pwc-archive/papers-with-abstracts huggingface dataset:
Machine learning dismantling and early-warning signals of disintegration in complex systems
Authors: Marco Grassia, Manlio De Domenico, Giuseppe Mangioni
Date: 2021-01-07 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

From physics to engineering, biology and social science, natural and artificial systems are characterized by interconnected topologies whose features - e.g., heterogeneous connectivity, mesoscale organization, hierarchy - affect their robustness to external perturbations, such as targeted attacks to their units. Identifying the minimal set of units to attack to disintegrate a complex network, i.e. network dismantling, is a computationally challenging (NP-hard) problem which is usually attacked with heuristics. Here, we show that a machine trained to dismantle relatively small systems is able to identify higher-order topological patterns, allowing to disintegrate large-scale social, infrastructural and technological networks more efficiently than human-based heuristics. Remarkably, the machine assesses the probability that next attacks will disintegrate the system, providing a quantitative method to quantify systemic risk and detect early-warning signals of system's collapse. This demonstrates that machine-assisted analysis can be effectively used for policy and decision making to better quantify the fragility of complex systems and their response to shocks.
Decision Support System for an Intelligent Operator of Utility Tunnel Boring Machines
Authors: Gabriel Rodriguez Garcia, Gabriel Michau, Herbert H. Einstein, Olga Fink
Date: 2021-01-07 00:00:00

In tunnel construction projects, delays induce high costs. Thus, tunnel boring machines (TBM) operators aim for fast advance rates, without safety compromise, a difficult mission in uncertain ground environments. Finding the optimal control parameters based on the TBM sensors' measurements remains an open research question with large practical relevance. In this paper, we propose an intelligent decision support system developed in three steps. First past projects performances are evaluated with an optimality score, taking into account the advance rate and the working pressure safety. Then, a deep learning model learns the mapping between the TBM measurements and this optimality score. Last, in real application, the model provides incremental recommendations to improve the optimality, taking into account the current setting and measurements of the TBM. The proposed approach is evaluated on real micro-tunnelling project and demonstrates great promises for future projects.
Information-theoretic bounds on quantum advantage in machine learning
Authors: Hsin-Yuan Huang, Richard Kueng, John Preskill
Date: 2021-01-07 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning

We study the performance of classical and quantum machine learning (ML) models in predicting outcomes of physical experiments. The experiments depend on an input parameter $x$ and involve execution of a (possibly unknown) quantum process $\mathcal{E}$. Our figure of merit is the number of runs of $\mathcal{E}$ required to achieve a desired prediction performance. We consider classical ML models that perform a measurement and record the classical outcome after each run of $\mathcal{E}$, and quantum ML models that can access $\mathcal{E}$ coherently to acquire quantum data; the classical or quantum data is then used to predict outcomes of future experiments. We prove that for any input distribution $\mathcal{D}(x)$, a classical ML model can provide accurate predictions on average by accessing $\mathcal{E}$ a number of times comparable to the optimal quantum ML model. In contrast, for achieving accurate prediction on all inputs, we prove that exponential quantum advantage is possible. For example, to predict expectations of all Pauli observables in an $n$-qubit system $\rho$, classical ML models require $2^{\Omega(n)}$ copies of $\rho$, but we present a quantum ML model using only $\mathcal{O}(n)$ copies. Our results clarify where quantum advantage is possible and highlight the potential for classical ML models to address challenging quantum problems in physics and chemistry.
Retrieval of Coloured Dissolved Organic Matter with Machine Learning Methods
Authors: Ana B. Ruescas, Martin Hieronymi, Sampsa Koponen, Kari Kallio, Gustau Camps-Valls
Date: 2021-01-07 00:00:00
Tasks: BIG-bench Machine Learning, GPR, regression, Retrieval

The coloured dissolved organic matter (CDOM) concentration is the standard measure of humic substance in natural waters. CDOM measurements by remote sensing is calculated using the absorption coefficient (a) at a certain wavelength (e.g. 440nm). This paper presents a comparison of four machine learning methods for the retrieval of CDOM from remote sensing signals: regularized linear regression (RLR), random forest (RF), kernel ridge regression (KRR) and Gaussian process regression (GPR). Results are compared with the established polynomial regression algorithms. RLR is revealed as the simplest and most efficient method, followed closely by its nonlinear counterpart KRR.
A spin-glass model for the loss surfaces of generative adversarial networks
Authors: Nicholas P Baskerville, Jonathan P Keating, Francesco Mezzadri, Joseph Najnudel
Date: 2021-01-07 00:00:00

We present a novel mathematical model that seeks to capture the key design feature of generative adversarial networks (GANs). Our model consists of two interacting spin glasses, and we conduct an extensive theoretical analysis of the complexity of the model's critical points using techniques from Random Matrix Theory. The result is insights into the loss surfaces of large GANs that build upon prior insights for simpler networks, but also reveal new structure unique to this setting.
Phishing Attacks and Websites Classification Using Machine Learning and Multiple Datasets (A Comparative Analysis)
Authors: Sohail Ahmed Khan, Wasiq Khan, Abir Hussain
Date: 2021-01-06 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Phishing attacks are the most common type of cyber-attacks used to obtain sensitive information and have been affecting individuals as well as organisations across the globe. Various techniques have been proposed to identify the phishing attacks specifically, deployment of machine intelligence in recent years. However, the deployed algorithms and discriminating factors are very diverse in existing works. In this study, we present a comprehensive analysis of various machine learning algorithms to evaluate their performances over multiple datasets. We further investigate the most significant features within multiple datasets and compare the classification performance with the reduced dimensional datasets. The statistical results indicate that random forest and artificial neural network outperform other classification algorithms, achieving over 97% accuracy using the identified features.
Continuous Glucose Monitoring Prediction
Authors: Julia Ann Jose, Trae Waggoner, Sudarsan Manikandan
Date: 2021-01-04 00:00:00
Tasks: Prediction

Diabetes is one of the deadliest diseases in the world and affects nearly 10 percent of the global adult population. Fortunately, powerful new technologies allow for a consistent and reliable treatment plan for people with diabetes. One major development is a system called continuous blood glucose monitoring (CGM). In this review, we look at three different continuous meal detection algorithms that were developed using given CGM data from patients with diabetes. From this analysis, an initial meal prediction algorithm was also developed utilizing these methods.
RANK: AI-assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks
Authors: Hazem M. Soliman, Geoff Salmon, Dušan Sovilj, Mohan Rao
Date: 2021-01-06 00:00:00
Tasks: graph construction, graph partitioning, Intrusion Detection

Advanced Persistent Threats (APTs) are sophisticated multi-step attacks, planned and executed by skilled adversaries targeting modern government and enterprise networks. Intrusion Detection Systems (IDSs) and User and Entity Behavior Analytics (UEBA) are commonly employed to aid a security analyst in the detection of APTs. The prolonged nature of APTs, combined with the granular focus of UEBA and IDS, results in overwhelming the analyst with an increasingly impractical number of alerts. Consequent to this abundance of data, and together with the crucial importance of the problem as well as the high cost of the skilled personnel involved, the problem of APT detection becomes a perfect candidate for automation through Artificial Intelligence (AI). In this paper, we provide, up to our knowledge, the first study and implementation of an end-to-end AI-assisted architecture for detecting APTs -- RANK. The goal of the system is not to replace the analyst, rather, it is to automate the complete pipeline from data sources to a final set of incidents for analyst review. The architecture is composed of four consecutive steps: 1) alert templating and merging, 2) alert graph construction, 3) alert graph partitioning into incidents, and 4) incident scoring and ordering. We evaluate our architecture against the 2000 DARPA Intrusion Detection dataset, as well as a read-world private dataset from a medium-scale enterprise. Extensive results are provided showing a three order of magnitude reduction in the amount of data to be reviewed by the analyst, innovative extraction of incidents and security-wise scoring of extracted incidents.
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
Authors: Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, Jonathan Berant
Date: 2021-01-06 00:00:00
Tasks: Question Answering, StrategyQA

A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly. In this work, we introduce StrategyQA, a question answering (QA) benchmark where the required reasoning steps are implicit in the question, and should be inferred using a strategy. A fundamental challenge in this setup is how to elicit such creative questions from crowdsourcing workers, while covering a broad range of potential strategies. We propose a data collection procedure that combines term-based priming to inspire annotators, careful control over the annotator population, and adversarial filtering for eliminating reasoning shortcuts. Moreover, we annotate each question with (1) a decomposition into reasoning steps for answering it, and (2) Wikipedia paragraphs that contain the answers to each step. Overall, StrategyQA includes 2,780 examples, each consisting of a strategy question, its decomposition, and evidence paragraphs. Analysis shows that questions in StrategyQA are short, topic-diverse, and cover a wide range of strategies. Empirically, we show that humans perform well (87%) on this task, while our best baseline reaches an accuracy of $\sim$66%.
Can RNNs learn Recursive Nested Subject-Verb Agreements?
Authors: Yair Lakretz, Théo Desbordes, Jean-Rémi King, Benoît Crabbé, Maxime Oquab, Stanislas Dehaene
Date: 2021-01-06 00:00:00
Tasks: Sentence

One of the fundamental principles of contemporary linguistics states that language processing requires the ability to extract recursively nested tree structures. However, it remains unclear whether and how this code could be implemented in neural circuits. Recent advances in Recurrent Neural Networks (RNNs), which achieve near-human performance in some language tasks, provide a compelling model to address such questions. Here, we present a new framework to study recursive processing in RNNs, using subject-verb agreement as a probe into the representations of the neural network. We trained six distinct types of RNNs on a simplified probabilistic context-free grammar designed to independently manipulate the length of a sentence and the depth of its syntactic tree. All RNNs generalized to subject-verb dependencies longer than those seen during training. However, none systematically generalized to deeper tree structures, even those with a structural bias towards learning nested tree (i.e., stack-RNNs). In addition, our analyses revealed primacy and recency effects in the generalization patterns of LSTM-based models, showing that these models tend to perform well on the outer- and innermost parts of a center-embedded tree structure, but poorly on its middle levels. Finally, probing the internal states of the model during the processing of sentences with nested tree structures, we found a complex encoding of grammatical agreement information (e.g. grammatical number), in which all the information for multiple words nouns was carried by a single unit. Taken together, these results indicate how neural networks may extract bounded nested tree structures, without learning a systematic recursive rule.
Multitask Learning for Emotion and Personality Detection
Authors: Yang Li, Amirmohammad Kazameini, Yash Mehta, Erik Cambria
Date: 2021-01-07 00:00:00
Tasks: Language Modeling, Language Modelling, Model Optimization

In recent years, deep learning-based automated personality trait detection has received a lot of attention, especially now, due to the massive digital footprints of an individual. Moreover, many researchers have demonstrated that there is a strong link between personality traits and emotions. In this paper, we build on the known correlation between personality traits and emotional behaviors, and propose a novel multitask learning framework, SoGMTL that simultaneously predicts both of them. We also empirically evaluate and discuss different information-sharing mechanisms between the two tasks. To ensure the high quality of the learning process, we adopt a MAML-like framework for model optimization. Our more computationally efficient CNN-based multitask model achieves the state-of-the-art performance across multiple famous personality and emotion datasets, even outperforming Language Model based models.
Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking
Authors: Yingjie Gu, Xiaoye Qu, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Xiaolin Gui
Date: 2021-01-07 00:00:00
Tasks: Entity Linking, Machine Reading Comprehension, Multiple-choice, Reading Comprehension

Entity linking (EL) for the rapidly growing short text (e.g. search queries and news titles) is critical to industrial applications. Most existing approaches relying on adequate context for long text EL are not effective for the concise and sparse short text. In this paper, we propose a novel framework called Multi-turn Multiple-choice Machine reading comprehension (M3}) to solve the short text EL from a new perspective: a query is generated for each ambiguous mention exploiting its surrounding context, and an option selection module is employed to identify the golden entity from candidates using the query. In this way, M3 framework sufficiently interacts limited context with candidate entities during the encoding process, as well as implicitly considers the dissimilarities inside the candidate bunch in the selection stage. In addition, we design a two-stage verifier incorporated into M3 to address the commonly existed unlinkable problem in short text. To further consider the topical coherence and interdependence among referred entities, M3 leverages a multi-turn fashion to deal with mentions in a sequence manner by retrospecting historical cues. Evaluation shows that our M3 framework achieves the state-of-the-art performance on five Chinese and English datasets for the real-world short text EL.
Towards a Smart Data Processing and Storage Model
Authors: Ronie Salgado, Marcus Denker, Stéphane Ducasse, Anne Etien, Vincent Aranega
Date: 2021-01-07 00:00:00
Tasks: model

In several domains it is crucial to store and manipulate data whose origin needs to be completely traceable to guarantee the consistency, trustworthiness and reliability on the data itself typically for ethical and legal reasons. It is also important to guarantee that such properties are also carried further when such data is composed and processed into new data. In this article we present the main requirements and theorethical problems that arise by the design of a system supporting data with such capabilities. We present an architecture for implementing a system as well as a prototype developed in Pharo.
LUCIDGames: onLine UnsCented Inverse Dynamic Games forAdaptive Trajectory Prediction and Planning
Authors: Simon Le Cleac’h, Mac Schwager and Zachary Manchester
Date: 2020-11-16 00:00:00
Tasks: Autonomous Driving, parameter estimation, Trajectory Prediction

Existing  game-theoretic  planning  methods  assumethat the robot knows the objective functions of the other agentsa  prioriwhile,  in  practical  scenarios,  this  is  rarely  the  case.This paper introduces LUCIDGames, an inverse optimal controlalgorithm  that  is  able  to  estimate  the  other  agents’  objectivefunctions  in  real  time,  and  incorporate  those  estimates  onlineinto  a  receding-horizon  game-theoretic  planner.  LUCIDGamessolves   the   inverse   optimal   control   problem   by   recasting   itin  a  recursive  parameter-estimation  framework.  LUCIDGamesuses  an  unscented  Kalman  filter  (UKF)  to  iteratively  update  aBayesian estimate of the other agents’ cost function parameters,improving  that  estimate  online  as  more  data  is  gathered  fromthe  other  agents’  observed  trajectories.  The  planner  then  takesaccount  of  the  uncertainty  in  the  Bayesian  parameter  estimatesof  other  agents  by  planning  a  trajectory  for  the  robot  subjectto  uncertainty  ellipse  constraints.  The  algorithm  assumes  noexplicit  communication  or  coordination  between  the  robot  andthe  other  agents  in  the  environment.  An  MPC  implementationof  LUCIDGames  demonstrates  real-time  performance  on  com-plex  autonomous  driving  scenarios  with  an  update  frequencyof   40   Hz.  Empirical   results   demonstrate  that   LUCIDGamesimproves  the  robot’s  performance  over  existing  game-theoreticand  traditional  MPC  planning  approaches.  Our  implementa-tion  of  LUCIDGames  is  available  athttps://github.com/RoboticExplorationLab/LUCIDGames.jl
Graph Representation Learning via Ladder Gamma Variational Autoencoders
Authors: Arindam Sarkar, Nikhil Mehta, Piyush Rai
Date: 2020-04-03 00:00:00
Tasks: Graph Representation Learning, Link Prediction, Representation Learning

We present a probabilistic framework for community discovery and link prediction for graph-structured data, based on a novel, gamma ladder variational autoencoder (VAE) architecture. We model each node in the graph via a deep hierarchy of gamma-distributed embeddings, and define each link probability via a nonlinear function of the bottom-most layer's embeddings of its associated nodes. In addition to leveraging the representational power of multiple layers of stochastic variables via the ladder VAE architecture, our framework offers the following benefits: (1) Unlike existing ladder VAE architectures based on real-valued latent variables, the gamma-distributed latent variables naturally result in non-negativity and sparsity of the learned embeddings, and facilitate their direct interpretation as membership of nodes into (possibly multiple) communities/topics; (2) A novel recognition model for our gamma ladder VAE architecture allows fast inference of node embeddings; and (3) The framework also extends naturally to incorporate node side information (features and/or labels). Our framework is also fairly modular and can leverage a wide variety of graph neural networks as the VAE encoder. We report both quantitative and qualitative results on several benchmark datasets and compare our model with several state-of-the-art methods.
Deep Topic Models for Multi-label Learning
Authors: Rajat Panda, Ankit Pensia, Nikhil Mehta, Mingyuan Zhou, Piyush Rai
Date: 2019-04-13 00:00:00
Tasks: Multi-Label Learning, Topic Models

We present a probabilistic framework for multi-label learning based on a deep generative model for the binary label vector associated with each observation. Our generative model learns deep multi-layer latent embeddings of the binary label vector, which are conditioned on the input features of the observation. The model also has an interesting interpretation in terms of a deep topic model, with each label vector representing a bag-of-words document, with the input features being its meta-data. In addition to capturing the structural properties of the label space (e.g., a near-low-rank label matrix), the model also offers a clean, geometric interpretation. In particular, the nonlinear classification boundaries learned by the model can be seen as the union of multiple convex polytopes. Our model admits a simple and scalable inference via efficient Gibbs sampling or EM algorithm. We compare our model with state-of-the-art baselines for multi-label learning on benchmark data sets, and also report some interesting qualitative results.
What does the consumer know about the environmental damage caused by the disposable cup and the need to replace it
Date: 2021-01-03 00:00:00

The objective of this work was to know the amount and frequency with which the people of Arandas in the Altos de Jalisco region use disposable cups and then know how willing they are to use edible cups made with natural gelatin. In this regard, it is worth commenting that these can not only be nutritious for those who consume them (since gelatin is a fortifying nutrient created from the skin and bone of pigs and cows), but they could also be degraded in a few days or be ingested by animals. To collect the information, a survey consisting of six questions was used, which was applied to 31 people by telephone and another 345 personally (in both cases they were applied to young people and adults). The results show that the residents of that town considerably use plastic cups in the different events that take place each week, which are more numerous during the patron saint festivities or at the end of the year. Even so, these people would be willing to change these habits, although for this, measures must be taken that do not affect the companies in that area, which work mainly with plastics and generate a high percentage of jobs.
Measurement of Global Value Chain (GVC) Participation in World Development Report 2020
Date: 2021-01-07 00:00:00

As we can understand with the spread of GVCs, a lot of new questions emerge regarding the measurement of participation and positioning in the globalised production process. The World Development Report (WDR) 2020 explains the GVC phenomenon and then focus on participation and the prospects especially in a world of change in technology. From the overview section, we can figure out that nowadays, goods and services flow across borders as intermediate inputs rather than final goods. In traditional trade, we need two countries with the notions of export and import. However, in GVC trade, the goods and services cross borders multiple times requiring more than two countries. Remarkable improvements in information, communication, and transport technologies have made it possible to fragment production across national boundaries. So the question is: how to conceptualise this type of new trade to justify the measurement of participation.