pwc-archive/papers-with-abstracts huggingface dataset:
Computing Optimal Decision Sets with SAT
Authors: Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey, Pierre Le Bodic
Date: 2020-07-29 00:00:00
Tasks: BIG-bench Machine Learning

As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use decision rules. This paper focuses on decision sets, a type of model with unordered rules, which explains each prediction with a single rule. In order to be easy for humans to understand, these rules must be concise. Earlier work on generating optimal decision sets first minimizes the number of rules, and then minimizes the number of literals, but the resulting rules can often be very large. Here we consider a better measure, namely the total size of the decision set in terms of literals. So we are not driven to a small set of rules which require a large number of literals. We provide the first approach to determine minimum-size decision sets that achieve minimum empirical risk and then investigate sparse alternatives where we trade accuracy for size. By finding optimal solutions we show we can build decision set classifiers that are almost as accurate as the best heuristic methods, but far more concise, and hence more explainable.
Localization with One-Bit Passive Radars in Narrowband Internet-of-Things using Multivariate Polynomial Optimization
Authors: Saeid Sedighi, Kumar Vijay Mishra, M. R. Bhavani Shankar, Björn Ottersten
Date: 2020-07-29 00:00:00
Tasks: TAR

Several Internet-of-Things (IoT) applications provide location-based services, wherein it is critical to obtain accurate position estimates by aggregating information from individual sensors. In the recently proposed narrowband IoT (NB-IoT) standard, which trades off bandwidth to gain wide coverage, the location estimation is compounded by the low sampling rate receivers and limited-capacity links. We address both of these NB-IoT drawbacks in the framework of passive sensing devices that receive signals from the target-of-interest. We consider the limiting case where each node receiver employs one-bit analog-to-digital-converters and propose a novel low-complexity nodal delay estimation method using constrained-weighted least squares minimization. To support the low-capacity links to the fusion center (FC), the range estimates obtained at individual sensors are then converted to one-bit data. At the FC, we propose target localization with the aggregated one-bit range vector using both optimal and sub-optimal techniques. The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint r\textit{an}ge-\textit{tar}get location \textit{es}timation (ANTARES) algorithm. Our overall one-bit framework not only complements the low NB-IoT bandwidth but also supports the design goal of inexpensive NB-IoT location sensing. Numerical experiments demonstrate feasibility of the proposed one-bit approach with a $0.6$\% increase in the normalized localization error for the small set of $20$-$60$ nodes over the full-precision case. When the number of nodes is sufficiently large ($>80$), the one-bit methods yield the same performance as the full precision.
Social Influences in Recommendation Systems
Authors: Diyah Puspitaningrum
Date: 2020-07-29 00:00:00
Tasks: Recommendation Systems, TAG

Social networking sites such as Flickr and Facebook allow users to share content with family, friends, and interest groups. Also, tags can often assign to resources. In the previous research using few association rules FAR, we have seen that high-quality and efficient association-based tag recommendation is possible, but the set-up that we considered was very generic and did not take social information into account. The proposed method in the previous paper, FAR, in particular, exhibited a favorable trade-off between recommendation quality and runtime. Unfortunately, recommendation quality is unlikely to be optimal because the algorithms are not aware of any social information that may be available. Two proposed approaches take a more social view on tag recommendation regarding the issue: social contact variants and social groups of interest. The user data is varied and used as a source of associations. The adoption of social contact variants has two approaches. The first social variant is User-centered Knowledge, to contrast Collective Knowledge. It improves tag recommendation by grouping historic tag data according to friend relationships and interests. The second variant is dubbed 'social batched personomy' and attempts to address both quality and scalability issues by processing queries in batches instead of individually, such as done in a conventional personomy approach. For the social group of interest, 'community batched personomy' is proposed to provide better accuracy groups of recommendation systems in contrast also to Collective Knowledge. By taking social information into account can enhance the performance of recommendation systems.
A superconducting nanowire spiking element for neural networks
Authors: Emily Toomey, Ken Segall, Matteo Castellani, Marco Colangelo, Nancy Lynch, Karl K. Berggren
Date: 2020-07-29 00:00:00

As the limits of traditional von Neumann computing come into view, the brain's ability to communicate vast quantities of information using low-power spikes has become an increasing source of inspiration for alternative architectures. Key to the success of these largescale neural networks is a power-efficient spiking element that is scalable and easily interfaced with traditional control electronics. In this work, we present a spiking element fabricated from superconducting nanowires that has pulse energies on the order of ~10 aJ. We demonstrate that the device reproduces essential characteristics of biological neurons, such as a refractory period and a firing threshold. Through simulations using experimentally measured device parameters, we show how nanowire-based networks may be used for inference in image recognition, and that the probabilistic nature of nanowire switching may be exploited for modeling biological processes and for applications that rely on stochasticity.
Finding Local Experts for Dynamic Recommendations Using Lazy Random Walk
Authors: Diyah Puspitaningrum, Julio Fernando, Edo Afriando, Ferzha Putra Utama, Rina Rahmadini, Y. Pinata
Date: 2020-07-29 00:00:00
Tasks: Recommendation Systems

Statistics based privacy-aware recommender systems make suggestions more powerful by extracting knowledge from the log of social contacts interactions, but unfortunately, they are static. Moreover, advice from local experts effective in finding specific business categories in a particular area. We propose a dynamic recommender algorithm based on a lazy random walk that recommends top-rank shopping places to potentially interested visitors. We consider local authority and topical authority. The algorithm tested on FourSquare shopping data sets of 5 cities in Indonesia with k-steps of 5,7,9 of (lazy) random walks and compared the results with other state-of-the-art ranking techniques. The results show that it can reach high score precisions (0.5, 0.37, and 0.26 respectively on precision at 1, precision at 3, and precision at 5 for k=5). The algorithm also shows scalability concerning execution time. The advantage of dynamicity is the database used to power the recommender system; no need to be very frequently updated to produce a good recommendation.
Improving Performance of Relation Extraction Algorithm via Leveled Adversarial PCNN and Database Expansion
Authors: Diyah Puspitaningrum
Date: 2020-07-29 00:00:00
Tasks: Relation, Relation Extraction

This study introduces database expansion using the Minimum Description Length (MDL) algorithm to expand the database for better relation extraction. Different from other previous relation extraction researches, our method improves system performance by expanding data. The goal of database expansion, together with a robust deep learning classifier, is to diminish wrong labels due to the incomplete or not found nature of relation instances in the relation database (e.g., Freebase). The study uses a deep learning method (Piecewise Convolutional Neural Network or PCNN) as the base classifier of our proposed approach: the leveled adversarial attention neural networks (LATTADV-ATT). In the database expansion process, the semantic entity identification is used to enlarge new instances using the most similar itemsets of the most common patterns of the data to get its pairs of entities. About the deep learning method, the use of attention of selective sentences in PCNN can reduce noisy sentences. Also, the use of adversarial perturbation training is useful to improve the robustness of system performance. The performance even further is improved using a combination of leveled strategy and database expansion. There are two issues: 1) database expansion method: rule generation by allowing step sizes on selected strong semantic of most similar itemsets with aims to find entity pair for generating instances, 2) a better classifier model for relation extraction. Experimental result has shown that the use of the database expansion is beneficial. The MDL database expansion helps improvements in all methods compared to the unexpanded method. The LATTADV-ATT performs as a good classifier with high precision P@100=0.842 (at no expansion). It is even better while implemented on the expansion data with P@100=0.891 (at expansion factor k=7).
Exploiting Cross-Lingual Knowledge in Unsupervised Acoustic Modeling for Low-Resource Languages
Authors: Siyuan Feng
Date: 2020-07-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Acquisition, speech-recognition, Speech Recognition

(Short version of Abstract) This thesis describes an investigation on unsupervised acoustic modeling (UAM) for automatic speech recognition (ASR) in the zero-resource scenario, where only untranscribed speech data is assumed to be available. UAM is not only important in addressing the general problem of data scarcity in ASR technology development but also essential to many non-mainstream applications, for examples, language protection, language acquisition and pathological speech assessment. The present study is focused on two research problems. The first problem concerns unsupervised discovery of basic (subword level) speech units in a given language. Under the zero-resource condition, the speech units could be inferred only from the acoustic signals, without requiring or involving any linguistic direction and/or constraints. The second problem is referred to as unsupervised subword modeling. In its essence a frame-level feature representation needs to be learned from untranscribed speech. The learned feature representation is the basis of subword unit discovery. It is desired to be linguistically discriminative and robust to non-linguistic factors. Particularly extensive use of cross-lingual knowledge in subword unit discovery and modeling is a focus of this research.
Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification
Authors: Xin Dong, Yaxin Zhu, Yupeng Zhang, Zuohui Fu, Dongkuan Xu, Sen yang, Gerard de Melo
Date: 2020-07-29 00:00:00
Tasks: Classification, General Classification, intent-classification, Intent Classification, Self-Learning, text-classification, Text Classification

In cross-lingual text classification, one seeks to exploit labeled data from one language to train a text classification model that can then be applied to a completely different language. Recent multilingual representation models have made it much easier to achieve this. Still, there may still be subtle differences between languages that are neglected when doing so. To address this, we present a semi-supervised adversarial training process that minimizes the maximal loss for label-preserving input perturbations. The resulting model then serves as a teacher to induce labels for unlabeled target language samples that can be used during further adversarial training, allowing us to gradually adapt our model to the target language. Compared with a number of strong baselines, we observe significant gains in effectiveness on document and intent classification for a diverse set of languages.
Bayesian Inference by Symbolic Model Checking
Authors: Bahare Salmani, Joost-Pieter Katoen
Date: 2020-07-29 00:00:00
Tasks: Bayesian Inference, model, Translation

This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks. We present a simple translation from Bayesian networks into tree-like Markov chains such that inference can be reduced to computing reachability probabilities. Using a prototypical implementation on top of the Storm model checker, we show that symbolic data structures such as multi-terminal BDDs (MTBDDs) are very effective to perform inference on large Bayesian network benchmarks. We compare our result to inference using probabilistic sentential decision diagrams and vtrees, a scalable symbolic technique in AI inference tools.
Brain Emotional Learning-based Prediction Model For the Prediction of Geomagnetic Storms
Authors: Mahboobeh Parsapoor
Date: 2020-07-28 00:00:00
Tasks: Prediction

This study suggests a new data-driven model for the prediction of geomagnetic storm. The model which is an instance of Brain Emotional Learning Inspired Models (BELIMs), is known as the Brain Emotional Learning-based Prediction Model (BELPM). BELPM consists of four main subsystems; the connection between these subsystems has been mimicked by the corresponding regions of the emotional system. The functions of these subsystems are explained using adaptive networks. The learning algorithm of BELPM is defined using the steepest descent (SD) and the least square estimator (LSE). BELPM is employed to predict geomagnetic storms using two geomagnetic indices, Auroral Electrojet (AE) Index and Disturbance Time (Dst) Index. To evaluate the performance of BELPM, the obtained results have been compared with ANFIS, WKNN and other instances of BELIMs. The results verify that BELPM has the capability to achieve a reasonable accuracy for both the short-term and the long-term geomagnetic storms prediction.
Forecasting Hyponatremia in hospitalized patients Using Multilayer Perceptron and Multivariate Linear Regression Techniques
Authors: Prasannavenkatesan Theerthagiri
Date: 2020-07-15 00:00:00
Tasks: Prediction, regression

The percentage of patients hospitalized due to hyponatremia is getting higher. Hyponatremia is the deficiency of sodium electrolyte in the human serum. This deficiency might indulge adverse effects and also associated with longer hospital stay or mortality, if it wasnt actively treated and managed. This work predicts the futuristic sodium levels of patients based on their history of health problems using multilayer perceptron and multivariate linear regression algorithm. This work analyses the patients age, information about other disease such as diabetes, pneumonia, liver-disease, malignancy, pulmonary, sepsis, SIADH, and sodium level of the patient during admission to the hospital. The results of the proposed MLP algorithm is compared with MLR algorithm based results. The MLP prediction results generates 23-72 of higher prediction results than MLR algorithm. Thus, proposed MLR algorithm has produced 57.1 of reduced mean squared error rate than the MLR results on predicting future sodium ranges of patients. Further, proposed MLR algorithm produces 27-50 of higher prediction precision rate.
An Experimental Study of The Effects of Position Bias on Emotion CauseExtraction
Authors: Jiayuan Ding, Mayank Kejriwal
Date: 2020-07-16 00:00:00
Tasks: Deep Learning, Emotion Cause Extraction, Position

Emotion Cause Extraction (ECE) aims to identify emotion causes from a document after annotating the emotion keywords. Some baselines have been proposed to address this problem, such as rule-based, commonsense based and machine learning methods. We show, however, that a simple random selection approach toward ECE that does not require observing the text achieves similar performance compared to the baselines. We utilized only position information relative to the emotion cause to accomplish this goal. Since position information alone without observing the text resulted in higher F-measure, we therefore uncovered a bias in the ECE single genre Sina-news benchmark. Further analysis showed that an imbalance of emotional cause location exists in the benchmark, with a majority of cause clauses immediately preceding the central emotion clause. We examine the bias from a linguistic perspective, and show that high accuracy rate of current state-of-art deep learning models that utilize location information is only evident in datasets that contain such position biases. The accuracy drastically reduced when a dataset with balanced location distribution is introduced. We therefore conclude that it is the innate bias in this benchmark that caused high accuracy rate of these deep learning models in ECE. We hope that the case study in this paper presents both a cautionary lesson, as well as a template for further studies, in interpreting the superior fit of deep learning models without checking for bias.
Does Causal Coherence Predict Online Spread of Social Media?
Authors: Pedram Hosseini, Mona Diab, David A. Broniatowski
Date: 2019-06-05 00:00:00
Proceeding: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation 2019 6
Tasks: Decision Making, Misinformation

Online misinformation is primarily spread by humans deciding to do so. We therefore seek to understand the factors making this content compelling and, ultimately, driving online sharing. Fuzzy-Trace Theory, a leading account of decision making, posits that humans encode stimuli, such as online content, at multiple levels of representation; namely, gist, or bottom-line meaning, and verbatim, or surface-level details. Both of these levels of representation are expected to contribute independently to online information spread, with the effects of gist dominating. Important aspects of gist in the context of online content include the presence of a clear causal structure, and semantic coherence – both of which aid in meaning extraction. In this paper, we test the hypothesis that causal and semantic coherence are associated with online sharing of misinformative social media content using Coh-Metrix – a widely-used set of psycholinguistic measures. Results support Fuzzy-Trace Theory’s predictions regarding the role of causally- and semantically-coherent content in promoting online sharing and motivate better measures of these key constructs.
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior
Authors: Ding Liu, Zhaowen Wang, Bihan Wen, Student Member, Jianchao Yang, Member, Wei Han, and Thomas S. Huang, Fellow, IEEE
Date: 2016-06-07 00:00:00
Proceeding: journals 2016 6
Tasks: Image Super-Resolution, Super-Resolution

Abstract—Single image super-resolution (SR) is an ill-posed problem, which tries to recover a high-resolution image from its low-resolution observation. To regularize the solution of the problem, previousmethods have focused on designing good priors for natural images, such as sparse representation, or directly learning the priors from a large data set with models, such as deep neural networks. In this paper, we argue that domain expertise from the conventional sparse coding model can be combined with the key ingredients of deep learning to achieve further improved results. We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data. The network has a cascaded structure, which boosts the SR performance for both ﬁxed and incremental scaling factors. The proposed training and testing schemes can be extended for robust handling of images with additional degradation, such as noise and blurring. A subjective assessment is conducted and analyzed in order to thoroughly evaluate various SR techniques. Our proposed model is tested on a wide range of images, and it signiﬁcantly outperforms the existing state-of-the-art methods for various scaling factors both quantitatively and perceptually. Index Terms—Image super-resolution, deep neural networks, sparse coding.
Discovering dispatching rules using data mining
Authors: XIAONAN LI AND SIGURDUR OLAFSSON
Date: 2005-08-01 00:00:00
Proceeding: journal 2005 8
Tasks: Scheduling

This paper introduces a novel methodology for generating scheduling rules using a data-driven approach.
We show how to use data mining to discover previously unknown dispatching rules by applying the learning
algorithms directly to production data. This approach involves preprocessing of historic scheduling data into
an appropriate data file, discovery of key scheduling concepts, and representation of the data mining results
in a way that enables its use for job scheduling. We also consider how by using this new approach unexpected
knowledge and insights can be obtained, in a manner that would not be possible if an explicit model of the
system or the basic scheduling rules had to be obtained beforehand. All of our results are illustrated via
numerical examples and experiments on simulated data
Referring Image Segmentation by Generative Adversarial Learning
Authors: Shuang Qiu, Yao Zhao, Jianbo Jiao, Yunchao Wei, Shikui Wei
Date: 2020-04-20 00:00:00
Proceeding: IEEE 2020 4
Tasks: Image Segmentation, Referring Expression, Referring Expression Segmentation, Segmentation, Semantic Segmentation, Sentence

Referring expression is a kind of language expression being used for referring to particular objects. In this paper, we focus on the problem of image segmentation from natural language referring expressions. Existing works tackle this problem by augmenting the convolutional semantic segmentation networks with an LSTM sentence encoder, which is optimized by a pixel-wise classification loss. We argue that the distribution similarity between the inference and ground truth plays an important role in referring image segmentation. Therefore we introduce a complementary loss considering the consistency between the two distributions. To this end, we propose to train the referring image segmentation model in a generative adversarial fashion, which well addresses the distribution similarity problem. In particular, the proposed adversarial semantic guidance network (ASGN) includes the following advantages: a) more detailed visual information is incorporated by the detail enhancement; b) semantic information counteracts the word embedding impact; c) the proposed adversarial learning approach relieves the distribution inconsistencies. Experimental results on four standard datasets show significant improvements over all the compared baseline models, demonstrating the effectiveness of our method