Reviewerly/exHarmony huggingface dataset:
CONVERSATIONAL MACHINES: CHATBOTS IN ENGINEERING EDUCATION

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 2450-2456ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.0630Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
ATTRIBUTES OF PHYSICAL COMPUTING SYSTEMS – THE PERSPECTIVE OF PEDAGOGY

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 5561-5565ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1309Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
DIGITAL TRANSFORMATION AS UNIVERSITY COURSE - DEVELOPMENT AND IMPLEMENTATION

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 7725-7729ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1799Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
An Empirical Investigation in Understanding the Recent Advancements in the Blockchain and Artificial Intelligence for Better Application in the Future

Artificial intelligence is refers as a simulation of the human intelligence through computer programmed that can mimic the action of human for making a system more efficient and adaptive to the realistic world. The collaboration of machine learning and AI has brought a huge development in the digital technology and business operation by implementing high level of coding and algorithm. With the help of AI, machine can learn new things automatically by creating and analysing patterns, that effective makes a work done faster. In this context, this research has shed light on the recent developments in AI and block chain technologies. This factor further may helpful for the business vertical in changing the way of doing work in the future.
Advanced Encryption Standard Algorithm with Optimal S-box and Automated Key Generation

Advanced Encryption Standard (AES) algorithm plays an important role in a data security application. In general S-box module in AES will give maximum confusion and diffusion measures during AES encryption and cause significant path delay overhead. In most cases, either L UTs or embedded memories are used for S- box computations which are vulnerable to attacks that pose a serious risk to real-world applications. In this paper, implementation of the composite field arithmetic-based Sub-bytes and inverse Sub-bytes operations in AES is done. The proposed work includes an efficient multiple round AES cryptosystem with higher-order transformation and composite field s-box formulation with some possible inner stage pipelining schemes which can be used for throughput rate enhancement along with path delay optimization. Finally, input biometric-driven key generation schemes are used for formulating the cipher key dynamically, which provides a higher degree of security for the computing devices.
Recommender System: Techniques, Comparison &amp; Solutions

There are several benefits of e-commerce websites that include cost effectiveness, convenience, flexibility, fast delivery, increase in income, etc. With these benefits, there is crucial role of e-commerce websites in business and users. However, e-commerce websites produce an overload of data, hence, Recommender Systems (RSs) provides a solution for the data overload problem. The present study, reviews different types of RSs and its pros and cons. Then, it does comparative study of different types of RSs. After the review, it&#x2019;s concluded that collaborating filtering technique used more than all other ones in e-commerce websites. There are problems with almost all techniques including the collaborative filtering technique too. However there is a novel model proposed that fixes the collaborative filtering technique of &#x2018;cold start&#x2019; at its best.
Electronic Radar Signal Recognition Based on Wavelet Transform and Convolutional Neural Network

In modern warfare, radar signals are becoming more and more complex. How to quickly and accurately obtain the category information of target tracks from various radar detection data with huge amount of data, and provide accurate and effective information for battlefield command is an urgent problem to be solved at present. With the development of wavelet theory, the application of wavelet transform is more and more extensive. In the training stage, the image is decomposed by stationary wavelet with scale 2 using the proposed algorithm, and then the high and low frequency components are respectively input into five designed residual networks for training. In the testing stage, inverse wavelet transform is used to obtain the final predicted image. Firstly, nine kinds of LPI radar signals are subjected to Choi-Williams distribution (CWD) time-frequency transformation to obtain two-dimensional time-frequency images. Then, the time-frequency images are denoised by denoised convolutional neural network. Finally, the images are sent to Incident-v4 network for feature extraction and classified by softmax classifier, so as to realize effective classification and identification of LPI radar signals. Convolution using a large number of analog datasets based on 4 typical LPI radar signals (Linear Frequency Modulation (LFM), Nonlinear Frequency Modulation (NLFM), Biphase Coded Signal (BPSK), COSTAS Frequency Coded signal) and white noise signals The neural network model is trained, and a small amount of measured signals (LFM, BPSK) are added as the verification set for adaptation, so as to better fit the detection model of the measured signals. Combining the classic and recent main techniques of radar signal processing, this paper discusses the value and potential of wavelet transform as a new and effective time-frequency analysis tool applied to wavelet transform and convolutional neural network in electronic radar signal recognition.
PartKG2Vec: Embedding of Partitioned Knowledge Graphs

Large-scale knowledge graphs with billions of nodes and edges are increasingly common in many domains. Such graphs often exceed the capacity of the systems storing the graphs in a centralized data store, not to mention the limits of today’s graph embedding systems. Unsupervised machine learning methods can be used for graph embedding, which can then be used for various machine learning tasks. State-of the art embedding techniques are often unable to achieve scalability without losing accuracy and efficiency. To overcome this, large knowledge graphs are frequently partitioned into multiple sub-graphs and placed in nodes of a distributed computing cluster. Graph embedding algorithms convert a graph into a vector space where the structure and the inherent property of the graph is preserved. Running such algorithms against these fragmented sub-graphs poses new challenges, such as maximizing the likelihood of preserving network neighborhood of nodes. Also, the learned embeddings of the individual graph partitions need to be merged into one overall embedding to maximize the likelihood of preserving network neighborhood of nodes. This paper introduces a novel method for embedding of partitioned knowledge graphs. It partitions the knowledge graph and executes learning algorithm in parallel on the partitions and merge their outputs to produce an overall embedding. Our evaluation demonstrates that the runtime performance is improved after partitioning of knowledge graph against complete knowledge graph and the quality of the embedding is like that of an embedding produced on the complete, unpartitioned graph.
Attentive Capsule Graph Neural Networks for Session-Based Recommendation

Considering recommendation scenarios in that user profiles are anonymous, the session-based recommendation is proposed to predict the items users are interested in from short sessions. However, most existing methods for session-based recommendation are insufficient to obtain diverse user interests. Meanwhile, they are susceptible to the negative impact of the cold start. To solve these problems, we propose a novel attentive capsule graph neural network for session-based recommendation (ACGNN) to mine more profound user preferences and minimize the impact of the cold start on the recommendation. In ACGNN, we model historical session sequences as graph-structured data and leverage graph neural networks to learn low-level item embedding to represent each session, which can capture complex transitions of items. Various low-level item embeddings are then aggregated into high-level item embeddings by an attentive capsule network, which significantly improves the expressiveness of the model. It can also enrich the features of cold-start users and items, which were difficult to be revealed by previous methods. Our experiments on two real-world datasets consistently show the superior performance of ACGNN over state-of-the-art methods.
Knowledge Structure-Aware Graph-Attention Networks for Knowledge Tracing

Knowledge Tracing (KT) aims to assess learners’ learning states and predict their performance based on prior interactions. However, most existing KT models depend on knowledge concepts instead of specific exercises, leading to the fine-grained information at the exercise level has been ignored, which may weaken the prediction performance of the models. We herein present Knowledge Structure-aware Graph-Attention Networks (KSGAN) for predicting learners’ performance, which uses improved Graph Attention Networks (GATs) to acquire effective exercise representations by taking full advantage of the knowledge structure between knowledge concepts and exercises. Additionally, a representation optimization is devised and integrated into the loss function to alleviate the sparsity of educational data and further improve the prediction performance. Finally, empirical validations on three open benchmark datasets show that our model well outperforms some state-of-the-art models in recent years. Remarkably, our model demonstrates superior prediction performance at exercise level compared to these previous models, without the additional information (e.g., exercise content, temporal information).
Deep User Multi-interest Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction is widely used in recommendation systems. Accurately modeling user interest is the key to improve the performance of CTR prediction task. Existing methods pay attention to model user interest from a single perspective to reflect user preferences, ignoring user different interests in different aspects, thus limiting the expressive ability of user interest. In this paper, we propose a novel Deep User Multi-Interest Network (DUMIN) which designs Self-Interest Extraction Network (SIEN) and User-User Interest Extraction Network (UIEN) to capture user different interests. First, SIEN uses attention mechanism and sequential network to focus on different parts in self-interest. Meanwhile, an auxiliary loss network is used to bring extra supervision for model training. Next, UIEN adopts multi-headed self-attention mechanism to learn a unified interest representation for each user who interacted with the candidate item. Then, attention mechanism is introduced to adaptively aggregate these interest representations to obtain user-user interest, which reflects the collaborative filtering information among users. Extensive experimental results on public real-world datasets show that proposed DUMIN outperforms various state-of-the-art methods.
Image Colorization: A Survey and Dataset

Image colorization is the process of estimating RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality. Deep learning techniques for image colorization have progressed notably over the last decade, calling the need for a systematic survey and benchmarking of these techniques. This article presents a comprehensive survey of recent state-of-the-art deep learning-based image colorization techniques, describing their fundamental block architectures, inputs, optimizers, loss functions, training protocols, and training data \textit{etc.} It categorizes the existing colorization techniques into seven classes and discusses important factors governing their performance, such as benchmark datasets and evaluation metrics. We highlight the limitations of existing datasets and introduce a new dataset specific to colorization. Using the existing datasets and our new one, we perform an extensive experimental evaluation of existing image colorization methods. Finally, we discuss the limitations of existing methods and recommend possible solutions as well as future research directions for this rapidly evolving topic of deep image colorization. Dataset and codes for evaluation are publicly available at https://github.com/saeed-anwar/ColorSurvey
DEVELOPING STUDENTS' DIGITAL COMPETENCIES - 21ST CENTURY TEACHING SKILLS: BASED ON SELF-ASSESSMENT OF HIGHER EDUCATION TEACHERS

DEVELOPING STUDENTS' DIGITAL COMPETENCIES - 21ST CENTURY TEACHING SKILLS: BASED ON SELF-ASSESSMENT OF HIGHER EDUCATION TEACHERS
A FOCUS GROUP STUDY ON TELEPRESENCE ROBOTS IN EDUCATION

1 University of Macedonia (GREECE)2 St Margaret College (MALTA)3 Danube University Krems (AUSTRIA)4 Karlsruhe Institute of Technology (GERMANY)
TEACHERS’ VIEW OF THE TEACHING PROFESSION AND ITS CHANGES IN CONNECTION WITH THE USE OF DIGITAL TECHNOLOGIES IN EDUCATION

TEACHERS’ VIEW OF THE TEACHING PROFESSION AND ITS CHANGES IN CONNECTION WITH THE USE OF DIGITAL TECHNOLOGIES IN EDUCATION
Multi-agent reinforcement learning for training and non-linear optimization

The field of Reinforcement Learning continues to show promise in solving old problems in new and innovative ways. Thanks to the algorithms’ ability to learn without an explicit set of labeled training data, the action, environment, reward approach has lured many researchers into framing old problems in this manner. This work focuses on utilizing a multi-agent reinforcement learning approach to develop an optimization algorithm which can be used for training gradient descent-based machine learning models. The work focuses on the agents’ abilities to collaboratively navigate a multi-dimension error space and locate an optimal solution for the model in training, and compares and contrasts the collaborative Reinforcement Learning approach with popular optimizers used today such as the Root Mean Square Propagation (RMSProp) [1] algorithm and the Adaptive Moment Estimation (Adam) algorithm [2]. The work will also discuss how the agents’ performance varies as the model in training gains complexity. Furthermore, the work will examine rare conditions under which the agents failed to find an optimal solution. Finally, the work will discuss similarities between machine learning model training and Multi-Domain Operations decision making. Additionally, the work will discuss how lessons learned during the agent training can be applied to developing decision agents for Multi Domain Operations.
Highly Efficient Implementation of Chaotic Systems Utilizing High-Level Synthesis Tools

Nowadays, the application of chaotic systems in digital image encryption received considerable attention. Since these systems have non-linear and complex structures, implementing chaotic systems on FPGA is encountered with design considerations. Designing these systems with hardware description languages such as Verilog and VHDL can lead to high cost and latency design. This paper presents a low-cost and high-performance approach for implementing chaotic systems on FPGA using high-level synthesis tools. First, we discretize the chaotic system with Forward Euler and Runge-Kutta’s fourth-order algorithm. Second, C++ codes with hardware aspects are written for these discretized models in the HLS compiler. Finally, we accelerate the design with exclusive directives in HLS and synthesize the design on target FPGA. In this design, we use a 32-bit fixed-point IQ-Math standard number (16I-I6Q) for data, and the target device is Xilinx Virtex-7 XC7VX330T. Proposed modules are implemented and synthesized utilizing Vivado HLS. The proposed implementation method reduces the latency, the number of Flip-Flops, and the number of Lookup tables with the ratio of 5.7, 13.48, and 8.14 on average, respectively in comparison with previous studies.
On the Evolution of Inheritance and Delegation Mechanisms and Their Impact on Code Quality

Source code reuse is considered one of the holy grails of modern software development. Indeed, it has been widely demonstrated that this activity decreases software development and maintenance costs while increasing its overall trustwor-thiness. The Object-Oriented (OO) paradigm provides different internal mechanisms to favor code reuse, i.e., specification inheritance, implementation inheritance, and delegation. While previous studies investigated how inheritance relations impact source code quality, there is still a lack of understanding of their evolutionary aspects and, more particular, of how these mechanisms may impact source code quality over time. To bridge this gap of knowledge, this paper proposes an empirical investigation into the evolution of specification inheritance, implementation inheritance, and delegation and their impact on the variability of source code quality attributes. First, we assess how the implementation of those mechanisms varies over 15 releases of three software systems. Second, we devise a statistical approach with the aim of understanding how inheritance and delegation let source code quality&#x2014;as indicated by the severity of code smells&#x2014;vary in either positive or negative manner. The key results of the study indicate that inheritance and delegation evolve over time, but not in a statistically significant manner. At the same time, their evolution often leads code smell severity to be reduced, hence possibly contributing to improve code maintainability.
Social inclusion, social network and community social work

“Social inclusion, social networks and community social work” is a theme for reflection and analysis on digitalisation and its positive and negative impacts on the social inclusion of each and every citizen, based on the maxim “no one left behind”. We reflect on empirical data on the organisation and planning of professional practices that were systematised during the pandemic period at the level of social action professionals, extracting recommendations for the digital transition process in contemporary society. We used a qualitative methodology based on documentary sources and the technique of observation. As a result, we leave open themes for reflection and debate at the political, academic, and professional levels, namely the impact of technology on social intervention; the concept of social inclusion and diversity; digital education and human rights; and community intervention and social work.
A Non-Content Multilayers Hybrid Machine Learning Web Phishing Detection Model

Phishing detection is one of the most important services for cyber security. Phishing is an easy to implement attack that depends on social engineering. It is implemented leveraging web, Short Message Service (SMS) and social media. This attack is the backbone of other attacks. In this work, a new web phishing detection algorithm is proposed based on machine learning algorithms. The proposed algorithm is a hybrid machine learning algorithm that consists of two main layers. The first layer is the feature extraction and reduction layer that consists of multiple machine learning algorithms that work in parallel. The second layer consists of one feed-forward neural network layer. Different numbers and types of algorithms can be leveraged in the first layer of the proposed algorithm. The proposed model has been tested with four algorithms in the first layer, namely, K-means, support vector machine, logistic regression and random forest. Four features have been extracted from this layer and fed into the second layer “the neural network layer”. The output of the second layer shows an accuracy that exceeded 99%.
Automatic Detection and Analysis of Technical Debts in Peer-Review Documentation of R Packages

Technical debt (TD) is a metaphor for code-related problems that arise as a result of prioritizing speedy delivery over perfect code. Given that the reduction of TDs can have long-term positive impact in the software engineering life-cycle (SDLC), TDs are studied extensively in the literature. However, very few of the existing research focused on the technical debts of R programming language despite its popularity and usage. Recent research by Codabux et al. [21] finds that <tex>$R$</tex> packages can have 10 diverse TD types analyzing peer-review documentation. However, the findings are based on the manual analysis of a small sample of R package review comments. In this paper, we develop a suite of Machine Learning (ML) classifiers to detect the 10 TDs automatically. The best performing classifier is based on the deep ML model BERT, which achieves F1-scores of 0.71 - 0.91. We then apply the trained BERT models on all available peer-review issue comments from two platforms, rOpenSci and BioConductor (13.5K review comments coming from a total of 1297 R packages). We conduct an empirical study on the prevalence and evolution of 10 TDs in the two R platforms. We discovered documentation debt is the most prevalent among all types of TD, and it is also expanding rapidly. We also find that R packages of generic platform (i.e. rOpenSci) are more prone to TD compared to domain-specific platform (i.e. BioConductor). Our empirical study findings can guide future improvements opportunities in R package documentation. Our ML models can be used to automatically monitor the prevalence and evolution of TDs in R package documentation.
A Thematic Content Analysis of the Cybersecurity Skills Demand in South Africa

The cybersecurity skills demand is a growing concern both globally and in South Africa, creating what is known as the cybersecurity skills gap. This means that there is a shortage of Information Technology (IT) and cybersecurity professionals that have the required knowledge, skills and abilities, to effectively fill this gap. This study aims to provide a better understanding of the cybersecurity skills demand in South Africa having analysed job postings in South Africa over a 4-month period from 1st October 2020 to 31st January 2021. This was done by conducting a thematic content analysis of the 280 job postings identified during this period. Results indicate a condensed set of knowledge, skills and abilities (KSAs) categorised according to five main job categories, namely: Cybersecurity, Operations and Support, Data and Artificial Intelligence, Strategy and Governance, and Software and Application Development. These results can assist universities, training institutions and organisations to address the cybersecurity skills gap in South Africa.
An IWT Based Reversible Watermarking Scheme Using Lagrange Interpolation Polynomial

Sharing the secret among the partners is an interesting technique that distributes the secret information in several shares for enhancing security. Nowadays, the reversible watermarking scheme becomes very popular due to increasing privacy and security issues in the day to day life. Here, a new watermarking scheme has been proposed using Integer Wavelet Transform (IWT) and Lagrange Interpolation Polynomial (LIP). The reversibility can meet with the help of the IWT scheme and LIP provides the flavor of secret sharing. Moreover, experimental results with comparison have been drawn at the end of the paper. The experimental shows that the proposed scheme can achieve 58.9 dB PSNR with a high payload of 1.5 bpp. From the experimental results, it can be concluded that the proposed scheme outperforms the other existing scheme.