supervised by the original complete model via selective distillation to compensate for some performance loss due to unstructured pruning. Finally, we restore the pruned neurons and only fine-tune them. Experimental results on several language pairs demonstrate the effectiveness of the proposed method.

关键词： Multilingual translation Importance estimation Pruning Selective knowledge distillation Negative language interference

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Multi-agent System for Multimodal Machine Learning Object Detection 18th

Multi-agent System for Multimodal Machine Learning Object De...

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18th International Conference on Hybrid artificial intelligence Systems

作者： Coelho, Eduardo Pimenta, Nuno Peixoto, Hugo Duraes, Dalila Melo-Pinto, Pedro Alves, Victor Bandeira, Lourenco Machado, Jose Novais, Paulo Univ Minho ALGORITMI Res Ctr LASI Braga Portugal Univ Tras Os Montes & Alto Douro CITAB Vila Real Portugal Schreder Hyper Carcavelos Portugal

ISBN: (纸本)9783031407246;9783031407253

Multi-agent systems have shown great promise in addressing complex problems that traditional single-agent approaches are not be able to handle. In this article, we propose a multi-agent system for the conception of a multimodal machine learning problem on edge devices. Our architecture leverages docker containers to encapsulate knowledge in the form of models and processes, enabling easy management of the system. Communication between agents is facilitated by Message Queuing Telemetry Transport, a lightweight messaging protocol ideal for Internet of Things and edge computing environments. Additionally, we highlight the significance of object detection in our proposed system, which is a crucial component of many multimodal machine learning tasks, by enabling the identification and localization of objects within diverse data modalities. In this manuscript an overall architecture description is performed, discussing the role of each agent and the communication protocol between them. The proposed system offers a general approach to multimodal machine learning problems on edge devices, demonstrating the advantages of multi-agent systems in handling complex and dynamic environments.

关键词： Multi-Agent System Multimodality Multimodal Machine Learning Object Detection

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Introducing an Atypical Loss: A Perceptual Metric Learning for Image Pairing 10th

Introducing an Atypical Loss: A Perceptual Metric Learning f...

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

作者： Dahmane, Mohamed CRIM Comp Res Inst Montreal Montreal PQ H3N 1M3 Canada

ISBN: (纸本)9783031206498;9783031206504

Recent works have shown an interest in comparing visually similar but semantically different instances. The paired Totally Looks Like (TLL) image dataset is a good example of visually similar paired images to figure out how humans compare images. In this research, we consider these more generic annotated categories to build a semantic manifold distance. We introduce an atypical triplet-loss using the inverse Kullback-Leibler divergence to model the distribution of the anchor-positive (a-p) distances. In the new redefinition of triplet-loss, the anchor-negative (a-n) loss is conditional to the a-p distance distribution which prevents the loss correction fluctuations in the plain summed triplet-loss function of absolute distances. The proposed atypical triplet-loss builds a manifold from relative distances to a "super" anchor represented by the a-p distribution. The evaluation on the paired images of the TLL dataset showed that the retrieving score from the first candidate guess (top-1) is 75% which is x 2.5 higher compared to the recall score of the baseline triplet-loss which is limited to 29%, and with a top-5 pairing score as high as 78% which represents a gain of x1.4.

关键词： Atypical loss Metric learning Visual relationship Image pairing Image perception Image retrieval

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Labelled Sequent Calculi for Conditional Logics: Conditional Excluded Middle and Conditional Modus Ponens Finally Together 1

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21st International Conference of the Italian-Association-for-artificial-intelligence (AIxIA)

作者： Olivetti, Nicola Panic, Nikola Pozzato, Gian Luca Aix Marseille Univ Univ Toulon CNRS ENSAMLSISUMR 7296 Marseille France Univ Torino Dipartimento Informat Turin Italy

ISBN: (数字)9783031271816

ISBN: (纸本)9783031271809;9783031271816

We introduce labelled sequent calculi for Conditional Logics with a selection function semantics. Conditional Logics are a sort of generalization of multimodal logics where modalities are labelled by formulas of the same language. Recently, they received a renewed attention and have found several applications in knowledge representation and artificial intelligence. In a previous work, we have considered the basic system CK and extensions with well known conditions ID, MP, CS and CEM, with the exception of those admitting both conditions CEM and MP, obtaining labelled sequent calculi called SeqS. Here we provide calculi for the whole cube of the extensions of CK generated by the above axioms, including also those with both CEM and MP: the basic idea is that of replacing the rule dealing with CEM in SeqS, which performs a label substitutions in both its premises, by a new one that avoids such a substitution and adopts a conditional formula on the right-hand side of a sequent as its principal formula. We have also implemented the proposed calculi in Prolog following the "lean" methodology, then we have tested the performances of the new prover, called CondLean2022, and compared them with those of CondLean, an implementation of SeqS, on the common systems. The performances of CondLean2022 are promising and seem to be better than those of CondLean, witnessing that the proposed calculi also provide a more efficient theorem prover for Conditional Logics.

关键词： Knowledge representation

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Tool Wear Monitoring Using Multi-sensor Time Series and Machine Learning 22nd

Tool Wear Monitoring Using Multi-sensor Time Series and Mach...

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22nd EPIA Conference on artificial intelligence (EPIA)

作者： Dreyer, Jonathan Carrino, Stefano Ghorbel, Hatem Cotofrei, Paul Univ Appl Sci & Arts Western Switzerland HES SO Haute Ecole Arc Ingn St Imier Switzerland Univ Neuchatel Informat Management Inst Neuchatel Switzerland

ISBN: (纸本)9783031490101;9783031490118

In the milling process of micro-machining, the optimization process is one of the keys to reduce production cost. By monitoring the tool wear and detecting when it is no longer acceptable, the machining process can be adjusted more accurately. This research explores four approaches using different machine learning models to predict machining tool wear during the milling process. The study is based on a dataset created with a face milling operation on stainless steel (AISI 303) round material. The machining is divided into a number of stairs and is performed with a 3 mm tungsten carbide. Three different types of sensors are used to measure the wearing process, with acoustic emission, accelerometers and axis currents. The better approach achieved a F1-score of 73% on five classes with an Extra-Trees Classifier.

关键词： Tool wear monitoring Milling machining Multi-sensors time series Machine learning

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RBNets: A Reinforcement Learning Approach for Learning Bayesian Network Structure

RBNets: A Reinforcement Learning Approach for Learning Bayes...

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

作者： Zheng, Zuowu Wang, Chao Gao, Xiaofeng Chen, Guihai Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9783031434174;9783031434181

Bayesian networks are graphical models that are capable of encoding complex statistical and causal dependencies, thereby facilitating powerful probabilistic inferences. To apply these models to real-world problems, it is first necessary to determine the Bayesian network structure, which represents the dependencies. Classic methods for this problem typically employ score-based search techniques, which are often heuristic in nature and have limited running times and performances that do not scale well for larger problems. In this paper, we propose a novel technique called RBNets, which uses deep reinforcement learning along with an exploration strategy guided by Upper Confidence Bound for learning Bayesian Network structures. RBNets solves the highest-value path problem and progressively finds better solutions. We demonstrate the efficiency and effectiveness of our approach against several state-of-the-art methods in extensive experiments using both real-world and synthetic datasets.

关键词： Bayesian network Structure learning Reinforcement learning

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Label Distribution Learning with Discriminative Instance Mapping 27th

Label Distribution Learning with Discriminative Instance Map...

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

作者： Zhang, Heng-Ru Bai, Run-Ting Tang, Wen-Tao Southwest Petr Univ Sch Comp Sci Chengdu Peoples R China

ISBN: (纸本)9783031333736;9783031333743

Label distribution learning (LDL) is an effective tool to tackle label ambiguity since it allows one instance to be associated with multiple labels in different degrees. Therefore, the more complex but informative label space makes it challenging to directly model the relationship between original features and label distributions. In this paper, an algorithm called Label Distribution Learning with Discriminative Instance Mapping (LDLDIM) is proposed to select a discriminative instance pool (DIP) to map the original features into a more discriminative space. First, we design a criterion that incorporates label information to quantify the discriminative power of each instance. Second, we select several instances with the highest discriminative ability to construct the DIP, and map the instances to the discriminative space through the DIP. By exploiting label information, this criterion enables the selected DIP to ensure that instances that are close (far away) in label space remain close (far away) in the discriminative space. Finally, multiple regressions for prediction are trained on the label distributions and the new features that are obtained by distance mapping with DIP. Experiments and comparisons on 16 datasets illustrate that our algorithm outperforms 6 state-of-the-art LDL methods in most cases.

关键词： Label distribution learning Instance mapping Discriminative ability

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PRACM: Predictive Rewards for Actor-Critic with Mixing Function in Multi-Agent Reinforcement Learning 16th

PRACM: Predictive Rewards for Actor-Critic with Mixing Funct...

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Yu, Sheng Liu, Bo Zhu, Wei Liu, Shuhong Natl Univ Def Technol Sch Informat & Commun Wuhan 430014 Peoples R China

ISBN: (纸本)9783031402913;9783031402920

Inspired by the centralised training with decentralised execution (CTDE) paradigm, the field of multi-agent reinforcement learning (MARL) has made significant progress in tackling cooperative problems with discrete action spaces. Nevertheless, many existing algorithms suffer from significant performance degradation when faced with large numbers of agents or more challenging tasks. Furthermore, some specific scenarios, such as cooperative environments with penalties, pose significant challenges to these algorithms, which often lack sufficient cooperative behavior to converge successfully. A new approach, called PRACM, based on the Actor-Critic framework is proposed in this study to address these issues. PRACM employs a monotonic mixing function to generate a global action value function, Qtot, which is used to compute the loss function for updating the critic network. To handle the discrete action space, PRACM uses Gumbel-Softmax. And to promote cooperation among agents and to adapt to cooperative environments with penalties, the predictive rewards is introduced. PRACM was evaluated against several baseline algorithms in "Cooperative Predator-Prey" and the challenging "SMAC" scenarios. The results of this study illustrate that PRACM scales well as the number of agents and task difficulty increase, and performs better in cooperative tasks with penalties, demonstrating its usefulness in promoting collaboration among agents.

关键词： Multi-agent reinforcement learning Discrete action Collaborative task Mixing function Predictive reward

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