Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical Techniques for Leveraging Experts for Sequential Decisions and Predictions Preliminary Oral Exam (Thesis Proposal)

Sequential decisions and predictions are common problems in many areas of natural language process, robotics, and video games. Essentially, an agent interacts with an environment to learn how to solve a particular problem. Research in the area of sequential decisions and predictions has increased due in part to the success of reinforcement learning. However, this success has come at a cost of algorithms being very data ineﬃcient, which makes doing learning in the real world diﬃcult. Our primary goal is to make these algorithms more data-eﬃcient using techniques related to imitation learning. Imitation learning is a technique for using expert feedback in sequential decision and prediction-making problems. Naive imitation learning has a covariate shift problem (i.e. training distribution is diﬀerent than test distribution). We propose methods and ideas to address this issue and address other issues that arise in other styles of imitation learning. In particular, we study two types of imitation learning style feedbacks, ‘interactive’ feedback and ‘demonstration’ feedback. We propose two ideas to help move towards learning in real-world settings. For my ﬁrst proposed work, I plan to study Imitation learning problems in the context of multi-player games. Modern multi-player games are a setting for studying imitation learning because of the amount of potential demonstration data available. Reinforcement learning techniques are hard to run in these settings because the reward is often sparse and the state space is usually large. Imitation learning techniques can help, but simply running imitation learning techniques out of the box could exaggerated issues already present. In this work, we use the fact that we are playing a game and that the expert provides good state coverage in the ‘demonstration’ feedback setting — and propose a new technique to learn from the ‘demonstration’ feedback. Extending beyond multi-player games, we propose to benchmark the current state of imitation learning algorithms. To understand how much progress we have made over the years, we plan to standardize comparing imitation learning algorithms with each other. At its simplest form imitation learning is supervised learning and a lot of progress has been made from benchmarks in supervised learning. We go a step further by introducing more standard ‘baselines’ besides behavior cloning and standardize ways of training behavior cloning. This benchmark will provide us with an understanding of how recent ideas hold against various situations, giving us an indicator of how practical and feasible it would be to run these algorithms in real-world settings.
Is Ireland the most intangible intensive economy in Europe? A growth accounting perspective

Assessing the contribution of intangible investment to growth is a challenging and complex task for any country. However, it has become increasingly difficult to determine both the exact magnitude of economic performance and its composition in the case of the Irish economy. This is mainly due to the impact of certain distortionary transactions by a select number of multinationals operating in the Irish jurisdiction. In this paper we address this issue by assessing, in a detailed manner, the contribution of intangible and tangible assets to the Irish growth story. We control for distortions in the official investment data series while also incorporating intangible assets which are not currently included in the National Accounts. Our results show that the observed unprecedented increase in the official intangible investment has a relatively minor contribution to the actual Irish labour productivity growth. Once the distortions are filtered out, Irish labour productivity growth is driven by tangible capital. More interestingly, non-national accounts intangible capital has a sizeable pro-cyclical impact on labour productivity growth.
The Nature of Coastal Hazard Risk Perceptions

Using survey data of homeowners from multiple locations on the U.S. east coast, this study characterizes the accuracy and determinants of homeowner misperceptions regarding flood and hurricane risks. Previous literature has examined the tendencies regarding homeowner flood risk misperceptions but has failed to reach a consensus. The results presented here add another data point to the literature on flood risk misperceptions using a novel data set and extend the literature by including an analysis of misperceptions of tropical cyclone risk. Findings suggest no general tendency in perceptions of flood probabilities, but a strong tendency to overestimate the probability of a major hurricane strike. Respondents also overwhelmingly overestimate the damage likely to be caused by a flood or major hurricane. Reduced form regressions suggest a variety of individual attributes may influence risk misperceptions including past flood experience, coastal experiences, and levels of worry. Notably, objective risk metrics appear to influence perceptions but only if the source of objective risk is highly publicized. Finally, estimation of six commonly referred to probability weighting functions reveals that the differences between subjective and objective flood probabilities cannot easily be explained by probability weighting suggesting that the source of the probability distortions is most likely due to mis-percieving the true risk.
Network Stochastic Call Center Model

A closed exponential queueing network with impatient customers is considered as a model of a call center. The process of handling calls in the call center is described. A detailed description of the model is given. The stay of the customer at a specific network node and its routing between the nodes correspond to the customer’s call status in the call center and the process of its routing between telephone agents of different levels. The process of changing the number of customers at the nodes is studied under the asymptotic assumption of a large number of customers. It is proved that its probability density function satisﬁes the Fokker–Planck– Kolmogorov equation. The system of diﬀerential equations for the expected value of this process is derived. The solution of this system allows predicting the dynamics of the expected number of customers in the network nodes and regulating the parameters of the call center operation. The asymptotic technique used is applicable both in transient and steady-state. The problem of finding the optimal staffing list of agents to maximize call center performance is investigated. The areas of implementation of research results are the design of call centers and improving their performance.
Head Pose as a Proxy for Gaze in Virtual Reality

—In this work, we posit that a user’s head pose can serve as a proxy for gaze in a VR object selection task. We describe a study in which participants were asked to describe a series of objects in a known order, providing approximate labels for the focus of attention. The participants’ head pose was then evaluated as a function of the position and orientation of the headset, and how closely that pose matched the location of known objects was calculated. The object that most closely matched the gaze was then evaluated using a mean reciprocal ranking. We demonstrate that using a concept of gaze derived from head pose can be used to effectively narrow the set of objects that are the target of participants’ attention.
Neighbor-Speciﬁc BGP: More Flexible Routing Policies Without Sacriﬁcing Global Stability

The Border Gateway Protocol (BGP) oﬀers network administrators considerable ﬂexibility in controlling how traﬃc ﬂows through their networks. However, the interaction between routing policies in diﬀerent Autonomous Systems (ASes) can lead to protocol oscillation. The best-known suﬃcient conditions of BGP global routing stability impose restrictions on the kinds of local routing policies individual ASes can safely implement. In this paper, we present neighbor-speciﬁc BGP (NS-BGP), a modest extension to BGP that enables a much wider range of local policies without compromising global stability. Whereas a conventional BGP-speaking router selects a single “best” route (for each destination preﬁx), NS-BGP allows a router to customize the route selection on behalf of each neighbor. For example, one neighbor may prefer the shortest route, another the most secure route, and yet another the least expensive route. Surprisingly, we prove that the much more ﬂexible NS-BGP is guaranteed to be stable under much less restrictive conditions on how routers “rank” the candidate routes. Moreover, unlike BGP, NS-BGP is able to support backup routing without oscillation and other anomalies such as “BGP wedgies”. We also show that it is safe to deploy NS-BGP incrementally, as a routing system with a partial deployment of NS-BGP is guaranteed to be stable, even in the presence of failure and other topology changes. In addition to our theoretical results, we also describe how NS-BGP can be deployed by individual ASes independently without changing to the BGP message format or collaboration from neighboring ASes.
Hybrid Estimation for Open-Ended Questions with Early-Age Students' Block-Based Programming Answers

Block-based programming is of great signiﬁcance for cultivating children’s computational thinking. However, due to the following challenges, it is diﬃcult to evaluate students’ programming ability in online learning systems: 1) compared with the traditional Online Judge (OJ) system, there is no standard answer for a given task in block-based programming; 2) in order to promote students’ interests, although the programs are not totally correct and unrelated to the task, the teacher will give a comparatively higher score. Therefore, current approaches involving output comparison and code analysis do not work eﬀectively. Furthermore, deep learning methods also suﬀer from the problem of how to represent block code for classiﬁcation. We propose a novel hybrid estimation model to address these challenges. We ﬁrst learn graph embedding from the parsed Abstract Syntax Tree (AST) to present the logicality of the code. Next, we provide some methods to measure the work-load and complexity of the code. Then, we extracted some key variables and task-irrelevant properties, introduced teacher bias. Finally, XGBoost was constructed for classiﬁcation. Based on real-world data collected from an online Scratch platform by early-age students, our model outperforms KimCNN, ResNet-18, and Graph2Vec+XGBoost. Moreover, we provided statistical analyses and intuitive explanations to interpret the characteristics in various groups.
Data Augmentation and Deep Learning Techniques to Improve Interface Noise Tolerance of Myoelectric Pattern Recognition Controllers

Background: Clinically available myoelectric PR controllers deteriorate under conditions that generate interface noise, such as electrode liftoff or wire failure. Previous solutions relied on additional processing steps like signal denoising and controller adaptation to mitigate these negative effects. However, there are no clinically practical controllers that are inherently robust to interface noise. This paper investigated the use of data-driven methods to build clinically practical, noise tolerant PR control strategies for transradial prostheses.Methods: We developed a data augmentation protocol to increase training data variability and two deep neural networks (MLP and CNN) to compress redundancies across a six-channel EMG array to a latent space that is robust to noise. Using data from fourteen intact limb subjects and seven below-elbow amputee subjects, we evaluated five control strategies: an LDA classifier (LDA), an existing adaptive LDA classifier that recalibrates after discarding noisy EMG channels (LDA-), an LDA classifier trained with an augmented data set (LDA+), an LDA classifier trained with MLP-aligned latent variables of an augmented data set (MLP-LDA), and an LDA classifier trained with CNN-aligned latent variables of an augmented data set (CNN-LDA). We computed the classification accuracies on clean signals and signals that had interface noise in up to four channels.Results: Compared to the baseline LDA method, LDA+ and MLP-LDA significantly increased classification accuracy on noisy data but decreased accuracy on clean data. On the other hand, LDA- and CNN-LDA improved noise tolerance while maintaining high performance with clean data. Conclusion: The CNN-LDA control strategy is robust to multichannel interface noise and can improve the reliability and usability of PR-based upper limb prostheses.
Investigating an Inverse Finite Element Approach for Characterising Soft Materials

R & D Journal of the South African Institution of Mechanical Engineering 2021, 37, 80-88 http://dx.doi. org/10.17159/2309-8988/2021/v37a9 http://www.saimeche. org.za (open access) © SAIMechE All rights reserved. 80 Abstract: Soft materials, such as soft biological tissue and soft silicone rubber, are non-linear materials which require the classical uniaxial and biaxial tensile testing methods for characterisation. Unfortunately, in special cases, such as for soft biological tissue, the samples are smaller than 10 mm × 10 mm in size and these classical tensile testing methods produce unwanted stress and strain gradients due to the fastening techniques associated with these methods. Micro-indentation is proposed as an alternative method for characterising soft materials. Using inverse Finite Element (FE) analysis and a known Mooney-Rivlin three parameter material model, six different micro-indentation tests were proposed. A theoretical approach was used to determine which indentation test best characterised a silicone sample, by using two FE models. The results showed that microindentation is capable of characterising a soft material in ideal conditions with a cylindrical indenter applied in a diagonal orientation over the sample, as the best indentation method. Finally, it was observed that the material model can either match the displacements with the smallest objective function or the stress vs. stretch curve can be matched to 99 % over the whole stretch range but not both simultaneously.
Simulation optimization for berth allocation in container terminals

This paper builds a simulation and optimization model for minimizing the total stay time of ships, which aims at the problem of berth and quay crane (QC) scheduling.The simulation and optimization method creates and evaluates berth allocation schemes by genetic algorithms,obtains feasible solutions meeting berth restriction and QC scheduling strategy by simulation mode[,and achieves a good solution including berthing times,berthing positions and the number of quay cranes allocated to ships.The speediness and validity of the method is illustrated by an instance.
Stylized Facts From Prices at Multi-Channel Retailers in Mexico

Using data gathered through web scraping techniques, this paper characterizes product categories' frequency, size and dispersion of price changes in eight retail chains in Mexico, and compare them with price statistics stemming from brick and mortar stores data of the same retailers. Notably, between 2016 and 2019, prices observed in brick and mortar stores (offline) change more frequently than those observed on websites (online). However, given a price change, online prices tend to exhibit larger price changes than their offline counterparts. In 2020, period affected by the pandemic, the above relationship across sales channel holds, while the frequency of price changes increased roughly by the same magnitude in both sales channels and the average size of price adjustments did not change relative to previous years. Results from this paper highlight the importance of recognizing the differences between survey and web scraped data.
Appraisal in Human-Robot Collaboration

We have investigated the mutual inﬂuence of aﬀective and collaborative processes in a cognitive theory to support interaction between humans and robots or virtual agents. We have developed new algorithms for appraisal processes, as part of a new overall computational model for implementing collaborative robots and agents. We build primarily on the cognitive appraisal theory of emotions and the Shared-Plans theory of collaboration to investigate the structure, fundamental processes and functions of emotions in a collaboration. We have evaluated our implemented algorithms by conducting an online user study.
Using Linked Data to build Recommender Systems ∗

This paper describes how Semantic Web technologies and especially the Linked Open Data (LOD) project can be used to build a new generation of recommender systems. While most of the current recommender systems use private data sets, we show how to exploit the beneﬁts of the LOD community eﬀort to build recommender systems. By providing public, collaboratively created and semantically structured data, it enables cross-domain recommendations by providing data which can be exploited for diﬀerent recommendation tasks and which is portable between systems, without changing the implementation of the recommendation algorithm. The contributions of this paper are (i) an overview of the LOD community eﬀort and of the data cloud generated by it, (ii) the description and evaluation of both a semantic-distance and a collaborative ﬁltering approach based on Linked Data, (iii) a method for evaluating recommendations using existing categorical data and (iv) the description of a reference architecture for implementing cross-domain recommender systems using Linked Data.
CEO Overseas Experience and Firm Internationalization: Before and After the Global Financial Crisis

Purpose – This study explores the contextual factors that affect the relationship between CEO overseas experience and firm internationalization. This study incorporates a wide range of contextual factors, including mega, macro, and micro variables. In particular, this study goes a step further from prior studies by incorporating a higher-order variable i.e., the global financial crisis that can constrain the managerial discretion of a CEO. Design/methodology – To structure the balanced data set before and after the 2008 global financial crisis, we used the data for the years from 2002 to 2014 from a sample of Korean manufacturing firms. Ultimately, 1101 firm-year unbalanced panel observations from 101 firms were used for the analysis. Findings – Our main findings can be summarized as follows. CEO overseas experience is positively related to firm internationalization. However, this relationship varies depending on the CEOs level of managerial discretion. As for the constraining moderation, the global financial crisis weakened the positive relationship between CEO overseas experience and firm internationalization. As for the enabling moderation, the CEOs tenure strengthened the relationship. Originality/value – This study adopted the knowledge, skills, and abilities (KSA) framework to explain the relationship between CEO overseas experience and firm internationalization. Moreover, we argue that the CEO-internationalization relationship depends on the specific context of the managerial discretion, focusing on the 2008 global financial crisis. Empirically, this study adopted the 2SLS procedure to correct endogeneity. Instead of taking the actual value of prior internationalization as a control, we estimated prior internationalization using the instrument variables at an industry level. This procedure made our estimation more robust.
Enabling On-Device Smartphone GPU based Training: Lessons Learned

Deep Learning (DL) has shown impressive performance in many mobile applications. Most existing works have focused on reducing the computational and resource overheads of running Deep Neural Networks (DNN) inference on resource-constrained mobile devices. However, the other aspect of DNN operations, i.e. training (forward and backward passes) on smartphone GPUs, has received little attention thus far. To this end, we conduct an initial analysis to examine the feasibility of on-device training on smartphones using mobile GPUs. We first employ the open-source mobile DL framework (MNN) and its OpenCL backend for running compute kernels on GPUs. Next, we observed that training on CPUs is much faster than on GPUs and identified two possible bottlenecks related to this observation: (i) computation and (ii) memory bottlenecks. To solve the computation bottleneck, we optimize the OpenCL backend’s kernels, showing 2x improvements (40-70 GFLOPs) over CPUs (15-30 GFLOPs) on the Snapdragon 8 series processors. However, we find that the full DNN training is still much slower on GPUs than on CPUs, indicating that memory bottleneck plays a significant role in the lower performance of GPU over CPU. The data movement takes almost 91% of training time due to the low bandwidth. Lastly, based on the findings and failures during our investigation, we present limitations and practical guidelines for future directions.
Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images

High-dimensional imaging is becoming increasingly relevant in many fields from astronomy and cultural heritage to systems biology. Visual exploration of such high-dimensional data is commonly facilitated by dimensionality reduction. However, common dimensionality reduction methods do not include spatial information present in images, such as local texture features, into the construction of low-dimensional embeddings. Consequently, exploration of such data is typically split into a step focusing on the attribute space followed by a step focusing on spatial information, or vice versa. In this paper, we present a method for incorporating spatial neighborhood information into distance-based dimensionality reduction methods, such as t-Distributed Stochastic Neighbor Embedding (t-SNE). We achieve this by modifying the distance measure between high-dimensional attribute vectors associated with each pixel such that it takes the pixel's spatial neighborhood into account. Based on a classification of different methods for comparing image patches, we explore a number of different approaches. We compare these approaches from a theoretical and experimental point of view. Finally, we illustrate the value of the proposed methods by qualitative and quantitative evaluation on synthetic data and two real-world use cases.
When, Why, and Which Pretrained GANs Are Useful?

The literature has proposed several methods to finetune pretrained GANs on new datasets, which typically results in higher performance compared to training from scratch, especially in the limited-data regime. However, despite the apparent empirical benefits of GAN pretraining, its inner mechanisms were not analyzed in-depth, and understanding of its role is not entirely clear. Moreover, the essential practical details, e.g., selecting a proper pretrained GAN checkpoint, currently do not have rigorous grounding and are typically determined by trial and error. This work aims to dissect the process of GAN finetuning. First, we show that initializing the GAN training process by a pretrained checkpoint primarily affects the model's coverage rather than the fidelity of individual samples. Second, we explicitly describe how pretrained generators and discriminators contribute to the finetuning process and explain the previous evidence on the importance of pretraining both of them. Finally, as an immediate practical benefit of our analysis, we describe a simple recipe to choose an appropriate GAN checkpoint that is the most suitable for finetuning to a particular target task. Importantly, for most of the target tasks, Imagenet-pretrained GAN, despite having poor visual quality, appears to be an excellent starting point for finetuning, resembling the typical pretraining scenario of discriminative computer vision models.