pwc-archive/papers-with-abstracts huggingface dataset:
The Role of Time and Data: Online Conformance Checking in the Manufacturing Domain
Authors: Florian Stertz, Juergen Mangler, Stefanie Rinderle-Ma
Date: 2021-05-04 00:00:00
Tasks: TAR

Process mining has matured as analysis instrument for process-oriented data in recent years. Manufacturing is a challenging domain that craves for process-oriented technologies to address digitalization challenges. We found that process mining creates high expectations, but its implementation and usage by manufacturing experts such as process supervisors and shopfloor workers remain unclear to a certain extent. Reason (1) is that even though manufacturing allows for well-structured processes, the actual workflow is rarely captured in a process model. Even if a model is available, a software for orchestrating and logging the execution is often missing. Reason (2) refers to the work reality in manufacturing: a process instance is started by a shopfloor worker who then turns to work on other things. Hence continuous monitoring of the process instances does not happen, i.e., process monitoring is merely a secondary task, and the shopfloor worker can only react to problems/errors that have already occurred. (1) and (2) motivate the goals of this study that is driven by Technical Action Research (TAR). Based on the experimental artifact TIDATE -- a lightweight process execution and mining framework -- it is studied how the correct execution of process instances can be ensured and how a data set suitable for process mining can be generated at run time in a real-world setting. Secondly, it is investigated whether and how process mining supports domain experts during process monitoring as a secondary task. The findings emphasize the importance of online conformance checking in manufacturing and show how appropriate data sets can be identified and generated.
Towards Accountability in the Use of Artificial Intelligence for Public Administrations
Authors: Michele Loi, Matthias Spielkamp
Date: 2021-05-04 00:00:00

We argue that the phenomena of distributed responsibility, induced acceptance, and acceptance through ignorance constitute instances of imperfect delegation when tasks are delegated to computationally-driven systems. Imperfect delegation challenges human accountability. We hold that both direct public accountability via public transparency and indirect public accountability via transparency to auditors in public organizations can be both instrumentally ethically valuable and required as a matter of deontology from the principle of democratic self-government. We analyze the regulatory content of 16 guideline documents about the use of AI in the public sector, by mapping their requirements to those of our philosophical account of accountability, and conclude that while some guidelines refer to processes that amount to auditing, it seems that the debate would benefit from more clarity about the nature of the entitlement of auditors and the goals of auditing, also in order to develop ethically meaningful standards with respect to which different forms of auditing can be evaluated and compared.
Two-Stage Facility Location Games with Strategic Clients and Facilities
Authors: Simon Krogmann, Pascal Lenzner, Louise Molitor, Alexander Skopalik
Date: 2021-05-04 00:00:00
Tasks: Vocal Bursts Valence Prediction

We consider non-cooperative facility location games where both facilities and clients act strategically and heavily influence each other. This contrasts established game-theoretic facility location models with non-strategic clients that simply select the closest opened facility. In our model, every facility location has a set of attracted clients and each client has a set of shopping locations and a weight that corresponds to her spending capacity. Facility agents selfishly select a location for opening their facility to maximize the attracted total spending capacity, whereas clients strategically decide how to distribute their spending capacity among the opened facilities in their shopping range. We focus on a natural client behavior similar to classical load balancing: our selfish clients aim for a distribution that minimizes their maximum waiting times for getting serviced, where a facility's waiting time corresponds to its total attracted client weight. We show that subgame perfect equilibria exist and give almost tight constant bounds on the Price of Anarchy and the Price of Stability, which even hold for a broader class of games with arbitrary client behavior. Since facilities and clients influence each other, it is crucial for the facilities to anticipate the selfish clients' behavior when selecting their location. For this, we provide an efficient algorithm that also implies an efficient check for equilibrium. Finally, we show that computing a socially optimal facility placement is NP-hard and that this result holds for all feasible client weight distributions.
Automatic Learning to Detect Concept Drift
Authors: Hang Yu, Tianyu Liu, Jie Lu, Guangquan Zhang
Date: 2021-05-04 00:00:00
Tasks: Active Learning, Drift Detection, Meta-Learning

Many methods have been proposed to detect concept drift, i.e., the change in the distribution of streaming data, due to concept drift causes a decrease in the prediction accuracy of algorithms. However, the most of current detection methods are based on the assessment of the degree of change in the data distribution, cannot identify the type of concept drift. In this paper, we propose Active Drift Detection with Meta learning (Meta-ADD), a novel framework that learns to classify concept drift by tracking the changed pattern of error rates. Specifically, in the training phase, we extract meta-features based on the error rates of various concept drift, after which a meta-detector is developed via a prototypical neural network by representing various concept drift classes as corresponding prototypes. In the detection phase, the learned meta-detector is fine-tuned to adapt to the corresponding data stream via stream-based active learning. Hence, Meta-ADD uses machine learning to learn to detect concept drifts and identify their types automatically, which can directly support drift understand. The experiment results verify the effectiveness of Meta-ADD.
An Overview of Laser Injection against Embedded Neural Network Models
Authors: Mathieu Dumont, Pierre-Alain Moellic, Raphael Viera, Jean-Max Dutertre, Rémi Bernhard
Date: 2021-05-04 00:00:00
Tasks: BIG-bench Machine Learning

For many IoT domains, Machine Learning and more particularly Deep Learning brings very efficient solutions to handle complex data and perform challenging and mostly critical tasks. However, the deployment of models in a large variety of devices faces several obstacles related to trust and security. The latest is particularly critical since the demonstrations of severe flaws impacting the integrity, confidentiality and accessibility of neural network models. However, the attack surface of such embedded systems cannot be reduced to abstract flaws but must encompass the physical threats related to the implementation of these models within hardware platforms (e.g., 32-bit microcontrollers). Among physical attacks, Fault Injection Analysis (FIA) are known to be very powerful with a large spectrum of attack vectors. Most importantly, highly focused FIA techniques such as laser beam injection enable very accurate evaluation of the vulnerabilities as well as the robustness of embedded systems. Here, we propose to discuss how laser injection with state-of-the-art equipment, combined with theoretical evidences from Adversarial Machine Learning, highlights worrying threats against the integrity of deep learning inference and claims that join efforts from the theoretical AI and Physical Security communities are a urgent need.
A Review of Confidentiality Threats Against Embedded Neural Network Models
Authors: Raphaël Joud, Pierre-Alain Moellic, Rémi Bernhard, Jean-Baptiste Rigaud
Date: 2021-05-04 00:00:00
Tasks: Medical Diagnosis, Model extraction, Side Channel Analysis

Utilization of Machine Learning (ML) algorithms, especially Deep Neural Network (DNN) models, becomes a widely accepted standard in many domains more particularly IoT-based systems. DNN models reach impressive performances in several sensitive fields such as medical diagnosis, smart transport or security threat detection, and represent a valuable piece of Intellectual Property. Over the last few years, a major trend is the large-scale deployment of models in a wide variety of devices. However, this migration to embedded systems is slowed down because of the broad spectrum of attacks threatening the integrity, confidentiality and availability of embedded models. In this review, we cover the landscape of attacks targeting the confidentiality of embedded DNN models that may have a major impact on critical IoT systems, with a particular focus on model extraction and data leakage. We highlight the fact that Side-Channel Analysis (SCA) is a relatively unexplored bias by which model's confidentiality can be compromised. Input data, architecture or parameters of a model can be extracted from power or electromagnetic observations, testifying a real need from a security point of view.
Implicit Regularization in Deep Tensor Factorization
Authors: Paolo Milanesi, Hachem Kadri, Stéphane Ayache, Thierry Artières
Date: 2021-05-04 00:00:00
Tasks: Matrix Completion

Attempts of studying implicit regularization associated to gradient descent (GD) have identified matrix completion as a suitable test-bed. Late findings suggest that this phenomenon cannot be phrased as a minimization-norm problem, implying that a paradigm shift is required and that dynamics has to be taken into account. In the present work we address the more general setup of tensor completion by leveraging two popularized tensor factorization, namely Tucker and TensorTrain (TT). We track relevant quantities such as tensor nuclear norm, effective rank, generalized singular values and we introduce deep Tucker and TT unconstrained factorization to deal with the completion task. Experiments on both synthetic and real data show that gradient descent promotes solution with low-rank, and validate the conjecture saying that the phenomenon has to be addressed from a dynamical perspective.
Large-scale Taxonomy Induction Using Entity and Word Embeddings
Authors: Petar Ristoski, Stefano Faralli, Simone Paolo Ponzetto, Heiko Paulheim
Date: 2021-05-04 00:00:00
Tasks: Word Embeddings

Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies. However, building taxonomies manually is a costly endeavor, and hence, automatic methods for taxonomy induction are a good alternative to build large-scale taxonomies. In this paper, we propose TIEmb, an approach for automatic unsupervised class subsumption axiom extraction from knowledge bases using entity and text embeddings. We apply the approach on the WebIsA database, a database of subsumption relations extracted from the large portion of the World Wide Web, to extract class hierarchies in the Person and Place domain.
VersaGNN: a Versatile accelerator for Graph neural networks
Authors: Feng Shi, Ahren Yiqiao Jin, Song-Chun Zhu
Date: 2021-05-04 00:00:00
Tasks: CPU, GPU, Graph Generation, Graph Matching, Graph Neural Network, Node Classification

\textit{Graph Neural Network} (GNN) is a promising approach for analyzing graph-structured data that tactfully captures their dependency information via node-level message passing. It has achieved state-of-the-art performances in many tasks, such as node classification, graph matching, clustering, and graph generation. As GNNs operate on non-Euclidean data, their irregular data access patterns cause considerable computational costs and overhead on conventional architectures, such as GPU and CPU. Our analysis shows that GNN adopts a hybrid computing model. The \textit{Aggregation} (or \textit{Message Passing}) phase performs vector additions where vectors are fetched with irregular strides. The \textit{Transformation} (or \textit{Node Embedding}) phase can be either dense or sparse-dense matrix multiplication. In this work, We propose \textit{VersaGNN}, an ultra-efficient, systolic-array-based versatile hardware accelerator that unifies dense and sparse matrix multiplication. By applying this single optimized systolic array to both aggregation and transformation phases, we have significantly reduced chip sizes and energy consumption. We then divide the computing engine into blocked systolic arrays to support the \textit{Strassen}'s algorithm for dense matrix multiplication, dramatically scaling down the number of multiplications and enabling high-throughput computation of GNNs. To balance the workload of sparse-dense matrix multiplication, we also introduced a greedy algorithm to combine sparse sub-matrices of compressed format into condensed ones to reduce computational cycles. Compared with current state-of-the-art GNN software frameworks, \textit{VersaGNN} achieves on average 3712$\times$ speedup with 1301.25$\times$ energy reduction on CPU, and 35.4$\times$ speedup with 17.66$\times$ energy reduction on GPU.
ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders
Authors: Yan Song, Tong Zhang, Yonggang Wang, Kai-Fu Lee
Date: 2021-05-04 00:00:00

Pre-trained text encoders have drawn sustaining attention in natural language processing (NLP) and shown their capability in obtaining promising results in different tasks. Recent studies illustrated that external self-supervised signals (or knowledge extracted by unsupervised learning, such as n-grams) are beneficial to provide useful semantic evidence for understanding languages such as Chinese, so as to improve the performance on various downstream tasks accordingly. To further enhance the encoders, in this paper, we propose to pre-train n-gram-enhanced encoders with a large volume of data and advanced techniques for training. Moreover, we try to extend the encoder to different languages as well as different domains, where it is confirmed that the same architecture is applicable to these varying circumstances and new state-of-the-art performance is observed from a long list of NLP tasks across languages and domains.
Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels using Variational Auto-Encoder
Authors: Weijia Zhang
Date: 2021-05-04 00:00:00
Tasks: Weakly-supervised Learning

Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be \textit{identically and independently distributed}, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Auto-Encoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.
Graph Pooling via Coarsened Graph Infomax
Authors: Yunsheng Pang, Yunxiang Zhao, Dongsheng Li
Date: 2021-05-04 00:00:00
Tasks: Contrastive Learning, Graph Representation Learning, Representation Learning

Graph pooling that summaries the information in a large graph into a compact form is essential in hierarchical graph representation learning. Existing graph pooling methods either suffer from high computational complexity or cannot capture the global dependencies between graphs before and after pooling. To address the problems of existing graph pooling methods, we propose Coarsened Graph Infomax Pooling (CGIPool) that maximizes the mutual information between the input and the coarsened graph of each pooling layer to preserve graph-level dependencies. To achieve mutual information neural maximization, we apply contrastive learning and propose a self-attention-based algorithm for learning positive and negative samples. Extensive experimental results on seven datasets illustrate the superiority of CGIPool comparing to the state-of-the-art methods.
Semantic Modeling for Food Recommendation Explanations
Authors: Ishita Padhiar, Oshani Seneviratne, Shruthi Chari, Daniel Gruen, Deborah L. McGuinness
Date: 2021-05-04 00:00:00
Tasks: Food recommendation, Knowledge Base Question Answering, Question Answering, Recommendation Systems

With the increased use of AI methods to provide recommendations in the health, specifically in the food dietary recommendation space, there is also an increased need for explainability of those recommendations. Such explanations would benefit users of recommendation systems by empowering them with justifications for following the system's suggestions. We present the Food Explanation Ontology (FEO) that provides a formalism for modeling explanations to users for food-related recommendations. FEO models food recommendations, using concepts from the explanation domain to create responses to user questions about food recommendations they receive from AI systems such as personalized knowledge base question answering systems. FEO uses a modular, extensible structure that lends itself to a variety of explanations while still preserving important semantic details to accurately represent explanations of food recommendations. In order to evaluate this system, we used a set of competency questions derived from explanation types present in literature that are relevant to food recommendations. Our motivation with the use of FEO is to empower users to make decisions about their health, fully equipped with an understanding of the AI recommender systems as they relate to user questions, by providing reasoning behind their recommendations in the form of explanations.
Causal factors discovering from Chinese construction accident cases
Authors: Zi-jian Ni, Wei Liu
Date: 2021-05-04 00:00:00

In China, construction accidents have killed more people than any other industry since 2012. The factors which led to the accident have complex interaction. Real data about accidents is the key to reveal the mechanism among these factors. But the data from the questionnaire and interview has inherent defects. Many behaviors that impact safety are illegal. In China, most of the cases are from accident investigation reports. Finding out the cause of the accident and liability affirmation are the core of incident investigation reports. So the truth of some answers from the respondents is doubtful. With a series of NLP technologies, in this paper, causal factors of construction accidents are extracted and organized from Chinese incident case texts. Finally, three kinds of neglected causal factors are discovered after data analysis.
Polynomial-Time Algorithms for Multi-Agent Minimal-Capacity Planning
Authors: Murat Cubuktepe, František Blahoudek, Ufuk Topcu
Date: 2021-05-04 00:00:00

We study the problem of minimizing the resource capacity of autonomous agents cooperating to achieve a shared task. More specifically, we consider high-level planning for a team of homogeneous agents that operate under resource constraints in stochastic environments and share a common goal: given a set of target locations, ensure that each location will be visited infinitely often by some agent almost surely. We formalize the dynamics of agents by consumption Markov decision processes. In a consumption Markov decision process, the agent has a resource of limited capacity. Each action of the agent may consume some amount of the resource. To avoid exhaustion, the agent can replenish its resource to full capacity in designated reload states. The resource capacity restricts the capabilities of the agent. The objective is to assign target locations to agents, and each agent is only responsible for visiting the assigned subset of target locations repeatedly. Moreover, the assignment must ensure that the agents can carry out their tasks with minimal resource capacity. We reduce the problem of finding target assignments for a team of agents with the lowest possible capacity to an equivalent graph-theoretical problem. We develop an algorithm that solves this graph problem in time that is \emph{polynomial} in the number of agents, target locations, and size of the consumption Markov decision process. We demonstrate the applicability and scalability of the algorithm in a scenario where hundreds of unmanned underwater vehicles monitor hundreds of locations in environments with stochastic ocean currents.
Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction
Authors: Zahra Zahedi, Mudit Verma, Sarath Sreedharan, Subbarao Kambhampati
Date: 2021-05-03 00:00:00
Tasks: Management

Trust between team members is an essential requirement for any successful cooperation. Thus, engendering and maintaining the fellow team members' trust becomes a central responsibility for any member trying to not only successfully participate in the task but to ensure the team achieves its goals. The problem of trust management is particularly challenging in mixed human-robot teams where the human and the robot may have different models about the task at hand and thus may have different expectations regarding the current course of action, thereby forcing the robot to focus on the costly explicable behavior. We propose a computational model for capturing and modulating trust in such iterated human-robot interaction settings, where the human adopts a supervisory role. In our model, the robot integrates human's trust and their expectations about the robot into its planning process to build and maintain trust over the interaction horizon. By establishing the required level of trust, the robot can focus on maximizing the team goal by eschewing explicit explanatory or explicable behavior without worrying about the human supervisor monitoring and intervening to stop behaviors they may not necessarily understand. We model this reasoning about trust levels as a meta reasoning process over individual planning tasks. We additionally validate our model through a human subject experiment.
EBIC.JL -- an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia
Authors: Paweł Renc, Patryk Orzechowski, Aleksander Byrski, Jarosław Wąs, Jason H. Moore
Date: 2021-05-03 00:00:00

Biclustering is a data mining technique which searches for local patterns in numeric tabular data with main application in bioinformatics. This technique has shown promise in multiple areas, including development of biomarkers for cancer, disease subtype identification, or gene-drug interactions among others. In this paper we introduce EBIC.JL - an implementation of one of the most accurate biclustering algorithms in Julia, a modern highly parallelizable programming language for data science. We show that the new version maintains comparable accuracy to its predecessor EBIC while converging faster for the majority of the problems. We hope that this open source software in a high-level programming language will foster research in this promising field of bioinformatics and expedite development of new biclustering methods for big data.