Reviewerly/exHarmony huggingface dataset:
ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging

Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label. However, this reasoning does not hold in many real-life scenarios, where the positive bag label is often a consequence of a certain percentage of positive instances. To address this issue, we introduce a dedicated instance-based method called ProMIL, based on deep neural networks and Bernstein polynomial estimation. An important advantage of ProMIL is that it can automatically detect the optimal percentage level for decision-making. We show that ProMIL outperforms standard instance-based MIL in real-world medical applications. We make the code available.
A Simple Debiasing Framework for Out-of-Distribution Detection in Human Action Recognition

In real-world scenarios, detecting out-of-distribution (OOD) action is important when deploying a deep learning-based human action recognition (HAR) model. However, HAR models are easily biased to static information in the video (e.g., background), which can lead to performance degradation of OOD detection methods. In this paper, we propose a simple debiasing framework for out-of-distribution detection in human action recognition. Specifically, our framework eliminates patches with static bias in video using attention maps extracted from the video vision transformer model. Experimental results show that our framework achieves consistent performance improvement on multiple OOD action detection methods and challenging benchmarks. Furthermore, we introduce two new OOD action detection tasks, Kinetics-400 vs. Kinetics-600 exclusive and Kinetics-400 vs. Kinetics-700 exclusive, to validate our method in a setting close to the real-world scenario. With extensive experiments, we demonstrate the effectiveness of our attention-based masking, and in-depth analysis validates the effect of static bias on OOD action detection. The source code and supplementary materials are available at: https://github.com/Simcs/attention-masking.
The Problem of Coherence in Natural Language Explanations of Recommendations

Providing natural language explanations for recommendations is particularly useful from the perspective of a non-expert user. Although several methods for providing such explanations have recently been proposed, we argue that an important aspect of explanation quality has been overlooked in their experimental evaluation. Specifically, the coherence between generated text and predicted rating, which is a necessary condition for an explanation to be useful, is not properly captured by currently used evaluation measures. In this paper, we highlight the issue of explanation and prediction coherence by 1) presenting results from a manual verification of explanations generated by one of the state-of-the-art approaches 2) proposing a method of automatic coherence evaluation 3) introducing a new transformer-based method that aims to produce more coherent explanations than the state-of-the-art approaches 4) performing an experimental evaluation which demonstrates that this method significantly improves the explanation coherence without affecting the other aspects of recommendation performance.
Towards Abstractive Timeline Summarisation Using Preference-Based Reinforcement Learning

This paper introduces a novel pipeline for summarising timelines of events reported by multiple news sources. Transformer-based models for abstractive summarisation generate coherent and concise summaries of long documents but can fail to outperform established extractive methods on specialised tasks such as timeline summarisation (TLS). While extractive summaries are more faithful to their sources, they may be less readable and contain redundant or unnecessary information. This paper proposes a preference-based reinforcement learning (PBRL) method for adapting pretrained abstractive summarisers to TLS, which can overcome the drawbacks of extractive timeline summaries. We define a compound reward function that learns from keywords of interest and pairwise preference labels, which we use to fine-tune a pretrained abstractive summariser via offline reinforcement learning. We carry out both automated and human evaluation on three datasets, finding that our method outperforms a comparable extractive TLS method on two of the three benchmark datasets, and participants prefer our method’s summaries to those of both the extractive TLS method and the pretrained abstractive model. The method does not require expensive reference summaries and needs only a small number of preferences to align the generated summaries with human preferences. Code available at https://github.com/Haruhi07/PBRL-TLS.
Disentangling Interaction Using Maximum Entropy Reinforcement Learning in Multi-Agent Systems

Research on multi-agent interaction involving both multiple artificial agents and humans is still in its infancy. Most recent approaches have focused on environments with collaboration-focused human behavior, or providing only a small, defined set of situations. When deploying robots in human-inhabited environments in the future, it will be unlikely that all interactions fit a predefined model of collaboration, where collaborative behavior is still expected from the robot. Existing approaches are unlikely to effectively create such behaviors in such “coexistence” environments. To tackle this issue, we introduce a novel framework that decomposes interaction and task-solving into separate learning problems and blends the resulting policies at inference time. Policies are learned with maximum entropy reinforcement learning, allowing us to create interaction-impact-aware agents and scale the cost of training agents linearly with the number of agents and available tasks. We propose a weighting function covering the alignment of interaction distributions with the original task. We demonstrate that our framework addresses the scaling problem while solving a given task and considering collaboration opportunities in a co-existence particle environment and a new cooking environment. Our work introduces a new learning paradigm that opens the path to more complex multi-robot, multi-human interactions.
A Convolutional Neural Network Approach to General Game Playing

General Game Playing (GGP), a research field aimed at developing agents that master different games in a unified way, is regarded as a necessary step towards creating artificial general intelligence. With the success of deep reinforcement learning (DRL) in games like Go, chess, and shogi, it has been recently introduced to GGP and is regarded as a promising technique to achieve the goal of GGP. However, the current work uses fully connected neural networks and is thus unable to efficiently exploit the topological structure of game states. In this paper, we propose an approach to applying general-purposed convolutional neural networks to GGP and implement a DRL-based GGP player. Experiments indicate that the built player not only outperforms the previous algorithm and UCT benchmark in a variety of games but also requires less training time.
Compilation of Tight ASP Programs

Answer Set Programming (ASP) is a well-known AI formalism. Traditional ASP systems, that follow the “ground&amp;solve” approach, are intrinsically limited by the so-called grounding bottleneck. Basically, the grounding step (i.e., variable-elimination) can be computationally expensive, and even unfeasible in several cases of practical interest. Recent work demonstrated that the grounding bottleneck can be partially overcome by compiling in external propagators subprograms acting as constraints. In this paper a novel compilation technique is presented that can be applied to tight normal programs; thus, the class of ASP programs that can be compiled is extended beyond constraints. The approach is implemented in the new system PROASP. PROASP skips entirely the grounding phase and performs solving by injecting custom propagators in GLUCOSE. An experiment, conducted on grounding-intensive ASP benchmarks, shows that PROASP is capable of solving instances that are out of reach for state-of-the-art ASP systems.
BMIPN: A Biased Multi-Granularity Interaction Prototype Network for Few-Shot Relation Extraction

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.
Answer Set Automata: A Learnable Pattern Specification Framework for Complex Event Recognition

Complex Event Recognition (CER) systems detect event occurrences in streaming input using predefined event patterns. Techniques that learn event patterns from data are highly desirable in CER. Since such patterns are typically represented by symbolic automata, we propose a family of such automata where the transition-enabling conditions are defined by Answer Set Programming (ASP) rules, and which, thanks to the strong connections of ASP to symbolic learning, are learnable from data. We present such a learning approach in ASP, capable of jointly learning the structure of an automaton, while synthesizing the transition guards from building-block predicates, and a scalable, incremental version thereof that progressively revises models learnt from mini-batches using Monte Carlo Tree Search. We evaluate our approach on three CER datasets and empirically demonstrate its efficacy.
Action-Failure Resilient Planning

In the real world, the execution of the actions planned for an agent is never guaranteed to succeed, as they can fail in a number of unexpected ways that are not explicitly captured in the planning model. Based on these observations, we introduce the task of finding plans for classical planning that are resilient to action execution failures. We refer to this problem as Resilient Planning and to its solutions as K-resilient plans; such plans guarantee that an agent will always be able to reach its goals (possibly by replanning alternative sequences of actions) as long as no more than K failures occur along the way. We also present RESPLAN, a new algorithm for Resilient Planning, and we compare its performance to methods based on compiling Resilient Planning to Fully-Observable-Non-Deterministic (FOND) planning.
Letting Go of Self-Domain Awareness: Multi-Source Domain-Adversarial Generalization via Dynamic Domain-Weighted Contrastive Transfer Learning

Domain generalization (DG), which aims to learn a model that can generalize to an unseen target domain, has recently attracted increasing research interest. A major approach is to learn domain invariant representations to avoid greedily capturing all the correlations found in source domains caused by empirical risk minimization. Nevertheless, overly emphasizing learning of domain invariant representations might lead to learning overly-compressed domain invariant representations, causing confusion of different classes in a same domain. To address this limitation, we introduce a novel dynamic domain-weighted contrastive loss, which maximizes the subdomain differences between different classes especially those belonging to the same domain, while minimizing the average distance between the points of the convex hull of the aligned source domains. We propose Multi-source domain-adversarial generalization via dynamic domain-weighted Contrastive transfer learning (MsCtrl), a novel domain-adversarial generalization framework, which optimizes the distribution alignment of source and potential target subdomains in an adversarial manner under the “control” of the aforementioned contrastive loss. Extensive experiments based on real-world datasets demonstrate significant advantages of MsCtrl over existing state-of-the-art methods.
Resource-Constrained Knowledge Diffusion Processes Inspired by Human Peer Learning

We consider a setting where a population of artificial learners is given, and the objective is to optimize aggregate measures of performance, under constraints on training resources. The problem is motivated by the study of peer learning in human educational systems. In this context, we study natural knowledge diffusion processes in networks of interacting artificial learners. By ‘natural’, we mean processes that reflect human peer learning where the students’ internal state and learning process is mostly opaque, and the main degree of freedom lies in the formation of peer learning groups by a coordinator who can potentially evaluate the learners before assigning them to peer groups. Among else, we empirically show that such processes indeed make effective use of the training resources, and enable the design of modular neural models that have the capacity to generalize without being prone to overfitting noisy labels.
How Does Diffusion Influence Pretrained Language Models on Out-of-Distribution Data?

Transformer-based pretrained language models (PLMs) have achieved great success in modern NLP. An important advantage of PLMs is good out-of-distribution (OOD) robustness. Recently, diffusion models have attracted a lot of work to apply diffusion to PLMs. It remains under-explored how diffusion influences PLMs on OOD data. The core of diffusion models is a forward diffusion process which gradually applies Gaussian noise to inputs, and a reverse denoising process which removes noise. The noised input reconstruction is a fundamental ability of diffusion models. We directly analyze OOD robustness by measuring the reconstruction loss, including testing the abilities to reconstruct OOD data, and to detect OOD samples. Experiments are conducted by analyzing different training parameters and data statistical features on eight datasets. It shows that finetuning PLMs with diffusion degrades the reconstruction ability on OOD data. The comparison also shows that diffusion models can effectively detect OOD samples, achieving state-of-the-art performance in most of the datasets with an absolute accuracy improvement up to 18%. These results indicate that diffusion reduces OOD robustness of PLMs.
TrojBits: A Hardware Aware Inference-Time Attack on Transformer-Based Language Models

Transformer-based language models demonstrate exceptional performance in Natural Language Processing (NLP) tasks but remain susceptible to backdoor attacks involving hidden input triggers. Trojan injection via hardware bitflips presents a significant challenge for contemporary language models. However, previous research overlooks practical hardware considerations, such as DRAM and cache memory structures, resulting in unrealistic attacks that demand the manipulation of an excessive number of parameters and bits. In this paper, we present TrojBits, a novel approach requiring minimal bit-flips to effectively insert Trojans into real-world Transformer language model systems. This is achieved through a three-module framework designed to efficiently target Transformer-based language models, consisting of Vulnerable Parameters Ranking (VPR), Hardware-aware Attack Optimization (HAO), and Vulnerable Bits Pruning (VBP). Within the VPR module, we are the first to employ Gradient-guided Fisher information to identify the most susceptible Transformer parameters, specifically in the word embedding layer. The HAO module then redistributes these parameters across multiple triggers, conforming to hardware constraints by incorporating a regularization term in the trojan optimization methodology. Finally, the VBP module aims to reduce the number of bit-flips by discarding less significant bits. We evaluate TrojBits on two representative NLP models, BERT and XLNE, on three classification tasks (SST2, OffensEval, and AG’s News). Our results demonstrate that our TrojBits successfully achieves the inference-time attack with only 64 parameters out of 116 million and 90-bit flips while maintaining the model performance.
Improving Bi-Objective Shortest Path Search with Early Pruning

Bi-objective search problems are a useful generalization of shortest path search. This paper reviews some recent contributions for the solution of this problem with emphasis on the efficiency of the dominance checks required for pruning, and introduces a new algorithm that improves time efficiency over previous proposals. Experimental results are presented to show the performance improvement using a set of standard problems over bi-objective road maps.
Adaptive Self-Supervised Continual Learning

Continual Learning (CL) studies the problem of developing a robust model that can learn new tasks while retaining previously learned knowledge. However, the current CL methods exclusively focus on data with annotations, disregarding that unlabelled data is the mainstream in real-world applications. To close this research gap, this study concentrates on continual self-supervised learning, which is plagued by challenges of memory over-fitting and class imbalance. Besides, these challenges are exacerbated throughout incremental training. Aimed at addressing these challenges from both loss and data perspectives, we introduce a framework, Adaptive Self-supervised Continual Learning (ASCL). Specifically, we devise an Adaptive Sharpness-Aware Minimization (ASAM) module responsible for identifying flatter local minima in the loss landscape with a smaller memory over-fitting risk. Additionally, we design an Adaptive Memory Enhancement (AME) module responsible for rebalancing self-supervised loss with new and old tasks from a data perspective. Finally, the adaptive mechanisms in AME and ASAM modules dynamically adjust the loss landscape sharpness and memory enhancement strength with the feedback of intermediate training results. The results of our extensive experiments demonstrate the state-of-the-art performance of our methods in continual self-supervised learning scenarios across multiple datasets.
A Paraconsistency Framework for Inconsistency Handling in Qualitative Spatial and Temporal Reasoning

Inconsistency handling is a fundamental problem in knowledge representation and reasoning. In this paper, we study this problem in the context of qualitative spatio-temporal reasoning, a framework for reasoning about space and time in a symbolic, human-like manner, by following an approach similar to that used for defining paraconsistent logics; paraconsistency allows deriving informative conclusions from inconsistent knowledge bases by mainly avoiding the principle of explosion. Inspired by paraconsistent logics, such as Priest’s logic LPm, we introduce the notion of paraconsistent scenario (i.e., a qualitative solution), which can be seen as a scenario that allows a conjunction of base relations between two variables, e.g., x precedes ∧ follows y. Further, we present several interesting theoretical properties that concern paraconsistent scenarios, including computational complexity results, and describe two distinct approaches for computing paraconsistent scenarios and solving other related problems. Moreover, we provide implementations of our two methods for computing paraconsistent scenarios and experimentally evaluate them using different strategies/metrics. Finally, we show that our paraconsistent scenario notion allows us to adapt to qualitative reasoning one of the well-known inconsistency measures employed in the propositional case, namely, contension measure.
Graph Neural Networks for Mapping Variables Between Programs

Automated program analysis is a pivotal research domain in many areas of Computer Science — Formal Methods and Artificial Intelligence, in particular. Due to the undecidability of the problem of program equivalence, comparing two programs is highly challenging. Typically, in order to compare two programs, a relation between both programs’ sets of variables is required. Thus, mapping variables between two programs is useful for a panoply of tasks such as program equivalence, program analysis, program repair, and clone detection. In this work, we propose using graph neural networks (GNNs) to map the set of variables between two programs based on both programs’ abstract syntax trees (ASTs). To demonstrate the strength of variable mappings, we present three use-cases of these mappings on the task of program repair to fix well-studied and recurrent bugs among novice programmers in introductory programming assignments (IPAs). Experimental results on a dataset of 4166 pairs of incorrect/correct programs show that our approach correctly maps 83% of the evaluation dataset. Moreover, our experiments show that the current state-of-the-art on program repair, greatly dependent on the programs’ structure, can only repair about 72% of the incorrect programs. In contrast, our approach, which is solely based on variable mappings, can repair around 88.5%.
Generating Replanning Goals Through Multi-Objective Optimization in Response to Execution Observation

In some applications, planning-monitoring systems generate plans and monitor their execution by other agents. During execution, agents might deviate from these plans for various reasons. The deviation from the expected behavior will be observed by the planning-monitoring system, which will replan in order to provide the agent a new suggested plan. Most existing replanning approaches maintain the goals and compute a plan that achieves them under the new circumstances. This is often not realistic, as achieving the original goal might be very costly or impossible under the current conditions. Furthermore, replanning approaches usually overlook agent’s behavior up to the observed deviation from the original plan. In this paper we introduce GREPLAN, a novel approach that proposes new replanning goals (and plans) by solving a multi-objective optimization problem that considers all goals within a perimeter of the original goal. Empirical results in several planning benchmarks show that GREPLAN successfully reacts to deviations from the original plan by generating new appropriate replanning goals.