pwc-archive/papers-with-abstracts huggingface dataset:
A Taxonomy of Explainable Bayesian Networks
Authors: Iena Petronella Derks, Alta de Waal
Date: 2021-01-28 00:00:00
Tasks: Decision Making, Diagnostic

Artificial Intelligence (AI), and in particular, the explainability thereof, has gained phenomenal attention over the last few years. Whilst we usually do not question the decision-making process of these systems in situations where only the outcome is of interest, we do however pay close attention when these systems are applied in areas where the decisions directly influence the lives of humans. It is especially noisy and uncertain observations close to the decision boundary which results in predictions which cannot necessarily be explained that may foster mistrust among end-users. This drew attention to AI methods for which the outcomes can be explained. Bayesian networks are probabilistic graphical models that can be used as a tool to manage uncertainty. The probabilistic framework of a Bayesian network allows for explainability in the model, reasoning and evidence. The use of these methods is mostly ad hoc and not as well organised as explainability methods in the wider AI research field. As such, we introduce a taxonomy of explainability in Bayesian networks. We extend the existing categorisation of explainability in the model, reasoning or evidence to include explanation of decisions. The explanations obtained from the explainability methods are illustrated by means of a simple medical diagnostic scenario. The taxonomy introduced in this paper has the potential not only to encourage end-users to efficiently communicate outcomes obtained, but also support their understanding of how and, more importantly, why certain predictions were made.
Making Responsible AI the Norm rather than the Exception
Authors: Abhishek Gupta
Date: 2021-01-28 00:00:00
Tasks: Ethics, Friction, Translation

This report prepared by the Montreal AI Ethics Institute provides recommendations in response to the National Security Commission on Artificial Intelligence (NSCAI) Key Considerations for Responsible Development and Fielding of Artificial Intelligence document. The report centres on the idea that Responsible AI should be made the Norm rather than an Exception. It does so by utilizing the guiding principles of: (1) alleviating friction in existing workflows, (2) empowering stakeholders to get buy-in, and (3) conducting an effective translation of abstract standards into actionable engineering practices. After providing some overarching comments on the document from the NSCAI, the report dives into the primary contribution of an actionable framework to help operationalize the ideas presented in the document from the NSCAI. The framework consists of: (1) a learning, knowledge, and information exchange (LKIE), (2) the Three Ways of Responsible AI, (3) an empirically-driven risk-prioritization matrix, and (4) achieving the right level of complexity. All components reinforce each other to move from principles to practice in service of making Responsible AI the norm rather than the exception.
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning
Authors: Amrita Saha, Shafiq Joty, Steven C. H. Hoi
Date: 2021-01-28 00:00:00
Tasks: Dependency Parsing, Language Modeling, Language Modelling, Machine Reading Comprehension, Question Answering, Reading Comprehension

Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic form of numerical reasoning over text in Machine Reading Comprehension (MRC). However, to achieve this, contemporary NMNs need strong supervision in executing the query as a specialized program over reasoning modules and fail to generalize to more open-ended settings without such supervision. Hence we propose Weakly-Supervised Neuro-Symbolic Module Network (WNSMN) trained with answers as the sole supervision for numerical reasoning based MRC. It learns to execute a noisy heuristic program obtained from the dependency parsing of the query, as discrete actions over both neural and symbolic reasoning modules and trains it end-to-end in a reinforcement learning framework with discrete reward from answer matching. On the numerical-answer subset of DROP, WNSMN out-performs NMN by 32% and the reasoning-free language model GenBERT by 8% in exact match accuracy when trained under comparable weak supervised settings. This showcases the effectiveness and generalizability of modular networks that can handle explicit discrete reasoning over noisy programs in an end-to-end manner.
Moral and Social Ramifications of Autonomous Vehicles
Authors: Veljko Dubljević, Sean Douglas, Jovan Milojevich, Nirav Ajmeri, William A. Bauer, George F. List, Munindar P. Singh
Date: 2021-01-28 00:00:00
Tasks: Autonomous Vehicles

Autonomous Vehicles (AVs) raise important social and ethical concerns, especially about accountability, dignity, and justice. We focus on the specific concerns arising from how AV technology will affect the lives and livelihoods of professional and semi-professional drivers. Whereas previous studies of such concerns have focused on the opinions of experts, we seek to understand these ethical and societal challenges from the perspectives of the drivers themselves. To this end, we adopted a qualitative research methodology based on semi-structured interviews. This is an established social science methodology that helps understand the core concerns of stakeholders in depth by avoiding the biases of superficial methods such as surveys. We find that whereas drivers agree with the experts that AVs will significantly impact transportation systems, they are apprehensive about the prospects for their livelihoods and dismiss the suggestions that driving jobs are unsatisfying and their profession does not merit protection. By showing how drivers differ from the experts, our study has ramifications beyond AVs to AI and other advanced technologies. Our findings suggest that qualitative research applied to the relevant, especially disempowered, stakeholders is essential to ensuring that new technologies are introduced ethically.
Compositionality Through Language Transmission, using Artificial Neural Networks
Authors: Hugh Perkins
Date: 2021-01-27 00:00:00

We propose an architecture and process for using the Iterated Learning Model ("ILM") for artificial neural networks. We show that ILM does not lead to the same clear compositionality as observed using DCGs, but does lead to a modest improvement in compositionality, as measured by holdout accuracy and topologic similarity. We show that ILM can lead to an anti-correlation between holdout accuracy and topologic rho. We demonstrate that ILM can increase compositionality when using non-symbolic high-dimensional images as input.
Characterising Fixed Parameter Tractability of Query Evaluation over Guarded TGDs
Authors: Cristina Feier
Date: 2021-01-27 00:00:00

We study the parameterized complexity of evaluating Ontology Mediated Queries (OMQs) based on Guarded TGDs (GTGDs) and Unions of Conjunctive Queries (UCQs), in the case where relational symbols have unrestricted arity and where the parameter is the size of the OMQ. We establish exact criteria for fixed-parameter tractability (fpt) evaluation of recursively enumerable classes of such OMQs (under the widely held Exponential Time Hypothesis). One of the main technical tools introduced in the paper is an fpt-reduction from deciding parameterized uniform CSPs to parameterized OMQ evaluation. The reduction preserves measures which are known to be essential for classifying recursively enumerable classes of parameterized uniform CSPs: submodular width (according to the well known result of Marx for unrestricted-arity schemas) and treewidth (according to the well known result of Grohe for bounded-arity schemas). As such, it can be employed to obtain hardness results for evaluation of recursively enumerable classes of parameterized OMQs both in the unrestricted and in the bounded arity case. Previously, in the case of bounded arity schemas, this has been tackled using a technique requiring full introspection into the construction employed by Grohe.
Knowledge-driven Natural Language Understanding of English Text and its Applications
Authors: Kinjal Basu, Sarat Varanasi, Farhad Shakerin, Joaquin Arias, Gopal Gupta
Date: 2021-01-27 00:00:00
Tasks: Natural Language Understanding, Question Answering

Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research. An ideal NLU system should process a language in a way that is not exclusive to a single task or a dataset. Keeping this in mind, we have introduced a novel knowledge driven semantic representation approach for English text. By leveraging the VerbNet lexicon, we are able to map syntax tree of the text to its commonsense meaning represented using basic knowledge primitives. The general purpose knowledge represented from our approach can be used to build any reasoning based NLU system that can also provide justification. We applied this approach to construct two NLU applications that we present here: SQuARE (Semantic-based Question Answering and Reasoning Engine) and StaCACK (Stateful Conversational Agent using Commonsense Knowledge). Both these systems work by "truly understanding" the natural language text they process and both provide natural language explanations for their responses while maintaining high accuracy.
Privacy-Preserving Clustering: A New ApproachBased on Invariant Order Encryption
Authors: Mihail-Iulian Pleșa, Cezar Pleșca
Date: 2020-12-20 00:00:00
Tasks: Cloud Computing, Clustering, Privacy Preserving

Cloud  computing  is  increasingly  used.  One  main  use  of  cloud  computing  is  the  running  of  a  machine  learning  algorithm.  Due  to the  large  amount  of  data  required  for  these  algorithms,  they  can  no  longer  be  run  on  personal  computers.  Uploading  personal  data  to  the  cloud  automatically  raises  the  issues  of  confidentiality  of  this  data.  In  this  paper,  we  show  through   a   series   of   experiments   that   an   order-preserving encryption algorithm can    be    applied    to    guarantee    the    confidentiality   of   the   input   of   two   well-known   clustering   algorithms: K-Means  and  DBSCAN.  We  show  that  K-Means can be modified to be applied over the encrypted data. We also proposed    a    slight    improvement    to    an    order-preserving encryption  scheme  to  ensure  that  it  is  randomized,  therefore increasing   its   security   level.   Finally,   after   studying   the   performance  of  clustering  algorithms  over  encrypted  data  we  show  a  practical  application  of  this  idea,  namely  the  color reduction over an encrypted image.
Optimal Disclosure of Information to a Privately Informed Receiver
Authors: Ozan Candogan, Philipp Strack
Date: 2021-01-25 00:00:00

We study information design settings where the designer controls information about a state, and there are multiple agents interacting in a game who are privately informed about their types. Each agent's utility depends on all agents' types and actions, as well as (linearly) on the state. To optimally screen the agents, the designer first asks agents to report their types and then sends a private action recommendation to each agent whose distribution depends on all reported types and the state. We show that there always exists an optimal mechanism which is laminar partitional. Such a mechanism partitions the state space for each type profile and recommends the same action profile for states that belong to the same partition element. Furthermore, the convex hulls of any two partition elements are such that either one contains the other or they have an empty intersection. In the single-agent case, each state is either perfectly revealed or lies in an interval in which the number of different signal realizations is at most the number of different types of the agent plus two. A similar result is established for the multi-agent case. We also highlight the value of screening: without screening the best achievable payoff could be as low as one over the number of types fraction of the optimal payoff. Along the way, we shed light on the solutions of optimization problems over distributions subject to a mean-preserving contraction constraint and additional side constraints, which might be of independent interest.
Computer simulation of surgical interventions for the treatment of refractory pulmonary hypertension
Authors: Seong Woo Han, Charles Puelz, Craig G. Rusin, Daniel J. Penny, Ryan Coleman, Charles S. Peskin
Date: 2021-01-28 00:00:00

This paper describes computer models of three interventions used for treating refractory pulmonary hypertension (RPH). These procedures create either an atrial septal defect, a ventricular septal defect, or, in the case of a Potts shunt, a patent ductus arteriosus. The aim in all three cases is to generate a right-to-left shunt, allowing for either pressure or volume unloading of the right side of the heart in the setting of right ventricular failure, while maintaining cardiac output. These shunts are created, however, at the expense of introducing de-oxygenated blood into the systemic circulation, thereby lowering the systemic arterial oxygen saturation. The models developed in this paper are based on compartmental descriptions of human hemodynamics and oxygen transport. An important parameter included in our models is the cross-sectional area of the surgically created defect. Numerical simulations are performed to compare different interventions and various shunt sizes and to assess their impact on hemodynamic variables and oxygen saturations. We also create a model for exercise and use it to study exercise tolerance in simulated pre-intervention and post-intervention RPH patients.
Use Non-Energy-Curtailment Resources for Primary Frequency Response in Future Low-Inertia Power Grids
Authors: Shutang You
Date: 2021-01-28 00:00:00

Power grid primary frequency response will be significantly impaired by Photovoltaic (PV) penetration increase because of the decrease in inertia and governor response. PV inertia and governor emulation requires reserving PV output and leads to solar energy waste. This paper exploits current grid resources and explores energy storage for primary frequency response under high PV penetration at the interconnection level. Based on the actual models of the U.S. Eastern Interconnection grid and the Texas grid, effects of multiple factors associated with primary frequency response, including the governor ratio, governor deadband, droop rate, and fast load response, are assessed under high PV penetration scenarios. In addition, performance of batteries and supercapacitors using different control strategies is studied in the two interconnections. The paper quantifies the potential of various resources to improve interconnection-level primary frequency response under high PV penetration without curtailing solar output.
Risk-sensitive safety analysis using Conditional Value-at-Risk
Authors: Margaret P. Chapman, Riccardo Bonalli, Kevin M. Smith, Insoon Yang, Marco Pavone, Claire J. Tomlin
Date: 2021-01-28 00:00:00

This paper develops a safety analysis method for stochastic systems that is sensitive to the possibility and severity of rare harmful outcomes. We define risk-sensitive safe sets as sub-level sets of the solution to a non-standard optimal control problem, where a random maximum cost is assessed via Conditional Value-at-Risk (CVaR). The objective function represents the maximum extent of constraint violation of the state trajectory, averaged over a given percentage of worst cases. This problem is well-motivated but difficult to solve tractably because the temporal decomposition for CVaR is history-dependent. Our primary theoretical contribution is to derive computationally tractable under-approximations to risk-sensitive safe sets. Our method provides a novel, theoretically guaranteed, parameter-dependent upper bound to the CVaR of a maximum cost without the need to augment the state space. For a fixed parameter value, the solution to only one Markov decision process problem is required to obtain the under-approximations for any family of risk-sensitivity levels. In addition, we propose a second definition for risk-sensitive safe sets and provide a tractable method for their estimation without using a parameter-dependent upper bound. The second definition is expressed in terms of a new coherent risk functional, which is inspired by CVaR. We demonstrate our primary theoretical contribution via numerical examples.
Measuring Intelligence and Growth Rate: Variations on Hibbard's Intelligence Measure
Authors: Samuel Alexander, Bill Hibbard
Date: 2021-01-25 00:00:00

In 2011, Hibbard suggested an intelligence measure for agents who compete in an adversarial sequence prediction game. We argue that Hibbard's idea should actually be considered as two separate ideas: first, that the intelligence of such agents can be measured based on the growth rates of the runtimes of the competitors that they defeat; and second, one specific (somewhat arbitrary) method for measuring said growth rates. Whereas Hibbard's intelligence measure is based on the latter growth-rate-measuring method, we survey other methods for measuring function growth rates, and exhibit the resulting Hibbard-like intelligence measures and taxonomies. Of particular interest, we obtain intelligence taxonomies based on Big-O and Big-Theta notation systems, which taxonomies are novel in that they challenge conventional notions of what an intelligence measure should look like. We discuss how intelligence measurement of sequence predictors can indirectly serve as intelligence measurement for agents with Artificial General Intelligence (AGIs).
TSO-DSO Operational Planning Coordination through "$l_1$-Proximal" Surrogate Lagrangian Relaxation
Authors: Mikhail Bragin, Yury Dvorkin
Date: 2021-01-25 00:00:00

The proliferation of distributed energy resources (DERs), located at the Distribution System Operator (DSO) level, bring new opportunities as well as new challenges to the operations within the grid, specifically, when it comes to the interaction with the Transmission System Operator (TSO). To enable interoperability, while ensuring higher flexibility and cost-efficiency, DSOs and the TSO need to be efficiently coordinated. Difficulties behind creating such TSO-DSO coordination include the combinatorial nature of the operational planning problem involved at the transmission level as well as the nonlinearity of AC power flow within both systems. These considerations significantly increase the complexity even under the deterministic setting. In this paper, a deterministic TSO-DSO operational planning coordination problem is considered and a novel decomposition and coordination approach is developed. Within the new method, the problem is decomposed into TSO and DSO subproblems, which are efficiently coordinated by updating Lagrangian multipliers. The nonlinearities at the TSO level caused by AC power flow constraints are resolved through a dynamic linearization while guaranteeing feasibility through "$l_1$-proximal" terms. Numerical results based on the coordination of the 118-bus TSO system with up to 32 DSO 34-bus systems indicate that the method efficiently overcomes the computational difficulties of the problem.
Deep PeNSieve: A deep learning framework based on the posit number system
Authors: Raul Murillo, Alberto A. Del Barrio, Guillermo Botella
Date: 2020-07-01 00:00:00
Tasks: Quantization

The Posit Number System (PNS) was introduced by John L. Gustafson in 2017. The interesting properties of this novel format can be exploited under the scenario of deep neural networks. In this paper, we propose Deep PeNSieve, a framework for entirely performing both training and inference of deep neural networks employing the PNS. Furthermore, an 8-bit posit quantization approach using fused operations is introduced. In comparison with the state-of-the-art posit frameworks, the proposal has been able to train more complex networks than the feedforward ones, achieving similar accuracies as the floating-point format. The case of CIFAR-10 is especially remarkable, as 16-bit posits even obtain 4% higher top-1 for such dataset. Overall, results show that the proposed quantization approach can preserve model accuracy in the same manner as common quantization techniques.
Diagnosis of Intelligent Reflecting Surface in Millimeter-wave Communication Systems
Authors: Rui Sun, Weidong Wang, Li Chen, Guo Wei, Wenyi Zhang
Date: 2021-01-11 00:00:00
Tasks: compressed sensing, Diagnostic

Intelligent reflecting surface (IRS) is a promising technology for enhancing wireless communication systems. It adaptively configures massive passive reflecting elements to control wireless channel in a desirable way. Due to hardware characteristics and deploying environments, an IRS may be subject to reflecting element blockages and failures, and hence developing diagnostic techniques is of great significance to system monitoring and maintenance. In this paper, we develop diagnostic techniques for IRS systems to locate faulty reflecting elements and retrieve failure parameters. Three cases of channel state information (CSI) availability are considered. In the first case where full CSI is available, a compressed sensing based diagnostic technique is proposed, which significantly reduces the required number of measurements. In the second case where only partial CSI is available, we jointly exploit the sparsity of the millimeter-wave channel and the failure, and adopt compressed sparse and low-rank matrix recovery algorithm to decouple channel and failure. In the third case where no CSI is available, a novel atomic norm is introduced as the sparsity-inducing norm of the cascaded channel, and the diagnosis problem is formulated as a joint sparse recovery problem. Finally, the proposed diagnostic techniques are validated through numerical simulations.
Game of Bloxorz Solving Agent Using Informed and Uninformed Search Strategies
Authors: Tahani Q. Alhassan, Shefaa S. Omar, Lamiaa A. Elrefaei
Date: 2020-01-22 00:00:00

Bloxorz is a block sliding puzzle game that can be categorized as a pathfinding problem. Pathfinding problems are well known problems in Artificial Intelligence field. In this paper, we proposed a single agent implementation to solve level-1 of Bloxorz game using Informed and Uninformed searching algorithms: Breadth-First Search (BFS), Depth-First Search (DFS), and A-star (A*) searching algorithms. The agent solves the problem using the three algorithms to compare their performance and conduct a conclusion that may help in improving the use of searching algorithms in this area. In this paper, A* and breadth-first search algorithms are founded to be more convenient to solve this problem.
Saak-Transform based Method for Image Noise Reduction
Authors: Quang Nhat Tran; Shih-Hsuan Yang
Date: 2019-08-22 00:00:00

This research proposes a novel approach to reduce noise in an image. In this study, the input image is considered being shot with high ISO in the low light condition and the noise is modeled as the additive white Gaussian noise. The Subspace Approximation with Augmented Kernels (Saak) transform, a state-of-the-art spatial-spectral representation, is used for extracting the local characteristics of an image. The clean coefficients are estimated based on optimal linear minimum mean square error (LMMSE) estimation with a shrinkage on Saak coefficients. The processed image provides subjectively satisfactory quality improvement and an increase in peak signal to noise ratio (PSNR) without harming edges or other image details. It shows that Saak transform is a promising tool for noise reduction.