OpenRaiser/Intern-Atlas huggingface dataset:
A Comparative User Study of Human Predictions in Algorithm-Supported Recidivism Risk Assessment
Venue: arXiv cs.CY 2022
Year: 2022

In this paper, we study the effects of using an algorithm-based risk assessment instrument to support the prediction of risk of criminalrecidivism. The instrument we use in our experiments is a machine learning version ofRiskEval(name changed for double-blindreview), which is the main risk assessment instrument used by the Justice Department ofCountry(omitted for double-blind review).The task is to predict whether a person who has been released from prison will commit a new crime, leading to re-incarceration,within the next two years. We measure, among other variables, the accuracy of human predictions with and without algorithmicsupport. This user study is done with (1)generalparticipants from diverse backgrounds recruited through a crowdsourcing platform,(2)targetedparticipants who are students and practitioners of data science, criminology, or social work and professionals who workwithRiskEval. Among other findings, we observe that algorithmic support systematically leads to more accurate predictions fromall participants, but that statistically significant gains are only seen in the performance of targeted participants with respect to thatof crowdsourced participants. We also run focus groups with participants of the targeted study to interpret the quantitative results,including people who useRiskEvalin a professional capacity. Among other comments, professional participants indicate that theywould not foresee using a fully-automated system in criminal risk assessment, but do consider it valuable for training, standardization,and to fine-tune or double-check their predictions on particularly difficult cases.
Rapid solution for searching similar audio items
Venue: arXiv cs.SD 2022
Year: 2022

A naive approach for finding similar audio items would be to compare each entry from the feature vector of the test example with each feature vector of the candidates in a k-nearest neighbors fashion. There are already two problems with this approach: audio signals are represented by high dimensional vectors and the number of candidates can be very large - think thousands. The search process would have a high complexity. Our paper will treat this problem through hashing methodologies more specifically the Locality Sensitive Hashing. This project will be in the spirit of classification and clustering problems. The computer sound production principles will be used to determine which features that describe an audio signal are the most useful. That will down-sample the size of the feature vectors and speed up the process subsequently.
Hyperparameter Tuning for Deep Reinforcement Learning Applications
Venue: arXiv cs.LG 2022
Year: 2022

Reinforcement learning (RL) applications, where an agent can simply learn optimal behaviors by interacting with the environment, are quickly gaining tremendous success in a wide variety of applications from controlling simple pendulums to complex data centers. However, setting the right hyperparameters can have a huge impact on the deployed solution performance and reliability in the inference models, produced via RL, used for decision-making. Hyperparameter search itself is a laborious process that requires many iterations and computationally expensive to find the best settings that produce the best neural network architectures. In comparison to other neural network architectures, deep RL has not witnessed much hyperparameter tuning, due to its algorithm complexity and simulation platforms needed. In this paper, we propose a distributed variable-length genetic algorithm framework to systematically tune hyperparameters for various RL applications, improving training time and robustness of the architecture, via evolution. We demonstrate the scalability of our approach on many RL problems (from simple gyms to complex applications) and compared with Bayesian approach. Our results show that with more generations, optimal solutions that require fewer training episodes and are computationally cheap while being more robust for deployment. Our results are imperative to advance deep reinforcement learning controllers for real-world problems.
Electrical Load Forecasting Using Edge Computing and Federated Learning
Venue: arXiv cs.CE 2022
Year: 2022

In the smart grid, huge amounts of consumption data are used to train deep learning models for applications such as load monitoring and demand response. However, these applications raise concerns regarding security and have high accuracy requirements. In one hand, the data used is privacy-sensitive. For instance, the fine-grained data collected by a smart meter at a consumer's home may reveal information on the appliances and thus the consumer's behaviour at home. On the other hand, the deep learning models require big data volumes with enough variety and to be trained adequately. In this paper, we evaluate the use of Edge computing and federated learning, a decentralized machine learning scheme that allows to increase the volume and diversity of data used to train the deep learning models without compromising privacy. This paper reports, to the best of our knowledge, the first use of federated learning for household load forecasting and achieves promising results. The simulations were done using Tensorflow Federated on the data from 200 houses from Texas, USA.
Neuro-Symbolic Entropy Regularization
Venue: arXiv cs.LG 2022
Year: 2022

In structured prediction, the goal is to jointly predict many output variables that together encode a structured object -- a path in a graph, an entity-relation triple, or an ordering of objects. Such a large output space makes learning hard and requires vast amounts of labeled data. Different approaches leverage alternate sources of supervision. One approach -- entropy regularization -- posits that decision boundaries should lie in low-probability regions. It extracts supervision from unlabeled examples, but remains agnostic to the structure of the output space. Conversely, neuro-symbolic approaches exploit the knowledge that not every prediction corresponds to a valid structure in the output space. Yet, they does not further restrict the learned output distribution. This paper introduces a framework that unifies both approaches. We propose a loss, neuro-symbolic entropy regularization, that encourages the model to confidently predict a valid object. It is obtained by restricting entropy regularization to the distribution over only valid structures. This loss is efficiently computed when the output constraint is expressed as a tractable logic circuit. Moreover, it seamlessly integrates with other neuro-symbolic losses that eliminate invalid predictions. We demonstrate the efficacy of our approach on a series of semi-supervised and fully-supervised structured-prediction experiments, where we find that it leads to models whose predictions are more accurate and more likely to be valid.
Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach
Venue: arXiv cs.NI 2022
Year: 2022

This paper studies the multi-agent resource allocation problem in vehicular networks using non-orthogonal multiple access (NOMA) and network slicing. To ensure heterogeneous service requirements for different vehicles, we propose a network slicing architecture. We focus on a non-cellular network scenario where vehicles communicate by the broadcast approach via the direct device-to-device interface. In such a vehicular network, resource allocation among vehicles is very difficult, mainly due to (i) the rapid variation of wireless channels among highly mobile vehicles and (ii) the lack of a central coordination point. Thus, the possibility of acquiring instantaneous channel state information to perform centralized resource allocation is precluded. The resource allocation problem considered is therefore very complex. It includes not only the usual spectrum and power allocation, but also coverage selection (which target vehicles to broadcast to) and packet selection (which network slice to use). This problem must be solved jointly since selected packets can be overlaid using NOMA and therefore spectrum and power must be carefully allocated for better vehicle coverage. To do so, we provide a optimization approach and study the NP-hardness of the problem. Then, we model the problem using multi-agent Markov decision process. Finally, we use a deep reinforcement learning (DRL) approach to solve the problem. The proposed DRL algorithm is practical because it can be implemented in an online and distributed manner. We show that our approach is robust and efficient when faced with different variations of the network parameters and compared to centralized benchmarks.
Interactive 3D Character Modeling from 2D Orthogonal Drawings with Annotations
Venue: arXiv cs.CV 2022
Year: 2022

We propose an interactive 3D character modeling approach from orthographic drawings (e.g., front and side views) based on 2D-space annotations. First, the system builds partial correspondences between the input drawings and generates a base mesh with sweeping splines according to edge information in 2D images. Next, users annotates the desired parts on the input drawings (e.g., the eyes and mouth) by using two type of strokes, called addition and erosion, and the system re-optimizes the shape of the base mesh. By repeating the 2D-space operations (i.e., revising and modifying the annotations), users can design a desired character model. To validate the efficiency and quality of our system, we verified the generated results with state-of-the-art methods.
Dissecting the impact of different loss functions with gradient surgery
Venue: arXiv cs.CV 2022
Year: 2022

Pair-wise loss is an approach to metric learning that learns a semantic embedding by optimizing a loss function that encourages images from the same semantic class to be mapped closer than images from different classes. The literature reports a large and growing set of variations of the pair-wise loss strategies. Here we decompose the gradient of these loss functions into components that relate to how they push the relative feature positions of the anchor-positive and anchor-negative pairs. This decomposition allows the unification of a large collection of current pair-wise loss functions. Additionally, explicitly constructing pair-wise gradient updates to separate out these effects gives insights into which have the biggest impact, and leads to a simple algorithm that beats the state of the art for image retrieval on the CAR, CUB and Stanford Online products datasets.
Efficient Distributed Learning in Stochastic Non-cooperative Games without Information Exchange
Venue: arXiv cs.GT 2022
Year: 2022

In this work, we study stochastic non-cooperative games, where only noisy black-box function evaluations are available to estimate the cost function for each player. Since each player's cost function depends on both its own decision variables and its rivals' decision variables, local information needs to be exchanged through a center/network in most existing work for seeking the Nash equilibrium. We propose a new stochastic distributed learning algorithm that does not require communications among players. The proposed algorithm uses simultaneous perturbation method to estimate the gradient of each cost function, and uses mirror descent method to search for the Nash equilibrium. We provide asymptotic analysis for the bias and variance of gradient estimates, and show the proposed algorithm converges to the Nash equilibrium in mean square for the class of strictly monotone games at a rate faster than the existing algorithms. The effectiveness of the proposed method is buttressed in a numerical experiment.
IMEXLBM 1.0: A Proxy Application based on the Lattice Boltzmann Method for solving Computational Fluid Dynamic problems on GPUs
Venue: arXiv cs.CE 2022
Year: 2022

The US Department of Energy launched the Exascale Computing Project (ECP) in 2016 as part of a coordinated effort to achieve the next generation of high-performance computing (HPC) and to accelerate scientific discovery. The Exascale Proxy Applications Project began within the ECP to: (1) improve the quality of proxies created by the ECP (2) provide small, simplified codes which share important features of large applications and (3) capture programming methods and styles that drive requirements for compilers and other elements of the tool chain. This article describes one Proxy Application (or "proxy app") suite called IMEXLBM which is an open-source, self-contained code unit, with minimal dependencies, that is capable of running on heterogeneous platforms like those with graphic processing units (GPU) for accelerating the calculation. In particular, we demonstrate functionality by solving a benchmark problem in computational fluid dynamics (CFD) on the ThetaGPU machine at the Argonne Leadership Computing Facility (ALCF). Our method makes use of a domain-decomposition technique in conjunction with the message-passing interface (MPI) standard for distributed memory systems. The OpenMP application programming interface (API) is employed for shared-memory multi-processing and offloading critical kernels to the device (i.e. GPU). We also verify our effort by comparing data generated via CPU-only calculations with data generated with CPU+GPU calculations. While we demonstrate efficacy for single-phase fluid problems, the code-unit is designed to be versatile and enable new physical models that can capture complex phenomena such as two-phase flow with interface capture.
Benchmarking learned non-Cartesian k-space trajectories and reconstruction networks
Venue: arXiv cs.LG 2022
Year: 2022

We benchmark the current existing methods to jointly learn non-Cartesian k-space trajectory and reconstruction: PILOT, BJORK, and compare them with those obtained from the recently developed generalized hybrid learning (HybLearn) framework. We present the advantages of using projected gradient descent to enforce MR scanner hardware constraints as compared to using added penalties in the cost function. Further, we use the novel HybLearn scheme to jointly learn and compare our results through a retrospective study on fastMRI validation dataset.
System Modeling and Performance Evaluation of Predictive QoS for Future Tele-Operated Driving
Venue: arXiv cs.NI 2022
Year: 2022

Future Tele-operated Driving (ToD) applications place challenging Quality of Service (QoS) demands on existing mobile communication networks that are of highly important to comply with for safe operation. New remote control and platooning services will emerge and pose high data rate and latency requirements. One key enabler for these applications is the newly available 5G New Radio (NR) promising higher bandwidth and lower latency than its predecessors. In addition to that, public 5G networks do not consistently deliver and do not guarantee the required data rates and latency of ToD. In this paper, we discuss the communication-related requirements of tele-operated driving. ToD is regarded as a complex system consisting of multiple research areas. One key aspect of ToD is the provision and maintenance of the required data rate for teleoperation by the mobile network. An in-advance prediction method of the end-to-end data rate based on so-called Radio Environmental Maps (REMs) is discussed. Furthermore, a novel approach improving the prediction accuracy is introduced and it features individually optimized REM layers. Finally, we analyze the implementation of tele-operated driving applications on a scaled vehicular platform combined with a cyber-physical test environment consisting of real and virtual objects. This approach enables large-scale testing of remote operation and autonomous applications.
ETAP: Energy-aware Timing Analysis of Intermittent Programs
Venue: arXiv cs.SE 2022
Year: 2022

Energy harvesting battery-free embedded devices rely only on ambient energy harvesting that enables stand-alone and sustainable IoT applications. These devices execute programs when the harvested ambient energy in their energy reservoir is sufficient to operate and stop execution abruptly (and start charging) otherwise. These intermittent programs have varying timing behavior under different energy conditions, hardware configurations, and program structures. This paper presents Energy-aware Timing Analysis of intermittent Programs (ETAP), a probabilistic symbolic execution approach that analyzes the timing and energy behavior of intermittent programs at compile time. ETAP symbolically executes the given program while taking time and energy cost models for ambient energy and dynamic energy consumption into account. We evaluated ETAP on several intermittent programs and compared the compile-time analysis results with executions on real hardware. The results show that ETAP's normalized prediction accuracy is 99.5%, and it speeds up the timing analysis by at least two orders of magnitude compared to manual testing.
Human-centered mechanism design with Democratic AI
Venue: arXiv cs.AI 2022
Year: 2022

Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here, we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that humans prefer by majority. A large group of humans played an online investment game that involved deciding whether to keep a monetary endowment or to share it with others for collective benefit. Shared revenue was returned to players under two different redistribution mechanisms, one designed by the AI and the other by humans. The AI discovered a mechanism that redressed initial wealth imbalance, sanctioned free riders, and successfully won the majority vote. By optimizing for human preferences, Democratic AI may be a promising method for value-aligned policy innovation.
Estimating the Capacities of Function-as-a-Service Functions
Venue: arXiv cs.DC 2022
Year: 2022

Serverless computing is a cloud computing paradigm that allows developers to focus exclusively on business logic as cloud service providers manage resource management tasks. Serverless applications follow this model, where the application is decomposed into a set of fine-grained Function-as-a-Service (FaaS) functions. However, the obscurities of the underlying system infrastructure and dependencies between FaaS functions within the application pose a challenge for estimating the performance of FaaS functions. To characterize the performance of a FaaS function that is relevant for the user, we define Function Capacity (FC) as the maximal number of concurrent invocations the function can serve in a time without violating the Service-Level Objective (SLO). The paper addresses the challenge of quantifying the FC individually for each FaaS function within a serverless application. This challenge is addressed by sandboxing a FaaS function and building its performance model. To this end, we develop FnCapacitor - an end-to-end automated Function Capacity estimation tool. We demonstrate the functioning of our tool on Google Cloud Functions (GCF) and AWS Lambda. FnCapacitor estimates the FCs on different deployment configurations (allocated memory & maximum function instances) by conducting time-framed load tests and building various models using statistical: linear, ridge, and polynomial regression, and Deep Neural Network (DNN) methods on the acquired performance data. Our evaluation of different FaaS functions shows relatively accurate predictions, with an accuracy greater than 75% using DNN for both cloud providers.
Quantile-Based Policy Optimization for Reinforcement Learning
Venue: arXiv cs.LG 2022
Year: 2022

Classical reinforcement learning (RL) aims to optimize the expected cumulative rewards. In this work, we consider the RL setting where the goal is to optimize the quantile of the cumulative rewards. We parameterize the policy controlling actions by neural networks and propose a novel policy gradient algorithm called Quantile-Based Policy Optimization (QPO) and its variant Quantile-Based Proximal Policy Optimization (QPPO) to solve deep RL problems with quantile objectives. QPO uses two coupled iterations running at different time scales for simultaneously estimating quantiles and policy parameters and is shown to converge to the global optimal policy under certain conditions. Our numerical results demonstrate that the proposed algorithms outperform the existing baseline algorithms under the quantile criterion.
FinGAN: Generative Adversarial Network for Analytical Customer Relationship Management in Banking and Insurance
Venue: arXiv cs.LG 2022
Year: 2022

Churn prediction in credit cards, fraud detection in insurance, and loan default prediction are important analytical customer relationship management (ACRM) problems. Since frauds, churns and defaults happen less frequently, the datasets for these problems turn out to be naturally highly unbalanced. Consequently, all supervised machine learning classifiers tend to yield substantial false-positive rates when trained on such unbalanced datasets. We propose two ways of data balancing. In the first, we propose an oversampling method to generate synthetic samples of minority class using Generative Adversarial Network (GAN). We employ Vanilla GAN [1], Wasserstein GAN [2] and CTGAN [3] separately to oversample the minority class samples. In order to assess the efficacy of our proposed approach, we use a host of machine learning classifiers, including Random Forest, Decision Tree, support vector machine (SVM), and Logistic Regression on the data balanced by GANs. In the second method, we introduce a hybrid method to handle data imbalance. In this second way, we utilize the power of undersampling and over-sampling together by augmenting the synthetic minority class data oversampled by GAN with the undersampled majority class data obtained by one-class support vigor machine (OCSVM) [4]. We combine both over-sampled data generated by GAN and the data under-sampled by OCSVM [4] and pass the resultant data to classifiers. When we compared our results to those of Farquad et al. [5], Sundarkumar, Ravi, and Siddeshwar [6], our proposed methods outperform the previous results in terms of the area under the ROC curve (AUC) on all datasets.
Surprisingly Robust In-Hand Manipulation: An Empirical Study
Venue: arXiv cs.RO 2022
Year: 2022

We present in-hand manipulation skills on a dexterous, compliant, anthropomorphic hand. Even though these skills were derived in a simplistic manner, they exhibit surprising robustness to variations in shape, size, weight, and placement of the manipulated object. They are also very insensitive to variation of execution speeds, ranging from highly dynamic to quasi-static. The robustness of the skills leads to compositional properties that enable extended and robust manipulation programs. To explain the surprising robustness of the in-hand manipulation skills, we performed a detailed, empirical analysis of the skills' performance. From this analysis, we identify three principles for skill design: 1) Exploiting the hardware's innate ability to drive hard-to-model contact dynamics. 2) Taking actions to constrain these interactions, funneling the system into a narrow set of possibilities. 3) Composing such action sequences into complex manipulation programs. We believe that these principles constitute an important foundation for robust robotic in-hand manipulation, and possibly for manipulation in general.