pwc-archive/papers-with-abstracts huggingface dataset:
Robust Graph Filter Identification and Graph Denoising from Signal Observations
Authors: Samuel Rey, Victor M. Tenorio, Antonio G. Marques
Date: 2022-10-16 00:00:00
Tasks: Denoising

When facing graph signal processing tasks, the workhorse assumption is that the graph describing the support of the signals is known. However, in many relevant applications the available graph suffers from observation errors and perturbations. As a result, any method relying on the graph topology may yield suboptimal results if those imperfections are ignored. Motivated by this, we propose a novel approach for handling perturbations on the links of the graph and apply it to the problem of robust graph filter (GF) identification from input-output observations. Different from existing works, we formulate a non-convex optimization problem that operates in the vertex domain and jointly performs GF identification and graph denoising. As a result, on top of learning the desired GF, an estimate of the graph is obtained as a byproduct. To handle the resulting bi-convex problem, we design an algorithm that blends techniques from alternating optimization and majorization minimization, showing its convergence to a stationary point. The second part of the paper i) generalizes the design to a robust setup where several GFs are jointly estimated, and ii) introduces an alternative algorithmic implementation that reduces the computational complexity. Finally, the detrimental influence of the perturbations and the benefits resulting from the robust approach are numerically analyzed over synthetic and real-world datasets, comparing them with other state-of-the-art alternatives.
Revisiting ensembling for improving the performance of deep learning models
Authors: Antonio Bruno, Davide Moroni, Massimo Martinelli
Date: 2022-08-21 00:00:00
Proceeding: 8th International Workshop Image Mining: Theory and Applications 2022 8
Tasks: Deep Learning, Image Classification

Ensembling is a very well-known strategy consisting in fusing several different models to achieve a new model for classification or
regression tasks. Over the years, ensembling has been proven to provide superior performance in various contexts related to pattern recognition and artificial intelligence. Moreover, the basic ideas that are at the basis of ensembling have been a source of inspiration for the design of the most recent deep learning architectures. Indeed, a close analysis of those architectures shows that some connections among layers and groups of layers achieve effects similar to those obtainable by bagging, boosting and stacking, which are the well-known three basic approaches to ensembling. However, we argue that research has not fully leveraged the
potential offered by ensembling. Indeed, this paper investigates some possible approaches to the combination of weak learners, or sub-components of weak learners, in the context of bagging. Based on previous results obtained in specific domains, we extend the approach to a reference dataset obtaining encouraging results.
Towards Provably Secure Encrypted Control Using Homomorphic Encryption
Authors: Kaoru Teranishi, Kiminao Kogiso
Date: 2022-10-17 00:00:00
Tasks: Mathematical Proofs

Encrypted control is a promising method for the secure outsourcing of controller computation to a public cloud. However, a feasible method for security proofs of control has not yet been developed in the field of encrypted control systems. Additionally, cryptography does not consider certain types of attacks on encrypted control systems; therefore, the security of such a system cannot be guaranteed using a secure cryptosystem. This study proposes a novel security definition for encrypted control under attack for control systems using cryptography. It applies the concept of provable security, which is the security of cryptosystems based on mathematical proofs, to encrypted control systems. Furthermore, this study analyzes the relation between the proposed security and the conventional security of cryptosystems. The results of the analysis demonstrated that the security of an encrypted control system can be enhanced by employing secure homomorphic encryption.
Sample Identifying Complexity of Encrypted Control Systems Under Least Squares Identification
Authors: Kaoru Teranishi, Kiminao Kogiso
Date: 2022-10-17 00:00:00

A sample identifying complexity has been introduced in the previous study to capture an adversary's estimation error of system identification. The complexity plays a crucial role in defining the security of encrypted control systems and designing a controller and security parameter for the systems. This study proposes a novel sample identifying complexity of encrypted control systems under an adversary who identifies system parameters using a least squares method. The proposed complexity is characterized by a controllability Gramian and ratio of identification input variance to the noise variance. We examine the tightness of the proposed complexity and its changes associated with the Gramian and variance ratio through numerical simulations. The simulation results demonstrate that the proposed complexity captures a behavior of estimation error with a sufficient level. Moreover, it confirmed that the effect of controllability Gramian in the proposed complexity becomes larger as the variance ratio increases.
Good AI for Good: How AI Strategies of the Nordic Countries Address the Sustainable Development Goals
Authors: Andreas Theodorou, Juan Carlos Nieves, Virginia Dignum
Date: 2022-10-08 00:00:00

Developed and used responsibly Artificial Intelligence (AI) is a force for global sustainable development. Given this opportunity, we expect that the many of the existing guidelines and recommendations for trustworthy or responsible AI will provide explicit guidance on how AI can contribute to the achievement of United Nations' Sustainable Development Goals (SDGs). This would in particular be the case for the AI strategies of the Nordic countries, at least given their high ranking and overall political focus when it comes to the achievement of the SDGs. In this paper, we present an analysis of existing AI recommendations from 10 different countries or organisations based on topic modelling techniques to identify how much these strategy documents refer to the SDGs. The analysis shows no significant difference on how much these documents refer to SDGs. Moreover, the Nordic countries are not different from the others albeit their long-term commitment to SDGs. More importantly, references to \textit{gender equality} (SDG 5) and \textit{inequality} (SDG 10), as well as references to environmental impact of AI development and use, and in particular the consequences for life on earth, are notably missing from the guidelines.
SAICL: Student Modelling with Interaction-level Auxiliary Contrastive Tasks for Knowledge Tracing and Dropout Prediction
Authors: Jungbae Park, Jinyoung Kim, Soonwoo Kwon, Sang Wan Lee
Date: 2022-10-07 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Knowledge Tracing

Knowledge tracing and dropout prediction are crucial for online education to estimate students' knowledge states or to prevent dropout rates. While traditional systems interacting with students suffered from data sparsity and overfitting, recent sample-level contrastive learning helps to alleviate this issue. One major limitation of sample-level approaches is that they regard students' behavior interaction sequences as a bundle, so they often fail to encode temporal contexts and track their dynamic changes, making it hard to find optimal representations for knowledge tracing and dropout prediction. To apply temporal context within the sequence, this study introduces a novel student modeling framework, SAICL: \textbf{s}tudent modeling with \textbf{a}uxiliary \textbf{i}nteraction-level \textbf{c}ontrastive \textbf{l}earning. In detail, SAICL can utilize both proposed self-supervised/supervised interaction-level contrastive objectives: MilCPC (\textbf{M}ulti-\textbf{I}nteraction-\textbf{L}evel \textbf{C}ontrastive \textbf{P}redictive \textbf{C}oding) and SupCPC (\textbf{Sup}ervised \textbf{C}ontrastive \textbf{P}redictive \textbf{C}oding). While previous sample-level contrastive methods for student modeling are highly dependent on data augmentation methods, the SAICL is free of data augmentation while showing better performance in both self-supervised and supervised settings. By combining cross-entropy with contrastive objectives, the proposed SAICL achieved comparable knowledge tracing and dropout prediction performance with other state-of-art models without compromising inference costs.
Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization
Authors: Chunpai Wang, Shaghayegh Sahebi, Siqian Zhao, Peter Brusilovsky, Laura O. Moraes
Date: 2022-10-06 00:00:00
Tasks: Knowledge Tracing

Knowledge Tracing (KT), which aims to model student knowledge level and predict their performance, is one of the most important applications of user modeling. Modern KT approaches model and maintain an up-to-date state of student knowledge over a set of course concepts according to students' historical performance in attempting the problems. However, KT approaches were designed to model knowledge by observing relatively small problem-solving steps in Intelligent Tutoring Systems. While these approaches were applied successfully to model student knowledge by observing student solutions for simple problems, they do not perform well for modeling complex problem solving in students.M ost importantly, current models assume that all problem attempts are equally valuable in quantifying current student knowledge.However, for complex problems that involve many concepts at the same time, this assumption is deficient. In this paper, we argue that not all attempts are equivalently important in discovering students' knowledge state, and some attempts can be summarized together to better represent student performance. We propose a novel student knowledge tracing approach, Granular RAnk based TEnsor factorization (GRATE), that dynamically selects student attempts that can be aggregated while predicting students' performance in problems and discovering the concepts presented in them. Our experiments on three real-world datasets demonstrate the improved performance of GRATE, compared to the state-of-the-art baselines, in the task of student performance prediction. Our further analysis shows that attempt aggregation eliminates the unnecessary fluctuations from students' discovered knowledge states and helps in discovering complex latent concepts in the problems.
Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda
Authors: Andrés Domínguez Hernández, Richard Owen, Dan Saattrup Nielsen, Ryan McConville
Date: 2022-10-05 00:00:00
Tasks: Misinformation

Machine learning (ML) enabled classification models are becoming increasingly popular for tackling the sheer volume and speed of online misinformation and other content that could be identified as harmful. In building these models, data scientists need to take a stance on the legitimacy, authoritativeness and objectivity of the sources of ``truth" used for model training and testing. This has political, ethical and epistemic implications which are rarely addressed in technical papers. Despite (and due to) their reported high accuracy and performance, ML-driven moderation systems have the potential to shape online public debate and create downstream negative impacts such as undue censorship and the reinforcing of false beliefs. Using collaborative ethnography and theoretical insights from social studies of science and expertise, we offer a critical analysis of the process of building ML models for (mis)information classification: we identify a series of algorithmic contingencies--key moments during model development that could lead to different future outcomes, uncertainty and harmful effects as these tools are deployed by social media platforms. We conclude by offering a tentative path toward reflexive and responsible development of ML tools for moderating misinformation and other harmful content online.
APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing
Authors: Haotian Zhang, Chenyang Bu, Fei Liu, Shuochen Liu, Yuhong Zhang, Xuegang Hu
Date: 2022-10-05 00:00:00
Tasks: Knowledge Tracing, Question Answering

Knowledge tracing (KT) is a fundamental task in educational data mining that mainly focuses on students' dynamic cognitive states of skills. The question-answering process of students can be regarded as a thinking process that considers the following two problems. One problem is which skills are needed to answer the question, and the other is how to use these skills in order. If a student wants to answer a question correctly, the student should not only master the set of skills involved in the question but also think and obtain the associative path on the skills graph. The nodes in the associative path refer to the skills needed and the path shows the order of using them. The associative path is referred to as the skill mode. Thus, obtaining the skill modes is the key to answering questions successfully. However, most existing KT models only focus on a set of skills, without considering the skill modes. We propose a KT model, called APGKT, that exploits skill modes. Specifically, we extract the subgraph topology of the skills involved in the question and combine the difficulty level of the skills to obtain the skill modes via encoding; then, through multi-layer recurrent neural networks, we obtain a student's higher-order cognitive states of skills, which is used to predict the student's future answering performance. Experiments on five benchmark datasets validate the effectiveness of the proposed model.
Towards sustainability assessment of artificial intelligence in artistic practices
Authors: Petra Jääskeläinen, Daniel Pargman, André Holzapfel
Date: 2022-10-03 00:00:00

An increasing number of artists use Ai in their creative practices (Creative-Ai) and their works have by now become visible at prominent art venues. The research community has, on the other hand, recognized that there are sustainability concerns of using Ai technologies related to, for instance, energy consumption and the increasing size and complexity of models. These two conflicting trajectories constitute the starting point of our research. Here, we discuss insights from our currently on-going fieldwork research and outline considerations for drawing various limitations in sustainability assessment studies of Ai art. We provide ground for further, more specific sustainability assessments in the domain, as well as knowledge on the state of sustainability assessments in this domain.
FAIR for AI: An interdisciplinary and international community building perspective
Authors: E. A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, Murali Emani, Ian Foster, Geoffrey Fox, Philip Harris, Lukas Heinrich, Shantenu Jha, Daniel S. Katz, Volodymyr Kindratenko, Christine R. Kirkpatrick, Kati Lassila-Perini, Ravi K. Madduri, Mark S. Neubauer, Fotis E. Psomopoulos, Avik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu
Date: 2022-09-30 00:00:00
Tasks: Management

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.
Prioritizing emergency evacuations under compounding levels of uncertainty
Authors: Lisa J. Einstein, Robert J. Moss, Mykel J. Kochenderfer
Date: 2022-09-30 00:00:00
Tasks: Decision Making, Humanitarian

Well-executed emergency evacuations can save lives and reduce suffering. However, decision makers struggle to determine optimal evacuation policies given the chaos, uncertainty, and value judgments inherent in emergency evacuations. We propose and analyze a decision support tool for pre-crisis training exercises for teams preparing for civilian evacuations and explore the tool in the case of the 2021 U.S.-led evacuation from Afghanistan. We use different classes of Markov decision processes (MDPs) to capture compounding levels of uncertainty in (1) the priority category of who appears next at the gate for evacuation, (2) the distribution of priority categories at the population level, and (3) individuals' claimed priority category. We compare the number of people evacuated by priority status under eight heuristic policies. The optimized MDP policy achieves the best performance compared to all heuristic baselines. We also show that accounting for the compounding levels of model uncertainty incurs added complexity without improvement in policy performance. Useful heuristics can be extracted from the optimized policies to inform human decision makers. We open-source all tools to encourage robust dialogue about the trade-offs, limitations, and potential of integrating algorithms into high-stakes humanitarian decision-making.
Active Informed Consent to Boost the Application of Machine Learning in Medicine
Authors: Marco Gerardi, Katarzyna Barud, Marie-Catherine Wagner, Nikolaus Forgo, Francesca Fallucchi, Noemi Scarpato, Fiorella Guadagni, Fabio Massimo Zanzotto
Date: 2022-09-27 00:00:00

Machine Learning may push research in precision medicine to unprecedented heights. To succeed, machine learning needs a large amount of data, often including personal data. Therefore, machine learning applied to precision medicine is on a cliff edge: if it does not learn to fly, it will deeply fall down. In this paper, we present Active Informed Consent (AIC) as a novel hybrid legal-technological tool to foster the gathering of a large amount of data for machine learning. We carefully analyzed the compliance of this technological tool to the legal intricacies protecting the privacy of European Citizens.
AI, Opacity, and Personal Autonomy
Authors: Bram Vaassen
Date: 2022-09-25 00:00:00
Tasks: Articles, Decision Making

Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings (Feller et al. 2016), medical diagnoses (Rajkomar et al. 2018; Esteva et al. 2019) and recruitment (Heilweil 2019, Van Esch et al. 2019). Academic articles (Floridi et al. 2018), policy texts (HLEG 2019), and popularizing books (O'Neill 2016, Eubanks 2018) alike warn that such algorithms tend to be _opaque_: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation (Lombrozo 2011, Hitchcock 2012), I formulate a moral concern for opaque algorithms that is yet to receive a systematic treatment in the literature: when such algorithms are used in life-changing decisions, they can obstruct us from effectively shaping our lives according to our goals and preferences, thus undermining our autonomy. I argue that this concern deserves closer attention as it furnishes the call for transparency in algorithmic decision-making with both new tools and new challenges.
Artificial Intelligence and Innovation to Reduce the Impact of Extreme Weather Events on Sustainable Production
Authors: Derrick Effah, Chunguang Bai, Matthew Quayson
Date: 2022-09-21 00:00:00

Frequent occurrences of extreme weather events substantially impact the lives of the less privileged in our societies, particularly in agriculture-inclined economies. The unpredictability of extreme fires, floods, drought, cyclones, and others endangers sustainable production and life on land (SDG goal 15), which translates into food insecurity and poorer populations. Fortunately, modern technologies such as Artificial Intelligent (AI), the Internet of Things (IoT), blockchain, 3D printing, and virtual and augmented reality (VR and AR) are promising to reduce the risk and impact of extreme weather in our societies. However, research directions on how these technologies could help reduce the impact of extreme weather are unclear. This makes it challenging to emploring digital technologies within the spheres of extreme weather. In this paper, we employed the Delphi Best Worst method and Machine learning approaches to identify and assess the push factors of technology. The BWM evaluation revealed that predictive nature was AI's most important criterion and role, while the mass-market potential was the less important criterion. Based on this outcome, we tested the predictive ability of machine elarning on a publilcly available dataset to affrm the predictive rols of AI. We presented the managerial and methodological implications of the study, which are crucial for research and practice. The methodology utilized in this study could aid decision-makers in devising strategies and interventions to safeguard sustainable production. This will also facilitate allocating scarce resources and investment in improving AI techniques to reduce the adverse impacts of extreme events. Correspondingly, we put forward the limitations of this, which necessitate future research.
Story Designer: Towards a Mixed-Initiative Tool to Create Narrative Structures
Authors: Alberto Alvarez, Jose Font, Julian Togelius
Date: 2022-10-11 00:00:00

Narratives are a predominant part of games, and their design poses challenges when identifying, encoding, interpreting, evaluating, and generating them. One way to address this would be to approach narrative design in a more abstract layer, such as narrative structures. This paper presents Story Designer, a mixed-initiative co-creative narrative structure tool built on top of the Evolutionary Dungeon Designer (EDD) that uses tropes, narrative conventions found across many media types, to design these structures. Story Designer uses tropes as building blocks for narrative designers to compose complete narrative structures by interconnecting them in graph structures called narrative graphs. Our mixed-initiative approach lets designers manually create their narrative graphs and feeds an underlying evolutionary algorithm with those, creating quality-diverse suggestions using MAP-Elites. Suggestions are visually represented for designers to compare and evaluate and can then be incorporated into the design for further manual editions. At the same time, we use the levels designed within EDD as constraints for the narrative structure, intertwining both level design and narrative. We evaluate the impact of these constraints and the system's adaptability and expressiveness, resulting in a potential tool to create narrative structures combining level design aspects with narrative.