Reviewerly/exHarmony huggingface dataset:
Analysis of Advances in Engineering Education Publications (2007-2020) to Examine Impact and Coverage of Topics

In this paper we present findings from analyses of articles published in Advances in Engineering Education (AEE) during the first 14 years (2007-2020) of its publication under the tenure of its founding editor. Our goal in this paper is two-fold: (1) to understand the impact of papers published in the journal as measured by citations, and (2) to understand the coverage of topics over time. To understand the impact and coverage of topics we qualitatively analyzed abstracts of all the articles published in the journal (N=242) and also used data mining techniques (text network mapping). In terms of impact, the topics that have been cited the most include curriculum development, use of technology for teaching and learning, teaching and learning strategies, and program development. We found that in keeping with the mission of the journal, papers largely reported evidence-driven program or curriculum development outcomes and papers published as part of special issues had the most impact as measured by citations.
Developing the CC2020 Knowledge Table and Tool to Globally Benchmark Computing Degrees

The ACM/IEEE-CS Computing Curricula 2020 report was published in March 2021. The report is global in its attention and covers all aspects of the computing disciplines that had ACM/IEEE approved curricula: Computer Engineering, Computer Science, Software Engineering, Information Systems, Information Technology and Cyber Security.The CC2020 Steering Committee analysed all the approved curricula in the six discipline areas and looked for any overlaps. For example, all the areas have some aspect of programming but not all at the same level. Using a thematic analysis methodology, a "Landscape of Computing Knowledge" table was then developed with six main topic areas which included 34 subsections. Each computing discipline specified a minimum and maximum value for each subsection for that discipline, suggesting an importance range within which most degree programs are likely to fall.The CC2020 project then developed online visualisations of the knowledge areas, where stakeholders can assess a degree program with the values required and then match it against any of the current approved curricula. This tool enables users to compare individual degrees with the ACM/IEEE curricula recommendations, to collect the structure and focus of computing degrees globally and compare computing degrees across institutions globally. It also enables industry to understand the different areas and contribute to curriculum development. This tool has now been used to gather structure and variation of degree programs globally.This paper will describe and reflect on the development of the Landscape of Computing Knowledge table, and the development of the visualization tool. Insights from the first workshops that have investigated how to use the tool and its utility will be reviewed.
Real time Vehicle Color Recognition on a budget: an investigation on the usage of CNN architectures

In this work, we consider the problem of vehicle color recognition and target scenarios with limited computational resources. Indeed, in real traffic monitoring systems running on the field, algorithms must be light in terms of inference time and memory, but also accurate and robust to the scene variability. We employ end-to-end Convolutional Neural Networks to investigate under which conditions the use of such methodologies– that are state-of-the-art in a multitude of vision-based tasks but often lead to a significant computational burden– can provide us a good compromise between efficiency and effectiveness. We reason on the structure and size of the networks, while monitoring the performance in terms of color classification accuracy and computational effort. We provide an extensive experimental analysis comparing the methods using a benchmark and a private dataset acquired on the field, with almost 20K of images covering a variety of scene conditions.
A Visual Surveillance System to Observe Realistic Road User Behavior for Improved Pedestrian and Cyclist Safety at Crossroads

Pedestrians and cyclists suffer the most serious injuries in traffic accidents. Existing Pedestrian Protection Systems and Road Safety Systems rely on an ideal model of pedestrian behavior and do not consider that people tend to take shortcuts, appear at unexpected places or can be distracted on the road, for example, by using a smartphone or wearing headphones. Collecting and analyzing realistic road user behavior is a crucial component to improve pedestrian and cyclist safety. However, such real-world data is still missing. To address this, we propose a visual surveillance system with two perpendicular partially overlapping fields of view, combined with a fully automated deep learning-based pipeline to process and collect video observations, detect and extract road user trajectories in real-world coordinates and estimate human attributes, such as age, gender, smartphone usage, etc. We demonstrate our prototype by deploying it in two locations in a European city.
Customer Behavior Recognition Adaptable for Changing Targets in Retail Environments

Recognizing Customer Behavior (CB) from videos of instore cameras is important to smart retail solutions. Because of possible changes in retail needs and environments, a high degree of adaptability to different target CBs is required for Customer Behavior Recognition (CBR) methods. Existing CBR methods are mainly machine learning based models due to their remarkable recognition accuracy. However, trained models are not reusable for different target CBs. Consequently, existing CBR methods are hard to adapt to different target CBs because the necessary recollecting data and retraining models. In this paper, we propose a CBR method that recognizes CBs by combinations of primitives, each of which represents an object’s motion or objects’ relationship. Since primitives can be reused in combinations for various CBs, the proposed method is easily adaptable to changed target CBs. Experiments on two datasets indicate the good adaptability and sufficient recognition accuracy of our method.
Review of the book of Kurashov Vladimir ignatievich «Knowledge of nature in interactions of scientific knowledge». Moscow: «KDU», 2021

The review is devoted to the book of the famous Kazan philosopher of science V.I. Kurashov. Cognition of nature in the interaction of scientific knowledge / V.I. Kurashov: Moscow: KDU, University book, 2021. 354 p. DOI: 10.31453/kdu.ru.91304.0146. As noted in the review, the work is devoted to the problems of cognition of nature in the system of modern ideas of natural science. The author of the monograph tries to take a fresh look at the interpretation of the phenomena of knowledge about nature in their systemic interconnectedness.
Optical microscopy image restoration and its application in strain measurement by digital image correlation method

As a direct and effective non-contact, full-field optical measurement method, digital image correlation (DIC) method has been widely used in 2D/3D displacement and strain measurement of materials and structures in various fields. Combined with advanced micro-scale speckle preparation technology, in-situ loading experiments can be carried out under the microscope, which allows micro-scale displacement and strain measurement by DIC. However, out-of-plane displacement is inevitable during the loading process. Due to the limited depth of field of the optical microscope, even a slight out-of-plane displacement can result in significant blurring of the speckle pattern. This will bring negative effects to the deformation measurement. In order to reduce the error caused by the image blurring, the blind deconvolution method was used in this paper to restore the blurred speckle image. Furthermore, Gaussian filtering was applied to solve the noise problem. The effect of the image restoration on the measurement accuracy of the DIC was analyzed quantitatively. A tensile experiment was carried out on the indium tin oxide (ITO) films. The experimental results showed that the error of the measured elastic modulus was reduced by 13.91%. The results indicate the measurement accuracy was improved effectively after image restoration.
Template-Based Universal Adversarial Perturbation for SAR Target Classification

With deep learning models been widely applied to SAR images interpretation, the adversarial robustness problem of models has drawn much attention. Due to the application in military fields, SAR image processing have extremely requirements on security. However, the popular optimized adversarial perturbation algorithms calculated the perturbation for one single image at a time, thus cannot be adapted to real-time adversarial scenarios. Also, the perturbation is designed in full digital space and lacks of sematic information of the targets. Faced with those problems, this paper proposes a SAR universal adversarial perturbation (SAR-UAP) for SAR images adversarial attacks based on template-based lightweight generative model without discriminator. The model has simple structure which consists of six convolutional layers and uses the fixed template as the input of the generator. Template-based UAP ensures that once training is finished, the disturbances remain unchanged in spite of the changing of images chosen to attack. Model performance was tested on the MSTAR dataset for eight classical CNNs. Compared with other GAN-based UAP methods, SAR-UAP gained slightly higher fooling rate which is over 85%, while the illustration showed SAR-UAP are much more target-focused. The transferability of perturbation was also verified.
Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

Link prediction, also known as Knowledge Graph Completion (KGC), is the common task in Knowledge Graphs (KGs) to predict missing connections between entities.Most existing methods focus on designing shallow, scalable models, which have less expressive than deep, multi-layer models.Furthermore, most operations like addition, matrix multiplications or factorization are handcrafted based on a few known relation patterns in several wellknown datasets, such as FB15k, WN18, etc.However, due to the diversity and complex nature of real-world data distribution, it is inherently difficult to preset all latent patterns.To address this issue, we propose KGE-ANS, a novel knowledge graph embedding framework for general link prediction tasks using automatic network search.KGE-ANS can learn a deep, multi-layer effective architecture to adapt to different datasets through neural architecture search.In addition, the general search space we designed is tailored for KG tasks.We perform extensive experiments on benchmark datasets and the dataset constructed in this paper.The results show that our KGE-ANS outperforms several state-of-the-art methods, especially on these datasets with complex relation patterns.
Kernel-Based Adaptive Filtering

This chapter explains the basic concepts of kernel-based methods, a widely used tool in machine learning. The idea is to present online parameter estimation of nonlinear models using kernel-based tools. The chapters aim is to introduce the kernel version of classical algorithms such as least mean square (LMS), recursive least squares (RLS), affine projection (AP), and set membership affine projection (SM-AP). In particular, we will discuss how to keep the dictionary of the kernel finite through a series of model reduction strategies. This way, all discussed kernel algorithms are tailored for online implementation.
First Contact: the Proper Category

We raise the ante, by explaining why the heuristic in Chapter 1 prima facie fails. The explanation requires some surgery theory, which will be important throughout the remainder of the book, and facts about lattices, from a variety of sources.
Adaptive Filtering on Graphs

The chapter discusses how to process data from irregular discrete domains, an emerging area called graph signal processing (GSP). Basically, the type of graph we deal with consists of a network with distributed vertices and weighted edges defining the neighborhood and the connections among the nodes. As such, the graph signals are collected in a vector whose entries represent the values of the signal nodes at a given time. A common issue related to GSP is the sampling problem, given the irregular structure of the data, where some sort of interpolation is possible whenever the graph signals are bandlimited or nearly bandlimited. These interpolations can be performed through the extension of the conventional adaptive filtering to signals distributed on graphs where there is no traditional data structure. The chapter presents the LMS, NLMS, and RLS algorithms for GSP along with their analyses and application to estimate bandlimited signals defined on graphs. In addition, the chapter presents a general framework for data-selective adaptive algorithms for GSP.
Evaluation of Sequential and Temporally Embedded Deep Learning Models for Health Outcome Prediction

Deep learning sequential models are increasingly being used to predict patients’ health outcomes by analyzing their medical histories. In this paper, we investigate the design decisions and challenges of using deep learning sequential models for predictive health modeling. Our results show that the most successful deep learning health models to date, called transformers, lack a mechanism to analyze the temporal characteristics of health records. To address this gap, we propose and evaluate a new model called DTTHRE: Decoder Transformer for Temporally Embedded Health Records Encoding. DTTHRE analyzes patients’ medical histories, including the elapsed time between visits. We also evaluate the performance of DTTHRE on a real-world medical dataset for two health outcomes: (1) diagnostic and (2) readmission prediction. DTTHRE successfully predicted patients’ final diagnosis (78.54 ± 0.22%) and readmission risk (99.91 ± 0.02%) with improved performance compared to existing deep learning sequential models in the literature. DTTHRE predicts the health outcome for each medical visit, which increases the training examples available from limited medical datasets with no additional training time.
Language Models for Deep Learning Programming: A Case Study with Keras

This chapter explores the application of language models to programming languages and our work in constructing a dataset for the task. More particularly, we focus on the Keras programming language, a popular framework for implementing Deep Learning experiments. Our original model KerasBERT has since been expanded by adding more data and re-training the language model. The original KerasBERT model was trained on two categories of Keras Code Examples and the Keras API reference. This chapter documents adding Keras GitHub Examples, Kaggle Notebook containing Keras Code, Medium articles describing how to use Keras, and StackOverflow questions regarding Keras. With these new data sources, we present new domain generalization analysis, as well as independent and identically distributed (i.i.d.) test set losses. We qualitatively evaluate how well KerasBERT learns the Keras Deep Learning framework through cloze test evaluation. We present miscellaneous properties of these cloze tests such as mask positioning and prompt paraphrasing. KerasBERT is an 80 million parameter RoBERTa model, which we compare to the Zero-Shot learning capability of the 6 billion parameter GPT-Neo model. We present a suite of cloze tests crafted from the Keras documentation to evaluate these models. We find some exciting completions that show KerasBERT is a promising direction for question answering and schema-free database querying. In this chapter, we document the reuse of KerasBERT and integration of additional data sources. We have tripled the size of the original data set and have identified five main sources of Keras information data. With these sources, we present new analyses of how models generalize to novel sources of data, such as a language model trained on Kaggle notebooks and tested on GitHub code. We conclude our work by discussing some future directions for KerasBERT and the development of language models for code documentation support.
Anomaly Detection and Debugging by Kernel Ridge Reconstruction

We developed an explainable reconstruction-based anomaly detection method called kernel ridge reconstruction (KRR). KRR replaces the non-linear decoder of an autoencoder (AE) with a linear regressor trained to reconstruct only normal data. Thanks to the linearity, its reconstruction can be written as the weighted sum of the normal training data, which prevents the reconstruction of anomalous data, and these weights help us to verify undesirable properties of the encoder and the training data. This linearity also reveals the desired property of the encoder for KRR to achieve high anomaly detection performance. We propose a training algorithm for the encoder to realize this property and an efficient data cleansing method. Our experiments on the MNIST, CIFAR10, and KDDCup99 datasets showed higher performance and lower computational cost of KRR than a recent reconstruction-based anomaly detection method (MemAE). The weights of the training data revealed that the AE’s encoder reconstructed anomalous data mainly composed of visually similar training data and our training algorithm prevented this problem. Our data cleansing method correctly removed harmful data from the training data and training on this cleaned dataset improved the performance of KRR.
Maritime Mathematics Course Renovation in Maritime Training Programs in New Zealand

Maritime Mathematics is a fundamental part of Maritime Training Programs, as it is deeply involved in maritime affairs, e.g. ship stability and navigation are the problems that can be solved by computing using mathematics. Standards of Training, Certification and Watchkeeping (STCW) requires the trainees to be assessed and deemed competent in maritime mathematics, a New Zealand Qualifications Authority (NZQA) Level 3 course with 15 credits is to assess for competency in maritime mathematics in the following six topics: Algebra, Mensuration, Graphs, Calculus, Geometry, Trigonometry. Until end of 2021, the program specific regulations allow open entry for domestic students and no mathematics access criteria for international students. This means that the students start their maritime mathematics class at varying levels of proficiency. However, all learning outcomes in six maritime mathematics topics must be achieved for a student to be deemed competent by completion of their training programs. A study is conducted to identify the gaps caused by the open entry policy, based on observation of interaction of teaching and learning with results in course final exam, to analyze the weakness of the current teaching contents and start Maritime Mathematics course renovation in maritime training programs in New Zealand. This study has contributions to Programme Change Application, which has been approved by NZQA at end of 2021, that Applicants must meet the following criteria for admission into the programme Applicants with no seagoing experience: 14 credits in either Mathematics (calculus, algebra or equivalent) and/or Physics. As the programme Applicants with seagoing experience still have no mathematics access criteria, this study has led to the production of a new Industry-oriented teaching strategy to the course development in 2022. And a formative test at the time of student enrolment to identify this group of students. thus, a pre-course session will be offered to the group of students who are lack of Mathematics foundation. Further study is to follow and enhance Maritime Mathematics course renovation in New Zealand.
Automated Environment for Individually Optimized Learning Experiences

This is a work-in-progress paper that describes research involving the development and use of technology to create social real-time learning experiences. This technology can provide rapid feedback between students and instructors, enabling the ability to make real-time data-driven decisions to automatically inform and individualize instruction. This paper discusses research being conducted in the following five supporting major areas: 1) Learning Assessment and Tracing, 2) Learning Outcome and Concept Dependencies, 3) Pre-Learning Intervention, 4) In-Process Learning Intervention, and 5) Future Learning Intervention using a learning environment called SlideSpace that has served as a living laboratory for experiments into human interactions.
Expanding the cybersecurity pipeline through early exposure in undergraduate programs

The cybersecurity field has exponentially grown in recent history, with little to no general understanding of the requirements for professionals in the area. In 2018, it was estimated that 3.5 million cybersecurity jobs would be unfilled by 2021 globally. Many students associate the cybersecurity field with computer programming and hacking, unaware of the societal importance of this career path and its connection to many majors unrelated to computer science or computer engineering. Previous work in cybersecurity education has focused on tools that aid students in understanding specific concepts. None has taken the approach of clarifying the cybersecurity profession in the form of a short-duration seminar series. With this goal, we propose an introductory seminar and a follow-on optional seminar series. The implementation of such a strategy early in undergraduate education may allow students to connect the cybersecurity career paths to one of their societal benefits. This series is designed to provide the student with direct practical examples and thought-provoking questions for the applications of security in their daily life, emphasizing multidisciplinary aspects of the field. This paper describes our experience with this seminar series at a large public university in the Fall of 2021 and Spring of 2022. We evaluate the effectiveness of the proposed approach by assessing its impact on students’ perceived awareness of cybersecurity careers and their interest in cybersecurity minors. Through this study, we observe that the seminar series resulted in an increase in student confidence regarding their understanding of the cybersecurity profession. The data also revealed an increased interest in the cybersecurity minors offered in our institution. Our implementation of this new seminar-based intervention has been limited to providing it as an extracurricular activity among many, therefore reaching only a small part of the first-year engineering students at the university. Still, the data indicates that such a seminar series is a viable instrument to make students aware of the opportunities in a cybersecurity career, its significant demand for professionals, and its potential for addressing societal problems. We make the seminar materials publicly available to facilitate the adoption of the proposed intervention at other institutions.