pwc-archive/papers-with-abstracts huggingface dataset:
Isconna: Streaming Anomaly Detection with Frequency and Patterns
Authors: Rui Liu, Siddharth Bhatia, Bryan Hooi
Date: 2021-04-04 00:00:00
Tasks: Anomaly Detection

An edge stream is a common form of presentation of dynamic networks. It can evolve with time, with new types of nodes or edges being continuously added. Existing methods for anomaly detection rely on edge occurrence counts or compare pattern snippets found in historical records. In this work, we propose Isconna, which focuses on both the frequency and the pattern of edge records. The burst detection component targets anomalies between individual timestamps, while the pattern detection component highlights anomalies across segments of timestamps. These two components together produce three intermediate scores, which are aggregated into the final anomaly score. Isconna does not actively explore or maintain pattern snippets; it instead measures the consecutive presence and absence of edge records. Isconna is an online algorithm, it does not keep the original information of edge records; only statistical values are maintained in a few count-min sketches (CMS). Isconna's space complexity $O(rc)$ is determined by two user-specific parameters, the size of CMSs. In worst case, Isconna's time complexity can be up to $O(rc)$, but it can be amortized in practice. Experiments show that Isconna outperforms five state-of-the-art frequency- and/or pattern-based baselines on six real-world datasets with up to 20 million edge records.
New Benchmarks for Learning on Non-Homophilous Graphs
Authors: Derek Lim, Xiuyu Li, Felix Hohne, Ser-Nam Lim
Date: 2021-04-03 00:00:00
Tasks: Attribute, Fraud Detection, Graph Representation Learning, Inductive Bias, Node Classification, Node Classification on Non-Homophilic (Heterophilic) Graphs, Representation Learning

Much data with graph structures satisfy the principle of homophily, meaning that connected nodes tend to be similar with respect to a specific attribute. As such, ubiquitous datasets for graph machine learning tasks have generally been highly homophilous, rewarding methods that leverage homophily as an inductive bias. Recent work has pointed out this particular focus, as new non-homophilous datasets have been introduced and graph representation learning models better suited for low-homophily settings have been developed. However, these datasets are small and poorly suited to truly testing the effectiveness of new methods in non-homophilous settings. We present a series of improved graph datasets with node label relationships that do not satisfy the homophily principle. Along with this, we introduce a new measure of the presence or absence of homophily that is better suited than existing measures in different regimes. We benchmark a range of simple methods and graph neural networks across our proposed datasets, drawing new insights for further research. Data and codes can be found at https://github.com/CUAI/Non-Homophily-Benchmarks.
MMBERT: Multimodal BERT Pretraining for Improved Medical VQA
Authors: Yash Khare, Viraj Bagal, Minesh Mathew, Adithi Devi, U Deva Priyakumar, CV Jawahar
Date: 2021-04-03 00:00:00
Tasks: Language Modeling, Language Modelling, Masked Language Modeling, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Images in the medical domain are fundamentally different from the general domain images. Consequently, it is infeasible to directly employ general domain Visual Question Answering (VQA) models for the medical domain. Additionally, medical images annotation is a costly and time-consuming process. To overcome these limitations, we propose a solution inspired by self-supervised pretraining of Transformer-style architectures for NLP, Vision and Language tasks. Our method involves learning richer medical image and text semantic representations using Masked Language Modeling (MLM) with image features as the pretext task on a large medical image+caption dataset. The proposed solution achieves new state-of-the-art performance on two VQA datasets for radiology images -- VQA-Med 2019 and VQA-RAD, outperforming even the ensemble models of previous best solutions. Moreover, our solution provides attention maps which help in model interpretability. The code is available at https://github.com/VirajBagal/MMBERT
Hypercorrelation Squeeze for Few-Shot Segmentation
Authors: Juhong Min, Dahyun Kang, Minsu Cho
Date: 2021-04-04 00:00:00
Tasks: Feature Correlation, Few-Shot Semantic Segmentation, Segmentation

Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class. This challenging task requires to understand diverse levels of visual cues and analyze fine-grained correspondence relations between the query and the support images. To address the problem, we propose Hypercorrelation Squeeze Networks (HSNet) that leverages multi-level feature correlation and efficient 4D convolutions. It extracts diverse features from different levels of intermediate convolutional layers and constructs a collection of 4D correlation tensors, i.e., hypercorrelations. Using efficient center-pivot 4D convolutions in a pyramidal architecture, the method gradually squeezes high-level semantic and low-level geometric cues of the hypercorrelation into precise segmentation masks in coarse-to-fine manner. The significant performance improvements on standard few-shot segmentation benchmarks of PASCAL-5i, COCO-20i, and FSS-1000 verify the efficacy of the proposed method.
Artificial Neural Network Modeling for Airline Disruption Management
Authors: Kolawole Ogunsina, Wendy A. Okolo
Date: 2021-04-05 00:00:00
Tasks: Management, Scheduling

Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems (UAS), operations and infrastructure, to the system. To this end, we use historical data on airline scheduling and operations recovery to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, we provide a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.
Probabilistic Programming Bots in Intuitive Physics Game Play
Authors: Fahad Alhasoun, Sarah Alnegheimish, Joshua Tenenbaum
Date: 2021-04-05 00:00:00
Tasks: Probabilistic Programming

Recent findings suggest that humans deploy cognitive mechanism of physics simulation engines to simulate the physics of objects. We propose a framework for bots to deploy probabilistic programming tools for interacting with intuitive physics environments. The framework employs a physics simulation in a probabilistic way to infer about moves performed by an agent in a setting governed by Newtonian laws of motion. However, methods of probabilistic programs can be slow in such setting due to their need to generate many samples. We complement the model with a model-free approach to aid the sampling procedures in becoming more efficient through learning from experience during game playing. We present an approach where combining model-free approaches (a convolutional neural network in our model) and model-based approaches (probabilistic physics simulation) is able to achieve what neither could alone. This way the model outperforms an all model-free or all model-based approach. We discuss a case study showing empirical results of the performance of the model on the game of Flappy Bird.
Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition
Authors: Haoqi Li, Yelin Kim, Cheng-Hao Kuo, Shrikanth Narayanan
Date: 2021-04-05 00:00:00
Tasks: Domain Adaptation, Emotion Recognition, Transfer Learning

Key challenges in developing generalized automatic emotion recognition systems include scarcity of labeled data and lack of gold-standard references. Even for the cues that are labeled as the same emotion category, the variability of associated expressions can be high depending on the elicitation context e.g., emotion elicited during improvised conversations vs. acted sessions with predefined scripts. In this work, we regard the emotion elicitation approach as domain knowledge, and explore domain transfer learning techniques on emotional utterances collected under different emotion elicitation approaches, particularly with limited labeled target samples. Our emotion recognition model combines the gradient reversal technique with an entropy loss function as well as the softlabel loss, and the experiment results show that domain transfer learning methods can be employed to alleviate the domain mismatch between different elicitation approaches. Our work provides new insights into emotion data collection, particularly the impact of its elicitation strategies, and the importance of domain adaptation in emotion recognition aiming for generalized systems.
DataOps for Societal Intelligence: a Data Pipeline for Labor Market Skills Extraction and Matching
Authors: Damian Andrew Tamburri, Willem-Jan van den Heuvel, Martin Garriga
Date: 2021-04-05 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Retrieval

Big Data analytics supported by AI algorithms can support skills localization and retrieval in the context of a labor market intelligence problem. We formulate and solve this problem through specific DataOps models, blending data sources from administrative and technical partners in several countries into cooperation, creating shared knowledge to support policy and decision-making. We then focus on the critical task of skills extraction from resumes and vacancies featuring state-of-the-art machine learning models. We showcase preliminary results with applied machine learning on real data from the employment agencies of the Netherlands and the Flemish region in Belgium. The final goal is to match these skills to standard ontologies of skills, jobs and occupations.
Automating Transfer Credit Assessment in Student Mobility -- A Natural Language Processing-based Approach
Authors: Dhivya Chandrasekaran, Vijay Mago
Date: 2021-04-05 00:00:00
Tasks: Decision Making, Semantic Similarity, Semantic Textual Similarity, Specificity

Student mobility or academic mobility involves students moving between institutions during their post-secondary education, and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student. In general, this process involves domain experts comparing the learning outcomes of the courses, to decide on offering transfer credits to the incoming students. This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity. The proposed research article focuses on identifying a model that exploits the advancements in the field of Natural Language Processing (NLP) to effectively automate this process. Given the unique structure, domain specificity, and complexity of learning outcomes (LOs), a need for designing a tailor-made model arises. The proposed model uses a clustering-inspired methodology based on knowledge-based semantic similarity measures to assess the taxonomic similarity of LOs and a transformer-based semantic similarity model to assess the semantic similarity of the LOs. The similarity between LOs is further aggregated to form course to course similarity. Due to the lack of quality benchmark datasets, a new benchmark dataset containing seven course-to-course similarity measures is proposed. Understanding the inherent need for flexibility in the decision-making process the aggregation part of the model offers tunable parameters to accommodate different scenarios. While providing an efficient model to assess the similarity between courses with existing resources, this research work steers future research attempts to apply NLP in the field of articulation in an ideal direction by highlighting the persisting research gaps.
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning
Authors: Quanlin Chen
Date: 2021-04-05 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), SMAC, SMAC+, Starcraft, Starcraft II

Multi-agent value-based approaches recently make great progress, especially value decomposition methods. However, there are still a lot of limitations in value function factorization. In VDN, the joint action-value function is the sum of per-agent action-value function while the joint action-value function of QMIX is the monotonic mixing of per-agent action-value function. To some extent, QTRAN reduces the limitation of joint action-value functions that can be represented, but it has unsatisfied performance in complex tasks. In this paper, in order to extend the class of joint value functions that can be represented, we propose a novel actor-critic method called NQMIX. NQMIX introduces an off-policy policy gradient on QMIX and modify its network architecture, which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for the joint action-value function. In addition, NQMIX takes the state-value as the learning target, which overcomes the problem in QMIX that the learning target is overestimated. Furthermore, NQMIX can be extended to continuous action space settings by introducing deterministic policy gradient on itself. Finally, we evaluate our actor-critic methods on SMAC domain, and show that it has a stronger performance than COMA and QMIX on complex maps with heterogeneous agent types. In addition, our ablation results show that our modification of mixer is effective.
An Artificial Intelligence Framework for Bidding Optimization with Uncertainty inMultiple Frequency Reserve Markets
Authors: Thimal Kempitiyaa, Seppo Sierla, Daswin De Silvaa, Matti Yli-Ojanpera, Damminda Alahakoona, Valeriy Vyatkin
Date: 2021-04-05 00:00:00

The global ambitions of a carbon-neutral society necessitate a stable and robust smart grid that capitalises on frequency reserves of renewable energy. Frequency reserves are resources that adjust power production or consumption in real time to react to a power grid frequency deviation. Revenue generation motivates the availability of these resources for managing such deviations. However, limited research has been conducted on data-driven decisions and optimal bidding strategies for trading such capacities in multiple frequency reserves markets. We address this limitation by making the following research contributions. Firstly, a generalised model is designed based on an extensive study of critical characteristics of global frequency reserves markets. Secondly, three bidding strategies are proposed, based on this market model, to capitalise on price peaks in multi-stage markets. Two strategies are proposed for non-reschedulable loads, in which case the bidding strategy aims to select the market with the highest anticipated price, and the third bidding strategy focuses on rescheduling loads to hours on which highest reserve market prices are anticipated. The third research contribution is an Artificial Intelligence (AI) based bidding optimization framework that implements these three strategies, with novel uncertainty metrics that supplement data-driven price prediction. Finally, the framework is evaluated empirically using a case study of multiple frequency reserves markets in Finland. The results from this evaluation confirm the effectiveness of the proposed bidding strategies and the AI-based bidding optimization framework in terms of cumulative revenue generation, leading to an increased availability of frequency reserves.
Integrating 2D and 3D Digital Plant Information Towards Automatic Generation of Digital Twins
Authors: Seppo Sierla, Mohammad Azangoo, Alexander Fay, Valeriy Vyatkin, Nikolaos Papakonstantinou
Date: 2021-04-05 00:00:00
Tasks: Graph Matching

Ongoing standardization in Industry 4.0 supports tool vendor neutral representations of Piping and Instrumentation diagrams as well as 3D pipe routing. However, a complete digital plant model requires combining these two representations. 3D pipe routing information is essential for building any accurate first-principles process simulation model. Piping and instrumentation diagrams are the primary source for control loops. In order to automatically integrate these information sources to a unified digital plant model, it is necessary to develop algorithms for identifying corresponding elements such as tanks and pumps from piping and instrumentation diagrams and 3D CAD models. One approach is to raise these two information sources to a common level of abstraction and to match them at this level of abstraction. Graph matching is a potential technique for this purpose. This article focuses on automatic generation of the graphs as a prerequisite to graph matching. Algorithms for this purpose are proposed and validated with a case study. The paper concludes with a discussion of further research needed to reprocess the generated graphs in order to enable effective matching.
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning
Authors: Chaoqun Wang, Xuejin Chen, Shaobo Min, Xiaoyan Sun, Houqiang Li
Date: 2021-04-05 00:00:00
Tasks: Contrastive Learning, Generalized Zero-Shot Learning, Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) targets recognizing new categories by learning transferable image representations. Existing methods find that, by aligning image representations with corresponding semantic labels, the semantic-aligned representations can be transferred to unseen categories. However, supervised by only seen category labels, the learned semantic knowledge is highly task-specific, which makes image representations biased towards seen categories. In this paper, we propose a novel Dual-Contrastive Embedding Network (DCEN) that simultaneously learns task-specific and task-independent knowledge via semantic alignment and instance discrimination. First, DCEN leverages task labels to cluster representations of the same semantic category by cross-modal contrastive learning and exploring semantic-visual complementarity. Besides task-specific knowledge, DCEN then introduces task-independent knowledge by attracting representations of different views of the same image and repelling representations of different images. Compared to high-level seen category supervision, this instance discrimination supervision encourages DCEN to capture low-level visual knowledge, which is less biased toward seen categories and alleviates the representation bias. Consequently, the task-specific and task-independent knowledge jointly make for transferable representations of DCEN, which obtains averaged 4.1% improvement on four public benchmarks.
When Can Liquid Democracy Unveil the Truth?
Authors: Ruben Becker, Gianlorenzo D'Angelo, Esmaeil Delfaraz, Hugo Gilbert
Date: 2021-04-05 00:00:00
Tasks: Misinformation

In this paper, we investigate the so-called ODP-problem that has been formulated by Caragiannis and Micha [10]. Here, we are in a setting with two election alternatives out of which one is assumed to be correct. In ODP, the goal is to organise the delegations in the social network in order to maximize the probability that the correct alternative, referred to as ground truth, is elected. While the problem is known to be computationally hard, we strengthen existing hardness results by providing a novel strong approximation hardness result: For any positive constant $C$, we prove that, unless $P=NP$, there is no polynomial-time algorithm for ODP that achieves an approximation guarantee of $\alpha \ge (\ln n)^{-C}$, where $n$ is the number of voters. The reduction designed for this result uses poorly connected social networks in which some voters suffer from misinformation. Interestingly, under some hypothesis on either the accuracies of voters or the connectivity of the network, we obtain a polynomial-time $1/2$-approximation algorithm. This observation proves formally that the connectivity of the social network is a key feature for the efficiency of the liquid democracy paradigm. Lastly, we run extensive simulations and observe that simple algorithms (working either in a centralized or decentralized way) outperform direct democracy on a large class of instances. Overall, our contributions yield new insights on the question in which situations liquid democracy can be beneficial.
Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection
Authors: Yan Xu, Yongliang Cheng
Date: 2021-04-05 00:00:00
Tasks: Anomaly Detection

The consumer Internet of Things (IoT) have developed in recent years. Mass IoT devices are constructed to build a huge communications network. But these devices are insecure in reality, it means that the communications network are exposed by the attacker. Moreover, the IoT communication network also faces with variety of sudden errors. Therefore, it easily leads to that is vulnerable with the threat of attacker and system failure. The severe situation of IoT communication network motivates the development of new techniques to automatically detect multi-anomaly. In this paper, we propose SS-VTCN, a semi-supervised network for IoT multiple anomaly detection that works well effectively for IoT communication network. SS-VTCN is designed to capture the normal patterns of the IoT traffic data based on the distribution whether it is labeled or not by learning their representations with key techniques such as Variational Autoencoders and Temporal Convolutional Network. This network can use the encode data to predict preliminary result, and reconstruct input data to determine anomalies by the representations. Extensive evaluation experiments based on a benchmark dataset and a real consumer smart home dataset demonstrate that SS-VTCN is more suitable than supervised and unsupervised method with better performance when compared other state-of-art semi-supervised method.
AST: Audio Spectrogram Transformer
Authors: Yuan Gong, Yu-An Chung, James Glass
Date: 2021-04-05 00:00:00
Tasks: Audio Classification, Audio Tagging, Classification, General Classification, Keyword Spotting, Speech Emotion Recognition, Time Series Analysis

In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels. To better capture long-range global context, a recent trend is to add a self-attention mechanism on top of the CNN, forming a CNN-attention hybrid model. However, it is unclear whether the reliance on a CNN is necessary, and if neural networks purely based on attention are sufficient to obtain good performance in audio classification. In this paper, we answer the question by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for audio classification. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech Commands V2.