pwc-archive/papers-with-abstracts huggingface dataset:
Operationalizing Specifications, In Addition to Test Sets for Evaluating Constrained Generative Models
Authors: Vikas Raunak, Matt Post, Arul Menezes
Date: 2022-11-19 00:00:00
Tasks: Domain Generalization, In-Context Learning

In this work, we present some recommendations on the evaluation of state-of-the-art generative models for constrained generation tasks. The progress on generative models has been rapid in recent years. These large-scale models have had three impacts: firstly, the fluency of generation in both language and vision modalities has rendered common average-case evaluation metrics much less useful in diagnosing system errors. Secondly, the same substrate models now form the basis of a number of applications, driven both by the utility of their representations as well as phenomena such as in-context learning, which raise the abstraction level of interacting with such models. Thirdly, the user expectations around these models and their feted public releases have made the technical challenge of out of domain generalization much less excusable in practice. Subsequently, our evaluation methodologies haven't adapted to these changes. More concretely, while the associated utility and methods of interacting with generative models have expanded, a similar expansion has not been observed in their evaluation practices. In this paper, we argue that the scale of generative models could be exploited to raise the abstraction level at which evaluation itself is conducted and provide recommendations for the same. Our recommendations are based on leveraging specifications as a powerful instrument to evaluate generation quality and are readily applicable to a variety of tasks.
Understanding the Energy Consumption of HPC Scale Artificial Intelligence
Authors: Danilo Carastan dos Santos
Date: 2022-11-14 00:00:00

This paper contributes towards better understanding the energy consumption trade-offs of HPC scale Artificial Intelligence (AI), and more specifically Deep Learning (DL) algorithms. For this task we developed benchmark-tracker, a benchmark tool to evaluate the speed and energy consumption of DL algorithms in HPC environments. We exploited hardware counters and Python libraries to collect energy information through software, which enabled us to instrument a known AI benchmark tool, and to evaluate the energy consumption of numerous DL algorithms and models. Through an experimental campaign, we show a case example of the potential of benchmark-tracker to measure the computing speed and the energy consumption for training and inference DL algorithms, and also the potential of Benchmark-Tracker to help better understanding the energy behavior of DL algorithms in HPC platforms. This work is a step forward to better understand the energy consumption of Deep Learning in HPC, and it also contributes with a new tool to help HPC DL developers to better balance the HPC infrastructure in terms of speed and energy consumption.
A Graph Neural Networks based Framework for Topology-Aware Proactive SLA Management in a Latency Critical NFV Application Use-case
Authors: Nikita Jalodia, Mohit Taneja, Alan Davy
Date: 2022-11-10 00:00:00
Tasks: Deep Reinforcement Learning, Management, Multivariate Time Series Forecasting, Time Series, Time Series Analysis, Time Series Forecasting

Recent advancements in the rollout of 5G and 6G have led to the emergence of a new range of latency-critical applications delivered via a Network Function Virtualization (NFV) enabled paradigm of flexible and softwarized communication networks. Evolving verticals like telecommunications, smart grid, virtual reality (VR), industry 4.0, automated vehicles, etc. are driven by the vision of low latency and high reliability, and there is a wide gap to efficiently bridge the Quality of Service (QoS) constraints for both the service providers and the end-user. In this work, we look to tackle the over-provisioning of latency-critical services by proposing a proactive SLA management framework leveraging Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL) to balance the trade-off between efficiency and reliability. To summarize our key contributions: 1) we compose a graph-based spatio-temporal multivariate time-series forecasting model with multiple time-step predictions in a multi-output scenario, delivering 74.62% improved performance over the established baseline state-of-art model on the use-case; and 2) we leverage realistic SLA definitions for the use-case to achieve a dynamic SLA-aware oversight for scaling policy management with DRL.
Resolving Uncertain Case Identifiers in Interaction Logs: A User Study
Authors: Marco Pegoraro, Merih Seran Uysal, Tom-Hendrik Hülsmann, Wil M. P. van der Aalst
Date: 2022-11-21 00:00:00
Tasks: Attribute

Modern software systems are able to record vast amounts of user actions, stored for later analysis. One of the main types of such user interaction data is click data: the digital trace of the actions of a user through the graphical elements of an application, website or software. While readily available, click data is often missing a case notion: an attribute linking events from user interactions to a specific process instance in the software. In this paper, we propose a neural network-based technique to determine a case notion for click data, thus enabling process mining and other process analysis techniques on user interaction data. We describe our method, show its scalability to datasets of large dimensions, and we validate its efficacy through a user study based on the segmented event log resulting from interaction data of a mobility sharing company. Interviews with domain experts in the company demonstrate that the case notion obtained by our method can lead to actionable process insights.
A Light-weight, Effective and Efficient Model for Label Aggregation in Crowdsourcing
Authors: Yi Yang, Zhong-Qiu Zhao, Quan Bai, Qing Liu, Weihua Li
Date: 2022-11-19 00:00:00

Due to the noises in crowdsourced labels, label aggregation (LA) has emerged as a standard procedure to post-process crowdsourced labels. LA methods estimate true labels from crowdsourced labels by modeling worker qualities. Most existing LA methods are iterative in nature. They need to traverse all the crowdsourced labels multiple times in order to jointly and iteratively update true labels and worker qualities until convergence. Consequently, these methods have high space and time complexities. In this paper, we treat LA as a dynamic system and model it as a Dynamic Bayesian network. From the dynamic model we derive two light-weight algorithms, LA\textsuperscript{onepass} and LA\textsuperscript{twopass}, which can effectively and efficiently estimate worker qualities and true labels by traversing all the labels at most twice. Due to the dynamic nature, the proposed algorithms can also estimate true labels online without re-visiting historical data. We theoretically prove the convergence property of the proposed algorithms, and bound the error of estimated worker qualities. We also analyze the space and time complexities of the proposed algorithms and show that they are equivalent to those of majority voting. Experiments conducted on 20 real-world datasets demonstrate that the proposed algorithms can effectively and efficiently aggregate labels in both offline and online settings even if they traverse all the labels at most twice.
Advanced Audio Aid for Blind People
Authors: Savera Sarwar, Muhammad Turab, Danish Channa, Aisha Chandio, M. Uzair Sohu, Vikram Kumar
Date: 2022-11-17 00:00:00
Tasks: object-detection, Object Detection

One of the most important senses in human life is vision, without it life is totally filled with darkness. According to WHO globally millions of people are visually impaired estimated there are 285 million, of whom some millions are blind. Unfortunately, there are around 2.4 million people are blind in our beloved country Pakistan. Human are a crucial part of society and the blind community is a main part of society. The technologies are grown so far to make the life of humans easier more comfortable and more reliable for. However, this disability of the blind community would reduce their chance of using such innovative products. Therefore, the visually impaired community believe that they are burden to other societies and they do not capture in normal activities separates the blind people from society and because of this believe did not participate in the normally tasks of society . The visual impair people mainly face most of the problems in this real-time The aim of this work is to turn the real time world into an audio world by telling blind person about the objects in their way and can read printed text. This will enable blind persons to identify the things and read the text without any external help just by using the object detection and reading system in real time. Objective of this work: i) Object detection ii) Read printed text, using state-of-the-art (SOTA) technology.
Incentivising cooperation by rewarding the weakest member
Authors: Jory Schossau, Bamshad Shirmohammadi, Arend Hintze
Date: 2022-10-04 00:00:00
Tasks: Decision Making

Autonomous agents that act with each other on behalf of humans are becoming more common in many social domains, such as customer service, transportation, and health care. In such social situations greedy strategies can reduce the positive outcome for all agents, such as leading to stop-and-go traffic on highways, or causing a denial of service on a communications channel. Instead, we desire autonomous decision-making for efficient performance while also considering equitability of the group to avoid these pitfalls. Unfortunately, in complex situations it is far easier to design machine learning objectives for selfish strategies than for equitable behaviors. Here we present a simple way to reward groups of agents in both evolution and reinforcement learning domains by the performance of their weakest member. We show how this yields ``fairer'' more equitable behavior, while also maximizing individual outcomes, and we show the relationship to biological selection mechanisms of group-level selection and inclusive fitness theory.
On the Effective Usage of Priors in RSS-based Localization
Authors: Çağkan Yapar, Fabian Jaensch, Ron Levie, Giuseppe Caire
Date: 2022-11-28 00:00:00
Tasks: Attribute

In this paper, we study the localization problem in dense urban settings. In such environments, Global Navigation Satellite Systems fail to provide good accuracy due to low likelihood of line-of-sight (LOS) links between the receiver (Rx) to be located and the satellites, due to the presence of obstacles like the buildings. Thus, one has to resort to other technologies, which can reliably operate under non-line-of-sight (NLOS) conditions. Recently, we proposed a Received Signal Strength (RSS) fingerprint and convolutional neural network-based algorithm, LocUNet, and demonstrated its state-of-the-art localization performance with respect to the widely adopted k-nearest neighbors (kNN) algorithm, and to state-of-the-art time of arrival (ToA) ranging-based methods. In the current work, we first recognize LocUNet's ability to learn the underlying prior distribution of the Rx position or Rx and transmitter (Tx) association preferences from the training data, and attribute its high performance to these. Conversely, we demonstrate that classical methods based on probabilistic approach, can greatly benefit from an appropriate incorporation of such prior information. Our studies also numerically prove LocUNet's close to optimal performance in many settings, by comparing it with the theoretically optimal formulations.
Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions
Authors: Armin Lederer, Azra Begzadić, Neha Das, Sandra Hirche
Date: 2022-12-01 00:00:00

Ensuring safety is of paramount importance in physical human-robot interaction applications. This requires both adherence to safety constraints defined on the system state, as well as guaranteeing compliant behavior of the robot. If the underlying dynamical system is known exactly, the former can be addressed with the help of control barrier functions. The incorporation of elastic actuators in the robot's mechanical design can address the latter requirement. However, this elasticity can increase the complexity of the resulting system, leading to unmodeled dynamics, such that control barrier functions cannot directly ensure safety. In this paper, we mitigate this issue by learning the unknown dynamics using Gaussian process regression. By employing the model in a feedback linearizing control law, the safety conditions resulting from control barrier functions can be robustified to take into account model errors, while remaining feasible. In order to enforce them on-line, we formulate the derived safety conditions in the form of a second-order cone program. We demonstrate our proposed approach with simulations on a two-degree-of-freedom planar robot with elastic joints.
Gaussian Process Barrier States for Safe Trajectory Optimization and Control
Authors: Hassan Almubarak, Manan Gandhi, Yuichiro Aoyama, Nader Sadegh, Evangelos A. Theodorou
Date: 2022-12-01 00:00:00
Tasks: Gaussian Processes

This paper proposes embedded Gaussian Process Barrier States (GP-BaS), a methodology to safely control unmodeled dynamics of nonlinear system using Bayesian learning. Gaussian Processes (GPs) are used to model the dynamics of the safety-critical system, which is subsequently used in the GP-BaS model. We derive the barrier state dynamics utilizing the GP posterior, which is used to construct a safety embedded Gaussian process dynamical model (GPDM). We show that the safety-critical system can be controlled to remain inside the safe region as long as we can design a controller that renders the BaS-GPDM's trajectories bounded (or asymptotically stable). The proposed approach overcomes various limitations in early attempts at combining GPs with barrier functions due to the abstention of restrictive assumptions such as linearity of the system with respect to control, relative degree of the constraints and number or nature of constraints. This work is implemented on various examples for trajectory optimization and control including optimal stabilization of unstable linear system and safe trajectory optimization of a Dubins vehicle navigating through an obstacle course and on a quadrotor in an obstacle avoidance task using GP differentiable dynamic programming (GP-DDP). The proposed framework is capable of maintaining safe optimization and control of unmodeled dynamics and is purely data driven.
Mechanism Design Theory in Control Engineering: A Tutorial and Overview of Applications in Communication, Power Grid, Transportation, and Security Systems
Authors: Ioannis Vasileios Chremos, Andreas A. Malikopoulos
Date: 2022-12-01 00:00:00

This article provides an introduction to the theory of mechanism design and its application to engineering problems. Our aim is to provide the fundamental principles of the theory of mechanism design for control engineers and theorists along with the state-of-the-art methods in engineering applications. We start our exposition with a brief overview of game theory highlighting the key notions that are necessary to introduce mechanism design, and then we offer a comprehensive discussion of the principles in mechanism design. Finally, we explore four key applications of mechanism design in engineering, i.e., communication networks, power grids, transportation, and security systems.
Equivalent Inertia Provided by Droop Control of Fast Frequency Regulation Resources
Authors: Ye Liu, Chen Shen
Date: 2022-12-01 00:00:00

Conventional inertia control strategies (i.e., the VSG-based and PD-based strategies) for the fast frequency regulation resources (FFRs) might lead to system oscillations due to their emulations of generator dynamics. The only-droop-based inertia control strategy is a feasible idea to address this issue. This letter quantitatively investigates the equivalent inertia provided by the droop control of the FFRs. First, an equivalent-scenario-based method is proposed to evaluate the equivalent inertia provided by the droop control, which shows that the droop control with a constant droop coefficient provides time-variant equivalent inertia. Then, the time-variant-droop-based equivalent inertia control (VDIC) strategy is proposed for the FFRs, which can provide the required constant inertia support without introducing generator-like dynamics. A case study verifies the effectiveness of the VDIC strategy.
Fast Verification of Control Barrier Functions via Linear Programming
Authors: Ellie Pond, Matthew Hale
Date: 2022-12-01 00:00:00

Control barrier functions are a popular method of ensuring system safety, and these functions can be used to enforce invariance of a set under the dynamics of a system. A control barrier function must have certain properties, and one must both formulate a candidate control barrier function and verify that it does indeed satisfy the required properties. Targeting the latter problem, this paper presents a method of verifying any finite number of candidate control barrier functions with linear programming. We first apply techniques from real algebraic geometry to formulate verification problem statements that are solvable numerically. Typically, semidefinite programming is used to verify candidate control barrier functions, but this does not always scale well. Therefore, we next apply a method of inner-approximating the set of sums of squares polynomials that significantly reduces the computational complexity of these verification problems by transcribing them to linear programs. We give explicit forms for the resulting linear programs, and simulation results for a satellite inspection problem show that the computation time needed for verification can be reduced by more than 95%.
Plug-and-Play Secondary Control for Safety of LTI Systems under Attacks
Authors: Yankai Lin, Michelle S. Chong, Carlos Murguia
Date: 2022-12-01 00:00:00

We consider the problem of controller design for linear time-invariant cyber-physical systems (CPSs) controlled via networks. Specifically, we adopt the set-up that a controller has already been designed to stabilize the plant. However, the closed loop system may be subject to actuator and sensor attacks. We first perform a reachability analysis to see the effect of potential attacks. To further ensure the safety of the states of the system, we choose a subset of sensors that can be locally secured and made free of attacks. Using these limited resources, an extra controller is designed to enhance the safety of the new closed loop. The safety of the system will be characterized by the notion of safe sets. Lyapunov based analysis will be used to derive sufficient conditions that ensure the states always stay in the safe set. The conditions will then be stated as convex optimization problems which can be solved efficiently. Lastly, our theoretical results are illustrated through numerical simulations.
Adaptive Safety Evaluation for Connected and Automated Vehicles with Sparse Control Variates
Authors: Jingxuan Yang, Haowei Sun, Honglin He, Yi Zhang, Shuo Feng, Henry X. Liu
Date: 2022-12-01 00:00:00

Safety performance evaluation is critical for developing and deploying connected and automated vehicles (CAVs). One prevailing way is to design testing scenarios using prior knowledge of CAVs, test CAVs in these scenarios, and then evaluate their safety performances. However, significant differences between CAVs and prior knowledge could severely reduce the evaluation efficiency. Towards addressing this issue, most existing studies focus on the adaptive design of testing scenarios during the CAV testing process, but so far they cannot be applied to high-dimensional scenarios. In this paper, we focus on the adaptive safety performance evaluation by leveraging the testing results, after the CAV testing process. It can significantly improve the evaluation efficiency and be applied to high-dimensional scenarios. Specifically, instead of directly evaluating the unknown quantity (e.g., crash rates) of CAV safety performances, we evaluate the differences between the unknown quantity and known quantity (i.e., control variates). By leveraging the testing results, the control variates could be well designed and optimized such that the differences are close to zero, so the evaluation variance could be dramatically reduced for different CAVs. To handle the high-dimensional scenarios, we propose the sparse control variates method, where the control variates are designed only for the sparse and critical variables of scenarios. According to the number of critical variables in each scenario, the control variates are stratified into strata and optimized within each stratum using multiple linear regression techniques. We justify the proposed method's effectiveness by rigorous theoretical analysis and empirical study of high-dimensional overtaking scenarios.
Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems
Authors: Jun Du, Bingqing Jiang, Chunxiao Jiang, Yuanming Shi, Zhu Han
Date: 2022-12-01 00:00:00
Tasks: Federated Learning, Privacy Preserving, Scheduling

To satisfy the expected plethora of computation-heavy applications, federated edge learning (FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency and privacy-preserving. To further improve the efficiency of wireless data aggregation and model learning, over-the-air computation (AirComp) is emerging as a promising solution by using the superposition characteristics of wireless channels. However, the fading and noise of wireless channels can cause aggregate distortions in AirComp enabled federated learning. In addition, the quality of collected data and energy consumption of edge devices may also impact the accuracy and efficiency of model aggregation as well as convergence. To solve these problems, this work proposes a dynamic device scheduling mechanism, which can select qualified edge devices to transmit their local models with a proper power control policy so as to participate the model training at the server in federated learning via AirComp. In this mechanism, the data importance is measured by the gradient of local model parameter, channel condition and energy consumption of the device jointly. In particular, to fully use distributed datasets and accelerate the convergence rate of federated learning, the local updates of unselected devices are also retained and accumulated for future potential transmission, instead of being discarded directly. Furthermore, the Lyapunov drift-plus-penalty optimization problem is formulated for searching the optimal device selection strategy. Simulation results validate that the proposed scheduling mechanism can achieve higher test accuracy and faster convergence rate, and is robust against different channel conditions.