pwc-archive/papers-with-abstracts huggingface dataset:
Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer
Authors: Qunsong Zeng, Yuqing Du, Kaibin Huang
Date: 2021-02-24 00:00:00

Federated edge learning (FEEL) is a widely adopted framework for training an artificial intelligence (AI) model distributively at edge devices to leverage their data while preserving their data privacy. The execution of a power-hungry learning task at energy-constrained devices is a key challenge confronting the implementation of FEEL. To tackle the challenge, we propose the solution of powering devices using wireless power transfer (WPT). To derive guidelines on deploying the resultant wirelessly powered FEEL (WP-FEEL) system, this work aims at the derivation of the tradeoff between the model convergence and the settings of power sources in two scenarios: 1) the transmission power and density of power-beacons (dedicated charging stations) if they are deployed, or otherwise 2) the transmission power of a server (access-point). The development of the proposed analytical framework relates the accuracy of distributed stochastic gradient estimation to the WPT settings, the randomness in both communication and WPT links, and devices' computation capacities. Furthermore, the local-computation at devices (i.e., mini-batch size and processor clock frequency) is optimized to efficiently use the harvested energy for gradient estimation. The resultant learning-WPT tradeoffs reveal the simple scaling laws of the model-convergence rate with respect to the transferred energy as well as the devices' computational energy efficiencies. The results provide useful guidelines on WPT provisioning to provide a guaranteer on learning performance. They are corroborated by experimental results using a real dataset.
On the Impact of Interpretability Methods in Active Image Augmentation Method
Authors: Flavio Santos, Cleber Zanchettin, Leonardo Matos, Paulo Novais
Date: 2021-02-24 00:00:00
Tasks: Image Augmentation

Robustness is a significant constraint in machine learning models. The performance of the algorithms must not deteriorate when training and testing with slightly different data. Deep neural network models achieve awe-inspiring results in a wide range of applications of computer vision. Still, in the presence of noise or region occlusion, some models exhibit inaccurate performance even with data handled in training. Besides, some experiments suggest deep learning models sometimes use incorrect parts of the input information to perform inference. Activate Image Augmentation (ADA) is an augmentation method that uses interpretability methods to augment the training data and improve its robustness to face the described problems. Although ADA presented interesting results, its original version only used the Vanilla Backpropagation interpretability to train the U-Net model. In this work, we propose an extensive experimental analysis of the interpretability method's impact on ADA. We use five interpretability methods: Vanilla Backpropagation, Guided Backpropagation, GradCam, Guided GradCam, and InputXGradient. The results show that all methods achieve similar performance at the ending of training, but when combining ADA with GradCam, the U-Net model presented an impressive fast convergence.
Nonlinear Invariant Risk Minimization: A Causal Approach
Authors: Chaochao Lu, Yuhuai Wu, Jośe Miguel Hernández-Lobato, Bernhard Schölkopf
Date: 2021-02-24 00:00:00
Tasks: BIG-bench Machine Learning, Representation Learning

Due to spurious correlations, machine learning systems often fail to generalize to environments whose distributions differ from the ones used at training time. Prior work addressing this, either explicitly or implicitly, attempted to find a data representation that has an invariant relationship with the target. This is done by leveraging a diverse set of training environments to reduce the effect of spurious features and build an invariant predictor. However, these methods have generalization guarantees only when both data representation and classifiers come from a linear model class. We propose invariant Causal Representation Learning (iCaRL), an approach that enables out-of-distribution (OOD) generalization in the nonlinear setting (i.e., nonlinear representations and nonlinear classifiers). It builds upon a practical and general assumption: the prior over the data representation (i.e., a set of latent variables encoding the data) given the target and the environment belongs to general exponential family distributions. Based on this, we show that it is possible to identify the data representation up to simple transformations. We also prove that all direct causes of the target can be fully discovered, which further enables us to obtain generalization guarantees in the nonlinear setting. Extensive experiments on both synthetic and real-world datasets show that our approach outperforms a variety of baseline methods. Finally, in the discussion, we further explore the aforementioned assumption and propose a more general hypothesis, called the Agnostic Hypothesis: there exist a set of hidden causal factors affecting both inputs and outcomes. The Agnostic Hypothesis can provide a unifying view of machine learning. More importantly, it can inspire a new direction to explore a general theory for identifying hidden causal factors, which is key to enabling the OOD generalization guarantees.
Summarizing Online Conversation of Indonesia Tourism Industry using Network Text Analysis
Authors: Andry Alamsyah, Sheila Shafira, Muhamad Alfin Yudhistira
Date: 2021-02-24 00:00:00

The tourism industry is one of the potential revenues and has an important role in economics in Indonesia. The tourism Industry brings job and business opportunities, foreign exchange earnings, and infrastructure development, tourism also plays the role of one of the main drivers in socio-economic progress in Indonesia. The number of foreign tourists visiting Indonesia increase cumulatively and has reached 10.41 million visits or an increase of 10.46 percent from the same period in the previous year. Government trying to increase the number of tourists to visit Indonesia by promoting many Indonesian tourist attractions.
AutoAI-TS: AutoAI for Time Series Forecasting
Authors: Syed Yousaf Shah, Dhaval Patel, Long Vu, Xuan-Hong Dang, Bei Chen, Peter Kirchner, Horst Samulowitz, David Wood, Gregory Bramble, Wesley M. Gifford, Giridhar Ganapavarapu, Roman Vaculin, Petros Zerfos
Date: 2021-02-24 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning, Model Selection, Time Series, Time Series Analysis, Time Series Forecasting

A large number of time series forecasting models including traditional statistical models, machine learning models and more recently deep learning have been proposed in the literature. However, choosing the right model along with good parameter values that performs well on a given data is still challenging. Automatically providing a good set of models to users for a given dataset saves both time and effort from using trial-and-error approaches with a wide variety of available models along with parameter optimization. We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf ) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset. With its flexible zero-conf design, AutoAI-TS automatically performs all the data preparation, model creation, parameter optimization, training and model selection for users and provides a trained model that is ready to use. For given data, AutoAI-TS utilizes a wide variety of models including classical statistical models, Machine Learning (ML) models, statistical-ML hybrid models and deep learning models along with various transformations to create forecasting pipelines. It then evaluates and ranks pipelines using the proposed T-Daub mechanism to choose the best pipeline. The paper describe in detail all the technical aspects of AutoAI-TS along with extensive benchmarking on a variety of real world data sets for various use-cases. Benchmark results show that AutoAI-TS, with no manual configuration from the user, automatically trains and selects pipelines that on average outperform existing state-of-the-art time series forecasting toolkits.
Memory-based Deep Reinforcement Learning for POMDPs
Authors: Lingheng Meng, Rob Gorbet, Dana Kulić
Date: 2021-02-24 00:00:00
Tasks: Deep Reinforcement Learning, Feature Engineering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A promising characteristic of Deep Reinforcement Learning (DRL) is its capability to learn optimal policy in an end-to-end manner without relying on feature engineering. However, most approaches assume a fully observable state space, i.e. fully observable Markov Decision Processes (MDPs). In real-world robotics, this assumption is unpractical, because of issues such as sensor sensitivity limitations and sensor noise, and the lack of knowledge about whether the observation design is complete or not. These scenarios lead to Partially Observable MDPs (POMDPs). In this paper, we propose Long-Short-Term-Memory-based Twin Delayed Deep Deterministic Policy Gradient (LSTM-TD3) by introducing a memory component to TD3, and compare its performance with other DRL algorithms in both MDPs and POMDPs. Our results demonstrate the significant advantages of the memory component in addressing POMDPs, including the ability to handle missing and noisy observation data.
Similarity measure for sparse time course data based on Gaussian processes
Authors: Zijing Liu, Mauricio Barahona
Date: 2021-02-24 00:00:00
Tasks: Clustering, Gaussian Processes, Time Series, Time Series Analysis

We propose a similarity measure for sparsely sampled time course data in the form of a log-likelihood ratio of Gaussian processes (GP). The proposed GP similarity is similar to a Bayes factor and provides enhanced robustness to noise in sparse time series, such as those found in various biological settings, e.g., gene transcriptomics. We show that the GP measure is equivalent to the Euclidean distance when the noise variance in the GP is negligible compared to the noise variance of the signal. Our numerical experiments on both synthetic and real data show improved performance of the GP similarity when used in conjunction with two distance-based clustering methods.
Functional neural network for decision processing, a racing network of programmable neurons with fuzzy logic where the target operating model relies on the network itself
Authors: Frederic Jumelle, Kelvin So, Didan Deng
Date: 2021-02-24 00:00:00
Tasks: Decision Making

In this paper, we are introducing a novel model of artificial intelligence, the functional neural network for modeling of human decision-making processes. This neural network is composed of multiple artificial neurons racing in the network. Each of these neurons has a similar structure programmed independently by the users and composed of an intention wheel, a motor core and a sensory core representing the user itself and racing at a specific velocity. The mathematics of the neuron's formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education and medicine including the opportunity to design an intelligent learning agent with application in business operations supervision. We believe that this functional neural network has a promising potential to transform the way we can compute decision-making and lead to a new generation of neuromorphic chips for seamless human-machine interactions.
Mapping Organization Knowledge Network and Social Media Based Reputation Management
Authors: Andry Alamsyah, Maribella Syawiluna
Date: 2021-02-24 00:00:00
Tasks: Management

Knowledge management is an important aspect of an organization, especially in the ICT industry. Having more control of it is essentials for the organization to stay competitive in the business. One way to assess the organization's knowledge capital is by measuring employee knowledge networks and their personal reputation in social media. Using this measurement, we see how employees build relationships around their peer networks or clients virtually. We are also able to see how knowledge networks support organizational performance. The research objective is to map knowledge network and reputation formulation in order to fully understand how knowledge flow and whether employee reputation has a higher degree of influence in the organization's knowledge network. We particularly develop formulas to measure knowledge networks and personal reputation based on their social media activities. As a case study, we pick an Indonesian ICT company that actively build their business around their employee peer knowledge outside the company. For the knowledge network, we perform data collection by conducting interviews. For reputation management, we collect data from several popular social media. We base our work on Social Network Analysis (SNA) methodology. The result shows that employees' knowledge is directly proportional to their reputation, but there are different reputations level on different social media observed in this research.
Interactive Power to Frequency Dynamics Between Grid-Forming Inverters and Synchronous Generators in Power Electronics-Dominated Power Systems
Authors: Rick Wallace Kenyon, Amirhossein Sajadi, Matt Bossart, Andy Hoke, Bri-Mathias Hodge
Date: 2021-02-24 00:00:00

With increased attention on grid-forming inverters as a power system stabilizing device during periods of high shares of inverter-based resource operations, there is a present need for a transparent and rigorous investigation of the inverted and direct power to frequency control capabilities, and associated impacts, of these devices on hybrid systems. Here, analysis of the frequency dynamics of the droop controlled grid-forming inverter and the synchronous generator illuminates the inverted active power-frequency relationship and the frequency response order reduction, forming the basis for novel, non-linear frequency droop control approaches. Device-level electromagnetic transient domain simulations corroborate the order-reduction findings, establish that a properly designed DC-side system has a negligible dynamical impact on active power transfer and will not impede frequency regulation, and confirm the frequency response improvement with non-linear control. Simulations of the 9- and 39-bus test systems validate the order reduction and associated decoupling of the nadir and rate of change of frequency in large networks. The primary system oscillatory mode confirms the correlation between high shares of grid-forming inverters and increased mode frequency and damping; a sharp decrease in damping is observed at shares above 80%, whether by grid-forming device quantity in large networks or rating variations in a small test system. Finally, simulation results on the Hawaiian island of Maui show a trend towards a first order frequency response with a grid-forming inverter, further corroborating the analytic findings and network impacts.
Re-Evaluating GermEval17 Using German Pre-Trained Language Models
Authors: M. Aßenmacher, A. Corvonato, C. Heumann
Date: 2021-02-24 00:00:00

The lack of a commonly used benchmark data set (collection) such as (Super-)GLUE (Wang et al., 2018, 2019) for the evaluation of non-English pre-trained language models is a severe shortcoming of current English-centric NLP-research. It concentrates a large part of the research on English, neglecting the uncertainty when transferring conclusions found for the English language to other languages. We evaluate the performance of the German and multilingual BERT-based models currently available via the huggingface transformers library on the four tasks of the GermEval17 workshop. We compare them to pre-BERT architectures (Wojatzki et al., 2017; Schmitt et al., 2018; Attia et al., 2018) as well as to an ELMo-based architecture (Biesialska et al., 2020) and a BERT-based approach (Guhr et al., 2020). The observed improvements are put in relation to those for similar tasks and similar models (pre-BERT vs. BERT-based) for the English language in order to draw tentative conclusions about whether the observed improvements are transferable to German or potentially other related languages.
An Overview of Direct Diagnosis and Repair Techniques in the WeeVis Recommendation Environment
Authors: Alexander Felfernig, Stefan Reiterer, Martin Stettinger, Michael Jeran
Date: 2021-02-24 00:00:00

Constraint-based recommenders support users in the identification of items (products) fitting their wishes and needs. Example domains are financial services and electronic equipment. In this paper we show how divide-and-conquer based (direct) diagnosis algorithms (no conflict detection is needed) can be exploited in constraint-based recommendation scenarios. In this context, we provide an overview of the MediaWiki-based recommendation environment WeeVis.
AGENT: A Benchmark for Core Psychological Reasoning
Authors: Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin A. Smith, Shari Liu, Dan Gutfreund, Elizabeth Spelke, Joshua B. Tenenbaum, Tomer D. Ullman
Date: 2021-02-24 00:00:00
Tasks: Core Psychological Reasoning

For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life. Intuitive psychology, the ability to reason about hidden mental variables that drive observable actions, comes naturally to people: even pre-verbal infants can tell agents from objects, expecting agents to act efficiently to achieve goals given constraints. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. Inspired by cognitive development studies on intuitive psychology, we present a benchmark consisting of a large dataset of procedurally generated 3D animations, AGENT (Action, Goal, Efficiency, coNstraint, uTility), structured around four scenarios (goal preferences, action efficiency, unobserved constraints, and cost-reward trade-offs) that probe key concepts of core intuitive psychology. We validate AGENT with human-ratings, propose an evaluation protocol emphasizing generalization, and compare two strong baselines built on Bayesian inverse planning and a Theory of Mind neural network. Our results suggest that to pass the designed tests of core intuitive psychology at human levels, a model must acquire or have built-in representations of how agents plan, combining utility computations and core knowledge of objects and physics.
Measuring Marketing Communications Mix Effort Using Magnitude Of Influence And Influence Rank Metric
Authors: Andry Alamsyah, Endang Sofyan, Tsana Hasti Nabila
Date: 2021-02-24 00:00:00
Tasks: Marketing

In the context of modern marketing, Twitter is considered a communication platform to spread information. Many companies create and acquire several Twitter accounts to support and perform varieties of marketing mix activities. Initially, each accounts used to capture a specific market profile. Together, the accounts create a network of information that provide consumer to the information they need depends on their contextual utilization. From many accounts available, we have the fundamental question on how to measure the influence of each account in the market based not only on their relations but also on the effects of their postings. The magnitude of Influence (MOI) metric is adapted together with Influence Rank (IR) measurement of accounts in their social network neighborhood. We use social network analysis approach to analyze 65 accounts in the social network of an Indonesian mobile phone network operator, Telkomsel which involved in marketing communications mix activities through series of related tweets. Using social network provide the idea of the activity in building and maintaining relationships with the target audience. This paper shows the results of the most potential accounts based on the network structure and engagement. Based on this research, the more number of followers one account has, the more responsibility it has to generate the interaction from their followers in order to achieve the expected effectiveness. The focus of this paper is to determine the most potential accounts in the application of marketing communications mix in Twitter.
GEM: Glare or Gloom, I Can Still See You -- End-to-End Multimodal Object Detection
Authors: Osama Mazhar, Robert Babuska, Jens Kober
Date: 2021-02-24 00:00:00
Tasks: Object, object-detection, Object Detection

Deep neural networks designed for vision tasks are often prone to failure when they encounter environmental conditions not covered by the training data. Single-modal strategies are insufficient when the sensor fails to acquire information due to malfunction or its design limitations. Multi-sensor configurations are known to provide redundancy, increase reliability, and are crucial in achieving robustness against asymmetric sensor failures. To address the issue of changing lighting conditions and asymmetric sensor degradation in object detection, we develop a multi-modal 2D object detector, and propose deterministic and stochastic sensor-aware feature fusion strategies. The proposed fusion mechanisms are driven by the estimated sensor measurement reliability values/weights. Reliable object detection in harsh lighting conditions is essential for applications such as self-driving vehicles and human-robot interaction. We also propose a new "r-blended" hybrid depth modality for RGB-D sensors. Through extensive experimentation, we show that the proposed strategies outperform the existing state-of-the-art methods on the FLIR-Thermal dataset, and obtain promising results on the SUNRGB-D dataset. We additionally record a new RGB-Infra indoor dataset, namely L515-Indoors, and demonstrate that the proposed object detection methodologies are highly effective for a variety of lighting conditions.
Set-valued classification -- overview via a unified framework
Authors: Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Titouan Lorieul
Date: 2021-02-24 00:00:00
Tasks: Classification, General Classification, Multi-class Classification

Multi-class classification problem is among the most popular and well-studied statistical frameworks. Modern multi-class datasets can be extremely ambiguous and single-output predictions fail to deliver satisfactory performance. By allowing predictors to predict a set of label candidates, set-valued classification offers a natural way to deal with this ambiguity. Several formulations of set-valued classification are available in the literature and each of them leads to different prediction strategies. The present survey aims to review popular formulations using a unified statistical framework. The proposed framework encompasses previously considered and leads to new formulations as well as it allows to understand underlying trade-offs of each formulation. We provide infinite sample optimal set-valued classification strategies and review a general plug-in principle to construct data-driven algorithms. The exposition is supported by examples and pointers to both theoretical and practical contributions. Finally, we provide experiments on real-world datasets comparing these approaches in practice and providing general practical guidelines.