bluuebunny/crossref_metadata_2025 huggingface dataset:
Sequential Vaccine Allocation with Delayed Feedback
Authors: Xiao Yichen , Ou Han-Ching , Chen Haipeng , Nguyen Van Thieu , Tran-Thanh Long
Published: July 2022

In this work we consider the problem of how to best allocate a limited supply of vaccines in the aftermath of an infectious disease outbreak by viewing the problem as a sequential game between a learner and an environment (specifically, a bandit problem). The difficulty of this problem lies in the fact that the payoff of vaccination cannot be directly observed, making it difficult to compare the relative effectiveness of vaccination on different population groups. Currently used vaccination policies make recommendations based on mathematical modelling and ethical considerations. These policies are static, and do not adapt as conditions change. Our aim is to design and evaluate an algorithm which can make use of routine surveillance data to dynamically adjust its recommendation. We evaluate the performance of our approach by applying it to a simulated epidemic of a disease based on real-world COVID-19 data, and show that our vaccination policy was able to perform better than existing vaccine allocation policies.

In particular, we show that with our allocation method, we can reduce the number of required vaccination by at least 50% in order to keep the peak number of hospitalised patients below a certain threshold. Also, when the same batch sizes are used, our method can reduce the peak number of hospitalisation by up to 20%.

We also demonstrate that our vaccine allocation does not vary the number of batches per group much, making it socially more acceptable (as it reduces uncertainty, hence results in better and more interpretable communication).
Conversational Inequality Through the Lens of Political Interruption
Authors: Yoo Clay H. , Wang Jiachen , Luo Yuxi , Khadilkar Kunal , KhudaBukhsh Ashiqur R.
Published: July 2022

We present a novel dataset of dialogues containing interruption with an aim to conduct a large-scale analysis of interruption patterns of people from diverse backgrounds in terms of gender, race/ethnicity, occupation, and political orientation. Our dataset includes 625,409 dialogues containing interruptions found in 275,420 transcripts from CNN, Fox News, and MSNBC spanning between January 2000 and July 2021. From this large, unlabeled pool of interruptions, we release an annotated dataset consisting of 2,000 dialogues with fine-grained interruption labels. We use this dataset to train an interruption classifier and predict the interruption type of a given dialogue. Our results reveal that male speakers (in our collected samples) tend to talk more than female speakers, while female speakers interrupt more. Moreover, people tend to use less intrusive interruptions when talking to others sharing the same political belief. This pattern becomes more pronounced among news media with stronger political bias.
A norm optimisation approach to SDGs: tackling poverty by acting on discrimination
Authors: Curto Georgina , Montes Nieves , Sierra Carles , Osman Nardine , Comim Flavio
Published: July 2022

Policies that seek to mitigate poverty by acting on equal opportunity have been found to aggravate discrimination against the poor (aporophobia), since individuals are made responsible for not progressing in the social hierarchy. Only a minority of the poor benefit from meritocracy in this era of growing inequality, generating resentment among those who seek to escape their needy situations by trying to climb up the ladder. Through the formulation and development of an agent-based social simulation, this study aims to analyse the role of norms implementing equal opportunity and social solidarity principles as enhancers or mitigators of aporophobia, as well as the threshold of aporophobia that would facilitate the success of poverty-reduction policies. The ultimate goal of the social simulation is to extract insights that could help inform and guide a new generation of policy making for poverty reduction by acting on the discrimination against the poor, in line with the UN “Leave No One Behind” principle. An “aporophobia-meter” will be developed and guidelines will be drafted based on both the simulation results and a review of poverty reduction policies at regional levels.
Interactive concept-map based summaries for SEND children
Authors: Galletti Martina , Anslow Michael , Bianchi Francesca , Calanca Manuela , Tomaiuoli Donatella , Van Trijp Remi , Vedovelli Diletta , Pasqua Eleonora
Published: July 2022

Equitable and inclusive quality education is a human right. It is crucial to provide for the learning needs of every child, especially those with learning disabilities. Traditional approaches to learning propose education paths performed with speech therapists. One of the most efficient strategies to help children with reading comprehension difficulties is the creation of a ``concept map'', a structured summary of the written text in a graph structure. Online tools that offer students the possibility to manually create or automatically extract concept maps from text have been created over the years. However, there is still a shortage of software that are specifically designed for children at risk and which produce a concept map that is tailored to the clinical profiles of individuals. In this Project Collaboration, we want to tackle this gap by implementing a multi-modal, online and open-access Artificial-Intelligence powered tool that could help these children to make sense of written text by enabling them to interactively create concept maps. The expected output is threefold. We will implement a new model for concept-map-based document summarization and a clinically appropriate web interface. We will evaluate them in real-world settings through user studies performed by speech therapists.
Climate Bot: A Machine Reading Comprehension System for Climate Change Question Answering
Authors: Rony Md Rashad Al Hasan , Zuo Ying , Kovriguina Liubov , Teucher Roman , Lehmann Jens
Published: July 2022

Climate change has a severe impact on the overall ecosystem of the whole world, including humankind. This demo paper presents Climate Bot - a machine reading comprehension system for question answering over documents about climate change. The proposed Climate Bot provides an interface for users to ask questions in natural language and get answers from reliable data sources. The purpose of the climate bot is to spread awareness about climate change and help individuals and communities to learn about the impact and challenges of climate change. Additionally, we open-sourced an annotated climate change dataset CCMRC to promote further research on the topic. This paper describes the dataset collection, annotation, system design, and evaluation.
On the Expressivity of Markov Reward (Extended Abstract)
Authors: Abel David , Dabney Will , Harutyunyan Anna , Ho Mark K. , Littman Michael L. , Precup Doina , Singh Satinder
Published: July 2022

Reward is the driving force for reinforcement-learning agents. We here set out to understand the expressivity of Markov reward as a way to capture tasks that we would want an agent to perform. We frame this study around three new abstract notions of "task": (1) a set of acceptable behaviors, (2) a partial ordering over behaviors, or (3) a partial ordering over trajectories. Our main results prove that while reward can express many of these tasks, there exist instances of each task type that no Markov reward function can capture. We then provide a set of polynomial-time algorithms that construct a Markov reward function that allows an agent to perform each task type, and correctly determine when no such reward function exists.
Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract)
Authors: Ali Mehdi , Berrendorf Max , Galkin Mikhail , Thost Veronika , Ma Tengfei , Tresp Volker , Lehmann Jens
Published: July 2022

For many years, link prediction on knowledge. graphs has been a purely transductive task, not allowing for reasoning on unseen entities.

Recently, increasing efforts are put into exploring semi- and fully inductive scenarios, enabling inference over unseen and emerging entities.

Still, all these approaches only consider triple-based KGs, whereas their richer counterparts, hyper-relational KGs (e.g., Wikidata), have not yet been properly studied.

In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.

Our experiments on a novel set of benchmarks show that qualifiers over typed edges can lead to performance improvements of 6% of absolute gains (for the Hits@10 metric) compared to triple-only baselines.

Our code is available at https://github.com/mali-git/hyper_relational_ilp.
Utilizing Treewidth for Quantitative Reasoning on Epistemic Logic Programs (Extended Abstract)
Authors: Besin Viktor , Hecher Markus , Woltran Stefan
Published: July 2022

Extending the popular Answer Set Programming (ASP) paradigm by introspective reasoning capacities has received increasing interest within the last years. Particular attention is given to the formalism of epistemic logic programs (ELPs) where standard rules are equipped with modal operators which allow to express conditions on literals for being known or possible, i.e., contained in all or some answer sets, respectively. ELPs thus deliver multiple collections of answer sets, known as world views. Employing ELPs for reasoning problems so far has mainly been restricted to standard deci- sion problems (complexity analysis) and enumeration (development of systems) of world views. In this paper, we first establish quantitative reasoning for ELPs, where the acceptance of a certain set of literals depends on the number (proportion) of world views that are compatible with the set. Second, we present a novel system capable of efficiently solving the underlying counting problems required for quantitative reasoning. Our system exploits the graph-based measure treewidth by iteratively finding (graph) abstractions of ELPs.
Measuring Data Leakage in Machine-Learning Models with Fisher Information (Extended Abstract)
Authors: Hannun Awni , Guo Chuan , van der Maaten Laurens
Published: July 2022

Machine-learning models contain information about the data they were trained on. This information leaks either through the model itself or through predictions made by the model. Consequently, when the training data contains sensitive attributes, assessing the amount of information leakage is paramount. We propose a method to quantify this leakage using the Fisher information of the model about the data. Unlike the worst-case a priori guarantees of differential privacy, Fisher information loss measures leakage with respect to specific examples, attributes, or sub-populations within the dataset. We motivate Fisher information loss through the Cramer-Rao bound and delineate the implied threat model. We provide efficient methods to compute Fisher information loss for output-perturbed generalized linear models. Finally, we empirically validate Fisher information loss as a useful measure of information leakage.
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent (Extended Abstract)
Authors: Kunstner Frederik , Kumar Raunak , Schmidt Mark
Published: July 2022

Expectation maximization (EM) is the default algorithm for fitting probabilistic models with missing or latent variables, yet we lack a full understanding of its non-asymptotic convergence properties. Previous works show results along the lines of “EM converges at least as fast as gradient descent” by assuming the conditions for the convergence of gradient descent apply. This approach is not only loose, in that it does not capture that EM can make more progress than a gradient step, but the assumptions fail to hold for textbook examples of EM like Gaussian mixtures. In this work, we show that for the common setting of exponential family distributions, viewing EM as a mirror descent algorithm leads to convergence rates in Kullback-Leibler (KL) divergence and how the KL divergence is related to first-order stationarity via Bregman divergences. In contrast to previous works, the analysis is invariant to the choice of parametrization and holds with minimal assumptions. We also show applications of these ideas to local linear (and superlinear) convergence rates, generalized EM, and non-exponential family distributions.
Open Data Science to Fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge (Extended Abstract)
Authors: Lozano Miguel Angel , Garibo-i-Orts Òscar , Piñol Eloy , Rebollo Miguel , Polotskaya Kristina , García-March Miguel Ángel , Conejero J. Alberto , Escolano Francisco , Oliver Nuria
Published: July 2022

We describe the deep learning-based COVID-19 cases predictor and the Pareto-optimal Non-Pharmaceutical Intervention (NPI) prescriptor developed by the winning team of the 500k XPRIZE Pandemic Response Challenge. The competition aimed at developing data-driven AI models to predict COVID-19 infection rates and to prescribe NPI Plans that governments, business leaders and organizations could implement to minimize harm when reopening their economies. In addition to the validation performed by XPRIZE with real data, our models were validated in a real-world scenario thanks to an ongoing collaboration with the Valencian Government in Spain. Our experience contributes to a necessary transition to more evidence-driven policy-making during a pandemic.
Asymmetric Hybrids: Dialogues for Computational Concept Combination (Extended Abstract)
Authors: Righetti Guendalina , Porello Daniele , Troquard Nicolas , Kutz Oliver , Hedblom Maria , Galliani Pietro
Published: July 2022

When considering two concepts in terms of extensional logic, their combination will often be trivial, returning an empty extension. Consider e.g. “a Fish Vehicle”, i.e., “a Vehicle which is also a Fish”. Still, people use sophisticated strategies to produce new, non-empty concepts. All these strategies involve the human ability to mend the conflicting attributes of the input concepts and to create new properties of the combination. We focus in particular on the case where a Head concept has superior ‘asymmetric’ control over steering the resulting combination (or hybridisation) with a Modifier concept. Specifically, we propose a dialogical model of the cognitive and logical mechanics of this asymmetric form of hybridisation. Its implementation is then evaluated using a combination of example ontologies.
Black-box Audit of YouTube's Video Recommendation: Investigation of Misinformation Filter Bubble Dynamics (Extended Abstract)
Authors: Tomlein Matus , Pecher Branislav , Simko Jakub , Srba Ivan , Moro Robert , Stefancova Elena , Kompan Michal , Hrckova Andrea , Podrouzek Juraj , Bielikova Maria
Published: July 2022

In this paper, we describe a black-box sockpuppeting audit which we carried out to investigate the creation and bursting dynamics of misinformation filter bubbles on YouTube. Pre-programmed agents acting as YouTube users stimulated YouTube's recommender systems: they first watched a series of misinformation promoting videos (bubble creation) and then a series of misinformation debunking videos (bubble bursting). Meanwhile, agents logged videos recommended to them by YouTube. After manually annotating these recommendations, we were able to quantify the portion of misinformative videos among them. The results confirm the creation of filter bubbles (albeit not in all situations) and show that these bubbles can be bursted by watching credible content. Drawing a direct comparison with a previous study, we do not see improvements in overall quantities of misinformation recommended.
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract)
Authors: Woodworth Blake , Bullins Brian , Shamir Ohad , Srebro Nathan
Published: July 2022

We resolve the min-max complexity of distributed stochastic convex optimization (up to a log factor) in the intermittent communication setting, where M machines work in parallel over the course of R rounds of communication to optimize the objective, and during each round of communication, each machine may sequentially compute K stochastic gradient estimates. We present a novel lower bound with a matching upper bound that establishes an optimal algorithm.
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval (Extended Abstract)
Authors: Zhan Jingtao , Mao Jiaxin , Liu Yiqun , Guo Jiafeng , Zhang Min , Ma Shaoping
Published: July 2022

Dense Retrieval (DR) has achieved state-of-the-art first-stage ranking effectiveness. However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space. Therefore, we present RepCONC, a novel retrieval model that learns discrete Representations via CONstrained Clustering. RepCONC jointly trains dual-encoders and the Product Quantization (PQ) method to learn discrete document representations and enables fast approximate NNS with compact indexes. It models quantization as a constrained clustering process, which requires the document embeddings to be uniformly clustered around the quantization centroids. We theoretically demonstrate the importance of the uniform clustering constraint and derive an efficient approximate solution for constrained clustering by reducing it to an instance of the optimal transport problem. Extensive experiments on two popular ad-hoc retrieval benchmarks show that RepCONC substantially outperforms a wide range of existing retrieval models in terms of retrieval effectiveness, memory efficiency, and time efficiency.
Text Transformations in Contrastive Self-Supervised Learning: A Review
Authors: Bhattacharjee Amrita , Karami Mansooreh , Liu Huan
Published: July 2022

Contrastive self-supervised learning has become a prominent technique in representation learning. The main step in these methods is to contrast semantically similar and dissimilar pairs of samples. However, in the domain of Natural Language Processing (NLP), the augmentation methods used in creating similar pairs with regard to contrastive learning (CL) assumptions are challenging. This is because, even simply modifying a word in the input might change the semantic meaning of the sentence, and hence, would violate the distributional hypothesis. In this review paper, we formalize the contrastive learning framework, emphasize the considerations that need to be addressed in the data transformation step, and review the state-of-the-art methods and evaluations for contrastive representation learning in NLP. Finally, we describe some challenges and potential directions for learning better text representations using contrastive methods.
Image-text Retrieval: A Survey on Recent Research and Development
Authors: Cao Min , Li Shiping , Li Juntao , Nie Liqiang , Zhang Min
Published: July 2022

In the past few years, cross-modal image-text retrieval (ITR) has experienced increased interest in the research community due to its excellent research value and broad real-world application. It is designed for the scenarios where the queries are from one modality and the retrieval galleries from another modality. This paper presents a comprehensive and up-to-date survey on the ITR approaches from four perspectives. By dissecting an ITR system into two processes: feature extraction and feature alignment, we summarize the recent advance of the ITR approaches from these two perspectives. On top of this, the efficiency-focused study on the ITR system is introduced as the third perspective. To keep pace with the times, we also provide a pioneering overview of the cross-modal pre-training ITR approaches as the fourth perspective. Finally, we outline the common benchmark datasets and evaluation metric for ITR, and conduct the accuracy comparison among the representative ITR approaches. Some critical yet less studied issues are discussed at the end of the paper.
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
Authors: Dong Haoyu , Cheng Zhoujun , He Xinyi , Zhou Mengyu , Zhou Anda , Zhou Fan , Liu Ao , Han Shi , Zhang Dongmei
Published: July 2022

Following the success of pre-training techniques in the natural language domain, a flurry of table pre-training frameworks have been proposed and have achieved new state-of-the-arts on various downstream tasks such as table question answering, table type recognition, column relation classification, table search, and formula prediction. Various model architectures have been explored to best capture the characteristics of (semi-)structured tables, especially specially-designed attention mechanisms. Moreover, to fully leverage the supervision signals in unlabeled tables, diverse pre-training objectives have been designed and evaluated, for example, denoising cell values, predicting numerical relationships, and learning a neural SQL executor. This survey aims to provide a comprehensive review of model designs, pre-training objectives, and downstream tasks for table pre-training, and we further share our thoughts on existing challenges and future opportunities.
A Survey on Dialogue Summarization: Recent Advances and New Frontiers
Authors: Feng Xiachong , Feng Xiaocheng , Qin Bing
Published: July 2022

Dialogue summarization aims to condense the original dialogue into a shorter version covering salient information, which is a crucial way to reduce dialogue data overload. Recently, the promising achievements in both dialogue systems and natural language generation techniques drastically lead this task to a new landscape, which results in significant research attentions. However, there still remains a lack of a comprehensive survey for this task. To this end, we take the first step and present a thorough review of this research field carefully and widely. In detail, we systematically organize the current works according to the characteristics of each domain, covering meeting, chat, email thread, customer service and medical dialogue. Additionally, we provide an overview of publicly available research datasets as well as organize two leaderboards under unified metrics. Furthermore, we discuss some future directions, including faithfulness, multi-modal, multi-domain and multi-lingual dialogue summarization, and give our thoughts respectively. We hope that this first survey of dialogue summarization can provide the community with a quick access and a general picture to this task and motivate future researches.
Legal Judgment Prediction: A Survey of the State of the Art
Authors: Feng Yi , Li Chuanyi , Ng Vincent
Published: July 2022

Automatic legal judgment prediction (LJP) has recently received increasing attention in the natural language processing community in part because of its practical values as well as the associated research challenges. We present an overview of the major milestones made in LJP research covering multiple jurisdictions and multiple languages, and conclude with promising future research directions.
Deep Learning with Logical Constraints
Authors: Giunchiglia Eleonora , Stoian Mihaela Catalina , Lukasiewicz Thomas
Published: July 2022

In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the background knowledge itself, e.g., for safety-critical applications. In this survey, we retrace such works and categorize them based on (i) the logical language that they use to express the background knowledge and (ii) the goals that they achieve.
Neural Re-ranking in Multi-stage Recommender Systems: A Review
Authors: Liu Weiwen , Xi Yunjia , Qin Jiarui , Sun Fei , Chen Bo , Zhang Weinan , Zhang Rui , Tang Ruiming
Published: July 2022

As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects users’ experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely adopted in industrial applications. This review aims at integrating re-ranking algorithms into a broader picture, and paving ways for more comprehensive solutions for future research. For this purpose, we first present a taxonomy of current methods on neural re-ranking. Then we give a description of these methods along with the historic development according to their objectives. The network structure, personalization, and complexity are also discussed and compared. Next, we provide a benchmark for the major neural re-ranking models and quantitatively analyze their re-ranking performance. Finally, the review concludes with a discussion on future prospects of this field. A list of papers discussed in this review, the benchmark datasets, our re-ranking library LibRerank, and detailed parameter settings are publicly available at https://github.com/LibRerank-Community/LibRerank.
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Authors: Liu Xin , Yan Mingyu , Deng Lei , Li Guoqi , Ye Xiaochun , Fan Dongrui , Pan Shirui , Xie Yuan
Published: July 2022

Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urgent demand is unsurprisingly made to accelerate GNNs for more efficient execution. In this paper, we provide a comprehensive survey on acceleration methods for GNNs from an algorithmic perspective. We first present a new taxonomy to classify existing acceleration methods into five categories. Based on the classification, we systematically discuss these methods and highlight their correlations. Next, we provide comparisons from aspects of the efficiency and characteristics of these methods. Finally, we suggest some promising prospects for future research.
Evaluation Methods for Representation Learning: A Survey
Authors: Nozawa Kento , Sato Issei
Published: July 2022

Representation learning enables us to automatically extract generic feature representations from a dataset to solve another machine learning task. Recently, extracted feature representations by a representation learning algorithm and a simple predictor have exhibited state-of-the-art performance on several machine learning tasks. Despite its remarkable progress, there exist various ways to evaluate representation learning algorithms depending on the application because of the flexibility of representation learning. To understand the current applications of representation learning, we review evaluation methods of representation learning algorithms. On the basis of our evaluation survey, we also discuss the future direction of representation learning. The extended version, https://arxiv.org/abs/2204.08226, gives more detailed discussions and a survey on theoretical analyses.
A Unified View of Relational Deep Learning for Drug Pair Scoring
Authors: Rozemberczki Benedek , Bonner Stephen , Nikolov Andriy , Ughetto Michaël , Nilsson Sebastian , Papa Eliseo
Published: July 2022

In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction, and combination therapy design tasks have been proposed. Here, we present a unified theoretical view of relational machine learning models which can address these tasks. We provide fundamental definitions, compare existing model architectures and discuss performance metrics, datasets, and evaluation protocols. In addition, we emphasize possible high-impact applications and important future research directions in this domain.
Fourier Analysis-based Iterative Combinatorial Auctions
Authors: Weissteiner Jakob , Wendler Chris , Seuken Sven , Lubin Ben , Püschel Markus
Published: July 2022

Recent advances in Fourier analysis have brought new tools to efficiently represent and learn set functions. In this paper, we bring the power of Fourier analysis to the design of combinatorial auctions (CAs). The key idea is to approximate bidders' value functions using Fourier-sparse set functions, which can be computed using a relatively small number of queries. Since this number is still too large for practical CAs, we propose a new hybrid design: we first use neural networks (NNs) to learn bidders’ values and then apply Fourier analysis to the learned representations. On a technical level, we formulate a Fourier transform-based winner determination problem and derive its mixed integer program formulation. Based on this, we devise an iterative CA that asks Fourier-based queries. We experimentally show that our hybrid ICA achieves higher efficiency than prior auction designs, leads to a fairer distribution of social welfare, and significantly reduces runtime. With this paper, we are the first to leverage Fourier analysis in CA design and lay the foundation for future work in this area. Our code is available on GitHub: https://github.com/marketdesignresearch/FA-based-ICAs.
Detecting and Understanding Harmful Memes: A Survey
Authors: Sharma Shivam , Alam Firoj , Akhtar Md. Shad , Dimitrov Dimitar , Da San Martino Giovanni , Firooz Hamed , Halevy Alon , Silvestri Fabrizio , Nakov Preslav , Chakraborty Tanmoy
Published: July 2022

The automatic identification of harmful content online is of major concern for social media platforms, policymakers, and society. Researchers have studied textual, visual, and audio content, but typically in isolation. Yet, harmful content often combines multiple modalities, as in the case of memes. With this in mind, here we offer a comprehensive survey with a focus on harmful memes. Based on a systematic analysis of recent literature, we first propose a new typology of harmful memes, and then we highlight and summarize the relevant state of the art. One interesting finding is that many types of harmful memes are not really studied, e.g., such featuring self-harm and extremism, partly due to the lack of suitable datasets. We further find that existing datasets mostly capture multi-class scenarios, which are not inclusive of the affective spectrum that memes can represent. Another observation is that memes can propagate globally through repackaging in different languages and that they can also be multilingual, blending different cultures. We conclude by highlighting several challenges related to multimodal semiotics, technological constraints, and non-trivial social engagement, and we present several open-ended aspects such as delineating online harm and empirically examining related frameworks and assistive interventions, which we believe will motivate and drive future research.
Towards Verifiable Federated Learning
Authors: Zhang Yanci , Yu Han
Published: July 2022

Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models. Nevertheless, due to the nature of open participation by self-interested entities, it needs to guard against potential misbehaviours by legitimate FL participants. FL verification techniques are promising solutions for this problem. They have been shown to effectively enhance the reliability of FL networks and build trust among participants. Verifiable FL has become an emerging topic of research that has attracted significant interest from the academia and the industry alike. Currently, there is no comprehensive survey on the field of verifiable federated learning, which is interdisciplinary in nature and can be challenging for researchers to enter into. In this paper, we bridge this gap by reviewing works focusing on verifiable FL. We propose a novel taxonomy for verifiable FL covering both centralised and decentralised settings, summarise the commonly adopted performance evaluation approaches, and discuss promising directions towards a versatile verifiable FL framework.
A Survey on Neural Open Information Extraction: Current Status and Future Directions
Authors: Zhou Shaowen , Yu Bowen , Sun Aixin , Long Cheng , Li Jingyang , Sun Jian
Published: July 2022

Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora. The technique well suits many open-world natural language understanding scenarios, such as automatic knowledge base construction, open-domain question answering, and explicit reasoning. Thanks to the rapid development in deep learning technologies, numerous neural OpenIE architectures have been proposed and achieve considerable performance improvement. In this survey, we provide an extensive overview of the state-of-the-art neural OpenIE models, their key design decisions, strengths and weakness. Then, we discuss limitations of current solutions and the open issues in OpenIE problem itself. Finally we list recent trends that could help expand its scope and applicability, setting up promising directions for future research in OpenIE. To our best knowledge, this paper is the first review on neural OpenIE.
Abstraction in Data-Sparse Task Transfer (Extended Abstract)
Authors: Fitzgerald Tesca , Goel Ashok , Thomaz Andrea
Published: July 2022

When a robot adapts a learned task for a novel environment, any changes to objects in the novel environment have an unknown effect on its task execution. For example, replacing an object in a pick-and-place task affects where the robot should target its actions, but does not necessarily affect the underlying action model. In contrast, replacing a tool that the robot will use to complete a task will effectively alter its end-effector pose with respect to the robot's base coordinate system, and thus the robot's motion must be replanned accordingly.

These examples highlight the relationship among (i) differences between the source and target environments, (ii) the level of abstraction at which a robot's task model should be represented to enable transfer to the target environment, and (iii) the information needed to ground the abstracted task representation in the target environment. In this abstract, summarizing our full article [Fitzgerald et al., 2021], we present our taxonomy of transfer problems based on this relationship. We also describe a knowledge representation called the Tiered Task Abstraction (TTA) and demonstrate its applicability to a variety of transfer problems in the taxonomy. Our experimental results indicate a trade-off between the generality and data requirements of a task representation, and reinforce the need for multiple transfer methods that operate at different levels of abstraction.
Experimental Comparison and Survey of Twelve Time Series Anomaly Detection Algorithms (Extended Abstract)
Authors: Freeman Cynthia , Merriman Jonathan , Beaver Ian , Mueen Abdullah
Published: July 2022

The existence of an anomaly detection method that is optimal for all domains is a myth. Thus, there exists a plethora of anomaly detection methods which increases every year for a wide variety of domains. But a strength can also be a weakness; given this massive library of methods, how can one select the best method for their application? Current literature is focused on creating new anomaly detection methods or large frameworks for experimenting with multiple methods at the same time. However, and especially as the literature continues to expand, an extensive evaluation of every anomaly detection method is simply not feasible. To reduce this evaluation burden, we present guidelines to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays such as seasonality, trend, level change concept drift, and missing time steps. We provide a comprehensive experimental validation and survey of twelve anomaly detection methods over different time series characteristics to form guidelines based on several metrics: the AUC (Area Under the Curve), windowed F-score, and Numenta Anomaly Benchmark (NAB) scoring model. Applying our methodologies can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods, especially in an online setting.
Mechanism Design with Predictions
Authors: Xu Chenyang , Lu Pinyan
Published: July 2022

Improving algorithms via predictions is a very active research topic in recent years. This paper initiates the systematic study of mechanism design in this model. In a number of well-studied mechanism design settings, we make use of imperfect predictions to design mechanisms that perform much better than traditional mechanisms if the predictions are accurate (consistency), while always retaining worst-case guarantees even with very imprecise predictions (robustness). Furthermore, we refer to the largest prediction error sufficient to give a good performance as the error tolerance of a mechanism, and observe that an intrinsic tradeoff among consistency, robustness and error tolerance is common for mechanism design with predictions.
Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract)
Authors: Zhu Ye , Ting Kai Ming
Published: July 2022

This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel. We show that Isolation kernel addresses two deficiencies of t-SNE that employs Gaussian kernel, and the use of Isolation kernel enables t-SNE to deal with large-scale datasets in less runtime without trading off accuracy, unlike existing methods used in speeding up t-SNE.
Irrational, but Adaptive and Goal Oriented: Humans Interacting with Autonomous Agents
Authors: Azaria Amos
Published: July 2022

Autonomous agents that interact with humans are becoming more and more prominent.

Currently, such agents usually take one of the following approaches for considering human behavior. Some methods assume either a fully cooperative or a zero-sum setting; these assumptions entail that the human's goals are either identical to that of the agent, or their opposite. In both cases, the agent is not required to explicitly model the human’s goals and account for humans' adaptation nature.

Other methods first compose a model of human behavior based on observing human actions, and then optimize the agent’s actions based on this model. Such methods do not account for how the human will react to the agent's actions and thus, suffer an overestimation bias.

Finally, other methods, such as model free reinforcement learning, merely learn which actions the agent should take at which states. While such methods can, theoretically, account for human adaptation nature, since they require extensive interaction with humans, they usually run in simulation.

By not considering the human’s goals, autonomous agents act selfishly, lack generalization, require vast amounts of data, and cannot account for human’s strategic behavior. Therefore, we call for pursuing solution concepts for autonomous agents interacting with humans that consider the human’s goals and adaptive nature.
Controllable Text Generation for Open-Domain Creativity and Fairness
Authors: Peng Nanyun (Violet)
Published: July 2022

Recent advances in large pre-trained language models have demonstrated strong results in generating natural languages and significantly improved performances for many natural language generation (NLG) applications such as machine translation and text summarization. However, when the generation tasks are more open-ended and the content is under-specified, existing techniques struggle to generate long-term coherent and creative content. Moreover, the models exhibit and even amplify social biases that are learned from the training corpora. This happens because the generation models are trained to capture the surface patterns (i.e. sequences of words), instead of capturing underlying semantics and discourse structures, as well as background knowledge including social norms. In this paper, I introduce our recent works on controllable text generation to enhance the creativity and fairness of language generation models. We explore hierarchical generation and constrained decoding, with applications to creative language generation including story, poetry, and figurative languages, and bias mitigation for generation models.
Towards New Optimized Artificial Immune Recognition Systems under the Belief Function Theory
Authors: Abdelkhalek Rihab
Published: July 2022

Artificial Immune Recognition Systems (AIRS) are powerful machine learning techniques, which aim to solve real world problems. A number of AIRS versions have produced successful prediction results. Nevertheless, these methods are unable to handle the uncertainty that could spread out at any stage of the AIRS approach. This issue is considered as a huge obstacle for having accurate and effective classification outputs. Therefore, our main objective is to handle this uncertainty using the belief function theory. We opt also in this article for an optimization over the classical AIRS approaches in order to enhance the classification performance.
Extending Decision Tree to Handle Multiple Fairness Criteria
Authors: Castelnovo Alessandro
Published: July 2022

The demand for machine learning systems that can provide both transparency and fairness is constantly growing. Since the concept of fairness depends on the context, studies in the literature have proposed various formalisation and mitigation strategies. In this work, we propose a novel, flexible, discrimination-aware classifier that allows the user to: (i) select and mitigate the desired fairness criterion from a set of available options; (ii) implement more than one fairness criterion; (iii) handle more than one sensitive attribute; and (iv) specify the desired level of fairness to meet specific business needs or regulatory requirements. Our approach is based on an optimised extension to the decision-tree classifier, and aims to provide transparent and fair rules to the final users.
Hybrid Learning System for Large-scale Medical Image Analysis
Authors: Cheng Zehua , Wu Lianlong
Published: July 2022

Adequate annotated data cannot always be satisfied in medical imaging applications. To address such a challenge, we would explore ways to reduce the quality and quantity of annotations requirements of the deep learning model by developing a hybrid learning system. We combined self-supervised learning, semi-supervised learning and weak-supervised learning to improve annotation utilization. Our primary research work on 2D medical image detection under poor annotation conditions has found that better regularization and adversarial loss can improve the robustness and performance with poor annotation conditions.
KRAKEN: A Novel Semantic-Based Approach for Keyphrases Extraction
Authors: D'Amico Simone
Published: July 2022

We propose KRAKEN, a novel approach for the extraction of keyphrases from texts. To this aim, KRAKEN makes use of distributional semantics to identify, as completely as possible, representative portions of documents, i.e. keyphrases. In addition, we define novel metrics to assess a weighted significance to the keyphrases extracted from a document, identifying the most important ones by assessing their semantic similarity with the text of the document they belong to.
Decomposition Methods for Solving Scheduling Problem Using Answer Set Programming
Authors: El-Kholany Mohammed  M. S.
Published: July 2022

This study proposes solving scheduling problems in industrial applications using the decomposition approach. The proposed model has been built using Multi-shot Answer Set Programming with Difference Logic. We tested our model with some benchmark instances and the results showed that our model is comparable to Constraint Programming to other heuristics in the literature.
Decentralized Autonomous Organizations and Multi-agent Systems for Artificial Intelligence Applications and Data Analysis
Authors: Facchini Sante Dino
Published: July 2022

The Ph.D research project aims to explore the potential of the Decentralized Autonomous Organization paradigm in conjunction with classic software architectures for Artificial Intelligence applications. The intended goal is to investigate and formalize a possible integration path between Multi-agent System architectures and Decentralized Autonomous Organizations. Starting from the Foundation for Intelligent Physical Agents standards, we will extend basic primitives to integrate Multi-agent Systems on Distributed Ledger Technology networks. Possible deployment of services and applications in the Internet-of-Things, Artificial Intelligence and Distributed Machine Learning areas will be tested. Application of Data Analysis techniques on datasets built on such a framework will be also addressed.
On the Complexity of Calculating Approval-Based Winners in Candidates-Embedded Metrics
Authors: Yang Yongjie
Published: July 2022

We study approval-based multiwinner voting where candidates are in a metric space and committees are valuated in terms of their distances to the given votes. In particular, we consider three different distance functions, and for each of them we study both the utilitarian rules and the egalitarian rules, resulting in six variants of winners determination problems. We focus on the (parameterized) complexity of these problems for both the general metric and several special metrics. For hardness results, we also discuss their approximability.
Engineering Socially-Oriented Autonomous Agents and Multiagent Systems
Authors: Montes Nieves
Published: July 2022

The emergent field of social AI is concerned with the development of autonomous agents that are able to act as part of larger community. Within this context, my research seeks to engineer meaningful social interactions among a group of agents from two different approaches. First, the societal level leverages constructs that apply to a society as a whole, like norms and social values. Second, the individual level endows agents with the ability to reason about others, by making use of Theory of Mind capabilities.
Data-Efficient Algorithms and Neural Natural Language Processing: Applications in the Healthcare Domain
Authors: Shim Heereen
Published: July 2022

Recently proposed pre-trained language models can be easily fine-tuned to a wide range of downstream tasks. However, fine-tuning requires a large training set. This PhD project introduces novel natural language processing (NLP) use cases in the healthcare domain where obtaining a large training dataset is difficult and expensive. To this end, we propose data-efficient algorithms to fine-tune NLP models in low-resource settings and validate their effectiveness. We expect the outcomes of this PhD project could contribute to the NLP research and low-resource application domains.
Information Injection to Deep Learning Solutions in Knowledge Transfer
Authors: Tomaszewska Paulina
Published: July 2022

Nowadays, with the thrive of AutoML techniques, it is expected that Machine Learning algorithms will work well without any human intervention. This is why the biggest focus is on introducing new neural network architectures, especially those capable of learning to learn. Recently, the Data-Centric AI Challenge was proposed by Andrew Ng whose goal was to change the paradigm and instead of having a fixed dataset and modifying the model, now the model is fixed and the data is preprocessed so that the model results in the best performance. In my thesis, I would like to focus on another approach, where I would not modify the given data nor introduce new architectures, instead, I would like to propose new ways of injecting additional information into knowledge transfer models to increase their performance.