bluuebunny/crossref_metadata_2025 huggingface dataset:
Obstruction Logic: A Strategic Temporal Logic to Reason About Dynamic Game Models
Authors: Catta Davide , Leneutre Jean , Malvone Vadim
Published: September 2023

Games that are played in a dynamic model have been studied in several contexts, such as cybersecurity and planning. In this paper, we introduce a logic for reasoning about a particular class of games with temporal goals played in a dynamic model. In such games, the actions of a player can modify the game model itself. We show that the model-checking problem for our logic is decidable in polynomial-time. Then, using this logic, we show how to express interesting properties of cybersecurity games defined on attack graphs.
Deep Reinforcement Learning with Implicit Imitation for Lane-Free Autonomous Driving
Authors: Chrysomallis Iason , Troullinos Dimitrios , Chalkiadakis Georgios , Papamichail Ioannis , Papageorgiou Markos
Published: September 2023

Implicit imitation assumes that learning agents observe only the state transitions of an agent they use as a mentor, and try to recreate them based on their own abilities and knowledge of their environment. In this paper, we put forward a deep implicit imitation Q-network (DIIQN) model, which incorporates ideas from three well-known Deep Q-Network (DQN) variants. As such, we enable a novel implicit imitation method for online, model-free deep reinforcement learning. Our thorough experimentation in the complex environment of the emerging lane-free traffic paradigm, verifies the benefits of our approach. Specifically, we show that deep implicit imitation RL dramatically accelerates the learning process when compared to a “vanilla” DQN method; and, unlike explicit imitation reinforcement learning, it is able to outperform mentor performance without resorting to additional information, such as the mentor’s actions.
Rectifying Binary Classifiers
Authors: Coste-Marquis Sylvie , Marquis Pierre
Published: September 2023

We elaborate on the notion of rectification of a classifier Σ based on Boolean features, introduced in [10]. The purpose is to determine how to modify Σ when the way it classifies a given instance is considered incorrect since it conflicts with some expert knowledge T. Given Σ and T, postulates characterizing the way Σ must be changed into a new classifier Σ ⋆ T that complies with T were presented. We focus here on the specific case of binary classifiers, i.e., there is a single target concept, and any instance is classified either as positive (an element of the concept), or as negative (an element of the complementary concept). In this specific case, our main contribution is twofold: (1) we show that there is a unique rectification operator ⋆ satisfying the postulates, and (2) when Σ and T are Boolean circuits, we show how a classification circuit equivalent to Σ ⋆ T can be computed in time linear in the size of Σ and T; when Σ is a decision tree (resp. a random forest, a boosted tree) and T is a decision tree, a decision tree (resp. a random forest, a boosted tree) equivalent to Σ ⋆ T can be computed in time polynomial in the size of Σ and T.
What Wikipedia Misses About Yuriko Nakamura? Predicting Missing Biography Content by Learning Latent Life Patterns
Authors: Duan Yijun , Liu Xin , Jatowt Adam , Zhuang Chenyi , Yu Hai-Tao , Lynden Steven , Kim Kyoung-Sook , Matono Akiyoshi
Published: September 2023

Action-related KnowledGe (AKG) is important for facilitating deeper understanding of people’s life patterns, objectives and motivations. In this study, we present a novel framework for automatically predicting missing human biography records in Wikipedia by generating such knowledge. The generation method, which is based on a neural network matrix factorization model, is capable of encoding action semantics from diverse perspectives and discovering latent inter-action relations. By correctly predicting missing information and correcting errors, our work can effectively improve the quality of data about the behavioral records of historical figures in the knowledge base (e.g., biographies in Wikipedia), thus contributing to the understanding and study of human actions by the general public on the one hand, and can be considered as a new paradigm for managing action-related knowledge in digital libraries on the other. Extensive experiments demonstrate that the AKG we generate can capture well missing or “forgotten” human biography related information in Wikipedia.
Exploiting Epistemic Uncertainty at Inference Time for Early-Exit Power Saving
Authors: Dymond Jack , Stein Sebastian , Gunn Steve
Published: September 2023

Distinguishing epistemic from aleatoric uncertainty is a central idea to out-of-distribution (OOD) detection. By interpreting adversarial and OOD inputs from this perspective, we can collect them into a single unclassifiable group. Rejecting such inputs mid-inference will reduce resource usage. To achieve this, we apply k-nearest neighbour (KNN) classifiers to the embedding space of branched neural networks. This introduces a novel means of additional power savings, through an early-exit reject. Our technique works out-of-the-box on any branched neural network and can be competitive on OOD benchmarks, achieving an area under receiver operator characteristic (AUROC) of over 0.9 in most datasets, and scores of 0.95+ when identifying perturbed inputs. A mixed input test set is introduced, we show how OOD inputs can be identified up to 50% of the time, and adversarial inputs up to 85% of the time. In a balanced test environment, this equates to power savings of up to 18% in the OOD scenario and 40% in the adversarial scenario. This allows a more stringent in-distribution (ID) classification policy, leading to accuracy improvements of 15% and 20% on the OOD and adversarial tests, respectively, when compared to conventional exit policies operating under the same conditions.
Failure Handling in BDI Plans via Runtime Enforcement
Authors: Ferrando Angelo , Cardoso Rafael C.
Published: September 2023

Engineering a software system can be a complex process and prone to failure. This is exacerbated when the system under consideration presents some degree of autonomy, such as in cognitive agents. In this paper, we use runtime verification as a way to enforce safety properties on Belief-Desire-Intention (BDI) agents by enveloping certain plans in safety shields. These shields function as a failure handling mechanism, they can detect and avoid violations in shielded plans. The safety shields also provide automated failure recovery by attempting alternative execution paths to avoid violations.
Double Logistic Regression Approach to Biased Positive-Unlabeled Data
Authors: Furmańczyk Konrad , Mielniczuk Jan , Rejchel Wojciech , Teisseyre Paweł
Published: September 2023

Positive and unlabelled learning is an important non-standard inference problem which arises naturally in many applications. The significant limitation of almost all existing methods addressing it lies in assuming that the propensity score function is constant and does not depend on features (Selected Completely at Random assumption), which is unrealistic in many practical situations. Avoiding this assumption, we consider parametric approach to the problem of joint estimation of posterior probability and propensity score functions. We show that if both these functions are logistic with different parameters (double logistic model) then the corresponding parameters are identifiable. Motivated by this, we propose two approaches to their estimation: a joint maximum likelihood method and the second approach based on an alternating maximization of two Fisher consistent approximations. Our experimental results show that the proposed methods perform on par or better than the existing methods based on Expectation-Maximisation scheme.
OER: Offline Experience Replay for Continual Offline Reinforcement Learning
Authors: Gai Sibo , Wang Donglin , He Li
Published: September 2023

The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline tasks likely leads to the catastrophic forgetting issue under resource-limited scenarios. In this paper, we formulate a new setting, continual offline reinforcement learning (CORL), where an agent learns a sequence of offline reinforcement learning tasks and pursues good performance on all learned tasks with a small replay buffer without exploring any of the environments of all the sequential tasks. For consistently learning on all sequential tasks, an agent requires acquiring new knowledge and meanwhile preserving old knowledge in an offline manner. To this end, we introduced continual learning algorithms and experimentally found experience replay (ER) to be the most suitable algorithm for the CORL problem. However, we observe that introducing ER into CORL encounters a new distribution shift problem: the mismatch between the experiences in the replay buffer and trajectories from the learned policy. To address such an issue, we propose a new model-based experience selection (MBES) scheme to build the replay buffer, where a transition model is learned to approximate the state distribution. This model is used to bridge the distribution bias between the replay buffer and the learned model by filtering the data from offline data that most closely resembles the learned model for storage. Moreover, in order to enhance the ability on learning new tasks, we retrofit the experience replay method with a new dual behavior cloning (DBC) architecture to avoid the disturbance of behavior-cloning loss on the Q-learning process. In general, we call our algorithm offline experience replay (OER). Extensive experiments demonstrate that our OER method outperforms SOTA baselines in widely-used Mujoco environments.
Iterative Reward Shaping Using Human Feedback for Correcting Reward Misspecification
Authors: Gajcin Jasmina , McCarthy James , Nair Rahul , Marinescu Radu , Daly Elizabeth , Dusparic Ivana
Published: September 2023

A well-defined reward function is crucial for successful training of an reinforcement learning (RL) agent. However, defining a suitable reward function is a notoriously challenging task, especially in complex, multi-objective environments. Developers often have to resort to starting with an initial, potentially misspecified reward function, and iteratively adjusting its parameters, based on observed learned behavior. In this work, we aim to automate this process by proposing ITERS, an iterative reward shaping approach using human feedback for mitigating the effects of a misspecified reward function. Our approach allows the user to provide trajectory-level feedback on agent’s behavior during training, which can be integrated as a reward shaping signal in the following training iteration. We also allow the user to provide explanations of their feedback, which are used to augment the feedback and reduce user effort and feedback frequency. We evaluate ITERS in three environments and show that it can successfully correct misspecified reward functions.
SMT-Based Satisfiability Checking of Strategic Metric Temporal Logic
Authors: Kacprzak Magdalena , Niewiadomski Artur , Penczek Wojciech , Zbrzezny Andrzej
Published: September 2023

The paper presents a novel SMT-based method for testing the satisfiability of formulae that express strategic properties of timed multi-agent systems represented by networks of timed automata. Strategic Metric Temporal Logic (SMTL) is introduced, which extends Metric Temporal Logic (MTL) with strategy operators. SMTL is interpreted over maximal continuous time runs of timed automata. We define a procedure that synthesises a model for a given SMTL formula if such a model exists. The method exploits Satisfiability Modulo Theories (SMT) techniques and Parametric Bounded Model Checking algorithms. The presented approach enables bounded satisfiability checking, where the model is partially given and needs to be completed in line with the given specification. Our method has been implemented, and its application is demonstrated through an example of the well-known dining philosophers problem extended with clocks and strategies. The experimental results are quite encouraging.
Cartesian Abstractions and Saturated Cost Partitioning in Probabilistic Planning
Authors: Klößner Thorsten , Seipp Jendrik , Steinmetz Marcel
Published: September 2023

Stochastic shortest path problems (SSPs) capture probabilistic planning tasks with the objective of minimizing expected cost until reaching the goal. One of the strongest methods to solve SSPs optimally is heuristic search guided by an admissible (lower-bounding) heuristic function. Recently, probability-aware pattern database (PDB) abstractions have been highlighted as an efficient way of generating such lower bounds, with significant advantages over traditional determinization-based approaches. Here, we follow this work, yet consider a more general type, Cartesian abstractions, which have been used successfully in the classical setting. We show how to construct probability-aware Cartesian abstractions via a counterexample-guided abstraction refinement (CEGAR) loop akin to classical planning. This method is complete, meaning it guarantees convergence to the optimal expected cost if not terminated prematurely. Furthermore, we investigate the admissible combination of multiple such heuristics using saturated cost partitioning (SCP), marking its first application in the probabilistic setting. In our experiments, we show that probability-aware Cartesian abstractions yield much more informative heuristics than their determinization-based counterparts. Finally, we show that SCP yields probability-aware abstraction heuristics that are superior to the previous state of the art.
BMIPN: A Biased Multi-Granularity Interaction Prototype Network for Few-Shot Relation Extraction
Authors: Li Yile , Yue Yinliang , Gu Xiaoyan , Fu Peng , Wang Weiping
Published: September 2023

Few-shot relation extraction (FSRE) focuses on detecting new relations through a few annotated instances. Most existing works adopt prototypical network-based models for FSRE. They compute prototype representations for each class in the support set separately, and then use prototype representations for relation prediction. In this way, they learn only the knowledge of each class, regardless of the high-level interactions among these classes. However, these interactions can help the model understand diversity and improve discrimination, which is essential for FSRE, especially for similar relations’ prediction. In this work, we introduce a novel Biased Multi-granularity Interaction Prototype Network (BMIPN). Specifically, we mimic human cognitive processes to model explicit and adaptive interactions from intra- and inter-class aspects. Furthermore, we propose a novel biased contrastive learning method that encourages the model to focus on contrasting similar relations, generating discriminative and robust prototype representations. Experimental results on two benchmark datasets demonstrate that BMIPN outperforms state-of-the-art models and achieves better performance with respect to similar relations.
Multilingual Lexical Simplification via Paraphrase Generation
Authors: Liu Kang , Qiang Jipeng , Li Yun , Yuan Yunhao , Zhu Yi , Hua Kaixun
Published: September 2023

Lexical simplification (LS) methods based on pretrained language models have made remarkable progress, generating potential substitutes for a complex word through analysis of its contextual surroundings. However, these methods require separate pretrained models for different languages and disregard the preservation of sentence meaning. In this paper, we propose a novel multilingual LS method via paraphrase generation, as paraphrases provide diversity in word selection while preserving the sentence’s meaning. We regard paraphrasing as a zero-shot translation task within multilingual neural machine translation that supports hundreds of languages. After feeding the input sentence into the encoder of paraphrase modeling, we generate the substitutes based on a novel decoding strategy that concentrates solely on the lexical variations of the complex word. Experimental results demonstrate that our approach surpasses BERT-based methods and zero-shot GPT3-based method significantly on English, Spanish, and Portuguese.
Graph-Based Abstractive Summarization of Extracted Essential Knowledge for Low-Resource Scenarios
Authors: Moro Gianluca , Ragazzi Luca , Valgimigli Lorenzo
Published: September 2023

Although current summarization models can process increasingly long text sequences, they still struggle to capture salient related information spread across the lengthy size of inputs with few labeled training instances. Today’s research still relies on standard input truncation without considering graph-based modeling of multiple semantic units to summarize only crucial facets. This paper proposes G-SEEK, a graph-based summarization of extracted essential knowledge. By representing the long source with a heterogeneous graph, our method extracts and provides salient sentences to an abstractive summarization model to generate the summary. Experimental results in low-resource scenarios, distinguished by data scarcity, reveal that G-SEEK consistently improves both the long- and multi-document summarization performance and accuracy across several datasets.
Blame Attribution for Multi-Agent Path Finding Execution Failures
Authors: Natan Avraham , Stern Roni , Kalech Meir
Published: September 2023

In Multi-Agent Systems (MAS), Multi-Agent Path Finding (MAPF) is the problem of finding a conflict-free plan for a group of agents from a set of starting points to a set of target points. Deviations from this plan are standard in real-world applications and may decrease overall system efficiency and even lead to accidents and deadlocks. In large MAS scenarios with physical robots, multiple faulty events occur over time, contributing to the overall degraded system performance. This raises the main problem we address in this work: how to attribute blame for a degraded MAS performance over a set of faulty events. We formally define this problem and propose using the Shapley values to solve it. Then, we propose an algorithm that efficiently approximates Shapley values by considering only some subsets of faulty events set. We analyze this algorithm theoretically and experimentally and demonstrate that it enables effectively trading off runtime for error.
Fast Pareto Optimization Using Sliding Window Selection
Authors: Neumann Frank , Witt Carsten
Published: September 2023

Pareto optimization using evolutionary multi-objective algorithms such as the classical GSEMO algorithm has been widely applied to solve constrained submodular optimization problems. A crucial factor determining the runtime of the used evolutionary algorithms to obtain good approximations is the population size of the algorithms which grows with the number of trade-offs that the algorithms encounter. In this paper, we introduce a sliding window speed up technique for recently introduced algorithms. We prove that our technique eliminates the population size as a crucial factor negatively impacting the runtime of the classical GSEMO algorithm and achieves the same theoretical performance guarantees as previous approaches within less computation time. Our experimental investigations for the classical maximum coverage problem confirms that our sliding window technique clearly leads to better results for a wide range of instances and constraint settings.
Unsupervised Zero-Shot Learning for Achieve Cross-Modal Alignment with Counterfactuals
Authors: Tai Jinyang , Guo Yike
Published: September 2023

Zero-Shot Learning (ZSL) is a technique that transfers knowledge from seen classes to unseen classes by establishing cross-modal mapping relationships. However, traditional ZSL methods heavily rely on a large number of expensive labeled data, which may not be readily available in practical applications. In practical applications there is often a lack of labels, and the approach implies that the lack of effective supervised information in the transfer process of seen classes can lead to ’negative causality’ problem between different modalities. Therefore, we propose an unsupervised counterfactual approach to solve the above problem. Therefore, we propose an unsupervised counterfactual approach to solve the above problem. In this paper, we propose an unsupervised learning model and use a Counterfactual Causal Inference framework to cross-modal mapping relationship adjustment (CMRA). Specifically, we aim to regard images as cause and Wikipedia text as effect form a causal relationship diagram. First, it uses multiple attributes attention to learn the visual semantic attributes of images and the corresponding Wikipedia text description words to form cross-modal alignment. Then, we combine contrastive learning and stop-gradient techniques to create a novel cross-modal mapping relationship. Finally, we conducted an investigation to assess the consistency of the multiple attribute attention in the distribution of visual semantic attributes before and after image transformation. To tackle this issue, we implemented a deactivation strategy specifically designed to eliminate the multiple attribute attention of visual semantic attributes that displayed noticeable distribution gaps at different stages. This approach also involves eliminating the mapping relationships between their corresponding Wikipedia text description words. This model evaluates the classification accuracy in AWA, CUB, APY, SUN. The experimental results show that the algorithm outperforms the state-of-the-art algorithms technology approaches.
A Logic-Based Framework for Explainable Agent Scheduling Problems
Authors: Vasileiou Stylianos Loukas , Xu Borong , Yeoh William
Published: September 2023

Agent Scheduling Problems (ASPs) are common in various real-world situations, requiring explainable decision-making processes to effectively allocate resources to multiple agents while fostering understanding and trust. To address this need, this paper presents a logic-based framework for providing explainable decisions in ASPs. Specifically, the framework addresses two types of queries: reason-seeking queries, which explain the reasoning behind scheduling decisions, and modification-seeking queries, which offer guidance on making infeasible decisions feasible. Acknowledging the importance of privacy in multi-agent scheduling, we introduce a privacy-loss function that measures the disclosure of private information in explanations, enabling a privacy-preserving aspect in our framework. By using this function, we introduce the notion of privacy-aware explanations and present an algorithm for computing them. Empirical evaluations demonstrate the effectiveness and versatility of our approach.
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing
Authors: Wu Jing , Hovakimyan Naira , Hobbs Jennifer
Published: September 2023

Classifying and segmenting patterns from a limited number of examples is a significant challenge in remote sensing and earth observation due to the difficulty in acquiring accurately labeled data in large quantities. Previous studies have shown that meta-learning, which involves episodic training on query and support sets, is a promising approach. However, there has been little attention paid to direct fine-tuning techniques. This paper repurposes contrastive learning as a pre-training method for few-shot learning for classification and semantic segmentation tasks. Specifically, we introduce a generator-based contrastive learning framework (GenCo) that pre-trains backbones and simultaneously explores variants of feature samples. In fine-tuning, the auxiliary generator can be used to enrich limited labeled data samples in feature space. We demonstrate the effectiveness of our method in improving few-shot learning performance on two key remote sensing datasets: Agriculture-Vision and EuroSAT. Empirically, our approach outperforms purely supervised training on the nearly 95,000 images in Agriculture-Vision for both classification and semantic segmentation tasks. Similarly, the proposed few-shot method achieves better results on the land-cover classification task on EuroSAT compared to the results obtained from fully supervised model training on the dataset.
Deep Ensemble Robustness by Adaptive Sampling in Dropout-Based Simultaneous Training
Authors: Wu Quanwei , Huang Bo , Wang Yi , Ke Zhiwei , Luo Da
Published: September 2023

Recent studies show that an ensemble of deep networks can have better adversarial robustness by increasing the deep feature learning diversity of base models to limit the adversarial transferability. However, existing schemes mostly rely on a second-order method for gradient regularization which usually involves a heavy computation overhead. In this paper, we propose a simple yet effective method which eliminates the use of a second-order optimization and significantly reduces the computation complexity of regularized simultaneous training of deep ensemble networks. For the first time, we show analytically that stochastic regularization by the proposed approach can promote both model smoothness and feature diversity of representation learning in the deep space. We also show that the proposed method is able to achieve a better gain of certified robustness. This is due to the effect of a prioritized feature selection enabled by an adaptive and continuous sampling of neuron activation among the base networks. Experimental results show that our method can improve adversarial robustness significantly comparing with the existing ensemble models on several image benchmark datasets. The ensemble performance can be further boosted by complementing the stochastic regularization approach with other defense paradigms such as adversarial training.
Task-Sensitive Discriminative Mutual Attention Network for Few-Shot Learning
Authors: Xu Baogui , Xu Chengjin , Lu Zhiwu , Su Bing
Published: September 2023

Many few-shot image classification methods focus on learning a fixed feature space from sufficient samples of seen classes that can be readily transferred to unseen classes. For different tasks, the feature space is either kept the same or only adjusted by generating attentions to query samples. However, the discriminative channels and spatial parts for comparing different query and support images in different tasks are usually different. In this paper, we propose a task-sensitive discriminative mutual attention (TDMA) network to produce task-and-sample-specific features. For each task, TDMA first generates a discriminative task embedding that encodes the inter-class separability and within-class scatter, and then employs the task embedding to enhance discriminative channels respective to this task. Given a specific query and different support images, TDMA further incorporates the task embedding and long-range dependencies to locate the discriminative parts in the spatial dimension. Experimental results on miniImageNet, tieredImageNet and FC100 datasets show the effectiveness of the proposed model.
Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties
Authors: Yan Xiankun , Do Anh Viet , Shi Feng , Qin Xiaoyu , Neumann Frank
Published: September 2023

Chance constraints are frequently used to limit the probability of constraint violations in real-world optimization problems where the constraints involve stochastic components. We study chance-constrained submodular optimization problems, which capture a wide range of optimization problems with stochastic constraints. Previous studies considered submodular problems with stochastic knapsack constraints in the case where uncertainties are the same for each item that can be selected. However, uncertainty levels are usually variable with respect to the different stochastic components in real-world scenarios, and rigorous analysis for this setting is missing in the context of submodular optimization. This paper provides the first such analysis for this case, where the weights of items have the same expectation but different dispersion. We present greedy algorithms that can obtain a high-quality solution, i.e., a constant approximation ratio to the given optimal solution from the deterministic setting. In the experiments, we demonstrate that the algorithms perform effectively on several chance-constrained instances of the maximum coverage problem and the influence maximization problem.
Enhanced Machine Reading Comprehension Method for Aspect Sentiment Quadruplet Extraction
Authors: Ye Shuqin , Zhai Zepeng , Li Ruifan
Published: September 2023

In the NLP domain, Aspect-Based Sentiment Analysis (ABSA) has gained significant attention in recent years due to its ability to perform fine-grained sentiment analysis. A challenging task in ABSA is Aspect Sentiment Quadruplet Extraction (ASQE), which involves the extraction of aspect terms and their associated opinion terms, sentiment polarities, and categories in the form of quadruplets. However, existing studies have ignored the strong dependence among the multiple subtasks involved in ASQE. In this paper, we propose a novel Enhanced Machine Reading Comprehension (EMRC) method and formalize ASQE task as a multi-turn MRC task. Our EMRC effectively learns and utilizes the relationships among different subtasks by incorporating previously generated query answers into the current queries. We design a hierarchical category classification strategy to perform the category prediction in a structured manner, enabling the model to tackle intricate categories with ease. Furthermore, we employ the bi-directional attention mechanism, i.e., context-to-query and query-to-context attentions, to map the context into a task-aware representation. We conduct extensive experiments on two benchmark datasets. The results demonstrate that EMRC outperforms the state-of-art baselines. The source code is publicly available at https://github.com/Little-Yeah/EMCR.
Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks
Authors: Yin Xiang , Potyka Nico , Toni Francesca
Published: September 2023

Argumentative explainable AI has been advocated by several in recent years, with an increasing interest on explaining the reasoning outcomes of Argumentation Frameworks (AFs). While there is a considerable body of research on qualitatively explaining the reasoning outcomes of AFs with debates/disputes/dialogues in the spirit of extension-based semantics, explaining the quantitative reasoning outcomes of AFs under gradual semantics has not received much attention, despite widespread use in applications. In this paper, we contribute to filling this gap by proposing a novel theory of Argument Attribution Explanations (AAEs) by incorporating the spirit of feature attribution from machine learning in the context of Quantitative Bipolar Argumentation Frameworks (QBAFs): whereas feature attribution is used to determine the influence of features towards outputs of machine learning models, AAEs are used to determine the influence of arguments towards topic arguments of interest. We study desirable properties of AAEs, including some new ones and some partially adapted from the literature to our setting. To demonstrate the applicability of our AAEs in practice, we conclude by carrying out two case studies in the scenarios of fake news detection and movie recommender systems.
Fair Deep Reinforcement Learning with Preferential Treatment
Authors: Yu Guanbao , Siddique Umer , Weng Paul
Published: September 2023

Learning fair policies in reinforcement learning (RL) is important when the RL agent may impact many users. We investigate a variant of this problem where equity is still desired, but some users may be entitled to preferential treatment. In this paper, we formalize this more sophisticated fair optimization problem in deep RL using generalized fair social welfare functions (SWF), provide a theoretical discussion to justify our approach, explain how deep RL algorithms can be adapted to tackle it, and empirically validate our propositions on several domains. Our contributions are both theoretical and algorithmic, notably: (1) We obtain a general bound on the suboptimality gap in terms of SWF-optimality using average reward of a policy SWF-optimal for the discounted reward, which notably justifies using standard deep RL algorithms, even for the average reward; (2) Our algorithmic innovations include a state-augmented DQN-based method for learning either deterministic or stochastic policies, which also applies to the usual fair optimization setting without any preferential treatment.
Boosting Visual Question Answering Through Geometric Perception and Region Features
Authors: Yu Hong , Wang Zhiyue , Liu Yuanqiu , Liu Han
Published: September 2023

Visual question answering (VQA) is a crucial yet challenging task in multimodal understanding. To correctly answer questions about an image, VQA models are required to comprehend the fine-grained semantics of both the image and the question. Recent advances have shown that both grid and region features contribute to improving the VQA performance, while grid features surprisingly outperform region features. However, grid features will inevitably induce visual semantic noise due to fine granularity. Besides, the ignorance of geometric relationship makes VQA models difficult to understand the object relative positions in the image and answer questions accurately. In this paper, we propose a visual enhancement network for VQA that leverages region features and position information to enhance grid features, thus generating richer visual grid semantics. First, the grid enhancement multi-head guided-attention module utilizes regions around the grid to provide visual context, forming rich visual grid semantics and effectively compensating for the fine granularity of the grid. Second, a novel geometric perception multi-head self-attention is introduced to process two types of features, incorporating geometric relations such as relative direction between objects while exploring internal semantic interactions. Extensive experiments demonstrate that the proposed method can obtain competitive results over other strong baselines.
Uncertainty-Driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning
Authors: Zhang Junjie , Lyu Jiafei , Ma Xiaoteng , Yan Jiangpeng , Yang Jun , Wan Le , Li Xiu
Published: September 2023

Equipped with the trained environmental dynamics, model-based offline reinforcement learning (RL) algorithms can often successfully learn good policies from fixed-sized datasets, even some datasets with poor quality. Unfortunately, however, it can not be guaranteed that the generated samples from the trained dynamics model are reliable (e.g., some synthetic samples may lie outside of the support region of the static dataset). To address this issue, we propose Trajectory Truncation with Uncertainty (TATU), which adaptively truncates the synthetic trajectory if the accumulated uncertainty along the trajectory is too large. We theoretically show the performance bound of TATU to justify its benefits. To empirically show the advantages of TATU, we first combine it with two classical model-based offline RL algorithms, MOPO and COMBO. Furthermore, we integrate TATU with several off-the-shelf model-free offline RL algorithms, e.g., BCQ. Experimental results on the D4RL benchmark show that TATU significantly improves their performance, often by a large margin. Code is available here.
Deep Co-Training for Cross-Modality Medical Image Segmentation
Authors: Zhu Lei , Chan Ling Ling , Ng Teck Khim , Zhang Meihui , Ooi Beng Chin
Published: September 2023

Due to the expensive segmentation annotation cost, cross-modality medical image segmentation aims to leverage annotations from a source modality (e.g. MRI) to learn a model for target modality (e.g. CT). In this paper, we present a novel method to tackle cross-modality medical image segmentation as semi-supervised multi-modal learning with image translation, which learns better feature representations and is more robust to source annotation scarcity. For semi-supervised multi-modal learning, we develop a deep co-training framework. We address the challenges of co-training on divergent labeled and unlabeled data distributions with a theoretical analysis on multi-view adaptation and propose decomposed multi-view adaptation, which shows better performance than a naive adaptation method on concatenated multi-view features. We further formulate inter-view regularization to alleviate overfitting in deep networks, which regularizes deep co-training networks to be compatible with the underlying data distribution. We perform extensive experiments to evaluate our framework. Our framework significantly outperforms state-of-the-art domain adaptation methods on three segmentation datasets, including two public datasets on cross-modality cardiac substructure segmentation and abdominal multi-organ segmentation and one large scale private dataset on cross-modality brain tissue segmentation. Our code is publicly available at https://github.com/zlheui/DCT.
Environmentally-Aware Bundle Recommendation Using the Choquet Integral
Authors: Bronzini Marco , Robbi Erich , Viappiani Paolo , Passerini Andrea
Published: September 2023

Nowadays, the environmental footprint of a process has become an important aspect to be considered in each human activity from industrial production to logistics. Despite this increased awareness, environmental friendliness is a quite new aspect in the IT sector and even less considered in the field of recommendation systems. Bundle recommendation aims to generate bundles of associated products that users tend to consume as a whole under certain circumstances, and poses additional challenges in terms of environmental friendliness. Nevertheless, current bundle recommendation systems fail to consider the environmental impact of the product bundle when generating recommendations. We introduce a new preference-based approach for bundle recommendation exploiting the Choquet integral. This allows us to formalize preferences for coalitions of environmental-related attributes, thus recommending product bundles accounting for synergies among product attributes. An experimental evaluation on a dataset of local food products in Northern Italy shows how the Choquet integral allows to naturally formalize a sensible notion of environmental friendliness, and that standard approaches based on weighted sums of attributes end up recommending bundles with lower environmental friendliness even if weights are explicitly learned to maximize it.
Association Between Hippocampal Volume and African Neuropsychology Memory Tests in Adult Individuals with Probable Alzheimer’s Disease in Democratic Republic of Congo
Authors: Ikanga Jean , Hickle Sabrina , Schwinne Megan , Epenge Emmanuel , Gikelekele Guy , Kavugho Immaculee , Tsengele Nathan , Samuel Mampunza , Zhao Liping , Qiu Deqiang , Stringer Anthony , Saindane Amit M. , Alonso Alvaro , Drane Daniel L.
Published: September 2023

Background:

Western studies indicate potential associations between hippocampal volume and memory in the trajectory of Alzheimer’s disease (AD). However, limited availability of neuroimaging technology and neuropsychological tests appropriate for sub-Saharan African (SSA) countries makes it difficult to establish neuroanatomical associations of hippocampus and memory in this locale.

Objective:

This study examined hippocampal volumes and memory in healthy control (HC) and probable AD groups in the Democratic Republic of Congo (DRC).

Methods:

Forty-six subjects with probable AD and 29 HC subjects were screened using the Community Instrument for Dementia and the Alzheimer Questionnaire. Participants underwent neuroimaging in Kinshasa, DRC, and memory was evaluated using the African Neuropsychology Battery (ANB). Multiple linear regression was used to determine associations between hippocampal volumes and memory.

Results:

Patients with probable AD performed significantly worse than HCs on ANB memory measures, and exhibited greater cerebral atrophy, which was significantly pronounced in the medial temporal lobe region (hippocampus, entorhinal cortex). Both AD and HC subjects exhibited high rates of white matter hyperintensities compared to international base rate prevalence, which was significantly worse for probable AD. Both also exhibited elevated rates of microhemorrhages. Regression analysis demonstrated a significant association between hippocampal volume and ANB memory tests. Hippocampal atrophy discriminated probable AD from the HC group.

Conclusions:

This study establishes the feasibility of conducting neuroimaging research in the SSA, demonstrates many known neuroimaging findings in probable AD patients hold up using culturally appropriate memory tasks, and suggest cardiovascular problems are a greater issue in SSA than in Western countries.
Cross-Sectional Analysis of Periodontal Disease and Cognitive Impairment Conducted in a Memory Clinic: The Pearl Study
Authors: Saji Naoki , Ishihara Yuichi , Murotani Kenta , Uchiyama Akira , Takeda Akinori , Sakurai Takashi , Matsushita Kenji
Published: September 2023

Background:

Periodontal disease (PeD) is a risk factor of Alzheimer’s disease and is associated with cognitive decline in older adults. However, the relationships between subitems of neuropsychological tests and PeD have not been fully clarified.

Objective:

To evaluate associations between PeD and subitems of neuropsychological tests.

Methods:

We performed a cross-sectional analysis of data of 183 participants (women: 50%, mean age: 79 years) from a clinical study. We enrolled patients who visited our memory clinic and assessed demographics, dementia-related risk factors, neuropsychological tests, brain magnetic resonance images, and a dental screening check. We evaluated the relationships between cognitive function and PeD using multivariable logistic regression analyses.

Results:

Participants with dementia were less likely to make periodical visits to the dentist, had fewer teeth, had less frequent tooth brushing habits, and were more likely to have PeD. Impaired cognitive function was significantly associated with an increasing degree of PeD. In multivariable logistic regression analyses, impaired visuospatial function and attention were associated with twice the risk of moderate or severe PeD compared with individuals with preserved visuospatial function and attention (odds ratio: 2.11, 95% confidence interval: 1.04–4.29, p = 0.037). Impaired word recall and recognition and following commands were associated with increased risk of PeD (odds ratio: 2.80, 95% confidence interval: 1.41–5.32, p = 0.003).

Conclusions:

Cognitive decline, such as impaired visuospatial function, attention, word recall and recognition, and inability to follow commands were independently and strongly associated with PeD. These items can be assessed easily on a daily basis.
Evaluation of children’s oral diagnosis and treatment using imaging examination using AI based Internet of Things
Authors: Li Yan , Qu Qizhi , Yue Yuxue , Guo Yuxuan , Yi Xiuna
Published: September 2023

BACKGROUND: Although cone beam computed tomography (CBCT) plays an important role in the diagnosis and treatment of oral diseases, its image segmentation method needs to be further improved, and there are still objections about the clinical application effect of general anesthesia (GA) on children’s dental fear (CDF). OBJECTIVE: This study aimed to investigate the application value of CBCT based on intelligent computer segmentation model in oral diagnosis and treatment of children in the context of biomedical signals, and to analyze the alleviating effect of GA on CDF. METHODS: Based on the regional level set (CV) algorithm, the local binary fitting (LBF) model was introduced to optimize it, and the tooth CBCT image segmentation model CV-LBF was established to compare the segmentation accuracy (SA), maximum symmetric surface distance (MSSD), average symmetric surface distance (ASSD), over segmentation rate (OR), and under segmentation rate (UR) between these model and other algorithms. 82 children with CDF were divided into general anesthesia group (GAG) (n= 38) and controls (n= 44) according to the voluntary principle of their families. Children in GAG were treated with GA and controls with protective fixed intervention. Children’s fear survey schedule-dental subscale (CFSS-DS) and Venham scores were counted before intervention in the two groups. CFSS-DS scores were recorded at 2 hours after intervention and after recovery in children in GAG. CFSS-DS and Venham scores were performed in all children 1 week after surgery. RESULTS: The results showed that the S⁢A value of CV-LBF algorithm was higher than that of region growing algorithm (P< 0.05). OR, UR, MSSD, and ASSD values of CV-LBF algorithm were evidently lower than those of other algorithms (P< 0.05). CFSS-DS scores were lower in GAG than in controls 2 hours after intervention and at return visits after 1 week of intervention (P< 0.001), and Venham scores were lower in GAG than in controls after intervention (P< 0.001). After intervention, the proportion of children with Venham grade 0, 1, 2, and 3 was obviously higher in GAG than in controls (P< 0.001), while the proportion of children with Venham grade 4 and 5 was clearly higher in controls than in GAG (P< 0.001). CONCLUSION: The results revealed that the computer intelligent segmentation model CV-LBF has potential application value in CBCT image segmentation of children’s teeth, and GA can effectively alleviate anxiety of children with CDF and can be used as biomedical signals.
3D human anatomy augmentation over a mannequin for the training of nursing skills
Authors: Kim Sun Kyung , Lee Youngho , Hwang Hye Ri , Park Su Yeon
Published: September 2023

BACKGROUND: The in-depth understanding of human anatomy is the foundation for safety in nursing practice. Augmented reality is an emerging technology that can be used for integrative learning in nursing education. OBJECTIVE: The study aimed to develop a human anatomy-based skill training system and pilot test its usability and feasibility. METHODS: Twenty-seven nursing students participated in 3D anatomy-based skill training for intramuscular injection and Levin tube feeding using HoloLens 2. Various user interfaces including pictures, videos, animation graphics, and annotation boxes assisted users with a comprehensive understanding of the step-by-step procedures for these techniques. A one-group pre-post test was conducted to observe changes in skill performance competency, usability, and learning satisfaction. RESULTS: After study participation, a statistically significant improvement in skill performance competency (p< 0.05) was observed. The usability results showed that students were satisfied with the usefulness of the program (9.55 ± 0.49) and scored highly for the intention to participate in other educational programs (9.62 ± 0.59). A high level of learning satisfaction was achieved (9.55 ± 0.49), with positive responses in fostering students’ engagement and excitement in the application of cutting-edge technology. CONCLUSION: The 3D anatomy-based nursing skill training demonstrated good potential to improve learning outcomes and facilitate engagement in self-directed practice. This can be integrated into undergraduate nursing education as an assistant teaching tool, contributing to the combination of knowledge and practice.