pwc-archive/papers-with-abstracts huggingface dataset:
Deep DA for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labeled Videos
Authors: Gnana Praveen R, Eric Granger, Patrick Cardinal
Date: 2020-10-28 00:00:00
Tasks: Domain Adaptation, regression, Weakly-supervised Learning

Automatic estimation of pain intensity from facial expressions in videos has an immense potential in health care applications. However, domain adaptation (DA) is needed to alleviate the problem of domain shifts that typically occurs between video data captured in source and target do-mains. Given the laborious task of collecting and annotating videos, and the subjective bias due to ambiguity among adjacent intensity levels, weakly-supervised learning (WSL)is gaining attention in such applications. Yet, most state-of-the-art WSL models are typically formulated as regression problems, and do not leverage the ordinal relation between intensity levels, nor the temporal coherence of multiple consecutive frames. This paper introduces a new deep learn-ing model for weakly-supervised DA with ordinal regression(WSDA-OR), where videos in target domain have coarse la-bels provided on a periodic basis. The WSDA-OR model enforces ordinal relationships among the intensity levels as-signed to the target sequences, and associates multiple relevant frames to sequence-level labels (instead of a single frame). In particular, it learns discriminant and domain-invariant feature representations by integrating multiple in-stance learning with deep adversarial DA, where soft Gaussian labels are used to efficiently represent the weak ordinal sequence-level labels from the target domain. The proposed approach was validated on the RECOLA video dataset as fully-labeled source domain, and UNBC-McMaster video data as weakly-labeled target domain. We have also validated WSDA-OR on BIOVID and Fatigue (private) datasets for sequence level estimation. Experimental results indicate that our approach can provide a significant improvement over the state-of-the-art models, allowing to achieve a greater localization accuracy.
Proceedings 9th International Workshop on Theorem Proving Components for Educational Software
Authors: Pedro Quaresma, Walther Neuper, João Marcos
Date: 2020-10-28 00:00:00
Tasks: Automated Theorem Proving

The 9th International Workshop on Theorem-Proving Components for Educational Software (ThEdu'20) was scheduled to happen on June 29 as a satellite of the IJCAR-FSCD 2020 joint meeting, in Paris. The COVID-19 pandemic came by surprise, though, and the main conference was virtualised. Fearing that an online meeting would not allow our community to fully reproduce the usual face-to-face networking opportunities of the ThEdu initiative, the Steering Committee of ThEdu decided to cancel our workshop. Given that many of us had already planned and worked for that moment, we decided that ThEdu'20 could still live in the form of an EPTCS volume. The EPTCS concurred with us, recognising this very singular situation, and accepted our proposal of organising a special issue with papers submitted to ThEdu'20. An open call for papers was then issued, and attracted five submissions, all of which have been accepted by our reviewers, who produced three careful reports on each of the contributions. The resulting revised papers are collected in the present volume. We, the volume editors, hope that this collection of papers will help further promoting the development of theorem-proving-based software, and that it will collaborate to improve the mutual understanding between computer mathematicians and stakeholders in education. With some luck, we would actually expect that the very special circumstances set up by the worst sanitary crisis in a century will happen to reinforce the need for the application of certified components and of verification methods for the production of educational software that would be available even when the traditional on-site learning experiences turn out not to be recommendable.
Interpretable Data-Driven Demand Modelling for On-Demand Transit Services
Authors: Nael Alsaleh, Bilal Farooq
Date: 2020-10-27 00:00:00
Tasks: BIG-bench Machine Learning

In recent years, with the advancements in information and communication technology, different emerging on-demand shared mobility services have been introduced as innovative solutions in the low-density areas, including on-demand transit (ODT), mobility on-demand (MOD) transit, and crowdsourced mobility services. However, due to their infancy, there is a strong need to understand and model the demand for these services. In this study, we developed trip production and distribution models for ODT services at Dissemination areas (DA) level using four machine learning algorithms: Random Forest (RF), Bagging, Artificial Neural Network (ANN) and Deep Neural Network (DNN). The data used in the modelling process were acquired from Belleville's ODT operational data and 2016 census data. Bayesian optimalization approach was used to find the optimal architecture of the adopted algorithms. Moreover, post-hoc model was employed to interpret the predictions and examine the importance of the explanatory variables. The results showed that the land-use type was the most important variable in the trip production model. On the other hand, the demographic characteristics of the trip destination were the most important variables in the trip distribution model. Moreover, the results revealed that higher trip distribution levels are expected between dissemination areas with commercial/industrial land-use type and dissemination areas with high-density residential land-use. Our findings suggest that the performance of ODT services can be further enhanced by (a) locating idle vehicles in the neighbourhoods with commercial/industrial land-use and (b) using the spatio-temporal demand models obtained in this work to continuously update the operating fleet size.
Generalized Insider Attack Detection Implementation using NetFlow Data
Authors: Yash Samtani, Jesse Elwell
Date: 2020-10-27 00:00:00
Tasks: Clustering

Insider Attack Detection in commercial networks is a critical problem that does not have any good solutions at this current time. The problem is challenging due to the lack of visibility into live networks and a lack of a standard feature set to distinguish between different attacks. In this paper, we study an approach centered on using network data to identify attacks. Our work builds on unsupervised machine learning techniques such as One-Class SVM and bi-clustering as weak indicators of insider network attacks. We combine these techniques to limit the number of false positives to an acceptable level required for real-world deployments by using One-Class SVM to check for anomalies detected by the proposed Bi-clustering algorithm. We present a prototype implementation in Python and associated results for two different real-world representative data sets. We show that our approach is a promising tool for insider attack detection in realistic settings.
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models
Authors: Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza
Date: 2020-10-27 00:00:00

In a real life process evolving over time, the relationship between its relevant variables may change. Therefore, it is advantageous to have different inference models for each state of the process. Asymmetric hidden Markov models fulfil this dynamical requirement and provide a framework where the trend of the process can be expressed as a latent variable. In this paper, we modify these recent asymmetric hidden Markov models to have an asymmetric autoregressive component, allowing the model to choose the order of autoregression that maximizes its penalized likelihood for a given training set. Additionally, we show how inference, hidden states decoding and parameter learning must be adapted to fit the proposed model. Finally, we run experiments with synthetic and real data to show the capabilities of this new model.
Manifold learning-based feature extraction for structural defect reconstruction
Authors: Qi Li, Dianzi Liu, Zhenghua Qian
Date: 2020-10-26 00:00:00
Tasks: Structural Health Monitoring

Data-driven quantitative defect reconstructions using ultrasonic guided waves has recently demonstrated great potential in the area of non-destructive testing. In this paper, we develop an efficient deep learning-based defect reconstruction framework, called NetInv, which recasts the inverse guided wave scattering problem as a data-driven supervised learning progress that realizes a mapping between reflection coefficients in wavenumber domain and defect profiles in the spatial domain. The superiorities of the proposed NetInv over conventional reconstruction methods for defect reconstruction have been demonstrated by several examples. Results show that NetInv has the ability to achieve the higher quality of defect profiles with remarkable efficiency and provides valuable insight into the development of effective data driven structural health monitoring and defect reconstruction using machine learning.
Designing learning experiences for online teaching and learning
Authors: Nachamma Sockalingam, Junhua Liu
Date: 2020-10-26 00:00:00

Teaching is about constantly innovating strategies, ways and means to engage diverse students in active and meaningful learning. In line with this, SUTD adopts various student-centric teaching and learning teaching methods and approaches. This means that our graduate/undergraduate instructors have to be ready to teach using these student student-centric teaching and learning pedagogies. In this article, I share my experiences of redesigning this teaching course that is typically conducted face-to-face to a synchronous online course and also invite one of the participant in this course to reflect on his experience as a student.
Using a Binary Classification Model to Predict the Likelihood of Enrolment to the Undergraduate Program of a Philippine University
Authors: Dr. Joseph A. Esquivel, Dr. James A. Esquivel
Date: 2020-10-26 00:00:00
Tasks: Binary Classification, Decision Making, Management

With the recent implementation of the K to 12 Program, academic institutions, specifically, Colleges and Universities in the Philippines have been faced with difficulties in determining projected freshmen enrollees vis-a-vis decision-making factors for efficient resource management. Enrollment targets directly impacts success factors of Higher Education Institutions. This study covered an analysis of various characteristics of freshmen applicants affecting their admission status in a Philippine university. A predictive model was developed using Logistic Regression to evaluate the probability that an admitted student will pursue to enroll in the Institution or not. The dataset used was acquired from the University Admissions Office. The office designed an online application form to capture applicants' details. The online form was distributed to all student applicants, and most often, students, tend to provide incomplete information. Despite this fact, student characteristics, as well as geographic and demographic data based on the students' location are significant predictors of enrollment decision. The results of the study show that given limited information about prospective students, Higher Education Institutions can implement machine learning techniques to supplement management decisions and provide estimates of class sizes, in this way, it will allow the institution to optimize the allocation of resources and will have better control over net tuition revenue.
Three computational models and its equivalence
Authors: Ciro Ivan Garcia Lopez
Date: 2020-10-26 00:00:00

The study of computability has its origin in Hilbert's conference of 1900, where an adjacent question, to the ones he asked, is to give a precise description of the notion of algorithm. In the search for a good definition arose three independent theories: Turing and the Turing machines, G\"odel and the recursive functions, Church and the Lambda Calculus. Later there were established by Kleene that the classic models of computation are equivalent. This fact is widely accepted by many textbooks and the proof is omitted since the proof is tedious and unreadable. We intend to fill this gap presenting the proof in a modern way, without forgetting the mathematical details.
Expert Selection in High-Dimensional Markov Decision Processes
Authors: Vicenc Rubies-Royo, Eric Mazumdar, Roy Dong, Claire Tomlin, S. Shankar Sastry
Date: 2020-10-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this work we present a multi-armed bandit framework for online expert selection in Markov decision processes and demonstrate its use in high-dimensional settings. Our method takes a set of candidate expert policies and switches between them to rapidly identify the best performing expert using a variant of the classical upper confidence bound algorithm, thus ensuring low regret in the overall performance of the system. This is useful in applications where several expert policies may be available, and one needs to be selected at run-time for the underlying environment.
5W1H-based Expression for the Effective Sharing of Information in Digital Forensic Investigations
Authors: Jaehyeok Han, Jieon Kim, Sangjin Lee
Date: 2020-10-26 00:00:00

Digital forensic investigation is used in various areas related to digital devices including the cyber crime. This is an investigative process using many techniques, which have implemented as tools. The types of files covered by the digital forensic investigation are wide and varied, however, there is no way to express the results into a standardized format. The standardization are different by types of device, file system, or application. Different outputs make it time-consuming and difficult to share information and to implement integration. In addition, it could weaken cyber security. Thus, it is important to define normalization and to present data in the same format. In this paper, a 5W1H-based expression for information sharing for effective digital forensic investigation is proposed to analyze digital forensic information using six questions--what, who, where, when, why and how. Based on the 5W1H-based expression, digital information from different types of files is converted and represented in the same format of outputs. As the 5W1H is the basic writing principle, application of the 5W1H-based expression on the case studies shows that this expression enhances clarity and correctness for information sharing. Furthermore, in the case of security incidents, this expression has an advantage in being compatible with STIX.
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control
Authors: Hamid Radmard Rahmani, Carsten Koenke, Marco A. Wiering
Date: 2020-10-25 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

In many reinforcement learning (RL) problems, it takes some time until a taken action by the agent reaches its maximum effect on the environment and consequently the agent receives the reward corresponding to that action by a delay called action-effect delay. Such delays reduce the performance of the learning algorithm and increase the computational costs, as the reinforcement learning agent values the immediate rewards more than the future reward that is more related to the taken action. This paper addresses this issue by introducing an applicable enhanced Q-learning method in which at the beginning of the learning phase, the agent takes a single action and builds a function that reflects the environments response to that action, called the reflexive $\gamma$ - function. During the training phase, the agent utilizes the created reflexive $\gamma$- function to update the Q-values. We have applied the developed method to a structural control problem in which the goal of the agent is to reduce the vibrations of a building subjected to earthquake excitations with a specified delay. Seismic control problems are considered as a complex task in structural engineering because of the stochastic and unpredictable nature of earthquakes and the complex behavior of the structure. Three scenarios are presented to study the effects of zero, medium, and long action-effect delays and the performance of the Enhanced method is compared to the standard Q-learning method. Both RL methods use neural network to learn to estimate the state-action value function that is used to control the structure. The results show that the enhanced method significantly outperforms the performance of the original method in all cases, and also improves the stability of the algorithm in dealing with action-effect delays.
Investigating the Robustness of Artificial Intelligent Algorithms with Mixture Experiments
Authors: Jiayi Lian, Laura Freeman, Yili Hong, Xinwei Deng
Date: 2020-10-10 00:00:00
Tasks: Autonomous Driving, Classification, General Classification, Prediction, Robust classification

Artificial intelligent (AI) algorithms, such as deep learning and XGboost, are used in numerous applications including computer vision, autonomous driving, and medical diagnostics. The robustness of these AI algorithms is of great interest as inaccurate prediction could result in safety concerns and limit the adoption of AI systems. In this paper, we propose a framework based on design of experiments to systematically investigate the robustness of AI classification algorithms. A robust classification algorithm is expected to have high accuracy and low variability under different application scenarios. The robustness can be affected by a wide range of factors such as the imbalance of class labels in the training dataset, the chosen prediction algorithm, the chosen dataset of the application, and a change of distribution in the training and test datasets. To investigate the robustness of AI classification algorithms, we conduct a comprehensive set of mixture experiments to collect prediction performance results. Then statistical analyses are conducted to understand how various factors affect the robustness of AI classification algorithms. We summarize our findings and provide suggestions to practitioners in AI applications.
ADABOOK & MULTIBOOK: Adaptive Boosting with Chance Correction
Authors: David M. W. Powers
Date: 2020-10-11 00:00:00

There has been considerable interest in boosting and bagging, including the combination of the adaptive techniques of AdaBoost with the random selection with replacement techniques of Bagging. At the same time there has been a revisiting of the way we evaluate, with chance-corrected measures like Kappa, Informedness, Correlation or ROC AUC being advocated. This leads to the question of whether learning algorithms can do better by optimizing an appropriate chance corrected measure. Indeed, it is possible for a weak learner to optimize Accuracy to the detriment of the more reaslistic chance-corrected measures, and when this happens the booster can give up too early. This phenomenon is known to occur with conventional Accuracy-based AdaBoost, and the MultiBoost algorithm has been developed to overcome such problems using restart techniques based on bagging. This paper thus complements the theoretical work showing the necessity of using chance-corrected measures for evaluation, with empirical work showing how use of a chance-corrected measure can improve boosting. We show that the early surrender problem occurs in MultiBoost too, in multiclass situations, so that chance-corrected AdaBook and Multibook can beat standard Multiboost or AdaBoost, and we further identify which chance-corrected measures to use when.
Probabilistic Transformers
Authors: Javier R. Movellan
Date: 2020-10-15 00:00:00

We show that Transformers are Maximum Posterior Probability estimators for Mixtures of Gaussian Models. This brings a probabilistic point of view to Transformers and suggests extensions to other probabilistic cases.
Exploring the Nuances of Designing (with/for) Artificial Intelligence
Authors: Niya Stoimenova, Rebecca Price
Date: 2020-10-22 00:00:00

Solutions relying on artificial intelligence are devised to predict data patterns and answer questions that are clearly defined, involve an enumerable set of solutions, clear rules, and inherently binary decision mechanisms. Yet, as they become exponentially implemented in our daily activities, they begin to transcend these initial boundaries and to affect the larger sociotechnical system in which they are situated. In this arrangement, a solution is under pressure to surpass true or false criteria and move to an ethical evaluation of right and wrong. Neither algorithmic solutions, nor purely humanistic ones will be enough to fully mitigate undesirable outcomes in the narrow state of AI or its future incarnations. We must take a holistic view. In this paper we explore the construct of infrastructure as a means to simultaneously address algorithmic and societal issues when designing AI.
Improving Accuracy of Federated Learning in Non-IID Settings
Authors: Mustafa Safa Ozdayi, Murat Kantarcioglu, Rishabh Iyer
Date: 2020-10-14 00:00:00
Tasks: Federated Learning

Federated Learning (FL) is a decentralized machine learning protocol that allows a set of participating agents to collaboratively train a model without sharing their data. This makes FL particularly suitable for settings where data privacy is desired. However, it has been observed that the performance of FL is closely tied with the local data distributions of agents. Particularly, in settings where local data distributions vastly differ among agents, FL performs rather poorly with respect to the centralized training. To address this problem, we hypothesize the reasons behind the performance degradation, and develop some techniques to address these reasons accordingly. In this work, we identify four simple techniques that can improve the performance of trained models without incurring any additional communication overhead to FL, but rather, some light computation overhead either on the client, or the server-side. In our experimental analysis, combination of our techniques improved the validation accuracy of a model trained via FL by more than 12% with respect to our baseline. This is about 5% less than the accuracy of the model trained on centralized data.
Shared Space Transfer Learning for analyzing multi-site fMRI data
Authors: Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew J. Greenshaw, Russell Greiner
Date: 2020-10-24 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Art Analysis, Transfer Learning

Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing different cognitive tasks -- e.g., watching movies or making decisions. MVPA works best with a well-designed feature set and an adequate sample size. However, most fMRI datasets are noisy, high-dimensional, expensive to collect, and with small sample sizes. Further, training a robust, generalized predictive model that can analyze homogeneous cognitive tasks provided by multi-site fMRI datasets has additional challenges. This paper proposes the Shared Space Transfer Learning (SSTL) as a novel transfer learning (TL) approach that can functionally align homogeneous multi-site fMRI datasets, and so improve the prediction performance in every site. SSTL first extracts a set of common features for all subjects in each site. It then uses TL to map these site-specific features to a site-independent shared space in order to improve the performance of the MVPA. SSTL uses a scalable optimization procedure that works effectively for high-dimensional fMRI datasets. The optimization procedure extracts the common features for each site by using a single-iteration algorithm and maps these site-specific common features to the site-independent shared space. We evaluate the effectiveness of the proposed method for transferring between various cognitive tasks. Our comprehensive experiments validate that SSTL achieves superior performance to other state-of-the-art analysis techniques.