bluuebunny/crossref_metadata_2025 huggingface dataset:
The Degree of Fairness in Efficient House Allocation
Authors: Hosseini Hadi , Kumar Medha , Roy Sanjukta
Published: October 2024

The classic house allocation problem is primarily concerned with finding a matching between a set of agents and a set of houses that guarantees some notion of economic efficiency (e.g. utilitarian welfare). While recent works have shifted focus on achieving fairness (e.g. minimizing the number of envious agents), they often come with notable costs on efficiency notions such as utilitarian or egalitarian welfare. We investigate the trade-offs between these welfare measures and several natural fairness measures that rely on the number of envious agents, the total (aggregate) envy of all agents, and maximum total envy of an agent. In particular, by focusing on envy-free allocations, we first show that, should one exist, finding an envy-free allocation with maximum utilitarian or egalitarian welfare is computationally tractable. We highlight a rather stark contrast between utilitarian and egalitarian welfare by showing that finding utilitarian welfare maximizing allocations that minimize the aforementioned fairness measures can be done in polynomial time while their egalitarian counterparts remain intractable (for the most part) even under binary valuations. We complement our theoretical findings by giving insights into the relationship between the different fairness measures and by conducting empirical analysis.
A Gale-Shapley View of Unique Stable Marriages
Authors: Gokhale Kartik , Mallik Amit Kumar , Misra Ankit Kumar , Nath Swaprava
Published: October 2024

Stable marriage of a two-sided market with unit demand is a classic problem that arises in many real-world scenarios. In addition, a unique stable marriage in this market simplifies a host of downstream desiderata. In this paper, we explore a new set of sufficient conditions for unique stable matching (USM) under this setup. Unlike other approaches that also address this question using the structure of preference profiles, we use an algorithmic viewpoint and investigate if this question can be answered using the lens of the deferred acceptance (DA) algorithm without actually running the algorithm. Our results yield a set of sufficient conditions for USM (viz., MP and MR) and show that these are disjoint from the previously known sufficiency conditions like sequential preference and no crossing. We provide a characterization of MP that makes it efficiently verifiable (without using DA), and shows the gap between MP and the entire USM class.
Objective-Informed Diversity for Multi-Objective Multiagent Coordination
Authors: Dixit Gaurav , Tumer Kagan
Published: October 2024

To coordinate in multiagent settings characterized by multiple objectives, asymmetric agents (agents with distinct capabilities and preferences) must learn diverse behaviors to balance trade-offs between agent-specific and team objectives. Hierarchical methods partially address this by leveraging a combination of Quality-Diversity methods that illuminate the behavior space and evolutionary algorithms that use non-dominated sorting over the explored behaviors to improve coverage in the objective space. However, optimizing diverse behaviors and trade-offs in isolation is susceptible to producing egocentric behaviors that favor agent-specific objectives at the cost of team objectives. This work introduces the Multi-Objective Informed Island Model (MOI-IM), an asymmetric multiagent learning framework that fosters diverse behaviors and rich inter-agent relationships, necessary to balance potentially conflicting and misaligned objectives. An evolutionary algorithm improves coverage in the objective space by evolving a population of teams, while a gradient-based optimization infers and progressively explores the behavior space by fluidly adapting search to regions that produce policies with non-dominated trade-offs. The two processes are coupled via shared replay buffers to ensure alignment between coverage in the behavior and objective space. Empirical results on an asymmetric multi-objective coordination problem highlight MOI-IM’s ability to produce teams that can express diverse trade-offs and robust relationships required to balance misaligned objectives.
FsPONER: Few-Shot Prompt Optimization for Named Entity Recognition in Domain-Specific Scenarios
Authors: Tang Yongjian , Hasan Rakebul , Runkler Thomas
Published: October 2024

Large Language Models (LLMs) have provided a new pathway for Named Entity Recognition (NER) tasks. Compared with fine-tuning, LLM-powered prompting methods avoid the need for training, conserve substantial computational resources, and rely on minimal annotated data. Previous studies have achieved comparable performance to fully supervised BERT-based fine-tuning approaches on general NER benchmarks. However, none of the previous approaches has investigated the efficiency of LLM-based few-shot learning in domain-specific scenarios. To address this gap, we introduce FsPONER, a novel approach for optimizing few-shot prompts, and evaluate its performance on domain-specific NER datasets, with a focus on industrial manufacturing and maintenance, while using multiple LLMs – GPT-4-32K, GPT-3.5-Turbo, LLaMA 2-chat, and Vicuna. FsPONER consists of three few-shot selection methods based on random sampling, TF-IDF vectors, and a combination of both. We compare these methods with a general-purpose GPT-NER method as the number of few-shot examples increases and evaluate their optimal NER performance against fine-tuned BERT and LLaMA 2-chat. In the considered real-world scenarios with data scarcity, FsPONER with TF-IDF surpasses fine-tuned models by approximately 10% in F1 score.
Translation and Transliteration Based Data Augmentation for Multilingual Semantic Parsing
Authors: Jauhari Sarthak , Nicosia Massimo , Chatterjee Ankush , Goel Rahul
Published: October 2024

Multilingual semantic parsing is one of the natural language understanding tasks powering modern virtual assistants. Annotating training data for supporting all languages is expensive and methods that rely on machine translation and label projection are used to perform language adaptation. In this paper, we revisit the assumption that a separate label projection step is necessary, with the goal of saving compute and reducing the complexity of the data augmentation pipeline. We create synthetic training examples by applying translation and transliteration directly at the slot level. We show that without a dedicated and expensive label projection component, we are able to achieve 97% of state-of-the-art data augmentation performance on multilingual semantic parsing, and obtain the same performance of the best systems for code mixed and code switched semantic parsing.
A Language Model as a Design Assistant for UI Design Recommendation and Evaluation
Authors: Sheng Lin , Deng Cheng , Zhang Junjie , Chang Fangyuan , Sun Qinghua , Liu Hongyu , Gu Zhenyu
Published: October 2024

In the digital era, the significance of design education is on the rise due to its ability to cultivate creativity. However, the disconnect between design practice and theory, coupled with the abundance of design knowledge, poses challenges to learning in this field. Despite the potential of large language models (LLMs) to integrate various data sources for facilitating design knowledge dissemination, they face obstacles such as the scarcity of design-related datasets and limited natural language representations. To overcome these challenges, we introduce DRELM, a design-centric language model that serves as an assistant providing UI design recommendations. We also offer corresponding resources to advance language modeling research in the design domain. Importantly, DesignInstruct stands out as a premier dataset for guiding user interface tasks, while DesignEvaluation significantly contributes to autonomous design evaluation and decision support. In our research, we utilize supervised data from DesignInstruct and DesignEvaluation to fine-tune pre-trained Qwen-7B models for design tasks. Experiments conducted on test data affirm the effectiveness of our dataset in enhancing knowledge comprehension, design execution, and evaluation. We commit to making all training data and DRELM models at https://github.com/sssala/DRELM-A-Language-Model-for-Design-Recommendation-and-Evaluation.
ChatZero: Zero-Shot Cross-Lingual Dialogue Generation via Pseudo-Target Language
Authors: Liu Yongkang , Shi Feng , Wang Daling , Zhang Yifei , Schütze Hinrich
Published: October 2024

Although large language models(LLMs) show amazing capabilities, among various exciting applications discovered for LLMs fall short in other low-resource languages. Besides, most existing methods depend on large-scale dialogue corpora and thus building systems for dialogue generation in a zero-shot scenario remains a considerable challenge. To address this challenge, we propose a novel end-to-end zero-shot dialogue generation model ChatZero based on cross-lingual code-switching method. First, we construct code-switching language and pseudo-target language with placeholders. Then for cross-lingual semantic transfer, we employ unsupervised contrastive learning to minimize the semantics gap of the source language, code-switching language, and pseudo-target language that are mutually positive examples in the high dimensional semantic space. Experiments on the multilingual DailyDialog and DSTC7-AVSD datasets demonstrate that ChatZero can achieve more than 90% of the original performance under the zero-shot case compared to supervised learning, and achieve state-of-the-art performance compared with other baselines.
DSPrompt: Prompt Learning with Relation Abstraction and Context Injection for Distant Supervised Relation Extraction
Authors: Meng Zechen , Zhang Wenbin , Zhao Mankun , Xu Tianyi , Yu Jian , Yu Ruiguo , Yu Mei
Published: October 2024

The distant supervision method automatically brings a large amount of training data to the relation extraction task through machine labelling, but inevitably introduces massive noise. Current methods aim to obtain precise bag representations to address the issue of noise by integrating multi-level information. However, they overlook the failure of point embeddings to capture the entailment among relations. In this paper, we propose a model called DSPrompt, which applies prompt learning to the DSRE task for the first time. To model the entailment among models, we use Gaussian embeddings, which indicate the generalized representations of relations, instead of traditional point embeddings. However, this can lead to the vanishing of contextual semantics, so we inject bag-specific contextual information into the Gaussian embeddings to offset the embeddings toward the actual semantics. We conduct extensive experiments on three widely used datasets, and the results show that our proposed model brings significant performance improvement compared with state-of-the-art DSRE methods. The codes are available at https://github.com/zc-meng/DSPrompt.
Enhancing Hate Speech Annotations with Background Semantics
Authors: Reyero Lobo Paula , Daga Enrico , Alani Harith , Fernandez Miriam
Published: October 2024

Most automated hate speech detection models rely on human annotations for training and evaluation. Logic and research indicate that people who belong to groups targeted by hate speech are better at identifying it, often due to their increased familiarity with the topic and associated hate speech terminology. However, most hate speech annotation practices overlook this issue, and hence the labels produced tend to have a reduced accuracy. In this paper, we describe an approach where the text to be annotated is supplemented with background semantics, to expose the meaning of hate speech terminology that is less likely to be known to general annotators. We test the impact of this approach by measuring change in inter-annotator agreement, before and after introducing semantics, between two groups of annotators; those who belong to the target group of hate speech, and those who are not. Our experiments show that infusing text with semantic background increases inter-annotator agreement by up to 11.3% on average, aligning the annotations from annotators who do not belong to the target groups with those from the target groups.
Iteratively Calibrating Prompts for Unsupervised Diverse Opinion Summarization
Authors: Wang Jian , Sun Yuqing , Liang Yanjie , Li Xin , Gong Bin
Published: October 2024

Diverse opinion summarization aims to generate a summary that captures multiple opinions in texts. Although large language models (LLMs) have become the main choice for this task, the performance is highly depend on prompts. In this paper, we propose a self-evaluation based prompt calibration framework to stimulate LLM for generating high quality summary. It adopts the reinforcement learning mechanism to calibrate prompts for maximizing the reward of summary. The framework contains three parts. In the prompt construction part, we design the prompt that contains topic, task instruction and key opinion reference. The topic indicates the main focus of documents, the instruction describes the task with natural language and the key opinion reference is the explicit constraint on the expected opinions. In the reward part, for each summary, its coverage score and diversity score are used to represent the semantic coverage to the source documents and the inter opinion differences, respectively. The prompt calibration part selects the sentences in generated summaries to calibrate the prompts for the next iteration. With this framework, we use a LLM with 7B parameters to generate summaries, which outperforms large GPT-4 and multiple strong baselines. The ablation studies indicate the effectiveness of the iterative calibration process. We analyze the opinion difference in terms of the tendencies of sentences in summaries and use the Natural Language Inference (NLI)-based method to evaluate the faithfulness of summaries. Experiment results show that our method generates summaries with high opinion difference and faithfulness.
Improving Aspect-Based Sentiment Analysis via Tuple-Order Learning
Authors: Hu Gongzhen , Liu Yuanjun , Zhou Xiabing , Zhang Min
Published: October 2024

In the field of Natural Language Processing (NLP), Aspect-Based Sentiment Analysis (ABSA) has gained significant attention in recent years due to its ability to perform fine-grained sentiment analysis. Generative methods tackle various ABSA tasks by autoregressively generating the target sequence of sentiment tuples in a specified format. However, the sentiment tuple is intrinsically an unordered set, and the method introduces an order bias between the generated sequence and the original target. Therefore, to investigate the impact of sentiment tuples order on model performance, we conduct a pilot experiment, unveiling that the order of tuples significantly influences the learning outcomes of the Seq2Seq model. Thus, we propose a novel tuple-order learning method that prioritizes tuples from simple to complex, facilitated by a discrete evaluation method that assesses the difficulty of each individual tuple. Specifically, we incorporate positional information on tuples and employ an effective strategy to expedite the assessment of individual tuples. The method optimizes the learning process while maintaining the structural integrity of existing generative models. Extensive experiments show that our approach significantly advances the performance on 14 datasets of 5 benchmark tasks. We will release our code at https://github.com/gongzhenhu/TOL.
Domain-Specific Long Text Classification from Sparse Relevant Information
Authors: D’Cruz Célia , Bereder Jean-Marc , Precioso Frédéric , Riveill Michel
Published: October 2024

Large Language Models have undoubtedly revolutionized the Natural Language Processing field, the current trend being to promote one-model-for-all tasks (sentiment analysis, translation, etc.). However, the statistical mechanisms at work in the larger language models struggle to exploit the relevant information when it is very sparse, when it is a weak signal. This is the case, for example, for the classification of long domain-specific documents, when the relevance relies on a single relevant word or on very few relevant words from technical jargon. In the medical domain, it is essential to determine whether a given report contains critical information about a patient’s condition. This critical information is often based on one or few specific isolated terms. In this paper, we propose a hierarchical model which exploits a short list of potential target terms to retrieve candidate sentences and represent them into the contextualized embedding of the target term(s) they contain. A pooling of the term(s) embedding(s) entails the document representation to be classified. We evaluate our model on one public medical document benchmark in English and on one private French medical dataset. We show that our narrower hierarchical model is better than larger language models for retrieving relevant long documents in a domain-specific context.
Comparing Lossless Compression Methods for Chess Endgame Data
Authors: Gomboc Dave , Shelton Christian R. , Miner Andrew S. , Ciardo Gianfranco
Published: October 2024

Chess endgame tables encode unapproximated game-theoretic values of endgame positions. The speed at which information is retrieved from these tables and their representation size are major limiting factors in their effective use. We explore and make novel extensions to three alternatives (decision trees, decision diagrams, and logic minimization) to the currently preferred implementation (Syzygy) for representing such tables. Syzygy is most compact, but also slowest at handling queries. Two-level logic minimization works well, though performing the compression takes significant time. Decision DAGs and multiterminal binary decision diagrams are both comparable and offer the best querying times, with decision diagrams providing better compression.
Planning with OWL-DL Ontologies
Authors: John Tobias , Koopmann Patrick
Published: October 2024

We introduce ontology-mediated planning, in which planning problems are combined with an ontology. Our formalism differs from existing ones in that we focus on a strong separation of the formalisms for describing planning problems and ontologies, which are only losely coupled by an interface. Moreover, we present a black-box algorithm that supports the full expressive power of OWL DL. This goes beyond what existing approaches combining automated planning with ontologies can do, which only support limited description logics such as DL-Lite and description logics that are Horn. Our main algorithm relies on rewritings of the ontology-mediated planning specifications into PDDL, so that existing planning systems can be used to solve them. The algorithm relies on justifications, which allows for a generic approach that is independent of the expressivity of the ontology language. However, dedicated optimizations for computing justifications need to be implemented to enable an efficient rewriting procedure. We evaluated our implementation on benchmark sets from several domains. The evaluation shows that our procedure works in practice and that tailoring the reasoning procedure has significant impact on the performance.
Count-Based Novelty Exploration in Classical Planning
Authors: Rosa Giacomo , Lipovetzky Nir
Published: October 2024

Count-based exploration methods are widely employed to improve the exploratory behavior of learning agents over sequential decision problems. Meanwhile, Novelty search has achieved success in Classical Planning through recording of the first, but not successive, occurrences of tuples. In order to structure the exploration, however, the number of tuples considered needs to grow exponentially as the search progresses. We propose a new novelty technique, classical count-based novelty, which aims to explore the state space with a constant number of tuples, by leveraging the frequency of each tuple’s appearance in a search tree. We then justify the mechanisms through which lower tuple counts lead the search towards novel tuples. We also introduce algorithmic contributions in the form of a trimmed open list that maintains a constant size by pruning nodes with bad novelty values. These techniques are shown to complement existing novelty heuristics when integrated in a classical solver, achieving competitive results in challenging benchmarks from recent International Planning Competitions. Moreover, adapting our solver as the frontend planner in dual configurations that utilize both memory and time thresholds demonstrates a significant increase in instance coverage, surpassing current state-of-the-art solvers.
Optimal Layout-Aware CNOT Circuit Synthesis with Qubit Permutation
Authors: Shaik Irfansha , van de Pol Jaco
Published: October 2024

CNOT optimization plays a significant role in noise reduction for Quantum Circuits. Several heuristic and exact approaches exist for CNOT optimization. In this paper, we investigate more complicated variations of optimal synthesis by allowing qubit permutations and handling layout restrictions. We encode such problems into Planning, SAT, and QBF. We provide optimization for both CNOT gate count and circuit depth. For experimental evaluation, we consider standard T-gate optimized benchmarks and optimize CNOT sub-circuits. We show that allowing qubit permutations can further reduce up to 56% in CNOT count and 46% in circuit depth. In the case of optimally mapped circuits under layout restrictions, we observe a reduction up to 17% CNOT count and 19% CNOT depth.
A Heuristic for Optimal Total-Order HTN Planning Based on Integer Linear Programming
Authors: Olz Conny , Lodemann Alexander , Bercher Pascal
Published: October 2024

Heuristic Search is still the most successful approach to hierarchical planning, both for finding any and for finding an optimal solution. Yet, there exist only a very small handful of heuristics for HTN planning – so there is still huge potential for improvements. It is especially noteworthy that there does not exist a single heuristic that’s tailored towards special cases. In this work we propose the very first specialized HTN heuristic, tailored towards totally ordered HTN problems. Our heuristic builds on an existing NP-complete and admissible delete-and-ordering relaxation ILP heuristic, but partially incorporates ordering constraints while reducing the number of ILP constraints. It exploits inferred preconditions and effects of compound tasks and is also admissible thus allowing to find optimal solutions. Our heuristic demonstrates improved performance (ILP) or comparable performance (LP) to the previous heuristic, suggesting the success of the model reduction. Compared to the current state-of-the art heuristic for optimal HTN planning, our heuristic is less efficient on average, but more informed and dominates it in roughly as many cases as it gets dominated by the other, making it a more efficient alternative in several domains.
Uncertainty in Real-World Vehicle Routing
Authors: Sobotka Václav , Rudová Hana
Published: October 2024

The motivation for our research arises from the limitations of traditional deterministic heuristic solvers for vehicle routing problems (VRP) observed in industrial practice. In general, the quantities provided to the solvers as inputs, e.g., loads or service times, are typically estimates or simplifying reflections of reality. While current state-of-the-art solvers are applicable to complex VRP variants at scale, their inability to reason about uncertainties limits their usefulness in real-world applications. Despite stochastic VRPs being a widely studied topic, related approaches are typically centered around the uncertainty in the problem rather than extending successful deterministic methods. Moreover, uncertainty-related methodologies and models are often strongly linked to computationally expensive sampling or exact algorithms making their scaling problematic. Thus, we aim for easy-to-integrate, reusable, and especially computationally efficient mechanisms, allowing us to naturally extend state-of-the-art heuristic solvers for a wide range of VRPs with reasoning about input uncertainties. We formulate four mechanisms fitting these criteria, including standard chance constraints, two data manipulation methods, and a novel penalty-based method. These four mechanisms are compared and analyzed for the most common sources of uncertainty in loads and times on both benchmark and complex real-world instances. Their favorable scaling properties are demonstrated on instances with up to 1,000 customers.
Good Things Come to Those Who Wait: The Power of Sensing in Social Laws
Authors: Tuisov Alexander , Shleyfman Alexander , Karpas Erez
Published: October 2024

Multiple agents operating in a shared environment can interfere with each other’s ability to reach their goals. One of the approaches to address this issue is enacting a social law – a set of rules that restricts some possible behaviors of the agents. A social law that ensures that each agent can achieve its goal, regardless of what the other agents do, is called robust. Recent work has shown how to verify that a given social law, encoded in an MA-STRIPS formalism, is robust by compilation to classical planning. That work also introduced the notion of waitfor preconditions, which assumes that the agent can check if these preconditions hold before executing its scheduled action and withhold from acting otherwise. In this work, we explore the connection between waitfor preconditions and sensing. In particular, we establish the semantics behind the waitfor mechanism and connect it to the agent’s sensing capabilities. Moreover, we reason about the expressive power of waitfors by juxtaposing environments where some sensing is allowed with “blind” environments. Using these insights, we derive methods for faster robustness validation, and present an empirical evaluation of these methods.
Evolving A* to Efficiently Solve the κ Shortest-Path Problem
Authors: Linares López Carlos , Herman Ian
Published: October 2024

The problem of finding the shortest path in a graph G(V, E) has been widely studied. However, in many applications it is necessary to compute an arbitrary number of them, κ. Even though the problem has raised a lot of interest from different research communities and many applications of it are known, it has not been addressed to the same extent as the single shortest path problem. The best algorithm known for efficiently solving this task has a time complexity of O (|E| + |V|log|V|+κ|V|) when computing paths in explicit form, and is based on best-first search. This paper introduces a new search algorithm with the same time complexity, which results from a natural evolution of A* thus, it preserves all its interesting properties, making it widely applicable to many different domains. Experiments in various testbeds show a significant improvement in performance over the state of the art, often by one or two orders of magnitude.
UDUC: An Uncertainty-Driven Approach for Learning-Based Robust Control
Authors: Zhang Yuan , Hoffmann Jasper , Boedecker Joschka
Published: October 2024

Learning-based techniques have become popular in both model predictive control (MPC) and reinforcement learning (RL). Probabilistic ensemble (PE) models offer a promising approach for modelling system dynamics, showcasing the ability to capture uncertainty and scalability in high-dimensional control scenarios. However, PE models are susceptible to mode collapse, resulting in non-robust control when faced with environments slightly different from the training set. In this paper, we introduce the uncertainty-driven robust control (UDUC) loss as an alternative objective for training PE models, drawing inspiration from contrastive learning. We analyze the robustness of the UDUC loss through the lens of robust optimization and evaluate its performance on the challenging real-world reinforcement learning (RWRL) benchmark, which involves significant environmental mismatches between the training and testing environments.
Placement Aware Grasp Planning for Efficient Sequential Manipulation
Authors: Park Juhan , Jin Daejong , Lee Kyungjae
Published: October 2024

In this paper, we address the problem of sequential pick-and-place with multiple objects when a specific goal configuration is given. The sequence of pick-and-place can generally be optimized using search-based methods. However, when the number of objects increases, there remains a challenge due to the exponential growth in computational complexity. Especially, the most challenging aspect is that a significant number of sequences are infeasible, leading to extended search times. In this regard, we propose an approach that efficiently addresses this issue by considering both pick and place aspects simultaneously while searching grasp poses with learning-based techniques to find feasible solutions expediently. In our experiments, the proposed method showed an enhancement of up to 90% in the average quality of trajectory for rearrangement benchmarks and about a 50% improvement in computational time in certain scenarios.
EVIDENT: Enhanced Visualization and Design Integration from Textual Edits and Prompts
Authors: Escarda-Fernández Miguel , Lin Xin , López-Riobóo-Botana Iñigo , González-Vázquez Sonia
Published: October 2024

In this work, we present EVIDENT (Enhanced VIsualization and DEsign iNTegration from Textual Edits and Prompts), a chatbot framework aimed at enhancing the creative process of generating prompts for background images in virtual reality sets. The system is designed to interpret initial user instructions for generating input prompts for text-to-image models and modify descriptions according to the user guidance through chat. Moreover, the system integrates a text-to-image diffusion model for testing the generated prompts quality, considering the prompt-image alignment via post hoc visual interpretation. This method empowers users to make informed adjustments to their prompts. We adapt a Large Language Model (LLM) with human preference data, using both synthetic and human-labelled custom datasets. We provide an intuitive platform for users to refine their prompts, continuously improving our LLM generation capabilities thanks to a feedback mechanism, allowing us to collect new human preference data for our fine-tuning and alignment pipelines.
Gas Grid Copilot: Can a MORL Agent Assist a Dispatcher in Managing a Gas Grid?
Authors: Schiendorfer Alexander , Lisca Gheorghe , Outafraout Karima , Michailov Lilia , Kätzel Pascal , Felix Rudolf
Published: October 2024

The distribution of fuel gases is undergoing major changes due to decarbonization efforts: Non-fossil gases such as biomethane or renewable hydrogen can lead to the reuse of existing gas infrastructure for gas storage, transport, and distribution to reduce greenhouse gas emissions while maintaining a high energy security. For safe and efficient operation, we propose Gas Grid Copilot (GGC) as a demonstrator of a multi-objective reinforcement learning agent that trains in a simulated gas grid environment to control a grid by modifying its inflow into a mass storage. Multiple, possibly conflicting reward signals are included. Their conflicts and synergies of rewards are analyzed using techniques from multi-criteria decision making, more specifically a conflict interaction matrix based on extended fuzzy logic. That way, dispatchers of a gas grid can explore the effects of reward prioritizations and their consequences safely.
Answerable Sociotechnical Systems
Authors: Kekulluoglu Dilara , Rovatsos Michael , Kokciyan Nadin
Published: October 2024

Sociotechnical systems (STSs) include artificial intelligence (AI) to make (semi-) automated decisions that impact our lives. Their reasoning processes still often remain unclear to people interacting with such systems, which may also harm people by making flawed decisions. The users do not have ways to challenge automated decisions and obtain proper restitution if necessary. Organizations may be willing to provide transparency about their decision-making process, but answering each of the questions people ask could be cumbersome. We propose a mediator agent framework that will bridge the gap between organizations that employ AI and people who were harmed by its automated decisions. Our approach helps the organizations to become answerable data practices, and it empowers people to report the harms, ask for clarifications, as well as remedies through dialogues. We implement a prototype to demonstrate the applicability of our approach through a real-world scenario.
AI-Powered Virtual Reality System for Training Wrist Amputees to Use Advanced Prosthetic Solutions
Authors: Aksiotis Vladislav , Sazonov Oleg , Nasrulina Kamila , Medvedeva Daria , Nekrasova Uliya , Ossadtchi Alexei
Published: October 2024

Individuals with upper limb amputations or congenital defects face substantial challenges in performing daily tasks, often exacerbated by limited functionality in the existing bionic prostheses. To address this, we present an AI-powered virtual reality (VR) system designed to train patients to control bionic prostheses with a high number of degrees of freedom using electromyographic (EMG) activity decoding. Current prosthetic control systems lack sophistication, relying on discrete muscle activity decoding and manual mode switching. Our system employs a novel neural network architecture pre-trained on healthy subjects, facilitating continuous decoding of residual muscle activity to control a virtual limb in the VR. Through three months of training, participants with congenital upper limb dysplasia achieved precise finger-specific control, demonstrating a low error rate and strong correlation between the target and the decoded kinematics. Importantly, the system’s adaptability and its user-friendly interface positively impacted patient experience, offering potential reduction of muscle atrophy risks and psychological benefits while preparing individuals for the use of an advanced prosthetic device. This innovative approach represents a significant step toward enhancing prosthetic functionality and improving the quality of life for upper limb amputees and congenital defect patients.
CA-SER: Cross-Attention Feature Fusion for Speech Emotion Recognition
Authors: Deeb Bashar M. , Savchenko Andrey , Makarov Ilya
Published: October 2024

In this paper, we introduce a novel tool for speech emotion recognition, CA-SER, that borrows self-supervised learning to extract semantic speech representations from a pre-trained wav2vec 2.0 model and combine them with spectral audio features to improve speech emotion recognition. Our approach involves a self-attention encoder on MFCC features to capture meaningful patterns in audio sequences. These MFCC features are combined with high-level representations using a multi-head cross-attention mechanism. Evaluation of speech emotion recognition on the IEMOCAP dataset shows that our system achieves a weighted accuracy of 74.6%, outperforming most existing techniques.
STV+FLY: On-the-Fly Model Checking of Strategic Ability in Multi-Agent Systems
Authors: Kurpiewski Damian , Kamiński Mateusz , Jamroga Wojciech
Published: October 2024

In this paper, we present a substantially enhanced version of our software tool STV (STrategic Verifier), dedicated to strategy synthesis and model checking of strategic abilities in multi-agent systems. The new extension, called STV+FLY, incorporates an advanced strategy synthesis algorithm that enables model checking with on-the-fly generation of the global model. This innovative approach allows for the verification of some strategic properties without generating the entire global state space, thus avoiding an important bottleneck and significantly improving the efficiency.
Equipment Condition-Integrated Predictive Modeling for Optimized Scheduling of Ion Implantation in Semiconductor Manufacturing
Authors: Laber Andreas , Gebser Martin , Schekotihin Konstantin
Published: October 2024

In view of the high total cost of semiconductor manufacturing assets, respective equipment needs to be as productive as possible. To avoid needless idling and unnecessary downtime, scheduling and maintenance strategies are important in practice. This paper presents a novel approach to reduce the substantial setup costs inherent to ion implantation by deriving scheduling constraints based on current equipment conditions. Consequently, a supervised learning pipeline is established that utilizes built-in sensors and process target data to accurately predict setup costs. The derived constraints are integrated into scheduling, thereby enhancing its efficiency through dynamic dispatching adaptations. The application of our method is projected to significantly improve equipment availability by avoiding more than 100 hours of potential downtime annually.
A Data-Driven Approach Supporting Location Decisions for Docking Stations in Bike-Sharing Systems
Authors: Spahiu Blerina , Briola Daniela , Sartori Riccardo , Vizzari Giuseppe
Published: October 2024

Bike-sharing systems (BSSs) have become integral to urban mobility, improving accessibility, multimodality of transportation, and sustainability. This paper presents a novel approach to supporting decisions on the positioning of docking stations for dock-based BSSs by leveraging real-world historical mobility data to estimate mobility demand. In particular, we used taxi travels data as a proxy to generate a synthetic mobility demand dataset that was exploited to estimate the locations of a dock-based BSS stations through clustering techniques. This study aims to improve the practicality of station positioning. By addressing challenges related to station placement, this research offers insights into the practical implementation of data-driven approaches in BSS planning and management, advancing the efficiency and sustainability of urban bike-sharing systems.
GIF: An Automated Genomic Information Finder to Extract Data from Reports
Authors: Rajendran Pavithra , Zenonos Alexandros , Sabu Sebin , Spiridou Anastasia , Goncalves Helena Spiridou , Laskowski Nastazja , Key Daniel , Patel Shiren , Pope Rebecca , Sebire Neil
Published: October 2024

Genomic testing is becoming increasingly used within the UK National Health Service (NHS) in an effort to deliver precision medicine that can directly benefit patient management and care. Typically, in hospital settings clinical scientists interpret genomic results and store the output in reports (unstructured format). Recent advances in the field of Natural Language Processing (NLP) techniques have significant advantage over the common time-consuming and laborious manual extraction of the relevant information from the reports. There are a plethora of open-source NLP models available, but limited evidence of their performance on real-world healthcare tasks, specifically for paediatric data. In this paper, we describe the development of an automated pipeline that uses a hybrid approach of combining rules and pretrained NLP models to extract gene variants, related information of these variants and any gene panel information present within the genomic test reports. We evaluated the performance of the pipeline against a manually-curated, expert-annotated, in-house data containing 372 reports. Our results and evaluation highlights the advantages and limitations of using existing pretrained models in a real-world setting, and in particular, when there are computation and resource constraints within the hospital setting.
Improving Process Yield Through Manufacturing Digital Twin Using Conditional Synthetic Data Engine (COSYNE)
Authors: Chandra Shantanu , Duvinage Matthieu , Prakash PKS , Davis Patrick , Timmer Sander
Published: October 2024

The pharmaceutical industry must adhere to rigorous regulations to meet specific quality standards. Additionally, the intricate nature of pharmaceutical manufacturing processes and long time to production necessitates timely detection of batch failures. AI/ML models are used for predictive maintenance in an automated and data-driven manner to detect these failures and aid timely intervention. However, these models require substantial amount of data for model training. This can lead to extended time-to-value before a predictive monitoring system can be deployed for any new process due to long process lead times. The current research proposes COSYNE, a generative AI-based approach to generate manufacturing digital twin, reducing the model development time by augmenting synthetic data with real data. The proposed solution is validated on a large pharmaceutical company’s batch manufacturing dataset, and the results are benchmarked across multiple dimensions of generation quality. Empirical results demonstrate that the proposed COSYNE outperforms the state-of-the-art approach by 2-3 times on average across all the generation quality metrics. Moreover, COSYNE enhances downstream AI/ML performance significantly through data augmentation and reduces time-to-value by creating high-fidelity digital twins with only 10% of real data and still achieve similar performance as current baseline trained on entire real data.
Reverse Engineering of Vinyl Acetate Polymerizations by Genetic Algorithm-Based Multi-Objective Optimization
Authors: Fiosina Jelena , Sievers Philipp , Drache Marco , Beuermann Sabine
Published: October 2024

This work proposes a machine learning (ML) assisted multi-objective optimization (MOO) approach for reverse engineering of vinyl acetate polymerization processes. The method leverages ML models trained on data from kinetic Monte Carlo (kMC) simulations to minimize number of expensive laboratory experiments. A genetic algorithm (GA) as the MOO optimizer is employed, considering reaction time, monomer conversion, and molar mass distribution (MMD) similarity as objectives. The trained ML models assist the optimization process and predict key polymer properties for candidate recipes generated by the GA, enabling rapid fitness function evaluation and allow for producing novel previously not simulated polymerization recipes. The proposed framework involves: (1) training ML models for monomer concentration and MMD prediction using kMC simulation data; (2) performing GA-based MOO to identify optimal recipes (Pareto front) for a target MMD (3) selecting the most suitable recipe based on user priorities from the resulting Pareto front, considering user-defined weights for each objective (time, conversion, MMD). The experiments demonstrate that the GA, coupled with simulation-supported ML, efficiently identifies optimal recipes with high accuracy. Notably, the ML models achieve good performance even with limited training data. This approach offers a rapid and cost-effective solution for reverse engineering of vinyl acetate polymerization processes.
Adaptive Context Embedding for Temperature Prediction in Residential Buildings
Authors: Kalidindi Sai Sushanth Varma , Banee Hadi , Karlsson Hans , Loutfi Amy
Published: October 2024

Transformer-based models have gained increasing popularity for time-series prediction; however, in specific applications such as residential heating systems, static contextual data of buildings is crucial to effectively capture and learn complex environmental dynamics. This paper presents a novel transformer-based model that adapts the contextual meta-data of residential buildings, generalizing across diverse environments. The model integrates temporal data with adaptive embedding of building-specific contextual meta-data such as geographic locations and building characteristics to dynamically learn and adapt to the variations. These adaptive context embeddings allow the model to comprehensively understand how different buildings respond to environmental changes over time. Initial results show improved accuracy and reliability in indoor temperature predictions of residential buildings, demonstrating the model’s potential to optimize district heating systems across a diverse array of residential buildings. This proposed model provides a basis for developing proactive heat management systems in buildings.
Leveraging User-Generated Reviews for Recommender Systems with Dynamic Headers
Authors: Vashishtha Shanu , Kumar Abhay , Morishetti Lalitesh , Nag Kaushiki , Achan Kannan
Published: October 2024

E-commerce platforms have a vast catalog of items to cater to their customers’ shopping interests. Most of these platforms assist their customers in the shopping process by offering optimized recommendation carousels, designed to help customers quickly locate their desired items. Many models have been proposed in academic literature to generate and enhance the ranking and recall set of items in these carousels. Conventionally, the accompanying carousel title text (header) of these carousels remains static. In most instances, a generic text such as “Items similar to your current viewing” is utilized. Fixed variations such as the inclusion of specific attributes “Other items from a similar seller” or “Items from a similar brand” in addition to “frequently bought together” or “considered together” are observed as well. This work proposes a novel approach to customize the header generation process of these carousels. Our work leverages user-generated reviews that lay focus on specific attributes (aspects) of an item that were favorably perceived by users during their interaction with the given item. We extract these aspects from reviews and train a graph neural network-based model under the framework of a conditional ranking task. We refer to our innovative methodology as Dynamic Text Snippets (DTS) which generates multiple header texts for an anchor item and its recall set. Our approach demonstrates the potential of utilizing user-generated reviews and presents a unique paradigm for exploring increasingly context-aware recommendation systems.
Fuzzy Ensemble Learning and Lightweight CNN for Stress Classification
Authors: Baran Katarzyna
Published: October 2024

Everyday life problems, armed conflicts, pandemics, and catastrophes – these are situations that are always accompanied by stress. Its chronic form can lead to so-called stress-related illnesses. Despite the development of health prevention, many people still get sick due to stress. Therefore, it is important to seek detective and classificatory solutions for stress, which may influence its reduction or control in the future. The example of this can be the thermographic stress registration presented in this article, combined with classification using lightweight CNN and Choquet fuzzy ensemble learning. The article proposed new ensemble frameworks for stress classification based on Choquet fuzzy integral, serving as an aggregation function. In the study, three pre-trained lightweight CNN models were used: MobileNetV2, Xception, and EfficientNet. The proposed fuzzy ensemble model achieves a classification accuracy above 90%. This work is of a prospective nature, with the possibility of implementing solutions in biomedical-psychological activities.
Physics-Assisted Explainable Anomaly Detection in Power Systems
Authors: Lau Matthew , Alsaeed Fahad , Thames Kayla , Suresettakul Nano , Zonouz Saman , Lee Wenke , Meliopoulos Athanasios P
Published: October 2024

Detection of cyber-attacks in power systems is crucial for rapid corrective actions like isolation, disinfection and asset restoration. For real-time deployment, detection methods must not only be accurate and computationally efficient, but also interpretable for further action. While physics models can reliably detect cyber-attacks, diagnosing where and how assets were attacked is computationally demanding. To supplement detection models, we propose Physics-Assisted Statistics for Anomaly Localization (PASAL), a domain-informed data-driven method that directly identifies anomalous devices. PASAL leverages domain knowledge of the grid topology and incorporates correlation and variance statistics to model inter-sensor causal relationships. Consequently, PASAL offers inherent interpretability and computational efficiency. Our study demonstrates that PASAL swiftly localizes data integrity attacks with minimal false positives and has the potential to identify the type of attack.