Reviewerly/exHarmony huggingface dataset:
A Study on the Pre- and Post-Pandemic Media of Instruction and Learning Effectiveness in Information Systems Courses

The objective of this study is to understand the effectiveness of medium of instruction based on student performance in courses offered face-to-face prior to the pandemic and virtual hybrid mode after the onset of the pandemic from a media richness theoretical perspective. This study analyzed data from 1157 students from 43 Information Systems courses over a 6-year pre- and post-pandemic period. This study analyzed student performance data from face to face and virtual hybrid courses across medium of instruction, type of course, course level, and gender. Overall, students performed better in face-to-face classes prior to the pandemic than the virtual hybrid courses. Across gender, and type of course, and course levels, face-to-face medium of instruction was found to be best for student performance. The post-hoc interviews with students and faculty indicated that face-to-face was the choice of instruction medium for technical courses and virtual hybrid mode was preferred for theory intensive conceptual courses, confirming the results from the data analysis.
Robust internal representations for domain generalization

Abstract This paper, which is part of the New Faculty Highlights Invited Speaker Program of AAAI'23, serves as a comprehensive survey of my research in transfer learning by utilizing embedding spaces. The work reviewed in this paper specifically revolves around the inherent challenges associated with continual learning and limited availability of labeled data. By providing an overview of my past and ongoing contributions, this paper aims to present a holistic understanding of my research, paving the way for future explorations and advancements in the field. My research delves into the various settings of transfer learning, including, few‐shot learning, zero‐shot learning, continual learning, domain adaptation, and distributed learning. I hope this survey provides a forward‐looking perspective for researchers who would like to focus on similar research directions.
Boosting Holistic Cybersecurity Awareness with Outsourced Wide-Scope CyberSOC: A Generalization from a Spanish Public Organization Study

Public sector organizations are facing an escalating challenge with the increasing volume and complexity of cyberattacks, which disrupt essential public services and jeopardize citizen data and privacy. Effective cybersecurity management has become an urgent necessity. To combat these threats comprehensively, the active involvement of all functional areas is crucial, necessitating a heightened holistic cybersecurity awareness among tactical and operational teams responsible for implementing security measures. Public entities face various challenges in maintaining this awareness, including difficulties in building a skilled cybersecurity workforce, coordinating mixed internal and external teams, and adapting to the outsourcing trend, which includes cybersecurity operations centers (CyberSOCs). Our research began with an extensive literature analysis to expand our insights derived from previous works, followed by a Spanish case study in collaboration with a digitization-focused public organization. The study revealed common features shared by public organizations globally. Collaborating with this public entity, we developed strategies tailored to its characteristics and transferrable to other public organizations. As a result, we propose the “Wide-Scope CyberSOC” as an innovative outsourced solution to enhance holistic awareness among the cross-functional cybersecurity team and facilitate comprehensive cybersecurity adoption within public organizations. We have also documented essential requirements for public entities when contracting Wide-Scope CyberSOC services to ensure alignment with their specific needs, accompanied by a management framework for seamless operation.
Depositional Architecture and Reservoir Quality Assessment of Abu Roash (G) Dolomite, as a Low Resistivity Pay, Razzak Oilfield, Western Desert, Egypt.

Abstract Abu-Roash (G) dolomite (A/R-G) is considered one of the most prolific oil-bearing reservoirs in the Western desert. In this study, Razzak Oilfield was selected to investigate the geological and petrophysical findings of these successions that were assigned stratigraphically to the upper Cretaceous (Late Cenomanian), in the Alamein basin. Sedimentologically, three main facies groups have been recognized in the studied successions of A/R-G and include dolostone, limestone and mudstone facies; and depositional; are divided into three parts: A) Back-reef to reef crest (Reefal depositional setting) - dolostone facies group; graded to B) Inner-ramp of limestone / dolomitic limestone facies types; then C) mud-flat (intertidal setting) of calcareous mudstone facies type. Petrophysically, most of the recorded pore sizes in the dolomite zone are mesopores (≈1 micron), with scattered macropores (2-10 microns), as identified by MICP on some selected 1.5” core plugs. In general; A/R-G dolomite displays low resistivity pay (high conductivity) due to: A) The heterogeneity of moldic and vuggy pore size distributions (macropores); associated with the dominant intercrystalline-dolomite mesopores; and B) Fe-rich minerals that increase the conductivity; where most of the identified dolomite crystals are secondary, late-diagenetic stage ferroan dolomite; and confirmed that petrographically by thin-section analysis.
Efficient Concatenated Bosonic Code for Additive Gaussian Noise

Bosonic codes offer noise resilience for quantum information processing. Good performance often comes at a price of complex decoding schemes, limiting their practicality. Here, we propose using a Gottesman-Kitaev-Preskill code to detect and discard error-prone qubits, concatenated with a quantum parity code to handle the residual errors. Our method employs a simple linear-time decoder that nevertheless offers significant performance improvements over the standard decoder. Our Letter may have applications in a wide range of quantum computation and communication scenarios.
Free Quantification in Four-Valued and Fuzzy Bilattice-Valued Logics

We introduce a variant of free logic (i.e., a logic admitting terms with nonexistent referents) that accommodates truth-value gluts as well as gaps. Employing a suitable expansion of the Belnap–Dunn four-valued logic, we specify a dual-domain semantics for free logic, in which propositions containing non-denoting terms can be true, false, neither true nor false, or both true and false. In each model, the dual domain semantics separates existing and non-existing objects into two subdomains, making it possible to quantify either over all objects or existing objects only. We also outline a fuzzy variant of the dual-domain semantics, accommodating non-denoting terms in fuzzy contexts that can be partially indeterminate or inconsistent.
Auditing the Blockchain

Information systems audit is the procedure of gathering and assessing data to determine whether an information system protects assets, upholds data integrity, successfully reaches organizational goals, effectively uses resources, and reviews business continuity plans. In this study, the IS audit framework by ISACA has been extended to information systems built on the blockchain technology or to systems integrated with a blockchain component. Blockchain is a relatively new technology, and its applications are being explored in different fields today. In fact, blockchain applications are being explored for improving the IS audit process. As such, the authors have explored the principles of audit and the blockchain itself in this chapter.
Programming language semantics: It’s easy as 1,2,3

Abstract Programming language semantics is an important topic in theoretical computer science, but one that beginners often find challenging. This article provides a tutorial introduction to the subject, in which the language of integers and addition is used as a minimal setting in which to present a range of semantic concepts in simple manner. In this setting, it is easy as 1,2,3.
Developing Audit Strategy for PaaS Applications

The adoption of cloud computing has become a mainstream strategy in IT today. Both product-based and service-based industries benefit greatly from the cloud model because of its ability to reduce an enterprise's Capex and allow focus on the growth of core business lines. However, before jumping onto the cloud bandwagon, evaluating the advantage and suitability of cloud technology for an enterprise is important. This chapter focuses on platform-as-a-service (PaaS) cloud computing offerings. The objective with this chapter is to understand the utility of PaaS and analyze and evaluate its risks, vulnerabilities, and impacts. The authors have carried out our investigation from an auditor's lens by studying the risks associated with the PaaS cloud service offering, understanding the methods to thoroughly assess these risks, and identifying controls that must be used to effectively mitigate them. They have aligned this research with the COBIT control framework as we recommend the formulation of cloud governance and business strategies in alignment with enterprise objectives and goals.
Digital instructional leadership in schools facing different levels of challenging contexts: A survey study during the COVID-19 pandemic

This study highlights the growing significance of integrating digital technology into instructional leadership in schools. Educational administration research cannot ignore the fact that digital technology has become an integral part of human interactions and organizational processes in twenty-first-century workplaces. This paper aims to explore digital instructional leadership behaviours of principals during the COVID-19 pandemic and explain behavioural differences in leadership related to different levels of socio-economic school settings. Data were collected through an online study from 380 teachers in Bahrain. We used a series of two-sample t-tests between percentages to compare differences in digital instructional leadership behaviours. The study revealed that means of principals’ digital instructional leadership behaviours were generally higher in schools serving student populations with a high rather than low socio-economic background. Principals in schools serving student populations with low socio-economic background were higher than their peers only in one digital instructional leadership behaviours, and this was related to socio-economic gaps. The study contributes to better understanding of digital instructional leadership, an approach that is becoming highly prevalent because of the pandemic, and sheds light on how levels of principals’ behaviours vary in different circumstances. Overall, the findings attest to the presence of a digital instructional leadership divide.
The Uniform Commercial Code Embraces Blockchain

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Learning Effects of Augmented Reality and Game-Based Learning for Science Teaching in Higher Education in the Context of Education for Sustainable Development

In the course of digitalization, new technologies and innovations are continuously introduced to the educational sector. For instance, augmented reality (AR) is increasingly applied in science teaching in both school and higher education. Combining real and virtual content potentially enhances interactivity and understanding of the learning process. This teaching and learning approach can positively impact various learning outcomes, such as learning gains and motivation. This paper aims to investigate the positive learning effects of AR using a game-based AR learning environment: “Beat the Beast”. In line with the concept of an education for sustainable development (ESD) topic, microplastics, this learning environment follows an interdisciplinary approach, combining the subjects of biology, chemistry, and engineering. To determine and distinguish the effects of implementing the technology AR in science learning environments as well as the principles of game-based learning, we contrasted the learning environment into two factors: one with AR and one without AR and the other with a game and without a game. A quasi-experimental design with 203 pre-service teachers of the first semester of all subjects was chosen to evaluate the four different types of settings with questionnaires on motivation, technology acceptance, user engagement, cognitive load, computer self-efficacy, knowledge, and ESD outcomes in higher education. Our research demonstrates that although augmented reality (AR) imposes a relatively elevated cognitive load, it does not negatively affect learning effects. In spite of the increased cognitive load, learners in AR settings do not exhibit lesser knowledge acquisition compared to those in alternative environments. Moreover, our investigation highlights AR’s potential to amplify motivation and user engagement. Contrary to expectations, in the context of the selected subject matter and target audience, game-based adaptations of the educational environment fail to enhance learning outcomes. These versions actually underperform compared to other formats in both motivational and engagement metrics.
Tackling Multi-Physics Nano-Scale Phenomena in Capillary Force Lithography with Small Data by Hybrid Intelligence

The scientific community has been looking for novel approaches to develop nanostructures inspired by nature. However, due to the complicated processes involved, controlling the height of these nanostructures is challenging. Nanoscale capillary force lithography (CFL) is one way to use a photopolymer and alter its properties by exposing it to ultraviolet radiation. Nonetheless, the working mechanism of CFL is not fully understood due to a lack of enough information and first principles. One of these obscure behaviors is the sudden jump phenomenon-the sudden change in the height of the photopolymer depending on the UV exposure time and height of nano-grating (based on experimental data). This paper uses known physical principles alongside artificial intelligence to uncover the unknown physical principles responsible for the sudden jump phenomenon. The results showed promising results in identifying air diffusivity, dynamic viscosity, surface tension, and electric potential as the previously unknown physical principles that collectively explain the sudden jump phenomenon.
Blind Modulation Classification of Wi-Fi 6 and 5G signals for Spectrum Sensing

Classification of modulation of Wi-Fi~6 and 5G downlink (DL) user data signals for spectrum sensing is studied. First, the orthogonal frequency division multiplexing (OFDM) symbol duration and cyclic prefix (CP) length are estimated based on the cyclic autocorrelation function (CAF). We propose a feature extraction algorithm characterizing the modulation of OFDM signals based on the estimated parameters. The algorithm includes removing the effects of a synchronization error and converting the obtained feature into a 2D histogram of phase and amplitude. This histogram is input to a convolutional neural network (CNN)-based classifier. Our system works without knowledge of a carrier frequency, Wi-Fi preamble, or resource allocation of 5G physical channels. We evaluate the classifier's performance with data with various protocol-compliant configurations. Our classifier achieves at least 98% accuracy when SNR is above the value required for data transmission.
Quantum circuits to measure scalar spin chirality

The scalar spin chirality is a three-body physical observable that plays an outstanding role both in classical magnetism, characterizing non-co-planar spin textures, and in quantum magnetism, as an order parameter for chiral spin liquids. In quantum information, the scalar spin chirality is a witness of genuine tripartite entanglement. Here we propose an indirect measurement scheme, based on the Hadamard test, to estimate the scalar spin chirality for general quantum states. We apply our method to study chirality in two types of quantum states: generic one-magnon states of a ferromagnet, and the ground state of a model with competing symmetric and antisymmetric exchange. We show a single-shot determination of the scalar chirality is possible for chirality eigenstates, via quantum phase estimation with a single auxiliary qutrit. Our approach provides a unified theory of chirality in classical and quantum magnetism.
Controlling LIME Kernel Width to Achieve Comprehensible Explanations on Tabular Data

LIME [9] is an Explainable AI (XAI) method that can offer local explanation for any Machine Learning model prediction. However, the design of LIME often leads to controversial problems in the explanation, which are mostly due to the randomness of LIME’s neighborhood generating process. In this paper, we contribute a method that can help LIME deliver comprehensible explanation by optimizing feature attribution and kernel width of the generating process. Our method ensures high level of features attribution while keeping kernel width lower than the default setting to remain high locality in the explanation. The study will focus mainly on LIME for tabular data.
Application of recurrence quantification analysis of acoustic emission time series to analysis of a plastic flow of metals

As a result of the application of recurrence quantification analysis to acoustic emission time series obtained in uniaxial tensile testing of copper and silver, we detected the existence of a characteristic interval in which the Shannon information entropy (as a parameter of the quantitative analysis of recurrence plots) increased rapidly. Using statistical analysis of the behavior of the dislocation ensemble, we established a relation between the physical parameters of the given interval and the global stability loss parameters of the plastic flow of metals, indicating the predictability of the distinctive point determined long before the critical state was attained.
Exploring the Perception of the Effect of Three-Dimensional Interaction Feedback Types on Immersive Virtual Reality Education

Immersive virtual reality (VR) systems are becoming widely used for education with three-dimensional (3D) information. Specifically, three-dimensional spaces to create virtual environments can help increase students’ learning interest and ability with spatial interaction. Also, with the use of multimodal interaction, VR systems can provide highly effective ways to solve problems through natural experiences. Additionally, immersive environments can bring together people in remote locations, which has been increasingly applied in education applications with the use of technology to simulate real situations. However, effective interaction methods that improve the learning ability of people participating in educational activities in 3D immersive environments are yet to be well defined. In this study, we investigated the effect of the interaction feedback types on the perception of students participating in VR environments. We conducted the experiment on three types of interaction responses, and our study was designed as a virtual chemistry class. Our experimental study showed that the interaction feedback type of a deformable object had a greater educational effect than other types of visual or audio feedback, and our results are expected to provide guidelines on how to create effective immersive education content and interaction methods.
A Novel Methodology for Real-Time Face Mask Detection Using PSO Optimized CNN Technique

The coronavirus (covid-19) pandemic has had a global impact on human beings. Many people have been infected, and millions have died due to COVID-19. A lot of precautionary measures are suggested by the World Health Organization (WHO) to prevent the spread of COVID-19, like the use of sanitizers, social distancing, and face masks. Wearing the face-masks incorrectly makes them useless and spreads the virus. In this manuscript, a convolution neural network (CNN) based real-time face mask detection technique is proposed. However, designing the architecture of CNN is a complex task and requires a deep knowledge of its internal structure and fine tuning of hyper-parameters for better accuracy. In this manuscript, the design complexity of the convolution neural network is reduced by optimizing the hyper-parameters of CNN using the particle swarm optimization (PSO) technique known as PCNN. The proposed PCNN model is tested on an image dataset. The obtained results are compared with other machine learning models like fundamental CNN, support vector machine, decision tree, and naive Bayes algorithms. The performance of the proposed model is tested using validation measures like Accuracy, precision, Recall, and F-1 score. It is observed that proposed model has higher accuracy compared to convention machine learning algorithms.
A Novel Power System Side Channel Attack Method Based on Machine Learning CNN-Transformer

Abstract Continuous advancements in cryptography and information technology have rendered current cryptographic algorithms highly robust against traditional cryptanalysis methods. However, in modern power systems, the equipment’s inherent process characteristics result in the leakage of side channel information during the operation of cryptographic algorithms. This information includes power consumption, electromagnetic signals, and timing data. Adversaries can exploit these side channels to compromise encryption keys. To address this issue, a groundbreaking power system side-channel attack method is introduced in this paper, leveraging the CNN-Transformer architecture in machine learning. The proposed approach utilizes power consumption analysis techniques to identify relevant points of interest in the side channel power consumption data. By employing a machine learning model for training, encryption can be breached. Empirical results demonstrate the superior attack efficiency of the model compared to LSTM and CNN models in side channel attacks.
A Federated Learning Model for Linear Fuzzy Clustering with Least Square Criterion

Federated learning is a hot topic on privacy preserving data analysis and has also been applied to fuzzy c-means clustering. In this paper, a federated learning scheme is proposed for linear fuzzy clustering with horizontally distributed data, where each cluster is represented by a linear-shape prototype. In order to merge the client-wise independent clustering results without violating personal privacy, gradient information of each prototype instead of original observation are shared at the centralized server. The objective function is defined with the least square criterion, which is useful in handling component-wise errors and makes it possible to find cluster basis vectors without solving an Eigen problem. Therefore, attribute-wise gradient decent learning can be realized by utilizing only gradient information of prototype parameters at the central server. The global prototypes are securely updated then distributed to clients for next updating. Experimental results demonstrate that the proposed algorithm is useful for reconstructing the whole data result under privacy preservation.