pwc-archive/papers-with-abstracts huggingface dataset:
An Improved Iterative Neural Network for High-Quality Image-Domain Material Decomposition in Dual-Energy CT
Authors: Zhipeng Li, Yong Long, Il Yong Chun
Date: 2020-12-02 00:00:00
Tasks: Image Reconstruction

Dual-energy computed tomography (DECT) has been widely used in many applications that need material decomposition. Image-domain methods directly decompose material images from high- and low-energy attenuation images, and thus, are susceptible to noise and artifacts on attenuation images. The purpose of this study is to develop an improved iterative neural network (INN) for high-quality image-domain material decomposition in DECT, and to study its properties. We propose a new INN architecture for DECT material decomposition. The proposed INN architecture uses distinct cross-material convolutional neural network (CNN) in image refining modules, and uses image decomposition physics in image reconstruction modules. The distinct cross-material CNN refiners incorporate distinct encoding-decoding filters and cross-material model that captures correlations between different materials. We study the distinct cross-material CNN refiner with patch-based reformulation and tight-frame condition. Numerical experiments with extended cardiactorso (XCAT) phantom and clinical data show that the proposed INN significantly improves the image quality over several image-domain material decomposition methods, including a conventional model-based image decomposition (MBID) method using an edge-preserving regularizer, a recent MBID method using pre-learned material-wise sparsifying transforms, and a noniterative deep CNN method. Our study with patch-based reformulations reveals that learned filters of distinct cross-material CNN refiners can approximately satisfy the tight-frame condition.
Localization of Malaria Parasites and White Blood Cells in Thick Blood Smears
Authors: Rose Nakasi, Aminah Zawedde, Ernest Mwebaze, Jeremy Francis Tusubira, Gilbert Maiga
Date: 2020-12-03 00:00:00
Tasks: Data Augmentation

Effectively determining malaria parasitemia is a critical aspect in assisting clinicians to accurately determine the severity of the disease and provide quality treatment. Microscopy applied to thick smear blood smears is the de facto method for malaria parasitemia determination. However, manual quantification of parasitemia is time consuming, laborious and requires considerable trained expertise which is particularly inadequate in highly endemic and low resourced areas. This study presents an end-to-end approach for localisation and count of malaria parasites and white blood cells (WBCs) which aid in the effective determination of parasitemia; the quantitative content of parasites in the blood. On a dataset of slices of images of thick blood smears, we build models to analyse the obtained digital images. To improve model performance due to the limited size of the dataset, data augmentation was applied. Our preliminary results show that our deep learning approach reliably detects and returns a count of malaria parasites and WBCs with a high precision and recall. We also evaluate our system against human experts and results indicate a strong correlation between our deep learning model counts and the manual expert counts (p=0.998 for parasites, p=0.987 for WBCs). This approach could potentially be applied to support malaria parasitemia determination especially in settings that lack sufficient Microscopists.
Multicriteria Group Decision-Making Under Uncertainty Using Interval Data and Cloud Models
Authors: Hadi A. Khorshidi, Uwe Aickelin
Date: 2020-12-01 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

In this study, we propose a multicriteria group decision making (MCGDM) algorithm under uncertainty where data is collected as intervals. The proposed MCGDM algorithm aggregates the data, determines the optimal weights for criteria and ranks alternatives with no further input. The intervals give flexibility to experts in assessing alternatives against criteria and provide an opportunity to gain maximum information. We also propose a novel method to aggregate expert judgements using cloud models. We introduce an experimental approach to check the validity of the aggregation method. After that, we use the aggregation method for an MCGDM problem. Here, we find the optimal weights for each criterion by proposing a bilevel optimisation model. Then, we extend the technique for order of preference by similarity to ideal solution (TOPSIS) for data based on cloud models to prioritise alternatives. As a result, the algorithm can gain information from decision makers with different levels of uncertainty and examine alternatives with no more information from decision-makers. The proposed MCGDM algorithm is implemented on a case study of a cybersecurity problem to illustrate its feasibility and effectiveness. The results verify the robustness and validity of the proposed MCGDM using sensitivity analysis and comparison with other existing algorithms.
Folding and Unfolding on Metagraphs
Authors: Ben Goertzel
Date: 2020-12-03 00:00:00

Typed metagraphs are defined as hypergraphs with types assigned to hyperedges and their targets, and the potential to have targets of hyperedges connect to whole links as well as targets. Directed typed metagraphs (DTMGs) are introduced via partitioning the targets of each edge in a typed metagraph into input, output and lateral sets; one can then look at "metapaths" in which edges' output-sets are linked to other edges' input-sets. An initial algebra approach to DTMGs is presented, including introduction of constructors for building up DTMGs and laws regarding relationships among multiple ways of using these constructors. A menagerie of useful morphism types is then defined on DTMGs (catamorphisms, anamorphisms, histomorphisms, futumorphisms, hylomorphisms, chronomorphisms, metamorphisms and metachronomorphisms), providing a general abstract framework for formulating a broad variety of metagraph operations. Deterministic and stochastic processes on typed metagraphs are represented in terms of forests of DTMGs defined over a common TMG, where the various morphisms can be straightforwardly extended to these forests. A variation of the approach to undirected typed metagraphs is presented; and it is indicated how the framework outlined can applied to realistic metagraphs involving complexities like dependent and probabilistic types, multidimensional values and dynamic processing including insertion and deletion of edges.
Distributed Thompson Sampling
Authors: Jing Dong, Tan Li, Shaolei Ren, Linqi Song
Date: 2020-12-03 00:00:00
Tasks: Multi-Armed Bandits, Thompson Sampling

We study a cooperative multi-agent multi-armed bandits with M agents and K arms. The goal of the agents is to minimized the cumulative regret. We adapt a traditional Thompson Sampling algoirthm under the distributed setting. However, with agent's ability to communicate, we note that communication may further reduce the upper bound of the regret for a distributed Thompson Sampling approach. To further improve the performance of distributed Thompson Sampling, we propose a distributed Elimination based Thompson Sampling algorithm that allow the agents to learn collaboratively. We analyse the algorithm under Bernoulli reward and derived a problem dependent upper bound on the cumulative regret.
Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems
Authors: Jan Strohschein, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke, Natalia Moriz, Thomas Bartz-Beielstein
Date: 2020-12-03 00:00:00

This paper presents the cognitive module of the cognitive architecture for artificial intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to test algorithms from different classes. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging-technology for module communication is used to evaluate a real-world use case.
Origin-Aware Next Destination Recommendation with Personalized Preference Attention
Authors: Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng, Rui Tan
Date: 2020-12-03 00:00:00
Tasks: Decoder

Next destination recommendation is an important task in the transportation domain of taxi and ride-hailing services, where users are recommended with personalized destinations given their current origin location. However, recent recommendation works do not satisfy this origin-awareness property, and only consider learning from historical destination locations, without origin information. Thus, the resulting approaches are unable to learn and predict origin-aware recommendations based on the user's current location, leading to sub-optimal performance and poor real-world practicality. Hence, in this work, we study the origin-aware next destination recommendation task. We propose the Spatial-Temporal Origin-Destination Personalized Preference Attention (STOD-PPA) encoder-decoder model to learn origin-origin (OO), destination-destination (DD), and origin-destination (OD) relationships by first encoding both origin and destination sequences with spatial and temporal factors in local and global views, then decoding them through personalized preference attention to predict the next destination. Experimental results on seven real-world user trajectory taxi datasets show that our model significantly outperforms baseline and state-of-the-art methods.
Mapping Patterns for Virtual Knowledge Graphs
Authors: Diego Calvanese, Avigdor Gal, Davide Lanti, Marco Montali, Alessandro Mosca, Roee Shraga
Date: 2020-12-03 00:00:00
Tasks: Knowledge Graphs, Management

Virtual Knowledge Graphs (VKG) constitute one of the most promising paradigms for integrating and accessing legacy data sources. A critical bottleneck in the integration process involves the definition, validation, and maintenance of mappings that link data sources to a domain ontology. To support the management of mappings throughout their entire lifecycle, we propose a comprehensive catalog of sophisticated mapping patterns that emerge when linking databases to ontologies. To do so, we build on well-established methodologies and patterns studied in data management, data analysis, and conceptual modeling. These are extended and refined through the analysis of concrete VKG benchmarks and real-world use cases, and considering the inherent impedance mismatch between data sources and ontologies. We validate our catalog on the considered VKG scenarios, showing that it covers the vast majority of patterns present therein.
Continuous Subject-in-the-Loop Integration: Centering AI on Marginalized Communities
Authors: Francois Roewer-Despres, Janelle Berscheid
Date: 2020-11-30 00:00:00

Despite its utopian promises as a disruptive equalizer, AI - like most tools deployed under the guise of neutrality - has tended to simply reinforce existing social structures. To counter this trend, radical AI calls for centering on the marginalized. We argue that gaps in key infrastructure are preventing the widespread adoption of radical AI, and propose a guiding principle for both identifying these infrastructure gaps and evaluating whether proposals for new infrastructure effectively center marginalized voices.
Machine learning prediction of critical transition and system collapse
Authors: Ling-Wei Kong, Hua-Wei Fan, Celso Grebogi, Ying-Cheng Lai
Date: 2020-12-02 00:00:00
Tasks: BIG-bench Machine Learning

To predict a critical transition due to parameter drift without relying on model is an outstanding problem in nonlinear dynamics and applied fields. A closely related problem is to predict whether the system is already in or if the system will be in a transient state preceding its collapse. We develop a model free, machine learning based solution to both problems by exploiting reservoir computing to incorporate a parameter input channel. We demonstrate that, when the machine is trained in the normal functioning regime with a chaotic attractor (i.e., before the critical transition), the transition point can be predicted accurately. Remarkably, for a parameter drift through the critical point, the machine with the input parameter channel is able to predict not only that the system will be in a transient state, but also the average transient time before the final collapse.
SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change
Authors: Maurício Gruppi, Sibel Adali, Pin-Yu Chen
Date: 2020-12-02 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Change Detection, Word Embeddings

This paper describes SChME (Semantic Change Detection with Model Ensemble), a method usedin SemEval-2020 Task 1 on unsupervised detection of lexical semantic change. SChME usesa model ensemble combining signals of distributional models (word embeddings) and wordfrequency models where each model casts a vote indicating the probability that a word sufferedsemantic change according to that feature. More specifically, we combine cosine distance of wordvectors combined with a neighborhood-based metric we named Mapped Neighborhood Distance(MAP), and a word frequency differential metric as input signals to our model. Additionally,we explore alignment-based methods to investigate the importance of the landmarks used in thisprocess. Our results show evidence that the number of landmarks used for alignment has a directimpact on the predictive performance of the model. Moreover, we show that languages that sufferless semantic change tend to benefit from using a large number of landmarks, whereas languageswith more semantic change benefit from a more careful choice of landmark number for alignment.
Domain Adaptation with Incomplete Target Domains
Authors: Zhenpeng Li, Jianan Jiang, Yuhong Guo, Tiantian Tang, Chengxiang Zhuo, Jieping Ye
Date: 2020-12-03 00:00:00
Tasks: Domain Adaptation, Imputation

Domain adaptation, as a task of reducing the annotation cost in a target domain by exploiting the existing labeled data in an auxiliary source domain, has received a lot of attention in the research community. However, the standard domain adaptation has assumed perfectly observed data in both domains, while in real world applications the existence of missing data can be prevalent. In this paper, we tackle a more challenging domain adaptation scenario where one has an incomplete target domain with partially observed data. We propose an Incomplete Data Imputation based Adversarial Network (IDIAN) model to address this new domain adaptation challenge. In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption. We conduct experiments on both cross-domain benchmark tasks and a real world adaptation task with imperfect target domains. The experimental results demonstrate the effectiveness of the proposed method.
Obstacle Avoidance Using a Monocular Camera
Authors: Kyle Hatch, John Mern, Mykel Kochenderfer
Date: 2020-12-03 00:00:00
Tasks: Collision Avoidance

A collision avoidance system based on simple digital cameras would help enable the safe integration of small UAVs into crowded, low-altitude environments. In this work, we present an obstacle avoidance system for small UAVs that uses a monocular camera with a hybrid neural network and path planner controller. The system is comprised of a vision network for estimating depth from camera images, a high-level control network, a collision prediction network, and a contingency policy. This system is evaluated on a simulated UAV navigating an obstacle course in a constrained flight pattern. Results show the proposed system achieves low collision rates while maintaining operationally relevant flight speeds.
Explainable AI for Software Engineering
Authors: Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, John Grundy
Date: 2020-12-03 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Artificial Intelligence/Machine Learning techniques have been widely used in software engineering to improve developer productivity, the quality of software systems, and decision-making. However, such AI/ML models for software engineering are still impractical, not explainable, and not actionable. These concerns often hinder the adoption of AI/ML models in software engineering practices. In this article, we first highlight the need for explainable AI in software engineering. Then, we summarize three successful case studies on how explainable AI techniques can be used to address the aforementioned challenges by making software defect prediction models more practical, explainable, and actionable.
Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Embeddings and the Implications to Representation Learning
Authors: Wei zhang, Murray Campbell, Yang Yu, Sadhana Kumaravel
Date: 2020-12-03 00:00:00
Tasks: Language Modelling, Representation Learning, text-classification, Text Classification, Word Similarity

Human judgments of word similarity have been a popular method of evaluating the quality of word embedding. But it fails to measure the geometry properties such as asymmetry. For example, it is more natural to say "Ellipses are like Circles" than "Circles are like Ellipses". Such asymmetry has been observed from a psychoanalysis test called word evocation experiment, where one word is used to recall another. Although useful, such experimental data have been significantly understudied for measuring embedding quality. In this paper, we use three well-known evocation datasets to gain insights into asymmetry encoding of embedding. We study both static embedding as well as contextual embedding, such as BERT. Evaluating asymmetry for BERT is generally hard due to the dynamic nature of embedding. Thus, we probe BERT's conditional probabilities (as a language model) using a large number of Wikipedia contexts to derive a theoretically justifiable Bayesian asymmetry score. The result shows that contextual embedding shows randomness than static embedding on similarity judgments while performing well on asymmetry judgment, which aligns with its strong performance on "extrinsic evaluations" such as text classification. The asymmetry judgment and the Bayesian approach provides a new perspective to evaluate contextual embedding on intrinsic evaluation, and its comparison to similarity evaluation concludes our work with a discussion on the current state and the future of representation learning.
Reversibility and Composition of Rewriting in Hierarchies
Authors: Russ Harmer, Eugenia Oshurko
Date: 2020-12-03 00:00:00

In this paper, we study how graph transformations based on sesqui-pushout rewriting can be reversed and how the composition of rewrites can be constructed. We illustrate how such reversibility and composition can be used to design an audit trail system for individual graphs and graph hierarchies. This provides us with a compact way to maintain the history of updates of an object, including its multiple versions. The main application of the designed framework is an audit trail of updates to knowledge represented by hierarchies of graphs. Therefore, we introduce the notion of rule hierarchy that represents a transformation of the entire hierarchy, study how rule hierarchies can be applied to hierarchies and analyse the conditions under which this application is reversible. We then present a theory for constructing the composition of consecutive hierarchy rewrites. The prototype audit trail system for transformations in hierarchies of simple graphs with attributes is implemented as part of the ReGraph Python library.
MelGlow: Efficient Waveform Generative Network Based on Location-Variable Convolution
Authors: Zhen Zeng, Jianzong Wang, Ning Cheng, Jing Xiao
Date: 2020-12-03 00:00:00

Recent neural vocoders usually use a WaveNet-like network to capture the long-term dependencies of the waveform, but a large number of parameters are required to obtain good modeling capabilities. In this paper, an efficient network, named location-variable convolution, is proposed to model the dependencies of waveforms. Different from the use of unified convolution kernels in WaveNet to capture the dependencies of arbitrary waveforms, location-variable convolutions utilizes a kernel predictor to generate multiple sets of convolution kernels based on the mel-spectrum, where each set of convolution kernels is used to perform convolution operations on the associated waveform intervals. Combining WaveGlow and location-variable convolutions, an efficient vocoder, named MelGlow, is designed. Experiments on the LJSpeech dataset show that MelGlow achieves better performance than WaveGlow at small model sizes, which verifies the effectiveness and potential optimization space of location-variable convolutions.
Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition
Authors: Genta Indra Winata, Guangsen Wang, Caiming Xiong, Steven Hoi
Date: 2020-12-03 00:00:00
Tasks: Language Modeling, Language Modelling, Multi-Task Learning, speech-recognition, Speech Recognition

One crucial challenge of real-world multilingual speech recognition is the long-tailed distribution problem, where some resource-rich languages like English have abundant training data, but a long tail of low-resource languages have varying amounts of limited training data. To overcome the long-tail problem, in this paper, we propose Adapt-and-Adjust (A2), a transformer-based multi-task learning framework for end-to-end multilingual speech recognition. The A2 framework overcomes the long-tail problem via three techniques: (1) exploiting a pretrained multilingual language model (mBERT) to improve the performance of low-resource languages; (2) proposing dual adapters consisting of both language-specific and language-agnostic adaptation with minimal additional parameters; and (3) overcoming the class imbalance, either by imposing class priors in the loss during training or adjusting the logits of the softmax output during inference. Extensive experiments on the CommonVoice corpus show that A2 significantly outperforms conventional approaches.