Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Experiences of Women Social Entrepreneurs: Advancing Understandings of ‘Emotional Capital’ in Women-only Networks

The field of social entrepreneurship, a domain focused on implementing solutions to social, cultural and environmental issues, remains highly male-dominated. Research continues to emphasise that women social entrepreneurs are often expected to behave in masculine ways in order to become successful. The study presented in this article explored the perceptions and experiences of thirty-three women living in the United Kingdom who were developing their skills in social entrepreneurship. Documenting their experiences, we sought to understand how women work in a male-dominated field. Our analysis primarily builds on a Bourdieusian-revisionist approach of emotional capital to advance understandings of how women’s networks with other women allow them to navigate social entrepreneurship. Drawing on emotional capital, as a theory, we examine the ways in which these women find these spaces generative and how it contributes to their problematising of masculine orthodoxies.
Effective Parallelization of the Vehicle Routing Problem

Capacitated Vehicle Routing Problem (CVRP) is an important combinatorial optimization problem, which is also NP-hard. A wide array of heuristics have been proposed in the literature to obtain an approximate solution to CVRP. To improve the execution time, parallel methods have been developed for accelerating metaheuristics-based algorithms, genetic algorithms, and evolutionary algorithms for CVRP. Despite these advances, our experiments with the state-of-the-art parallel solutions indicate that their run times are too high to be practically useful. The combinatorial explosion is so high that the execution time is prohibitively large even on mid-sized CVRP instances having a few hundred customers. In this work, we propose a novel technique which combines local search and randomization for solving CVRP faster with reasonable accuracy, even on large problem instances. Our usage of randomization enables searching a large space of candidate solutions. We experimentally compare our proposed method with the state-of-the-art GPU implementations on diverse input instances and demonstrate the efficacy of our approach. Our sequential and shared-memory parallel implementations are on an average 36--1189× faster than the state-of-the-art GPU-parallel genetic algorithms while also achieving a superior solution quality. Furthermore, our reported solutions are close to the current best-known solutions from CVRPLIB.
How Fitness Aggregation Methods Affect the Performance of Competitive CoEAs on Bilinear Problems

Competitive co-evolutionary algorithms (CoEAs) do not rely solely on an external function to assign fitness values to sampled solutions. Instead, they use the aggregation of outcomes from interactions between competing solutions allowing to rank solutions and make selection decisions. This makes CoEAs a useful tool for optimisation problems that have intrinsically interactive domains. Over the past decades, many ways to aggregate the outcomes of interactions have been considered. At the moment, it is unclear which of these is the best choice. Previous research is fragmented and most of the fitness aggregation methods (fitness measures) proposed have only been studied empirically. We argue that a proper understanding of the dynamics of CoEAs and their fitness measures can only be achieved through rigorous analysis of their behaviour. In this work we make a step towards this goal by using runtime analysis to study two commonly used fitness measures. We show a dichotomy in the behaviour of a (1, Λ) CoEA when optimising a Bilinear problem. The algorithm finds a Nash equilibrium efficiently if the worst interaction is used as a fitness measure but it takes exponential time w.o. p. if the average of all interactions is used instead.
Combined Layout Optimization of Wind Farm and Cable Connection on Complex Terrain Using a Genetic Algorithm

To improve the economic efficiency of wind farms, this paper proposes a method for simultaneously optimizing wind farm layout and cabling on complex terrain such as mountainous areas, which most previous studies have not considered. Multiple wind turbines should be placed to maximize energy production while minimizing the cable length (between wind turbines and between the substation and wind turbines). To optimize both, especially on complex terrain where wind speeds at a site are not constant, the proposed method combines a genetic algorithm (NSGA-II) and a capacitated minimum spanning tree approximation algorithm (Esau-Williams algorithm). For five sites with complex terrain, the proposed method is compared with the exact optimal solution obtained by the weighted sum method using the integer linear programming formulation. For a small number of candidate locations, the proposed method obtains a hyper-volume equivalent to the exact solution. In comparison, the proposed method can obtain a larger hyper-volume even in the case of many candidate locations where the weighted sum method is computationally infeasible in terms of practical resources and time. These results indicate that the proposed method effectively contributes to the wind farm design on complex terrain.
EA-based ASV Trajectory Planner for Detecting Cyanobacterial Blooms in Freshwater

Cyanobacterial Blooms (CBs) constitute a relevant ecological and public health problem since they often produce toxic metabolites that endanger the lives of many species, and they prevent human water consumption and recreational use. To determine the locations of CBs in lentic water bodies, we present a new planner based on Evolutionary Algorithms (EAs) that optimizes the trajectory of an Autonomous Surface Vehicle (ASV) equipped with a probe capable of detecting CBs. The planner 1) exploits the information provided by a particle transport simulator that determines the CB distribution from the water currents and the inherent CB behavior (in particular, its biological growth and vertical displacements) and 2) is supported by an EA that optimizes the mission duration, the ASV trajectory length, and the contributions of each simulated particle to the predicted cyanobacterial concentration along the ASV trajectory. The planner also ensures the trajectory feasibility from the ASV, probe, and water body perspective; and refines the trajectory shape by increasing the number of the decision variables during the iteration of an EA supported by usual NSGA-II operations. The results over different scenarios show that the planner determines overall good solutions that adapt the ASV trajectory to the evolution of CB distribution.
Learning Synergies for Multi-Objective Optimization in Asymmetric Multiagent Systems

Agents in a multiagent system must learn diverse policies that allow them to express complex inter-agent relationships required to optimize a single team objective. Multiagent Quality Diversity methods partially address this by transforming the agents' large joint policy space to a tractable sub-space that can produce synergistic agent policies. However, a majority of real-world problems are inherently multi-objective and require asymmetric agents (agents with different capabilities and objectives) to learn policies that represent diverse trade-offs between agent-specific and team objectives. This work introduces Multi-objective Asymmetric Island Model (MO-AIM), a multi-objective multiagent learning framework for the discovery of generalizable agent synergies and trade-offs that is based on adapting the population dynamics over a spectrum of tasks. The key insight is that the competitive pressure arising from the changing populations on the team tasks forces agents to acquire robust synergies required to balance their individual and team objectives in response to the nature of their teams and task dynamics. Results on several variations of a multi-objective habitat problem highlight the potential of MO-AIM in producing teams with diverse specializations and trade-offs that readily adapt to unseen tasks.
Enhanced Strongly typed Genetic Programming for Algorithmic Trading

This paper proposes a novel strongly typed Genetic Programming (STGP) algorithm that combines Technical (TA) and Sentiment analysis (SA) indicators to produce trading strategies. While TA and SA have been successful when used individually, their combination has not been considered extensively. Our proposed STGP algorithm has a novel fitness function, which rewards not only a tree's trading performance, but also the trading performance of its TA and SA subtrees. To achieve this, the fitness function is equal to the sum of three components: the fitness function for the complete tree, the fitness function of the TA subtree, and the fitness function of the SA subtree. In doing so, we ensure that the evolved trees contain profitable trading strategies that take full advantage of both technical and sentiment analysis. We run experiments on 35 international stocks and compare the STGP's performance to four other GP algorithms, as well as multilayer perceptron, support vector machines, and buy and hold. Results show that the proposed GP algorithm statistically and significantly outperforms all benchmarks and it improves the financial performance of the trading strategies produced by other GP algorithms by up to a factor of two for the median rate of return.
Vertical-Axis Wind Turbine Design Using Surrogate-assisted Optimization with Physical Experiments In-loop

Most of the existing wind power comes from the use of traditional Horizontal-Axis Wind Turbines (HAWT), which typically require installation over vast countryside areas (in wind farms) due to considerations such as wake effects and noise. Recently, there has been an increasing interest to explore an alternative solution - Vertical-Axis Wind Turbines (VAWT) - that may be more suitable for compact urban areas. In this paper, we conduct design optimization of twin-blade VAWTs by evaluating the candidate designs through direct small-scale prototyping and physical experiments in-loop. The problem is a practical example of expensive optimization where the number of evaluations affordable are severely limited. In addition to the conventional single-objective form (maximize rotational speed), we also solve a multi-objective version of the problem (maximize rotational speed and minimize mass). For conducting optimization, we leverage surrogate-assisted approaches that make use of both predicted mean and uncertainties in modeling for an efficient exploration of the design space. The experiments demonstrate that the approach is able to generate non-intuitive designs that are competitive or better than the baseline (classic twin-blade Savonius design) within a small evaluation budget. The study also strengthens the case for applying this approach for design optimization in general.
Effects of Objective Space Normalization in Multi-Objective Evolutionary Algorithms on Real-World Problems

In real-world multi-objective problems, each objective has a totally different scale. However, some frequently-used multi-objective evolutionary algorithms (MOEAs) have no objective space normalization mechanisms. The effect of objective space normalization on the performance of decomposition-based MOEAs (e.g., MOEA/D and NSGA-III) has already been examined for artificial test problems (e.g., DTLZ and WFG) in the literature. In this paper, we examine its practical usefulness for real-world multi-objective problems using various MOEAs. Our experimental results clearly show that objective space normalization is needed not only in decomposition-based MOEAs but also in hypervolume-based MOEAs. We also explain why objective space normalization is needed in these two types of MOEAs.
Fourier Transform-based Surrogates for Permutation Problems

In the context of pseudo-Boolean optimization, surrogate functions based on the Walsh-Hadamard transform have been recently proposed with great success. It has been shown that lower-order components of the Walsh-Hadamard transform have usually a larger influence on the value of the objective function. Thus, creating a surrogate model using the lower-order components of the transform can provide a good approximation to the objective function. The Walsh-Hadamard transform in pseudo-Boolean optimization is a particularization in the binary representation of a Fourier transform over a finite group, precisely defined in the framework of group representation theory. Using this more general definition, it is possible to define a Fourier transform for the functions over permutations. We propose in this paper the use of surrogate functions based on the Fourier transforms over the permutation space. We check how similar the proposed surrogate models are to the original objective function and we also apply regression to learn a surrogate model based on the Fourier transform. The experimental setting includes two permutation problems for which the exact Fourier transform is unknown based on the problem parameters: the Asteroid Routing Problem and the Single Machine Total Weighted Tardiness.
A Two-Stage Multi-Objective Evolutionary Reinforcement Learning Framework for Continuous Robot Control

Real-world continuous control problems often require optimizing for multiple conflicting objectives. Various works in multi-objective reinforcement learning have been conducted to tackle such issues and obtained impressive performance. At the same time, evolutionary algorithms (EAs), which are extensively used in multi-objective optimization, have recently been demonstrated their competitiveness to RL algorithms, including multi-objective control for environments with discrete action spaces. However, using EAs for multi-objective continuous robot control is still an under-explored topic. For the single-objective setting, the Proximal Distilled Evolutionary Reinforcement Learning (PDERL) framework succeeds in combining the robustness of EA and the efficiency of RL methods. In this work, we bring the strengths of PDERL to the multiobjective realm to create the novel multi-objective PDERL framework called MOPDERL that consists of a warm-up stage and an evolution stage. In particular, MOPDERL collaboratively optimizes policies for each separate objective in the warm-up stage, and then exchanges that knowledge for further policy improvement during the multi-objective evolution stage. We benchmark MOPDERL on six MuJoCo robot locomotion environments, which have been modified for the multi-objective context. The results show that MOPDERL produces better-quality Pareto fronts and higher metric scores than the state-of-the-art PGMORL algorithm across five out of six environments.
Effects of Including Optimal Solutions into Initial Population on Evolutionary Multiobjective Optimization

A long-standing question in the evolutionary multi-objective (EMO) community is how to generate a good initial population for EMO algorithms. Intuitively, as the starting point of optimization, a good initial population can have positive effects on the performance of EMO algorithms. However, in most existing EMO algorithms, one of the commonly-used initialization methods is to randomly generate a set of solutions as an initial population. One possible approach to improve random initialization is to include one or more Pareto optimal (near Pareto optimal) solution(s) in the initial population, which are expected to provide useful information and knowledge on the optimized problem. In this paper, to investigate the effectiveness of this initialization idea, we examine and quantify the effects of including one or more Pareto optimal solution(s) in the initial population on the performance of EMO algorithms. Experimental results demonstrate that it is worthwhile to first obtain and then include some Pareto optimal solutions in the initial population. Through a number of experiments and algorithm behavior analysis, this study provides supports and insights into EMO algorithm design and motivates further research on population initialization for EMO algorithms.
Findings of the SIGMORPHON 2023 Shared Task on Interlinear Glossing

This paper presents the findings of the SIGMORPHON 2023 Shared Task on Interlinear Glossing. This first iteration of the shared task explores glossing of a set of six typologically diverse languages: Arapaho, Gitksan, Lezgi, Natügu, Tsez and Uspanteko. The shared task encompasses two tracks: a resource-scarce closed track and an open track, where participants are allowed to utilize external data resources. Five teams participated in the shared task. The winning team Tü-CL achieved a 23.99%-point improvement over a baseline RoBERTa system in the closed track and a 17.42%-point improvement in the open track.
Investigating Phoneme Similarity with Artificially Accented Speech

While the deep learning revolution has led to significant performance improvements in speech recognition, accented speech remains a challenge. Current approaches to this challenge typically do not seek to understand and provide explanations for the variations of accented speech, whether they stem from native regional variation or non-native error patterns. This paper seeks to address non-native speaker variations from both a knowledge-based and a data-driven perspective. We propose to approximate non-native accented-speech pronunciation patterns by the means of two approaches: based on phonetic and phonological knowledge on the one hand and inferred from a text-to-speech system on the other. Artificial speech is then generated with a range of variants which have been captured in confusion matrices representing phoneme similarities. We then show that non-native accent confusions actually propagate to the transcription from the ASR, thus suggesting that the inference of accent specific phoneme confusions is achievable from artificial speech.
KGQA Without Retraining

Popular models for Knowledge Graph Question Answering (KGQA), including semantic parsing and End-to-End (E2E) models, decode into a constrained space of KG relations. Although E2E models accommodate novel entities at test-time, this constraint means they cannot access novel relations, requiring expensive and time-consuming retraining whenever a new relation is added to the KG. We propose KG-Flex, a new architecture for E2E KGQA that instead decodes into a continuous embedding space of relations, which enables use of novel relations at test-time. KG-Flex is the first to support KG updates with entirely novel triples, free of retraining, while still supporting end-to-end training with simple, weak supervision of (Q, A) pairs. Our architecture saves on time, energy, and data resources for retraining, yet we retain performance on standard benchmarks. We further demonstrate zero-shot use of novel relations, achieving up to 82% of base-line hit@1 on three QA datasets. KG-Flex can also fine-tune, requiring significantly shorter time than full retraining; fine-tuning on target data for 10% of full training increases hit@1 to 89-100% of baseline.
SIGMORPHON–UniMorph 2023 Shared Task 0: Typologically Diverse Morphological Inflection

The 2023 SIGMORPHON–UniMorph shared task on typologically diverse morphological inflection included a wide range of languages: 26 languages from 9 primary language families. The data this year was all lemma-split, to allow testing models’ generalization ability, and structured along the new hierarchical schema presented in (Batsuren et al., 2022). The systems submitted this year, 9 in number, showed ingenuity and innovativeness, including hard attention for explainability and bidirectional decoding. Special treatment was also given by many participants to the newly-introduced data in Japanese, due to the high abundance of unseen Kanji characters in its test set.
Glossy Bytes: Neural Glossing using Subword Encoding

This paper presents several different neural subword modelling based approaches to interlinear glossing for seven under-resourced languages as a part of the 2023 SIGMORPHON shared task on interlinear glossing. We experiment with various augmentation and tokenization strategies for both the open and closed tracks of data. We found that while byte-level models may perform well for greater amounts of data, character based approaches remain competitive in their performance in lower resource settings.
LISN @ SIGMORPHON 2023 Shared Task on Interlinear Glossing

This paper describes LISN”’“s submission to the second track (open track) of the shared task on Interlinear Glossing for SIGMORPHON 2023. Our systems are based on Lost, a variation of linear Conditional Random Fields initially developed as a probabilistic translation model and then adapted to the glossing task. This model allows us to handle one of the main challenges posed by glossing, i.e. the fact that the list of potential labels for lexical morphemes is not fixed in advance and needs to be extended dynamically when labelling units are not seen in training. In such situations, we show how to make use of candidate lexical glosses found in the translation and discuss how such extension affects the training and inference procedures. The resulting automatic glossing systems prove to yield very competitive results, especially in low-resource settings.
Distinguishing Romanized Hindi from Romanized Urdu

We examine the task of distinguishing between Hindi and Urdu when those languages are romanized, i.e., written in the Latin script. Both languages are widely informally romanized, and to the extent that they are identified in the Latin script by language identification systems, they are typically conflated. In the absence of large labeled collections of such text, we consider methods for generating training data. Beginning with a small set of seed words, each of which are strongly indicative of one of the languages versus the other, we prompt a pretrained large language model (LLM) to generate romanized text. Treating text generated from an Urdu prompt as one class and text generated from a Hindi prompt as the other class, we build a binary language identification (LangID) classifier. We demonstrate that the resulting classifier distinguishes manually romanized Urdu Wikipedia text from manually romanized Hindi Wikipedia text far better than chance. We use this classifier to estimate the prevalence of Urdu in a large collection of text labeled as romanized Hindi that has been used to train large language models. These techniques can be applied to bootstrap classifiers in other cases where a dataset is known to contain multiple distinct but related classes, such as different dialects of the same language, but for which labels cannot easily be obtained.
Effect Graph: Effect Relation Extraction for Explanation Generation

Argumentation is an important means of communication. For describing especially arguments about consequences, the notion of effect relations has been introduced recently. We propose a method to extract effect relations from large text resources and apply it on encyclopedic and argumentative texts. By connecting the extracted relations, we generate a knowledge graph which we call effect graph. For evaluating the effect graph, we perform crowd and expert annotations and create a novel dataset. We demonstrate a possible use case of the effect graph by proposing a method for explaining arguments from consequences.
Textual Entailment for Temporal Dependency Graph Parsing

We explore temporal dependency graph (TDG) parsing in the clinical domain. We leverage existing annotations on the THYME dataset to semi-automatically construct a TDG corpus. Then we propose a new natural language inference (NLI) approach to TDG parsing, and evaluate it both on general domain TDGs from wikinews and the newly constructed clinical TDG corpus. We achieve competitive performance on general domain TDGs with a much simpler model than prior work. On the clinical TDGs, our method establishes the first result of TDG parsing on clinical data with 0.79/0.88 micro/macro F1.