pwc-archive/papers-with-abstracts huggingface dataset:
Configuring Multiple Instances with Multi-Configuration
Authors: Alexander Felfernig, Andrei Popescu, Mathias Uta, Viet-Man Le, Seda Polat-Erdeniz, Martin Stettinger, Müslüm Atas, Thi Ngoc Trang Tran
Date: 2021-09-20 00:00:00

Configuration is a successful application area of Artificial Intelligence. In the majority of the cases, configuration systems focus on configuring one solution (configuration) that satisfies the preferences of a single user or a group of users. In this paper, we introduce a new configuration approach - multi-configuration - that focuses on scenarios where the outcome of a configuration process is a set of configurations. Example applications thereof are the configuration of personalized exams for individual students, the configuration of project teams, reviewer-to-paper assignment, and hotel room assignments including individualized city trips for tourist groups. For multi-configuration scenarios, we exemplify a constraint satisfaction problem representation in the context of configuring exams. The paper is concluded with a discussion of open issues for future work.
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
Authors: JongSeok Lee, Jianxiang Feng, Matthias Humt, Marcus G. Müller, Rudolph Triebel
Date: 2021-09-20 00:00:00
Tasks: Gaussian Processes, object-detection, Object Detection

This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs). Our main contribution is a practical and principled combination of DNNs with sparse Gaussian Processes (GPs). We prove theoretically that DNNs can be seen as a special case of sparse GPs, namely mixtures of GP experts (MoE-GP), and we devise a learning algorithm that brings the derived theory into practice. In experiments from two different robotic tasks -- inverse dynamics of a manipulator and object detection on a micro-aerial vehicle (MAV) -- we show the effectiveness of our approach in terms of predictive uncertainty, improved scalability, and run-time efficiency on a Jetson TX2. We thus argue that our approach can pave the way towards reliable and fast robot learning systems with uncertainty awareness.
The Case for Claim Difficulty Assessment in Automatic Fact Checking
Authors: Prakhar Singh, Anubrata Das, Junyi Jessy Li, Matthew Lease
Date: 2021-09-20 00:00:00
Tasks: Fact Checking

Fact-checking is the process of evaluating the veracity of claims (i.e., purported facts). In this opinion piece, we raise an issue that has received little attention in prior work -- that some claims are far more difficult to fact-check than others. We discuss the implications this has for both practical fact-checking and research on automated fact-checking, including task formulation and dataset design. We report a manual analysis undertaken to explore factors underlying varying claim difficulty and identify several distinct types of difficulty. We motivate this new claim difficulty prediction task as beneficial to both automated fact-checking and practical fact-checking organizations.
Actionable Approaches to Promote Ethical AI in Libraries
Authors: Helen Bubinger, Jesse David Dinneen
Date: 2021-09-20 00:00:00
Tasks: Ethics

The widespread use of artificial intelligence (AI) in many domains has revealed numerous ethical issues from data and design to deployment. In response, countless broad principles and guidelines for ethical AI have been published, and following those, specific approaches have been proposed for how to encourage ethical outcomes of AI. Meanwhile, library and information services too are seeing an increase in the use of AI-powered and machine learning-powered information systems, but no practical guidance currently exists for libraries to plan for, evaluate, or audit the ethics of intended or deployed AI. We therefore report on several promising approaches for promoting ethical AI that can be adapted from other contexts to AI-powered information services and in different stages of the software lifecycle.
Predicting vehicles parking behaviour in shared premises for aggregated EV electricity demand response programs
Authors: Vinicius Monteiro de Lira, Fabiano Pallonetto, Lorenzo Gabrielli, Chiara Renso
Date: 2021-09-20 00:00:00
Tasks: energy management, Management, Scheduling

The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. Demand response aggregation and load control will enable greater grid stability and greater penetration of renewable energies into the grid. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging
Authors: Karim Lekadir, Richard Osuala, Catherine Gallin, Noussair Lazrak, Kaisar Kushibar, Gianna Tsakou, Susanna Aussó, Leonor Cerdá Alberich, Kostas Marias, Manolis Tsiknakis, Sara Colantonio, Nickolas Papanikolaou, Zohaib Salahuddin, Henry C Woodruff, Philippe Lambin, Luis Martí-Bonmatí
Date: 2021-09-20 00:00:00
Tasks: Fairness, Image Reconstruction, Image Segmentation, Medical Image Segmentation, Semantic Segmentation

The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning. Notwithstanding the successes and future potential of AI in medical imaging, many stakeholders are concerned of the potential risks and ethical implications of imaging AI solutions, which are perceived as complex, opaque, and difficult to comprehend, utilise, and trust in critical clinical applications. Addressing these concerns and risks, the FUTURE-AI framework has been proposed, which, sourced from a global multi-domain expert consensus, comprises guiding principles for increased trust, safety, and adoption for AI in healthcare. In this paper, we transform the general FUTURE-AI healthcare principles to a concise and specific AI implementation guide tailored to the needs of the medical imaging community. To this end, we carefully assess each building block of the FUTURE-AI framework consisting of (i) Fairness, (ii) Universality, (iii) Traceability, (iv) Usability, (v) Robustness and (vi) Explainability, and respectively define concrete best practices based on accumulated AI implementation experiences from five large European projects on AI in Health Imaging. We accompany our concrete step-by-step medical imaging development guide with a practical AI solution maturity checklist, thus enabling AI development teams to design, evaluate, maintain, and deploy technically, clinically and ethically trustworthy imaging AI solutions into clinical practice.
Asymptotic Causal Inference
Authors: Sridhar Mahadevan
Date: 2021-09-20 00:00:00
Tasks: Causal Inference, Experimental Design

We investigate causal inference in the asymptotic regime as the number of variables approaches infinity using an information-theoretic framework. We define structural entropy of a causal model in terms of its description complexity measured by the logarithmic growth rate, measured in bits, of all directed acyclic graphs (DAGs), parameterized by the edge density d. Structural entropy yields non-intuitive predictions. If we randomly sample a DAG from the space of all models, in the range d = (0, 1/8), almost surely the model is a two-layer DAG! Semantic entropy quantifies the reduction in entropy where edges are removed by causal intervention. Semantic causal entropy is defined as the f-divergence between the observational distribution and the interventional distribution P', where a subset S of edges are intervened on to determine their causal influence. We compare the decomposability properties of semantic entropy for different choices of f-divergences, including KL-divergence, squared Hellinger distance, and total variation distance. We apply our framework to generalize a recently popular bipartite experimental design for studying causal inference on large datasets, where interventions are carried out on one set of variables (e.g., power plants, items in an online store), but outcomes are measured on a disjoint set of variables (residents near power plants, or shoppers). We generalize bipartite designs to k-partite designs, and describe an optimization framework for finding the optimal k-level DAG architecture for any value of d \in (0, 1/2). As edge density increases, a sequence of phase transitions occur over disjoint intervals of d, with deeper DAG architectures emerging for larger values of d. We also give a quantitative bound on the number of samples needed to reliably test for average causal influence for a k-partite design.
TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method
Authors: Zeqian Ju, Peiling Lu, Xu Tan, Rui Wang, Chen Zhang, Songruoyao Wu, Kejun Zhang, Xiangyang Li, Tao Qin, Tie-Yan Liu
Date: 2021-09-20 00:00:00
Tasks: Rhythm

Lyric-to-melody generation is an important task in automatic songwriting. Previous lyric-to-melody generation systems usually adopt end-to-end models that directly generate melodies from lyrics, which suffer from several issues: 1) lack of paired lyric-melody training data; 2) lack of control on generated melodies. In this paper, we develop TeleMelody, a two-stage lyric-to-melody generation system with music template (e.g., tonality, chord progression, rhythm pattern, and cadence) to bridge the gap between lyrics and melodies (i.e., the system consists of a lyric-to-template module and a template-to-melody module). TeleMelody has two advantages. First, it is data efficient. The template-to-melody module is trained in a self-supervised way (i.e., the source template is extracted from the target melody) that does not need any lyric-melody paired data. The lyric-to-template module is made up of some rules and a lyric-to-rhythm model, which is trained with paired lyric-rhythm data that is easier to obtain than paired lyric-melody data. Second, it is controllable. The design of template ensures that the generated melodies can be controlled by adjusting the musical elements in template. Both subjective and objective experimental evaluations demonstrate that TeleMelody generates melodies with higher quality, better controllability, and less requirement on paired lyric-melody data than previous generation systems.
"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World
Authors: Emily Wenger, Max Bronckers, Christian Cianfarani, Jenna Cryan, Angela Sha, Haitao Zheng, Ben Y. Zhao
Date: 2021-09-20 00:00:00
Tasks: Deep Learning, Speaker Recognition, Speech Synthesis

Advances in deep learning have introduced a new wave of voice synthesis tools, capable of producing audio that sounds as if spoken by a target speaker. If successful, such tools in the wrong hands will enable a range of powerful attacks against both humans and software systems (aka machines). This paper documents efforts and findings from a comprehensive experimental study on the impact of deep-learning based speech synthesis attacks on both human listeners and machines such as speaker recognition and voice-signin systems. We find that both humans and machines can be reliably fooled by synthetic speech and that existing defenses against synthesized speech fall short. These findings highlight the need to raise awareness and develop new protections against synthetic speech for both humans and machines.
Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play
Authors: Arkady Arkhangorodsky, Scot Fang, Victoria Knight, Ajay Nagesh, Maria Ryskina, Kevin Knight
Date: 2021-09-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Task-oriented dialog systems are often trained on human/human dialogs, such as collected from Wizard-of-Oz interfaces. However, human/human corpora are frequently too small for supervised training to be effective. This paper investigates two approaches to training agent-bots and user-bots through self-play, in which they autonomously explore an API environment, discovering communication strategies that enable them to solve the task. We give empirical results for both reinforcement learning and game-theoretic equilibrium finding.
Some Critical and Ethical Perspectives on the Empirical Turn of AI Interpretability
Authors: Jean-Marie John-Mathews
Date: 2021-09-20 00:00:00
Tasks: Ethics

We consider two fundamental and related issues currently faced by Artificial Intelligence (AI) development: the lack of ethics and interpretability of AI decisions. Can interpretable AI decisions help to address ethics in AI? Using a randomized study, we experimentally show that the empirical and liberal turn of the production of explanations tends to select AI explanations with a low denunciatory power. Under certain conditions, interpretability tools are therefore not means but, paradoxically, obstacles to the production of ethical AI since they can give the illusion of being sensitive to ethical incidents. We also show that the denunciatory power of AI explanations is highly dependent on the context in which the explanation takes place, such as the gender or education level of the person to whom the explication is intended for. AI ethics tools are therefore sometimes too flexible and self-regulation through the liberal production of explanations do not seem to be enough to address ethical issues. We then propose two scenarios for the future development of ethical AI: more external regulation or more liberalization of AI explanations. These two opposite paths will play a major role on the future development of ethical AI.
MeetDot: Videoconferencing with Live Translation Captions
Authors: Arkady Arkhangorodsky, Christopher Chu, Scot Fang, Yiqi Huang, Denglin Jiang, Ajay Nagesh, Boliang Zhang, Kevin Knight
Date: 2021-09-20 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Translation

We present MeetDot, a videoconferencing system with live translation captions overlaid on screen. The system aims to facilitate conversation between people who speak different languages, thereby reducing communication barriers between multilingual participants. Currently, our system supports speech and captions in 4 languages and combines automatic speech recognition (ASR) and machine translation (MT) in a cascade. We use the re-translation strategy to translate the streamed speech, resulting in caption flicker. Additionally, our system has very strict latency requirements to have acceptable call quality. We implement several features to enhance user experience and reduce their cognitive load, such as smooth scrolling captions and reducing caption flicker. The modular architecture allows us to integrate different ASR and MT services in our backend. Our system provides an integrated evaluation suite to optimize key intrinsic evaluation metrics such as accuracy, latency and erasure. Finally, we present an innovative cross-lingual word-guessing game as an extrinsic evaluation metric to measure end-to-end system performance. We plan to make our system open-source for research purposes.
A proactive malicious software identification approach for digital forensic examiners
Authors: Muhammad Ali, Stavros Shiaeles, Nathan Clarke, Dimitrios Kontogeorgis
Date: 2021-09-20 00:00:00

Digital investigators often get involved with cases, which seemingly point the responsibility to the person to which the computer belongs, but after a thorough examination malware is proven to be the cause, causing loss of precious time. Whilst Anti-Virus (AV) software can assist the investigator in identifying the presence of malware, with the increase in zero-day attacks and errors that exist in AV tools, this is something that cannot be relied upon. The aim of this paper is to investigate the behaviour of malware upon various Windows operating system versions in order to determine and correlate the relationship between malicious software and OS artifacts. This will enable an investigator to be more efficient in identifying the presence of new malware and provide a starting point for further investigation.
Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge
Authors: Xinzhu Liu, Di Guo, Huaping Liu, Fuchun Sun
Date: 2021-09-20 00:00:00
Tasks: Efficient Exploration

In visual semantic navigation, the robot navigates to a target object with egocentric visual observations and the class label of the target is given. It is a meaningful task inspiring a surge of relevant research. However, most of the existing models are only effective for single-agent navigation, and a single agent has low efficiency and poor fault tolerance when completing more complicated tasks. Multi-agent collaboration can improve the efficiency and has strong application potentials. In this paper, we propose the multi-agent visual semantic navigation, in which multiple agents collaborate with others to find multiple target objects. It is a challenging task that requires agents to learn reasonable collaboration strategies to perform efficient exploration under the restrictions of communication bandwidth. We develop a hierarchical decision framework based on semantic mapping, scene prior knowledge, and communication mechanism to solve this task. The results of testing experiments in unseen scenes with both known objects and unknown objects illustrate the higher accuracy and efficiency of the proposed model compared with the single-agent model.
A Novel Online Incremental Learning Intrusion Prevention System
Authors: Christos Constantinides, Stavros Shiaeles, Bogdan Ghita, Nicholas Kolokotronis
Date: 2021-09-20 00:00:00
Tasks: Incremental Learning, Intrusion Detection

Attack vectors are continuously evolving in order to evade Intrusion Detection systems. Internet of Things (IoT) environments, while beneficial for the IT ecosystem, suffer from inherent hardware limitations, which restrict their ability to implement comprehensive security measures and increase their exposure to vulnerability attacks. This paper proposes a novel Network Intrusion Prevention System that utilises a SelfOrganizing Incremental Neural Network along with a Support Vector Machine. Due to its structure, the proposed system provides a security solution that does not rely on signatures or rules and is capable to mitigate known and unknown attacks in real-time with high accuracy. Based on our experimental results with the NSL KDD dataset, the proposed framework can achieve on-line updated incremental learning, making it suitable for efficient and scalable industrial applications.
Automatic Generation of Board Game Manuals
Authors: Matthew Stephenson, Eric Piette, Dennis J. N. J. Soemers, Cameron Browne
Date: 2021-09-20 00:00:00
Tasks: Board Games, Translation

In this paper we present a process for automatically generating manuals for board games within the Ludii general game system. This process requires many different sub-tasks to be addressed, such as English translation of Ludii game descriptions, move visualisation, highlighting winning moves, strategy explanation, among others. These aspects are then combined to create a full manual for any given game. This manual is intended to provide a more intuitive explanation of a game's rules and mechanics, particularly for players who are less familiar with the Ludii game description language and grammar.
Dyadformer: A Multi-modal Transformer for Long-Range Modeling of Dyadic Interactions
Authors: David Curto, Albert Clapés, Javier Selva, Sorina Smeureanu, Julio C. S. Jacques Junior, David Gallardo-Pujol, Georgina Guilera, David Leiva, Thomas B. Moeslund, Sergio Escalera, Cristina Palmero
Date: 2021-09-20 00:00:00
Tasks: Long-range modeling

Personality computing has become an emerging topic in computer vision, due to the wide range of applications it can be used for. However, most works on the topic have focused on analyzing the individual, even when applied to interaction scenarios, and for short periods of time. To address these limitations, we present the Dyadformer, a novel multi-modal multi-subject Transformer architecture to model individual and interpersonal features in dyadic interactions using variable time windows, thus allowing the capture of long-term interdependencies. Our proposed cross-subject layer allows the network to explicitly model interactions among subjects through attentional operations. This proof-of-concept approach shows how multi-modality and joint modeling of both interactants for longer periods of time helps to predict individual attributes. With Dyadformer, we improve state-of-the-art self-reported personality inference results on individual subjects on the UDIVA v0.5 dataset.
A Survey of Text Games for Reinforcement Learning informed by Natural Language
Authors: Philip Osborne, Heido Nõmm, Andre Freitas
Date: 2021-09-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning has shown success in a number of complex virtual environments. However, many challenges still exist towards solving problems with natural language as a core component. Interactive Fiction Games (or Text Games) are one such problem type that offer a set of partially observable environments where natural language is required as part of the reinforcement learning solutions. Therefore, this survey's aim is to assist in the development of new Text Game problem settings and solutions for Reinforcement Learning informed by natural language. Specifically, this survey summarises: 1) the challenges introduced in Text Game Reinforcement Learning problems, 2) the generation tools for evaluating Text Games and the subsequent environments generated and, 3) the agent architectures currently applied are compared to provide a systematic review of benchmark methodologies and opportunities for future researchers.