Computer Sciences Commons^™

An International Comparison Of K-12 Computer Science Education Intended And Enacted Curricula, Katrina Falkner, Sue Sentance, Rebecca Vivian, Sarah Barksdale, Leonard Busuttil, Elizabeth Cole, Christine Liebe, Francesco Maiorana, Monica M. Mcgill, Keith Quille

Conference Papers

This paper presents an international study of K-12 Computer Science implementation across Australia, England, Ireland, Italy, Malta, Scotland and the United States. We present findings from a pilot study, comparing CS curriculum requirements (intended curriculum) captured through country reports, with what surveyed teachers (n=244) identify as enacting in their classroom (the enacted curriculum). We address the extent that teachers are implementing the intended curriculum as enacted curriculum, exploring specifically country differences in terms of programming languages and CS topics implemented. Our findings highlight the similarities and differences of intended and enacted CS curriculum within and across countries and the value …

An International Benchmark Study Of K-12 Computer Science Education In Schools, Katrina Falkner, Sue Sentance, Rebecca Vivian, Sarah Barksdale, Leonard Busuttil, Elizabeth Cole, Christine Liebe, Francesco Maiorana, Monica M. Mcgill, Keith Quille

Conference Papers

There has been a growing interest and increase in work shared about national K-12 Computer Science Education (CSED) curriculum and implementation efforts around the world. Much of this work focuses on curriculum analysis, country reports, experience reports and case studies. The K-12 CSED community would benefit from an international strategic effort to compare, contrast and monitor K-12 CSED over time, across multiple countries and regions, to understand pedagogy, practice, resources and experiences from the perspective of teachers working in classrooms. Furthermore, there is a need for validated and robust instruments that can support comparable investigations into the current state of …

Multi-Person Tracking By Multi-Scale Detection In Basketball Scenarios, Adria Arbués-Sanguesa, Gloria Haro, Coloma Ballester

Session 1: Active Vision, Tracking, Motion Analysis

Tracking data is a powerful tool for basketball teams in order to extract advanced semantic information and statistics that might lead to a performance boost. However, multi-person tracking is a challenging task to solve in single-camera video sequences, given the frequent occlusions and cluttering that occur in a restricted scenario. In this paper, a novel multi-scale detection method is presented, which is later used to extract geometric and content features, resulting in a multi-person video tracking system. Having built a dataset from scratch together with its ground truth (more than 10k bounding boxes), standard metrics are evaluated, obtaining notable results …

On The Mental Workload Assessment Of Uplift Mapping Representations In Linked Data, Ademar Crotti Junior, Christophe Debruyne, Luca Longo, Declan O'Sullivan

Conference papers

Self-reporting procedures have been largely employed in literature to measure the mental workload experienced by users when executing a specific task. This research proposes the adoption of these mental workload assessment techniques to the task of creating uplift mappings in Linked Data. A user study has been performed to compare the mental workload of “manually” creating such mappings, using a formal mapping language and a text editor, to the use of a visual representation, based on the block metaphor, that generate these mappings. Two subjective mental workload instruments, namely the NASA Task Load Index and the Workload Profile, were applied …

The Use Of Deep Learning Distributed Representations In The Identification Of Abusive Text, Susan Mckeever, Hao Chen, Sarah Jane Delany

Conference papers

The selection of optimal feature representations is a critical step in the use of machine learning in text classification. Traditional features (e.g. bag of words and n-grams) have dominated for decades, but in the past five years, the use of learned distributed representations has become increasingly common. In this paper, we summarise and present a categorisation of the stateof-the-art distributed representation techniques, including word and sentence embedding models. We carry out an empirical analysis of the performance of the various feature representations using the scenario of detecting abusive comments. We compare classification accuracies across a range of off-the-shelf embedding models …

Image-Based Malware Classification: A Space Filling Curve Approach, Stephen O Shaughnessy

Conference Papers

Anti-virus (AV) software is effective at distinguishing between benign and malicious programs yet lack the ability to effectively classify malware into their respective family classes. AV vendors receive considerably large volumes of malicious programs daily and so classification is crucial to quickly identify variants of existing malware that would otherwise have to be manually examined. This paper proposes a novel method of visualizing and classifying malware using Space-Filling Curves (SFC's) in order to improve the limitations of AV tools. The classification models produced were evaluated on previously unseen samples and showed promising results, with precision, recall and accuracy scores of …

Noise Reduction In Eeg Signals Using Convolutional Autoencoding Techniques, Conor Hanrahan

Dissertations

The presence of noise in electroencephalography (EEG) signals can significantly reduce the accuracy of the analysis of the signal. This study assesses to what extent stacked autoencoders designed using one-dimensional convolutional neural network layers can reduce noise in EEG signals. The EEG signals, obtained from 81 people, were processed by a two-layer one-dimensional convolutional autoencoder (CAE), whom performed 3 independent button pressing tasks. The signal-to-noise ratios (SNRs) of the signals before and after processing were calculated and the distributions of the SNRs were compared. The performance of the model was compared to noise reduction performance of Principal Component Analysis, with …

Ucc/Bdcat Tutorial Chairs’ Welcome, Yan Tang, Tamara Matthews

Other resources

The call for tutorials at UCC'19 and BDCAT'19 attracted submissions from Australia and Europe. The tutorial chairs reviewed and accepted two revised tutorials, and decided to award a third spot on a FCFS basis to ensure that conference attendees have access to learning resources across all conference topics.

A Guide To A Successful Industry-Academia Collaboration, Hublinked Consortium

Reports

This report, developed by the HubLinked Consortium, aims at determining what works best when higher education institutions work with industry on software innovation. The range of potential mechanisms for U-I linkages is extensive and they differ in effectiveness. Many of them are examined in the following pages under different sections. This report tries to identify the most efficient ways for HEIs and companies to engage in different types of collaborations as well as identify different needs, obstacles, enablers, preferences and perceptions that CS faculties and industry hold. This research enables a better understanding of the dynamics of U-I linkages in …

Csinc: An Inclusive K-12 Outreach Model, Karen Nolan, Roisin Faherty, Keith Quille, Brett Becker, Susan Bergin

Conference Papers

This poster describes the early development of a K-12 outreach model, named CSinc, to promote CS in Ireland. It has already been piloted with over 4500 K-12 students in its first year. At the heart of the model is a two-hour camp that incorporates an on-site school delivery. Schools from all over Ireland self-selected to participate, including male only, female only and mixed schools. The no-cost nature of the model meant a range of schools participated from officially designated "disadvantaged" to private fee-paying. During the initial deployment over 2500 pre- and post- surveys have been collected. This data will allow …

An International Study Piloting The Measuring Teacher Enacted Computing Curriculum (Metrecc) Instrument, Katrina Falkner, Sue Sentance, Rebecca Vivian, Sarah Barksdale, Leonard Busuttil, Elizabeth Cole, Christine Liebe, Francesco Maiorana, Monica M. Mcgill, Keith Quille

Conference Papers

As the discipline of K-12 computer science (CS) education evolves, international comparisons of curriculum and teaching provide valuable information for policymakers and educators. Previous academic analyses of K-12 CS intended and enacted curriculum has been conducted via curriculum analyses, country reports, experience reports, and case studies, with K-12 CS comparisons distinctly lacking teacher input.

This report presents the process of an international Working Group to develop, pilot, review and test validity and reliability of the MEasuring TeacheR Enacted Computing Curriculum (METRECC) instrument to survey teachers in K-12 schools about their implementation of CS curriculum to understand pedagogy, practice, resources and …

A Deep Recurrent Q Network Towards Self-Adapting Distributed Microservice Architecture, Basel Magableh, Muder Almiani

Articles

One desired aspect of microservice architecture is the ability to self-adapt its own architecture and behavior in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements distributed microservice architecture model running a swarm cluster, as informed by the Monitor, Analyze, Plan, and Execute over a shared Knowledge (MAPE-K) model. The proposed architecture employs multiadaptation agents supported by a centralized controller, which can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-learning network (DRQN). It is argued that such integration between DRQN …

Presentations & Infographics, Quality Blended Learning Consortium

Open Educational Resources

This tutorial introduces the users to the fundamental concepts on how to create presentations and infographics, as well as provides guidance on the tools to be used, best practices, and mistakes to avoid. The tutorial includes quizzes for self-assessment.

High-Speed Distributed Data Process Of Photometric Astronomical Data, Paul Doyle

Other

Since the 1970s the CCD has been the principle method of measuring flux to calculate the apparent magnitude of celestial objects within astronomical photometry. Each CCD image must be digitally cleaned and calibrated prior to its use. As data archives increase in size to Petabytes, the data processing challenge requires image processing techniques to continue to exceed the rate of data capture.

This paper describes NIMBUS, a rapidly scalable, failure resilient distributed network architecture capable of processing CCD image data at a rate of hundreds of Terabytes per day. NIMBUS is implemented using a decentralized web queue to control the …

Analysing The Impact Of Machine Learning To Model Subjective Mental Workload: A Case Study In Third-Level Education, Karim Moustafa, Luca Longo

Conference papers

Mental workload measurement is a complex multidisciplinary research area that includes both the theoretical and practical development of models. These models are aimed at aggregating those factors, believed to shape mental workload, and their interaction, for the purpose of human performance prediction. In the literature, models are mainly theory-driven: their distinct development has been influenced by the beliefs and intuitions of individual scholars in the disciplines of Psychology and Human Factors. This work presents a novel research that aims at reversing this tendency. Specifically, it employs a selection of learning techniques, borrowed from machine learning, to induce models of mental …

Test: A Terminology Extraction System For Technology Related Terms, Murhaf Hossari, Soumyabrata Dev, John Kelleher

Conference papers

Tracking developments in the highly dynamic data-technology landscape are vital to keeping up with novel technologies and tools, in the various areas of Artificial Intelligence (AI). However, It is difficult to keep track of all the relevant technology keywords. In this paper, we propose a novel system that addresses this problem. This tool is used to automatically detect the existence of new technologies and tools in text, and extract terms used to describe these new technologies. The extracted new terms can be logged as new AI technologies as they are found on-the-fly in the web. It can be subsequently classified …

Intelligent Intrusion Detection Using Radial Basis Function Neural Network, Alia Abughazleh, Muder Almiani, Basel Magableh, Abdul Razaque

Conference papers

Recently we witness a booming and ubiquity evolving of internet connectivity all over the world leading to dramatic amount of network activities and large amount of data and information transfer. Massive data transfer composes a fertile ground to hackers and intruders to launch cyber-attacks and various types of penetrations. As a consequence, researchers around the globe have devoted a large room for researches that can handle different types of attacks efficiently through building various types of intrusion detection systems capable to handle different types of attacks, known and unknown (novel) ones as well as have the capability to deal with …

Hierarchical Cluster Analysis: A New Type Of Ranking Criteria Based On Arwu Ranking Data, Zhengshuo Li

Dissertations

The advent of big data leads to many applications of Machine Learning techniques. University rankings is one of the applicable domains, which is currently playing a crucial role in the assessment of the universities' performance. Currently, the rankings are usually carried out by some authoritative ranking institutions by means of weighting techniques and the results are conveyed in numerical rankings. Three of the most famous university ranking institutions have been introduced from a technical perspective. However, these institutions have been proven to be subjective in relation to their data selection and weighting method.

Using Supervised Learning To Predict English Premier League Match Results From Starting Line-Up Player Data, Runzuo Yang

Dissertations

Soccer is one of the most popular sports around the world. Many people, whether they are a fan of a soccer team, a player of online soccer games or even the professional coach of a soccer team, will attempt to use some relevant data to predict the result of a match. Many of these kinds of prediction models are built based on data from the match itself, such as the overall number of shots, yellow or red cards, fouls committed, etc. of the home and away teams. However, this research attempted to predict soccer game results (win, draw or loss) …

Predicting Violent Crime Reports From Geospatial And Temporal Attributes Of Us 911 Emergency Call Data, Vincent Corcoran

Dissertations

The aim of this study is to create a model to predict which 911 calls will result in crime reports of a violent nature. Such a prediction model could be used by the police to prioritise calls which are most likely to lead to violent crime reports. The model will use geospatial and temporal attributes of the call to predict whether a crime report will be generated. To create this model, a dataset of characteristics relating to the neighbourhood where the 911 call originated will be created and combined with characteristics related to the time of the 911 call. Geospatial …

Enhancing Partially Labelled Data: Self Learning And Word Vectors In Natural Language Processing, Eamon Mcentee

Dissertations

There has been an explosion in unstructured text data in recent years with services like Twitter, Facebook and WhatsApp helping drive this growth. Many of these companies are facing pressure to monitor the content on their platforms and as such Natural Language Processing (NLP) techniques are more important than ever. There are many applications of NLP ranging from spam filtering, sentiment analysis of social media, automatic text summarisation and document classification.

Performance Comparison Of Hybrid Cnn-Svm And Cnn-Xgboost Models In Concrete Crack Detection, Sahana Thiyagarajan

Dissertations

Detection of cracks mainly has been a sort of essential step in visual inspection involved in construction engineering as it is the commonly used building material and cracks in them is an early sign of de-basement. It is hard to find cracks by a visual check for the massive structures. So, the development of crack detecting systems generally has been a critical issue. The utilization of contextual image processing in crack detection is constrained, as image data usually taken under real-world situations vary widely and also includes the complex modelling of cracks and the extraction of handcrafted features. Therefore the …

Hard: A Heterogeneity-Aware Replica Deletion For Hdfs, Hilmi Egemen Ciritoglu, John Murphy, Christina Thorpe

Articles

The Hadoop distributed fle system (HDFS) is responsible for storing very large datasets reliably on clusters of commodity machines. The HDFS takes advantage of replication to serve data requested by clients with high throughput. Data replication is a trade-of between better data availability and higher disk usage. Recent studies propose diferent data replication management frameworks that alter the replication factor of fles dynamically in response to the popularity of the data, keeping more replicas for in-demand data to enhance the overall performance of the system. When data gets less popular, these schemes reduce the replication factor, which changes the data …

Bigger Versus Similar: Selecting A Background Corpus For First Story Detection Based On Distributional Similarity, Fei Wang, Robert J. Ross, John D. Kelleher

Articles

The current state of the art for First Story Detection (FSD) are nearest neighbourbased models with traditional term vector representations; however, one challenge faced by FSD models is that the document representation is usually defined by the vocabulary and term frequency from a background corpus. Consequently, the ideal background corpus should arguably be both large-scale to ensure adequate term coverage, and similar to the target domain in terms of the language distribution. However, given these two factors cannot always be mutually satisfied, in this paper we examine whether the distributional similarity of common terms is more important than the scale …

Cs1: How Will They Do? How Can We Help? A Decade Of Research And Practice, Keith Quille, Susan Bergin

Articles

Background and Context: Computer Science attrition rates (in the western world) are very concerning, with a large number of students failing to progress each year. It is well acknowledged that a significant factor of this attrition, is the students’ difficulty to master the introductory programming module, often referred to as CS1.

Objective: The objective of this article is to describe the evolution of a prediction model named PreSS (Predict Student Success) over a 13-year period (2005–2018).

Method: This article ties together, the PreSS prediction model; pilot studies; a longitudinal, multi-institutional re-validation and replication …

Is There A Correlation Between Wikidata Revisions And Trending Hashtags On Twitter?, Paula Dooley

Dissertations

Twitter is a microblogging application used by its members to interact and stay socially connected by sharing instant messages called tweets that are up to 280 characters long. Within these tweets, users can add hashtags to relate the message to a topic that is shared among users. Wikidata is a central knowledge base of information relying on its members and machines bots to keeping its content up to date. The data is stored in a highly structured format with the added SPARQL protocol and RDF Query Language (SPARQL) endpoint to allow users to query its knowledge base.

Forecasting Anomalous Events And Performance Correlation Analysis In Event Data, Sonya Leech [Thesis]

Dissertations

Classical and Deep Learning methods are quite common approaches for anomaly detection. Extensive research has been conducted on single point anomalies. Collective anomalies that occur over a set of two or more durations are less likely to happen by chance than that of a single point anomaly. Being able to observe and predict these anomalous events may reduce the risk of a server’s performance. This paper presents a comparative analysis into time-series forecasting of collective anomalous events using two procedures. One is a classical SARIMA model and the other is a deep learning Long-Short Term Memory (LSTM) model. It then …

Comparing Procedural Content Generation Algorithms For Creating Levels In Video Games, Zina Monaghan

Dissertations

Procedural Content Generation (PCG) is used frequently in games to increase replayability by introducing variety to playghrough of a game and reduce development time by allowing complex game worlds to be developed by a smaller team over a more limited amount of time.

Detection Of Offensive Youtube Comments, A Performance Comparison Of Deep Learning Approaches, Priyam Bansal

Dissertations

Social media data is open, free and available in massive quantities. However, there is a significant limitation in making sense of this data because of its high volume, variety, uncertain veracity, velocity, value and variability. This work provides a comprehensive framework of text processing and analysis performed on YouTube comments having offensive and non-offensive contents.

YouTube is a platform where every age group of people logs in and finds the type of content that most appeals to them. Apart from this, a massive increase in the use of offensive language has been apparent. As there are massive volume of new …

The Role Of Previous Discourse In Identifying Public Textual Cyberbullying, Aurelia Power, Anthony Keane, Brian Nolan, Brian O'Neill

Articles

In this paper we investigate the contribution of previous discourse in identifying elements that are key to detecting public textual cyberbullying. Based on the analysis of our dataset, we first discuss the missing cyberbullying elements and the grammatical structures representative of discourse-dependent cyberbullying discourse. Then we identify four types of discourse dependent cyberbullying constructions: (1) fully inferable constructions, (2) personal marker and cyberbullying link inferable constructions, (3) dysphemistic element and cyberbullying link inferable constructions, and (4) dysphemistic element inferable constructions. Finally, we formalise a framework to resolve the missing cyberbullying elements that proposes several resolution algorithms. The resolution algorithms target …