Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis: Discovering Semi-Markov Models From Event Logs

Process mining is a well-established discipline of data analysis focused on the discovery of process models from information systems' event logs. Recently, an emerging subarea of process mining - stochastic process discovery has started to evolve. Stochastic process discovery considers frequencies of events in the event data and allows for more comprehensive analysis. In particular, when durations of activities are presented in the event log, performance characteristics of the discovered stochastic models can be analyzed, e.g., the overall process execution time can be estimated. Existing performance analysis techniques usually discover stochastic process models from event data and then simulate these models to evaluate their execution times. These methods rely on empirical approaches. This paper proposes analytical techniques for performance analysis allowing for the derivation of statistical characteristics of the overall processes' execution times in the presence of arbitrary time distributions of events modeled by semi-Markov processes. The proposed methods can significantly simplify the what-if analysis of processes by providing solutions without resorting to simulation.
Human-Multirobot Collaborative Mobile Manipulation: The Omnid Mocobots

The Omnid human-collaborative mobile manipulators are an experimental platform for testing control architectures for autonomous and human-collaborative multirobot mobile manipulation. An Omnid consists of a mecanum-wheel omnidirectional mobile base and a series-elastic Delta-type parallel manipulator, and it is a specific implementation of a broader class of mobile collaborative robots (“mocobots”) suitable for safe human co-manipulation of delicate, flexible, and articulated payloads. Key features of mocobots include passive compliance, for the safety of the human and the payload, and high-fidelity end-effector force control independent of the potentially imprecise motions of the mobile base. We describe general considerations for the design of teams of mocobots; the design of the Omnids in light of these considerations; manipulator and mobile base controllers to achieve multirobot collaborative behaviors; and experiments in human-multirobot collaborative mobile manipulation of large and articulated payloads, where the mocobot team renders the payloads weightless for effortless human co-manipulation. In these experiments, the only communication among the humans and Omnids is mechanical, through the payload.
Hardness and Algorithms for Robust and Sparse Optimization

We explore algorithms and limitations for sparse optimization problems such as sparse linear regression and robust linear regression. The goal of the sparse linear regression problem is to identify a small number of key features, while the goal of the robust linear regression problem is to identify a small number of erroneous measurements. Specifically, the sparse linear regression problem seeks a $k$-sparse vector $x\in\mathbb{R}^d$ to minimize $\|Ax-b\|_2$, given an input matrix $A\in\mathbb{R}^{n\times d}$ and a target vector $b\in\mathbb{R}^n$, while the robust linear regression problem seeks a set $S$ that ignores at most $k$ rows and a vector $x$ to minimize $\|(Ax-b)_S\|_2$. We first show bicriteria, NP-hardness of approximation for robust regression building on the work of [OWZ15] which implies a similar result for sparse regression. We further show fine-grained hardness of robust regression through a reduction from the minimum-weight $k$-clique conjecture. On the positive side, we give an algorithm for robust regression that achieves arbitrarily accurate additive error and uses runtime that closely matches the lower bound from the fine-grained hardness result, as well as an algorithm for sparse regression with similar runtime. Both our upper and lower bounds rely on a general reduction from robust linear regression to sparse regression that we introduce. Our algorithms, inspired by the 3SUM problem, use approximate nearest neighbor data structures and may be of independent interest for solving sparse optimization problems. For instance, we demonstrate that our techniques can also be used for the well-studied sparse PCA problem.
Power-efficient Joint Link Selection and Multihop Routing for Throughput Maximization in UAV Assisted FANETs

This paper considers a multi-UAV network with a ground station (GS) that uses multihop relaying structure for data transmission in a power-efficient manner. In order to increase the total network performance of a flying ad-hoc network (FANET) of UAVs, the objective is to investigate the best possible multihop routing structure for data dissemination. We formulate a problem to jointly optimize the multihop routing structure with the communication link selection for a given power budget so that the overall network throughput can be maximized. It appears that the formulated problem belongs to a class of nonconvex and integer optimization problems, thus making it NP-hard. To solve this problem efficiently, it is decoupled into two subproblems i) power allocation with known Bellman Ford-based multihop routing structure and ii) link selection problem. Further, these two subproblems are independently converted into convex problems by relaxation and solved in tandem for the best suboptimal solution to the main problem. Simulation results indicate that the proposed multihop routing schemes can achieve a significant improvement in network throughput compared to the other benchmark scheme.
Can Push-forward Generative Models Fit Multimodal Distributions?

Many generative models synthesize data by transforming a standard Gaussian random variable using a deterministic neural network. Among these models are the Variational Autoencoders and the Generative Adversarial Networks. In this work, we call them"push-forward"models and study their expressivity. We show that the Lipschitz constant of these generative networks has to be large in order to fit multimodal distributions. More precisely, we show that the total variation distance and the Kullback-Leibler divergence between the generated and the data distribution are bounded from below by a constant depending on the mode separation and the Lipschitz constant. Since constraining the Lipschitz constants of neural networks is a common way to stabilize generative models, there is a provable trade-off between the ability of push-forward models to approximate multimodal distributions and the stability of their training. We validate our findings on one-dimensional and image datasets and empirically show that generative models consisting of stacked networks with stochastic input at each step, such as diffusion models do not suffer of such limitations.
ZoDIAC: Zoneout Dropout Injection Attention Calculation

Recently the use of self-attention has yielded to state-of-the-art results in vision-language tasks such as image captioning as well as natural language understanding and generation (NLU and NLG) tasks and computer vision tasks such as image classiﬁcation. This is since self-attention maps the internal interactions among the elements of input source and target sequences. Although self-attention successfully calculates the attention values and maps the relationships among the elements of input source and target sequence, yet there is no mechanism to control the intensity of attention. In real world, when communicating with each other face to face or vocally, we tend to express diﬀerent visual and linguistic context with various amounts of intensity. Some words might carry (be spoken with) more stress and weight indicating the importance of that word in the context of the whole sentence. Based on this intuition, we propose Zoneout Dropout Injection Attention Calculation (ZoDIAC) in which the intensities of attention values in the elements of the input sequence are calculated with respect to the context of the elements of input sequence. The results of our experiments reveal that employing ZoDIAC leads to better performance in comparison with the self-attention module in the Transformer model. The ultimate goal is to ﬁnd out if we could modify self-attention module in the Transformer model with a method that is potentially extensible to other models that leverage on self-attention at their core. Our ﬁndings suggest that this particular goal deserves further attention and investigation by the research community. The code for ZoDIAC is available on www.github. com/zanyarz/zodiac.
Linear Model Against Malicious Adversaries with Local Differential Privacy

—Scientiﬁc collaborations beneﬁt from collaborative learning of distributed sources, but remain difﬁcult to achieve when data are sensitive. In recent years, privacy preserving techniques have been widely studied to analyze distributed data across different agencies while protecting sensitive information. Most existing privacy preserving techniques are designed to resist semi-honest adversaries and require intense computation to perform data analysis. Secure collaborative learning is signif-icantly difﬁcult with the presence of malicious adversaries who may deviates from the secure protocol. Another challenge is to maintain high computation efﬁciency with privacy protection. In this paper, matrix encryption is applied to encrypt data such that the secure schemes are against malicious adversaries, including chosen plaintext attack, known plaintext attack, and collusion attack. The encryption scheme also achieves local differential pri- vacy. Moreover, cross validation is studied to prevent overﬁtting without additional communication cost. Empirical experiments on real-world datasets demonstrate that the proposed schemes are computationally efﬁcient compared to existing techniques against malicious adversary and semi-honest model.
A contraction theory approach to observer-based controller design for glucose regulation in type 1 diabetes

Artificial Pancreas has evolved to be effective in regulating the blood glucose in the safe range of 70–180 mg/dl in type 1 diabetic patients. However, the high intra-patient variability, as well as exogenous meal disturbances, pose serious challenges. Moreover, the unavailability of insulin sensors in Artificial Pancreas adds to the difficulty. In the present work, a subcutaneous model of type 1 diabetes (T1D) is considered for observer-based controller design in the framework of contraction analysis. A variety of realistic multiple-meal scenarios for different virtual TlD patients have been investigated and results are presented.
vMFNet: Compositionality Meets Domain-generalised Segmentation

Training medical image segmentation models usually requires a large amount of labeled data. By contrast, humans can quickly learn to accurately recognise anatomy of interest from medical (e.g. MRI and CT) images with some limited guidance. Such recognition ability can easily generalise to new images from different clinical centres. This rapid and generalisable learning ability is mostly due to the compositional structure of image patterns in the human brain, which is less incorporated in medical image segmentation. In this paper, we model the compositional components (i.e. patterns) of human anatomy as learnable von-Mises-Fisher (vMF) kernels, which are robust to images collected from different domains (e.g. clinical centres). The image features can be decomposed to (or composed by) the components with the composing operations, i.e. the vMF likelihoods. The vMF likelihoods tell how likely each anatomical part is at each position of the image. Hence, the segmentation mask can be predicted based on the vMF likelihoods. Moreover, with a reconstruction module, unlabeled data can also be used to learn the vMF kernels and likelihoods by recombining them to reconstruct the input image. Extensive experiments show that the proposed vMFNet achieves improved generalisation performance on two benchmarks, especially when annotations are limited. Code is publicly available at: https://github.com/vios-s/vMFNet.
Two-Stage Neural Contextual Bandits for Personalised News Recommendation

We consider the problem of personalised news recommendation where each user consumes news in a sequential fashion. Existing personalised news recommendation methods focus on exploiting user interests and ignores exploration in recommendation, which leads to biased feedback loops and hurt recommendation quality in the long term. We build on contextual bandits recommendation strategies which naturally address the exploitation-exploration trade-off. The main challenges are the computational efficiency for exploring the large-scale item space and utilising the deep representations with uncertainty. We propose a two-stage hierarchical topic-news deep contextual bandits framework to efficiently learn user preferences when there are many news items. We use deep learning representations for users and news, and generalise the neural upper confidence bound (UCB) policies to generalised additive UCB and bilinear UCB. Empirical results on a large-scale news recommendation dataset show that our proposed policies are efficient and outperform the baseline bandit policies.
Reliable Vehicle State and Parameter Estimation

Diverse vehicle active safety systems including vehicle electronic stability control (ESC) system, anti-lock braking system (ABS), and traction control system (TCS) are significantly relying on information about the vehicle’s states and parameters, as well as the vehicle’s surroundings. However, many important states or parameters, such as sideslip angle, tire-road friction coefficient, road gradient and vehicle mass are hard to directly measure, and hence advanced estimation algorithms are needed. Furthermore, enhancements of sensor technologies and the emergence of new concepts such as Internet of Things and their automotive version, Internet of Vehicles, facilitate reliable and resilient estimation of vehicle states and road conditions. Consequently, developing a resilient estimation structure to operate with the available sensor data in commercial vehicles and be flexible enough to incorporate new information in future cars is the main objective of this thesis. This thesis presents a reliable corner-based vehicle velocity estimation and a road condition classification algorithm. For vehicle velocity estimation, a combination of vehicle kinematics and the LuGre tire model is introduced in the design of a corner-based velocity observer. Moreover, the observability condition for both cases of time-invariant and parameter varying is studied. The effect of suspension compliance on enhancing the accuracy of the vehicle corner velocity estimation is also investigated and the results are verified via several experimental tests. The performance and the robustness of the proposed corner-based vehicle velocity estimation to model and road condition uncertainties is analyzed. The stability of the observer is discussed, and analytical expressions for the boundedness of the estimation error in the presence of system uncertainties for the case of fixed observer gains are derived. Furthermore, the stability of the observer under arbitrary and stochastic observer gain switching is studied and the performances of the observer for these two switching scenarios are compared. At the end, the sensitivity of the proposed observer to tire parameter variations is analyzed. These analyses are referred to as offline reliability methods. In addition to the off-line reliability analysis, an online reliability measure of the proposed velocity estimation is introduced, using vehicle kinematic relations. Moreover, meth-
Generative Neural Articulated Radiance Fields

Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress. These 3D GANs, however, have not been demonstrated for human bodies and the generated radiance fields of existing frameworks are not directly editable, limiting their applicability in downstream tasks. We propose a solution to these challenges by developing a 3D GAN framework that learns to generate radiance fields of human bodies or faces in a canonical pose and warp them using an explicit deformation field into a desired body pose or facial expression. Using our framework, we demonstrate the first high-quality radiance field generation results for human bodies. Moreover, we show that our deformation-aware training procedure significantly improves the quality of generated bodies or faces when editing their poses or facial expressions compared to a 3D GAN that is not trained with explicit deformations.
Prediction of Bead Profile Parameters for Submerged Arc Welding of Structural Pipe Steel

— n oil industries, submerged arc welding is the only primarily used process to weld high thickness, large distance structural oil pipes. Submerged arc welding process parameters play an important role in determining the quality of a weld joint. In the work presented here; on structural pipe steel (API 5L X65 grade) has been performed to estimate the effect of welding parameters on the weld bead geometry parameters. For design of experiments, response surface methodology (RSM) had been used. Mathematical models for bead geometry parameters; bead width, bead height and form factor in terms of welding parameters; welding current, arc voltage and welding speed had been formed and checked for adequacy with ANOVA (F-Test). It was found that welding current was the most significant parameter controlling all the responses and arc voltage had increasing effect.
Open Street Map for Multi-Modal Freight Transport Planning

Route planning for transportation of goods is an essential task. Determining a route for e.g. a truck is simple if the road network including the legal and technical limitations like truck ban or weight restriction are known. Planning of multi-modal routes is a bit more complex. The change of mode of transportation is only possible at specific locations. This change takes time and is often subjet to additional technical or legal restrictions. Thus the different networks, e.g., road and rail, need to be connected. An obvious approach for multi-modal transport planning would be the use of Open Street Map (OSM) data since it does not only contain information about roads but also about trains and rivers. In this paper we first present some existing solutions for multi-modal transport planning and then discuss the use of OSM data in such a process. We identify missing elements in OSM and show opportunities for further development and interventions.
A New Adjacency Matrix Configuration in GCN-based Models for Skeleton-based Action Recognition

Human skeleton data has received increasing attention in action recognition due to its background robustness and high efficiency. In skeleton-based action recognition, graph convolutional network (GCN) has become the mainstream method. This paper analyzes the fundamental factor for GCN-based models -- the adjacency matrix. We notice that most GCN-based methods conduct their adjacency matrix based on the human natural skeleton structure. Based on our former work and analysis, we propose that the human natural skeleton structure adjacency matrix is not proper for skeleton-based action recognition. We propose a new adjacency matrix that abandons all rigid neighbor connections but lets the model adaptively learn the relationships of joints. We conduct extensive experiments and analysis with a validation model on two skeleton-based action recognition datasets (NTURGBD60 and FineGYM). Comprehensive experimental results and analysis reveals that 1) the most widely used human natural skeleton structure adjacency matrix is unsuitable in skeleton-based action recognition; 2) The proposed adjacency matrix is superior in model performance, noise robustness and transferability.
The Qualitative Believing is Seeing: Korean Immigrants' Perception of the Former Believing is Seeing: Korean Immigrants' Perception of the Former South Korean President Roh South Korean President Roh

Abstract This study explores how individual characteristics interact with news media choice and people’s perception of mediated news events and public figures focusing on South Korean immigrants’ perception of the former South Korean President Roh. Thirteen South Korean immigrants were interviewed, and the results demonstrate a three-way relationship between (a) interviewees’ political orientation, (b) media choice, and (c) perception of Roh and the newspapers. The interviewees supporting Roh, who read online news on a regular basis but did not read conservative newspapers, recognized the political claim that the oligopoly of the conservative newspapers has influenced public opinion on Roh, while the readers of the conservative newspapers, who do not read on-line news, did not acknowledge the claim. Individuals’ opinions on Roh and the newspapers were not directed by media framing, but interviewees selectively chose what they read and accept. This study explores how individual characteristics interact with news media choice and people’s perception of mediated news events and public figures focusing on South Korean immigrants’ perception of the former South Korean President Roh. Thirteen South Korean immigrants were interviewed, and the results demonstrate a three-way relationship between (a) interviewees’ political orientation, (b) media choice, and (c) perception of Roh and the newspapers. The interviewees supporting Roh, who read online news on a regular basis but did not read conservative newspapers, recognized the political claim that the oligopoly of the conservative newspapers has influenced public opinion on Roh, while the readers of the conservative newspapers, who do not read on-line news, did not acknowledge the claim. Individuals’ opinions on Roh and the newspapers were not directed by media framing, but interviewees selectively chose what they read and accept.
Educational strategy to develop nursing students’ management competencies in hospital practice

ABSTRACT Objectives: to apply and analyze an educational strategy to develop management skills in nursing students to work in hospital practice. Methods: exploratory, intervention, qualitative study conducted from February 2020 to 2021. Fifty-four nursing students from a public higher education institution participated in this study, in which thirteen workshops were held to discuss management cases. Subsequently, semi-structured interviews were conducted using inductive thematic analysis. Results: the case studies addressed the management competencies of communication, decision making, leadership, and interpersonal relationships. After the intervention, the strategy was evaluated through interviews, identifying positive aspects regarding knowledge acquisition; and other limiting aspects, such as limited time to discuss the cases. Final Considerations: the workshops proved to be effective as teaching strategies for students, adding new management knowledge that should help their performance as future nurses, capable of reflection, and the subjects of knowledge construction for the professional practice of nursing.