Reviewerly/exHarmony huggingface dataset:
CERM: Context-Aware Literature-Based Discovery via Sentiment Analysis

Driven by the abundance of biomedical publications, we introduce a sentiment analysis task to understand food-health relationship. Prior attempts to incorporate health into recipe recommendation and analysis systems have primarily focused on ingredient nutritional components or utilized basic computational models trained on curated labeled data. Enhanced models that capture the inherent relationship between food ingredients and biomedical concepts can be more beneficial for food-related research, given the wealth of information in biomedical texts. Considering the costly data labeling process, these models should effectively utilize both labeled and unlabeled data. This paper introduces Entity Relationship Sentiment Analysis (ERSA), a new task that captures the sentiment of a text based on an entity pair. ERSA extends the widely studied Aspect Based Sentiment Analysis (ABSA) task. Specifically, our study concentrates on the ERSA task applied to biomedical texts, focusing on (entity-entity) pairs of biomedical and food concepts. ERSA poses a significant challenge compared to traditional sentiment analysis tasks, as sentence sentiment may not align with entity relationship sentiment. Additionally, we propose CERM, a semi-supervised architecture that combines different word embeddings to enhance the encoding of the ERSA task. Experimental results showcase the model's efficiency across diverse learning scenarios.
R-STAR: Robust Self-Taught Task-Wise Reweighting for Rehearsal-Based Class Incremental Learning

Class incremental learning (CIL) requires a model to learn the knowledge of new classes without overwriting that of old classes. The main challenge thus lies in catastrophic forgetting. Among all advances in addressing this challenge, rehearsal-based methods are the most widely-used due to their convenience and effectiveness. However, the (classification) scores bias between the old and new classes, known as the main cause of catastrophic forgetting for rehearsal-based methods, is still not fully addressed. Although some recent strategies are proposed to reduce the scores bias, they either take extra training time or sacrifice too much performance on the current task. In this paper, we propose a novel Robust Self-Taught Task-Wise Reweighting (R-STAR) method, which can act as a flexible and key component for improving existing rehearsal-based methods. Concretely, on top of the standard training process, it measures the forgetting degree of the model over the augmented buffer (for robust evaluation) on each task. Further, following the self-taught paradigm, it directly activates the task-wise forgetting degree into a reweighting ratio for scores bias reduction during the inference stage. Extensive experiments show that our R-STAR can improve most rehearsal-based methods with remarkable margins, but with (almost) no extra training cost or excessive performance sacrifice on the new task. Moreover, it also shows its advantages over existing scores bias correction strategies.
Optimal Alignment of Temporal Knowledge Bases

Answering temporal CQs over temporalized Description Logic knowledge bases (TKB) is a main technique to realize ontology-based situation recognition. In case the collected data in such a knowledge base is inaccurate, important query answers can be missed. In this paper we introduce the TKB Alignment problem, which computes a variant of the TKB that minimally changes the TKB, but entails the given temporal CQ and is in that sense (cost-) optimal. We investigate this problem for ALC TKBs and conjunctive queries with LTL operators and devise a solution technique to compute (cost-optimal) alignments of TKBs that extends techniques for the alignment problem for propositional LTL over finite traces.
Progression for Monitoring in Temporal ASP

In recent years, there has been growing interest in the application of temporal reasoning approaches and non-monotonic logics from artificial intelligence in dynamic systems that generate data. A well-known approach to temporal reasoning is the use of a progression technique, which allows for the online computation of logical consequences of a logical knowledge base over time. We consider a progression technique for Temporal Here and There and Temporal Equilibrium Logic, which is the logic underlying answer programming over linear-temporal logic (LTL). Compared to usual LTL online computation, where the goal is to check whether a trace is compliant with a temporal specification, our approach provides also the means to compute non-monotonic temporal reasoning over a trace of observations. Besides formal notions and results, we also present an algorithm for performing progression to monitor a dynamic system, which has been implemented as a proof of concept and allows for handling expressive application scenarios.
Using Earley Parser for Recognizing Totally Ordered Hierarchical Plans

Earley Parser is a top-down parser proposed for context-free grammars and used, for example, in the grammar constraint. Parsing trees of context-free grammars are very close to task decomposition trees used in hierarchical planning, specifically when the actions are totally ordered. This paper suggests using the Earley Parser to recognize totally ordered hierarchical plans. Given a sequence of actions – a prefix of the plan – and a task decomposition model, the plan recognition problem asks which task decomposes to a plan containing the given plan prefix. We will show that the Earley parser significantly increases the speed of plan recognition compared to the existing bottom-up parsing-based plan recognizer. The Earley parser’s performance is also on a par with the planning-based plan recognizer despite not using any planning heuristics.
Robust Federated Learning Method Against Data and Model Poisoning Attacks with Heterogeneous Data Distribution

Federated Learning (FL) is essential for building global models across distributed environments. However, it is significantly vulnerable to data and model poisoning attacks that can critically compromise the accuracy and reliability of the global model. These vulnerabilities become more pronounced in heterogeneous environments, where clients’ data distributions vary broadly, creating a challenging setting for maintaining model integrity. Furthermore, malicious attacks can exploit this heterogeneity, manipulating the learning process to degrade the model or even induce it to learn incorrect patterns. In response to these challenges, we introduce RFCL, a novel Robust Federated aggregation method that leverages CLustering and cosine similarity to select similar cluster models, effectively defending against data and model poisoning attacks even amidst high data heterogeneity. Our experiments assess RFCL’s performance against various attacker numbers and Non-IID degrees. The findings reveal that RFCL outperforms existing robust aggregation methods and demonstrates the capability to defend against multiple attack types.
SPAT: Semantic-Preserving Adversarial Transformation for Perceptually Similar Adversarial Examples

Although machine learning models achieve high classification accuracy against benign examples, they are vulnerable to adversarial machine learning (AML) attacks which generate adversarial examples by adding well-crafted perturbations to the benign examples. The perturbations can be increased to enhance the attack success rate, however, if the perturbations are added without considering the semantic or perceptual similarity between the benign and adversarial examples, the attack can be easily perceived/detected. As such, there exists a trade-off between the attack success rate and the perceptual similarity. In this paper, we propose a novel Semantic-Preserving Adversarial Transformation (SPAT) framework which facilitates an advantageous trade-off between the two metrics. SPAT modifies the optimisation objective of an AML attack to include the goal of increasing the attack success rate as well as the goal of maintaining the perceptual similarity between benign and adversarial examples. Our experiments on a variety of datasets including CIFAR-10, GTSRB, and MNIST demonstrate that SPAT-transformed AML attacks achieve better perceptual similarity while maintaining the attack success rates as the conventional AML attacks.
Towards Trustworthy NLP: An Adversarial Robustness Enhancement Based on Perplexity Difference

The vulnerability of artificial intelligence has emerged as a bottleneck, with adversarial attacks posing a significant threat to natural language processing. Although multiple defense mechanisms have been proposed, they often suffer from strict constraints, weak generalization, and low scalability. To address these challenges, we propose leveraging perplexity to quantify the difference between clean and adversarial examples based on the observation of numerous cases. We then statistically prove the substantial difference between them using Bayesian hypothesis testing. Subsequently, we develop an adversarial defense framework named UMPS, which contains two branches: “Uncovering the Mask”(UM) and “Perplexity-guided Sampling”(PS). UM utilizes a masked language model and Jaro-Winkler distance constraint to recover out-of-vocabulary words, while PS employs perplexity to locate the optimal sample within a convex hull which is constructed with integrated gradients. Theoretically, the proposed framework fulfills three requirements: effectiveness, universality, and portability. The experimental results demonstrate that UMPS effectively enhances the robustness of language models including BERT, against advanced attacks and outperforms three strong baseline methods. Furthermore, we conduct an instance analysis to illustrate how UMPS functions and what it outputs, an ablation study to support the validity and necessity of the two branches, and an post-hoc test on the difference in perplexity to explains the defense performance of our framework.
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations

Modern recommender systems utilize users’ historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminished user satisfaction and trust in the systems. Acknowledging the recent advancements in explainable recommender systems that enhance users’ understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability. In this paper, we present a user-controllable recommender system that seamlessly integrates explainability and controllability within a unified framework. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system by interacting with these explanations. Furthermore, we introduce and assess two attributes of controllability in recommendation systems: the complexity of controllability and the accuracy of controllability. Experimental evaluations on MovieLens and Yelp datasets substantiate the effectiveness of our proposed framework. Additionally, our experiments demonstrate that offering users control options can potentially enhance recommendation accuracy in the future. Source code and data are available at https://github.com/chrisjtan/ucr.
MFC: A Multishot Approach to Federated Data Clustering

The work explores the federated data clustering problem. The primary goal is to perform k-means clustering of data distributed over multiple clients while preserving privacy during an exchange with the central server. Existing solutions to unsupervised federated data clustering are either computationally challenging or effective only in heterogeneous regimes, i.e., when the number of clusters per client (kz) is less than the total number of clusters (k) (specifically, kz ≤ k). Moreover, existing one-shot approaches assume that the information about kz is available for each client. In this paper, we propose two multi-shot approaches which we call MFC and MFCH, that perform well on both heterogeneous and non-heterogeneous regimes, i.e., are independent of the underlying client data distribution. Both MFC and MFCH stand out as they do not rely on prior knowledge about kz. We theoretically bound the closeness of the local centers obtained by MFC to that of the optimal global centers and prove that under some well-separability assumption, the centers will be close enough. MFCH improvises MFC by only sharing a single cluster center from each client, thus ensuring more privacy. Our theoretical analysis shows that when at least O(k2log k) clients are involved, centers obtained by MFCH will closely approximate optimal global centers. Experiments on synthetic and real-world datasets validate the proposed approaches’ efficacy showcasing lower objective costs in non-heterogeneous regimes while having comparable performance in heterogeneous regimes. In addition, as a byproduct MFC exhibits higher device-level fairness in terms of the individual objective cost compared to existing state-of-the-art algorithms. The code is publicly available at https://github.com/shivi98g/MFC.
On the Structural Complexity of Grounding – Tackling the ASP Grounding Bottleneck via Epistemic Programs and Treewidth

Answer Set Programming is widely applied research area for knowledge representation and for solving industrial domains. One of the challenges of this formalism focuses on the so-called grounding bottleneck, which addresses the efficient replacement of first-order variables by means of domain values. Recently, there have been several works in this direction, ranging from lazy grounding, hybrid solving, over translational approaches. Inspired by a translation from non-ground normal programs to ground disjunctive programs, we attack the grounding bottleneck from a more general angle. We provide a polynomial reduction for grounding disjunctive programs of bounded domain size by reducing to propositional epistemic logic programs (ELPs). By slightly adapting our reduction, we show new complexity results for non-ground programs that adhere to the measure treewidth. We complement these results by matching lower bounds under the exponential time hypothesis, ruling out significantly better algorithms.
A Semantics-Driven Methodology for High-Quality Image Annotation

Recent work in Machine Learning and Computer Vision has highlighted the presence of various types of systematic flaws inside ground truth object recognition benchmark datasets. Our basic tenet is that these flaws are rooted in the many-to-many mappings which exist between the visual information encoded in images and the intended semantics of the labels annotating them. The net consequence is that the current annotation process is largely under-specified, thus leaving too much freedom to the subjective judgment of annotators. In this paper, we propose vTelos, an integrated Natural Language Processing, Knowledge Representation, and Computer Vision methodology whose main goal is to make explicit the (otherwise implicit) intended annotation semantics, thus minimizing the number and role of subjective choices. A key element of vTelos is the exploitation of the WordNet lexico-semantic hierarchy as the main means for providing the meaning of natural language labels and, as a consequence, for driving the annotation of images based on the objects and the visual properties they depict. The methodology is validated on images populating a subset of the ImageNet hierarchy.
A Hybrid Approach to Preference Learning with Interaction Terms

Preference learning is an essential component in numerous applications, such as recommendation systems, decision-making processes, and personalized services. We propose here a novel approach to preference learning that interleaves Gaussian Processes (GP) and Robust Ordinal Regression (ROR). A Gaussian process gives a probability distribution on the latent function values that generate users’ preferences. Our method extends the traditional non-parametric Gaussian process framework by approximating the latent function by a very flexible parameterized function, that we call θ-additive function, where θ is the parameter set. The set θ reflects the degree of sophistication of the generalized additive model that can potentially represent the user’s preferences. To learn what are the components of θ, we update a probability distribution on the space of all possible sets θ, depending on the ability of the parameterized function to approximate the latent function. We predict pairwise preferences by using the parameter set θ that maximizes the posterior distribution and by performing robust ordinal regression based on this parameter set. Experimental results on synthetic data demonstrate the effectiveness and robustness of our proposed methodology.
AlegAATr the Bandit

One design strategy for developing intelligent agents is to create N distinct behaviors, each of which works effectively in particular tasks and circumstances. At each time step during task execution, the agent, or bandit, chooses which of the N behaviors to use. Traditional bandit algorithms for making this selection often (1) assume the environment is stationary, (2) focus on asymptotic performance, and (3) do not incorporate external information that is available to the agent. Each of these simplifications limits these algorithms such that they often cannot be used successfully in practice. In this paper, we propose a new bandit algorithm, called AlegAATr, as a step toward overcoming these deficiencies. AlegAATr leverages a technique called Assumption-Alignment Tracking (AAT), proposed previously in the robotics literature, to predict the performance of each behavior in each situation. It then uses these predictions to decide which behavior to use at any given time. We demonstrate the effectiveness of AlegAATr in selecting behaviors in three problem domains: repeated games, ad hoc teamwork, and a human-robot pick-n-place task.
Epistemic JAADL: A Modal Logic for Joint Abilities with Imperfect Information

Coordination and joint ability are important problems in representation and reasoning about multi-agent systems. Ghaderi et al. presented a formalization of joint ability of coalitions in the expressive first-order language of the situation calculus. Essentially, a coalition has joint ability to achieve a goal if after iterated elimination of dominated strategies, any remaining joint strategy achieves the goal. Based on their work, Liu et al. proposed JAADL, a modal logic for joint abilities under strategy commitments. In this paper, we propose EJAADL, an epistemic extension of JAADL, for imperfect information games where agents may have incomplete knowledge or even false beliefs about the world. Like Ghaderi et al.’s work, elimination of dominated strategies is now based on beliefs about the world, rather than facts about the world as in JAADL. Strategies are required to be uniform, i.e., they select the same action in all accessible histories. We illustrate EJAADL with examples, analyze its properties, and show that model checking memoryless EJAADL is in EXPTIME. Moreover, we consider the fragment of EJAADL without the iterated elimination operator, and show that model-checking the memoryless version of this fragment can be done in PSPACE.
Contrastive Learning with Diverse Samples

Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent contrastive learning achievements. Current work mainly adopts instance discrimination as the pretext task, which treats every single instance as a different class (negative) and uses a collection of data augmentation techniques to generate more examples (positive) for each class. The idea is straightforward and efficient but will generally cause similar instances to be classified into different classes. Such problem has been defined as “class collision” in some previous works and is shown to hurt the representation ability. Motivated by this observation, we present a solution to address this issue by filtering similar negative examples from each mini-batch. Concretely, we model the problem as a Determinantal Point Process (DPP) so that similar instances can be filtered stochastically, and diverse samples are expected to be sampled for contrastive training. Besides, we further introduce a priority term for each instance, which indicates the hardness of its positives, so that instances with more hard positives are more likely to be sampled for contributing to the optimization. Our sampling can be efficiently implemented in a feed-forward manner and further accelerated by our encouraged complement DPP. Extensive experimental results demonstrate our priority over the standard setup of contrastive learning.
Improving Text Semantic Similarity Modeling Through a 3D Siamese Network

Siamese networks have gained popularity as a method for modeling text semantic similarity. Traditional methods rely on pooling operation to compress the semantic representations from Transformer blocks in encoding, resulting in two-dimensional semantic vectors and the loss of hierarchical semantic information from Transformer blocks. Moreover, this limited structure of semantic vectors is akin to a flattened landscape, which restricts the methods that can be applied in downstream modeling, as they can only navigate this flat terrain to capture semantic nuances. To address this issue, we propose a novel 3D Siamese network for text semantic similarity modeling, which maps semantic information to a higher-dimensional space. The three-dimensional semantic tensors not only retains more precise spatial and feature domain information but also provides the necessary structural condition for comprehensive downstream modeling strategies to capture them. Leveraging this structural advantage, we introduce several modules to reinforce this 3D framework, focusing on three aspects: feature extraction, attention, and feature fusion. Our extensive experiments on four text semantic similarity benchmarks demonstrate the effectiveness and efficiency of our 3D Siamese Network.
Hierarchical Text Classification Using Contrastive Learning Informed Path Guided Hierarchy

Hierarchical Text Classification (HTC) has recently gained traction given the ability to handle complex label hierarchy. This has found applications in domains like E- commerce, customer care and medicine industry among other real-world applications. Existing HTC models either encode label hierarchy separately and mix it with text encoding or guide the label hierarchy structure in the text encoder. Both approaches capture different characteristics of label hierarchy and are complementary to each other. In this paper, we propose a Hierarchical Text Classification using Contrastive Learning Informed Path guided hierarchy (HTC-CLIP), which learns hierarchy-aware text representation and text informed path guided hierarchy representation using contrastive learning. During the training of HTC-CLIP, we learn two different sets of class probabilities distributions and during inference, we use the pooled output of both probabilities for each class to get the best of both representations. Our results show that the two previous approaches can be effectively combined into one architecture to achieve improved performance. Tests on two public benchmark datasets showed an improvement of 0.99 – 2.37% in Macro F1 score using HTC-CLIP over the existing state-of-the-art models.
Strategy Repair in Reachability Games

We introduce Strategy Repair, the problem of finding a minimal amount of modifications to turn a strategy for a reachability game from losing into winning. The problem is relevant for a number of settings in Planning and Synthesis, where solutions essentially correspond to winning strategies in a suitably defined reachability game. We show, via reduction from Vertex Cover, that Strategy Repair is NP-complete and devise two algorithms, one optimal and exponential and one polynomial but sub-optimal, which we compare experimentally. The reported experimentation includes some heuristics for strategy modification, which proved crucial in dramatically improving performance.