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 Carlos Soares
Luis Torgo (Eds.)
LNAI 12986

Discovery Science
24th International Conference, DS 2021
Halifax, NS, Canada, October 11–13, 2021
Proceedings

123
Lecture Notes in Artificial Intelligence 12986

Subseries of Lecture Notes in Computer Science

Series Editors
Randy Goebel
University of Alberta, Edmonton, Canada
Yuzuru Tanaka
Hokkaido University, Sapporo, Japan
Wolfgang Wahlster
DFKI and Saarland University, Saarbrücken, Germany

Founding Editor
Jörg Siekmann
DFKI and Saarland University, Saarbrücken, Germany
More information about this subseries at http://www.springer.com/series/1244
Carlos Soares · Luis Torgo (Eds.)

Discovery Science
24th International Conference, DS 2021
Halifax, NS, Canada, October 11–13, 2021
Proceedings
Editors
Carlos Soares Luis Torgo
Universidade do Porto and Fraunhofer Dalhousie University
Portugal AICOS Halifax, NS, Canada
Porto, Portugal

ISSN 0302-9743 ISSN 1611-3349 (electronic)

Lecture Notes in Artificial Intelligence
ISBN 978-3-030-88941-8 ISBN 978-3-030-88942-5 (eBook)
https://doi.org/10.1007/978-3-030-88942-5
LNCS Sublibrary: SL7 – Artificial Intelligence

© Springer Nature Switzerland AG 2021

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the
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broadcasting, reproduction on microfilms or in any other physical way, and transmission or information
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The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
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 Preface

This volume contains the papers selected for presentation at the 24th International
Conference on Discovery Science (DS 2021), which was organized to be held in
Halifax, Canada, during October 11–13, 2021. Due to the restrictions associated with
the COVID-19 pandemic, the conference was moved online and held virtually over the
same time period.
DS is a conference series that started in 1986. Held every year, DS continues its
tradition as the unique venue for the latest advances in the development and analysis
of methods for discovering scientific knowledge, coming from machine learning, data
mining, and intelligent data analysis, along with their application in various scientific
domains. In particular, major areas selected for DS 2021 included the folllowing: appli-
cations (including a relevant number of papers addressing the COVID-19 pandemic),
classification, data streams, feature selection, graph and network mining, neural net-
works and deep learning, preferences, recommender systems, representation learning,
responsible artificial intelligence, and spatial, temporal and spatiotemporal data.
DS 2021 received 76 international submissions that were carefully reviewed by three
or more Program Committee (PC) members or external reviewers, with a few exceptions.
After a rigorous reviewing process, 15 regular papers and 21 short papers were accepted
for presentation at the conference and publication in the DS 2021 volume.
We would like to sincerely thank all people who helped this volume come into being
and made DS 2021 a successful and exciting event. In particular, we would like to express
our appreciation for the work of the DS 2021 PC members and external reviewers who
helped assure the high standard of accepted papers. We would like to thank all authors
of submissions to DS 2021, without whose work it would not have been possible to have
such high-quality contributions in the conference.
We are grateful to the Steering Committee chair, Sašo Džeroski, for his extraordinary
support in critical decisions concerning the event plan, particularly important in these
challenging times. We are also very grateful to the Program Committee chairs of DS
2020, Annalisa Appice and Grigorios Tsoumakas, for all the information provided, which
made the whole organization much easier. We wish to express our thanks to the local
organization chairs, David Langstroth, Nuno Moniz, Paula Branco, Vitor Cerqueira, and
Yassine Baghoussi, for their support and incredible work. We would like to express
our deepest gratitude to all those who served as organizers, session chairs, and hosts,
who made great efforts to meet the online challenge to make the virtual conference
a real success. Finally, our thanks are due to Alfred Hofmann and Anna Kramer of
Springer for their continuous support and work on the proceedings. We are grateful
to Springer for a special issue on Discovery Science to be published in the Machine
Learning journal. All authors were given the possibility to extend and rework versions
of their papers presented at DS 2021 for a chance to be published in this prestigious
journal. For DS 2021, Springer also supported a Best Student Paper Award, which was
given to Bart Bussmann and his co-authors, Jannes Nys and Steven Latré, for their paper
“Neural Additive Vector Autoregression Models for Causal Discovery in Time Series.”
vi Preface

This paper presents high quality work on a very relevant topic and is complemented
with the materials to reproduce it. We would like to congratulate the authors for this
achievement.

September 2021 Carlos Soares

Luis Torgo
 Organization

Program Committee Chairs

Carlos Soares Universidade do Porto and Fraunhofer Portugal AICOS,
Portugal
Luis Torgo Dalhousie University, Canada

Program Committee

Martin Atzmueller Tilburg University, The Netherlands

Colin Bellinger National Research Council of Canada, Canada
Paula Branco Ottawa University, Canada
Alberto Cano Virginia Commonwealth University, USA
Daniel Castro Silva University of Porto, Portugal
Michelangelo Ceci University of Bari Aldo Moro, Italy
Victor Cerqueira Dalhousie University, Canada
Bruno Cremilleux University of Caen Normandy, France
Nicola Di Mauro University of Bari Aldo Moro, Italy
Ivica Dimitrovski Ss. Cyril and Methodius University in Skopje, North
Macedonia
Wouter Duivesteijn Eindhoven University of Technology, The Netherlands
Sašo Džeroski Jožef Stefan Institute, Slovenia
Johannes Fürnkranz Johannes Kepler University Linz, Austria
Mohamed Gaber Birmingham City University, UK
Dragan Gamberger Rudjer Bošković Institute, Croatia
Manco Giuseppe Italian National Research Council, Italy
Kouichi Hirata Kyushu Institute of Technology, Japan
Dino Ienco Irstea, France
Nathalie Japkowicz American University, USA
Stefan Kramer Johannes Gutenberg University Mainz, Germany
Vincenzo Lagani Ilia State University, Georgia
Pedro Larranaga University of Madrid, Spain
Nada Lavrač Jožef Stefan Institute, Slovenia
Tomislav Lipic Rudjer Bošković Institute, Croatia
Gjorgji Madjarov Ss. Cyril and Methodius University in Skopje, North
Macedonia
Rafael Gomes Mantovani Federal University of Technology – Paraná, Brazil
Elio Masciari Institute for High Performance Computing and
Networking, Italy
Nuno Moniz University of Porto, Portugal
Anna Monreale University of Pisa, Italy
viii Organization

Catarina Oliveira Portucalense University, Portugal

Sageev Oore Dalhousie University and Vector Institute, Canada
Rita P. Ribeiro University of Porto, Portugal
George Papakostas International Hellenic University, Greece
Ruggero G. Pensa University of Turin, Italy
Pedro Pereira Rodrigues University of Porto, Portugal
Bernhard Pfahringer University of Waikato, New Zealand
Gianvito Pio University of Bari Aldo Moro, Italy
Pascal Poncelet LIRMM Montpellier, France
Jan Ramon Inria, France
André L. D. Rossi Universidade Estadual Paulista, Brazil
Kazumi Saito University of Shizuoka, Japan
Tomislav Smuc Rudjer Bošković Institute, Croatia
Marina Sokolova University of Ottawa and Dalhousie University, Canada
Jerzy Stefanowski Poznan University of Technology, Poland
Luis Filipe Teixeira University of Porto, Portugal
Herna Viktor University of Ottawa, Canada
Baghoussi Yassine University of Porto, Portugal
Albrecht Zimmermann University of Caen Normandy, France

Additional Reviewers

Ana Mestrovic João Alves

Daniela Santos Leonardo Ferreira
Emanuel Guberovic Miha Keber
Fabrizio Lo Scudo Narjes Davari
Francesca Pratesi
Nima Dehmamy
Francesco S. Pisani
Francesco Spinnato Nunziato Cassavia
Isabel Rio-Torto Paolo Mignone
Ivan Grubisic Rafaela Leal
João Almeida Roberto Interdonato
 Contents

Applications

Automated Grading of Exam Responses: An Extensive Classification

Benchmark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Jimmy Ljungman, Vanessa Lislevand, John Pavlopoulos,
Alexandra Farazouli, Zed Lee, Panagiotis Papapetrou, and Uno Fors

Automatic Human-Like Detection of Code Smells . . . . . . . . . . . . . . . . . . . . . . . . . 19

Chitsutha Soomlek, Jan N. van Rijn, and Marcello M. Bonsangue

HTML-LSTM: Information Extraction from HTML Tables in Web Pages

Using Tree-Structured LSTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Kazuki Kawamura and Akihiro Yamamoto

Predicting Reach to Find Persuadable Customers: Improving Uplift

Models for Churn Prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Théo Verhelst, Jeevan Shrestha, Denis Mercier,
Jean-Christophe Dewitte, and Gianluca Bontempi

Classification

A Semi-supervised Framework for Misinformation Detection . . . . . . . . . . . . . . . . 57

Yueyang Liu, Zois Boukouvalas, and Nathalie Japkowicz

An Analysis of Performance Metrics for Imbalanced Classification . . . . . . . . . . . 67

Jean-Gabriel Gaudreault, Paula Branco, and João Gama

Combining Predictions Under Uncertainty: The Case of Random Decision

Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
Florian Busch, Moritz Kulessa, Eneldo Loza Mencía, and Hendrik Blockeel

Shapley-Value Data Valuation for Semi-supervised Learning . . . . . . . . . . . . . . . . . 94

Christie Courtnage and Evgueni Smirnov

Data Streams

A Network Intrusion Detection System for Concept Drifting Network

Traffic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Giuseppina Andresini, Annalisa Appice, Corrado Loglisci,
Vincenzo Belvedere, Domenico Redavid, and Donato Malerba
x Contents

Incremental k-Nearest Neighbors Using Reservoir Sampling for Data

Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Maroua Bahri and Albert Bifet

Statistical Analysis of Pairwise Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

Georg Krempl, Daniel Kottke, and Tuan Pham

Graph and Network Mining

FHA: Fast Heuristic Attack Against Graph Convolutional Networks . . . . . . . . . . 151

Haoxi Zhan and Xiaobing Pei

Ranking Structured Objects with Graph Neural Networks . . . . . . . . . . . . . . . . . . . 166

Clemens Damke and Eyke Hüllermeier

Machine Learning for COVID-19

Knowledge Discovery of the Delays Experienced in Reporting COVID-19

Confirmed Positive Cases Using Time to Event Models . . . . . . . . . . . . . . . . . . . . . 183
Aleksandar Novakovic, Adele H. Marshall, and Carolyn McGregor

Multi-scale Sentiment Analysis of Location-Enriched COVID-19 Arabic

Social Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
Tarek Elsaka, Imad Afyouni, Ibrahim Abaker Targio Hashem,
and Zaher AL-Aghbari

Prioritization of COVID-19-Related Literature via Unsupervised

Keyphrase Extraction and Document Representation Learning . . . . . . . . . . . . . . . 204
Blaž Škrlj, Marko Jukič, Nika Eržen, Senja Pollak, and Nada Lavrač

Sentiment Nowcasting During the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . 218

Ioanna Miliou, John Pavlopoulos, and Panagiotis Papapetrou

Neural Networks and Deep Learning

A Sentence-Level Hierarchical BERT Model for Document Classification

with Limited Labelled Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
Jinghui Lu, Maeve Henchion, Ivan Bacher, and Brian Mac Namee

Calibrated Resampling for Imbalanced and Long-Tails in Deep Learning . . . . . . 242

Colin Bellinger, Roberto Corizzo, and Nathalie Japkowicz

Consensus Based Vertically Partitioned Multi-layer Perceptrons for Edge

Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
Haimonti Dutta, Saurabh Amarnath Mahindre, and Nitin Nataraj
 Contents xi

Controlling BigGAN Image Generation with a Segmentation Network . . . . . . . . 268

Aman Jaiswal, Harpreet Singh Sodhi, Mohamed Muzamil H,
Rajveen Singh Chandhok, Sageev Oore, and Chandramouli Shama Sastry

GANs for Tabular Healthcare Data Generation: A Review on Utility

and Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
João Coutinho-Almeida, Pedro Pereira Rodrigues,
and Ricardo João Cruz-Correia

Preferences and Recommender Systems

An Ensemble Hypergraph Learning Framework for Recommendation . . . . . . . . . 295

Alireza Gharahighehi, Celine Vens, and Konstantinos Pliakos

KATRec: Knowledge Aware aTtentive Sequential Recommendations . . . . . . . . . 305

Mehrnaz Amjadi, Seyed Danial Mohseni Taheri, and Theja Tulabandhula

Representation Learning and Feature Selection

Elliptical Ordinal Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

Aïssatou Diallo and Johannes Fürnkranz

Unsupervised Feature Ranking via Attribute Networks . . . . . . . . . . . . . . . . . . . . . . 334

Urh Primožič, Blaž Škrlj, Sašo Džeroski, and Matej Petković

Responsible Artificial Intelligence

Deriving a Single Interpretable Model by Merging Tree-Based Classifiers . . . . . 347

Valerio Bonsignori, Riccardo Guidotti, and Anna Monreale

Ensemble of Counterfactual Explainers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358

Riccardo Guidotti and Salvatore Ruggieri

Learning Time Series Counterfactuals via Latent Space Representations . . . . . . . 369

Zhendong Wang, Isak Samsten, Rami Mochaourab,
and Panagiotis Papapetrou

Leveraging Grad-CAM to Improve the Accuracy of Network Intrusion

Detection Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385
Francesco Paolo Caforio, Giuseppina Andresini, Gennaro Vessio,
Annalisa Appice, and Donato Malerba

Local Interpretable Classifier Explanations with Self-generated Semantic

Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401
Fabrizio Angiulli, Fabio Fassetti, and Simona Nisticò
xii Contents

Privacy Risk Assessment of Individual Psychometric Profiles . . . . . . . . . . . . . . . . 411

Giacomo Mariani, Anna Monreale, and Francesca Naretto

The Case for Latent Variable Vs Deep Learning Methods in Misinformation

Detection: An Application to COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422
Caitlin Moroney, Evan Crothers, Sudip Mittal, Anupam Joshi,
Tülay Adalı, Christine Mallinson, Nathalie Japkowicz,
and Zois Boukouvalas

Spatial, Temporal and Spatiotemporal Data

Local Exceptionality Detection in Time Series Using Subgroup Discovery:

An Approach Exemplified on Team Interaction Data . . . . . . . . . . . . . . . . . . . . . . . . 435
Dan Hudson, Travis J. Wiltshire, and Martin Atzmueller

Neural Additive Vector Autoregression Models for Causal Discovery

in Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446
Bart Bussmann, Jannes Nys, and Steven Latré

Spatially-Aware Autoencoders for Detecting Contextual Anomalies

in Geo-Distributed Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461
Roberto Corizzo, Michelangelo Ceci, Gianvito Pio, Paolo Mignone,
and Nathalie Japkowicz

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473

Applications
 Automated Grading of Exam Responses:
An Extensive Classification Benchmark

Jimmy Ljungman(B) , Vanessa Lislevand, John Pavlopoulos,

Alexandra Farazouli, Zed Lee, Panagiotis Papapetrou, and Uno Fors

Department of Computer and Systems Sciences,

Stockholm University, Stockholm, Sweden
{jimmy.ljungman,lislevand,ioannis,alexandra.farazouli,zed.lee,
panagiotis,uno}@dsv.su.se

Abstract. Automated grading of free-text exam responses is a very

challenging task due to the complex nature of the problem, such as lack
of training data and biased ground-truth of the graders. In this paper, we
focus on the automated grading of free-text responses. We formulate the
problem as a binary classification problem of two class labels: low- and
high-grade. We present a benchmark on four machine learning meth-
ods using three experiment protocols on two real-world datasets, one
from Cyber-crime exams in Arabic and one from Data Mining exams in
English that is presented first time in this work. By providing various
metrics for binary classification and answer ranking, we illustrate the
benefits and drawbacks of the benchmarked methods. Our results sug-
gest that standard models with individual word representations can in
some cases achieve competitive predictive performance against deep neu-
ral language models using context-based representations on both binary
classification and answer ranking for free-text response grading tasks.
Lastly, we discuss the pedagogical implications of our findings by iden-
tifying potential pitfalls and challenges when building predictive models
for such tasks.

Keywords: Automated grading · Answer grading · Natural language

processing · Machine learning

1 Introduction
The assessment of students’ knowledge and understanding in academic courses
plays a crucial role in effective teaching and usually takes place in the form
of distance or on-the-spot formal examinations. Exams are typically composed
of different types of questions, such as multiple-choice, true/false, and free-text
questions. Free-text questions, in particular, have benefits in terms of students’
learning as students are required to recall external knowledge, and freely and
concisely elaborate on a subject [14].

Supported by the AutoGrade project of Stockholm University.

c Springer Nature Switzerland AG 2021
C. Soares and L. Torgo (Eds.): DS 2021, LNAI 12986, pp. 3–18, 2021.
https://doi.org/10.1007/978-3-030-88942-5_1
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for anything except a dog-fight, but they flops down in their chairs
at the front of the stage and acts like they meant business.
Scenery recovers his sawed-off shotgun and sets down on the
corner of the stage, where he can watch them disgrunted husbands.
Me and Dirty follows Magpie to a place he’s got partitioned off for
a dressing-room. Through the curtain we can hear Yaller Rock
County beginning to come in. Me and Dirty are just sober enough to
kinda be indifferent to death or taxation.
Magpie gives us our costumes, which consists of cowhide pants
with a tail tied on, and a jigger made like a cap, with yearlin’ calf
horns sticking out the side. He also gives us each a little whistle
made of a willer.
“Where’s the shirt?” asks Dirty.
“Fauns don’t wear shirts.”
“What do you wear, Magpie?”
Magpie holds up a mountain-lion skin and a breech-clout. Dirty
looks things over and then says to Magpie:
“If you escape, Magpie, will yuh do me a favor? In my cabin—in a
old trunk, is a suit of clothes. I paid sixteen dollars for it the year
Bryan run for free silver, but I never wore it. Will yuh see that they
lays me out in it? Lawd knows I don’t want to be buried in a outfit
like this.”
From outside we hears “Fog-horn” Foster’s voice—
“We-e-e-ll, come on, you mockin’-birds!”
“The house must be full,” opines Magpie, fastening his lionskin.
“Full of hootch and ⸺” sighs Dirty, sliding into his cow skins.
“I’m goin’ to die like a ⸺ cow, I know that.”
“My gosh!” grunts Magpie. “I’ve plumb forgot we ain’t got no
announcer since the judge quit. Ike, will you do the announcin’?”
“Then I won’t have to dance?”
“Sure you’ll have to dance, but all you’ve got to do, Ike, is to tell
’em what is comin’ next. The first thing on the program is a solo
dance, which is knowed as ‘The Gatherin’ Storm,’ by Mrs. Smith; and
then she gets assisted by the five ‘Raindrops,’ consistin’ of Mrs. Holt,
Mrs. Tilton, Mrs. Steele, Mrs. Gonyer and Mrs. Wheeler. Mrs. Smith is
doin’ the solo in place of the departed champeen dancer of the
world. Will yuh do this for me, Ike?”
“Do it for Magpie,” urges Dirty. “Do anythin’ to get it over.”

I went on to the stage, and I got the shock of my life. Them

females are out there, and I’m a danged liar if they ain’t undressed
about as much as possible. I takes one look and staggers for the
curtain. I hears one of them women bust out in a “haw! haw!” as I
went past, but I never stopped to think that I wasn’t wearing any
more than the law allows.
I steps out through the curtain and looks around. Never did the
old hall hold as many folks. Fog-horn Foster and Half-Mile Smith are
settin’ in the front row, across the aisle from each other. They stares
at me for a moment; then both gets up like they was walking in their
sleep, steps for the aisle and bumps together.
Fog-horn hit Half-Mile and Half-Mile hit the floor, after which Fog-
Horn went right on up the aisle. Half-Mile got up, looks at me again,
and follers Fog-Horn, but he ain’t tryin’ to catch Fog-Horn—he’s tryin’
to go past him.
“My ⸺” gasps “Cinch” Culler, lookin’ wild-like around. “Won’t
somebody please hold me? I won’t be responsible⸺”
“Ladies and gents,” says I. “I’m out here to let yuh know what’s
comin’ off.”
“Wait a minute,” says Abe Mudgett, standing up. “I’ve got my two
sisters here with me, and if anything more’s comin’ off⸺”
“Set down!” squeaks Scenery, waving his shotgun at Abe, and Abe
sets down.
“Now,” says I, “I’m out here to announce that the first thing on
the program is Mrs. Smith. She’s goin’ to imitate a storm comin’ up,
and then Mrs. Holt, Mrs. Tilton, Mrs. Wheeler, Mrs. Steele and Mrs.
Gonyer are goin’ to show yuh what raindrops look like. This here
⸺”
“Haw! Haw! Haw!” roars Pete Gonyer, but his laugh don’t show
that he’s tickled so awful much.
 “Haw! Haw! Haw! Mrs. Smith is goin’ to imitate— Haw! Haw!
Haw!”
“Haw! Haw!” howls Wick. “My wife looks as much like a storm as
yours does like a raindrop, Pete.”
“My wife,” states the judge, standing up, “my wife ain’t goin’ to do
no ⸺ fool thing of the kind. I’ll show her⸺”
“Set down!” yelps Scenery. “Set down, you old Blackstone blatter!
This is once when you don’t hand down no decisions.”
“Git off the stage and let ’er rain!” howls Telescope Tolliver. “I’ll
see it through if I have to wear a slicker.”
“Ready for us to play?” asks Bill Thatcher, kicking Frenchy to wake
him up.
“Use your own judgment, Bill,” says I. “I’ve done all I can, and
now I’m goin’ to let nature take her course.”
I starts to step back through the curtain, when “Polecat” Perkins
yells—
“Ike, I was wrong—you’re only half-cow.”
I gets back inside. Them women are all scared plumb stiff, but
Mrs. Smith wheezes—
“Ladies, we’ve made our bluff—let ’er go!”
Just then Bill Thatcher’s instrument begins to wail and wail,
shutting off all chances for Frenchy Deschamps to be heard.
“Sweet Marie!” howls Mrs. Smith. “Gee cripes, don’t he never
learn a new tune?”
I ducks out of sight and the curtain slides back.

If Mrs. Smith knew anything about dancing she forgot every step.
She trots out on the stage and starts something like Kid Carson used
to call “shadow-boxing.” Then she turns around about three times,
stubs her toe and falls down. Standing in a line across the stage is
the rest of them females, with their hands up in the air like they was
being held up by somebody with a gun.
“A-arabellie!” wails Wick. “My ⸺, woman, git out of sight!”
Mrs. Smith gets to her feet and yelps back at Wick:
 “Git out of sight yourself—if you don’t like it! I’ll teach you to flirt
with a dancer. Start the music over again, Bill.”
“Em-m-m-i-lee!” shrieks Sam Holt. “Ain’tcha got no modesty? Go
put on your shoes and socks!”
Bill Thatcher starts squealing on his instrument again, and Mrs.
Smith starts doing some fancy steps.
Wow! Here comes Judge Steele, Art Wheeler, Pete Gonyer,
Testament Tilton, Wick Smith and Sam Holt, climbing right over the
top of folks.
“Git ba-a-a-ck!” squeaks Seenery, waving his shotgun. “Stop it!
Whoa, Blaze!”
“Look at the wild man!” howls somebody, and here comes Magpie
across the stage hopping high and handsome.
“Stop ’em, Scenery!” whoops Magpie. “Dog-gone ’em, they can’t
bust up my show!”
Man, I’ll tell all my grandchildren this tale. Them outraged
husbands came up on that stage, while Yaller Rock County yelled
itself hoarse and made bets on whether it would be an odd or even
number of deaths. Magpie hit Pete in the neck and Pete lit with one
leg on each side of Bill Thatcher’s head. Wick Smith got hold of his
wife and them two started a tug of war.
Me and old Sam Holt got to waltzing around and around, which
wasn’t a-tall pleasant, being as I’m barefooted and Sam ain’t. I seen
Mrs. Wheeler and Art locked in mortal combat, and just then I hears
Dirty Shirt Jones yelp—
“Heavy, heavy hangs over your head—”
I whirls just in time to see what’s coming, but I can’t escape. Dirty
Shirt has turned the atmosphere loose. Them four he-sheep—four
ungentlemanly woollies, with corkscrew horns, are buck-jumping
across that stage, seeking what they may hit. I swung around to
meet the attack, and I reckon the leading sheep hit him a dead
center, ’cause I felt the shock plumb to me.
Maybe it hit Sam a little low, because it knocked all four of our
feet off the floor, and the next in line picked us in the air and stood
us on our heads.
 I seen Wick Smith, braced against the edge of the stage, trying to
pull his wife over the edge, the same of which is a invitation to a
sheep, and the old ram accepted right on the spot. Mrs. Smith
grunted audibly and shot into Wick’s arms. Scenery Sims starts to
skip across the stage, but a ram outsmarted him, and I seen
Scenery turn over gracefully in the air and shoot, regardless, with
both barrels of that sawed-off shotgun.
Them load of shot hived up in the chandelier, the same of which
cut off our visible supply of light.
I heard the crashing of glass, and I figures that the hallway is too
crowded for some of the audience. I lays still, being wise, until the
noise subsides, and the crowd has escaped. Then I moves slowly to
my hands and knees. I feels a hand feeling of my legs, and then a
hand taps gently on my horned cap.
“I—I thought,” whispers old Sam’s voice kinda quavering-like, “I—
I thought they was all old ones, but a sheep’s a sheep to me.”
Bam! Something landed on my head, and I seen more bright
lights than there is in a million dollars worth of skyrockets. Then
things kinda clear up, and I hears old Sam saying to himself:
“Well, I killed one of the ⸺ things. If I go carefully⸺”
I can dimly see old Sam sneaking for the front of the stage. I’m
mad. I got up and sneaked right after him. No man can mistake me
for a sheep and get away with it. I jumps for old Sam’s back, and
just then he seems to kinda drop away from me. I reckon he forgot
about the five-feet drop from the stage, and I know danged well I
did. I reckon I sort of lit on my head and shoulders on top of
somebody. There comes a squeak from Bill Thatcher’s instrument,
and then all is quiet.

I wriggled loose and starts to get up, but a strong hand grabs me
by the ankle, yanks me off my feet, and I hit my head on a chair. I
kinda remember being dragged down them stairs, and then I feels
my carcass being dragged over rough ground. It was a long, hard
trip, and I reckon I lost about all the skin on the upper half of my
body. Finally I bumps over a step, gets yanked inside on to a carpet,
and then I hears a voice very dimly—
“Sweetheart, I brought thee home.”
Then a light is lit, and I sees Mrs. Smith putting the chimney on a
lamp. Without turning she says—
“I reckon you’ll confine your love to me after this, eh?”
Then she turns and looks at me, setting there on the floor with
my back propped up against a chair. I looks around. Just inside the
door, sitting on the floor, is Wick. Mrs. Smith looks at me and then at
him. Then she wipes her lips and stares at Wick.
“Sweetheart, eh?” grunts Wick, getting to his feet. “Arabellie, ain’t
you got no shame? Dancin’ up there without nothing on to speak of,
and then you has the gall to bring your sweetheart home with yuh.”
“Did—did—didn’t I—bring you home, Wicksie?”
“You—know—danged—well—you—didn’t. I always knowed you
was kinda sweet on Ike Harper.”
“On that!” She actually yelped, and pointed her finger at me.
“Sweet on him?”
I gets to my feet, but my legs ain’t very strong. I says:
“Lemme a-alone. I don’t want no man’s wife’s love—especially one
what hauls me home by the ankle. When I git married I want a
clingin’ vine—not a pile driver.”
I never did have much sense. A feller in my condition ought to
keep his mouth shut and sneak away soft-like. I turns my head
toward the door, and just then the weight of the world hit me from
behind, and it was a lucky thing for that house that the door was
open.
I landed on my hands and knees in the yard, with all the wind
knocked out of my system. Wick has got some rose-bushes in his
yard. Like a animal wounded unto death, I reckon I tried to crawl
around on my hands and knees to find a spot to die in.
All to once I sees one of them ⸺ sheep. It’s only a short
distance from me. I know if I move it’s going to hit me sure as ⸺
so I remains still. I’ll bet that me and the sheep never moved a
muscle for fifteen minutes.
Then all at once the sheep spoke.
“For ⸺’s sake, if you’re goin’ to butt—butt and have it over
with!”
I got to my feet.
“Get up, Dirty Shirt Jones,” says I. “What kind of a way is that to
act?”
Dirty weaves to his feet and stumbles over to me.
“Ike, thank the Lord, we’re alive!”
“Don’t presume too much. Medical science says that a man can
live after losin’ a certain amount of skin, but I’m bettin’ I’ve passed
that certain limit. Let’s sneak home and save what life we’ve got
left.”
We sneaked around the Mint Hall and Wick’s store, and at the
corner we stumbles into somebody.
“Who goes there?” asks Dirty.
“Go ⸺!” wails Magpie Simpkins. “Help me, will yuh? I wrastled
all the way down here with one of them ⸺ sheep and now I’m
afraid to let loose.”
“You and your ⸺ atmosphere!” groans Dirty.
“I’m settin’ on it,” wails Magpie, “I’ve got a kink in my neck. Will
yuh hold it down until I can get up?”
Just then a voice from under him starts singing very soft and low
—
“There’s a la-a-a-nd that is fairer than this⸺”
Magpie gets to his feet and takes a deep breath.
“Testament,” says he, “what made yuh blat like a sheep?”
But Testament’s mind is not dwelling on sheep—not the kind of
sheep that Magpie meant.
Then the three of us starts limping toward home.
“Mebbe,” says Magpie, kinda painful-like, “mebbe we progressed
too fast. Piperock don’t appreciate it, gents, but this night the old
town jumped ahead at least fifty years.”
 “Jumpin’,” says Dirty, reflective-like, “Jumpin’ don’t hurt nobody,
but, holy hen-hawks, it sure does hurt to jump that far and light so
hard.”
We pilgrims along, everybody trying hard to make their legs track.
Finally Magpie says—
“Personally, I think that interpretive dancin’ has anythin’ skinned I
ever seen.”
“Me too,” says I, “and parts I never have seen.”

Transcriber’s Note: This story appeared in the April 30, 1922 issue
of Adventure Magazine.
*** END OF THE PROJECT GUTENBERG EBOOK TOO MUCH
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