Reviewerly/exHarmony huggingface dataset:
Case Studies in the Socio-technical Analysis of Cybersecurity Incidents: Comparing Attacks on the UK NHS and Irish Healthcare Systems

Cybersecurity in health care is a complex socio-technical problem. In a critical infrastructure context, like hospitals, the risks of cyberthreats do not just result from technical vulnerabilities alone but also from the degradation of working practices over time. This paper argues that organizational, operational vulnerabilities, and governance structures create a pressing need for systematic socio-technical risk analysis for cybersecurity of healthcare organizations. Yet current risk analysis methodologies are not designed to detect these kinds of systemic risks. We address these problems by the use of System-Theoretic Accident Modeling Process (STAMP). In the first case study—WannaCry cyberincident on UK National Health Service (NHS)—we applied the STAMP method to identify socio-technical factors related to the incident. Our analysis shows that the STAMP-based control taxonomies tend to be generic, which provides expressive power, but also makes them hard to apply to the specific circumstances of a cyberincident. We have, therefore, integrated the US National Institute of Science and Technology (NIST) control taxonomies to provide the level of detail required to identify potential mitigations for the control failures identified using the STAMP approach. After WannaCry, governments around the world have adopted national strategies to reduce future risks. However, ransomware threats have continued to emerge, including an attack on the Irish healthcare systems, our second case study. Our results show that the integration of a more detailed taxonomy to support the higher level STAMP analysis can increase consistency between analysts and enables direct comparisons to be made between similar incidents.
A Preventative Moving Target Defense Solution for Web Servers Using Iptables

Web servers are targets for cyberattacks because they contain valuable information, which could facilitate interactions with another system or damage an organization’s reputation. In the last two decades, Moving Target Defense (MTD) research has gained attention as a cyber resilient technique to mitigate cyber threats. However, most MTD work focuses on the network layer, and there is not much work to support the service layer. This research is an experimental evaluation of Dynamic Application Rotational Environment (DARE) and Dare IMproved (DIM). DIM is an enhanced version of DARE that leverages a host-based firewall to rotate between web servers located on the same host. The main contribution of this work is furthering the understanding of implementing a centralized host-based MTD architecture for web servers. Results show that DIM can maintain availability while thwarting attacks, whereas DARE limits the availability of the web server.
Exploring the MITRE ATT&amp;CK® Matrix in SE Education

Cybersecurity is a multidisciplinary field that requires understanding of human behavior. To reinforce this idea and encourage non-technical students to participate in cybersecurity, an experiential learning project was implemented in an upper-level undergraduate criminal justice class. This paper is focused on that proof-of-concept class project in which groups of students mapped a social engineering case study onto the MITRE ATT&CK framework to understand the adversarial mindset. The paper provides background information on the ATT&CK framework, compares groups’ mappings to others within the class as well as against a mapping done by an ATT&CK representative, and it offers a discussion on the lessons learned and opportunities to expand our application and understanding of educational cybersecurity principles. This paper emphasizes that while someone with more knowledge and experience using a framework that focuses on the technical aspects of cybersecurity may map a SE case study differently than multidisciplinary students who are experiencing it for the first time, there is not a single correct way to interpret and correspondingly defend adversary behaviors. Having students experience this mapping project allows them to understand the breakdown of an adversary’s behavior and contextualize key tactics and techniques in a way that fits their perspective and skillset. This paper also demonstrates how a SE case study can be mapped onto the ATT&CK framework despite SE not being the focus of the framework, and that SE uses tactics and techniques that are also prevalent within more technical cyber campaigns. The authors hope to encourage more interdisciplinary cybersecurity education by sharing this experiential learning course project.
Mathematical Error Propositional Logic

Each chapter should be preceded by an abstract (10–15 lines long) that summarizes the content. The abstract will appear online at www.SpringerLink.com and be available with unrestricted access. This allows unregistered users to read the abstract as a teaser for the complete chapter. As a general rule the abstracts will not appear in the printed version of your book unless it is the style of your particular book or that of the series to which your book belongs. Please use the ‘starred’ version of the new Springer abstract command for typesetting the text of the online abstracts (cf. source file of this chapter template abstract) and include them with the source files of your manuscript. Use the plain abstract command if the abstract is also to appear in the printed version of the book.
It's Getting Hot in Here: Assessing Thermal Responses of Different Clothing Ensembles for Wheelchair Users Using Thermal Camera

When spending extensive amounts of time sitting in a wheelchair, heat can build up where the body is in contact with the chair. A thermal camera can be used to access the impact that this can have and help determine solutions to create more thermally comfortable options for these users. This change could allow an individual to feel more comfortable in their environment and, in turn, give them more confidence. The purpose of this research was to investigate heat generated by an individual in a wheelchair wearing different clothing ensembles using a thermal camera. Testing resulted in the conclusion that different material types and combinations will have a significant impact on the heat generated by the user and therefore transferred to the back of the wheelchair. This study indicates that there are opportunities for research to help the community create a better understanding of how heat affects wheelchair users.&nbsp
The Application of iFLYTEK FiF Speaking Training System in Language Learning

The Artificial Intelligence has aroused tremendous and profound transforms in contemporary education. An increasing number of college students are adopting the Artificial Intelligence to conduct individualized learning. In English learning, the speaking ability of college students needs to be further improved and spoken language teaching should be attached great significance.This paper focuses on the application of iFLYTECK Speaking Training System in college English class. Based on intelligent speech and artificial intelligent technologies, this iFLYTECK Speaking Training System provides a new direction to improve the listening and speaking ability of college students.
Portrait Sketch Synthesis via Mish-Gated U-net and GANs

GANs structure. However, those methods based on GANs did not get a better effect, their results usually have obvious quality problems such as serious distortion and visual artifacts. In this paper, we propose a novel portrait sketch synthesis approach based on the mish-gated U-net (MGUnet) and GANs to address the above problems. The MGUnet is an improvement on the classical U-net, which utilizes mish activation function to build a new gating module, and embeds the module in U-net to enhance the synthesis performance of the neural network. The proposed method can effectively eliminate artifacts and enhance the effect of synthesized images. The results of our experiments demonstrate that our method can convert satisfactory portrait sketches for portrait sketch synthesis.
Leveraging GCN and User Friendship for Next POI Recommendation

The next POI (point of interest) recommendation aims to explore the behaviour patterns from users’ historical check-in records and recommend the next location. However, solving the data sparsity problem and improving the model recommendation performance are still considerable challenges in POI recommendations. This paper proposes a GFUC model consisting of the GCN (Graph Convolutional Networks) and FUC (Friendship of User Context) modules. The GCN module integrates weighted graph convolutional networks to obtain the best representation of users and POIs. The FUC module divides user check-in records with multiple fine-grain manners. Then it incorporates the user friendship and context information, significantly improving the model’s performance while solving the data sparsity problem. More specifically, experimental results show that our proposed GFUC model improves the performance of POI recommendations by more than 10% on both Yelp and Gowalla datasets with the evaluation metrics [email protected]
Prompt-based Few-shot Learning for Table-based Fact Verification

Natural language processing has been a hot topic of research, but existing research is mainly limited to unstructured information such as natural language sentences and documents, and less research has been done on structured information such as tables. The main object of this paper is the table-based fact verification task, under which there is only one TABFACT dataset. Most of the existing methods on this dataset are based on pre-trained models and need to be fine-tuned again if a new dataset appears. And some previous work on natural language sentences has shown that prompt approach can achieve good performance with few samples. Therefore, in this paper, we adopt the prompt approach for experiments on the table fact detection task by manually designing templates for hinting the pre-trained model. Meanwhile, to enhance the generalization of the model, we introduce a multi-pair mapping relationship in the Answer Engineering phase. Experiments on the TABFACT dataset show that using the prompt method for table-based fact verification task in the case of few samples can be effective, providing a new way for optimizing table-related tasks in the case of few samples.
Answering questions over tables based on TAPAS and graph attention model

Question answering over tables is seen as a semantic parsing task in which the question is translated to a logical form (e.g. an SQL statement) that can be executed against the table to retrieve the answer. However, the generated logical form is only an intermediate step in retrieving the answer, and collecting such logical form is time and money consuming, and requires some SQL syntax for the worker annotating the dataset. To address these issues, some models have been proposed to directly answer questions over tables without generating logical forms. TAPAS encodes table as input by extending BERT’s architecture, pre-trains on millions of table-text pairs extracted from Wikipedia, and is used in the downstream tasks of question answering over tables. However, in the downstream task of question answering over tables, TAPAS only uses two fully connected layers to select table cells and a corresponding aggregate operator, which we believe does not fully exploit the capability of the TAPAS encoder. According to the characteristics of question answering over tables, We propose a graph attention model to utilize and fuse feature vectors extracted from TAPAS encoder. Our experimental results on the WikiSQL dataset show that the accuracy of the TAPAS-GAT containing TAPAS encoder and graph attention model is 87.0%, which exceeds the accuracy of the original TAPAS containing TAPAS encoder and two fully connected layers. In addition, the TAPAS is limited to the text length of the input table question pair, typically 512 tokens, which leads to the inability of TAPAS to handle larger tables. We propose a method to reduce the size of the table by extracting the columns in the table that are relevant to the question, and we call such a preprocessing operation as a snapshot. After taking a snapshot of the table, the accuracy of our model on WIkiSQL dataset rises to 89.8%.
Optimization of Feature Extraction in Indonesian Speech Recognition Using PCA and SVM Classification

Speech recognition is a technology application that communicates with machines by identifying the speaker's words. The main processes in speech recognition are pre-processing, feature extraction and classification. The feature extraction method is one of the most studied sections in speech recognition research because it plays an important role in obtaining optimal features. MFCC has become a popular feature extraction method widely applied to speech recognition systems. In addition, the IMFCC, GFCC, BFCC, and LFCC methods have also begun to be widely applied. Each method can model some information and, by doing a combination, is considered able to represent more information. The feature extraction combination system generates more feature data, and optimization is done to reduce the feature coefficient. This paper discusses testing machine learning models on each feature extraction method and a combination of feature extraction methods to determine the best level of accuracy. Furthermore, this paper also discusses the effect of using the PCA method, which is applied to a combination of feature extraction methods in reducing the number of features to find out the best number of features. Support vector machine (SVM) is proposed as a classification process in this research. The voice sample consisted of 800 in the form of words spoken in Indonesian, namely the numbers zero to nine (0–9). The results showed that combining feature extraction methods resulted in better accuracy than individual methods. The combined GFCC+LFCC method produces the best accuracy of 99.38% while using independent methods produces the best accuracy of 99.38% using the GFCC method. Applying the PCA method to GFCC+LFCC can reduce feature dimensions to 16 coefficients while maintaining the highest level of accuracy. This paper is expected to be a reference in further speech recognition research.
Sentiment Analysis of Airline Ticket and Hotel Booking of Traveloka Using Support Vector Machine

Digital information technology has penetrated most parts of the world, especially customer service. Internet support has made systems running in human life into digitalized activities, including, in this case, booking airline tickets and hotel bookings. One application that has been developing since 2012 is Traveloka. This study utilizes sentiment analysis to assess this application review via Twitter. The method used in this sentiment analysis is a support vector machine (SVM). This research aims to find the confusion matrix in the model resulting from the review of this application. These results are then used to create desktop-based applications. The evaluation results of the model show the accuracy, precision, recall, and F1-Score of 0.75, 0.67, 1, and 0.8, respectively.
Comparison Between Deep Learning and Parametric Methods in Target Imaging from Narrowband Radar Data

Data interpolation techniques via parametric method and deep learning network are investigated to enhance range resolution on images generated from complex radar data collected at narrow bandwidths. A mathematical framework for the parametric method is presented and the very deep super-resolution (VDSR) network developed for real electro-optical data is adapted to deal with complex data. The techniques are applied to data collected on a canonical target at 80 and 120 MHz bandwidths that comprise, respectively, 3 and 4 frequency samples over the range of aspect angles that span from -5 degrees to 95 degrees. Composite images generated from the interpolated data of both techniques show their practicality and usefulness to unravel a target body where images from the original data failed. By comparing results from both interpolated methods, it is demonstrated that the parametric signal processing approach is still relevant in this era of deep learning and may be best suited in domains where data is complex, the samples are very limited in frequency and the target body exhibits complex features.
Load Forecasting for Data Centers Based on Time Series

With the booming development of the data center industry around the world, the power load forecasting of the data center plays an important role. In this paper, based on wavelet analysis and time series-based power load forecasting methods, data center power load forecasting is carried out. This paper uses the monthly power load data of a data center to carry out medium and long-term load forecasting, and achieves a good forecasting effect. This paper also uses daily load data to carry out short-term load forecasting, and uses a combination of one-step forecasting and time series-based methods for forecasting to achieve better forecasting results.
Learning Agile, Vision-Based Drone Flight: From Simulation to Reality

We present our latest research in learning deep sensorimotor policies for agile, vision-based quadrotor flight. We show methodologies for the successful transfer of such policies from simulation to the real world. In addition, we discuss the open research questions that still need to be answered to improve the agility and robustness of autonomous drones toward human-pilot performance.
Teaching Responsible Computing in Context

Recent news and national reports have significantly increased interest in new approaches for teaching responsible computing to help students understand, evaluate, and address the social impact of existing and emerging computing technologies. This 3-hour workshop will be offered in two workshop format sessions: in-person and online. The first part of each session will introduce responsible computing and its connections to RESPECT and Cultural Competence in Computing (3C). Next, we will provide a short overview of our own work in teaching responsible computing along with frameworks, tools, and best practices. We will showcase four different approaches to teaching responsible computing across institutional settings (high school, college, university), interdisciplinary partnerships (computing, philosophy, STS, digital humanities), and instructional formats (dedicated courses, embedded lessons, design challenges, bootcamps). The workshop presentations will focus on practical advice about how to get started, available resources, securing support from administration and colleagues, and other considerations for this work. In the second half of the workshop, participants will work in small groups to co-design potential lessons based on shared topical interests, institutional settings, and/or learning objectives. Facilitators will provide guidance, recommendations, and classroom examples to help the small groups to complete draft lessons that will be disseminated among workshop participants and on the workshop website. A laptop or internet connected device is needed to participate in the small group activity. Handouts/materials will be provided on the workshop website.
Providing Students with Standardized, Cloud-Based Programming Environments at Term's Start (for Free)

For CS50 at Harvard, we have long provided students with a standardized programming environment, to avoid start-of-term technical difficulties that might otherwise arise if students had to install and configure compilers, interpreters, and debuggers on their own Macs and PCs. (For many students, "hello, world" is challenge enough on day 0, without also encountering "command not found" at the same time!) We originally provided students with shell accounts on a university-managed cluster of systems. We then transitioned to a cloud-based equivalent so as to manage the systems ourselves, root access and all. We transitioned thereafter to client-side virtual machines, to scale to more students and enable GUI-based assignments. We have since transitioned to web-based environments, complete with code tabs, terminal windows, and file explorers, initially implemented atop AWS Cloud9 and now, most recently, GitHub Codespaces, an implementation of Visual Studio (VS) Code in the cloud, free for teachers and students alike. In this workshop, we'll discuss the pedagogical and technological advantages and disadvantages of every approach and focus most of our time, hands-on, on using and configuring GitHub Codespaces itself for teaching and learning. Along the way, attendees will learn how to create their own Docker images and "devcontainers" for their own classes and any languages they teach. Attendees will learn what is possible educationally by writing their own VS Code extensions as well. And how, at term's end, to "offboard" students to VS Code itself on their own Macs and PCs, so as to continue programming independent of Codespaces.
RecursionVisualizer

Dynamic Programming (DP) is one of the most difficult algorithm techniques for undergraduate computer science students to master. RecursionVisualizer is an open-source Python package that seeks to improve how students learn DP. With one line of code, RecursionVisualizer enables users to create beautiful, interactive animations of any DP problem. This addresses specific misunderstandings students have about DP and makes it easier for both educators to teach DP and for students to learn about DP. This paper describes the features of RecursionVisualizer, its educational benefits and various uses. Source code, documentation, and examples can be found at https://ez2rok.github.io/recursion-visualizer.
Source Academy

The Source Academy is a community-built immersive online environment for learning computing with the book Structure and Interpretation of Computer Programs (SICP). An interactive version of the textbook is integrated into the system, and the programming environment of the Source Academy allows learners to focus on SICP-specific sublanguages of JavaScript (Python and Scheme versions in preparation). The environment includes tools to support SICP's mental models for computational processes and data, including a stepper that animates the substitution model of Chapter 1, a data visualizer that supports the box-and-pointer diagrams of Chapter 2, and a visualizer for the environment model of Chapter 3.
A Framework to Develop Automatic Speech Recognition for Low Resource Languages

Current Automatic Speech Recognition (ASR) systems, like Google Assistant, Apple's Siri, or Amazon's Alexa, continue to only support a small number of languages (English, Mandarin, Arabic, etc.), primarily those spoken in developed nations with abundant resources. While these languages have been able to reap the benefits of having such technology at their disposal, places like Ethiopia, and Vietnam are still far behind. This work represents a global collaboration to create a framework for customizing ASR systems for low resource languages (LRLs), or languages with limited human and financial resources. This paper describes the methodology for using an existing application (Kaldi) to implement an ASR system for two such languages, Amharic and Vietnamese, with the least amount of annotated speech. The languages are chosen to leverage available student expertise and create cross-cultural connections. The objective of the research is to create a procedure by which, given enough training records and annotation, any language can be added.
Mozart Musical Dice Game

A Musikalisches Würfelspiel (musical dice game) is a process for generating music by concatenating a random sequence of precomposed musical fragments. In this programming assignment, students "compose" a Viennese waltz in the style of Mozart by playing a sequence of 32 sound files that are chosen at random (from a library of 272) according to the results of throwing dice. It is a captivating demonstration not only of the power of randomness but also of how much can be accomplished by combining a simple idea with a tiny bit of code.